European Union Risk Assessment Report

Transcription

1 European Union Risk Assessment Report NICKEL AND NICKEL COMPOUNDS CAS-No.: EINECS-No.: Sulphate CAS-No.: EINECS-No.: Carbonate CAS-No.: EINECS-No.: Chloride CAS-No.: EINECS-No.: Dinitrate CAS-No.: EINECS-No.: (SECTION 3.2) Effects Assessment RISK ASSESSMENT

2 LEGAL NOTICE Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa Server ( Cataloguing data can be found at the end of this publication Luxembourg: Office for Official Publications of the European Communities, [ECB: year] ISBN [ECB: insert number here] European Communities, [ECB: insert year here] Reproduction is authorised provided the source is acknowledged. Printed in Italy

3 NICKEL CAS No: EINECS No: RISK ASSESSMENT Final Version 30 May, 2008 Denmark Rapporteur for the risk assessment of is Denmark Contact point: Henrik Tyle, Danish Environmental Protection Agency, Strandgade 29, 1401 København K, Denmark, Assisted by Janeck Scott-Fordsmand, Department of Terrestrial Ecology, National Environmental Research Institute,University of Aarhus, P.O. Box 314, Vejlsoevej 25, DK Silkeborg, Denmark

4 Date of Last Literature Search : March 2008 Review of report by MS Technical Experts finalised: April 2008 Final report: May 2008

5 Foreword This Draft Risk assessment Report is carried out in accordance with Council Regulation (EEC) 793/93 1 on the evaluation and control of the risks of existing substances. Existing substances are chemical substances in use within the European Community before September 1981 and listed in the European Inventory of Existing Commercial Chemical Substances. Regulation 793/93 provides a systematic framework for the evaluation of the risks to human health and the environment of these substances if they are produced or imported into the Community in volumes above 10 tonnes per year. There are four overall stages in the Regulation for reducing the risks: data collection, priority setting, risk assessment and risk reduction. Data provided by Industry are used by Member States and the Commission services to determine the priority of the substances which need to be assessed. For each substance on a priority list, a Member State volunteers to act as Rapporteur, undertaking the in-depth Risk Assessment and recommending a strategy to limit the risks of exposure to the substance, if necessary. The methods for carrying out an in-depth Risk Assessment at Community level are laid down in Commission Regulation (EC) 1488/94 2, which is supported by a technical guidance document 3. Normally, the Rapporteur and individual companies producing, importing and/or using the chemicals work closely together to develop a draft Risk Assessment Report, which is then presented at a Meeting of Member State technical experts for endorsement. The Risk Assessment Report is then peer-reviewed by the Scientific Committee on Toxicity, Ecotoxicity and the Environment (CSTEE) which gives its opinion to the European Commission on the quality of the risk assessment. This Draft Risk Assessment Report is currently under discussion in the Competent Group of Member State experts with the aim of reaching consensus. During the course of these discussions, the scientific interpretation of the underlying scientific information may change, more information may be included and even the conclusions reached in this draft may change. The Competent Group of Member State experts seek as wide a distribution of these drafts as possible, in order to assure as complete and accurate an information basis as possible. The information contained in this Draft Risk Assessment Report does not, therefore, necessarily provide a sufficient basis for decision making regarding the hazards, exposures or the risks associated with the priority substance. This Draft Risk Assessment Report is the responsibility of the Member State rapporteur. In order to avoid possible misinterpretations or misuse of the findings in this draft, anyone wishing to cite or quote this report is advised to contact the Member State rapporteur beforehand. Contact Details of the Rapporteur(s) 1 O.J. No L 084, 05/04/199 p O.J. No L 161, 29/06/1994 p Technical Guidance Document, Part I V, ISBN [1234]

6 0 OVERALL RESULTS OF THE RISK ASSESSMENT 4 [Note: In the final report, chapters 0 and 5 should be as close as possible to the OJ] CAS Number: [click here to insert CAS No.] EINECS Number: [click here to insert EINECS No.] IUPAC Name: [click here to insert IUPAC name] Environment (X) Conclusion (i) There is a need for further information and/or testing. (X) Conclusion (ii) There is at present no need for further information and/or testing and no need for risk reduction measures beyond those which are being applied already. (X) Conclusion (iii) There is a need for limiting the risks; risk reduction measures which are already being applied shall be taken into account. Conclusion (i) is reached because: There is a need for additional testing to provide robust data for the derivation of the PNECsediment. Conclusion (ii) is reached because: The risk assessment has shown that exposure of nickel at the regional scale results in no risk for most scenarios in the aquatic and terrestrial compartments. Additionally, no regional risk was shown for secondary poisoning. Conclusion (iii) is reached because: The risk assessment has shown that exposure of nickel at the regional scale causes potential risk to aquatic organisms in waters with high ph and low Dissolved Organic Carbon. Also, subsets of soils from Spain and the UK show potential risk. Several instances of risk to the aquatic and terrestrial compartments were observed for specific sites in the Local Scale risk characterization. 4 Conclusion (i) There is a need for further information and/or testing. Conclusion (ii) There is at present no need for further information and/or testing and no need for risk reduction measures beyond those which are being applied already. Conclusion (iii) There is a need for limiting the risks; risk reduction measures which are already being applied shall be taken into account.

7 CONTENTS 1 GENERAL SUBSTANCE INFORMATION (SEPARATE DOCUMENT) GENERAL INFORMATION ON EXPOSURE (SEPARATE DOCUMENT) ENVIRONMENT ENVIRONMENTAL EXPOSURE (SEPARATE DOCUMENT) EFFECTS ASSESSMENT: HAZARD IDENTIFICATION AND DOSE (CONCENTRATION) RESPONSE (EFFECT ASSESSMENT) Aquatic Effects Assessment Sources and selection of ecotoxicological data Sources of ecotoxicological data Selection of ecotoxicological data Derivation of L(E)C10 and NOEC values (methods) Aggregation of L(E)C10 or NOEC data Derivation of PNEC values using statistical extrapolation (methods) Results Toxicity to freshwater algae & higher plants Toxicity to freshwater invertebrates Toxicity to freshwater fish and amphibians Water chemistry of the test media Comparison with water characteristics of European freshwaters Implementation of bioavailability HC5freshwater Uncertainty analysis Toxicity test results for sediment organisms Introduction Provisional results conclusion i) sediment testing program Conclusion i) sediment research program Marine Effects Assessment Introduction Chronic toxicity to marine organisms Chronic toxicity to marine algae Chronic toxicity to marine invertebrates Chronic toxicity to marine fish Summaries of literature PNEC Derivation for the marine compartment Evaluation of data available for the marine environment Assessment Factor Approach SSD Approach PNEC derivation Bioavailability Uncertainty analysis Terrestrial Effects Assessment Sources and selection of ecotoxicological data Sources of ecotoxicological data Selection of ecotoxicological data Derivation of NOEC or L(E)Cx values Averaging thresholds for same process/species Derivation of PNEC values using statistical extrapolation (methods) Results Toxicity to higher plants Toxicity to soil invertebrates Toxicity to soil micro-organisms Essentiality and Toxicity Soil characteristics of the test media

8 Comparison with soil characteristics for European soils Implementation of bioavailability PNEC derivation Uncertainty analysis Secondary Poisoning Assessment Introduction Bioaccumulation and Biomagnification Identification of Relevant Food Chains Aquatic Derivation of PNECoral Values Birds Mammalian Tier 2 Mammalian PNECoral Values Derivation of PECoral Values Aquatic Terrestrial Bioavailability of Dietborne Mammals Birds Risk Characterization Aquatic - Marine Aquatic Freshwater Terrestrial Uncertainties Summary and Conclusions EUSES Calculations can be viewed as part of the report at the website of the European Chemicals Bureau: TABLES Table Ranges of ph, hardness, and Mg used for data selection... 2 Table Overview of the accepted high quality nickel chronic NOEC values for algae/highe r plants.. 8 Table Overview of the rejected low quality nickel chronic NOEC values for algae/higher plants Table Overview of the accepted high quality nickel chronic NOEC values for freshwater invertebrates.. All selected values for the most sensitive endpoint used for the derivation of HC5 are marked in bold. In addition the species, test duration, effect parameter and endpoint is marked in bold the first time Table Overview of the rejected low quality chronic nickel NOEC values for freshwater invertebrates. 25 Table Overview of the high quality chronic nickel NOEC values for freshwater invertebrates not further used for normalisation because the phys.-chem. falls outside the boundaries of the BLMs/ DOC could not be estimated Table Overview of the accepted high quality nickel chronic NOEC values for freshwater amphibians and fish.. All selected values for the most sensitive endpoint used for the derivation of HC5 are marked in bold. In addition the species, test duration, effect parameter and endpoint is marked in bold the first time Table Overview of the rejected low quality nickel chronic NOEC values for freshwater amphibians and fish Table Overview of the high quality chronic nickel NOEC values for freshwater amphibians/fish not further used for normalisation because the physico.-chemical properties fall outside the boundaries of the BLMs/ DOC could not be estimated Table Physico-chemical parameters of the selected toxicity studies (min-max values) and European freshwaters (reported as 10 th, 50 th and 90 th %) Table Abiotic boundaries for the different chronic BLMs Table Validation boundaries for the different chronic BLMs Table Site locations and water quality parameters of test waters used for the spot checking study.. 86 Table Justification for choice of BLM for read-across Table Summary of the physico-chemical characteristics of the different selected scenarios Table Comparison of the physico-chemical conditions of the different scenarios versus EU surface waters (Swad database)

9 Table The reduction in intra-species variability (expressed as max/min ratios) after normalization of the L(E)C 10 /NOEC data Table The reduction in intra-species variability (expressed as max/min ratios) after normalization of the effects data. The BLM that exhibited the lowest (most stringent) point estimate (i.e., ECx) value was used.. 99 Table Summary of the species mean NOEC or EC10 values (total risk approach) in µg Ni/L (with most sensitive endpoint) Table Non-normalised species mean EC10s or NOECs (total risk approach) that are used as input values for deriving the HC 5 values as a basis for the freshwater Table Summary of the most sensitive endpoint and number of datapoints after normalisation using the BLMs Table Summary of the HC 5 for the best fitting distribution functions using the A/D and the K/S Go-F approaches Table : Summary of the HC 5 for the best fitting and log-normal distributions derived for the different selected scenarios Table Goodness-of-fit statistics (according to Andersen-Darling (A/D) and Kolmogorov-Smirnov (K/S)) for the best fitting and log-normal frequency distributions Table Minimum taxonomic groups requirements for the extrapolation method (London workshop, 2001) Table Individual species covered in the Ni database Table Individual families covered in the Ni database Table HC 5 at 50 th % confidence limit (together with 5 th and 95 th confidence limits) derived from the conventional log-normal distribution Table Some selected parameters of typical EU freshwater as compared to typical seawater. All values are in meq/l except ph Table Overview of the accepted high quality nickel chronic NOEC values for marine algae Table Overview of the accepted nickel chronic NOEC values for marine invertebrates Table Overview of the rejected nickel chronic NOEC values for marine invertebrates Table Overview of the accepted high quality nickel chronic NOEC values for marine fish Table Individual species covered in the Ni marine ecotoxicity database Table Individual families covered in the Ni marine ecotoxicity database Table Chronic Toxicity Data of to Aquatic Animals and Plants Table Summary of test statistics for the Anderson-Darling and Kolmogorov-Smirnov Goodness of Fit tests, and HC5(50%) values, for the distributions that are considered in Appendix 2 of the Aquatic Effects Assessment Table Comparison of Assessment Factor approach (i.e., application of lowest chronic EC10 value with an assessment factor of 10) with HC5 values derived from a variety of statistically-based approaches Table Overview of the accepted NOEC/EC10 values for higher plants (estimated background nickel concentrations and CEC** are indicated in italics) Table : Reported higher plant studies NOT used in the effects assessment Table Overview of the accepted EC10/NOEC values for soil invertebrates (estimated background nickel concentrations and CEC** are indicated in italics). Values selected for the effects assessment are underlined. EC10/NOEC indices: m: mortality, r: reproduction Table Reported higher invertebrate studies NOT used in the effects assessment Table Overview of the accepted EC10/NOEC values for microbial processes/species. Values selected for the effects assessment are underlined (estimated background nickel concentrations and CEC** are indicated in italics). EC10 in italics refer to enzymatic processes and are included in a sensitivity analysis of the PNEC calculation Table Reported microbial/enzymatic studies NOT used in the effects assessment Table Soil parameters of the selected toxicity studies (min-max values) and European soils (reported as 10 th and 90 th %) Table The effect of ageing of a Ni spiked soils on the change in isotopically exchangeable fraction of the added Ni Table Changes in Ni toxicity (ED 10 based) in NiCl 2 spiked soils between 1 week ( freshly spiked ) and 1.5 year ( aged ) incubation. Model predictions: see text Table :. Changes in Ni toxicity (ED 50 based or ED 20 based for maize resid. Decomp.) in NiCl 2 spiked soils between 1 week ( freshly spiked ) and 1.5 year ( aged ) incubation Table Overview of the soils used in the conclusion (i) research programme Table Overview of all significant regression models relating the toxicity of nickel to abiotic factors in both aged and non-aged soils (total risk approach). Selected models are underlined Table Summary of the physico-chemical characteristics of the different selected examples

10 Table Comparison of the physico-chemical conditions of the different soil type scenarios versus EU soils (according to the Foregs database) Table The reduction in intra-species variability (expressed as max/min ratios) after normalization of the NOEC data Table summary of the HC 5 for the best fitting and log-normal distributions derived for the different selected scenarios Table Goodness-of-fit statistics (according to Andersen-Darling (A/D) and Kolmogorov-Smirnov (K/S)) for the best fitting and log-normal frequency distributions. For all six scenarios, the Weibull distribution is selected as the best fit function Table Overview of all families covered by the terrestrial database Table HC 5 (50%) (together with 5 th and 95 th confidence limits) derived from the conventional lognormal distribution Table Summary of Dietary Composition of Candidate Marine Mammals Table Summary of Toxicity Studies with Birds Table concentrations in bivalve molluscs collected from European marine waters Table concentrations in fish collected from European marine waters Table Dissolved nickel concentrations in representative European freshwaters Table PECoral values for the freshwater food chain Table Representative soils for evaluating the terrestrial food chain secondary poisoning model Table bioaccumulation factors (BAFs) for earthworms. (Note: Soil content was voided from the earthworm gut, at least partially, in each study.) Table PECoral values based on estimated nickel concentrations in earthworm tissue Table PECoral/PNECoral ratios for the oystercatcher Table PECoral/PNECoral ratios for the harbor seal Table PECoral/PNECoral ratios for a bivalve-eating bird Table PECoral/PNECoral ratios for otter Table PECoral/PNECoral ratios for a worm-eating bird Table PECoral/PNECoral ratios for common shrews Table Summary of PEC:PNEC ratios and their respective margins of safety Table Comparison of European mole and shrew PEC:PNEC ratios List of Figures Figure Distribution of the observed phs in the ecotoxicity tests Figure Distribution of the observed DOC values in the ecotoxicity tests Figure Distribution of the observed Ca 2+ and Mg2 + cation values in the toxicity tests Figure Distribution of the observed alkalinity values in the toxicity tests Figure Distribution of the observed hardness in EU freshwaters Figure Distribution of the observed ph in EU freshwaters Figure Distribution of the observed DOC content in EU freshwaters Figure Water parameters of the selected toxicity studies (min-max values), hardness and ph boundaries of the BLMs (min-max values for the broadest and narrowest BLMs), ranges of DOC from the BLM validation exercise (min-max values for the broadest and narrowest BLMs), and European freshwaters (reported as 10 th and 90 th %). Ranges of the narrowest BLMs for hardness (algae BLM), DOC (rainbow trout BLM), and ph (Ceriodaphnia dubia BLM) are shown by the vertical arrows Figure FOREGS maps showing regions that are not covered by the BLMs (red circles) Figure Predictive capability of the Ni BLM when applied to D. magna (circles) and C. dubia (inverted triangles) (Di Toro et al., 2005). Data from Schubauer-Berigan et al., 1993, Bossuyt et al., 2001, Chapman et al 1980, Keithly et al., Figure Predictive capacity of the acute D. magna model as shown by predicted vs. observed 48h-LC 50. Data with synthetic waters are from Deleebeeck et al. (2005). Data with natural waters are from Deleebeeck et al. (2005) and Bossuyt et al. (2001) Figure Predictive capacity of the acute C. dubia model in natural waters as shown by predicted vs. observed 48h-LC

11 Figure Predictive capacity of the chronic D. magna models Figure Observed and predicted chronic ECx of Ni to C. dubia in synthetic and natural waters (datset from University of Ghent and from Wirtz et al., 2004) Figure Predictive capacity of the model for LC 50 s and LCxs for x>10% and <100% after 17 days of exposure. Filled symbols indicate extrapolated LC 50 s and are less reliable to evaluate the predictive capacity of the models Figure Observed vs. predicted 72h-E r C 50 s and 72h-E r C 10 s. Filled data points were obtained at ph < Figure Relationship between observed and predicted 4 d EC 50 for Hordeum vulgare exposed to Ni Figure The combined effect of hardness and DOC on chronic Ni toxicity (plotted at a ph of 7.5).. 76 Figure The combined effect of ph and DOC on chronic Ni toxicity (plotted at a hardness of 100 mg CaCO 3 /L) Figure Relationship between ph, DOC, and hardness and nickel toxicity to Ceriodaphnia dubia. Realtionships are shown at hardnesses of 40, 100, and 310 mg CaCO 3 /L. The horizontal axis to the right shows DOC (mg/l), the horizontal axis to the left shows ph, and the vertical axis shows the EC10 (µg Ni/L) Figure Predictive capacity of the merged C. dubia chronic Ni toxicity model for natural waters.. 81 Figure Predictive capacity of the D. magna chronic Ni toxicity models for natural waters; left: developed based on natural waters only; right: merged model Figure Overview of the different steps involved in the normalisation and HC 5 drivation Figure TC NES agreed read-across approach and read-across criteria Figure Observed nickel toxicity (EC20, in µg Ni/L) to the rotifer Brachionus calyciflorus compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of Figure Observed nickel toxicity (EC20, in µg Ni/L) to the insect Chironomus tentans compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of Figure Observed nickel toxicity (EC20, in µg Ni/L) to the snail Lymnaea stagnalis compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of Figure Observed nickel toxicity (EC520, in µg Ni/L) based on inhibition of specific root growth length of the vascular plant Lemna minor compared with predicted toxicity using the Biotic Ligand Model developed for the alga Pseudokirchneriella subcapitata (A), the vascular plant Hordeum vulgaris (B), the crustacean Daphnia magna (C), and the crustacean Ceriodaphnia dubia (D). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of Figure The intra-species variability (expressed as max/min ratios) of the EC 10 /NOECs expressed as dissolved µg Ni/l test medium and BLM-normalised, using the chronic bioavailability models (underlined species are those for which BLMs (P subcapitata, D. magna, C. dubia, O. mykiss) have been developed) Figure Intra-species variability (expressed as max/min ratios) of the effects concentrations expressed as dissolved µg Ni/l test medium and BLM-normalized, using the chronic bioavailability models Figure The cumulative frequency distributions of the non-normalised species mean EC10 or NOEC values from the chronic Ni toxicity tests in the dataset of freshwater organisms. Observed data and Log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the non-normalised species mean EC10 or NOEC values from the chronicni toxicity tests in the dataset of freshwater organisms. Observed data and Gamma distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario ditches in The Netherlands). Geochemical parameters for this scenario were: ph = 6.9, hardness = 260 mg/l CaCO 3, DOC = 12.0 mg/l. Observed data and Extreme Value distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario ditches in The Netherlands). Geochemical parameters for this scenario were: ph = 6.9, hardness = 260 mg/l CaCO 3, DOC = 12.0 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data

12 Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Otter in the United Kingdom). Geochemical parameters for this scenario were: ph = 8.1, hardness = 165 mg/l CaCO 3, DOC = 3.2 mg/l. Observed data and log-normal distribution distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Teme in the United Kingdom). Geochemical parameters for this scenario were: ph = 7.6, hardness = 159 mg/l CaCO 3, DOC = 8.0 mg/l. Observed data and Gamma distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Teme in the United Kingdom). Geochemical parameters for this scenario were: ph = 7.6, hardness = 159 mg/l CaCO 3, DOC = 8.0 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Rhine in The Netherlands). Geochemical parameters for this scenario were: ph = 7.8, hardness = 217 mg/l CaCO 3, DOC = 2.8 mg/l. Observed data and Logistic distribution curve (best fitting curve) for the dataset fitted on the data. 105 Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Rhine in The Netherlands). Geochemical parameters for this scenario were: ph = 7.8, hardness = 217 mg/l CaCO 3, DOC = 2.8 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Ebro in Spain). Geochemical parameters for this scenario were: ph = 8.2, hardness = 273 mg/l CaCO 3, DOC = 3.7 mg/l. Observed data and log-normal distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Lake Monate in Italy). Geochemical parameters for this scenario were: ph = 7.87; hardness = 48.3 mg/l CaCO 3, DOC = 2.5 mg/l. Observed data and Logistic distribution curve (best fitting curve) for the dataset fitted on the data Figure : The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Lake Monate in Italy). Geochemical parameters for this scenario were: ph = 7.87; hardness = 48.3 mg/l CaCO 3, DOC = 2.5 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Swedish neutral acidic lake). Geochemical parameters for this scenario were: ph = 6.7; hardness = 27.8 mg/l CaCO 3, DOC = 3.8 mg/l. Observed data and Pearson VI distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the normalised species mean NOEC or EC 10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Swedish neutral. acidic lake). Geochemical parameters for this scenario were: ph = 6.7; hardness = 27.8 mg/l CaCO 3, DOC = 3.8 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data Figure Overview of the SSD and HC 5 for the different freshwater eco-regions Figure Occurrence of Lymnaea stagnalis in the Netherlands (from Figure Proposed marine toxicity tests as part of the Conclusion I research proposal for nickel Figure SSD relationship for chronic Ni marine test data without Diadema antellarum using a log-normal distribution. Relationship shown for median hazardous concentrations (solid line) and the lower (5%; upper dotted line) and upper (95%; lower dotted line) estimate of hazardous concentrations. HC5(50%) = 8.96 ( ). Horizontal error bars represent the lowest and highest 95% confidence limit for endpoints shown as the geometric mean of multiple tests for a given species Figure SSD relationship for chronic Ni marine test data without fish (Atherinops affinis and Cyprinodon variegatus) or Dunaliella tertiolecta using a log-normal distribution. Relationship shown for median hazardous concentrations (solid line) and the lower (5%; upper dotted line) and upper (95%; lower dotted line) estimate of hazardous concentrations. HC5(50%) = 23.7 µg Ni/L ( µg Ni/L) Figure Summary of the statistically not rejected frequency distributions used to fit the nickel marine ecotoxicity data Figure Mean (± Stdev) NOECs or EC 10 s for various nickel compounds for two plant species, Oat (Avena sativa) and Alfalfa (Medigo sativa) Figure : Predicted of soil background concentration based on two correlations obtained for Danish (Bak, 1997) (n=72) and Dutch soil (n=108) (Slooff, 1992)

13 Figure Distribution of the observed background Ni concentration in the ecotoxicity tests Figure Distribution of the observed phs in the ecotoxicity tests & boundaries of regression models. 214 Figure Distribution of the observed organic matter content in the ecotoxicity tests & boundaries of regression models Figure Distribution of the observed clay content in the ecotoxicity tests & boundaries of regression models Figure Distribution of the observed CEC in the ecotoxicity tests & boundaries of regression models. 215 Figure Distribution of the observed CEC content in EU soils Figure Distribution of the observed ph in EU soils Figure Distribution of the observed organic matter content in EU soils Figure Distribution of the observed clay content in EU soils Figure : Soil parameters of the selected toxicity studies (min-max values), boundaries of the regression models (min-max values) and for the European soil programme programme and the FOREGS data (reported as 10 th and 90 th %) Figure General approach used for the incorporation of Ni bioavailability in soils Figure The effect of leaching three NiCl 2 spiked soils on Ni toxicity. Soil ph is 4.6 (soil 1 = Jyndevad), 6.1 (soil 2 = Woburn) and 7.6 (soil 3 = Cordoba 2). Leaching factors >1.0 denoted reduced toxicity after leaching. The leaching factor is the ratio of the ED 50 (measured concentrations) after leaching to that before leaching. The range of the factors refers to maximally 7 assays, i.e. 3 microbial assays, 2 chronic invertebrate assays and 2 plant growth assays (from NiPERA, 2005). The median ( med ) of leaching factors ranged from 1.0 for soil 1 (Jyndevad) to 2.5 for soil 3 (Cordoba 2) Figure The fixation factors calculated based on the isotopically exchangeable fraction in soil between 1 day after spiking and 540 days after spiking. Full line is an empirical curve fitted to the data: fixation factor=1+exp(1.4*(ph-7.0)). The proportion of EU soils covered at each ph interval is indicated. Coverage is based on the ph distribution in soils collected by Parametrix Figure The ageing factors based on toxicity (symbols) and the predicted factor changes in labile pool of Ni in soil (line). Toxicity changes estimated from ED 10 (top) or ED 50 (bottom). Open symbols are unbounded values and are a lower estimate of the ageing factor. None of the ageing factors are significantly lower than that predicted by the chemical model except for 1 point (ED 10 based in most acid soil) Figure Single linear regressions of Ni toxicity threshold values (as total aged and non-aged EC 50 (mg Ni/kg) for microbial processes, higher plants and invertebrates) and soil parameters driving the bioavailability.232 Figure a: Prediction of EC 50 (EC 20 for maize respiration) thresholds (based on total soil Ni concentrations; mg/kg) derived from microbial testing on spiked soils using soil CEC. The dotted lines represent a factor of 2 difference from the solid 1:1 line Figure b: Prediction of EC 50 thresholds (based on total soil Ni concentrations; mg/kg) derived from invertebrate testing on spiked soils using soil CEC for both E. fetida and F. candida. The dotted lines represent a factor of 2 difference from the solid 1:1 line Figure c: Prediction of EC50 thresholds (based on total soil Ni concentrations; mg/kg) derived from higher plants on spiked soils using soil. The dotted lines represent a factor of 2 difference from the solid 1:1 line.234 Figure Illustration of the difference in slopes between the different species/functions Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario acid sandy soil in Sweden). Geochemical parameters for this scenario were: ph = 4.8, CEC = 2.4 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario acid sandy soil in Sweden). Geochemical parameters for this scenario were: ph = 4.8, CEC = 2.4 cmol/kg. Observed data and Lognormal distribution curve for the dataset fitted on the data Figure : The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario loamy soil in The Netherlands). Geochemical parameters for this scenario were: ph = 7.5, CEC = 20 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario loamy soil in The Netherlands). Geochemical parameters for this scenario were: ph = 7.5, CEC = 20 cmol/kg. Observed data and Log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario peaty soil in The Netherlands). Geochemical parameters for this scenario were: ph = 4.7, CEC = 35 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data

14 Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario peaty soil in The Netherlands). Geochemical parameters for this scenario were: ph = 4.7, CEC = 35 cmol/kg. Observed data and Log-normal distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario acid sandy soil in Germany). Geochemical parameters for this scenario were: ph = 3.0, CEC = 6.0 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario acid sandy soil in Germany). Geochemical parameters for this scenario were: ph = 3.0, CEC = 6.0 cmol/kg. Observed data and Log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario clay soil in Greece). Geochemical parameters for this scenario were: ph = 7.4, CEC = 36 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario clay soil in Greece). Geochemical parameters for this scenario were: ph = 7.4, CEC = 36 cmol/kg. Observed data and Log-normal distribution curve for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario soil in Denmark). Geochemical parameters for this scenario were: ph = 6.3, CEC = 10.4 cmol/kg. Observed data and Weibull distribution curve (best fitting curve) for the dataset fitted on the data Figure The cumulative frequency distributions of the aged/normalised species mean NOEC or EC 10 values from the Ni toxicity tests in the dataset of terrestrial organisms/processes (scenario soil in Denmark). Geochemical parameters for this scenario were: ph = 6.3, CEC = 10.4 cmol/kg. Observed data and Log-normal distribution curve for the dataset fitted on the data Figure : Overview of the HC5(50%) for the different eco-regions as a function of the CEC content. Note that the symbols for the peaty soil from the Netherlands and the clay soil from Greece overlay each other because the CEC values were essentially the same Figure The intra-species variability (expressed as max/min ratios) of the EC 10 /NOECs expressed as mg Ni/kg test medium and normalised, using the chronic regression models Figure Relationship between bioconcentration factor and exposure concentration for nickel in fish (from McGeer et al. 2003). Relevant seawater concentrations are in the range of 1-2 µg Ni L -1, which corresponds to a Log BCF of approximately Figure : Distribution of earthworm BAFs Figure Tiered Risk Characterization Approach

15 1 GENERAL SUBSTANCE INFORMATION (SEPARATE DOCUMENT) 2 GENERAL INFORMATION ON EXPOSURE (SEPARATE DOCUMENT) 3 ENVIRONMENT 3.1 ENVIRONMENTAL EXPOSURE (SEPARATE DOCUMENT) 3.2 EFFECTS ASSESSMENT: HAZARD IDENTIFICATION AND DOSE (CONCENTRATION) RESPONSE (EFFECT ASSESSMENT) Aquatic Effects Assessment Sources and selection of ecotoxicological data Sources of ecotoxicological data The ecotoxicological data in this report are derived from original papers on the subject, published in international journals and from research projects. Review articles covering in the environment were also searched for data sources. Only original literature was quoted Selection of ecotoxicological data The assessment of data adequacy involves a review of individual data elements with respect to how the study is conducted and how the results are interpreted in order to accept (or reject) a study in accordance with the purpose of the assessment. The term adequacy covers both the reliability of the available data and the relevance of the data for environmental hazard and risk assessment. These two basic elements have been defined by the TGD as follows: Reliability: covering the inherent quality of a test relating to test methodology and the way that the performance and results of a test are described. Relevance: covering the extent to which a test is appropriate for a particular risk assessment. Only ecotoxicity data that comply with all of the above-mentioned criteria can be considered valid and may be used in the risk assessment. 1

16 Relevance Biological relevancy The toxicity data on algae, higher plants, invertebrates, amphibians and fish are from singlespecies tests that study relevant ecotoxicological parameters such as survival, growth and/or reproduction. Relevancy of the test substance Ni-only exposures are considered relevant for the effects assessment. Studies should be rejected if indications exist that impurities or other substances might have an effect on the toxic properties of the substance under investigation. Only the results of tests with soluble nickel salts are used, thus excluding tests with insoluble nickel salts (e.g. NiO). Ecological relevancy of data The assessment of the ecological relevancy of a particular study requires that certain basic ecological considerations be taken into account (e.g., test organisms should be representative for the environment). This is especially true when ecologically irrelevant species are the driver for the PNEC. Relevance of the test medium The range of the physico-chemistry of the test media should be within the range of the developed/validated BLMs. For algae, the BLM was developed/validated within a hardness range of mg/l CaCO 3 (Mg range between 0.1 to 4.8 mm) and a ph range of For the higher plant Hordeum vulgare, the BLM was developed within a Mg of 0.04 to 3.9 mm and a ph range of For the chronic BLM with Daphnia magna the BLM was developed/validated within a hardness range of mg/l CaCO 3 and a ph range of The physico-chemical boundaries of the chronic Ceriodaphnia dubia model ranged between mg/l CaCO 3 for hardness and between for ph. For the fish Oncorhynchus mykiss the BLM was developed/validated within a hardness range of mg/l CaCO 3 and a ph range of Table summarizes the ranges of ph and hardness (Mg concentration) used for data selection: Table Ranges of ph, hardness, and Mg used for data selection Test organism ph range Hardness range (mg/l CaCO3) Mg (mg/l) Algae P. subcapitata * Higher plants H. vulgare NA Invertebrates D. magna Invertebrates C. dubia Fish O. mykiss *: Hardness range for algae was based on the range of Mg concentrations used in unilateral experiments conducted to determine the relationship between Mg and Ni toxicity. This basis was used because Mg was shown to be more important than Ca in the amelioration of Ni toxicity to P. subcapitata. Therefore, only those toxicity data with physico-chemical parameters within the boundaries of the BLMs were retained for bioavailability normalisation. Reliability Standardised tests, as prescribed by organisations such as OECD and US EPA, are used as a reference when test methodology, performance and data treatment/reporting are considered. 2

17 Indeed, the thorough description of key requirements guarantees the (high) reliability of the reported ecotoxicity data. Non-standardised test data, however, may also have a high reliability, but require a more thorough check on their compliance with reliability criteria before being used in hazard identification and/or risk assessment. The specific items considered in this study for data selection are the following: Type of test Data from approved OECD test guidelines and based on a case by case evaluation data from non standardised tests have been considered as suitable. Preference is given to data extracted from peer reviewed publications, but data from national environment agencies (US EPA, RIVM, ) are also retained. In the present study only chronic tests are being considered. Chronic exposure depends upon the exposure duration and is also a function of the life cycle of the test organisms. A priori fixed exposure durations are therefore not relevant and should instead be related to the species, their typical life cycle and to the recommended exposure duration as described in standard ecotoxicity protocols (e.g. 7 days for Ceriodaphnids (ASTM, 2004), 21 days for daphnids (OECD, 1998). As in the EU Zn risk assessment, chronic exposure was defined as > 4 days for all invertebrates and fish. For nickel it was found more appropriate to make a case by case evaluation taking the factors mentioned above into account. With respect to the chronic effect values it is noted that the fact whether or not a toxicity value (i.e., L(E)C 10 or NOEC) is considered chronic is not determined exclusively by the above exposure duration in the test. For unicellular algae, other micro-organisms (bacteria, protozoa) and even invertebrates (e.g. rotifers), an exposure time of 3 days or less already covers one or more generations, thus for these organisms chronic NOEC values may be derived from experiments lasting less than 4 days. Description of test material and methods Tests should be performed according standard operational procedures. A detailed description of methods employed in the study should be provided. These methods should include, but are not limited to, preparation of the test solutions (environment), timing of administration and observations recorded, protocol used for chemical and nickel analysis, etc. Chemical analysis In this effects assessment, clearly the focus is on the use of actual (measured) effect concentrations for PNEC derivation. This is especially relevant for Ni as in many cases the effects concentrations are close to the background concentrations. However, nominal concentrations could also be used in case chemical analysis showed that the nominal concentrations did not differ substantially from the measured concentrations (<20%). In addition, if no measured values exist for a specific species, nominal concentrations could also be considered especially when these values are well above the HC5 and the Ni background concentrations. The following hierarchical approach was used to evaluate the acceptability of data based on nominal nickel concentrations: Nominal data will not be used if measured data exist for the same species and endpoint from studies conducted under similar test duration and conditions including physical/chemical conditions, where the clear preference will be for measured data; If no measured data exist for a given species: nominal data may be maintained if NOEC/EC 10 values are sufficiently above the background concentration of nickel as these data will be within the range of data from studies that report measured nickel concentrations. All other quality and relevance criteria must be satisfied before data from such studies can be included in the database. 3

18 If no measured data exist for a given species: If NOEC/EC 10 values are close to the nickel background concentration then relevance of each study is discussed on a case by case basis. As in the EU Zn risk assessment, these results can be regarded as being dissolved nickel concentrations, because under laboratory conditions it is assumed that almost all of the nickel is present in the dissolved phase. If it is not mentioned whether the NOEC/L(E)C 10 values are based on measured or nominal concentrations, they were considered as nominal concentrations. Test acceptability Minimal requirements for endpoints such as mortality, growth, reproduction (e.g. control mortality for chronic exposure < 20 %) are often given in standard procedures. Therefore chronic data were rejected if evidence was provided that such unacceptable control mortality was observed in the control. For algae, control division rate was checked for conformity with OECD ( ) and ASTM (2003) guidelines. A cell division rate of 1.33 is referred to in the OECD guideline (i.e. cell concentration in the control cultures should have increased by a factor of at least 16 within 3 days) and 1.0 for the ASTM (2003) guideline (i.e. cell concentration in the control cultures should have increased by a factor of at least 16 within 4 days). However as stated in the rev. OECD TG 201 the increase of cells should under optimal conditions have increased more than 16 times (often 50 to 100 times). The biomass increase in the controls will be provided in this report to show the relative increase among the different tests to allow the reader to judge the sensitivity of each test. Concentration-effect relationships A clear concentration-response should be observed. Because effect concentrations are statistically derived values, information concerning the statistics should be used as a criterion for data selection. In that respect, L(E)C 10 values are considered as equivalent to NOEC values. If no methodology is reported or if values were only visually derived, the data were considered too unreliable for use in this context. Effect levels derived from toxicity tests using only 1 test concentration will always result in unbounded, and are therefore considered unreliable data. Therefore, only the results from toxicity tests using 1 control and at least 2 Ni concentrations were retained. Tests that do not comply with the above-mentioned stipulations are not used in this risk assessment. However, the use of unbounded NOEC values could be justified on a case by case basis, e.g. when no other toxicity values are available for a particular species or when scientific evidence exist that the true toxicity towards a specific organism would be biased if such data were not taken into account. In the latter case a justification will be given Derivation of L(E)C10 and NOEC values (methods) The toxicological variables are estimated based on NOEC (No Observed Effect concentration) or L(E)C 10 values. The methods that have been used for the derivation of NOEC values, being real NOEC values or NOEC values derived from effect concentrations, are based on the recommendations outlined in the revised TGD (2003). If L(E)C 10 data are reported or if both NOEC and L(E)C 10 data are available (as is often the case in research projects), the L(E)C 10 value was used in the effects assessment. All EC 10 5 Draft rev. TG 201. This version was exactly the same version as that adopted by the OECD Council (cf. 16 Addendum to the OECD Test guidelines) March

19 values included in the PNEC derivation should preferably be within the tested interval (i.e above the lowest test concentration above control) for each test Estimation of L(E)C 10 values below the lowest added test concentration has been asserted to introduce uncertainty (ISO, 2004). On the other hand, it is noted that toxicity tests always also include a control with no added test substance. When testing naturally occurring substances like nickel, this control often will include either background concentrations of nickel (tests in natural water) or traces of nickel (tests in artificial media), which may reduce concerns about estimating L(E)C 10 values that are below the lowest test concentration (because the control is effectively the lowest test concentration). Whatever decision is taken, for metals/metal compounds extrapolating L(E)C 10 values below the natural background concentration should be avoided, regardless of the test medium. The EC 10 values below the lowest applied test concentration are rejected if the lowest test concentration resulted in 20% inhibition or more and/or if the EC 10 is more than twofold below the lowest applied (non-zero) test concentration.this data selection procedure is not a strict one to used without further consideration. The robustness of the NOEC derivation is also relevant to consider in this regard, including for example the difference between the NOEC and the LOEC value. If no reliable L(E)C 10 values are available, real NOEC values is derived from the data reported, i.e. the NOEC is one of the concentrations actually used in the test and should be derived using appropriate statistics (significance level usually: p = 0.05 (optional: the p = 0.01 level if reported instead of the p = 0.05 level)). The use of LOEC/MATC or unbounded NOEC/LOEC values are considered in specific cases, e.g. if other toxicity values are not available for a particular species. For example, if no effects were observed at the highest or the only tested concentration, then this concentration can be used as a conservative estimate for the real NOEC Aggregation of L(E)C10 or NOEC data To avoid over-representation of ecotoxicological data from one particular species and in accordance with the developing MERAG guidance, the chronic Ni toxicity data values used here were treated as follows: If for one species several chronic NOEC orl(e)c10 values based on the same toxicological endpoint are available, these values are averaged by calculating the geometric mean, resulting in the species mean NOEC orl(e)c10. This aggregation approach can be performed as long as the NOEC orl(e)c10 values were obtained from equivalent tests, for i.e.from tests with the same test species and endpoint and similar exposure conditions and duration. However, NOEC values generated from different exposure times are aggregated in case exposure duration does not affect significantly the sensitivity of the organisms. Also, NOEC values derived from tests with a relatively short exposure time may be used instead of NOEC values derived from tests with a longer exposure time if the data indicate that a more sensitive life stage and/ or toxicity endpoint were tested in the former tests. This if for one species several chronic NOEC or L(E)C10 values based on different toxicological endpoints are available, the lowest value is selected. The lowest value is determined on the basis of the geometric mean if more than one value for the same endpoint, species and similar duration and test conditions are available (see above). In some cases, NOEC values for different life stages of a specific organism are available in a specific publication. If from these data it becomes evident that a distinct life stage is 5

20 more sensitive, the result for the most sensitive life stage is selected. The life stage of the organism is indicated in the tables as the life stage at start of the test (e.g. fish: yearlings) or as the life stage(s) during the test (e.g. eggs larvae, which is a test including the egg and larval stage) Derivation of PNEC values using statistical extrapolation (methods) The PNEC values were derived from the ecotoxicity data (either NOEC values or EC 10 values from laboratory tests), using the ecotoxicological statistical extrapolation method (also referred to as the Specxies Sensitivity Distribution (SSD-) method), which is described in the TGD (Chapter 3, Appendix V). To evaluate the toxicity data, the statistical extrapolation method was used, calculating the median fifth percentile (HC5) of the species sensitivity distribution. The log-normal distribution (e.g. the methods of Wagner & Løkke (1991) and Aldenberg & Jaworska (2000)) and the log-logistic distribution (Aldenberg & Slob, 1993) are pragmatic choices because of their mathematical properties (methods exist that allow for most in-depth analysis of various uncertainties). However, several other SSD curve fitting functions could be used in order to derive variability distributions (i.e. species-sensitivity distributions, SSD) and percentiles from parametric (e.g. Log-normal, Weibull distributions, ) and / or even from nonparametric methods. To avoid overfitting it was recommended that the selected SSD functions should not be too complex (2-3 paremeters functions are preferred over multiparameters functions). Indeed, a perfect fit can always be obtained by using for example a high degree polynomial distribution. Fitting of the normalised chronic Ni toxicity data is assessed towards the classical log normal distribution function or by selecting the fitting function giving the best goodness of curve fit in individual cases by selecting among 10 different frequency distributions which could be described by 2 parameters:, i.e. Erlang, Normal, Logistic, Inverse Gaussian, Extreme Value, Weibull, Pearson V, Iniform, Pareto, and to 2 distributions which could be described using 3 parameters, i.e. Triangular and Pearson VI. The main characteristics desribing these SSD curve fitting functions are reported in Appendix G.1. The selection of the distributions is based on the available distributions in the BestFit software. In order to evaluate the fit of various distribution functions for a given data set, goodness-offit statistics (software BestFit, Palisade Inc.) were used for evaluating the SSD curve fit for these curve fit functions. Goodness-of-fit tests are formal statistical tests of the hypothesis that the data represent an independent sample from an assumed continuous distribution. These tests involve a comparison between the actual data and the theoretical distribution (i.e. curve fit function) under consideration. The Andersen-Darling (A-D) test places most emphasis on tail values whereas the Kolmogorov-Smirnov (K-S) test investigates the data fit for the middle of the distribution curve. The lower the Goodness of Fit statistics, the better the distribution under consideration fits the data. Appendix G.1 provides background information on the goodness of fit statistics used in this report. Differences in the measure of goodness of fitting of the tails of the distribution curves may have an impact on the goodness of fit evaluations of the curves employed. If the approach is used to select the fitting function, which has the best goodness of fit to the particular data in each case, the actual choice of the goodness of fit function may determine the selection of the particular fitting function and hence the derivation of the HC 5. The A-D and the K-S tests belong to the wide class of quadratic statistics measuring vertical discrepancy in a cumulative distribution function-type probability plot (Stephens, 1982). The calculated goodness-of-fit 6

21 statistic measures how good the fit is: critical values are calculated and used in order to determine whether a fitted distribution should be accepted or rejected at a specific level of confidence. Typically, these values depend on the type of distribution fit, the number of data points and the confidence interval. The level at which one distinguishes between likely and unlikely values of the test statistic is a matter of judgement. Typically a significance level of 0.05 is used, implying that a value of the test statistic below the 95 th percentile of the distribution for the statistic is acceptable and leads to the inability to reject the hypothesis. A value of the calculated A-D statistic above the 95 th percentile of the distribution leads to the rejection of the null hypothesis, i.e. the distribution is not a good fit (Cullen & Frey, 1999). Additional guidance on the application of SSD statistics on small data sets is provided in Section 4 (Marine Effects Assessment of ), Annex II of the EU Risk Assessment of Results An overview of all gathered chronic toxicity data on Ni towards freshwater organisms is provided in the following sections. Both the accepted and rejected data were separately listed to enhance transparency.the toxicity data are only retained for normalization if the physicochemical parameters (ph, H, DOC) are within the boundaries of the BLMs (see Tables with accepted data). Ifthe physico-chemical parameters fall outside the boundaries of the BLMs, they are not further used in the normalization (see Tables with rejected data). All accepted toxicity data are then further normalized using an appropriate BLM, the species geometric mean per endpoint are calculated and the most sensitive endpoint is further propagated in the SSD and used for the PNEC calculation. (see further details in later sections). Thus in the following tables showing the available test data endpoints, species and NOEC- or EC10- values, the values that have been carried forward for the HC5-& PNEC derivation have been indicated in bold. The values marked in bold have been identified after normalisation of all accepted high quality chronic data Toxicity to freshwater algae & higher plants Accepted data on chronic single-species toxicity tests resulting in accepted high quality reliable NOEC/L(E)C10 values (expressed as Ni) for algae and higher plants are summarised in Table Rejected low quality data are summarized in Table A third category of toxicity data has been developed, i.e. in case the only reason for data rejection was that the physico-chemistry is not within the boundaries of the BLMs. However, for the algae/ higher plants no toxicity data has been rejected on the basis of falling outside the bioavailability modelling boundaries. From the database, 58 individual NOEC s (ranging between 12.3 (for the species Scenedesmus accuminatus) and 425 µg/l for the algae species (i.e. Pseudokirchneriella subcapitata) were reported by Deleebeeck et al. (2005) & De Schamphelaere et al. (2006). Six individual NOEC/L(E)C10 values, ranging between 8.2 and 80 µg/l for the higher plant Lemna gibba and Lemna minor were reported. 8 different algae species and 2 higher plant species was represented in the database. The data quality of all individual studies is discussed below. 7

22 Table Overview of the accepted high quality nickel chronic NOEC values for algae/highe r plants. Substance Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Analysis of concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Factor Increase over initial cell density 6 Test water Pseudokirchneriell a subcapitata / 3 days Growth rate EC measured yes static Natural water Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured Yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured Yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured Yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured Yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured yes static 25 7,7 0* <1 Artificial subcapitata medium Pseudokirchneriella / 3 days Growth rate EC measured yes static 25 7,7 0* <1 Artificial subcapitata medium Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC10 25,3 measured yes static 25 7,48 20,1 0* <1 34 Artificial subcapitata medium 6 This factor relates to the control growth in the experiment. The OECD TG has a quality criteria that this factor should at least be 16, but under optimal test conditions with Pseudokirchneriella subcapitata this factor is up to 100. If the factor is less than 16 the particular test can be regarded as insensitive and not acceptable. In 3 tests out of a total of x tests this OECD TG 201 quality criterion was not fulfilled. These data were not rejected because the poor control growth in a total of 3 out of 42 cases in total (but out of 10 tests with DOC > 1mg/l) was attributed to mass significance. In the three particular tests nickel toxicity was measured at low, medium and high hardness conditions, low to medium ph and high to moderate DOC concentrations. 8

23 Substance Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Analysis of concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Factor Increase over initial cell density 6 Pseudokirchneriella subcapitata / 3 days Growth rate EC10 75,2 measured yes static 25 7,51 54,5 0* <1 45 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7,5 92,2 0* <1 59 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 56 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 31 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 32 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 25 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 30 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 18 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 30,3 measured yes static 25 7,4 19,9 0* <1 40 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 36,6 measured yes static 25 7,38 46,4 0* <1 45 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 37,3 measured yes static 25 7,4 81,4 0* <1 50 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 51,9 measured yes static 25 7, * <1 40 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 31,5 measured yes static 25 7, * <1 49 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 42,1 measured yes static 25 7, * <1 45 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 40,5 measured yes static 25 7, * <1 33 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 47,5 measured yes static 25 6,01 21,2 0* <1 75 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 51,9 measured yes static 25 6,45 21,5 0* <1 59 Artificial medium Pseudokirchneriella / 3 days Growth rate EC measured yes static 25 7,29 20,8 0* <1 53 Artificial Test water 9

24 Substance Species Age and/or Test Effect Endpoint Value Analysis of Conc. Administration Temp. ph Hardness DOC Nicb Factor Test water size of test organism duration parameter (µg/l) concentrations response of test substance ( C) (mg/l) (mg/l) (µg/l) Increase over initial cell density 6 subcapitata medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 44,3 measured yes static 25 7,65 20,7 0* <1 45 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 35,9 measured yes static 25 7,92 21,3 0* <1 35 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 57,6 measured yes static 25 6,23 24,5 0* <1 28 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 6, * <1 16 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 6, * <1 15 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 26,5 measured yes static 25 7,2 28,9 0* <1 60 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 44 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static 25 7, * <1 29 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 31,5 measured yes static 25 7, * <1 26 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 44,3 measured yes static 25 7, * <1 21 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC10 91,2 measured yes static 25 7, * <1 19 Artificial medium Deleebeeck et al., 2005 [1] Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static * <1 Artificial medium Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static * <1 Artificial medium Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella / 3 days Growth rate EC measured yes static Natural water subcapitata Pseudokirchneriella subcapitata / 3 days Growth rate EC measured yes static Natural water De Schamphelaere et al., 2006 [2] 10

25 Substance Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Analysis of concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Factor Increase over initial cell density 6 Test water Chlamydomonas / 3 days Growth rate EC measured yes static <1 Artificial sp. medium Chlamydomonas sp. / 3 days Growth rate EC measured yes static <1 Artificial medium Ankistrodesmus / 3 days Growth rate EC measured yes static <1 Artificial falcatus medium Ankistrodesmus / 3 days Growth rate EC measured yes static <1 Artificial falcatus medium Scenedesmus / 3 days Growth rate NOEC 12.3 measured yes static <1 Artificial accuminatus medium Chlorella / 3 days Growth rate EC measured yes static <1 Artificial medium Desmodesmus / 3 days Growth rate NOEC 22.5 measured yes static <1 Artificial spinosus medium Pediastrum duplex / 3 days Growth rate EC measured yes static <1 Artificial medium Pediastrum duplex / 3 days Growth rate EC measured yes static <1 Artificial medium Coelastrum / 3 days Growth rate EC measured yes static <1 Artificial microporum medium Coelastrum / 3 days Growth rate EC measured yes static <1 Artificial microporum medium Deleebeeck et al., 2006 [3] Lemna gibba fronds 7 days Growth (dry EC10 80 measured yes Static renewal * not Artificial sulphate weight) reported medium Lemna gibba fronds 7 days Growth EC10 50 measured yes Static renewal * not Artificial sulphate (frond count) reported medium Lemna gibba fronds 7 days Growth rate EC10 70 measured yes Static renewal * not Artificial sulphate reported medium Klaine & Knuteson, 2003 [4] nitrate Lemna minor fronds 7 days Growth (root EC measured yes Static renewal Natural length) medium nitrate Lemna minor fronds 7 days Growth (root EC measured yes Static renewal Natural length) medium nitrate Lemna minor fronds 7 days Growth (root EC measured yes Static renewal <2 Natural 11

26 Substance Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Analysis of concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) length) Antunes, 2007 [5] *: estimated values: reconstituted water DOC concentration is assumed to be 0 mg/l (DOC in reconstituted water does not bind significant amounts of Ni; De Schamphelaere et al., 2006). DOC (mg/l) Nicb (µg/l) Factor Increase over initial cell density 6 Test water medium 12

27 Table Overview of the rejected low quality nickel chronic NOEC values for algae/higher plants. Substance Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Analysis of concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) sulphate Lemna gibba Fronds 7 days Growth (dry weight) EC measured yes static not reported sulphate Lemna gibba Fronds 7 days Growth (frond EC measured yes static not count) reported sulphate Lemna gibba Fronds 7 days Growth (dry EC measured yes Static not weight) reported sulphate Lemna gibba Fronds 7 days Growth (frond EC measured yes Static not count) reported sulphate Lemna gibba Fronds 7 days Growth rate EC measured yes Static not reported nitrate Lemna minor fronds 7 days Growth (root length) nitrate Lemna minor fronds 7 days Growth (root length) nitrate Lemna minor fronds 7 days Growth (root length) Nicb (µg/l) Test water Artificial medium Artificial medium River Klaine & Knuteson, 2003 [4] Data were rejected due to fluctuation in ph, which resulted in a decrease in the proportion of nickel present as Ni 2+ EC measured yes Static renewal Natural medium EC measured yes Static renewal Artificial medium Antunes, 2007 [5] Data were rejected because the EC10 values were >2x below the lowest tested concentration EC measured yes Static renewal Natural medium Stantec, 2007 [5] Data were rejected due to the occurrence of naturally-occurring stressors River River 13

28 [1] Deleebeeck et al., 2005 & [2] De Schamphelaere et al., Species: Pseudokirchneriella subcapitata. All chemicals were reagent grade. All test media were prepared by adding different volumes of stock solutions of CaCl2 and MgCl2 to ISO medium (ISO, 1989) in which Na2-EDTA (100 µg/l) was replaced by 32 µg/l artificial dissolved organic carbon (Aldrich Humic Acid AHA, Sigma-Aldrich Chemie, Steinheim, Germany). The ph in the tests was controlled by adding 0.75 g/l MOPS (3-Nmorpholinopropanesulfonic acid) to the test solution. MOPS is also reported to be noncomplexing for metals (Kandegedara & Rorabacher, 1999). For each bioassay, the prepared test medium was then used as the dilution water to make a nickel concentration series (added as NiCl2, 5 to 6 nickel concentrations and a control). concentrations in control media were always below the method detection level (MDL) of the GF-AAS (see further), which is 3 µg/l. All media were prepared 24h prior to being used in the toxicity tests and stored at 25 C. The 72h growth inhibition test with P. subcapitata was performed according to the revised draft OECD Guideline 201 (OECD, 2004). Algal tests were performed in 100 ml Erlenmeyer flasks, each containing 50 ml test medium. Each test consisted of three control replicates as well as three replicates for each of the five or six nickel concentrations. Each replicate was inoculated with cells/ml of P. subcapitata. Erlenmeyer flasks were then incubated at 25 C on a light table and were manually shaken two times a day. Cell densities were determined after 48 and 72h with the aid of a particle counter. According to the OECD Guideline 201 (OECD, 2004), Dissolved nickel concentrations in each test concentration were determined at the beginning and at the end of the test using a flame or a graphite furnace atomic absorption spectrophotometer. 72h EC50 and EC10 values were calculated using growth rate as an endpoint (ErC50, ErC10 = concentrations that caused a 50% and 10% reduction in growth rate, respectively). For exponentially growing cultures, the average specific growth rate (µ) was calculated using the following formula (OECD Guideline 201, OECD, 2004). Formatted: English (U.S.) Formatted: English (U.S.) Formatted: English (U.S.) Formatted: English (U.S.) ln Nx ln N0 µ x = t x where: µ x = growth rate after x days of exposure (d -1 ) N x, N 0 = cell density after x days (t x ) and at the start of the test (t 0 ) t x = time (days) since start of test Reliability/relevance: study was accepted. The results from the static tests resulted in reliable 72 h ErC10 values varied between 25.3 and 425 µg/l. [3] Deleebeeck et al., 2006 Species: Chlamydomonas sp.;ankistrodesmus falcatus; Scenedesmus accuminatus; Chlorella sp.; Desmodesmus spinosus; Pediastrum duplex and Coelastrum microporum. Soft lakes (from so-called soft and hard waters) in Sweden were sampled and live phytoplankton samples were taken. The live phytoplankton samples were transported to the laboratory in complete darkness in a cooling device (temperature ca. 15 C). Once in the laboratory, they were stored in a cooling chamber at 15 C and under a low-intensity light cycle of 8L:14D. The samples were used for inoculation of agar plates. After several days (up to 1 week) of incubation under continuous light at 25 C, 61 isolates of green algae were made through 14

29 micromanipulation with Pasteur pipettes (single cells were isolated). Isolates were transferred to WC medium (Guillard and Lorenzen, 1972). Na2SiO3.9H2O was not added because silicates are not of importance for green algae, but only for diatoms). The isolates were then allowed to grow for several days under the same circumstances (25 C and continuous light) and were then stored again at 15 C and low light. During inoculation on agar plates, isolation and culturing, cells were always kept at a hardness comparable to the water hardness of their lake of origin. Hard and soft water species were cultured at a hardness of 43.3 and 6.29 mg CaCO3/l, respectively. For the algae testing, test media were based on the OECD medium proposed by the draft revised OECD guideline 201 (OECD, 2004). For the soft, the medium hard and the hard test medium (having a hardness of 6.29, 16.4 and 43.3 mg CaCO 3 /L, respectively), three different stock solutions 1 with adjusted hardness levels were prepared. In stock solution 2, AHA-DOC (Aldrich Humic Acid Dissolved Organic Carbon) was used to replace EDTA as an Fe chelator. In the final test media, ph was between 7.1 and 7.3. All growth inhibition tests were performed according to the draft revised OECD guideline 201 (OECD, 2004). Tests were inoculated at appropriate cell densities using cultures that had been allowed to grow in hardness-adjusted WC medium (Guillard and Lorenzen, 1972) over a period of 5 to 7 days in order to attain the exponential growth phase. All tests lasted 72 hours. After 48 and 72 hours, light absorption at 660 nm (chlorophyll a) was measured in each replica at each test concentration. At the end of each test, ph was measured and samples were taken for measurement of dissolved Ni, total Ca and Mg and dissolved inorganic carbon concentrations. At the end of testing, the following calculations were made using dissolved Ni concentrations and measurements of light absorption at 660 nm: 72-h NOEC and LOEC values (Mann Whitney U test, p < 0.05). 72-h EC50 and EC10 values (logistic model described by De Schamphelaere and Janssen, 2004) and their 95% confidence intervals (Levenberg-Marquardt method (Levenberg, 1944; Marquardt, 1963)). Reliability/relevance: study was accepted. The results from the static tests resulted in reliable 72 h ErC10 values varied between 12.6 and 59.4 µg/l. [4] Klaine and Knuteson, 2003 Species: Lemna gibba. Data for higher plants were recently generated by Klaine & Knuteson (2003). All test organisms were maintained prior to testing in artificial 20X-APP growth medium. Three duckweed tests were conducted with Lemna gibba using the draft OECD test guidelines for Lemna sp. (draft OECD TG ). Long term nickel toxicity in OECD artificial medium (20X-APP growth medium without EDTA and further sterilized and filtered at 0.2 µm) and natural (collected from the Lynches river (South Carolina, USA) and further sterilized and filtered at 0.2 µm) water was tested in both static and renewal systems. Ni was added as Ni-sulfate using 6 different treatments and 1 control (4 replicates) using a 2 fold increasing concentration series. The ph of the test solution in the static tests was 7.2, in the static trenewal 6.5. Hardness of the artificial medium was ± 300 mg/l CaCO 3, while the hardness of the natural medium was much lower, i.e. 41 mg/l CaCO 3. Approximately 2 to 3 duckweed colonies, totalling 8 to 10 fronds, were axenically transferred to the test containers for 7 days. Ni concentrations were measured using AAS. Growth (dry weight and frond count) and growth rate were the measured endpoints at day 7 and generated EC 10 values between 50 and 190 µg Ni/l. All effective concentrations were determined using linear interpolation method. The validity criterion, i.e. a 5 (ASTM, 2003) to 7 (OECD, 2003) fold doubling, was met in all the tests. 7 This test guideline was essentially the same as that adopted by the OECD Council, March 23,

30 Reliability/relevance: Results from the static-renewal portion of the study were accepted. ph in the static test reached an equilibrium value of 7.2. At this ph and at the test hardness of 300 mg/l CaCO 3, chemical speciation modeling indicated that precipitation of NiCO 3 was possible. Results from the static test were therefore rejected. The reliable/relevant EC 10 values varied between 50 and 80 µg Ni/l. [5] Antunes, 2007 Species: Lemna minor. For each of the tests, 100 ml of the test solution and two Lemna minor plants having three fronds each, were added to a 250 ml Erlenmeyer flask. Flasks were placed in a growth chamber that was programmed to have a set temperature of 25 C and a 24 h light cycle. The light intensity within the growth cabinets varied from 4186 to 4568 lux. All solutions in the test flasks were completely replaced daily to ensure a minimal amount of drift in the water chemistry parameters. Waters filtered through the 1 µm glass filter were measured out and spiked with Ni(NO3)2. The Ni spike was added in a logarithmic concentration series. Tests were performed using both artificial (APHA medium) and natural waters collected in the USA. The ph of the test solution in the tests varied between 6.95 and Hardness of the medium varied between 35 and 236 mg/l CaCO 3, DOC concentrations between 0.7 and 7.1 mg/l. After 7 d of exposure, all plants were harvested, and measurements were taken of the number of fronds and the total root length of the plants in each test flask. Plants from each test flask were then patted dry and placed on a strip of aluminum foil which was pre-dried and weighed. All samples were placed in a drying oven set at 60 C, and reweighed after 24 h. All data were plotted using the software package SigmaPlot (Version 9.01, Systat Software Inc., San Jose, CA, USA). The IC50 and IC10 values and confidence intervals for data expressed as the total dissolved Ni in solution. Dissolved Ni was measured using AAS. Reliability/relevance: study was accepted. The results from the tests resulted in reliable 7 d EC10 values varying between 8.2 and 75 µg/l). The high variability among 7 d EC10 values could be explained because of differences in abiotic factors (ph, DOC, hardness) among the different natural waters, which influences the toxicity. Average specific growth rate based on root length was chosen as the endpoint to take forward. Because this endpoint was the most sensitive one The extrapolated EC10 values are only accepted when they are within a two-fold difference with the lowest test concentration. More specifically, for the river Platte, a NOEC was determined, and the EC10 (i.e. 75 µg/l) about 1.5 times lower than the NOEC. For another water (Zollner), the EC10 (i.e. 36 µg/l) was slightly above the lowest test concentration. For the third water (Santiam), the EC10 (i.e. 8.2 µg/l) was about two times below the lowest test concentration. Taking these factors into consideration, it was suggested that these values could be accepted, based on the previous conditions for accepting test results Toxicity to freshwater invertebrates Data on chronic single-species toxicity tests resulting in accepted high quality L(E)C 10 /NOEC values (expressed as Ni) for freshwater invertebrates are summarised in Table Rejected low quality data are summarized in Table A third category of toxicity data has been developed, i.e. in case the only reason for data rejection was that the physicochemistry is not within the boundaries of the BLMs or because no reliable estimation of the DOC content could be made (b). From the database 113 individual NOEC s for 15 different individual species were selected. Chronic data on invertebrates belonging to different taxonomic groups being cladoceran (79% of the data points), amphipods (0.9% of the data), 16

31 insects (7% of the data), hydrozoans (0.9% of the data), rotifers (5% of the data) and a mollusc (7% of the data) were observed in the database. The percentages of the invertabrate species (calculated as the proportion of each group compared to the entire database) represented in the database used to calculate the SSD curve were for cladoceran (23.3%), amphipods (3.3%), insects (6.7%), hydrozoans (3.3%) and for molluscs (6.6%). The NOEC/L(E)C 10 varied between 2.8 µg/l (Wirtz et al., 2004) and µg/l for Chironomus tentans (De Schamphelaere et al., 2007). It is noted that the invertebrate data are heavily dominated by data on cladocerans. The data quality of all individual studies is discussed below. 17

32 Table Overview of the accepted high quality nickel chronic NOEC values for freshwater invertebrates.. All selected values for the most sensitive endpoint used for the derivation of HC5 are marked in bold. In addition the species, test duration, effect parameter and endpoint is marked in bold the first time. Substance Species Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 158 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 149 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 56.5 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 281 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 164 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days mortality NOEC 60.1 measured yes Static renewal Natural water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,79 42,8 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,81 68,2 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC10 47 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 27.1 measured yes Static renewal 20 6,79 42,8 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 47.6 measured yes Static renewal 20 6,81 68,2 0* <1 Artificial water 18

33 Substance Species Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water Daphnia magna Cladoceran neonates 21 days mortality NOEC 49.3 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 84.7 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 93.8 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 84.7 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,85 41,7 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,81 59,7 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,81 97,1 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 25.2 measured yes Static renewal 20 6,85 41,7 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 44.4 measured yes Static renewal 20 6,81 59,7 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 25 measured yes Static renewal 20 6,81 97,1 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 45.5 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 78.1 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 82 measured yes Static renewal 20 6, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 5, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,4 50 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 6,97 50,2 0* <1 Artificial water 19

34 Substance Species Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 7,35 50,1 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 7,62 49,8 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days reproduction EC measured yes Static renewal 20 8, * <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 39.3 measured yes Static renewal * <1 Artificial water Daphnia magna Cladoceran neonates 21 days Mortality NOEC 39.1 measured yes Static renewal 20 6,4 50 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days Mortality NOEC 37 measured yes Static renewal 20 6,97 50,2 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 37.4 measured yes Static renewal 20 7,35 50,1 0* <1 Artificial water Daphnia magna Cladoceran neonates 21 days mortality NOEC 22.8 measured yes Static renewal 20 7, * <1 Artificial water Deleebeeck et al., 2005 [1] acetate acetate Daphnia magna Cladoceran neonates 21 days reproduction NOEC 90 nominal Not reported Static renewal * not reported Reconstituted water Daphnia magna Cladoceran neonates 21 days mortality NOEC 90 nominal Not reported Static renewal * not reported Reconstituted water Kuhn et al., 1989 [3] Daphnia magna Cladoceran neonates 42 days reproduction NOEC 40 measured yes Static renewal * not reported Filtered lake water Munzinger, 1990 [4] Daphnia magna Cladoceran neonates 70 days reproduction NOEC 80 measured yes Static renewal * not reported Filtered lake water Daphnia magna Cladoceran neonates 70 days growth NOEC 80 measured yes Static renewal * not reported Filtered lake water Munzinger, 1994 [5] Ceriodaphnia Cladoceran Neonates 7 days reproduction NOEC 5.3 measured Yes Static renewal Not reported Artificial water dubia Ceriodaphnia Cladoceran Neonates 7 days reproduction NOEC 3.4 measured Yes Static renewal Not reported Artificial water dubia Ceriodaphnia Cladoceran Neonates 7 days reproduction NOEC 5.8 measured Yes Static renewal Not reported Artificial water 20

35 Substance Species dubia Taxonomic group Age and/or Test Effect size of test duration parameter organism Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 5.3 measured Yes Static renewal Not reported Artificial water Cladoceran Neonates 7 days survival NOEC 15.3 measured Yes Static renewal Not reported Artificial water Cladoceran Neonates 7 days survival NOEC 9.6 measured yes Static renewal Not reported Artificial water Keithly et al., 2004 [6] (But Unbound NOECs not used) Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal Artificial water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal Artificial water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal Artificial water Cladoceran Neonates 7 days survival EC measured yes Static renewal Artificial water Wirtz et al., 2004 [7] (But unbound NOECs not used) Cladoceran Neonates 10 days Reproduction EC measured yes Static renewal Natural water Cladoceran Neonates 10 days Reproduction EC measured yes Static renewal Natural water Cladoceran Neonates 10 days Reproduction EC measured yes Static renewal Natural water Cladoceran Neonates 10 days Reproduction EC measured yes Static renewal Natural water Cladoceran Neonates 10 days Reproduction EC measured yes Static renewal Natural water De Schamphelaere et al., 2006 [8] Clistorina magnifica insect Larvae & pupae 8 months Survival NOEC 66 measured Not reported Flow through * Not reported Well water Nebeker et al., 1984 [9] Juga plicifera mollusc mature 21 days survival NOEC 124 measured Not reported Flow through * Not reported Well water Nebeker et al., 1986 [10] 21

36 Substance Species Taxonomic group Hydra littoralis Radiata Age and/or size of test organism Not reported Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water 12 days Growth rate NOEC 60 nominal yes Static renawal * Not reported Artificial water Santiago-Fandino, 1983 [11] Hyalella azteca Amphipod 7-8 days old 14 days mortality NOEC 29 measured yes Static renewal Not reported Artificial water Keithly et al., 2004 [6] Ceriodaphnia cladoceran <48 h old 17 days mortality NOEC 2.0 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days reproduction NOEC 3.5 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days mortality NOEC 10.3 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days reproduction NOEC 10.3 measured yes Static renewal * 0.6 Artificial water quadrangula Peracantha cladoceran <48 h old 17 days mortality NOEC 11.3 measured yes Static renewal * 0.6 Artificial water truncata Peracantha cladoceran <48 h old 17 days reproduction NOEC 2.5 measured yes Static renewal * 0.6 Artificial water truncata Peracantha cladoceran <48 h old 17 days mortality NOEC 25.8 measured yes Static renewal * 0.6 Artificial water truncata Peracantha cladoceran <48 h old 17 days reproduction NOEC 25.8 measured yes Static renewal * 0.6 Artificial water truncata Ceriodaphnia cladoceran <48 h old 17 days mortality NOEC 11.5 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days reproduction NOEC 11.5 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days mortality NOEC 12.7 measured yes Static renewal * 0.6 Artificial water quadrangula Ceriodaphnia cladoceran <48 h old 17 days reproduction NOEC 34.9 measured yes Static renewal * 0.6 Artificial water quadrangula Daphnia cladoceran <48 h old 21 days mortality NOEC 26.6 measured yes Static renewal * 0.6 Artificial water longispina Daphnia cladoceran <48 h old 21 days reproduction NOEC 56.6 measured yes Static renewal * 0.6 Artificial water longispina Daphnia Cladoceran <48 h old 21 days mortality NOEC 29.0 measured yes Static renewal * 0.6 Artificial water longispina 22

37 Substance Species Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water Daphnia longispina Cladoceran <48 h old 21 days reproduction NOEC measured yes Static renewal * 0.6 Artificial water Alona affinis Cladoceran <48 h old 16 days mortality NOEC 25.0 measured yes Static renewal * 0.6 Artificial water Ceriodaphnia Cladoceran <48 h old 17 days mortality NOEC 9.9 measured yes Static renewal * 0.6 Artificial water pulchella Ceriodaphnia Cladoceran <48 h old 17 days reproduction NOEC 9.9 measured yes Static renewal * 0.6 Artificial water pulchella Ceriodaphnia Cladoceran <48 h old 17 days mortality NOEC 28.2 measured yes Static renewal * 0.6 Artificial water pulchella Ceriodaphnia Cladoceran <48 h old 17 days reproduction NOEC 28.2 measured yes Static renewal * 0.6 Artificial water pulchella Simocephalus Cladoceran <48 h old 21 days mortality NOEC 9.2 measured yes Static renewal * 0.6 Artificial water vetulus Simocephalus Cladoceran <48 h old 21 days reproduction NOEC 9.2 measured yes Static renewal * 0.6 Artificial water vetulus Simocephalus Cladoceran <48 h old 21 days mortality NOEC 28.9 measured yes Static renewal * 0.6 Artificial water vetulus Simocephalus Cladoceran <48 h old 21 days reproduction NOEC 28.9 measured yes Static renewal * 0.6 Artificial water vetulus Deleebeeck et al., 2006 [12] Lymnea stagnalis Lymnea stagnalis Lymnea stagnalis Lymnea stagnalis Lymnea stagnalis Lymnea stagnalis Lymnea stagnalis Chironomus tentans mollusc Newly hatched 30 days mortality NOEC measured yes Static renewal Natural water mollusc Newly 30 days mortality NOEC measured yes Static renewal Natural water hatched mollusc Newly 30 days mortality NOEC 50.3 measured yes Static renewal <2 Natural water hatched mollusc Newly 30 days mortality NOEC 10.2 measured yes Static renewal Natural water hatched mollusc Newly 30 days Growth (wet EC measured yes Static renewal Natural water hatched weight) mollusc Newly 30 days Growth (wet EC measured yes Static renewal <2 Natural water hatched weight) mollusc Newly 30 days Growth (wet NOEC 12.0 measured yes Static renewal Natural water hatched weight) insect larvae 10 days mortality NOEC measured yes Static renewal Natural water 23

38 Substance Species Chironomus tentans Chironomus tentans Chironomus tentans Chironomus tentans Chironomus tentans Chironomus tentans Brachionus calyciflorus Brachionus calyciflorus Brachionus calyciflorus Brachionus calyciflorus Brachionus calyciflorus Brachionus calyciflorus Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value Analysis of (µg/l) concentrations Conc. response Administration of test substance Temp. ( C) ph Hardness (mg/l) DOC (mg/l) Nicb (µg/l) Test water insect larvae 10 days mortality NOEC measured yes Static renewal Natural water insect larvae 10 days mortality NOEC measured yes Static renewal <2 Natural water 3 insect larvae 10 days mortality NOEC measured yes Static renewal Natural water insect larvae 10 days biomass EC measured yes Static renewal Natural water insect larvae 10 days biomass EC measured yes Static renewal <2 Natural water insect larvae 10 days biomass EC measured yes Static renewal Natural water rotifer neonates 2 days Growth rate EC measured yes Static Natural water rotifer neonates 2 days Growth rate EC measured yes Static Natural water 3 rotifer neonates 2 days Growth rate EC measured yes Static <2 Natural water rotifer neonates 2 days Growth rate EC measured yes Static <1 Artificial water rotifer neonates 2 days Growth rate EC measured yes Static <1 Artificial water rotifer neonates 2 days Growth rate EC measured yes Static <1 Artificial water Stubblefield and Van Genderen, 2007 [18] *: robust estimated DOC values: data from Nebeker et al. (1984; 1986) using well water: DOC concentration = 1.1 mg/l (McCrady and Chapman 1979); data from Munzinger et al. (1990; 1994) using water from Lake Maggiore, Italy = 1.1 mg/l (Dueri et al. 2005); reconstituted water DOC concentration: 0 mg/l (De Schamphelaere et al., 2006) 24

39 Table Overview of the rejected low quality chronic nickel NOEC values for freshwater invertebrates. Substance Species nitrate Chironomus riparius Clistorina magnifica Ceriodaphnia dubia Ceriodaphnia dubia Daphnia magna Daphnia magna Hyalella azteca Hyalella azteca Hyalella azteca Daphnia magna Ceriodaphnia dubia Taxonomic Age Test Effect Endpoin Value Analysis of Dose Administratio Temp. ph Hardness Ni cb (µg/l) Test group and/or duration parameter t (µg/l) concentrations response n of test ( C) (mg/l) water size of test substance organism insect larvae 30 days growth MATC 1,100 measured yes Static renewal Not artificial reported Powlesland & George, 1986 [13] only unbound NOECs insect Larvae 19 weeks survival NOEC 55 measured Not Flow through <20 tapwater reported Van Frankenhuysen & Geen, 1987 [14] High control mortalæity o.a. Cladoceran Neonates 7 days reproduction NOEC 3.8 nominal Yes Static renewal Not Artificial reported water Cladoceran Neonates 7 days reproduction NOEC 7.5 nominal Yes Static renewal Not Artificial reported water Cladoceran Neonates 21 days Survival NOEC 160 nominal Yes Static renewal Not Artificial reported water Kszos et al., 1992 [15] Cladoceran Not reported 21 days reproduction EC16 30 nominal Not reported Nominal values and alternative data available filtered lake water Static renewal not reported Biesinger & Christensen, 1972 [16] Not possible to estimate NOEC or EC10 Amphipod 0-1 week 28 days mortality LC measured Not Static renewal 24 Not Not reported Not Artificial old reported reported reported water Amphipod 0-1 week 28 days mortality LC measured Not Static renewal 24 Not Not reported Not Artificial old reported reported reported water Amphipod 0-1 week 28 days mortality LC measured Not Static renewal 24 Not Not reported Not Artificial old reported reported reported water Borgmann et al., 2001 [17] Control mortality > 20% Cladoceran neonates 21 days reproduction NOEC >20 nominal Reported Static renewal not Artificial reported water Kszos et al., 1992 [15] Unbound NOEC values Cladoceran Neonates 7 days reproduction LOEC <3.8 nominal Not Static renewal Not Artificial reported reported water 25

40 Substance Species Taxonomic group Age and/or size of test Test duration Effect parameter Endpoin Value Analysis of t (µg/l) concentrations Dose response organism Cladoceran Neonates 7 days survival LOEC <3.8 nominal Not reported Administratio n of test substance Temp. ( C) ph Hardness (mg/l) Ni cb (µg/l) Test water Ceriodaphnia dubia Static renewal Not reported Artificial water Keithly et al., 2004 [6] Unbound NOEC values Ceriodaphnia Cladoceran Neonates 7 days Reproductio NOEC <2.6 measured yes Static renewal Artificial dubia n water Ceriodaphnia Cladoceran Neonates 7 days survival NOEC >11.8 measured yes Static renewal Artificial dubia water Ceriodaphnia Cladoceran Neonates 7 days survival NOEC >11.7 measured yes Static renewal Artificial dubia water Ceriodaphnia Cladoceran Neonates 7 days Reproductio NOEC >51.1 measured yes Static renewal Artificial dubia n water Ceriodaphnia dubia Lymnea stagnalis Chironomus tentans Lymnea stagnalis Chironomus tentans Chironomus tentans Brachionus calyciflorus Lymnea stagnalis Lymnea stagnalis Cladoceran Neonates 7 days survival NOEC >51.1 measured yes Static renewal Artificial water Wirtz et al., 2004 [7] Unbound NOEC values mollusc Newly hatched 30 days mortality NOEC <3.0 measured yes Static renewal Natural water insect larvae 10 days biomass NOEC <558.4 measured yes Static renewal Natural water Stubblefield and Van Genderen, 2007 Unbound NOEC value mollusc Newly 30 days Growth (wet EC measured yes Static renewal Natural hatched weight) water insect larvae 10 days mortality NOEC measured yes Static renewal Natural water insect larvae 10 days biomass EC measured yes Static renewal Natural water rotifer neonates 2 days Growth rate EC measured yes Static Natural water Stubblefield and Van Genderen, 2007 [18] Data were rejected due to the occurrence of naturally-occurring stressors mollusc Newly 30 days mortality EC measured yes Static renewal Natural hatched water mollusc Newly 30 days Growth (wet EC measured yes Static renewal Natural hatched weight) water 26

41 Substance Species Chironomus tentans Taxonomic group Age and/or size of test Test duration Effect parameter Endpoin Value Analysis of t (µg/l) concentrations Dose response Administratio n of test substance Temp. ( C) ph Hardness (mg/l) Ni cb (µg/l) Test water organism insect larvae 10 days biomass EC measured yes Static renewal Natural water Stubblefield and Van Genderen, 2007 [18] Data were rejected because the EC10 values were >2x below the lowest tested concentration 27

42 Table Overview of the high quality chronic nickel NOEC values for freshwater invertebrates not further used for normalisation because the phys.-chem. falls outside the boundaries of the BLMs/ DOC could not be estimated. Substance Species Taxonomic Age and/or Test Effect Endpoint Value Analysis of Dose Administration Temp. ph Hardness Nicb (µg/l) Test water group size of test duration parameter organism (µg/l) concentrationresponse s of test substance ( C) (mg/l) Daphnia magna Cladoceran Cohorts of various age 17 days reproduction EC measured Not reported Flow through not reported filtered lake water Daphnia magna Cladoceran Cohorts of various age 17 days survival EC measured Not reported Flow through not reported filtered lake water Daphnia magna Cladoceran neonates 21 days reproduction NOEC 200 measured Not reported Renewal not reported filtered lake water Daphnia magna Cladoceran neonates 21 days survival NOEC 200 measured Not reported Renewal not reported filtered lake water Daphnia magna Cladoceran neonates 21 days growth NOEC 200 measured Not reported Renewal not reported filtered lake water Enserink et al [2]: No robust DOC estimate could be made **. Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 51.5 measured yes Static renewal * Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 232* 8.5 Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 48.5 measured yes Static renewal * 232* 8.5 Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 230* 8.4 Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 31.6 measured yes Static renewal * 230* 8.4 Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * Artificial water Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 6.8 measured yes Static renewal * Artificial water Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction NOEC 27.9 measured yes Static renewal * Natural water Ceriodaphnia dubia Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 234* 3.0 Natural water Ceriodaphnia Cladoceran Neonates 7 days survival NOEC 19.3 measured yes Static renewal * 234* 3.0 Natural dubia Ceriodaphnia dubia water Cladoceran Neonates 7 days Reproduction NOEC 12.0 measured yes Static renewal * 236* 2.9 Natural water 28

43 Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 21.4 measured yes Static renewal * 236* 2.9 Natural water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 236* 3.6 Natural water Cladoceran Neonates 7 days survival NOEC 17.5 measured yes Static renewal * 236* 3.6 Natural water Cladoceran Neonates 7 days Reproduction NOEC 3.3 measured yes Static renewal * 228* 1.9 Artificial water Cladoceran Neonates 7 days survival NOEC 4.9 measured yes Static renewal * 228* 1.9 Artificial water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 262* 9.5 Natural water Cladoceran Neonates 7 days survival NOEC 53.6 measured yes Static renewal * 262* 9.5 Natural water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 246* 1.2 Artificial water Cladoceran Neonates 7 days survival NOEC 19.0 measured yes Static renewal * 246* 1.2 Artificial water Cladoceran Neonates 7 days Reproduction NOEC 1.8 measured yes Static renewal * Artificial water Cladoceran Neonates 7 days survival EC measured yes Static renewal * Artificial water Cladoceran Neonates 7 days Reproduction NOEC 2.5 measured yes Static renewal * Artificial water Cladoceran Neonates 7 days survival NOEC 4.1 measured yes Static renewal * Artificial water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * 292* 1.2 Artificial water Cladoceran Neonates 7 days survival EC measured yes Static renewal * 292* 1.2 Artificial water Cladoceran Neonates 7 days survival NOEC 4.5 measured yes Static renewal * Artificial water Cladoceran Neonates 7 days Reproduction NOEC 4.4 measured yes Static renewal * 310* 2.7 Artificial water Cladoceran Neonates 7 days survival NOEC 6.8 measured yes Static renewal * 310* 2.7 Artificial water Cladoceran Neonates 7 days Reproduction NOEC 8.6 measured yes Static renewal * 586* 6.3 Artificial water Cladoceran Neonates 7 days survival EC measured yes Static renewal * 586* 6.3 Artificial water Cladoceran Neonates 7 days Reproduction EC measured yes Static renewal * Artificial water 29

44 Ceriodaphnia dubia Ceriodaphnia dubia Cladoceran Neonates 7 days survival NOEC 3.9 measured yes Static renewal * Artificial water Cladoceran Neonates 7 days survival NOEC 52.9 measured yes Static renewal * Natural water Wirtz et al., 2004 [7] Outside BLM range Brachionus calyciflorus rotifer neonates 2 days Growth rate EC measured yes Static * Natural water Stubblefield and Van Genderen, 2007 [18] Outside BLM range *: ph and or hardness range of the test is situated outside the range of the developed/validated BLMs hardness between mg/l CaCO3 (D. magna & C. dubia) and ph between for Daphnia magna; for C. dubia. **: no robust estimation of the DOC conc. for toxicity data from Enserink et al. (1991) could be made. Formatted: Section start: Continuous 30

45 [1] Deleebeeck et al., 2005 Species: Daphnia magna. For each bioassay, the prepared test medium (natural water) was used as the dilution water to make a nickel concentration series (added as NiCl 2, 5 nickel concentrations and a control). Dissolved nickel concentrations in control media were always below the method detection level (MDL) of the GF-AAS (see further), which is 3 µg/l. In order to obtain near-equilibrium situations, all media were prepared 24h prior to being used in the toxicity tests and stored at 20 C. 21 days Chronic toxicity tests were performed according to draft OECD guideline 211 (OECD, 1998). Test organisms originated from a healthy clone (K6) of D. magna, which has been cultured under standardized conditions in M4 medium (Elendt and Bias, 1990) for several years. Dissolved nickel concentrations in the culture medium were always below the MDL of the GF-AAS, which is 3 µg/l. At the start of each test, 10 juvenile animals (< 24h old) per concentration were transferred individually to polyethylene cups containing 50 ml of test medium (i.e., 10 replicates per concentration). Animals were fed every day with an algal mix of P. subcapitata and Chlamydomonas reinhardtii in a 3:1 ratio (on a cell number basis). Each organism received 8 x 10 6 cells per day in the first week, 16 x 10 6 cells per day in the second week and 24 x 10 6 cells per day in the third week of exposure. Every other day, the medium was renewed, parent mortality noted, and the number of produced juveniles counted. At the end of the test, reproduction was calculated in two different ways for each nickel concentration: R0 = (total number of offspring after 21 days of exposure)/(total number of parent animals at the start of the test) and R1 = (total number of offspring after 21 days of exposure produced by parent animals that survived the test)/(total number of surviving parent animals). Following OECD guideline 211 (OECD, 1996), a test should be considered valid when the mortality of the parent animals in the control medium does not exceed 20% at the end of the test, and when the mean number of live offspring produced per parent animal surviving at the end of the test in the control medium is at least 60. The first criterium was fulfilled in all of the tests conducted in this study. The second criterium was not fulfilled in all tests. Average control reproduction was 56 offspring per live parent animal at the end of testing. A reason for the slightly lower control reproduction could possibly have resulted from the use of natural media insetad of the artificial OECD medium. Nevertheless the slightly lower control reproduction than required by the OECD TG was not considered as reason enough for not accepting the data. Dissolved nickel concentrations (0.45 µm filter, Gelman Sciences, Ann Arbor, MI, USA) in each test concentration were determined at the beginning of the tests and at the end of each of the three exposure weeks using a flame or a graphite furnace atomic absorption spectrophotometer. Clear conc.- response curves were reported. Both EC50 and EC10 are parameters to be estimated, allowing a straight-forward estimation of confidence intervals directly from the fitting procedure. Parameter estimation and calculation of the 95% confidence limits was carried out using the Levenberg-Marquardt method (Levenberg, 1944, Marquardt, 1963). DOC in synthetic laboratory water was considered to be 0 mg/l, whereas DOC in natural waters ranged from 2.5 to 17.3 mg/l. Reliability/relevance: study was accepted. From the chronic toxicity tests reliable EC 10 values between 32.7 and 444 µg Ni/l for reproduction/ and NOEC values between 52.8 and 389 µg/l for survival were extracted from the paper. [2] Enserink et al, Species: Daphnia magna. The experiments were carried out at a constant temperature of 20 C in filtred (25 µm) water from Lake Ijssel (hardness 225 mg/l CaCO 3 ; ph: 8.1). The semi static life table experiments were started in 5-fold with neonates (< 24 old) randomly distributed into cohorts of 10 animals each. A control and 5 to 6 toxicant concentrations with a dilution ratio of 3.2 was used. The test medium was renewed 3 times a week.the daphnids were fed 31

46 with green algae. The surviving females and the number of neonates were recorded daily during the 21 days experiment. Statistics (p = 0.01) were reported for the semi static experiments. Statistics are used but no dose response curve is reported. Because only the lowest effect concentrations (LOEC) with respect to growth (320 µg/l), survival (320 µg/l) and intrinsic rate of natural increase (320 µg/l), which is related to both survival and reproduction, were reported, the NOEC values were derived from these concentrations using a factor of 3.2, i.e. the ratio used between test concentrations. A NOEC of 200 µg Ni/l was therefore estimated for the different endpoints (survival, growth and reproduction). All concentrations were measured using AAS. In addition, population dynamics were investigated in a 17 days test using an intermittent flow system. The tests were initiated with exponentially growing populations of 20 daphnids consisting of cohorts of various ages. The effects of the test compounds on the population dynamics were analyzed by means of a parametric model. Population growth could be represented as a function of both reproduction and survival. The same approach for deriving reliable NOEC values from the Enserink et al. (1991) was used in the EU Zn RAR. Reliability/relevance: study was accepted. From the chronic toxicity tests reliable NOEC/EC 10 values of 200 µg Ni/l (from the semi static life-table experiments) and 540 µg Ni/l (from the flow through population studies) for reproduction/survival were extracted from the paper. However no robust estimate could be made on the DOC concentration in the test media. These data were therefore not used for the normalisation and hence not carried over to the effect assessment. These data are however included in Table where all valid data outside the domain of the BLMs or for which no reliable DOC values are currently available. [3] Kuhn et al., 1989 Species: Daphnia magna. The test organisms were maintained in temperature controlled dechlorinated tap water from the city of Berlin. Daphnia magna were fed daily with dry Scenedesmus alga. Renewal of the culture water was performed twice a week. All experiments were carried out in synthetic freshwater, where all salts were prepared in deionized water. The final Ca and Mg concentrations in the experiments were 82.4 and 24.9 mg/l. The ph of the solutions was 8.0 ± 0.2. For this reconsittuted test water a default DOC of zero mg/l was assumed. Before preparing the dilution series the substance was fully dissolved in the dilution water using magnetic stirrers. The dilution steps correspond to a ratio 1:2. Four parallel test vessels per concentration level and the controls comprising at least 4 vessels were filled with 24 h old Daphnia magna (1 organism/50 ml or 20 organisms per concentration level). A semistatic procedure was used (3 times a week renewal). The offspring/dead organisms were counted and used for further analysis. Tetramin (fish feed) and activated sludge was given as feeds. The tests were performed at 25 C during a period of 21days. Samples were taken twice from selected concentration levels of the test series during the test period and chemically analyzed. The test concentration range reported varied between 0.09 and 23.6 mg Ni/l. The tested Ni compound was Ni acetate. The endpoints followed in this 21 days reproduction test were mortality, reproduction and appearance of the first offspring. The Student s t-test and the U-test were the statistical methods used. The results (NOEC values) were expressed as nominal concentrations. However, the chemical analysis showed that the nominal concentrations did not differ more than 20% and therefore the data were further used in the effects assesssment. The control reproduction was 88.8 offspring (cv of 14.8%), the control mortality 7.1 %. conc.- response curve is not reported. Formatted: English (U.S.) Formatted: English (U.S.) Reliability/relevance: study was accepted. From the chronic toxicity tests reliable NOEC value of 90 µg Ni/l for reproduction/survival were extracted from the paper. [4] Munzinger,

47 Species: Daphnia magna. The experiments with Daphnia magna were carried out with strains adapted to Lake Maggiore water (hardness: mg/l CaCO3; alkalinity mg/l; ph: ; Ni background: < 1 µg/l). Lake water was also used to prepare solutions which contained 5 different Ni concentrations (between 40 and 200 µg/l). Temperature of the water was 21 C. Those nominal concentrations were confirmed by AAS. At the beginning of the experiments 30 individuals (24-44 h old) were chosen randomly and transferred to the control or test water. Individual D. magna were examined, medium was changed, neonates counted and discarded and food was added every day. Significance testing was performed (p=0.05). Control mortality never exceeded 10 %. The total number of progeny (offspring) showed a NOEC of 40 µg/l. Clear conc.- response curve was reported. The experiments were terminated after 42 days. DOC was estimated from Dueri et al. (2005), which reports a mean value of 1.1 mg/l from long term monitoring of Lake Maggiore. Reliability/relevance: study was accepted. From the chronic toxicity tests reliable NOEC value of 40 µg Ni/l for reproduction was extracted from the paper. [5] Munzinger, 1994 Species: Daphnia magna. The experiments with Daphnia magna were carried out with strains adapted to Lake Maggiore water (hardness: mg/l CaCO3; alkalinity mg/l; ph: ; Ni background: < 1 µg/l). Lake water was also used to prepare solutions which contained 5 different Ni concentrations (between 40 and 200 µg/l). Temperature of the water was 21 C. Those nominal concentrations were confirmed by AAS. At the beginning of the experiments 30 individuals (24-44 h old) were chosen randomly and transferred to the control or test water. Individual D. magna were examined, medium was changed, neonates counted and discarded and food was added every day. Significance testing was performed (p=0.05). Control mortality never exceeded 10 %. The brood size and body length showed a NOEC of 80 µg/l. Clear dose response curve was reported. The experiments were terminated after 42 days. DOC was estimated from Dueri et al. (2005), which reports a mean value of 1.1 mg/l from long term monitoring of Lake Maggiore. Reliability/relevance: study was accepted. From the chronic toxicity tests reliable NOEC value of 80 µg Ni/l for reproduction/body length was extracted from the paper. [6] Keithly et al., 2004 Species: Ceriodaphnia dubia. The Keithly et al. (2004) study assessed the 7 days chronic Ni toxicity to C. dubia at several water hardnesses (between 50 and 250 mg/l CaCO 3 ) and constant ph (i.e. between 7.6 and 7.8) and DOC (0.7 to 1.3 mg Ni/L). Organisms were otained from laboratory cultures maintained at 25 C and 90 mg/l CaCO 3. These tests were conducted with neonates (<24 days old) in accordance with the US EPA guidelines in static renewal systems using a formulated moderately hard water as test medium. To ensure Ni was in equilibrium between the dissolved/total phases, test concentrations were prepared 24 h prior to use.daily renewal tests were conducted at C and organisms were fed (artificial YCT diet and algae) during exposure. For each test, a control and 6 to 7 exposure concentratrations (10 replicates) were used. Ni concentrations in solution was measured using ICP-MS. Water chemistry (Ni, Ca, DOC, ) was performed on the dissolved fraction and was measured 3 times during testing (the Ni concentrations for the 50 mg/l CaCO 3 tests are based on nominal concentrations). NOEC values (calculated using the Dunnett s or Steel Many One Rank tests) varied between <3.8 and 15.3 µg/l. In general, reproduction seems to be a more sensitive endpoint compared to survival. Keithly et al. (2004) also studied the chronic effects of Ni on the amphipod Hyalella azteca. The amphipods were 7 to 8 days old at test initiation and exposed for 14 days to 7 different Ni 33

48 concentrations (10 replicates) and 1 control. The organisms were fed (artificial YCT diet) during exposure. The 14 days tests were conducted under static renewal conditions. Temperature was maintained at C, ph fluctuated between 8.0 and 8.3, hardness was maintained at 91 mg/l CaCO 3. Ni concentrations in solution were measured using ICP-MS. The test generated a strong dose-response relationship. Control mortality was <10%. A 14 days NOEC value (calculated using the Dunnett s or Steel Many One Rank tests) of 29 µg/l was calculated. Reliability/relevance: study was accepted. The results from the toxicity tests performed with C. dubia at the hardness of 50 mg/l CaCO 3 were based on nominal concentrations and resulted in unbounded NOEC values. Therefore these results were not retained in the effects assessment. All other chronic toxicity test results with C. dubia were accepted and resulted in NOEC values between between 5.3 and 15.3 µg Ni/l for the endpoint survival and between 3.4 and 5.8 µg Ni/l for the endpoint reproduction The chronic toxicity test with H. azteca resulted in a reliable NOEC value of 29 µg Ni/l. [7] Wirtz et al., Species: Ceriodaphnia dubia. Wirtz et al. (2004) also generated toxicity data with C. dubia using different test medium (reconstituted and natural) with different water characteristics (i.e. ph between 7.2 and 8.6; with (up to 8.2 mg/l) and without DOC addition, hardness between 40 and 900 mg/l CaCO 3 ). Water used in culturing was moderately hard reconstituted laboratory water prepared with reagent grade chemicals. The test methods used were in accordance with US EPA and ASTM guidelines (ASTM, 2001; US EPA, 2002). Tests using natural waters were conducted using unaltered water collected from each site and water that was filtered (0.45 µm) prior to test initiation. All toxicity tests were performed using <24 h old neonates. Tests were maintained at 25 C. The 7 days tests were conducted under static renewal conditions using 5 different concentrations (10 replicates). A 50% dilution serie was used. The organisms were fed an YCT/algae mix. Chemical analysis of the Ni concentrations (using AAS) was performed at test initiation and at 6 days following exposure. Water chemistry (hardness, ph, DOC ) was measured during testing. Tests were accepted if control survival >80% and there were an average of 15 youngs/adult was achieved. NOEC/EC 10 was calculated using appropriate statistical analysis, i.e. ANOVA and least squares non linear regression analysis. Water chemistry seemed to affect considerably the chronic toxicity of Ni towards C. dubia. Again, the test results indicate that reproduction is a more sensitive endpoint compared to survival Formatted: English (U.S.) Reliability/relevance: study was accepted. From the chronic toxicity tests NOEC/EC 10 values varying between 2.8 and 53.6 µg Ni/l were calculated. Unbounded NOEC values were not retained for the effects assessement. NOEC values with a ph and/or hardness outside the range used for BLM development/validation were not used in the further assessment. These data are however included in Table where all valid data outside the domain of the BLMs or for which no reliable DOC values are currently available. [8] De Schamphelaere et al., 2006 Species: Ceriodaphnia dubia. Chronic (10d) toxicity experiments were carried out with Ceriodaphnia dubia (Crustacea: Cladocera). The test protocol was designed to comply as much as possible with the Ceriodaphnia dubia TG of the US EPA (2002) and OECD TG 211 on daphnids (1984, 1998). The most important deviation from the original US EPA test method was that no YTC slurry was provided as food source and that instead only a green algal mix was given in chronic toxicity experiments. The test concentrations (nominal nickel concentrations) were selected based on the results of preliminary static-acute rangefinding tests and information obtained from the literature. Test concentrations were spaced by Formatted: English (U.S.) Formatted: English (U.S.) 34

49 a factor of 1.8 (nominal). For each chronic test, 5 or 6 nickel concentrations and a non-nickel spiked water control were tested. Test solutions were equilibrated for 24 hours before test initiation. C. dubia was obtained from a monoclonal in-house culture, which is routinely maintained on carbon-filtered city tap water (Gent, Belgium), conditioned by continuous passage over a biological filter. Six weeks (> three generations) before all experiments were started, organisms were acclimated to moderately hard reconstituted water (US EPA, 1993) with added Se and Vitamin B12 and Zn to optimize culture health. Dissolved Ni was below the detection limit of the GF-AAS, i.e. < 2 µg/l). Cultures were fed ad libitum with an algal mix of Pseudokirchneriella subcapitata and Chlamydomonas reinhardtii in a 3:1 ratio on cell basis and were maintained on a light cycle of 12 hours light and 12 hours dark. Chronic toxicity tests were initiated with 16 to 24 hour old juveniles. Tests were conducted in a temperature-controlled room at 25º±1ºC under a light cycle of 16 hours light and 8 hours dark. Test containers were 30 ml polyethylene cups containing 20 ml of test solution. One Ceriodaphnia dubia individual was impartially assigned to each test vessel and ten replicates were tested per treatment. Mortality and reproduction (number of juveniles) were determined every 24 hours. Feeding was by an algal mix of P. subcapitata and C. reinhardtii in a 3:1 ratio on cell basis. No extra food was added on the 3rd day of exposure, because food remaining from the two previous feedings was still abundant and because overloading of food has previously been observed to act adversely on our Ceriodaphnia clone. Tests lasted 10 days, which was sufficient to ensure three broods to be completed in control organisms. Ni was measured using GF-AAS after acidification of the samples. Test data were analyzed using Statistica software. The Mann-Whitney U test was used to test for significant differences between the reproduction of the Ni treatments and the control. The EC10 effects concentrations were calculated by the logistic regression. Toxicity tests were conducted in six natural waters, which ranged in DOC from 3.1 to 23.6 mg/l. Reliability/relevance: study was accepted. From the chronic toxicity tests reliable EC 10 values between 7.4 and 44.2 µg Ni/l for reproduction. [9] Nebeker et al., 1984 Species: Clistorina magnifica. The test species (Insecta: Tricoptera) was obtained from the Oregon State University. Well water for all flow through tests is used as test water (delivery rate of 37 ml/min). Water temperature ranged between 14.5 and 16 C. Reported hardness and alkalinity of testmedia are 54 and 45 mg/l CaCO 3 respectively, the reported ph value ranged between 6.8 and 7.4. Stock solutions for Ni were prepared by dissolving reagent grade nickel in acidified well water. Metal solutions were delivered at a flow rate of 100 ml/min. Water samples for total Ni analysis were taken weekly and analyzed using AAS. Ni solutions were serially diluted (0.7 dilution series). All tests were initiated with larvae and lastaed 8 months. Chi-square analysis of the chronic data was used to determine statistical significance. Ni concentration series are reported (6 concentrations between 66 and 3669 µg/l Ni). A concentration-response is reported. Control mortality is 10%. DOC concentrations are based on measurements made by McCrady and Chapman (1979) on the same well water. Reliability/relevance: study was accepted. Reliable NOEC values forsurvival of larvae and pupae of C. magnifica is 66 µg/l Ni. [10] Nebeker et al., 1986 Species: Juga plicifera. The snail (Mollusca: Gastropoda) was collected from an Oregon coastal stream. They were held in culture for several months during testing. They received leaves, fish food pellets, algae and Cerophyl as food.water was obtained from a well at a temperature of 15 C. Stock solutions were prepared by dissolving reagent grade nickel in acidified well water. Water samples were for total metal analyses were taken at Formatted: English (U.S.) Formatted: English (U.S.) 35

50 least weekly during the chronic exposure experiments and analyzed using AAS. Ni solutions were serially diluted (0.7 dilution series). Metal solutions were delivered at a flow rate of 100 ml/min. Ten snails were exposed at each concentration with replicates. Tests were started with mature snails. The chronic exposure time was 21 days. NOEC concentrations were determined using chi-square analysis. Reported hardness and alkalinity of testmedia are 59 and 50 mg/l CaCO 3 respectively, the reported ph value 7.1 Mortality was the endpoint used. No concentration- response curve was reported. No control mortaility was reported. DOC concentrations are based on measurements made by McCrady and Chapman (1979) on the same well water. Reliability/relevance: study was accepted. Reliable NOEC value for survival of J. plicifera is 124 µg/l Ni. [11] Santiago-Fandino, 1983 Species: Hydra littoralis. The hydrozoan (Coelenterata) was grown in 1 l artificial medium (i.e DOC was very low and assumed to be zero). The medium did not contain EDTA and has the following characteristics: hardness of 70 mg/l CaCO3 and a ph of 8.2. The salts were diluted in distilled water. The cultures were kept in a darkened constant temperature room at C. The organaisms were fed daily with nauplii of Artemia. The number of replicates was 4/6 in each of which 10 individuals were placed. was added as. 5 test concentrations were used (between 1and 1,000 µg/l). Growth rates were calculated by determining the production of new organisms via asexual reproduction over time. Effect of nickel exposure was determined by comparing growth rates in nickel treatments with that of the control. The duration of the experiment was 12 days. The sublethal threshold concentration (lowest conc at which the growth rate becomes significantly less than that of the controls) was 60 µg/l. This threshold concentration was estimated using the Barlett s test. Dose response curve is reported. Reliability/relevance: the observed no effect concentration for growth rate of Hydra (i.e. 60 µg/l) was reported as a nominal value but the study was accepted because no other data were available for that specific species and because this threshold concentration was much higher than the background nickel concentration in the artificial test medium. [12] Deleebeeck et al., Test species: Peracantha truncata; Ceriodaphnia quadrangula; Daphnia longispina; Alona affinis; Ceriodaphnia pulchella; Simocephalus vetulus. Six different cladoceran species were sampled from selected regions in the middle of Sweden. In this area, there is a very low anthropogenic input (N, P) since population density is very low and agricultural land use is limited (the major part of the natural environment consists of forests and lakes). In the laboratory, cultures were kept at 20 C and a light cycle of 12L:12D. The animals were fed ad libitum with an algal mix of Pseudokirchneriella subcapitata and Chlamydomonas reinhardtii in a 3:1 ratio (on a cell number basis). The culture medium (natural water) was partly refreshed every week. The experimental design was to conduct toxicity tests in soft, medium hard and hard test media (artificial test medium with very low level of DOC assumed to be zero). Organisms originating from soft waters were tested in soft and medium hard test media and organisms originating from hard waters were tested in medium hard and hard test media. The hardness of the test media was 6.29, and 43.3 mg CaCO3/l in soft, medium hard and hard medium, respectively. ph was in all tests similar, i.e

51 For each bioassay, a control and 5 Ni concentrations Ni added as NiCl 2 were prepared. At the start of each test, 10 juvenile animals (< 48h old) per concentration were transferred individually to polyethylene cups containing 50 ml of test medium (i.e. 10 replicates per concentration). Animals were fed every day with an algal mix of P. subcapitata and C. reinhardtii in a 3:1 ratio (on a cell number basis). Tests were kept at 20 C under a light cycle of 12L:12D. Every other day, the medium was renewed, parent mortality noted, and the number of produced juveniles counted. Tests were finished as soon as control animals had released a third brood (depending on the species this occurred between 16 and 21 days after test initiation). Weekly, dissolved Ni, total Ca and Mg, and dissolved inorganic carbon concentrations were measured in the test medium. NOEC and LOEC values for the endpoint mortality (Fisher s Exact Test (Finney, 1948, Pearson and Hartley, 1962) recommended by US-EPA, 1994; p < 0.05)). NOEC and LOEC values for the endpoint reproduction (Mann Whitney U test, p < 0.05). Reliability/relevance: study was accepted. From the chronic toxicity tests reliable NOEC values between 2.0 and 34.9 µg Ni/l for Ceriodaphnia quadrangular (mortality); between 2.5 and 25.8 µg Ni/l for Peracantha truncata (reproduction); between 26.6 and µg Ni/l for Daphnia longispina (mortality); 25.0 µg Ni/l for Alona affinis (mortality); between 9.9 and 28.2 µg Ni/l for Ceriodaphnia pulchella (reproduction); between 9.2 and 28.9 µg Ni/l for Simocephalus vetulus (reproduction). [13] Powlesland & George, 1986 Test species: Chironomus riparius. The toxicity tests were conducted with the midge using the methods outlined by US EPA (1975). The tests were performed in artificial dilution water consisting of 384 mg/l NaHCO3, 240 mg/l CaSO4.2H2O and 240 mg/l MgSO4 (all dissolved in distilled water). All organisms are acclimated to this medium prior to testing. All tests were conducted at 22 C. Analyses of nickel in the test solutions were conducted by AAS. Toxicant concentrations were prepared from a solution of nickel nitrate. In the chronic toxicity tests 800 ml of the test solution was used and each concentration was replicated 3 times. At twice weekly intervals, 500 ml of the test medium was replaced. A single egg mass (eggs < 18 h old) was added to each replicate on day 1. Larvae were further fed with pellets. Two different tests were performed using 3 different Ni concentrations (dilution factor between 0.3 and 0.7). The ph of the test solutions varied between 8.6 and 8.7, the alkalinity between 247 and 267 mg/l CaCO3. Significance testing (method not reported) shows that growth was inhibited at the lowest tested concentration, i.e µg Ni/l. The authors reported a MATC of 1.1 mg/l. However, this MATC was not calculated as the geometric mean between the NOEC and the LOEC concentrations and was therefore not further retained. In addition, the calculated MATC was below the lowest tested concentration. Clear dose response was reported. Reliability/relevance: The authors reported only unbounded NOEC values and are therefore inadequate for risk assessment purposes. [14] Van Frankenhuysen & Geen, 1987 Test species: Clistorinia magnifica. (Insecta, Tricoptera) Egg masses were from Marion Lake (Vancouver). During the experiments the larvae were held in dechlorinated tapwater flowing at a rate of 100 ml/min. Dissolved background concentration of Ni was <20 µg/l. The ph of the test media ranges between 5.6 and 6.4, the hardness was 4.5 mg/l CaCO3 (1.6.mg/l Ca; 0.1 mg/l Mg). The DOC concentration was reported: mg/l. Stock solutions of reagent grade NiCl2.6H2O was prepared in distilled water. The water temperature was maintained at C. The larvae were fed leaves of alder supplemented with wheat grains. The toxic effect of Ni was studied by exposing larvae from first instar until pupation. Three different Ni Formatted: English (U.S.) Formatted: English (U.S.) 37

52 concentrations were tested: 55, 215 and 700 µg Ni/l. All concentrations were obtained from measurements using AAS (detection limit 20 µg/l). The Ni reported concentration in the test media was dissolved. Each experiment was replicated with 50 larvae per replicate. Larval development and survival was recorded weekly for the first 5 weeks and every other week thereafter. The experiment was terminated after 19 weeks. Mean survival times were calculated and compared using the Kruskal Wallis test. Significance was tested at the 95% confidence level. The effect of Ni on the caddisfly was tested at 3 different phs: 4.1, 5.5 and 6.2. Clear dose response curves were reported for all experiments. Control survival at the end of the experiments never exceeded 50% Reliability/relevance: study was rejected because of the high control mortality (> 50%). [15] Kszos et al., 1992 Test species: Ceriodaphnia dubia; Daphnia magna. The test organisms were cultured in laboratory using dilute mineral water (for C. dubia) or natural well/spring water (for D. magna). The mineral water was further amended with a low concentration of trace metals. Water temperature was maintained at 25 C for C. dubia and 21 C for D. magna. The daphnids were fed a mixture of yeast, YCT and algae. Each C. dubia test was initiated with neonates (< 24 h). Daily renewal of the test water during the 7 days test was applied. The toxicity tests with C. dubia were performed in 10% (ph: 7.0; hardness: 42 mg/l CaCO 3 ) and 30% % (ph: 8.5; hardness: 117 mg/l CaCO 3 ) diluted mineral water. The 21 days tests with D. magna were performed in well water with a ph of 7.7 and hardness of 109 mg/l CaCO 3. The test conditions with D. magna were similar: neonates were used, fed the same mixture nominal test concentrations and the water was renewed every other day. The NOEC for all tests were computed using ANOVA. Dose response curves were reported for both organisms. Control survival was 100% for the C. dubia tests and 90% for the D. magna tests. All values are reported as nominal concentrations. Reliability/relevance: study was rejected as all effect concentrations were reported as nominal concentrations, and because other data for this species are available from studies that reported measured nickel concentrations. [16] Biesinger & Christensen, Test species: Daphnia magna. The strain was originally obtained from the University of Michigan. The chronic daphnid tests were placed in 200 ml of medium at a constant temperature of 18 C. Four test chambers were used with a total of 20 organisms for each experimental and control conditions. Neonates (12 h old) were used into the test chambers and the medium was renewed at the beginning of each week. Reproduction was assessed by counting the young produced each week. Lake Superior water was used as medium (ph: 7.74; hardness: 45.3 mg/l; Ni background: <0.5 µg/l) in both the culture and testing media. Geometric series of Ni concentrations were used. The test duration was 21 days. Statistics (Litchfield & Wilcoxon) were used but no dose response curve was reported. All concentrations were reported as added. Both 50 and 16% impairment of the reproduction are reported. Reagent grade chemicals were used in the testing (NiCl2.6H2O). The publication reported a 16% reproductive impairment at 30 µg/l Ni. However, as no dose response curve was reported it was not possible to calculate the NOEC/EC10 concentration. Reliability/relevance: study was rejected. From the chronic toxicity tests it was not possible to estimate a reliable NOEC/EC 10 value, as only an EC16 and no dose response curve was reported. [17] Borgmann et al.,

53 Test species: Hyalella azteca. Water-only exposures using the amphipod H. azteca were also performed by Borgmann et al. (2001). The amphipods were cultured in dechloribated city tap water (from Lake Ontario, hardness: 130 mg/l; alkalinity 90 mg/l; ph ). Twenty 0-1 week old larvae were exposed for 28 days in a static renewal system. LC 10 values were estimated using regression analysis. Ni concentrations in the water samples were analyzed using AAS. Tests were maintained at 23 C. The organisms were exposed to 9 to 10 different Ni concentrations (2 to 3 replicates with 20 animals per replicate) and 1 control. Results of the water only experiments were reported as LC 25 and LC 50 values. Additional information on LC 10 values and individual raw data were provided by the author (test concentration, individual survival per treatment, dose response curve was given by Borgmann). Reliability/relevance: The author reported LC 10 values for 3 experiments, i.e. 6.3, 5.5 and 20.9 µg Ni/l (geometric mean of 9.0 µg Ni/l). However, inadequate control survival in the experiments, i.e. it varied between 70 and 79%, was observed and the results were therefore rejected. [18] Stubblefield and Van Genderen, 2007 Test species: Lymnea stagnalis; Chironomus tentans; Brachionus calyciflorus. Exposure treatments were prepared by adding appropriate volumes of NiCl2.6H2O stock to site waters in polypropylene dilution chambers and were allowed to equilibrate for at least 18 hours at test temperature prior to distributing to exposure chambers for test initiation or renewal. Test designs consisted of at least five toxicant concentrations (50% dilution series) and a negative control (unspiked dilution water). Exposures were initiated by randomly distributing organisms directly into test solutions. The hardness of the test media varied between 16 and 256 mg/l CaCO3, the ph between 6.9 and 8.0, the DOC between.69 and 7.1 mg/l. Tests with snails. Egg masses (L. stagnalis) were obtained from Dr. Martin Grosell (RSMAS, University of Miami, Miami, FL). Adults that produced the egg masses used for testing were cultured in dechlorinated tap water (average hardness and ph of 57 mg/l as CaCO3 and 7.4, respectively). Egg masses were shipped overnight to the testing laboratory and subsamples (approximately 200) were placed into each of the respective site water samples. Water was maintained at 25 ± 2 C, continuously aerated and changed out (80% volume replacement) every other day. Upon hatch, snails were randomly transferred to exposure chambers to initiate testing. Test methods followed those proposed by Bhargava (1992) and Sloof and Canton (1983). The test endpoint (blot-dried wet weight) was determined to the nearest 0.01 mg using an analytical balance (Mettler-Toledo, Columbus, OH, USA). Reliability/relevance: the study was accepted. From the chronic toxicity tests reliable EC10 values (growth) between 1.4 and µg Ni/l for Lymnea stagnalis. The extrapolated EC10 values are only accepted when they are within a two-fold difference with the lowest test concentration. Thus for L. stagnalis (for the most sensitive endpoint wet weight ), the EC10 values for the river Platte (i.e µg/l) and the Santiam river (i.e. 1.4 µg/l) were retained, while the EC10 values for the river Calapooia (i.e. 1.1 µg/l) and Zollner Creek (i.e. 1.3 µg/l) were rejected Tests with insect. Second instar larvae (C. tentans) were obtained from Aquatic Biosystems (Fort Collins, CO). Adults that produced the larvae used for testing were cultured in moderately-hard reconstituted laboratory water (average hardness and ph of 96 mg/l as CaCO3 and 8.2, respectively). Larvae were shipped overnight to the testing laboratory in polypropylene containers which were immediately placed in an environmental chamber (25 ± 2 C) under light aeration. Organisms were observed for 48 h prior to test initiation and fed fish flake slurry once daily. At test initiation containers holding larvae were poured into a shallow glass dish for close observation of population health and randomly transferred to exposure chambers. Test methods followed those proposed by the U.S. EPA (2000) and OECD (2006). 39

54 Test chambers contained approximately 50 ml of autoclaved and thoroughly washed beach sand. The test endpoint (ash free dry weight) was determined using a drying oven (105 C for at least 12 hours; VWR Internations, West Chester, PA) and a muffle furnace (550 C for at least 2 hours; Neytech). The ash free weight was calculated by subtracting the ashed weight (non-biological material) from the dry weight. Reliability /relevance: the study was accepted. From the chronic toxicity tests reliable EC10 values (biomass) between and µg Ni/l for Chironomus tentans. The extrapolated EC10 values are only accepted when they are within a two-fold difference with the lowest test concentration. For the species C. tentans (for the most sensitive endpoint dry weight ), the EC10 values estimated for the river Calapooia (i.e µg/l), Platte (i.e µg/l) and Santiam (i.e µg/l) were accepted. The EC10 value for the Zollner Creek (i.e µg/l) was rejected. Tests with rotifer. Rotifer (B. calyciflorus) test kits (ROTOXKIT F) were obtained from MicroBioTests Inc. (Belgium). Cysts were hatched according to manufacturer s directions except that the hatching media (moderately-hard reconstituted laboratory water) and food (Selenastrum capricornutum) were prepared fresh at the testing laboratory. Test methods followed those proposed by the Snell and Moffat (1992). Chronic effect concentrations that represented 10 and 95% confidence limits were calculated using non-linear interpolation (Norberg-King, 1993). Toxicological were determined using an analysis of variance (ANOVA) following back-calculation of standard error values from 95% confidence limits. The toxicant concentrations used in these calculations were averages of all dissolved measurements for each treatment within each toxicity test. Linear regressions (univariate and stepwise) were determined using a statistical program (JMP 6.0, SAS Institute, Cary, NC). An alpha value of 0.05 was used to judge the significance of each statistical relationship, unless otherwise noted. Reliability /relevance: the study was accepted. From the chronic toxicity tests reliable EC10 (intrinsic growth rate) between and µg Ni/l for Brachionus calyciflorus. All the EC10 (i.e µg/l) on the population growth (most sensitive endpoint) for B. calyciforus were accepted for the different rivers. The rather large diference between the accepted EC10 values could to a large extent be explained because of differences in abiotic factors (ph, DOC, hardness) among the different natural waters, which influences the toxicity Toxicity to freshwater fish and amphibians Accepted data on chronic single-species toxicity tests resulting in high quality NOEC/L(E)C 10 values (expressed as Ni) for freshwater fish and amphibians (n= 38) are summarised in Table An overview of the rejected low quality data is provided in Table A third category of toxicity data has been developed, i.e. in case the only reason for data rejection was that the physico-chemistry is not within the boundaries of the BLMs or because no reliable estimation of the DOC content could be made (Table ). Chronic data for 3 different fish species and 3 different amphibians were extracted and used in the effects assessment. The NOEC/L(E)C 10 values for fish range from 40 µg/l for Brachydanio rerio (Dave & Xiu, 1991) to 1,100 µg/l for the Oncorhynchus mykiss (Nebeker et al., 1985). The NOEC/L(E)C 10 values for the amphibians range from 84.5 µg/l to 13,147 µg/l for Xenopus laevis (Hopfer et al., 1991). The data quality of all individual studies is discussed below. 40

55 Formatted: Section start: New page, Width: pt, Height: pt Table Overview of the accepted high quality nickel chronic NOEC values for freshwater amphibians and fish.. All selected values for the most sensitive endpoint used for the derivation of HC5 are marked in bold. In addition the species, test duration, effect parameter and endpoint is marked in bold the first time. Substance Species (scientific) sulphate Pimephales promelas Taxonomic group Fish Age and/or Test Effect Endpoint Value Analysis of Dose Administration of Temp. ph Hardness DOC Nicb Test water size of test duration organism parameter (µg/l) concentrations response test substance ( C) (mg/l) (mg/l) (µg/l) ELS 30 days mortality NOEC 109 measured yes Flow through * <6 Lake water (embryo) Lind et al., 1978 [1] Pimephales promelas Pimephales promelas Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Fish ELS 32 days mortality NOEC 57 measured yes Flow through * Not Reconstituted reported water Fish ELS 32 days length NOEC 57 measured yes Flow through * Not Reconstituted reported water Birge et al., 1984 [3] Fish juvenile 17 days mortality LC measured yes Flow through Natural medium Fish juvenile 17 days mortality LC measured yes Flow through Natural medium Fish juvenile 17 days mortality LC measured yes Flow through Natural medium Fish juvenile 17 days mortality LC measured yes Flow through <1.0 Natural medium Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water 41

56 Substance Species (scientific) Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Oncorhynchus mykiss Taxonomic Age and/or Test Effect Endpoint Value Analysis of Dose Administration of Temp. ph Hardness DOC Nicb Test water group size of test duration organism parameter (µg/l) concentrations response test substance ( C) (mg/l) (mg/l) (µg/l) Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Fish juvenile 17 days mortality LC measured yes Flow through * <1.0 Reconstituted water Deleebeeck et al., 2005 [4] Oncorhynchus mykiss Oncorhynchus mykiss Fish Fish ELS (eyed 38 days mortality NOEC 700 measured yes Flow through * Not eggs) reported ELS (eyed 38 days growth NOEC 134 measured yes Flow through * Not eggs) reported Nebeker et al., 1985 [5] Well water Well water sulphate Brachydanio rerio Fish ELS (bastula stage) 8 days hatchability NOEC 40 nominal Not reported Static renewal * Not reported Dave & Xiu, 1991 [7] Artificial water Xenopus laevis Frog ELS 4 days malformation EC measured yes Static renewal * <29 Artificial medium Xenopus laevis Frog ELS 4 days mortality LC10 13,147 measured yes Static renewal * <29 Artificial medium Hopfer et al., 1991 [8] Xenopus laevis Frog ELS 4 days mortality LC10 4,630 measured yes Static renewal * 1.7 Artificial medium Xenopus laevis Frog ELS 4 days mortality LC10 4,790 measured yes Static renewal * 1.7 Artificial medium Xenopus laevis Frog ELS 4 days malformation EC measured yes Static renewal * 1.7 Artificial medium Xenopus laevis Frog ELS 4 days malformation EC measured yes Static renewal * 1.7 Artificial medium Xenopus laevis Frog ELS 4 days growth NOEC 90 measured yes Static renewal * 1.7 Artificial medium Bufo terrestris Toad ELS 7 days mortality LC measured yes Static renewal * <0.5 Artificial medium 42

57 Substance Species Taxonomic Age and/or Test Effect Endpoint Value Analysis of Dose Administration of Temp. ph Hardness DOC Nicb Test water (scientific) group size of test duration parameter (µg/l) concentrations response test substance ( C) (mg/l) (mg/l) (µg/l) organism Bufo terrestris Toad ELS 7 days mortality LC measured yes Static renewal * <0.5 Artificial medium Bufo terrestris Toad ELS 7 days Malformation EC measured yes Static renewal * <0.5 Artificial medium Bufo terrestris Toad ELS 7 days Malformation EC measured yes Static renewal * <0.5 Artificial medium Bufo terrestris Toad ELS 7 days growth NOEC 640 measured yes Static renewal * <0.5 Artificial medium Gastrophryne Toad ELS 7 days mortality LC measured yes Static renewal * <0.5 Artificial medium carolensis Gastrophryne Toad ELS 7 days mortality LC measured yes Static renewal * <0.5 Artificial medium carolensis Gastrophryne Toad ELS 7 days malformation EC measured yes Static renewal * <0.5 Artificial medium carolensis Gastrophryne Toad ELS 7 days malformation EC measured yes Static renewal * <0.5 Artificial medium carolensis Gastrophryne Toad ELS 7 days growth NOEC 450 measured yes Static renewal * <0.5 Artificial medium carolensis Fort et al., 2004 [9] *: robust estimated DOC values: data from Nebeker et al. (1985) using well water: DOC concentration = 1.1 mg/l (McCrady and Chapman 1979); data from Lind et al. (1978) using Lake Superior water: DOC concentration: 1.0 mg/l (Erickson et al., 1996); reconstituted water DOC concentration: 0 mg/l (De Schamphelaere et al., 2006). 43

58 Table Overview of the rejected low quality nickel chronic NOEC values for freshwater amphibians and fish. Substance sulphate Species (scientific) Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value (µg/l) Analysis of concentrations Dose response Administration of test substance Temp. ( C) ph Hardness (mg/l) Nicb (µg/l) Test water Bufo arenarum Toad ELS (embryo) 7 days mortality LC10 1,270 nominal Not reported Static renewal Not reported Not reported Artificial water Herkovits et al., 2000 [10] No hardness reported, only nominal value reported. Oncorhynchus Fish ELS (embryo) 85 days mortality NOEC >466 measured Not Folw through Not Not reported mykiss reported reported Brix et al, 2004 [11] Unbounded NOEC, and other data are available for this species. Oncorhynchus Fish ELS (fertilised 28 days mortality LC Not reported Not Static renewal Not Artificial water mykiss eggs) reported reported Oncorhynchus Fish ELS (fertilised 28 days mortality LC Not reported Not Static renewal Not Filtered mykiss eggs) reported reported tapwater Oncorhynchus Fish ELS (fertilised 28 days mortality LC Not reported Not Static renewal Not River water mykiss eggs) reported reported Gastrophryne Toad ELS (fertilised 7 days mortality LC measured Not Static renewal Not Not Artificial water carolensis eggs) reported reported reported Bufo fowleri Toad ELS (fertilised 7 days mortality LC measured Not Static renewal Not Not Artificial water eggs) reported reported reported Ambystoma Salamander ELS (fertilised 7 days mortality LC measured Not Static renewal Not Not Artificial water opacum eggs) reported reported reported Carassius Fish ELS (fertilised 28 days mortality LC Not reported Not Static renewal Not Not Artificial water auratus eggs) reported reported reported Ictalurus Fish ELS (fertilised 28 days mortality LC10 38 Not reported Not Static renewal Not Not Artificial water punctatus eggs) reported reported reported Micropterus Fish ELS (fertilised 28 days mortality LC Not reported Not Static renewal Not Not Artificial water salmoides eggs) reported reported reported Birge & Black, 1980 [12] Insufficient data for verifying concentration response, no DOC reported, and other data are available for many of the test species. Cyprinus carpio Fish larvae 10 days mortality LC measured yes Static renewal Not Spring water reported Blaylock & Frank, 1979 [13] Only LC50 values were reported. 44

59 Table Overview of the high quality chronic nickel NOEC values for freshwater amphibians/fish not further used for normalisation because the physico.-chemical properties fall outside the boundaries of the BLMs/ DOC could not be estimated. Substance Species (scientific) Taxonomic group Age and/or size of test organism Test Effect duration parameter Endpoint Value (µg/l) Analysis of concentrations Dose response Administration of test substance Temp. ( C) ph Hardness (mg/l) Nicb (µg/l) Test water nitrate Salmo salar Fish ELS (egg) 40 days mortality EC10 67 measured yes Static renewal * <5 Tap water Pimephales promels Grande & Andersen, 1983 [6] Hardness outside of BLM range. Fish fry 330 days reproduction NOEC 380 measured yes Flow through /** Not reported *: Fish BLM hardness ranges was between mg/l CaCO3 and ph range was between **: no robust estimation of the DOC conc. for toxicity data from Pickering (1974) could be made. Pickering, 1974 [2] Robust estimation of DOC was not possible, preventing normalization ** 45

60 [1] Lind et al., 1978 Test species: Pimephales promelas. Embryos were exposed to Ni in Lake Superior water (DOC measured: 1 mg/l; Source to be determined) for a period of 30 days. In each experiment 5 test concentrations (dilution factor between 0.2 and 0.4) and a control were supplied by a continuous flow dilution apparatus. Exposure began with embryos 1 day after fertilization and continued for 30 days. Temperature in all experiments was 25 C. Total Ni concentrations were measured at regular intervals during each test. Significance testing was applied at p=0.05 (Dunnett s test). No significant effect on mean fish weight after 30 days was detected. Clear dose response curves are reported for embryonal/larval survival. Control mortality was very low, i.e. between 2 and 3%. Deleted: Section Break (Next Page) Formatted: Danish Formatted: Danish Reliability/relevance: The study was accepted. A NOEC of µg/l (survival) could be extracted. [2] Pickering, 1974 Test species: Pimephales promelas. All tests with the fish were performed using a proportional diluter which delivered a control and 5 concentrations of nickel with a dilution factor of 0.5. The retention time was 5 hours. The dilution water was a mixture of pond water originating from a spring and carbon filtered demineralised Cincinnati tapwater. The mean hardness of this water was 210 mg/l CaCO3, the ph 7.8 and the alkalinity 161 mg/l CaCO3. Before spawning took place, the temperature averaged 18 C, after spawning the temperature averaged 24 C. The stock solution was made by dissolving reagent grade nickel in demin. water. All Ni concentrations were measured using a polarograph. The fathead minnow were obtained from the Newton Fish Farm. Twenty five fish (6 weeks old, 0.3 g) were randomly assigned to each exposure chamber. Ni exposure began in late November and was continued till November (± 330 days). Statistical analyses of the data on egg production and hatchability was performed using Anova (p=0.05). the mean number of eggs per female/spawning was significantly reduced at the 2 highest concentrations, resulting in NOEC values of 380 µg/l. A dose response curve was observed. Reliability/relevance: The study was accepted. A NOEC of 380 µg/l (reproduction) could be extracted. However no robust estimate could be made on the DOC concentration in the test media. These data were therefore not used for the normalisation.and establishment of the SSD curve. These data are however included in Table where all valid data outside the domain of the BLMs or for which no reliable DOC values are currently available. [3] Birge et al., 1984 Test species: Pimephales promelas. All experiments were performed using the fathead minnow Pimephales promelas. Freshly fertilized embryos for the early life stage tests were obtained from the EPA laboratory. Reagent grade nickel was used in the testing. A 32 day early life stage was performed following the procedures outlined by ASTM. The tests were performed using a continuous flow system. The experiments were performed in a temperature regulated room (25.1 C). Reconstituted dilution water (with assumed zero DOC) was delivered to the mixing chambers. The tests were performed using 6 Ni concentrations, with controls. All tests were performed in duplicate. Initially 35 eggs were placed in a 500 ml exposure chamber. The eggs were examined daily and dead organisms were removed. Hatching began after 4 days after test initiation. 1-day old larvae were then transferred from each exposure chamber to a corresponding beaker. The fish were fed with brine shrimp. The dilution water was prepared using reagent grade salts to distilled deionized water. The hardness of the water was mg/l CaCO3, the ph 7.4, the alkalinity 54.6 mg/l CaCO3. Exposure concentrations for Ni were analyzed using AAS. The test responses were analyzed 46

61 for statistical significance using Anova/least squares means test. Clear dose response curves were obtained for mortality and growth. Mortality in the control was 10%. A reliable NOEC value for survival of 57 µg/l could be extracted. No reductions in mean weights were observed at any concentration (NOEC > 733 µg/l). Mean standard length produced a NOEC of 423 µg/l. Reliability/relevance: The study was accepted. A NOEC of 57 µg/l (survival) and 423 µg/l (length) could be extracted. Use only the lowest NOEC (i.e. that from the most sensitive endpoint) [4] Deleebeeck et al., 2005 Test species: Oncorhynchus mykiss. All test media were prepared using carbon filtered, deionized water (conductivity < 2 µs/cm, DOC assumed to be zero) and chemicals (reagent grade) purchased from VWR (Leuven, Belgium). The composition of the basic medium (test medium with low concentrations of all major cations: 0.12 mm Ca, 0.12 mm Mg, mm K, mm Na) was based on the medium recommended for algal growth inhibition tests as described by OECD guideline 201 (1996). For each bioassay, the prepared test medium was used as the dilution water to make a nickel concentration series (added as NiCl2, 5 nickel concentrations and a control treatment). Dissolved nickel concentrations in the control treatments were always lower than the method detection level (MDL) of the GF-AAS, which is 3 µg/l. In order to obtain near-equilibrium situations, all media were prepared 24h prior to being used in the toxicity tests and stored at 14.5 C. Juvenile (± 200 mg, ca. 35 days post hatch, swim fry, fully resorbed yolk sac) rainbow trout (O. mykiss) were purchased from Houghton Springs Fish Farm (United Kingdom). Upon arrival in the laboratory, the fish were held in 200 l aquaria (at a density of about 400 fish per aquarium) for at least 4 days in an aerated mixture of ½ deionized water and ½ carbon filtered city tap water (Gent, Belgium). Temperature was kept between 14 and 16 C. Water hardness was ~ 180 mg CaCO3/l, ph ~ 7.8 and background nickel < 3 µg/l. Before testing, fish were acclimated for 1 week to the unspiked exposure media. This was done under exactly the same circumstances as during the toxicity tests (see further in this paragraph). During this week, mortality was less than 5% in all cases, which is in accordance with the OECD guideline 215 (1996). After this week, fish were exposed to nickel for 21 days in a flow-through system. under various conditions of abiotic factors (hardness and ph) A few tests were stopped earlier (17 days). Tests were allowed to last longer (up to 26 days), as long as performance of the fish in the control media was good enough. Each concentration was tested in a polyethylene aquarium containing 15 l of test medium and 20 animals. At all times, water temperature was within 1 C of the target temperature of 14.5 C. Aquaria were vigorously aerated (O 2 concentration > 90% of saturation) and a light cycle of 12L:12D was used, with gradual changes (spread over half an hour in the morning as well as in the evening) from dark to light and vice versa. During the first two weeks of the exposure to nickel, fish were fed daily at 4% of the mean wet body weight measured at the start of the test. Although none of the tests conducted in this study lasted 28 days, these control criteria were fulfilled in all the tests. The endpoints tested were growth reduction, mortality and behaviour. At regular intervals a number of physicochemical parameters were determined. For determination of dissolved nickel concentrations, samples were taken twice a week and filtered through 0.45 µm filters (Gelman Sciences, Ann Arbor, MI, Australia). Dissolved nickel concentrations in each test concentration were determined using a flame or a graphite furnace atomic absorption spectrophotometer. Pseudo-specific growth rates were calculated for each surviving fish according to the following equation (OECD, 1996). Growth rates in each exposure concentration were statistically compared to control growth rates using the Mann Whitney U test. P-values < 0.05 were considered significant. 47

62 Reliability/relevance: The study was accepted. LC10 for mortality varied between 164 and 1,548 µg/l. [5] Nebeker et al., 1985 Test species: Oncorhynchus mykiss. Rainbow trout were used for all testing. Newly fertilized eggs (< 4 h post-fertilization), eyed eggs and pre-swim larval fish were used for the early leife stage tests. All eggs were obtained from the Oregon Dept. of Fish and Wildlife Oakbridge Hatchery. Water for all testing was obtained from wells. The hardness of the water during testing was mg/l CaCO3, the alkalinity between mg/l CaCO3 and the ph between The test duration varied from 38 days (for the tests with the pre-swim-up larvae) and 85 days (for the tests with the newly fertilized eggs). Initial water temperature was set up at 9.5 C and gradually raised to 12 C and maintained between 12 and 14 C. Reagent grade nickel was used for all tests. The stock solution was prepared and pumped to the diluter. The delivery rate was 37 ml/min. Water samples for Ni analysis were taken at least weekly during the chronic tests and analyzed using AAS. Dunnett s multiple comparison and chi-square analyses were used to determine significant differences (p<0.05). Four different tests were performed. The results from test 1 and 2 could not be used because of the low control survival, i.e. 83 and 50%. The results from the tests 3 and 4 could be further used as the control survival was 98 and 95 % respectively. Test 3 (using eyed eggs as test organisms) used 8 different test concentrations (dilution factor between ), varyring between 35 and 3,730 µg/l. Test 4 used 4 different test concentrations (dilution factor between ), varyring between 134 and 3,730 µg/l. Dose response curves for both survival abd growth (length and wet weight) were reported. No background concentration in the control water was reported. A reliable NOEC value for survival of 1,100 µg/l and growth of 134 µg/l (wet weight) /431 µg/l (length) could be extracted from test 3. A reliable NOEC value for survival of 700 µg/l and growth of 134 µg/l (wet weight/length) could be extracted from test 4. Reliability/relevance: The study was accepted. A most senssitive NOEC concentration of 700 µg/l (survival) and 134 µg/l (growth) could be extracted. Only the latter lowest NOEC (i.e most sensitiuve endpoint) is carried over to the effect assessment (SSD) [6] Grande & Andersen, 1983 Test species: Salmo salar. The fish originated from the river Sandviksela (Sweden). The experiments were conducted in 5 l aquaria with continuous aeration.50 eggs were exposed to each metal concentration. The experiments were run for 3-4 months till the fish had absorbed the yolk sack. When all the eggs were hatched the number of fish was reduced to 20. The solutuins were renewed each second day. Temperature varied between 4.0 (at the start) and 8 C (dilution factor between 0.2 and 0.5). Ni nitrate was used as test substance. Tap water originating from an oligotrophic lake was used as dilution water. The characteristics of this water were the following: hardness: 11 mg/l CaCO3, ph: 6.3. Ni concentration in the test water was <5.0 µg Pb/l.Samples of the test solutions were analyzed periodically using AAS. 5 test concentrations were used. A clear dose response relationship was reported, but no statistics. However, from the dose response curve of the mortality of eggs towards Ni, a reliable EC10 value of 67 µg/l Ni could be calculated.the paperfurther shows that the hatching process (time) was disturbed at 100 µgl. Control mortality was 0%. Reliability/relevance: The results generated a reliable EC10 value of 67 µg/l (egg mortality). However, the hardness of the test (i.e. 11 mg/l CaCO3) is situated outside the boundaries of the fish BLM (i.e mg/l CaCO3) and is therefore rejected from the effects analysis. These data are however included in Table where all valid data outside the domain of the BLMs or for which no reliable DOC values are currently available. 48

63 [7] Dave & Xiu, 1991 Test species: Brachydanio rerio. The dilution water used in these experiments consisted of Milli-Q water to which analytical grade salts were added. The test is started with addition of eggs in the blastula stage. Test solutions (50 ml in a petri dish) were used (dilution factor 0.5). Test solutions were renewed daily. No food is provided in the tests. sulphate (analytical grade) was used. All dishes were incubated at 26 C. The dilution water has a hardness of 100 mg/l CaCO3, a ph of 7.5. Hatching and embryo mortality were investigated. For hatching a dose response curve is observed, not for survival. After critical evaluation of this paper the following conclusion can be formulated: from the paper, two different approaches were used in order to determine the no effect concentration of Ni. The first method applies the ZEP (i.e. Zero Equivalent Point, being the intersection between the dose response curve and the control line) and the MATC method. Using the first approach, a no effect concentration (reported as ZEP value) of 40 µg/l Ni for hatching was calculated. Reliability/relevance: Although the no effect level was reported as nominal values, the study was accepted because no other data were available for that specific species and because the threshold concentration was much higher than the background nickel concentration in this artificial test medium. NOEC concentration of 40 µg/l (hatching) was extracted. [8] Hopfer et al., 1991 Test species: Xenopus laevis. Mature X. laevis were held at 20 C in demin. deionized water (with no DOC) to which NaCl was added. reagent grade nickel was used for testing. 3 to 4 days before each assay, an adult Xenopus female was primed to ovulate by an injection of pregnant mare serum. On the evening before the assay the female was given an injection of human chorionic gonadotropin. The female was then kept overnight at 16 C. Next morning batches of eggs were expelled from the female. The eggs were then fertilized and the developing embryos were kept in an incubator at 23 C until the Fetax assay began at 5 h post fertlization (embryos at the blastula stage). The ph of the medium was adjusted daily to 6.8. The standard Fetax procedure was performed using embryos at the blastula stage. The assay was repeated 7 times using 22 different concentrations ragning from 5.9 µg/l to 176 mg/l. The Fetax medium contained <29.3 µg/l Ni based on analysis with AAS. During testing, the Petri dishes were kept at 23 C. The media were changed 3 times during testing. After 4 days of exposure, mortality was counted and malformation/development stages were determined. Values for L(E)C10 were computed by the Litchfield Wilcoxson test. In the control embryos the survival at 101 h postfertilization was >95% and the incidence of malformation was <7%. A LC10 concentration of 13.1 mg/l was reported for survival, an EC 10 of 84.5 µg/l for malformation. Clear dose response curves for both malformations and mortality were reported. Hardness was not reported but standard Fetax assay recommended a hardness of 100 mg/l. Reliability/relevance: The study was accepted. A reliable LC 10 value (survival) of 13.1 mg/l and a reliable EC 10 (malformation) of 84.5 µg/l could be extracted. Only the lowest EC10 (most sensitive endpoint) is carried over for the effect assessment (SSD curve) [9] Fort et al., 2004 Test species: Bufo terrestris; Xenopus laevis; Gastrophryne carolinensis. The Fort et al. (2004) study assessed the chronic Ni toxicity to the amphibians X. laevis, B. terrestris and G. carolinensis. The field collected adult specimens of B. terrestris and G. carolinensis were maintained in outdoor mesocosms using well water (dissolved Ni background concentration of <0.5 µg/l). Breeding and maintaining of adult Xenopus specimens was performed in the laboratory using dechlorinated tap water (dissolved Ni background concentration of 1.7 µg/l). Formatted: English (U.S.) Formatted: English (U.S.) 49

64 These tests were conducted with larval stages (late blastula/early gastrula) in accordance with the ASTM guidelines in static renewal systems using dechlorinated tap water as test medium with no DOC Daily renewal tests were conducted at 23 C for the X. laevis larvae and at 21 C for the other amphibians. The test duration is 4 days for X. laevis and 7 days for the other amphibians. For each test, a control and 14 exposure concentratrations were used. Ni concentrations in solution was measured, at start and at the end of the test, using ICP-MS. LC10 values (calculated using linear interpolation or probit analysis) and NOEC values (calculated using ANOVA or Kruskal ANOVA at or 0.05). The above mentioned study was conducted in accordance with the principles set forth in the GLP regulations. Control mortality in FETAX solution was 0% in the controls of the X. laevis bioassays, between 0 and 6.3% for the B. terrestris bioassays, and between 5 and 10% in the G. carolinensis bioassays. Clear dose response curves were reported. No malformation was observed in any of the controls for the different bioassays. Reliability/relevance: study was accepted. Reliable L(E)C10 and NOEC concentrations were reported for the following organisms and endpoints: X. laevis: 4630 and 4790 µg/l for survival; 230 and 260 µg/l for malformation; 90 µg/l for growth. B. terrestris: 880 and 1360 µg/l for survival; 970 and 1430 µg/l for malformation; 640 µg/l for growth. G. carolinensis: 180 and 190 µg/l for survival; 220 and 180 µg/l for malformation; 450 µg/l for growth. Only the lowest NOECs or EC10s (most sensitive endpoint per species and test condition and duration) is carried over for the effect assessment (SSD curve) [10] Herkovits et al., 2000 Test species: Bufo arenarum. Ovulation of B. arenarum females was induced by means of injection. Oocytes were fertilized in vitro with sperm, and embryos werev maintained in Holtfreter solution (HS) at 20 C. 10 embryos were placed in glass Petri dish in 40 ml of HS containing Ni at 13 nominal concentrations ranging from 5 to 35 mg/l. Experiments were repeated 3 times with embryos from different couples of parents. By means of probit analysis LC10 values were calculated. Survival was evaluated daily up to 7 days of exposure. Test solutions were replaced once a day. Ni solutions were prepared from a standard Ni solution. The ph values of Ni solutions increased from 7 to 8 in relation to the test concentration. The paper reports a LC10 value of 1.27 mg Ni/l. The dose response information highlights the overlapping between the confidence intervals of the LC10, LC50 and LC90 indicating no clear dose response curve. Reliability/relevance: The study was rejected. The LC10 value (1.27 mg/l) was far below the lowest tested Ni concentration, i.e. 5 mg/l. No hardness was reported and no clear concentration - response curve was found. Only nominal concentrations reported. [11] Brix et al., 2004 Test species: Oncorhynchus mykiss. (>97% purity) was used for preparation of the stock solutions. Dilution water was natural spring water collected from Woodinville. The characteristics of the dilution water used for chronic testing were the following, ph: 7.9; hardness: 89 mg/l CaCO3; alkalinity: 74 mg/l CaCO3; DOC: 1.1 mg/l. The tests with the rainbow trout Oncorhynchus mykiss were performed according to the ASTM guidelines. The chronic toxicity tests were initiated with embryos, obtained from Nisqually trout farm, less than 4 h after fertilization. The test system used a proportional diluter. The test design consisted of 5 concentrations and a control using 4 replcates in 5.7 l test chambers. The test concentrations varied frm 29 to 466 µg/l (dilution factor 0.5). The flow through system replaced one tank volume every day. Aqueous concentrations of Ni were analyzed in filtered samples using ICP. Toxicity data were used to calculate the NOEC using Dunnett s multiple comparison test. The total exposure duration was 85 days. In the chronic studies no 50

65 statistically significant effects were observed for any of the endpoints evaluated, hatchability, survival, growth. Reliability/relevance: The results were not used as only unbounded NOEC were reported (> 466 µg/l) and reliable NOEC values for the same species are available from the research work performed at the University of Ghent (Deleebeck et al. 2005). [12] Birge & Black, 1980 Test species: Oncorhynchus mykiss, Ictalurus punctatus, Micropterus salmoides, Carassius auratus, Gastrophryne carolinensis, Ambystoma opacum and Bufo fowleri. The bioassays were performed using static renewal procedures. All bioassays were initiated at or soon after fertilization and continued through 4 days posthatching. was used in all bioassays and all concentrations were confirmed by AAS. The ph varied from 7.2 to 7.7, and ranges for hardness and alkalinity were and mg/l CaCO3 respectively. All tests were performed in reconstituted water. Control survival ranged from 91 to 98% and log-probit analysis was used to compute LC10 values. Test responses were reported for the different tests. To determine whether the use of reconstituted water could have altered the response, the bioassays were repeated with trout eggs in carbon filtered tap water (ph: 7.6; hardness: 125 mg/l CaCO3; alkalinity: 81 mg/l CaCO3) and in stream water (ph: 7.8; hardness: 174 mg/l CaCO3; alkalinity: 155 mg/l CaCO3). The author reported LC10 values of 10.6 (Oncorhynchus mykiss), 38.4 (Ictalurus punctatus), (Micropterus salmoides), (Carassius auratus), 4.1 (Gastrophryne carolinensis), 60.4 (Ambystoma opacum) and µg/l (Bufo fowleri) for the tests performed with reconstituted water. The LC10 values using O. mykiss for the tests performed with tapwater and stream water were 11.1 and 14.8 µg/l respectively. The toxicity results reported by Birge and Black (1980) were not retained for the effects assessment. One fundamental reason for questioning the reliability of the LC 10 value reported by Birge and Black (1980) is that this study was not conducted for risk assessment purposes; otherwise, concentration ranges that would have yielded a true NOEC would have been used. An additional technical reason for questioning the reliability of this LC 10 value includes that the reliability of the Birge-studies for risk assessment purposes has been questionned by the Rapporteur country for Diantimony trioxide (DAT). More specifically, the results of the Birge (1977), Birge (1978) and Birge et al. (1979) which present results of toxicity tests on antimony, were rejected for the Existing Substances Risk Assessment on this compound. The basis for rejecting results of these studies was the following: even though these studies are well performed, with measured test concentrations etc., neither of these studies are considered valid in the DAT RAR. The reason for this is that the test concentrations used are never reported, which makes it difficult to decide whether or not the calculated NOEC values are included in the tested concentration range, or not. Reliability/relevance: The species and endpoints of this reference were addressed in newer repeat studies of high quality, because it was felt based on general experience concerning the data quality of this reference, that re-testing would be useful for obtaining new and more reliable alternative data on the same species and endpoint & similar testing methods as those originally used. (Fort et al, 2004). Thus all results for the different organisms from this particular publication were rejected. [13] Blaylock & Frank, 1979 Test species: Cyprinus carpio. Spawning carp (Cyprinus carpio) were collected from a local impoundment. Eggs and milt were stripped into bottles containing selected Ni concentrations, i.e. between 3 and 10 mg/l of NiSO4 in a 0.6% solution of NaCl and spring water. After 51

66 fertilization, the eggs were transferred to hatching boxes containing the same concentyration of Ni sulphate in spring water. Three replicates were run for controls and for each of the 8 Niconcentrations. Water in the hatching boxes was maintained at 25 C. The Ni solutions were changed daily. After 3 days the number of eggs that hatched and the number of abnormal larvae were scored. Carp larvae that hatched from the control were further used to determine the toxicity of Ni to the larval stage. 1-day old larvae were exposed to Ni concentrations ranging between 1 and 10 mg/l. 21 replicates of 25 larvae were placed in test and control water. The temperature ranged between C. The mortality of the larvae was recorded till 10 days of exposure. The ph of the spring water was 7.4, the total hardness 128 mg/l. samples of the water was taken at the beginning and end of the test and analyzed with AAS. LC50 values were estimated using a probit analysis. Control survival was between 80-90% at the end of the test. Reliability/relevance: The results were not used as only 10 days LC50 values were reported for the larvae. In addition, the results from the hatching could not be used as the exposure time was 3 days (acute) Water chemistry of the test media In order to better (1) understand the observed differences in intraspecies sensitivity for nickel and (2) investigate the composition of the database (i.e. range of freshwater characteristics covered), the physico-chemical characteristics encountered in the toxicity tests were investigated thoroughly as follows: - ph The ph values extracted from the literature/reserach database varied between 5.5 and 8.5 (10 th %-90 th %: ) and seemed representative for those encountered in European surface waters (see Table ). The distribution of the observed ph values used in the ecotoxicological tests is given in Figure cumulative distribution ph Figure Distribution of the observed phs in the ecotoxicity tests. - Dissolved Organic Carbon (DOC) DOC is an important abiotic factor mitigating the chronic toxicity of Ni towards freshwater species. However, often the DOC concentrations used in the toxicity tests were not reported 52

67 and therefore it might be considered to estimate the DOC concentrations in those test media for which no measured DOC levels are available. In case measured or robust estimated DOC concentrations are available for toxicity tests with a particular species it is proposed not to use other toxicity tests for the same species with default DOC levels. This approach does not result in any lost of species and reduces the uncertainty in effects analysis. It is further proposed only to consider default DOC concentrations for those toxicity tests for which no alternative measured or robust estimated DOC levels are available for a particular species. Assumed DOC concentrations are preferably estimated based on reported measurements from literature or existing environmental databases for the same water source. The following DOC estimations were extracted from literature/databases: Nebeker et al. (1984; 1985; 1986) provided toxicity data for O. mykiss, C. magnifica and J. plicifera. These tests were peformed at the US EPA Corvalis (USA) facility using well water as a dilution water. A robust estimate of 1.1 mg/l DOC was provided by US EPA (personal communication), Kuhn et al. (1989), Santiago-Fandino (1983), Birge et al. (1984), Dave & Xiu (1991) provided toxicity data with organisms tested in reconstituted waters. For reconstituted water a default DOC concentration of 0 mg/l (De Schamphelaere et al., 2006) was assumed. This assumption is based on measured DOC concentrations in reconstituted waters at the University of Ghent (see Appendix G.2), Lind et al. (1978) provided toxicity data with P. promelas using Lake Superior as the dilution water. An estimated DOC concentration of 1.0 mg/l is proposed based on the historic measurements performed by Erickson et al. (1996), Munzinger et al., 1990; 1994 provided toxicity data with D. magna performed in Lake Maggiore. An estimated DOC concentration of 1.0 mg/l is proposed based on the measurements performed by Dueri et al. (2005). Formatted: Danish No estimation of DOC concentrations in the following tests could be made. These specific datapoints were therefore not further propagated in the bioavailability normalisation: Data with D. magna performed in Lake Ijssel (Enserink et al., 1991), Data with P. promelas in an unknown mixture of pond/tapwater (Pickering, 1974). Toxicity values for species from those studies that were not included in the normalization were either within or above the range of values that were used for normalization. For example, NOECs for D. magna from Enserink et al. (1991) ranged from 200 to 540 µg Ni/L, The range of NOEC/EC10 values for D. magna from other studies was 16.9 to 281 µg Ni/L. Likewise, the NOEC for Pimaphales promelas reported by Pickering (1974) was 340 µg Ni/L, which was above the range of NOECs for this species (57 to µg Ni/L) from studies that were accepted for normalization. DOC values extracted/estimated from the literature database varied between 0 and 25.8 mg/l DOC (10 th %- 90 th %: mg/l) and seemed therefore to cover those encountered in European surface waters (see Table ). The distribution of the observed DOC values used in the ecotoxicological tests is given in Figure

68 cumulative distribution DOC (mg/l) Figure Distribution of the observed DOC values in the ecotoxicity tests. - Hardness The hardness values extracted from the literature database varied between 6.3 and 481 mg/l CaCO 3 (10 th %-90 th %: mg/l) and seemed therefore to cover those encountered in European surface waters. The 10 th %-90 th % for the Ca 2+ cation varied between 3.4 and 67.1 mg/l, for the Mg 2+ cation between 0.97 and 35.0 mg/l (see Table ). The distribution of the observed Ca 2+ and Mg 2+ cation values used in the ecotoxicological tests is given in Figure

69 cumulative distribution Ca 2+ (mg/l) cumulative distribution Mg 2+ (mg/l) Figure Distribution of the observed Ca 2+ and Mg2 + cation values in the toxicity tests. Alkalinity The alkalinity values extracted from the literature database varied between 0.05 and 244 mg/l CaCO 3 (10 th %-90 th %: mg/l). The distribution of the observed alkalinity values used in the ecotoxicological tests is given in Figure Whenever possible, alkalinity was based on reported measurements from the database. In the absence of any reported concentrations, estimates were calculated using the WHAM VI speciation software. Alkalinity in open systems is calculated based on the ph value of the system and considering equilibrium between the air/water compartments. 55

70 cumulative distribution Alkalinity (mg/l) Figure Distribution of the observed alkalinity values in the toxicity tests Comparison with water characteristics of European freshwaters Physico-chemistry of the test media vs EU surface waters The values of the freshwater abiotic parameters in the ecotoxicity tests were compared with ranges reported for European freshwaters (Table ) compiled from the Swad 8 and the Foregs ( databases. An extensive database comprising information on the physico-chemistry of the European surface waters has been compiled by Euras/University of Ghent. The databases covered by the Swad database are the following: - Belgium The monitoring data from the Flemish surface waters were obtained from the Flemish Environment Agency (VMM). The database contains information on more than 1000 stations spread over 6 different river systems in Flanders. Data were collected between 1989 and The monitoring data for the Walloon region were generated and reported by the Scientific Institute for Public Services (ISSeP). The database includes 62 stations representing 42 rivers and 4 different water catchment areas (Scheldt, Meuse, Rhine and Seine). Each station was sampled 6 to 12 times in From the databases 12,884 individual datapoints on ph, 1,931 on DOC and 1,944 on hardness were collected. - Germany The German monitoring data included in this report originate from the Wassergütestelle Elbe (Hamburg) and are only representative for the river Elbe. Eleven locations were monitored monthly or bimonthly in 1996 and From this German database 362 individual datapoints on ph, 325 on DOC and 263 on hardness were collected. - The Netherlands 8 An analysis of the probabilistic distribution of nickel and abiotic factors (e.g., hardness, ph, and DOC) was submitted for discussion at TC NES I 2005, and an updated version was submitted for discussion at TC NES I 2006 (R311_312_419_420_424_ENV_PEC_Appendix D1.doc). The latter document is annexed to this report c.f. Annex XX 56

71 The monitoring data from the Netherlands were gathered by the Rijkswaterstaat (RWS; executive organisation of the Dutch Ministry of Transport, Public Works and Water Management), and represent 25 different rivers and stations throughout the Netherlands. Data are available for the period From this Dutch database 1,510 individual datapoints on ph and 821 on DOC were collected. - Sweden The Swedish monitoring data in SWAD were gathered from the Swedish University of Agricultural Science. Various national, regional and reference surface waters (incl. the four large Swedish lakes) were monitored between 1995 and From this Swedish database 3,428 individual datapoints on ph, 2,952 on DOC and 3,192 on hardness were collected. - United Kingdom The data for the United Kingdom were collected in the framework of The Harmonised Monitoring Scheme a monitoring campaign that is conducted since 1974 and which covers the whole of England and Wales. The database contains the measurements of 143,079 samplings obtained at 275 different locations and 195 rivers. Up to 116 different parameters were determined, including physico-cemical parameters. From this UK database 3,583 individual datapoints on ph, 1,146 on DOC and 3,075 on hardness were collected. - Spain The Commps database reports data on the physico-chemical characteristics of Spanish freshwaters in From this Spanish database 5,901 individual datapoints on ph were collected. The estimation of representative concentrations of the variables driving the bioavailability of Ni in freshwaters is based on the construction of frequency distributions incorporating all data gathered from the different databases (Figure , and ). - Hardness The hardness content in EU freshwaters ranges between 37.4 and mg/l CaCO 3 (range as 10 th and 90 th percentiles) as shown in Figure A typical hardness (50 th %) of 99.4 mg/l CaCO 3 was estimated in EU freshwaters. It is realised that these ranges may not be representative for certain European aquatic systems, e.g. for soft waters (defined as waters with a hardness below around 20 mg CaCO 3 /l). 57

72 cumulative distribution Hardness (mg/l as CaCO3) Figure Distribution of the observed hardness in EU freshwaters. ph The ph in EU freshwaters ranges between 6.6 and 8.1 (range as 10 th and 90 th percentiles) as shown in Figure A typical ph value (50 th %) of 7.5 was estimated in EU freshwaters. cumulative distribution ph Figure Distribution of the observed ph in EU freshwaters Dissolved organic carbon The dissolved organic carbon content in EU freshwaters ranges between 2.6 and 12.4 mg/l (range as 10 th and 90 th percentiles) as shown in Figure A typical DOC concentration (50 th mg/l) of 6.4 mg/l was estimated in EU freshwaters. 58

73 cumulative distribution DOC (mg/l) Figure Distribution of the observed DOC content in EU freshwaters An overview of the maps representing the range of physico-chemistry encountered in the EU surface waters according to the Foregs database ( is given in Figure Comparison between the freshwater properties used in the acceptable ecotoxictiy tests and those encountered in EU freshwaters (according to the Swad & Foregs database) revealed similar ranges for ph, hardness and DOC (Table ; Figure ).The ph, DOC and hardness of the ecotoxicity tests and the EU freshwater fit well, but the toxicity studies still contains some limited toxicity data performed at higher and lower ph/doc/hardness values compared to the 10 th 90 th % in EU surface waters. Furthermore, the range of abiotic factors from the toxicity tests and the BLM development are similar for ph and hardness (see chapter 1.2.1). For the DOC, the toxicity dataset contains higher DOC concentrations compared to the range for the BLM development. Table Physico-chemical parameters of the selected toxicity studies (min-max values) and European freshwaters (reported as 10 th, 50 th and 90 th %). Parameter Range (min-max /(10 th % -50 th % - 90 th %) ph Toxicity studies European freshwaters (Swad) European freshwaters (Foregs) 6.4* DOC (mg/l) Toxicity studies European freshwaters (Swad) European freshwaters (Foregs) 0.9* Hardness (mg/l CaCO 3) Toxicity studies European freshwaters (Swad) Ca (mg/l) Toxicity studies European freshwaters (Foregs) 3.0* Mg (mg/l) Toxicity studies European freshwaters (Foregs) 0.8* *: estimated value from graphical interpolation 59

74 Hardness range EU waters Toxicity data BLMs Hardness (mg/l CaCO3) DOC range EU waters (Swad) EU waters (Foregs) Toxicity data BLMs DOC (mg/l) ph range EU waters (Swad) EU waters (Foregs) Toxicity data BLMs ph Figure Water parameters of the selected toxicity studies (min-max values), hardness and ph boundaries of the BLMs (min-max values for the broadest and narrowest BLMs), ranges of DOC from the BLM validation exercise (min-max values for the broadest and narrowest BLMs), and European freshwaters (reported as 10 th and 90 th %). Ranges of the narrowest BLMs for hardness (algae BLM), DOC (rainbow trout BLM), and ph (Ceriodaphnia dubia BLM) are shown by the vertical arrows. Compared to the Foregs database (see Figure for maps and range in abiotic factors across EU surface waters), a similar but somewhat narrower range in ph/doc was observed from the data collected in the Swad database. 60

75 A visual representation of the ranges of hardness, DOC, and ph shows that the minimum and maximum values from the toxicity database are broader than the 10 th 90 th percentiles of the distribution of these parameters that were derived from the EU monitoring programs (Figure ). Also, the boundaries of the broadest BLMs for hardness and ph cover the the 10 th 90 th percentiles of the distribution of these parameters. The broadest range of DOC from the validation exercises also covered the 10 th and 90 th percentiles of DOC distribution in EU waters. The range of the narrowest BLM does not appear to cover the 10 th and 90 th percentile range for DOC or ph however. This is not a concern for DOC, as the effects of DOC were not evaluated experimentally in the development of the BLMs. Rather effects of DOC on nickel toxicity are quantified through the speciation modules within the BLM computational platform, i.e., WHAM VI. That is, the effect of DOC is abiotic (sorption of Ni 2+ ), rather than physiological (e.g., competition with Ni 2+ for uptake, as seen for Ca 2+, Mg 2+, and H + ). The BLM with the narrowest ph range (6.5 to 8.2) was that of C. dubia, development of which is described in later portions of the text. This ph range was subsequently used as one of the criteria for choosing the typical freshwater scenarios within the ecoregion approach for implementing bioavilability. ph values for all of the freshwater scenarios were within the ph range of the C. dubia model. Boundaries of the BLMs vs EU surface waters The BLMs aimed at establishing relationships between concentrations of H +, Ca 2+ and Mg 2+ and the chronic nickel toxicity, taking into account speciation, for the derivation of the necessary BLM parameters. As mentionned in section 1.2.1, the chronic BLMs were developed/validated within specific boundaries selected based on the typical range (10-90 th %) of abiotic factors occurring in the EU surface waters. Although these models were developed/validated within specific agreed boundaries of abiotic factors representative for the EU surface waters (Table ), it is probable that certain areas/countries in the EU are not within the boundaries of the BLMs. It is therefore clear that these areas outside the BLM boundaries are not representative for the typical environments normally occurring in the EU surface waters. Table Abiotic boundaries for the different chronic BLMs. Algae BLM Daphnia BLM Ceriodaphnia BLM Fish BLM EU surface waters (10-90 th %) - FOREGS ph *-8.3 Mg (mg/l) *-27.3 Ca (mg/l) *-119 *: estimated value from graphical interpolation The Foregs database provides maps with ranges of abiotic factors (ph, Ca, Mg) which could be used for comparison with the physico-chemical boundaries of the chronic BLM (Figure ). From these maps, the most critical competitive ions mitigating the toxicity of Ni, i.e. ph, and Ca/Mg were further investigated. Although the BLMs were developed/validated within the 10 th -90 th % boundaries of the abiotic factors, the maps show that the BLM for C. dubia is not covering some low ph areas (with ph<6.1; below the 10 th % of the ph values in EU surface waters) e.g. in the Scandivian countries. On the other hand, some sensitive areas with high ph (with ph>8.5; above the 90 th % of the ph values in EU surface waters) outside the boundaries of all the developed/validated BLMs are found in the southern part of the EU, i.e. Spain, northern Italy, southern France and Greece. Comparing the lower boundaries of the BLMs with the Ca/Mg concentrations in EU surface waters suggests that a large part of the 61

76 Scandinavian countries (and a small part of northern Spain/Scotland) with Ca/Mg concentrations as low as respectively 3.0 and 1.0 mg/l are not covered by the boundaries of the fish/algae BLMs. Full coverage of the Ca/Mg levels is almost achieved with the BLMs for the most sensitive trophic level (i.e. the invertebrates). However, such especially sensitive regions with high ph or regions with low hardness will thus not be covered with the BLM normalisation approach developed in this risk assessment. A map of the distribution of DOC within the EU is included in Figure , but there are no legitimate DOC boundaries with respect to the BLMs, as the effects of DOC on nickel toxicity were not explicitly measured in the experiments used to develop the BLMs. Rather, effects of DOC on nickel toxicity occur from the sorption of Ni 2+. The magnitude of this sorption is modeled by WHAM VI, which takes into account effects of competing cations for DOC sorption sites. The effect of DOC is not physiological, and it is therefore appropriate to model the effect of DOC across the range of DOC that occurs in EU surface waters. The ability of the models to estimate effects of DOC was confirmed in the independent validation of the BLMs, which are presented in Section Therefore, the DOC map in Figure does not show areas that are not covered by the BLMs. 62

77 63

78 Figure FOREGS maps showing regions that are not covered by the BLMs (red circles) 64

79 Implementation of bioavailability Description of the model General introduction Several papers have been published focusing on factors influencing nickel toxicity to daphnids (Schubauer-Berigan et al., 1993; Keithly et al., 2004) and fish (Schubauer-Berigan et al., 1993; Meyer et al., 1999; Pyle et al., 2002; Pane et al a&b; Hoang et al., 2004; Pane et al., 2004 a&b). The most important factors studied were hardness (Ca+Mg, or Ca and Mg tested separately), ph, alkalinity and dissolved organic carbon (DOC). Overall, higher levels of hardness and DOC were shown to reduce nickel toxicity to the organism studied. More specifically, nickel appeared to selectively antagonize magnesium homeostasis in Daphnia magna (Pane et al., 2003). However, a more intensive program of acute/chronic toxicity testing was needed to test this hypothesis and to quantify the magnitude of the influence of these parameters. The understanding of the mechanisms involved in nickel toxicity resulted in the development of a mechanistically based model that is able to describe nickel bioavailability and toxicity to aquatic biota. This biotic ligand modeling (BLM) approach was developed to incorporate aqueous speciation reactions and competition of cations for binding to biotic receptors. This BLM is based on a conceptual model similar to the gill site interaction model (GSIM) originally proposed by Pagenkopf (1983) and the free ion activity model (FIAM) as described by Campbell (1995). The model is based on the hypothesis that toxicity is not simply related to total or dissolved nickel concentration but that metal complexation and interaction at the site of action need to be considered. toxicity is simulated as the accumulation of the metal at the biologically sensitive receptor, the biotic ligand, which represents the site of action of metal toxicity. It is therefore assumed to occur as the result of the free metal ion reacting with the physiologically active binding sites at the site of action. This is represented as the formation of a metal-biotic ligand complex. The development of a chronic nickel Biotic Ligand Model (BLM) is, therefore, explicitly based on the premise that the following assumptions hold: 1. The plasma membrane is the primary site for nickel interaction with living organisms (i.e. the plasma membrane at the gill epithelial cell; therefore, adsorption through the gut is ignored) 2. this interaction with the plasma membrane can be described as a complexation reaction, forming Ni-X-cell 3. transport in solution, towards the membrane, and the subsequent surface complexation reaction occur rapidly, such that a (pseudo-) equilibrium is established between metal species in the bulk solution and those at the biological surface(rapid meaning faster than uptake, faster than the expression of the biological response) 4. the biological response, whether it be nickel uptake, or toxicity is strickly dependent on the concentration of the Ni-X-Cell surface complex, 5. In the range of metal concentrations of toxicological interest, the concentration of free sites (-X-cell), remains constant and variation in Ni-X-Cell follow those of Ni 2+ in solution. 6. During the exposure to Ni, the nature of the biological surface remains constant (i.e. the metal does not induce any change in the nature of the plasma membrane Formatted: Bullets and Numbering Formatted: English (U.S.) 65

80 For fish the biotic ligand appears to be sites on the surface membrane of the gill. The principal feature that distinguishes the biotic ligand from considering only the free metal ion as the toxic species is that the free metal ion competes with other cations, e.g. Ca ²+, H +, for binding at the biotic ligand. As a result, the presence of these cations in solution can mitigate toxicity, with the degree of mitigation depending on their concentrations and the strength of their binding to the biotic ligand. Biotic ligand models for Ni have recently been developed for the different trophic levels (algae, daphnids, fish) using the different methodologies proposed by De Schamphelaere and Janssen (2002). The University of Ghent (Deleebeeck et al.,2005) derived their chronic Ni- BLMs for D. magna, O. mykiss and the green alga Pseudokirchneriella subcapitata using the observed linear relationships between the observed toxicity effect concentration of the metal and the individual present competing cation concentration (both expressed as free ion activity). Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic Validation in natural surface waters The BLM development was based on a series of (univariate) toxicity experiments in artificially composed test media in which the major water quality parameters that were expected to affect nickel toxicity were varied (i.e. Mg 2+, Ca 2+, Na +, K + and H + for acute toxicity and Mg 2+, Ca 2+ and H + for chronic toxicity). External validation of the developed models was performed using nickel spiked natural surface waters, which were chemically characterized with respect to the BLM input parameters. The results of these tests provided an opportunity to validate the developed models and to evaluate the effect of natural DOC on chronic nickel bioavailability and toxicity to the test organisms. External validation of the developed BLMs was performed in different surface waters representing various European regions. The results of these tests allow us to validate the developed models and to evaluate the effect of natural dissolved organic carbon on chronic nickel bioavailability and toxicity. A short description of the sites used for the validation study is given hereunder: Bihain: This site is a small creek (named Ruisseau de S t. Martin) located in the nature reserve named Hoge Venen, which is a highland, peat rich area in Belgium. The water of this creek is characterized by low ph and low to intermediate DOC concentration. Usually hardness is low, but on one of the sampling dates, a higher hardness was measured due to an elevated Ca concentration (sample taken during drought period). Ankeveen: This body of water, a narrow side arm of a larger lake system called the Ankeveensche Plassen (NL), is located in a lowland peat area in the Netherlands close to the village of Nederhorst-den-Berg. Surface water ph of this site is rather variable, ranging from 6.5 to 8.4, depending on the season and the time of the day. At both sampling dates, ph was around 7. Further, Ankeveen has a high DOC concentration and high hardness. River Mole: The river Mole is a tributary of the Thames (England, UK) and is characterized by high hardness, low DOC concentration and a ph between 7 and 8. Beneden Regge: This river in the north of the Netherlands is situated in the province of Overijssel and joins the Vecht in the neighbourhood of Ommen, where the sample was taken. At the time of sampling, the water had an intermediate DOC concentration, high hardness and a ph of around 7.5. Markermeer: Markermeer has high ph, high hardness and an intermediate DOC concentration. This site is a large lake in the north of the Netherlands which is part of the bigger Lake Ijssel, which has been cut off from the North Sea by a dam. 66

81 Brisy: This sampling site is part of the eastern branch of the river Ourthe (B) near the village Brisy (Belgium) and is situated in a forested area. This site is characterized by low DOC concentration, low hardness and a ph usually just below 7. Eau d Eppe: This is a small stream just outside the protected forest area called Le Val Joly. The sample was taken close to where it joins the river Helpe Majeure, which is a tributary of the Samber. At the time of sampling the water had a rather low DOC concentration, intermediate hardness and a ph around 7.5. Lake Clywedog: Lake Clywedog (Wales, UK) is a man made reservoir formed by the Clywedog dam, which was built to regulate the flow of water in the river Severn. Lake Clywedog is characterized by low DOC concentration, low hardness and low ph. Voyon: Voyon has rather high ph, medium hardness and low DOC. The location is at the mouth of the small stream Le Voyon and is located in a protected forested area called Le Val Joly in northern France. 11 different water samples from the above mentioned surface waters were used for the validation of the chronic D. magna BLM, while respectively 9 and 5 water samples were used for the validation of the chronic algae and fish BLMs. The range of the physico-chemical parameters for the freshwater systems used to validate the different chronic BLMs are summarized in Table These ranges represents the measured minimum and maximum values for ph, Ca, Mg, and DOC for the natural waters collected in the validation exercise. These ranges were one source of information used to define the physico-chemical boundaries of the BLMs. The other source was the range of physico-chemical parameters from tests using synthetic laboratory waters in univariate experiments that were used to develop the models. The ph ranges of the natural waters used in the validation exercise were within the synthetic laboratory water ph boundary for all species. The lower range of Ca and Mg in natural waters was lower than the lowest concentration used in laboratory tests, but the differences were minor (e.g., the lower Ca boundary for synthetic laboratory waters ranged from 3.0 mg/l for algae to 7.2 mg/l for D. magna). As indicated in Section 2.6.3, the models accurately predicted Ni toxicity in those natural waters with lower Ca and Mg concentrations than were observed in laboratory tests with synthetic water. Ca and Mg concentrations from the natural water were then used as the lower boundary for these parameters, as indicated in Table Determining effects of DOC on nickel toxicity was not a component of the laboratory tests, and DOC concentrations were assumed to be 0 mg/l in these tests. Table Validation boundaries for the different chronic BLMs. Algae BLM Daphnia BLM Fish BLM ph DOC (mg/l) Mg (mg/l) Ca (mg/l) Application & validation of the model acute toxicity By demonstrating that 24 h gill-nickel accumulation was a constant predictor of acute toxicity (96 h LC 50 values) in fathead minnow across a wide range of water hardness levels, Meyer et al. (1999) showed that the fundamental principle of the BLM approach is applicable to nickel. Although Pane et al. (2004) recently demonstrated that nickel is not an acute gill ionregulatory toxicant in fish but rather a respiratory toxicant - in contrast to all other metals for which BLMs have been developed - current research has demonstrated that the BLM concept also holds for nickel. The basic framework (affinity constants for gill cation complexes as 67

82 well as LA 50 ) of an acute Ni-BLM has recently been developed based on the data provided by Meyer et al. (1999). Moreover the nickel model was extended to daphnids with reasonable success by adjusting the LA 50 value (from the conceptual aspect of the present BLM approach, only one LA 50 should exist for a given species and life stage) through the calibration of previously reported acute nickel toxicity data in D. magna and C. dubia (Ca 2+, Mg 2+, and ph were the primary variables) and keeping all other parameters the identical (Figure ). Figure Predictive capability of the Ni BLM when applied to D. magna (circles) and C. dubia (inverted triangles) (Di Toro et al., 2005). Data from Schubauer-Berigan et al., 1993, Bossuyt et al., 2001, Chapman et al 1980, Keithly et al., De Schamphelaere et al. (2006) also developed acute BLM for D. magna based on WHAM VI speciation modelling from existing datasets of Deleebeeck et al. (2005) and Bossuyt et al. (2001). The WHAM VI model used by DeSchamphelaere et al. (2006) was calibrated directly to nickel speciation measurements made in natural waters. Specifically, the model was modified to increase the accuracy of nickel-doc binding (Van Laer et al. In Press). The D. magna datasets that were used report 48h-LC50 dissolved for a range of spiked natural surface waters and covers a large range of DOC (1.8 to 25.8 mg/l), hardness (13 to 266 mg CaCO 3 /L), Ca (3.0 to 73 mg/l), Mg (1.1 to 21 mg/l), ph (6.0 to 8.1), and alkalinity (0.4 to 161 mg CaCO 3 /L), see Figure Formatted: English (U.S.) 68

83 predicted 48h-LC50 (µg Ni/L) 10,000 synthetic waters natural waters 1,000 1,000 10,000 observed 48h-LC50 (µg Ni/L) Figure Predictive capacity of the acute D. magna model as shown by predicted vs. observed 48h-LC50. Data with synthetic waters are from Deleebeeck et al. (2005). Data with natural waters are from Deleebeeck et al. (2005) and Bossuyt et al. (2001). Figure illustrates that the model is well-calibrated to the synthetic waters in the first place, but of course the true value of a model is that it should work in natural waters too. It illustrates that 15 out of 16 of the 48h-LC 50dissolved are predicted by an error of less than factor 2. Toxicity was underestimated (48h-LC 50 overestimated) by a factor of more than 2 for Bihain (2) and by factors close to 2 for Bihain (1) and Clywydog (1). Compared with the other test waters, all these waters are characterized by very low Mg concentrations, i.e. between 1.1 and 1.5 mg/l, which is far below the lowest hardness and Mg concentration in test solutions used for model development. This may have created a situation where the daphnids were stressed and therefore were particularly sensitive to Ni stress. De Schamphelaere et al. (2005) concluded that in normal test waters, the acute D. magna model exhibits good predictive capacity for use in spiked natural surface waters. De Schamphelaere et al. (2006) also developed acute BLM for C. dubia based on the acute D. magna BLM-algorithms. The observed and predicted 48h-LC 50 s are reported in Figure with predictions errors between a factor 1.1 to 2.0. De Schamphelaere et al. (2006) concluded that the acute D. magna Ni-BLM can be used to predict acute Ni toxicity to C. dubia in natural waters. However, non-linear relationship between ph and nickel toxicity has been observed, with the slope increasing as ph exceeds 8.2. DeSchamphelaere et al. 2006). Therefore, it is not recommended to use the current model in solutions well over ph

84 predicted 48h-LC50 (µg Ni/L) 1, ,000 observed 48h-LC50 (µg Ni/L) Figure Predictive capacity of the acute C. dubia model in natural waters as shown by predicted vs. observed 48h-LC50. Application & validation of the model chronic toxicity The BLMs were derived directly from toxicity tests in which major freshwater cations (Ca 2+, Mg 2+, Na 2+, K + )and ph were unilaterally modified. Results of these experiments indicated that both Ca 2+ and Mg 2+ (i.e., hardness) ameliorated Ni toxicity, as did ph. The same trend decreasing Ni toxicity with increasing hardness and decreasing ph was observed for all three species (i.e., D. magna, O. mykiss, and P. subcapitata). The stability constants were used to assemble the models, the validity of which were then tested by evaluating the accuracy of BLM predictions to nickel toxicity tests performed using a series of natural fresh waters that exhibited ranges of ph, hardness, and DOC. Details of the outcomes of these validation exercises are provided below for each of the nickel BLMs that have been developed. On the one hand, it could be argued that including the data from natural waters in the effects database means that the BLMs have not been field validated. If this position is adopted, then the data from the natural waters should be removed from the effects database. On the other hand, it can be argued that the validation was an independent exercise, and that whether or not the data are used in the SSD is irrelevant to testing the ability of the laboratory-based models to predict toxicity in natural waters. Also, the data collected from natural waters will be normalized to the conditions that are agreed upon for regional and local PNEC derivation, as will data from the laboratory testing. Invertebrates De Schamphelaere et al. (2006) have developed a chronic BLM for D. magna based on the the dataset from Deleebeeck et al. (2005) using WHAM VI. The predictive capacity of the models is plotted in Figure , for natural waters, as well as for the artificial test waters. The model is able to predict most 21d-ECx values in artificial test solutions by an error of less than factor 2, indicating that the model is well calibrated to this dataset. This does not appear to be the case for the natural surface waters, where there is a trend of underestimating 21d-ECx values or overestimating the chronic Ni toxicity. Prediction errors were on average factor 2.0, 2.3 and 2.4 for the EC 50, EC 20 and EC 10 -levels respectively. Formatted: English (U.S.) 70

85 predicted 21d-ECx (µg Ni/L) Artificial EC50 Artificial EC10 artificial EC10 Natural EC50 Natural EC20 Natural EC observed 21d-ECx (µg Ni/L) Figure Predictive capacity of the chronic D. magna models. According to De Schamphelaere et al. (2006), possible hypotheses to explain why the chronic D. magna model is less accurate in natural waters include that at the time of ecotoxicity testing with natural waters, the chronic sensitivity of the daphnids to Ni might have been considerably lower, or the model based on univariate testing does not capture potential interactive effects of different (co-varying) physico-chemical characteristics in natural waters or the presence of DOC (humic substances) in natural waters that (as opposed to artificial waters) ameliorates Ni toxicity beyond its effect on speciation. Similarly, De Schamphelaere et al. (2006) have developed a chronic BLM for C. dubia based on newly generated data and the Wirtz et al. (2004) dataset using the speciation model WHAM VI. The optimal calibration results in prediction errors lower than factor 2 for all test waters and effect levels from the University of Ghent dataset, with an average prediction error for EC50 dissolved of factor 1.4 (Figure ). 100 PMX-Synthetic PMX-Natural predicted ECx (µg Ni/L) 10 EC10 EC20 EC observed ECx (µg Ni/L) predicted ECx (µg Ni/L) observed ECx (µg Ni/L) Figure Observed and predicted chronic ECx of Ni to C. dubia in synthetic and natural waters (datset from University of Ghent and from Wirtz et al., 2004). 71

86 The data in Figure further indicate that all EC50 dissolved from the Wirtz et al. (2004) dataset) are predicted by an error of less than factor 3, whereas the variability was originally a factor of 20. However, the developed C. dubia model, developed for ph levels < 8.2 results in clearly biased predictions for the Wirtz et al. (2004) dataset, i.e. low EC 50 s are generally overestimated and high EC 50 s are generally underestimated. Because of this important prediction bias, which is mainly due to a more pronounced ph effect at ph > 8.2 vs. at ph <8.2, and because of the fact that ph 8.2 is the upper 95 th percentile of EU surface waters. Therefore C. dubia ecotoxicity data are not used for the EU nickel risk assessment concerning use of BLMs if these data were obtained in test solutions with ph > 8.2. The Wirtz et al. (2004) dataset, however, offers a unique possibility towards assessing the risk of surface waters with a ph > 8.2, because C. dubia is currently the species known to be most sensitive to Ni. It would be important, however, to also use the associated higher slope of the ph function in such a case. These data are inserted in Table which includes all valid data on species where the abiotic factors were outside the range of the BLM which is used for the species in question. Fish De Schamphelaere et al. (2006) developed chronic Ni bioavailability models for the fish O. mykiss based on datasets generated by Deleebeeck et al.(2005), using WHAM VI instead of the BLM software which is based on WHAM V. Formatted: English (U.S.) 72

87 10000 predicted 17d-LC50 (µg Ni/L) 1000 ph set Mg set Ca set Natural observed 17d-LC50 (µg Ni/L) predicted 17d-LC50 (µg Ni/L) 1000 ph set Mg set Ca set Natural observed 17d-LC50 (µg Ni/L) Figure Predictive capacity of the model for LC50s and LCxs for x>10% and <100% after 17 days of exposure. Filled symbols indicate extrapolated LC50s and are less reliable to evaluate the predictive capacity of the models. Observed vs. predicted 17d-LC 50 s and 17d-LCx are plotted in Figure The data illustrate that most LC 50 s are predicted by an error of less than a factor of 2, not only for the synthetic test waters, but also for the natural test waters, the data of which have also been taken from Deleebeeck et al. (2005). Meaningful validations were performed for four test waters (i.e. Ankeveen, Bihain, Brisy, and Markermeer). The LC 50 s in Ankeveen, Brisy and Markermeer were predicted very accurately, i.e. by an error of 1.1-fold on average. The LC 50 in Bihain was overestimated by 2.9-fold. The latter may perhaps be explained by the fact that this test water had properties at the border of or even outside the chemistry ranges for which our model was developed. While in this water the ph of 5.6 was at the border, its water hardness of 14 mg CaCO 3 /L (Ca = 0.09 mm, Mg = 0.05 mm, Table ) was clearly lower than in all synthetic test waters, which had a minimum hardness of 25 mg CaCO 3 /L (Ca = Mg = 0.12 mm). Algae Formatted: English (U.S.) 73

88 De Schamphelaere et al. (2006) developed/validated chronic Ni bioavailability models for the alga P. subcapitata based on datasets generated by Deleebeeck et al.(2005) and Bossuyt et al. (2001), using WHAM VI instead of the BLM software which is based on WHAM V predicted 72h-EC50 (µg Ni/L) 1000 Bossuyt - natural this study - natural Bossuyt - synthetic this study - synthetic Deleebeeck - natural predicted 72h-EC10 (µg Ni/L) Bossuyt - natural this study - natural Bossuyt - synthetic this study - synthetic Deleebeeck - natural observed 72h-EC50 (µg Ni/L) observed 72h-EC10 (µg Ni/L) Figure Observed vs. predicted 72h-ErC50s and 72h-ErC10s. Filled data points were obtained at ph < 6.4. When the data points obtained at ph < 6.45 are not considered, all EC 50 s and most EC 10 s are predicted by an error of less than 2-fold, with average prediction errors of 1.4-fold for EC 50 s and 1.5-fold for EC 10 s. Interestingly, The EC 50 and EC 10 in the Bihain test waters investigated by Bossuyt et al. (2001) were also predicted reasonably well, despite the fact that ph of these waters was below De Schamphelaere et al. (2006) suggest that the model may be reasonably safely extended to a ph as low as 6.0, although the uncertainty about model predictions between ph 6.0 and 6.4 is likely to be higher than at ph > 6.4 Higher plants Lock et al. (2005) developed a model for higher plants using H. vulgare as test organism. These plants were exposed for 4 days in nutrient solutions with different concentratins of Ca, Mg, Na, and H + and EC 50 s were calculated and used for the development of a higher plant BLM for nickel (Figure ). 74

89 Predicted 4 d EC50 (µm Ni 2+ ) Observed 4 d EC50 (µm Ni 2+ ) Figure Relationship between observed and predicted 4 d EC50 for Hordeum vulgare exposed to Ni 2+. Autovalidation (i.e., comparison of model predictions with observed toxicity values that were determined in tests used to derive model parameters) of the developed nickel BLM for H. vulgare indicated that the model could predict the EC 50 s within a factor of less than 2, indicating the BLMs can also be used to predict metal toxicity to higher plants. However, the authors stated that further field validation would be relevant. Chronic Ni toxicity as a function of ph, DOC and hardness Visualization, by means of 3D-surface response plots, of the effect of chronic Ni toxicity with ph, DOC and hardness is provided in Figures through NOECs (or EC 10 s) were predicted for a matrix of ph, hardness and DOC values for C. dubia, D. magna, O. mykiss and Pseudokirchneriella subcapitata using the chronic bioavailability models described by De Schamphelaere et al. (2006). ph and hardness were varied within ranges that are common to the boundaries of all models used, i.e. ph 6.5 to 8.2, and hardness 6 to 320 mg CaCO 3 /L. DOC was varied between 1 and 25 mg DOC/L. We assumed equilibrium with atmospheric pco 2 = atm, molar Ca:Mg ratios of 3:1. Other ions were estimated from their correlation with Ca concentration (Heijerick et al., 2002). DOC was modeled according to De Schamphelaere et al. (2006), i.e. assuming 40% active fulvic acid. All 3D-figures were constructed with Statistica 6.0 software (STATSOFT, Tulsa, OK) and are summarized in Figures through The combined effect of hardness and DOC is plotted at a ph of 7.5 (50 th percentile of occurrence in EU surface waters according to the Swad, EURAS, Gent, Belgium) (see Figure ). The combined effect of ph, DOC, and hardness on nickel toxicity is plotted at a hardness of 100 mg CaCO 3 /L (50 th percentile of occurrence in EU surface waters according to the Swad, EURAS, Gent, Belgium).(see Figure ) The combined effect of ph, DOC, and hardness on nickel toxicity to a particulat organism, Ceriodaphnia dubia, is shown at hardnesses of 40, 100, and 310 mg CaCO 3 /L (see 75

90 Figure ). C. dubia was chosen because it shows the greatest sensitivity of the BLM species to nickel. Similar figures are shown in Appendix G.4. Figure The combined effect of hardness and DOC on chronic Ni toxicity (plotted at a ph of 7.5) 76

91 Figure The combined effect of ph and DOC on chronic Ni toxicity (plotted at a hardness of 100 mg CaCO3/L) 77

92 Figure Relationship between ph, DOC, and hardness and nickel toxicity to Ceriodaphnia dubia. Realtionships are shown at hardnesses of 40, 100, and 310 mg CaCO3/L. The horizontal axis to the right shows DOC (mg/l), the horizontal axis to the left shows ph, and the vertical axis shows the EC10 (µg Ni/L) 78

93 The following general observations are made: The effect of DOC on EC10s is linear for all species and for any ph or hardness values under consideration. The effect of DOC is more pronounced (steeper slope) when hardness is lower and when ph is higher. This relationship is particularly evident for C. dubia, which is the most sensitive of the species addressed. The inter-relation of hardness and ph on effects of DOC occurs because at lower hardness and higher ph, more binding sites are available for Ni binding. The effect of hardness is generally linear down to a hardness of 20 to 60 mg CaCO 3 /L, depending on the species considered. Down to this level, decreasing hardness reduces EC10- levels. Further decreasing the hardness to lower levels (below 20 to 60 mg CaCO 3 /L), results in increasing EC10s. This is because, at low hardness levels, the effect of less competition between Ni and Mg 2+ and Ca 2+ ions for DOC-binding sites overrules the effect of less competition on the biotic ligand. Hence, at very low hardness levels, reduced hardness is predicted to reduce toxicity, when expressed on a dissolved Ni basis. This is predicted to be the case for all species. The effect of ph on the different organisms can be divided in 2 groups: invertebrates + fish on the one hand and algae on the other. For invertebrates and fish, increasing ph results in decreased EC10sover the whole ph range. The algae model predicts a curved response, with both low and high ph levels resulting in increased EC10s. At low ph, the competition between H + and Ni 2+ for the biotic ligand overrules the competition for the DOC binding sites. At high ph the effect of more available binding sites on the DOC and binding to carbonate becomes more important. Implementation of the BLM in risk assessment procedures General approach The correction for bioavailability is applied to the effect concentrations (NOECs or EC 10 ) obtained in the accepted high quality ecotoxicity tests obtained from the literature search and from the conclusion (i) research program. The first step in the approach consists of the determination of a critical biotic ligand accumulation calculated from the experimentally generated organism specific toxicity values (NOEC or L(E)C 10 values) and knowledge about the abiotic factors influencing nickel toxicity (hardness, ph and DOC) in the induvidual tests of the database. Ideally, organism-specific bioavailability models should be used for that purpose. This is because the competition for uptake between the free metal ion and other cations and protons at the site of toxicity is influenced by biological factors, e.g., the relative affinity for a metal ion versus a cation or proton at the uptake site can vary among species. The use of organism-specific models will preserve this ecologicaly relevant interspecies variability in the risk assessment. However BLMs are not available for all organisms that have been accepted for use in this risk assessment. Thus, extrapolation across species is necessary. Assuming similar mechanisms of toxicity for phylogenetically similar species implicitly suggests that the models are able to predict the toxicity towards other phylogenetically similar species. This assumption may, however, be questioned because it is well known that e.g. the physiology of taxonomically closely related species may be significantly different because the physiology generally is more a consequence of adaptation to ecological niches than of phylogenetic relationship. However in absence of BLMs on all species represented in the database on accepted tests, the above assumption is simply necessary to use the bioavailability 79

94 normalisation approach if data on species without BLMs are used. The uncertainty of employment of this extrapolation approach to species without established BLMs will be considered in the uncertainty analysis for the pelagic compartment. For now, the approach to extrapolate from species with an established BLM to species without means that the D. magna/c. dubia BLM models will be used to predict the influence of abiotic factors on the chronic toxicity to other invertebrates, that the P. subcapitata model will be used to predict the abiotic factors influence on the chronic toxicity to other algae; and so on. In the second step of the approach, each organism specific critical biotic ligand accumulation is translated into a critical bioavailable dissolved nickel concentrations for a specific set of water-quality conditions (ph, H, DOC) representative of EU surface water environments. The set of water quality conditions thus represents a number of typical freshwater scenarios that are representative of the EU. These scenarios will be described in detail in a later section. When normalizing the toxicity data for invertebrates it should be kept in mind that the ph slope parameter varied significantly between C. dubia and D. magna. Specifically, the slope for the relationship between ph and nickel toxicity is steeper for C. dubia than for D. magna. To this end, it is further recommended that, for carrying out normalizations for risk assessment purposes, species-specific models should be used when they are available for a given species. For other invertebrate species it is recommended that the normalizations are carried out with both the C. dubia and the D. magna ph slopes, and the most conservative toxicity estimate is further carried on in the HC 5 derivation. The result of the normalisation of the toxicity data (for different species) using the most sensitive/conservative BLM is that the application of this approach will lead to a conservative HC 5 value. It could be further argued that different BLMs exist (different ph slopes) for closely related species such D. magna and C. dubia, and therefore that this might question the read across of BLMs for less closely related species. However, this must be interpreted somewhat differently because the differences between both models are not related to metal uptake/competition physiology. Indeed, both models have similar stability constants for Ca and Mg. The differences between the two models are related to: 1) a higher intrinsic sensitivity for C. dubia and 2) a steeper ph slope for C. dubia. Difference #1 is not related to metal uptake/competition physiology, but is rather a reflection of the extreme sensitivity of this organism. Difference # 2 is partly due to the fact that nickel speciation needs to be handled differently at the extremely low concentrations that cause toxicity to C. dubia compared with the much higher concentrations that are relevant for D. magna. It is therefore likely that the differences between C. dubia and D. magna are largely a reflection of the extreme sensitivity of C. dubia. Furthermore, the influence of the difference on the predictive capacity of both BLMs is very limited. Indeed, when a unified BLM is elaborated using the merged average ph slope (the mean for both species (D. magna and C. dubia) very good predictive capacity of the models is observed for both D. magna and C. dubia. This is illustrated in the Figures and below. 80

95 100 predicted ECx (µg Ni/L) 10 1 EC10 EC20 EC observed ECx (µg Ni/L) Figure Predictive capacity of the merged C. dubia chronic Ni toxicity model for natural waters Natural EC50 Natural EC20 Natural EC10 Natural EC50 Natural EC20 Natural EC10 predicted 21d-ECx (µg Ni/L) observed 21d-ECx (µg Ni/L) predicted 21d-ECx (µg Ni/L) observed 21d-ECx (µg Ni/L) Figure Predictive capacity of the D. magna chronic Ni toxicity models for natural waters; left: developed based on natural waters only; right: merged model Therefore the different BLMs between C. dubia & D. magna should be seen as a fine tuning of the model rather than the development of a totally different BLM. Similarly, the algae, the D. magna, the C. dubia and the higher plant BLM are used to normalise the Lemna toxicity data. Here also, the lowest (most stringent) normalised NOEC or EC10 values extracted from the best fitting model (i.e. D. magna BLM) and the P. subcapitata modes (most ecologically relevant BLM) is used for the normalisation, SSD curve and final PNEC derivation. It must be emphasized that the higher plant model was developed based on toxicity experiments in hydroponic exposure conditions using the terrestrial vascular plant Hordeum vulgare (barley). However, the toxicity data from these experiments were not retained in the database as the organism (i.e. barley) is not endemic to aquatic habitats. The availability of stability constants provided information that could potentially be used in the risk assessment to normalize toxicity data for the higher plants, which in the case of nickel is restricted to Lemna gibba. 81

96 Finally for fish and amphibian species without BLM, the BLM on rainbow trout is used to normalise the abiotic factors influence in the chronic toxiicty observed in the tests with these species. A flow chart of the different steps involved in the normalisation and HC 5 drivation is outlined in Figure Figure Overview of the different steps involved in the normalisation and HC5 drivation. For normalising for bioavailability chronic BLM have to be developed (using laboratory waters) and validated (using EU surface waters). Thes BLMs are further used to normalise all high quality chronic toxicity data from the database. This high quality database contains both chronic toxicity data obtained from the research activities (cfr. BLM development/validation exercise) and literature. Normalisation for bioavailability using the read-across approach (see below) is peformed towards the abiotic conditions occurring in the selected eco-regions. Aggregation of the normalised chronic toxicity data is performed based on the calculation of the species geometric mean value for the most sensitive endpoint per species. A species sensitivity distribution (SSD) is then constructed by application of an appropriate curve fit 82

97 distribution to the normalised chronic toxicity data. Finally a median HC 5 value is derived and subsequently a PNEC is derived. Cross species normalisation approach The BLMs developed as described above will account for both the interactions of a metal ion with the media, which are common to every metal, and the interaction of the available forms of the metal with the organism, which is species specific. It is therefore important to demonstrated that a BLM for one taxonomic group may be applicable to other species without a BLM. For the purpose of read-across between species, the approach below is based on TCNES discussions and the approach proposed by MERAG and integrating the views of TCNES experts, expressing their view at TCNES and in writing. Based on this a discussion paper generally agreed by TCNES was proposed which includes read-across acceptance criteria, as outlined in Figure Figure TC NES agreed read-across approach and read-across criteria. 1. The first step consists of the read across approach thus consists in evaluating if full read-across to all species (full read-across) within a trophic level may be justified. The application of a bioavailability model across species assumes similar mechanism of actions (e.g. similar stability constants between the cations (Ca, Mg, H) and the biotic ligands, similar site of action) and therefore the applicability across species needs to be investigated and validated. Information on the applicability of the bioavailability model across species can be obtained from information on the mechanism of action (MOA) of the metal under consideration, physiological similarities between the 83

98 species, observed changes in intra-species variability 9 after application of the bioavailability model across species and by other means, 2. The TC NES agreed read across approach (Figure ) provides the following read-across concepts If quantitative evidence is available to confirm the applicability of at least 3 BLMs to at least 3 dissimilar representative taxonomic groups full BLM normalization of the SSD may be justified. Such quantitative information should consist of spot checking of the BLMs for species for which no validation had been undertaken. The spot checking would amount to confirmation that the BLMs are able to adequately predict the toxicity of metals to dissimilar taxonomic groups, in chronic or acute tests under varying abiotic conditions which are known to influence the bioavailability as modelled by the available BLMs. The level of checking, e.g. testing of additional taxa to confirm applicability of the BLM would be determined on a case by case basis taking into account the level of uncertainty in the extrapolations, and the extent to which it is necessary to reduce uncertainty. The accuracy of such predictions should be within a factor of 5 (should preferentially be evaluated on a case by case) depending on the intrinsic variability of the species endpoint tested. If the above information is not available, other evidence related to read-across of existing BLMs to other species can be used. Each of these bioavailability refinement considerations (e.g. mode of actions, species, decrease in intra-species variability) bring some inherent uncertainties when used for full BLM normalisation. The TC NES agreed approach further considers that bioavailability corrections, based on the three BLMs only is considered as the baseline correction. In this approach, if read-across can not be sufficiently demonstrated, the most conservative bioavailability factor (BioF) is subsequently used because this approach is expected to provide the most conservative implementation of bioavailability. 3. If full read-across is justified as described above the next step consists of applying the bioavailability model across species of similar trophic levels (e.g. applying the Daphnia magna BLM 10 for normalization of the toxicity data from other invertebrates like amphipods, insects, ) towards a specific set of geochemical conditions (e.g. a defined eco-region). The bioavailability model normalizes the no-effects threshold concentration (NOEC or EC10) of the metal for each species endpoint and the model therefore retains the intrinsic sensitivities of the different species and endpoints to the chronic toxicity of the metal. The species-specific normalized geomean NOEC s for the most sensitive endpoints are then used to derive the PNEC using the assessment factor approach (AF) (data poor metals) or by constructing an SSD (data rich metals) from which the HC 5, as outlined in the TGD, can be derived. In case read-across is only justified for some species and not for others (e.g. insufficient readacross data or unexplained significant increase in variability after normalization or different mode of action) the cautious approach (most strict correction by employing the BioF approach need to be applied (c.f. further in the MERAG report and the ERAR for zinc) In the Ni risk assessment the general principles of Figure and was followed and, the read across approach of using BLMs towards different, but phylogenetically similar, 9 Induced reductions in intra-species variability can be assessed by e.g. comparing the predicted vs. observed toxicity for the different species or by means of the max./min. ratio between toxicity thresholds. 10 Normalisation of toxicity data is only allowed within the boundaries of the developed/validated bioavailability model 84

99 species has been applied. Chronic BLMs, were initially developed for invertebrates (D. magna/c. dubia), algae (R. subcapitata) and fish (O. mykiss) The D. magna model were applied to the other invertebrates through normalizing the NOECs, gathered in the Ni ecotoxicity database and characterized by varying physico-chemical test conditions. The data show that the chronic Ni D. magna BLM significantly reduced the observed intra-species variability for all invertebrate species for which multiple datapoints on chronic nickel toxicity under different abiotic factor combinations were available (i.e. D. magna, C. quadrangula, P. truncata, D. longispina, C. pulchella and S. vetulus) (see chapter 2.7.3). Similar results were obtained when the alga BLM is used for the normalization of other algae species (i.e. Chlamydomonas, A. falcatus, P. duplex and C. microporum) from the database. The D. magna BLM reduced however only a part of the intraspecies variability for Ceriodaphnia dubia. In particular, the model did not predict the most sensitive data accurately. Furthermore, the effects database and the normalization exercise indicated that the HC 5 to a large extent was influenced by C. dubia toxicity values (see chapter 2.2; Appendix G.6). Based on these two factors, a specific BLM model for C. dubia was developed. The chronic BLM for C. dubia was developed within the boundaries of abiotic factors occurring in EU surface waters and showed a good predictive capacity. Subsequently, the C. dubia model was used to normalize the C. dubia data in the database. However, it is recognized that remaining uncertainties exist in the extrapolation of a chronic BLM towards other phylogenetically dissimilar species for which no BLM has been developed. In particular, concerns were raised on the extrapolation of the chronic crustacean C.dubia/D. magna towards non-crustacean species like molluscs, insects, rotifers and plants. To resolve these uncertainties, an agreement was reached at TC NES IV 06 (December 2006) on the information and decision framework needed to support employmenet of the full cross species BLM extrapolation :Full normalization of the nickel aquatic toxicity database via the four chronic nickel BLMs could be proposed and considered on the condition that a spotcheck study would be performed validating the relevance and reliability of BLM predictions for a suitable number of species, on which no BLM has yet been developed and validated The four non-blm organisms used for the spot checking included three invertebrate species, the insect Chironomus tentans, the rotifer Brachionus calicyflorus, and the fresh water snail Lymneae stagnalis and one plant species Lemna minor. Standard test protocols exist for C. tentans, L. minor and B. calicyflorus, but not for L. stagnalis. The latter species has been tested for other metals and was chosen to determine if the existing BLMs apply to gastropod molluscs, as data for one gastropod is included in the Ni aquatic toxicity database. These spot checking tests were performed in five natural waters that represent those typically found within the major ecoregions of Europe. Evaluation of the Cross-species Extrapolation of the BLM The accuracy of the BLM predictions was evaluated by comparing the observed chronic toxicity for each of the non-blm species with the predicted toxicity from the chronic BLMs of D.magna or C. dubia for the selected invertebrate species and with the P. subcapitata and H. vulgaris BLMs for L. minor. An overview of the sample sites (all located in the US) and abiotic factors of the waters used for the spot checking study is provided in Table When these naturally occurring test waters were selected the overall aim was to maximize the variability of the abiotic factors which influences the chronic toxicity for the species on which BLMs have been developed. In the same time these selected water have been chosen to be representative for a wide variety of ambient surfacewater types witin the EU as regards the abiotic factors driving the BLMs. 85

100 Table Site locations and water quality parameters of test waters used for the spot checking study Site ph H (mg/l CaCO 3) DOC (mg/l) S. Platte River (ph-amended) Zollner Creek Calapooia River S. Platte River S. Santiam River Brachionus calyciflorus. Predictions for B. calyciflorus were better with the D. magna BLM than with the C. dubia BLM (Figure ). Predictions for all waters were accurate within a factor of two using the D. magna BLM with the exception of the prediction for Zollner Creek, which differed by a factor of 2.2. Here, the predicted EC20 was 691 µg Ni/L, and the observed EC20 was 1498 µg Ni/L. The 95 th percentile confidence intervals were also within a factor of four for all of the natural waters. Chironomus tentans. Predictions for C. tentans were equally accurate with either the D. magna or the C. dubia BLMs, both of which predicted toxicity for all waters within a factor of 2 (Figure ). Additionally, the 95 th percentile confidence intervals were within a factor of four for all of the natural waters. Lymnaea stagnalis. Predictions for L. stagnalis were better with the C. dubia BLM than with the D. magna BLM (Figure ). All waters were predicted within factor of 2 using the C. dubia BLM, and the 95 th percentile confidence intervals were within a factor of four for all of the natural waters. Lemna minor. Both the Pseudokirchneriella subcapitata BLM and the Hordeum vulgaris BLM (Figure ) predicted the toxicity of Lemna minor within a factor of 2 for three out of the five waters, and within a factor of three for the remaining two waters. The 95 th percentile confidence intervals for both of these BLMs were all within a factor of 4. 86

101 10000 A. Brachionus - growth rate - EC20 - D. magna BLM B. Brachionus - growth rate - EC20 - C. dubia BLM Predicted ECx (µg/l) Predicted ECx (µg/l) Observed ECx (µg/l) Observed ECx (µg/l) Figure Observed nickel toxicity (EC20, in µg Ni/L) to the rotifer Brachionus calyciflorus compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of A. Chironomus - biomass - EC20 - D. magna BLM B. Chironomus - biomass - EC20 - C. dubia BLM Predicted ECx (µg/l) 1000 Predicted ECx (µg/l) Observed ECx (µg/l) Observed ECx (µg/l) Figure Observed nickel toxicity (EC20, in µg Ni/L) to the insect Chironomus tentans compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of 4. 87

102 100 A. Lymnaea - weight - EC50 - D. magna BLM 100 B. Lymnaea - weight - EC50 - C. dubia BLM Predicted ECx (µg/l) 10 Predicted ECx (µg/l) Observed ECx (µg/l) Observed ECx (µg/l) Figure Observed nickel toxicity (EC20, in µg Ni/L) to the snail Lymnaea stagnalis compared with predicted toxicity using the Biotic Ligand Model developed for Daphnia magna (A) and Ceriodaphnia dubia (B). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of 4. 88

103 1000 A. Lemna minor - root length - Algae BLM 1000 B. Lemna minor - Root length - Hordeum BLM Predicted EC50 (µg/l) 100 Predicted EC50 (µg/l) Observed EC50 (µg/l) Observed EC50 (µg/l) 1000 C. Lemna minor - root length - D. magna BLM 1000 D. Lemna minor - fronds - C. dubia BLM Predicted EC50 (µg/l) 100 Predicted EC50 (µg/l) Observed EC50 (µg/l) Observed EC50 (µg/l) Figure Observed nickel toxicity (EC520, in µg Ni/L) based on inhibition of specific root growth length of the vascular plant Lemna minor compared with predicted toxicity using the Biotic Ligand Model developed for the alga Pseudokirchneriella subcapitata (A), the vascular plant Hordeum vulgaris (B), the crustacean Daphnia magna (C), and the crustacean Ceriodaphnia dubia (D). Error bars represent 95% confidence intervals. Solid line ( ) represents a 1:1 relationship. The interior dashed line (---) represents predictions within a factor of 2, and the exterior dashed line ( - ) represents predictions within a factor of 4. The evaluation of the validity of applying BLMs to non-blm organisms was made using weight-of-evidence. Even after thourough evaluation, some residual uncertainty remains about the approach to take with certain non-blm organisms, where either the outcome of the Spot Check exercise was not clear, or for non-blm species for which the species themselves or closely-related organisms were were not included in the Spot-Check exercise. An example of where the Spot Check exercise was not perfectly clear is Lymnaea stagnalis, for which the issue of ph variation in the test medium during the testing period introduces uncertainty about the validity of normalizing data from L. stagnalis and other molluscs with the Ceriodaphnia dubia BLM (although the C. dubia model showed the best fit for the L. stagnalis toxicity data, the C. dubia model on the other hand also indicated that the bioavailable Ni fraction would impact toxicity much more than the case would be according to the Daphnia magna BLM). 89

104 Another example of where the Spot Check exercise did not remove all uncertainty is with the Lemna minor toxicity data. For this species, the BLM for D. magna showed a betterfit that the BLM for algae. The application of an animal-based BLM to a vascular plant may indicate that it cannot simply be assumed that the closer species are in evolutionary terms, the more likely it is that it also will be affected by similar impact of abiotic factors as regards chronic nickel toxicity. I,.e. this may in general cast some doubt on the assumption behind the full cross species normalisation approach that the closer species are taxonomically, the more likely it is that they can share the same BLM for chronic nickel toxicity. On the other hand Lemna is like animals multicellurar organisms and even though Lemna is sharing the same kingdom taxonomically (Chromista) as single cell algae species, it is still not very closely related to these organisms in evolutionary terms. The proposal for moving forward in the face of these uncertainties is to perform a sensitivity analysis in which the use of the best fitting BLM for the species in question is compared to the use of the most stringent BLM for some of the species in question (i.e., L. stagnalis), or between that vary in terms of ecological significance (i.e., for L. minor the D. magna BLM provided the best fit, but the P. subcapitata model is felt to be more ecologically significant). The outcome of the analysis is two HC5 values for each of the Ecoregion scenarios, where the outcome of the different model choices is compared. This sensitivity analysis is performed using the following framework, which matches the species in the Ni Ecotoxocity Database with the BLMs that will be used. For certain species, i.e., L. stagnalis and Lemna sp., two alternative BLMs will be evaluated. For the other species, the choice of the BLM is justified in Table Table Justification for choice of BLM for read-across Species in the ecotoxicity database Pseudokirchneriella subcapitata (algae) Chlamydomonas sp. (algae) BLM used for normalization Pseudokirchneriella subcapitata Pseudokirchneriella subcapitata Justification for choice of BLM 1. BLM species, so the species-specific BLM is to be used. 2. Finding of similar mechanisitic explanation of toxicity between Pseudokirchneriella subcapitata and a dissimilar species (Chlamydomonas reinhardtii) by Worms and Wilkinson (2007). Ankistrodesmus falcatus (algae) Scenedesmus accuminatus (algae) Pseudokirchneriella subcapitata See 2. Pseudokirchneriella subcapitata See 2. Chlorella sp. (algae) Pseudokirchneriella subcapitata See 2. Desmodesmus spinosus (algae) Pediastrum duplex (algae) Coelastrum microporum (algae) Pseudokirchneriella subcapitata See 2. Pseudokirchneriella subcapitata See 2. Pseudokirchneriella subcapitata See 2. 90

105 Species in the ecotoxicity database BLM used for normalization Justification for choice of BLM Lemna gibba (vascular plant: duckweed) Lemna minor (vascular plant: duckweed) Compare between Pseudokirchneriella subcapitata and Daphnia magna Sensitivity Analysis Compare between Pseudokirchneriella subcapitata and Daphnia magna Sensitivity Analysis 3. Outcome of Spot Check exercise identified that the D. magna model provided a better fit, but the P. subcapitata model was felt to be more ecologically relevant. For the purposes of comparison, the model that shows the most stringent normalized EC10 value will be included with the most stringent result for the other species subject to the sensitivity analysis (e.g., L. stagnalis). 3. Outcome of Spot Check exercise identified that the D. magna model provided a better fit, but the P. subcapitata model was felt to be more ecologically relevant. For the purposes of comparison, the model that shows the most stringent normalized EC10 value will be included with the most stringent result for the other species subject to the sensitivity analysis (e.g., L. stagnalis). Daphnia (crustacean : cladoceran) magna Daphnia magna See 1 (Species-specific BLM is used) Ceriodaphnia (crustacean : cladoceran) dubia Ceriodaphnia dubia See 1 (Species-specific BLM is used) Ceriodaphnia quadrangula (crustacean : cladoceran) More stringent of the C. dubia and D. magna models 4. Decision taken in Aquatic Effects Assessment, on the basis that both the test species and the proposed BLM species are cladoceran crustaceans Daphnia longispina (crustacean : cladoceran) Ceriodaphnia pulchella (crustacean : cladoceran) More stringent of the C. dubia and D. magna models More stringent of the C. dubia and D. magna models See 4. See 4. Simocephalus (crustacean : cladoceran) Alona (crustacean : cladoceran) Peracantha (crustacean : cladoceran) vetulus affinis truncata More stringent of the C. dubia and D. magna models More stringent of the C. dubia and D. magna models More stringent of the C. dubia and D. magna models See 4. See 4. See 4. Hyalella (crustacean : amphipod) azteca More stringent of the C. dubia and D. magna models 5. Decision taken in Aquatic Effects Assessment, and supported by the basis that both the test species and the proposed BLM species are crustaceans 91

106 Species in the ecotoxicity database Clistoronia magnifica (insect: caddisfly) Chironomus tentans (insect: midge) Juga plicifera (mollusc: snail) Lymnaea stagnalis (mollusc : snail) Hydra littoralis (coelentrate : hydra) Brachionus calyciflorus (rotifer) Gastrophryne carolinensis (amphibian: Eastern narrow mouthed toad) BLM used for normalization More stringent of the C. dubia and D. magna models More stringent of the C. dubia and D. magna models Best fitting model versus the most stringent model, which will be determined on a water-by-water basis Sensitivity Analysis Best fitting model versus the most stringent model, which will be determined on a water-by-water basis Sensitivity Analysis Best fitting model versus the most stringent model, which will be determined on a water-by-water basis Sensitivity Analysis Daphnia magna model Oncorhynchus mykiss BLM Justification for choice of BLM 6. Outcome of the Spot Check exercise for the insect Chironomus tentans showed that either of the cladoceran models showed a good fit. See Due to uncertainty surrounding the reliability of the test results for the toxicity test performed with Lymnaea stagnalis during the Spot Check, and the relationship between observed toxicity and BLM predictions, a comparison will be made between the best-fitting and the most stringent BLMs. See As there are no BLM organisms or Spot Check organisms that are closely related to H. littoralis, a comparison will be made between the best-fitting and the most stringent BLMs. 9. The D. magna model was the bst-fitting model according to the Spot Check analysis, and the choice of BLM will not impact the HC5 determination due to the insensitivity of B. calyciflorus. 10. Decision taken in Aquatic Effects Assessment, and supported by the basis that both the test species and the proposed BLM species are vertebrates Bufo terrestris (amphibian: Southern toad) Xenopus laevis (amphibian: African clawed frog) Oncorhynchus mykiss BLM See 10. Oncorhynchus mykiss BLM See 10. Brachydanio rerio (fish: zebrafish) Oncorhynchus mykiss BLM 11. Decision taken in Aquatic Effects Assessment, and supported by results of Deleebeeck et al. (2007), which showed that the O. mykiss BLM provided a good fit for toxicity data for the fish Pimephales promelas Pimephales promelas (fish: fathead minnow) Oncorhynchus mykiss BLM See 11. Oncorhynchus mykiss (fish: rainbow trout) Oncorhynchus mykiss BLM See 1 (Species-specific BLM is used) 92

107 In summary, the following normalisation approach was followed for the HC5-50 derivation: for algae, the Pseudokirchneriella subcapitata BLM was used; for higher aquatic plants, the D. magna (best fitting BLM) BLMs was pused; for cladocerans, insects and amphipods, the most stringent from the D. magna and C. dubia BLM is used; for rotifers, the D. magna BLM is used; for molluscs and hydra a the Ceriodaphnia dubia (best fitting BLM) BLMs was used; for fish and amphibians, the Oncorhynchus mykiss BLM was used. Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic A sensitivity analysis comparing the use of the best fitting versus the most stringent BLM for the different eco-region scenarios can be found in Appendix G.7. Results of this sensitivity analysis demonstrate that the differences between use of the best-fitting model versus the most stringent model are extremely minor. Based on this it is proposed to use the most stringent model, when using BLM normalization for species on which a species specific BLM has not yet been developed and validated. A number of limitations have been raised on the use of BLMs in metals risk assessment, including the following: BLMs do not account for possible effects caused by dietborne metal exposure, which may be substantial for many aquatic organisms; Formatted: Bullets and Numbering BLMs do not account for exposures of multiple metals or organic contaminants; BLMs are equilibrium-based models that may not accurately account for the dynamic behavior of metal speciation in some aquatic environments. In most cases, these limitations have been addressed or are the focus of current research. For example, while it is true that dietborne metal exposures can be substantial for many aquatic organisms, observations of toxicological effects of dietborne metal exposures are rare, and in the field are limited to situations where waterborne exposures exceed Environmental Quality Guidelines for metals (Borgmann et al. 2005). Additionally, dietborne metals exposure did occur in the toxicity tests used to develop the nickel BLMs, and the models were capable of accurately predicting nickel toxicity even though dietborne exposure is not accounted for. And while the multiple contaminant exposure is environmentally relevant, the Existing Substances regulation was developed to assess the risk of specific chemical substances in isolation. Appendix G-8 addresses the possible limitation interactions between nickel and one specific class of organic contaminants, the carbamate pesticides. These compounds are of particular interest because they have the potential to form neutral complexes with nickel, which will not be accurately accounted for by the current speciation module within the BLM. This in turn may result in situations where BLM predictions of nickel toxicity are underpredicted. The conclusions of Appendix G.8 are that underpredictions of toxicity are theoretically possible, but that the short half-life of carbamates and their sorption to organic matter reduce the possibility that carbamate-nickel complexes would be formed in the field. Also, the limited information on the distribution of carbamates prevents a quantitative assessment of this interaction Scenario development for eco-regions 93

108 The toxicity data are as mentioned above further normalized towards typical physico-chemical conditions occurring in typical region specific EU surface waters. In order to achieve this, abiotic factors mitigating chronic Ni toxicity from both lakes and rivers were collected from different regions in the EU (i.e. Sweden, Italy, The Netherlands United Kingdom and Spain). The different scenarios were selected to provide examples of typical conditions covering a wide range of physico-chemical conditions (ph between 6.67 and 8.2; hardness between 27.8 and 260 mg/l CaCO 3, DOC between 2.5 and 27.5 mg/l) occurring in EU surface waters. The different considered scenarios are summarized in Table and In this exercise small (± 1,000 m³/d), medium sized (± 200,000 m³/d) and large (± 1,000,000 m³/d) alluvial (eutrophic) rivers were considered. In addition, an example of a typical Mediterranean river was also included in this report. For the lakes, the focus was on the gathering of physicochemical data for sensitive systems, i.e. oligotrophic and neutral-acidic lakes. Table Summary of the physico-chemical characteristics of the different selected scenarios. Rivers Lakes Boundarie s Type Name Country ph H (mg/l DOC Reference CaCO3) (mg/l) Small (ditches with / The Van Tilborg, 2002 flow rate of ± 1,000 m³/d) Netherlands Medium (rivers with flow rate of ± 200,000 m³/d) Large (rivers with flow rate of ± 1,000,000 m³/d) River Otter River Teme River Rhine United Kingdom The Netherlands HMS database Van Tilborg, 2002 Mediterranean River Ebro Spain Heijerick, 2006 river Oligotrophic systems Lake Monate Italy Euro-Ecole, 2002 Neutral-acidic / Sweden Riksinventering system lakes database Foregs database / Swad database BLMs The physico-chemical conditions of the different scenarios considered were further compared with the phys.-chem. conditions occurring in EU surface waters (see Table , according to the SWAD database). Based on this comparison it seems that the Swedish acid lake has a low ph, hardness and DOC concentration. The oligotrophic lakes are typically characaterised by a medium ph, a low hardness and a low to very low DOC concentration. The ditches are typically characterised by a high hardness and a high DOC (very close to the 90 th % of the DOC concentration in EU surface waters, while the lowland rivers typically have medium to low DOC levels and a medium to high ph/hardness. Table Comparison of the physico-chemical conditions of the different scenarios versus EU surface waters (Swad database). Type Name Country Scenario Low (L): when the phys.-chem. in the system 10 th % of abiotic factor in EU surface waters; Medium (M): when the phys.-chem. in the system 50 th % of abiotic factor in EU surface waters; High (H): when the phys.-chem. in the system 90 th % of abiotic factor in EU surface waters. 94

109 Rivers Lakes ph H (mg/l DOC (mg/l) CaCO3) Small (ditches with flow / The Netherlands Low High High rate of ± 1,000 m³/d) Medium (rivers with River Otter United Kingdom High Medium Low Medium flow rate of ± 200,000 River Teme m³/d) Medium Medium Large (rivers with flow River Rhine The Netherlands Medium High Low rate of ± 1,000,000 m³/d) Mediterranean river River Ebro Spain High High Low Oligotrophic systems Lake Monate Italy Medium Low Low Neutral-acidic system / Sweden Low Low Low It is emphasized that the abiotic factors of all selected scenarios are within the boundaries of the chronic BLMs for Ni. Intraspecies variability Prior to the integration of the chronic BLMs into the risk assessment, the uncertainty related to the extrapolation of the BLM across species was assessed based on the gathered database. Normalisation of the NOEC data explicitely implies 1) the understanding of the NOECvariability within each species and therefore 2) the reduction in the intra-species variability. Figure and Table shows the original (non-normalised) and BLM normalised (for the specific scenario of the River Rhine) intra-species variability (expressed as the ratio between the highest and lowest NOEC from a specific species, i.e. max/min). 95

110 ratio (max/min) 35,00 30,00 25,00 20,00 15,00 10,00 5,00 0,00 non-normalised BLM normalised P. subcapitata D. magna C. dubia C. quadrangula P. truncata D. longispina C. pulchella S. vetulus P. promelas O. mykiss Chlamydomonas sp, A. falcatus P. duplex C. microporum Figure The intra-species variability (expressed as max/min ratios) of the EC10/NOECs expressed as dissolved µg Ni/l test medium and BLM-normalised, using the chronic bioavailability models (underlined species are those for which BLMs (P subcapitata, D. magna, C. dubia, O. mykiss) have been developed). The chronic BLMs, developed for D. magna and C. dubia were applied to the other invertebrates through normalising the NOECs, gathered in the Ni ecotoxicity database and characterised by varying physico-chemical test conditions, to the specific River Rhine ph, hardness, DOC. The data show that use of the chronic Ni D. magna/c. dubia BLM significantly reduced the observed intra-species variability for all invertebrate species in the Ni effects data base. The max/min ratios for the normalised invertebrate L(E)C 10 /NOEC data show to vary between 1.7 and 12.7, while originally a considerably higher intra-species variability between 2.9 and 31.9 was observed for the non-normalised L(E)C 10 /NOEC data. Normalisation of the chronic fish data using the developed chronic fish BLM resulted in max/min ratios all below a factor of 6.6 while originally a considerably higher intra-species variability (up to 11.6 for O. mykiss) was observed for the non-normalised NOEC data. However, no intraspecies reduction is observed for the fathead minnow P. promelas. It must be stressed that after normalisation the intra-species variability is still very low, i.e. a factor of 3.6. Figure and Table demonstrates that the algae bioavailability model developed for P. subcapitata also reduce the variability observed in the ecotoxicity of Ni, by decreasing the intra-species variability from a factor 17 to a factor of 8. For other algae species (i.e., Chlamydoemonas sp., A. falcatus, P. duplex, and C. microporum), two tests were conducted under roughly similar conditions, with the only difference between the two tests being hardness (6 and 16 mg CaCO 3 /L). The small difference in water hardness explains why the intraspecies variability was not large before normalization. However, in all cases a reduction in intra-species variability for the other algae species was achieved through the use of the P. subcapitata BLM. Very similar results were obtained when the toxicity data were normalised towards the physico-chemical conditions of the other eco-region scenarios. An overview of these results is provided in Appendix G.9. Deleted: 8 96

111 Table The reduction in intra-species variability (expressed as max/min ratios) after normalization of the L(E)C10/NOEC data. Species (number of datapoints) Invertebrates Nonnormalised Normalised Variability reduction (%) Range of abiotic factors Daphnia magna (# 54) ph: ; DOC:0-17; H: Ceriodaphnia dubia (# 14) ph: ; DOC:1-24; H : Ceriodaphnia quadrangula (# 8) ph:7.2; DOC:0; H:6-43 Peracantha truncata (# 4) ph:7.2; DOC:0; H:6-16l Daphnia longispina (# 4) ph:7.2; DOC:0; H:16-43 Ceriodaphnia pulchella (# 4) ph:7.2; DOC:0; H:16-43 Simocephalus vetulus (# 4) ph:7.2; DOC:0; H:16-43 Fish Oncorhynchus mykiss (# 19) ph: ; DOC:0-18; H: Pimephales promelas (# 3) / ph: ; DOC:0-1 ; H: Algae Pseudokirchner. subcapitata (# 47) ph: ; DOC:0-26; H: Chlamydomonas sp. (# 2) ph:7.2; DOC:0; H:6-16 Ankistodesmus falcatus (# 2) ph:7.2; DOC:0; H:6-16 Pediastrum duplex (# 2) ph:7.2; DOC:0; H:16-43 Coelastrum microporum (# 2) ph:7.2; DOC:0; H:16-43 Note 1: Organisms in the table are restricted to those for which intra-species variability could be quantified, i.e., those organisms for which data from tests with variable water chemistry were available (DOC and hardness (H) are reported as mg/l). Note 2: For species indicated with bold a BLM has been developed. Thus only for the other species the validity of the cross species BLM extrapolation is indicated by the reduction in the intra-species variability of the data. In general, application of the chronic BLMs reduced the uncertainty associated with the effect assessment of species for which chronic nickel data are available from tests with different combinations of abiotic factors. Fish are currently a notable exception to this generalization. The organisms in Table that showed reductions in intraspecies variability via BLM normalization are, for the most part, the same species for which a BLM was developed, or are closely related to such species. This is especially true for the invertebrates, where intraspecies variability could only be quantified for cladoceran species. No information is available on the reduction of intraspecies variability for species taxonomically clearly different from the species wih a BLM ie. for hydra, insects, amphibians, and higher plants (cf. section 2.6.5). Such information is provided here below. Intraspecies Variability of spot checking species The uncertainty related to the extrapolation of the BLMs across species was assessed based on the toxicity data generated for B. calyciflorus, C. tentans, L. stagnalis, and L. minor. The principle function of the BLM is to take account of the influence of abiotic factors on 97

112 intraspecies variability that occur within the chronic toxicity as a result of tests that have been conducted in waters exhibiting different chemistries. This is achieved by normalizing the data for a given species to a common set of abiotic factors (ph, hardness, and DOC in the case of nickel) resulting in a reduction in intra-species variability. The ability of the BLMs to reduce intraspecies variability was assessed by comparing intraspecies variability between nonnormalized toxicity data and data that were normalized to a common set of abiotic factors (Figure and Table ). Following a similar approach, the intraspecies variability was expressed as the ratio between the highest and lowest NOEC from a specific species, i.e. max/min ratio. Also consistent with the approach taken above, the toxicity data were normalized to the River Rhine scenario (ph=7.8, hardness = 217 mg CaCO 3, DOC = 2.8 mg/l). max/min ratio Non-normalized P. subcapitata BLM H. vulgaris BLM D. magna BLM C. dubia BLM 2 0 Lymnaea EC50 Chironomus EC20 Brachionus EC20 Lemna EC50 Figure Intra-species variability (expressed as max/min ratios) of the effects concentrations expressed as dissolved µg Ni/l test medium and BLM-normalized, using the chronic bioavailability models 98

113 Table The reduction in intra-species variability (expressed as max/min ratios) after normalization of the effects data. The BLM that exhibited the lowest (most stringent) point estimate (i.e., ECx) value was used. Non-BLM Species (Number of datapoints) BLM Used Nonnormalized Normalized Reduction in intraspecies variability (%) Brachionus calyciflorus (6) D. magna Chironomus tentans (4) C. dubia Lymnaea stagnalis (4) C. dubia Lemna minor (5) P. subcapitata Lemna minor (5) D. magna The C. dubia yielded the lowest EC50 value for C. tentans, but the min/max ratio was slightly (3.5%) higher than the non-normalized min/max ratio. The data show that use of the chronic Ni BLMs significantly reduced the observed intraspecies variability for three of the four non-blm species on which spot checks were performed (Figure , Table ). The exception was Chironomus tentans, for which the min/max ratio of normalized data was slightly (3.5%) higher than the non-normalized data. The intraspecies variability for C. tentans was low to begin with, however (i.e. similar to the variability for L. stagnalis and L. minor after normalization). Additionally, the BLMs were able to predict the toxicity of C. tentans within a factor of 2. Moreover, the BLMs predicted that the expected variability was only slightly higher (factor 5) than the variation observed in the toxicity tests outcomes for this species (factor of 2.9). Intraspecies variability was substantially reduced for the two species with the highest degree of intraspecies differencne in nickel toxicity depending of the abiotic water characteristics in the selected test waters, i.e. for B. calyciflorus and L. stagnalis. Intraspecies variability was reduced by 70.2% for B. calyciflorus and by 55.4% for L. stagnalis. The use of the P. subcapitata BLM reduced the intraspecies variability for L. minor by 27%; however, a larger reduction of 57% was observed when the D. magna BLM was used for L. minor HC5 Freshwater An overview of the species sensitivity distributions and HC 5 values derived by using the lognormal and the best fitting function amongst 10 tested fit funtions (c.f. Appendix G.1) for the 7 considered scenarios is presented hereunder. An overview of calculated normalised NOEC,or EC 10 values for a number of selected species in relation to the above mentioned different scenarios (EU surface water sites) is presented in Appendix G.8. Non-normalised HC5-50 for surface waters Table presents the non-normalised species geometric mean NOEC or EC10 values for the most sensitive endpoint per species used for HC5/PNEC derivation in surface waters. It must be stressed that this approach shows high intraspecies variability for some species and 99

114 is therefore considered less ecologically relevant for PNEC derivation compared to the use of BLM-normalised dissolved toxicity data. Table Summary of the species mean NOEC or EC10 values (total risk approach) in µg Ni/L (with most sensitive endpoint) Taxonomic group Algae Higher plants Species Most sensitive endpoint Species mean NOEC/EC10 value (µg/l) Scenedesmus accuminatus Desmodesmus spinosus Pediastrum duplex Chlamydomonas sp Ankistodesmus falcatus Chlorella sp Coelastrum microporum Pseudokirchneriella subcapitata Lemna minor Lemna gibba Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth Growth rate Rotifer Brachionus calyciflorus Intrinsic rate of growth Molluscs Lymnea stagnalis Juga plicifera Growth mortality Cladocerans Ceriodaphnia dubia Ceriodaphnia quadragula Peracantha truncata Simocephalus vetulus Ceriodaphnia pulchella Alona affinis Daphnia longispina Daphnia magna Reproduction Mortality Reproduction Reproduction & mortality Reproduction & mortality Mortality Mortality Reproduction Insects Clistoronia magnifica Chironomus tentans Mortality Biomass Hydrozoans Hydra littoralis Growth 60.0 Amphipods Hyalella azteca Mortality 29.0 Fish Amphibians Brachydanio rerio Pimephales promelas Oncorhynchus mykiss Xenopus laevis Gastrophryne carolensis Bufo terrestris Hatchability Growth Growth Malformation Mortality Growth The cumulative frequency distribution (SSD) of the non-normalised species mean NOEC values towards Ni is presented in Figures and

115 cumulative distribution Generic scenario - HC5-50 = 5.6 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Xenopus laevis Oncorhynchus mykiss Pseudokircheneriella J uga plic ifera Clistoronia subcapitata magnifica Hydra lit t ora lis Pimephales promelas Lemna gibba Coelastrum microporum Chlore lla sp. Brachydanio rerio Daphnia magna Hyalella azteca Ankistodesmus falcatus Le mna minor Chlamydomonas sp Daphnia longispina Alona affinis Pediastrum duplex Desmodesmus spinosus Ceriodaphnia pulchella Simocephalus vetulus Scenedesmus accuminatus Peracantha truncata Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea st a gnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the non-normalised species mean EC10 or NOEC values from the chronic Ni toxicity tests in the dataset of freshwater organisms. Observed data and Log-normal distribution curve for the dataset fitted on the data cumulative distribution Generic scenario - HC5-50 = 6.3 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Xenopus laevis Oncorhynchus mykiss J uga plic ifera P. subcapitata Clist oronia magnifica Hydra lit t oralis Pimephales promelas Lemna gibba Coelastrum microporum Chlorella sp. Brachydanio rerio Daphnia magna Hyalella azteca Ankistodesmus falcatus Lemna minor Chlamydomonas sp Daphnia longispina Alona affinis Pediastrum duplex Desmodesmus spinosus Ceriodaphnia pulchella Simocephalus vetulus Scenedesmus accuminatus Peracantha truncata Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the non-normalised species mean EC10 or NOEC values from the chronicni toxicity tests in the dataset of freshwater organisms. Observed data and Gamma distribution curve (best fitting curve) for the dataset fitted on the data 101

116 Using statistical extrapolation results in a HC 5 at 50% confidence value of 6.3 µg/l ( µg/l) (best fitting curve) and 5.6 µg/l ( µg/l) (log-normal curve) based on the nonnormalised EC10 or NOEC data (Table ). Table Non-normalised species mean EC10s or NOECs (total risk approach) that are used as input values for deriving the HC5 values as a basis for the freshwater. Non-normalised scenario Total risk approach (n =31) Median 5 th percentile log-normal (µg/l) Median 5 th percentile best fit (µg/l) 5.6 ( ) 6.3 ( ) Gamma distribution Note: Median 5 th percentile values with 90% confidence bound (HC5 with 5% and 95% CI) for freshwater ecosystem in case of statistical extrapolation and using the assessment factor approach. The approach for HC5/PNEC derivation using dissolved (not normalized) toxicity data shows a very high intra-species variability in NOEC for some species. The derived species mean values and HC5 are therefore subjected to a relative high level of uncertainty. A visual overview of the observed intra-species variability observed for the most sensitive endpoint for the different organisms is provided in Appendix G.10. The observed high intra-species variability observed on some of the non-normalised toxicity data could be explained because those tests with a specific species were performed under very different physico-chemical conditions (i.e. ph, DOC, hardness). Deleted: 9 Normalised HC 5-50 for rivers Small river (ditch in The Netherlands); low ph/ high hardness/ high DOC The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the ditch scenario are presented in Figure and

117 cumulative distribution Scenario Ditch - HC5-50 = 56.1 ( ) µg/l Bufo terrestris Hydra littoralis Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Oncorhynchus mykiss J uga plicifera Xenopus laevis Clistoronia magnifica Daphnia longispina Pimephales promelas Brachydanio rerio Hyalella azteca Chlorella sp. Coelastrum microporum Alona affinis P. subcapitata Ceriodaphnia pulchella S imocephalus vet ulus Ankistodesmus falcatus Chlamydomonas sp Daphnia magna Peracantha truncata Pediastrum duplex Desmodesmus spinosus Lemna minor Ceriodaphnia quadrangula Scenedesmus accuminatus Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario ditches in The Netherlands). Geochemical parameters for this scenario were: ph = 6.9, hardness = 260 mg/l CaCO3, DOC = 12.0 mg/l. Observed data and Extreme Value distribution curve (best fitting curve) for the dataset fitted on the data. cumulative distribution Scenario Ditch - HC5-50 = 43.6 ( ) µg/l Bufo terrestris Hydra lit t oralis Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Oncorhynchus mykiss Juga plicifera Xenopus laevis Clist oronia magnifica Daphnia longispina Pimephales promelas Brachydanio rerio Hyalella azteca Chlorella sp. Coelastrum microporum Alona affinis P. subcapitata Ceriodaphnia pulchella Simocephalus vetulus Ankistodesmus falcatus Chlamydomonas sp Daphnia magna Peracantha truncata Pediastrum duplex Desmodesmus spinosus Lemna minor Ceriodaphnia quadrangula Scenedesmus accuminatus Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario ditches in The Netherlands). Geochemical parameters for this scenario were: ph = 6.9, hardness = 260 mg/l CaCO3, DOC = 12.0 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data. Medium sized (River Otter in the United Kingdom); high ph/ medium hardness/ low DOC The species sensitivity distribution and the calculated HC 5 based on the conventional lognormal (best fitting approach) for the medium sized river with low DOC scenario are presented in Figure

118 cumulative distribution Scenario River Otter - HC5-50 = 8.1 ( ) µg/l Bufo terrestris Brachionus calyciflorus Gastrophryne carolensis Chironomus tentans Oncorhynchus mykiss Xenopus laevis Hydra littoralis Chlorella sp. Coelastrum microporum P. subcapitata Ankistodesmus falcatus Chlamydomonas sp Pimephales promelas Brachydanio rerio Hyalella azteca Pediastrum duplex Desmodesmus spinosus J uga plicifera Scenedesmus accuminatus Daphnia magna Clistoronia magnifica Lemna minor Daphnia longispina Alona affinis Ceriodaphnia pulchella Simocephalus vetulus Lemna gibba Peracantha truncata Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Otter in the United Kingdom). Geochemical parameters for this scenario were: ph = 8.1, hardness = 165 mg/l CaCO3, DOC = 3.2 mg/l. Observed data and log-normal distribution distribution curve (best fitting curve) for the dataset fitted on the data. Medium sized river (River Teme in the United Kingdom);medium ph/ medium hardness/ medium DOC The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the medium sized river with medium DOC scenario are presented in Figures and cumulative distribution Scenario River Teme - HC5-50 = 18.7 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Hydra littoralis Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Juga plicifera Chlore lla sp. Coelastrum microporum Pimephales promelas Brachydanio rerio P. subcapitata Hyalella azteca Clistoronia magnifica Ankistodesmus falcatus Chlamydomonas sp Daphnia longispina Pediastrum duplex Desmodesmus spinosus Alona affinis Daphnia magna Ceriodaphnia pulchella Simocephalus vetulus Lemna minor Scenedesmus accuminatus Peracantha truncata Ceriodaphnia quadrangula Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Teme in the United Kingdom). Geochemical parameters for this scenario were: ph = 7.6, hardness = 159 mg/l CaCO3, DOC = 8.0 mg/l. Observed data and Gamma distribution curve (best fitting curve) for the dataset fitted on the data. 104

119 cumulative distribution Scenario River Teme - HC5-50 = 19.0 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Hydra lit t ora lis Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Juga plicifera Chlore lla sp. Coelastrum microporum Pimephales promelas Brachydanio rerio P. subcapitata Hyalella azteca Clistoronia magnifica Ankistodesmus falcatus Chlamydomonas sp Daphnia longispina Pediastrum duplex Desmodesmus spinosus Alona affinis Daphnia magna Ceriodaphnia pulchella S imocephalus vet ulus Lemna minor Scenedesmus accuminatus Peracantha truncata Ceriodaphnia quadrangula Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Teme in the United Kingdom). Geochemical parameters for this scenario were: ph = 7.6, hardness = 159 mg/l CaCO3, DOC = 8.0 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data. Large river (River Rhine in The Netherlands); medium ph/ high hardness/ low DOC The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the large sized river with low DOC scenario are presented in Figure and cumulative distribution Scenario River Rhine - HC5-50 = 7.5 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Hydra lit t oralis Oncorhynchus mykiss Xenopus laevis Chlorella sp. Coelastrum microporum Juga plicifera Pimephales promelas Brachydanio rerio P. subcapitata Hyalella azteca Ankistodesmus falcatus Chlamydomonas sp Clist oronia magnifica Pediastrum duplex Desmodesmus spinosus Daphnia longispina Daphnia magna Lemna minor Alona affinis Scenedesmus accuminatus Ceriodaphnia pulchella Simocephalus vetulus Peracantha truncata Lemna gibba Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Rhine in The Netherlands). Geochemical parameters for this scenario were: ph = 7.8, hardness = 217 mg/l CaCO3, DOC = 2.8 mg/l. Observed data and Logistic distribution curve (best fitting curve) for the dataset fitted on the data. 105

120 cumulative distribution Scenario River Rhine - HC5-50 = 10.8 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Gastrophryne carolensis Hydra littoralis Oncorhynchus mykiss Xenopus laevis Chlorella sp. Coelastrum microporum J uga plicifera Pimephales promelas Brachydanio rerio P. subcapitata Hyalella azteca Ankistodesmus falcatus Chlamydomonas sp Clistoronia magnifica Pediastrum duplex Desmodesmus spinosus Daphnia longispina Daphnia magna Lemna minor Alona affinis Scenedesmus accuminatus Ceriodaphnia pulchella Simocephalus vetulus Peracantha truncata Lemna gibba Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Rhine in The Netherlands). Geochemical parameters for this scenario were: ph = 7.8, hardness = 217 mg/l CaCO3, DOC = 2.8 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data. Mediterranean river (River Ebro in Spain); high ph/ high hardness /low DOC The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the Meditteranean river scenario are presented in Figure cumulative distribution Scenario River Ebro - HC5-50 = 8.7 ( ) µg/l Bufo terrestris Brachionus calyciflorus Gastrophryne carolensis Chironomus tentans Oncorhynchus mykiss Xenopus laevis Hydra lit t oralis Chlorella sp. Coelastrum microporum P. subcapitata Ankistodesmus falcatus Pimephales promelas Chlamydomonas sp Brachydanio rerio Hyalella azteca Pediastrum duplex Desmodesmus spinosus J uga plicifera Daphnia magna Scenedesmus accuminatus Lemna minor Clist oronia magnifica Daphnia longispina Alona affinis Ceriodaphnia pulchella Lemna gibba Simocephalus vetulus Peracantha truncata Ceriodaphnia quadrangula Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario River Ebro in Spain). Geochemical parameters for this scenario were: ph = 8.2, hardness = 273 mg/l CaCO3, DOC = 3.7 mg/l. Observed data and lognormal distribution curve (best fitting curve) for the dataset fitted on the data. Normalised HC 5-50 for lakes 106

121 Oligotrophic systems (Lake Monate in Italy); medium ph/low hardness /low DOC The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the oligotrophic scenario are presented in Figure and cumulative distribution Scenario Lake Monate - HC5-50 = 5.3 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Hydra littoralis Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Chlorella sp. Pseudokircheneriella Coelastrum microporum Ankistodesmus subcapitata falcatus J uga plicifera Chlamydomonas sp Pimephales promelas Pediastrum duplex Desmodesmus spinosus Brachydanio rerio Hyalella azteca Clist oronia magnifica Daphnia longispina Scenedesmus accuminatus Daphnia magna Alona affinis Lemna minor Ceriodaphnia pulchella Simocephalus vetulus Peracantha truncata Ceriodaphnia quadrangula Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Lake Monate in Italy). Geochemical parameters for this scenario were: ph = 7.87; hardness = 48.3 mg/l CaCO3, DOC = 2.5 mg/l. Observed data and Logistic distribution curve (best fitting curve) for the dataset fitted on the data. cumulative distribution Scenario Lake Monate - HC5-50 = 7.1 ( ) µg/l Bufo terrestris Brachionus calyciflorus Chironomus tentans Hydra lit t oralis Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Chlorella sp. P seudokircheneriella Coelastrum microporum Ankistodesmus subcapitata falcatus J uga plicifera Chlamydomonas sp Pimephales promelas Pediastrum duplex Desmodesmus spinosus Brachydanio rerio Hyalella azteca Clistoronia magnifica Daphnia longispina Scenedesmus accuminatus Daphnia magna Alona affinis Lemna minor Ceriodaphnia pulchella Simocephalus vetulus Peracantha truncata Ceriodaphnia quadrangula Lemna gibba Ceriodaphnia dubia Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure : The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Lake Monate in Italy). Geochemical parameters for this scenario were: ph = 7.87; hardness = 48.3 mg/l CaCO3, DOC = 2.5 mg/l. Observed data and lognormal distribution curve for the dataset fitted on the data. Neutral acidic system in Sweden; low ph/ low hardness /low DOC 107

122 The species sensitivity distribution and the calculated HC 5 based on the best fitting and conventional log-normal approaches for the Swedish neutral acidic lake are presented in Figure and cumulative distribution Scenario Lake Sweden - HC5-50 = 14.3 ( ) µg/l Hydra lit t oralis Bufo terrestris Brachionus calyciflorus Chironomus tentans J uga plicifera Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Chlorella sp. Coelastrum microporum Clistoronia magnifica P. subcapitata Daphnia longispina Ankistodesmus falcatus Chlamydomonas sp Pimephales promelas Hyalella azteca Brachydanio rerio Pediastrum duplex Desmodesmus spinosus Alona affinis Ceriodaphnia pulchella Simocephalus vetulus Daphnia magna Peracantha truncata Scenedesmus accuminatus Lemna minor Ceriodaphnia quadrangula Ceriodaphnia dubia Lemna gibba Lymnea st agnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Swedish neutral acidic lake). Geochemical parameters for this scenario were: ph = 6.7; hardness = 27.8 mg/l CaCO3, DOC = 3.8 mg/l. Observed data and Pearson VI distribution curve for the dataset fitted on the data. cumulative distribution Scenario Lake Sweden - HC5-50 = 12.1 ( ) µg/l Hydra lit t oralis Bufo terrestris Brachionus calyciflorus Chironomus tentans J uga plicife ra Gastrophryne carolensis Oncorhynchus mykiss Xenopus laevis Chlore lla sp. Coelastrum microporum Clistoronia magnifica P. subcapitata Daphnia longispina Ankistodesmus falcatus Chlamydomonas sp Pimephales promelas Hyalella azteca Brachydanio rerio Pediastrum duplex Desmodesmus spinosus Alona affinis Ceriodaphnia pulchella S imocephalus vet ulus Daphnia magna Peracantha truncata Scenedesmus accuminatus Lemna minor Ceriodaphnia quadrangula Ceriodaphnia dubia Lemna gibba Lymnea stagnalis NOEC/EC10 (µg Ni/L) Figure The cumulative frequency distributions of the normalised species mean NOEC or EC10 values from the chronic Ni toxicity tests in the dataset of freshwater organisms (scenario Swedish neutral. acidic lake). Geochemical parameters for this scenario were: ph = 6.7; hardness = 27.8 mg/l CaCO3, DOC = 3.8 mg/l. Observed data and log-normal distribution curve for the dataset fitted on the data. Summary The main physico-chemical variable mitigating chronic Ni toxicity in surface waters, i.e. DOC, Hardness and ph, varied between, respectively, mg/l, mg/l CaCO 3 and for the different selected typical eco-regions. This results in HC 5-50 values from log- 108

123 normals SSDs for the different eco-regions varying between 7.1 and 43.6 µg/l depending on the DOC/Hardness/pH of the surface waters (Figure ). An overview of all individual normalised toxicity data for the different species for all selected scenarios is provided in Appendix G.11. Comparison between the eco-region HC 5 values with the natural background concentrations of Ni in EU freshwaters is provided in Appendix G.12. A summary of the most sensitive endpoints per species obtained after normalisation with the BLM is given in Table Deleted: 0 Deleted: 11 Table Summary of the most sensitive endpoint and number of datapoints after normalisation using the BLMs Taxonomic group species Most sensitive endpoint Number of datapoints Algae Higher plants Scenedesmus accuminatus Desmodesmus spinosus Pediastrum duplex Chlamydomonas sp Ankistodesmus falcatus Chlorella sp Coelastrum microporum Pseudokirchneriella subcapitata Lemna minor Lemna gibba Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth rate Growth Growth rate Rotifer Brachionus calyciflorus Intrinsic rate of growth 6 Molluscs Lymnea stagnalis Juga plicifera Growth mortality 3 1 Cladocerans Ceriodaphnia dubia Ceriodaphnia quadragula Peracantha truncata Simocephalus vetulus Ceriodaphnia pulchella Alona affinis Daphnia longispina Daphnia magna Reproduction Mortality Reproduction Reproduction & mortality Reproduction & mortality Mortality Mortality Reproduction Insects Clistoronia magnifica Chironomus tentans Mortality Biomass 1 3 Hydrozoans Hydra littoralis Growth 1 Amphipods Hyalella azteca Mortality 1 Fish Amphibians Brachydanio rerio Pimephales promelas Oncorhynchus mykiss Xenopus laevis Gastrophryne carolensis Bufo terrestris Hatchability Growth Growth Malformation Mortality Growth

124 cumulative distribution Scenario neutral acid lake - Sweden/HC5-50: 12.1 µg/l Scenario river Rhine - Netherlands/HC5-50: 10.8 µg/l Scenario river Teme - UK/HC5-50: 19.0 µg/l Scenario river Otter - UK/HC5-50: 8.1 µg/l Scenario lake Monate - Italy/HC5-50: 7.1 µg/l Scenario ditch - The Netherlands/HC5-50: 43.6 µg/l Scenario river Ebro - Spain/HC5-50: 8.7 µg/l NOEC/EC10 (µg Ni/L) Figure Overview of the SSD and HC5 for the different freshwater eco-regions. 110

125 Influence of the choice of species sensitivity curve fitting function on the HC 5 The values in Table to are presented as the HC5 (which does not account for the sampling uncertainty) and are different from the HC5 at the 50 th percentile (i.e. the media HC5 value which incorporates the sampling uncertainty according to the approach of Aldenberg & Jaworska, 2000) which are presented in Figures through and in Table HC 5 for best fitting distributions using A/D versus K/S goodness-of-fit approaches Goodness-of-fit statistics (Go-F) were used to select the best fitting distribution among all distributions tested. The influence of the choice of the Go-F, i.e. the Anderson-Darling (A/D) versus the Kolmogorov-Smirnov (K/S) approaches, on the selected frequency distribution and estimated HC 5 value has been assessed for the different scenarios is summarized in Table Table Summary of the HC5 for the best fitting distribution functions using the A/D and the K/S Go-F approaches Scenario A/D Ditch in The Netherlands 64.9 Extreme Value River Otter in the United Kingdom 8.3 Log-normal River Teme in the United Kingdom 22.6 Gamma River Rhine in The Netherlands 11.5 Logistic River Ebro in Spain 8.9 Log-normal Lake Monate in Italy 7.5 Logistic Neutral-acidic lake in Sweden 16.4 Pearson VI HC5 (µg/l) K/S 64.9 Extreme Value 8.3 Weibull 27.7 Extreme Value 16.7 Extreme Value 8.8 Weibull 7.5 Logistic 17.2 Extreme Value Table shows that the influence of the choice of the Go-F, i.e. the Anderson-Darling (A/D) versus the Kolmogorov-Smirnov (K/S) approaches, on the estimated HC 5 value is very limited. A factor of difference between 1.0 and 1.22 is observed between the two alternative Go-F approaches. HC 5 for best fitting approach (using A/D) versus the conventional log-normal approach A summary of the HC 5 for the best fitting (using the A/D goodness-of-fit approach) and the conventional log-normal distributions derived for the different selected scenarios is provided in Table

126 Table : Summary of the HC5 for the best fitting and log-normal distributions derived for the different selected scenarios Scenario HC5 (µg/l) Log-normal distribution Best fitting distribution (A/D based approach) Ditch in The Netherlands River Otter in the United Kingdom River Teme in the United Kingdom River Rhine in The Netherlands River Ebro in Spain Lake Monate in Italy Neutral-acidic lake in Sweden Table demonstrates that the use of the conventional log-normal frequency distribution resulted in HC 5 values for the different scenarios that are similar but occasionally slightly lower/higher than the HC 5 values derived from best fitting distributions (maximum factor of difference 1.3). Goodness-of-fit statistics (using A/D & K/S) for the best fitting and the log-normal frequency distributions A summary of the goodness-of-fit statistics (using A/D & K/S) for the best fitting and the lognormal frequency distributions s is provided in Table Table Goodness-of-fit statistics (according to Andersen-Darling (A/D) and Kolmogorov-Smirnov (K/S)) for the best fitting and log-normal frequency distributions. Rivers Lakes Scenario Goodness-of-fit statistic (A/D) Goodness-of-fit statistic (K/S) Best fitting Log-normal Best fitting Log-normal Small ditches (The Netherlands) Medium rivers (United Kingdom) - River Otter - River Teme Large rivers (Germany) River Rhine Mediterranean river (Spain) River Ebro Oligotrophic systems (Italy) Lake Monate Acidic system (Sweden) The best fitting distribution always resulted in a better fit of the data towards both the tails (from the A/D test) and the middle (from the K/S test) of the frequency distributions compared to the conventional log-normal distribution. However in all cases the curve fitting functions here fit reasonably well to the chronic toxicity SSD data and none of the fit functions can be rejected at the 5 % significance level. Both choices of functions (best fit versus log-normal) come with strengths and limitations. On the one hand, the choice of the best fit function option of course means that the goodness of fits is optimised. However, among-site comparisons with this approach can be questioned because the data are not treated / evaluated the same way at each site. Also, there are no obvious reasons why different fit functions should be relevent at individual sites. Futhermore the classical log-normal approach could not be rejected at the 5 % significance level and 112

127 provides HC 5 values that are derived in exactly the same way and thus fully comparable among sites. Finally there it is emphasised that there is no technical basis that explains why species sensitivity should be log-normally distributed or distributed according to any other particular distribution functions. Based on the weight-of-evidence and for the purposes of comparability with Risk Assessments of other substances, the decision was taken in this risk assessment of nickel to utilize the log-normal distribution unless it was shown to be rejected on the basis of Goodness-of-Fit tests Uncertainty analysis The use of statistical extrapolation is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC/L(E)C10. Based on uncertainty considerations the TGD (2003) recommends, for the freshwater compartment, to apply an additional assessment factor on the 50% confidence value of the 5th percentile value (thus PNEC = 5th percentile value (50th c.i.)/af), with an AF between 1 and 5, to be judged on a case by case basis. Based on the available chronic NOEC data, the following points were considered when determining the size of the assessment factor: 1. The overall quality of the database and the end-points covered, e.g., if all the data are generated from true chronic studies using relevant endpoints; representativity of the fysicochemistry of the test media The freshwater Ni-database covered only ecological relevant endpoints. The selected endpoints were all very relevant for potential effects at population level: mortality, reproduction, hatching, growth, and abnormalities, Covering chronic exposure times are also achieved for all trophic levels in the Ni database. What comprises chronic exposure depends on the exposure duration and is also a function of the life cycle of the test organisms. The duration should therefore be related to the typical life cycle and to the recommended exposure duration from standard ecotoxicity protocols (e.g. 7 days for Ceriodaphnids (ASTM), 21 days for daphnids (OECD),, days for H. azteca, etc.). Following the latest OECD test guidelines, relatively short term studies, focusing on sensitive life stages rather than focusing on the full life stage are also deemed appropriate. Typical reported exposure time for algae is 3 days, for higher plants 7 days which is in accordance with international guidelines (e.g. OECD, ASTM). Chronic toxicity tests with crustaceans have typical exposure durations of 7 days for C. dubia and between days for D. magna. The tests with insects have a duration of up to 240 days. The early life stages with toads/frogs have a test duration varying between 4 days and follow therefore the ASTM guideline. The early life stage tests with fish have an exposure duration varying between 30 and 40 days. According to ASTM guidelines, early life stage tests with fish (e.g. with O. mykiss, P. promelas) should have a test duration of 30 days which is in accordance with the selected toxicity data for fish in the database. The chronic tests with juvenuiles/fry have exposure durations between 17 and 330 days. The length is very much dependent of the spoecies of fish and the type of chronic test: sac fry test, growth inhibition test on juveniles or the FELS. Sensitive life stages were covered in the database. Almost all chronic toxicity tests were performed using sensitive life stages (e.g. all fish tests used early life stage/juvenile organisms, the tests with crustaceans were started with newly born organisms, the tests with insect were initiated with larvae). 113

128 The L(E)C10/NOEC data were extracted from tests performed in a variety of natural/artificial freshwaters, covering a considerable part of the wide range of the freshwater characteristics (e.g. ph value and hardness) that are normally found in European freshwaters (Table ). Ranges of ph, DOC and hardness (as 10th %-90th %) used in the ecotoxicological tests varied, respectively, between , mg/l and between mg/l CaCO3. Ranges of ph and hardness used in the EU surface wates varied respectively between , mg/l and between mg/l CaCO3. Therefore the Ni-data seemed to properly reflect the variability in physicochemical conditions encountered in European surface waters (calculated from measured water chemistry in EU surface waters). 2. The diversity and representativeness of the taxonomic groups covered by the database; From the extracted data, it seems that the Ni-database do fulfill the requirement of different NOEC values. Indeed, the database is comprised of 31 different species mean NOEC values for 19 different families, including 214 individual high quality NOEC (58 individual NOEC for algae, 6 for higher plants; 113 for invertebrates; 37 for fish/amphibians. In addition, organisms belonging to the different taxonomic groups, i.e. 8 different groups, as defined by the London workshop (2001), are found in the Ni-database (Table ). However the invertebrate database was heavily dominated by cladoceran / crustecean / arthropod species. Thus the database contained 4 non-arthropod invertebrate species i.e. a snail, a gastropod, a rotifer and a coelenterata species. The non-cladoceran invertebrate species did however not seem to be especially sensitive. The higher plants are represented by 2 species, which however seemed to be relatively sensitive. Table Minimum taxonomic groups requirements for the extrapolation method (London workshop, 2001). Taxonomic groups 1) Fish (usually tested species like salmons, bluegill, channel catfish, etc.) 2) A second family in the phylum Chordata (fish, amphibian, etc.) 3) A crustacean (e.g. cladoceran, copepod, ostracod, isopod, amphipod, crayfish etc.) 4) AN INSECT (E.G. MAYFLY, DRAGONFLY, DAMSELFLY, STONEFLY, CADDISFLY, MOSQUITO, MIDGE, ETC.) 5) A family in a phylum other than Arthropoda or Chordata (e.g. Rotifera, Annelida, Mollusca, etc.) 6) A family in any order of insect or any phylum not already represented Ni-database OK (e.g. Pimephales promelas) OK (e.g. Oncorhynchus mykiss) OK (e.g. Ceriodaphnia dubia) OK (e.g. Clistorinia magnifica) OK (e.g. Juga plicifera) OK (e.g. Xenopus laevis) 7) ALGAE OK (e.g. Pseudokirchneriella subcapitata) 8) Higher plants OK (E.G. LEMNA GIBBA) An overview of the total number of individual species and families covered in the Ni database is provided in respectively Table and 26. From this table it seems that 27 individual species are covered belonging to 16 different families from different trophic levels (primary producers, primary consumers and secondary consumers). It must be stressed that the database for invertebrates is dominated by the cladocerans. Indeed, 96% of all selected chronic 114

129 datapoints originated from cladocerans (9 different species), while only one single chronic datapoint is available for one species among the other invertebrate taxonomic groups that are represented, i.e., amphipods, molluscs, insects and hydrozoans. Table Individual species covered in the Ni database. Number of species Individual species in Ni database 1 Pseudokirchneriella subcapitata (algae) 2 Chlamydomonas sp. (algae) 3 Ankistrodesmus falcatus (algae) 4 Scenedesmus accuminatus (algae) 5 Chlorella sp. (algae) 6 Desmodesmus spinosus (algae) 7 Pediastrum duplex (algae) 8 Coelastrum microporum (algae) 9 Lemna gibba (vascular plant: duckweed) 10 Lemna minor (vascular plant: duckweed) 11 Daphnia magna (crustacean : cladoceran) 12 Ceriodaphnia dubia (crustacean : cladoceran) 13 Ceriodaphnia quadrangula (crustacean : cladoceran) 14 Daphnia longispina (crustacean : cladoceran) 15 Ceriodaphnia pulchella (crustacean : cladoceran) 16 Simocephalus vetulus (crustacean : cladoceran) 17 Alona affinis (crustacean : cladoceran) 18 Peracantha truncata (crustacean : cladoceran) 19 Hyalella azteca (crustacean : amphipod) 20 Clistoronia magnifica (insect: caddisfly) 21 Chironomus tentans (insect: midge) 22 Juga plicifera (mollusc: snail) 23 Lymnea stagnalis (mollusc: gastropod) 24 Brachionus calyciflorus (rotifer) 25 Hydra littoralis (coelentrate : hydra) 26 Gastrophryne carolinensis (amphibian: Eastern narrow mouthed toad) 115

130 Number of species Individual species in Ni database 27 Bufo terrestris (amphibian: Southern toad) 28 Xenopus laevis (amphibian: African clawed frog) 29 Brachydanio rerio (fish: zebrafish) 30 Pimephales promelas (fish: fathead minnow) 31 Oncorhynchus mykiss (fish: rainbow trout) Footnote: In bold: species with chronic BLMs; underlined: Species with intraspecies reduction of variability by application of BLM of species from the same trophic level Table Individual families covered in the Ni database Number of families Individual familiy in Ni database 1 Scenedesmaceae (Pseudokirchneriella subcapitata; Coelastrum microporum; Desmodesmus spinosus; Scenedesmus accuminatus) 2 Hydrodictyaceae (Pediastrum duplex) 3 Chlamydomonadaceae (Chlamydomonas sp.) 4 Chlorellaceae (Chlorella sp.; Ankistrodesmus falcatus) 5 Daphnidae (Daphnia magna, Ceriodaphnia dubia, Ceriodaphnia quadrangula, Daphnia longispina, Ceriodaphnia pulchella, Simocephalus vetulus) 6 Hyalellidae (Hyalella azteca) 7 Limnephilidae (Clistoronia magnifica) 8 Chironomidae (Chironomus tentans) 9 Pleuroceridae (Juga plicifera) 10 Lymnaeidae (Lymnea stagnalis) 11 Microhylidae (Gastrophryne carolinensis) 12 Bufonidae (Bufo terrestris) 13 Pipidae (Xenopus laevis) 14 Cyprinidae (Pimephales promelas, Brachydanio rerio) 15 Salmonidae (Oncorhynchus mykiss) 16 Lemnaceae (Lemna gibba; Lemna minor) 17 Chidoridae (Alona affinis, Peracantha truncata) 18 Hydridae (Hydra littoralis) 19 Brachionidae (Brachionus calyciflorus) 116

131 3. Use of BLMs. Four chronic BLMs for nickel toxicity were used. BLMs from two cladoceran species, one algae species, and one fish species were used to normalise data on those species. Normalization of the database reduced intraspecies variability for 16 species for which intraspecies variability could be quantified. Intraspecies variability was reduced between 10 and 70% among species. Reduction in intraspecies variability decreases the uncertainty of the species mean (i.e., geometric mean) that is used in the SSD, and should therefore reduce uncertainty in the derivation of HC 5 values. However, chronic BLMs were not available for 27 species and BLM or data concerning intraspecies reduction in variability by use of BLMs were not available for 15 species. For the remaining species it was assumed that the best fitting BLMs or the BLMs of taxonomically similar species could be used. The application of BLMs across species can be discussed, as insufficient data exist to show that competitive effects between cations and metals at the site of toxicity are universal for a specific metal. For some invertebrate species without a BLM the normalisation was however employed by choosing the most conservative between two BLMs for the species under the water characteristics of the typical freshwater scenarios that were considered (see Section 2.6.4). Furthermore, a BLM was available for Ceriodaphnia dubia, a very sensitive species in the nickel ecotoxicity database. This species influences the outcome of the HC 5 to a large extent. Both facts remove some of the concerns about the application of this BLM to less sensitive species. Furthermore application of BLM to 5 different cladoceran species without BLMs reduced the intraspecies variation of nickel toxicity under different conditions of abiotic factors. The same was not the case for a fish species without a BLM when the BLM for another species was used. However the absolute intraspecies variation for the fish species without BLM was anyway only small. Thus this reduction of intraspecies variability of nickel toxicity under different abiotic factors for species without BLMs illustrated that at least for closely related species cross species extrapolation is justified. Furthermore the spot check exercise illustratedthe concept of this read across approach. Indeed, this exercise seems to indicate that the existing BLLMs were able to predict reasonably well for a variety of surface water conditions the chronic toxicity to four species which are not so closely related to the species with existing BLMs. The use of the full normalisation approach is also to some extent supported by the fact that the conditional stability constants for the BLMs were very consistent across those four species for which a BLM has been developed (De Schamphelaere et al. 2006). This may indicate existence of a general consistency in the interaction among cations, protons, and nickel ions for uptake at the site of toxicity regardless of species. In summary, more species specific BLMs have been developed for chronic nickel toxicity on pelagic organisms than for any other metal. Nevertheless, the SSD and thus the HC 5 were obtained based on the assumption that BLMs could be read across to species without an established BLM. However, some scientific justification for employment of a full read across is provided with the results from the reduction in intra-species variability and with the spot checking exercise. 4. Statistical extrapolation and HC 5 estimation Curve fitting and goodness of curve fit approach. Different types of SSD curve fitting functions and goodness of fit approaches were investigated when estimating the HC 5 for 7 different surface water characteristics having 117

132 abiotic factors (DOC, hardnes and ph) within both the range observed in EU surface waters and the employed BLMs. The choice of SSD curve fitting and goodness of fit approaches impacted the derivation of the HC 5 to some degree. But the impact was no greater than a factor of 1.3 and thus not of large significance. Nevertheless it should be considered which of the presented approaches that should be selected or if all of the approaches should be carried over to the risk characterisation. 12 Comparing log-normal versus best fit models, the decision has been made to utilize the log-normal model. Calculation of the HC5 based on the lognormal curve fitting resulted in some cases in a more conservative estimate compared to the use of the best fitting curves. Estimation of the 50 th % confidence limit on the HC 5. According to the TGD (2003) the PNEC should be derived from the HC 5 at 50 th % conficence limit (µg/l) and considering the application of an additional assessment factor. Table provides a summary of the HC 5 at 50 th % confidence limit (together with 5 th and 95 th confidence limits) derived from the conventional log-normal distribution according to the methodology of Aldenberg & Jaworska (2000). Table HC5 at 50 th % confidence limit (together with 5 th and 95 th confidence limits) derived from the conventional log-normal distribution. Scenario Ditch in The Netherlands River Otter in the United Kingdom River Teme in the United Kingdom River Rhine in The Netherlands River Ebro in Spain Lake Monate in Italy Neutral-acidic lake in Sweden Physico-chemical characteristics ph:6.9; H:260 mg/l; DOC:12.0 mg/l ph:8.1; H:165 mg/l; DOC:3.2 mg/l ph:7.6; H:159 mg/l; DOC:8.0 mg/l ph:7.8; H:217 mg/l; DOC:2.8 mg/l ph:8.2; H:273 mg/l; DOC:3.7 mg/l ph:7.7; H:48.3 mg/l; DOC:2.5 mg/l ph:6.7; H:27.8 mg/l; DOC:3.8 mg/l HC 5 at 50 th % conficence HC 5 at 50 th % conficence limit limit (µg/l) using Lognormal (µg/l) using best fitting distribution distribution 43.6 ( ) 56.1 ( ) 8.1 ( ) 8.1 ( ) 19.0 ( ) 18.7 ( ) 10.8 ( ) 7.5 ( ) 8.7 ( ) 8.7 ( ) 7.1 ( ) 5.3 ( ) 12.1 ( ) 14.3 ( ) Although the best fitting distribution always resulted in a better fit of the data towards both the tails (from the A/D goodness-of-fit test) and the middle (from the K/S goodness-of-fit test) of the frequency distributions compared to the conventional log-normal distribution, in all cases the log-normal curve fitting functions fit reasonably well to the chronic toxicity SSD data and none (except using the A/D goodness-of-fit statistics for the Swedish lake scenario) 12 Opposed to the small impact of curve and goodness of fit approaches, the difference between the HC5 estimated values for the selected surface water sites were ± an order of magnitude different. This illustrates that derivation of one generic PNEC surface water for EU may be misleading or at least not helpful. Instead PNEC for surface water is propsoed to be expressed in a way to allow for the variation and impact of the water characteristics be taken into account in the risk characterisation (where also the exposure concentration PEC can be normalised to the varying abiotic factors prevailing in different EU surface waters). 118

133 of the fit log-normal functions can be rejected, using both the A/D and K/S goodness-of-fit tests, at the 5 % significance level. 5. Comparisons between field and mesocosm studies and the 5 th percentile and mesocosm/field studies to evaluate the laboratory to field extrapolation. No field data were available that allow deriving threshold concentrations of Ni in freshwaters at the field scale. 6. Species sensitivity below HC 5. The different eco-region SSD show that for 5 of the 7 selected scenario, there is 2 species mean value below the proposed HC Indeed, for the river Otter, Teme, Rhine, Ebro and for the lake Monate, the species mean value for the cladoceran C. dubia and the snail L. stagnalis is respectively between a factor and below the proposed HC 5 (at 50 th % c.i.). The EU TGD recommends discussing NOEC values below the 5 th percentile. If all such NOECs are from one trophic level, then this could be an indication that a particular sensitive group exists, implying that some of the underlying assumptions for applying the SSD method may not be met. Depending on the way of calculating the cumulative probability of the NOEC values, NOECs below the HC 5 will typically not occur when the sample size is below The availability of more datapoints, increases the probability of having NOECs below the HC 5. Therefore, identifying NOEC values below the HC 5 should not be considered in the application of an additional assessment factor without taking the total number of datapoints into account. Of particular interest is to note that in all eco-region scenarios tested that the snail L. stagnalis is found to be the most sensitive species from the database. Howver, L. stagnalis is a very common species which can be found in almost all watercourses all over The Netherlands where different metal loads can occur. The distribution of L. stagnalis (pond snail) is given below (preliminary data from a Dutch distribution project in Figure Figure Occurrence of Lymnaea stagnalis in the Netherlands (from 119

134 On this background it has been considered whether the high sensitivity of L. stagnalis may be particular to the test organisms used in the spot checking exercise,. L.stagnalis has a wide spread occurrence in Dutch surfacewaters where exposure to nickel and other metals (and other contaminants) may occur. This wide occurrence cannot however be taken as an indication of insensitivity of the wild Dutch L. stagnalis populations to chronic nickel toxicity, but only to the fact that these populations may probably not be severly affected by the ambient concentrations of nickel and other metals (and other contaminants) in Dutch surfacewater. This does of course also not exclude the theoretical possibility that the natural L. Stagnalis populations in Dutch surfacewater are adapted to nickel exposure but scientific evidence for that does currently not exist. Therefore in conclusion scientific evidence does not currently extist that L. stagnalis used in the spot check testing was more sensitive than naturally occurring populations. Furthermore a high sensitivity of this species is generally supported by the physiology of the organism, i.e., it s high demand for Ca ++ ions during growth, which may be interefered with by exposure to divalent metal ions such as nickel. There is however as discussed before a general reliability issue related to the apperant high sensitivity of L. stagnalis observed during the toxicity spot testing, namely the reliability of data related to the issue of ph dynamics. The ph was highly variable in these tests due to algal growth and the use of none buffered test medium. This generally made these results somewhat questionable, when comparing the test results with BLM predictions, which are using as one model parameter ph. Overall conclusion: Based on weight of evidence and related to also the overall assessment of the size of the extra assessment factor for other datarich metals it is proposed to use an AF of 2. This factor is proposed being used when estimating the site/ water course/ region specific PNECs based on the HC 5 -drivation employing the log-normal fit function for SSD curve fitting and based on the full normalisation approach as described in this report Toxicity test results for sediment organisms Introduction According to the guidance provided in the Technical Guidance Document (TGD) potential risks for the sediment compartment should best be evaluated using whole-sediment tests with representative benthic organisms. This approach is deemed the most realistic one since it is only by using such tests that it is possible to adequately address all routes of exposure typically encountered in sediments (e.g. exposure through pore water and ingestion of sediment particles). When the risk assessment of nickel under the Existing Substance Regulation started the number of sediment toxicity data published in international peer reviewed journals was rather limited and restricted to a few benthic species only. It was therefore felt appropriate to set up a sediment testing conclusion i) program to investigate the toxicity and availability of nickel towards various benthic freshwater organisms. The set-up of the sediment conclusion i) program, conducted by Ghent University (De Schamphelaere et al, 2006; Vandegehuchte et 120

135 al., 2006), was discussed and supported by TCNES member states 13. The objectives were twofold: 1. To assess the chronic ecotoxicity of nickel to various benthic freshwater organisms with distinct live strategies, feeding behaviors, ecological niches and life-cycles on the basis of lethal and sub-lethal endpoints. 2. To assess the effect of potential modifying factors on nickel toxicity to these organisms, notably acid volatile sulfide (AVS), organic carbon and iron-oxyhydroxide (FeOOH) The data generated by this program in combination with additional data collected from open literature formed the basis for the PNEC sediment derivation using a Species Sensitivity Distribution (SSD). However, the results of the laboratory experiments were confounded by the diffusion of nickel from the spiked sediments into the overlying water. In fact it was shown that the concentration of nickel in the overlying water in the test system generally correlated better with the observed effect concentrations on the sediment organisms than the nickel concentrations measured in the bulk sediment. Therefore extensive calculations were necessary to extrapolate from water born nickel concentrations to sediment concentrations using the equilibrium partitioning approach. The very cautious assumptions that were necessarily taken in this process introduced a high degree of scientific uncertainty. As a consequence, a high assessment factor was deemed appropriate when calculating the PNEC from the estimated HC5(50%) based on the above mentioned approach. The scientific uncertainty of the described approach yielded a PNECsed of 18.3 mg Ni/kg, which is close to or below nickel background concentrations in many sediments of EU countries. The results of the draft Sediment Effects Assessment were discussed at TC NES III Specifically, the approach taken to derive the PNECsed range of 18.3 mg Ni/kg was presented. The impact of this PNECsed on risk characterization for the sediment compartment was also discussed. It was estimated that 100% of local sites would be concluded to be at risk, and that risk would be concluded at the regional scale as well. Because the PNECsed was also shown to be below a generic natural background concentration of 29 mg Ni/kg for EU countries the TC NES Member State representatives concluded that the current sediment data set should not be used to derive a PNEC sediment and that additional research is warranted to allow scientifically justified approaches to be incorporated into the nickel sediment toxicity test program in order to derive a reliable PNEC for the sediment compartment. Both the provisional results of the sediment testing conclusion i) program and the scope of the sediment research conclusion i) program are discussed in more detail in the subsequent sections. A full analysis of the data can be found in the specific draft sediment effects assessment report and the sediment research conclusion i) proposal Provisional results conclusion i) sediment testing program The sediment conclusion i) testing program (Vandegehuchte et al, 2006) and additional data from open literature (Milani et al, 2003) yielded a sediment effects data bases with chronic toxicity test results (n = 50) for 7 different sediment-dwelling organisms covering different 13 Sediment toxicity testing was one component of the Conclusion i) Research Program for nickel that was approved by Member States at TC NES I

136 habitats and feeding habits. Data for the following species were available: the amphipods Hyalella azteca (14 individual L(E)C 10 /NOEC values) and Gammarus pulex (2 individual NOEC/L(E)C 10 values); the oligochaetes Tubifex tubifex (15 individual L(E)C 10 /NOEC values) and Lumbriculus variegatus (3 individual L(E)C 10 /NOEC values); the insects Chironomus riparius (6 individual NOEC values), Ephoron virgo (4 individual NOEC values), and Hexagenia sp. (6 individual L(E)C 10 /NOEC values). The selected L(E)C 10 /NOEC values range on a total Ni basis between 12.5 mg/kg dry wt. (for H. azteca) and 3,058 mg/kg dry wt. (for T. tubifex). The lowest L(E)C 10 /NOEC values were consistently found in sediments with low AVS (Acid Volatile sulfides) and low OC (organic carbon) content. Hence, it was deemed appropriate to explore if the underlying pore water paradigm of the SEM-AVS model is valid for the sediment toxicity results at hand and could be used to normalize the sediment effects data set. The analysis of the laboratory test results from the conclusion i) program indicated, however, that on a bulk-sediment basis the SEM-AVS concept only seemed to explain the observed effects for C. riparius, but not for the other five (epi) benthic species. Toxicity was observed in these cases at SEM-AVS values < 0 while the SEM-AVS model would predict the absence of toxicity under these circumstances. These results were not consistent with the results of the field recolonization study on nickel contaminated sediments (Burton et al, 2005). The field study suggested a much higher threshold effects concentrations: absence of nickel toxicity in overlying water; in three out of four cases a relationship between AVS-SEM 14 ; and, absence of nickel toxicity to sediment dwelling organisms in relation to the recolonization. In contrast with the laboratory data, results of the field experiment conducted with nickel showed that benthic re-colonization was unaffected by sediment nickel concentrations up to 100 mg Ni/kg in some sediments, and up to 500 mg Ni/kg in other sediments after nine-months of exposure, which is more than 10 times higher than the threshold levels derived from the laboratory effects data sets. Even though the recolonization endpoint investigated in the multispecies field study cannot directly be compared with the L(E)C10- or NOEC-values in the single species long-term laboratory studies, the discrepancy between these results was remarkable. A possible explanation for this discrepancy could be the contribution of Ni present in the overlying water in the laboratory tests to the observed toxicity. Wang et al. (2004) did indeed show that in static and semi-static test designs the sediment may act as a source of dissolved metals to the overlying water column. In addition both short equilibration times and high spiked metal concentrations in sediments typically used in laboratory experiments may accentuate partitioning of metals disproportionately to the dissolved phase and increase as such the probability of exposure and/or toxicity via the overlying water (Lee et al, 2004). Although in the current laboratory experiments sediments equilibrated for more than 70 days, considerable amounts of dissolved Ni in the overlying water were present in all lab-exposures conducted by Vandegehuchte et al. (2006) (even under conditions where SEM-AVS<0), that could have attributed to the observed toxicity. The importance of exposure through the overlying water for the laboratory test results of Vandegehuchte et al., 2006 and Milani et al, 2003 was then explored by constructing concentration-response curves (expressed as µg dissolved Ni/L) for all six organisms used in these laboratory studies. Since (bio)availability may differ between the different test systems 14 In the fourth case (the Pallanza site), the sediments contained very low AVS to begin with, and impacts on recolonization were observed in both control and nickel-treated sediments, which may have been caused by high flow events. These sediments were not relevant to test the AVS-SEM theory because there was little to no AVS, and because impacts from other stressors occurred over the exposure period. 122

137 it was also deemed appropriate to evaluate the speciation and competition effects that might have influenced the test results. Speciation effects were taken into account using WHAM VI (concentration response curves expressed as µg Ni 2+ -activity/l.) In this analysis by de Schamphelaere et al (2006) the following key findings were: available data seems indeed to suggest that nickel in the overlying water contributes in a significant way to the observed toxicity in these particular laboratory studies. expressing the different toxicity values on the basis of dissolved Ni, Ni 2+ activity and bioavailable Ni in the overlying water predicts the toxicity of Ni to all organisms reasonably well, independent of the type of sediment tested. both Ni 2+ and bioavailable Ni predict toxicity almost equally well since the overlying water chemistry parameters were rather similar across all sediments investigated within each species and therefore differences in bioavailability in these particular laboratory studies were limited. The observation that the toxicity of nickel to the benthic organisms was more accurately explained by measured nickel concentrations in the overlying water than nickel in the sediment phases in these particular laboratory studies hampered a straightforward calculation of a PNECsed value and necessitated the development of an alternative approach. The approach taken was to extrapolate the critical overlying water concentrations to corresponding sediment concentrations using the equilibrium partitioning approach. The cautious assumption was taken that only organic carbon would play a pivotal role in binding with nickel in sediments (this was also a pragmatic decision, because organic carbon is the only solid phase that the WHAM VI speciation model can account for). This assumption is cautious as many other sediment phases, such as iron and manganese oxy-hydroxides, are known to bind cationic metals like (Tessier and Campbell, 1987). This approach, however, yielded PNEC values close to background concentrations; this was partly due to the fact that higher assessment factors needed to be applied to take into account the larger uncertainty introduced by using this alternative approach. The outcome of this provisional PNEC derivation was discussed at several TC NES meetings and it was decided to set up an additional conclusion i) sediment research program in order to evaluate further in detail the remaining technical issues concerning the fate, geochemical behavior, and toxicity of nickel in sediments. A better understanding of the mechanisms of nickel binding and bridging the gaps between what is observed under a laboratory experimental set up and the conditions actually occurring in the field is necessary for development of a more robust effect assessment approach for the sediment compartment Conclusion i) sediment research program The results of the field recolonization studies for nickel generally support the SEM-AVS concept for nickel, but the inability to apply a reliable bioavailability correction for nickel in the laboratory experiments is important to resolve. The reason that the laboratory data did not show a strong relationship between AVS and nickel toxicity is mainly due to the fact that overlying water exposure occurred, in a degree that probably deviates significantly from what may be observed in the field. Thus the conducted laboratory toxicity tests did not constitute a proper test of the AVS-SEM theory, which assumes that toxicity to in-faunal benthic organisms occurs from exposure to metals in the pore water phase. 123

138 The main factor in creating the high uncertainty is the diffusional loss of soluble nickel from the sediment phase into the overlying water of the laboratory test systems that caused the observed toxic effects. In the field, the impact of the net loss of soluble nickel from the sediment phase on nickel concentrations in overlying water will be lower than in the laboratory due to the larger natural dilution capacity of natural water systems, which depends on the hydrological conditions at hand. Rivers with significant flow rates and lakes with winddriven turbulent flows will probably not result in high metal concentrations in the overlying water. However conditions in stagnant natural water bodies and perhaps more pronounced for shallow waters may resemble more closely those of the above mentioned test systems which were used for the testing of nickel on sediment organisms. Another issue that needs proper attention is that the present test methodologies (i.e., mixing of the sediment with soluble Ni salt at high concentrations at once) is not an accurate representation of the process of nickel accumulation in real freshwater systems (i.e., accumulation of freshly deposited nickel associated with sediment particles settling from the overlying water column). Indeed, the enrichment process of field sediments with nickel is substantially different from the way sediments are spiked in the laboratory. In laboratory sediment toxicity tests, the concentration is applied by adding soluble nickel salts at once in high concentrations. Contamination of sediments in the field, on the other hand, occurs over longer time scales via the deposition of particle-associated nickel, which is chemically distinct from the soluble and bioavailable forms used in sediment toxicity tests. A remaining concern requiring investigation is the need to address bioavailability to aerobic as well as anaerobic sediments. Recent efforts in predicting the bioavailability and toxicity of sediment-associated metals have focused almost exclusively on anaerobic phases (i.e., AVS), but the behavior of metals in aerobic portions has been extensively studied as well. Although the AVS concept also applies to aerobic sediments (AVS concentrations can be set close to 0 and no binding to other sediment phases is assumed, resulting in a cautious approach) it is known that OC and Fe/MnOOH influence the distribution of nickel between solid and pore water phases significantly and hence may be used to quantify the influence of the bioavailability of nickel in aerobic sediments. The availability of such data/relationships would greatly enhance the ability to perform bioavailability normalisation in aerobic as well as anaerobic sediments. The concerns addressed above have led to a proposal for a new sediment research program. Shortly the key difference in the research proposed and the work that was performed under the original Existing Substance Regulation Conclusion i) research program (i.e., the research that yielded the PNECsed range of approx.18.3 mg Ni/kg) is that the new proposal includes steps to limit diffusional loss of nickel from the sediment phase. Limiting diffusional loss will receive special attention because it was felt that this was an artifact driven by the introduction of massive loadings of soluble nickel into the laboratory sediments employed in semi-static test systems. The proposed research will therefore be comprised of three components, including 1. An evaluation of optimal sediment spiking techniques. The goals of this preliminary step will be to understand the kinetics of nickel sorption to solid sediment phases, effects of time on nickel speciation in sediments, and to develop a spiking technique that minimizes the diffusion of nickel to overlying water to that which may be considered realistic for conditions in the field. Different scenarios may have to be considered such as for example a scenario related to rivers and lakes with significant 124

139 water flow rates on the one hand and to stagnant shallow water bodies on the other hand. 2. Generation of new ecotoxicity data on sediment dwelling organisms providing effect concentrations relating to bulk sediment concentrations using testing methods that will limit overlying water exposure to nickel to levels realistic for field situations (see above). This will produce sufficient reliable ecotoxicity data to populate a Species Sensitivity Distribution (SSD) for the scenario with significant water flow. It has to be considered further whether, and if so how, the scenario with insignificant water flow may be covered (see above). 3. The development of an integrated, equilibrium-partitioning based bioavailability model for normalizing the sediment effect concentrations to (bio)available sediment concentrations under both aerobic and anaerobic conditions. This will demonstrate and determine the relevance of (bio)availability corrections in aerobic and anaerobic sediments by accounting for both OC and Fe/MnOOH beyond the proposed default SEM-AVS model. The results of the above proposed conclusion i) sediment research program will be used to directly calculate a PNECsed value eliminating much of the uncertainty in the current PNECsed derivation. A Technical Conclusion i) Group is currently being formed to provide technical oversight to the different steps mentioned above. At TC NES I 2008, Member State representatives gave support to the development of a Technical Conclusion i) Group open for participation of experts from the Member States. A number of TCNES participants, including those from D, DK (Rapporteur), ES, the NL, and the UK, indicated already that they would participate. The first meeting is being held in early June 2008, with testing anticipated to begin in the summer Marine Effects Assessment Introduction Geochemical conditions in coastal marine waters are vastly different from those of typical freshwater. Table shows a comparison between several factors that have been shown to ameliorate nickel toxicity to freshwater organisms, including ph and the constituents of hardness, Ca 2+ and Mg 2+. While there is little difference in mean ph between typical EU freshwater systems and seawater, seawater contains much higher concentrations of hardness constituents. The nickel BLMs were not developed to estimate nickel speciation or nickel toxicity under these conditions, and it is therefore not possible to normalize the freshwater nickel ecotoxicity data to typical marine conditions. Furthermore, marine species have developed different mechanism to handle ion-transport across tissue and hence will deal differently with environmental conditions. 125

140 Table Some selected parameters of typical EU freshwater as compared to typical seawater. All values are in meq/l except ph. Typical EU Freshwater Typical Seawater Ratio SW/FW ph [H + ]/[H + ] [Ca 2+ ] [Mg 2+ ] [Na + ] [Cl - ] [SO4 2- ] [K + ] Besides the above differences there are distinct differences in the concentration of Fe 2+ concentrations in fresh and marine water. Because there were constraints for using the freshwater database for the marine environment, testing of nickel to marine organisms was proposed at TC NES IV 06 to develop the data necessary to develop a Species Sensitivity Distribution (SSD) that can be used to determine a PNEC marine value for nickel. The major constraints to reject the use of freshwater data were: Pooling of freshwater and marine data will introduce a certain degree of uncertainty. Pooling would only be justified if it can be demonstrated that the species distributions for freshwater and marine chronic toxicity were statistically the same when compared on a free nickel ion basis. Abiotic factors of marine waters are outside the boundaries of the freshwater Ni BLMs, and therefore the BLMs could not be used to normalize the freshwater data to marine conditions. Because of these constraints, it was decided that using the results of toxicity testing of marine organisms would decrease the uncertainty of the PNECmarine derivation. The proposed testing covered ten species (Fig. 1). 126

141 Marine Effects Assessment Proposed Marine Aquatic Toxicity Testing Organism Cyprinodon variegates A Macrocystis pyrifera A Strongylocentrotus purpuratus B Dendraster excentricus B Mytilus galloprovincialis B Common Test Method Name Fish: Sheepshead ASTM E (2004) minnow Plant: Giant Kelp US EPA/600/R-95/136, 1995 Invertebrate: Purple sea ASTM E1563 urchin Invertebrate: Sand dollar ASTM E1563 Invertebrate: Blue mussel ASTM E724 Crassostrea sp. B Neanthes arenaceodentata B Champia parvula B Skeletonema costatum B Dunaliella tertiolecta B Invertebrate: Common oyster Invertebrate: Polychaete worm Plant: Rhodophyte Plant: Diatom Plant: Flagellate ASTM E724 ASTM E1562 ASTM E1498 ASTM E1218 ASTM E1218 A Golder & Associates, Ltd. (Vancouver, BC, Canada) B Parametrix, Inc. (Corvallis, OR, USA) Figure Proposed marine toxicity tests as part of the Conclusion I research proposal for nickel. Testing for the ten species was completed by September Data sources This report analyses the available chronic nickel toxicity data for marine organisms. In addition to newly generated data from a testing initiated under the nickel risk assessment program, the literature was reviewed for other sources of relevant and reliable chronic toxicity data on nickel. Relevance and reliability Relevance and reliability criteria followed that established for effects assessments of nickel in the aquatic, sediment, and terrestrial compartments. Relevancy of test medium The focus of this report is to determine effects of nickel on marine organisms. Therefore, the relevant test medium is full strength seawater. The TGD does not define the salinity range of full strength sea water, but the standardized protocols used in the toxicity tests do provide a recommended range of salinities for use in the tests. For example, ASTM E1498, the standard test guidance for testing with seaweeds, recommends that natural waters collected for testing with salinity below 28 g/kg should be modified so that the salinity at the time of testing is 30 g/kg. In general, the salinity of coastal marine waters is considered to range between 28 and 34 g/kg. All of the tests used in the effects assessments here used artificial or natural seawater with salinity in the range between 28 and 34 g/kg. An exception to this is the test on the Mediterranean sea urchin, Paracentrotus lividus, which was tested in natural seawater collected from the Mediterranean Sea with an ambient salinity of g/kg. Effects of nickel on estuarine organisms were not included in this assessment. Estuaries are geochemically and ecologically different from freshwater and marine systems. The salinity of estuaries can vary from 0.5 to 28 mg/kg, and in certain salinity ranges the ecological 127

142 communities are completely distinct from freshwater and marine communities. The speciation and toxicity of metals like nickel are known to vary according to salinity. For example, the distribution coefficient of nickel decreases with increasing salinity (Turner et al. 2002), and the acute toxicity of nickel (and other metals) to the estuarine mysid, Neomysis integer, decreases with increasing salinity (Verslyke et al. 2003). The combination of dynamic salinity conditions in estuaries and the complex interaction of salinity with metal geochemistry and toxicity suggest that a specific effects assessment for estuarine waters is required. This is relevant as many emission sites in Europe are located on coasts and emit to estuarine water. It should be pointed out that this is a generic, multi-contaminant issue. Because the estuarine ecosystem is unique, it is not specific to nickel or even other metals. Therefore specific testing and assessment of chronic nickel toxicity to estuarine organisms has at this point not been included in this risk assessment of nickel Chronic toxicity to marine organisms In general the ecotoxicity data for marine organisms have been evaluated according to the same principles that the ecotoxicity data on freshwater organisms. For further details of the studies see also section 3 and the background reports Chronic toxicity to marine algae Accepted data on chronic single-species toxicity tests resulting in accepted high quality reliable NOEC/L(E)C10 values (expressed as total Ni concentration) for algae are summarized in Table From the database, EC10 values for four species are reported, ranging from 97 µg Ni/L for growth of giant kelp (Macrocystis pyrifera) to µg Ni/L for growth of the dinoflagellate, Dunaliella tertiolecta. 128

143 Table Overview of the accepted high quality nickel chronic NOEC values for marine algae. Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) Substance Species (95% CI) Golder 2007 sulfate Macrocystis Macrocystis 48 h Germination EC pyrifera zoospores ( ) sulfate Macrocystis Macrocystis 48 h Growth EC pyrifera zoospores ( ) Parametrix 2007a Champia parvula Adult 10 d Reproduction EC ( ) Parametrix 2007b A Skelatonema / 72 h Specific EC costatum Growth Rate ( ) Skelatonema / 72 h Specific EC costatum Growth Rate ( ) Skelatonema / 72 h Specific EC costatum Growth Rate ( ) Skelatonema / 72 h Specific EC costatum Growth Rate ( ) Skelatonema / 72 h Specific EC costatum Growth Rate ( ) Parametrix 2007c Dunaliella / 72 h Specific EC tertiolecta Growth Rate ( ) Conc. response Analysis of concentrations Administration of test substance Temp ( C) ph Salinity (PSU) DOC (mg/l) Nicb (µg/l) Test water Yes Yes Static Nm 3 Natural water Yes Yes Static Nm 3 Natural water Yes Yes Staticrenewal <1 Natural water Yes Yes Static <1 Synthetic seawater Yes Yes Static <1 Natural water (Yaquina Bay, OR, USA) Yes Yes Static <1 Natural water (Shannon Point, WA, USA) Yes Yes Static <1 Natural water (Cape Fear, NC, USA) Yes Yes Static <1 Natural water (Vallejo, CA, USA) Yes Yes Static <1 Natural water In the range of 0.22 mg/l to 2.7 mg/l Dissolved Organic Carbon, no relationship between DOC and Ni toxicity was observed. All controls had exponential growth during the entire experimental period. 129

144 Chronic toxicity to marine invertebrates Accepted data on chronic single-species toxicity tests resulting in accepted high quality reliable NOEC or L(E)C10 values (expressed as total Ni concentration) for marine invertebrates are summarized in Table Rejected low quality data are summarized in Table From the database, EC10 values are reported for 10 species, ranging from 2.9 µg Ni/L for development of the echinoderm, Diadema antillum, to 335 µg Ni/L for development of the echinoderm, Strongylocentrotus purpuratus. Mortimer and Miller (1994) measured effects of chromium, of copper, and of nickel exposure to larvae and juveniles of the Australian sand crab, Portunus pelagicus. This study was evaluated for reliability and relevance, and it was rejected for the following reasons: 1. Limited number of nickel concentrations: The test design involved exposure to 0, 10, and 100 µg Ni/L. This limited number of test concentrations makes it difficult to calculate reliable EC10 values. 2. No replication of nickel concentrations: The tests were conducted in a single test chamber for each nickel concentration, and the organisms within the test chamber were considered to be replicate observations. This is known as pseudo-replication, and does not replicate the treatment effect, which is the nickel concentration. Without replication, the treatment variability can not be quantified, and therefore ANOVA analysis can not be performed. The NOECs that are reported by Mortimer and Miller (1994) are therefore invalid. 3. concentrations were not measured: The nickel concentrations reported in the paper are based on nominal, as opposed to measured, concentrations. The Aquatic Effects Assessment of nickel uses nominal nickel concentrations under certain circumstances, including when the test was otherwise considered to be reliable and relevant and when the control concentration of nickel was anyway much lower that the NOEC or L(E)C10 value. As indicated above, the case for Mortimer and Miller (1994) had other sever limitations, and the absence of measured nickel concentrations becomes then a more critical factor. 130

145 Table Overview of the accepted nickel chronic NOEC values for marine invertebrates Substance Species Age and/or size of test organism Effect parameter Endpoint Value (µg/l) (95% CI) Analy-sis of conc. Conc. Response Test duration Administration of test response Temp ( C) ph Salinity (PSU) DOC (mg/l) Nicb (µg/l) Test Water Bielmeyer et al Parametrix 2007d Parametrix 2007d Diadema antillarum Dendraster excentricus Strongylocentrot us purpuratus Hunt et al Mysidopsis intii Gentile et al Soluble Hunt et al Parametrix 2007e Mysidopsis bahia Haliotis rufescens Mytilus galloprovincialis Mytilus galloprovincialis Embryo 40 h Development EC10¹ 2.9 ( ) Embryo 48 h Development EC (46-280) Embryo 48 h Development EC ( ) Juvenile (2 d) Juvenile (2 d) 28 d Growth EC ( ) Yes Yes Static NM 2 NM 2 Artificial seawater Yes Yes Static <1 Natural seawater Yes Yes Static <1 Natural seawater Yes Yes Flowthrough 36 d Reproduction NOEC 61 Yes Yes Staticrenewal Embryo 22 d Metamorphosis EC ( ) Embryo 48 h Development EC ( ) Embryo 48 h Development EC ( ) Yes Yes Staticrenewal Natural seawater Natural seawater Natural seawater Yes Yes Static <1 Natural seawater (Yaquine Bay, OR, USA) Yes Yes Static <1 Natural seawater (Shannon Point, WA, USA) Mytilus gallopro- Embryo 48 h Development EC Yes Yes Static <1 Natural seawater (Cape 131

146 Substance Species Age and/or size of test organism Effect parameter Endpoint Value (µg/l) (95% CI) Analy-sis of conc. Conc. Response Test duration Administration of test response Temp ( C) ph Salinity (PSU) DOC (mg/l) Nicb (µg/l) Test Water vincialis (82-354) Fear, NC, USA) Parametrix 2007 f Parametrix 2007 g Mytilus galloprovincialis Neanthes arenaceodentata Crassostrea gigas Novelli et al nitrate Paracentrotus lividus Pagano 2007 Paracentrotus lividus Embryo 48 h Development EC (98-386) Juvenile 90 d Reproduction EC ( ) Embryo 48 h Development EC ( ) Embryo 72 h Development EC (57-121) Embryo 72 h Development EC ( ) ¹ EC10 values calculated from original data provided from author(s). 2 NM: Not Measured Yes Yes Static <1 Natural seawater (Vallejo, CA, USA) Yes Yes Staticrenewal <0.5 <1 Natural seawater Yes Yes Static <1 Natural seawater (Yaquine Bay, OR, USA) No Yes Static NM 2 NM 2 Artificial seawater (Ocean Fish, Prodac International, Padua, Italy). Yes Yes Static Natural Seawater (Mytilene, Greece) 132

147 Table Overview of the rejected nickel chronic NOEC values for marine invertebrates Substance Species Mortimer and Miller Protunus pelagicus Petrich and Reisch Ctenodrillus serratus Age and/or size of test organism Effect parameter Endpoint Value (µg/l) Test duration Analysis of conc. Conc. Response Administration of test response Zooea 6 w Development NOEC 10 No Yes Static-renewal NM 3 3 Natural seawater Juvenile NA 4 Repro-duction NOEC 100 No Yes Static-renewal NA Temp ( C) ph Salinity (PSU) DOC (mg/l) Nicb (µg/l) Test Water NA 4 NM 3 Natural seawater 1 Reason for rejection: Mortimer and Miller (1994) measured effects of chromium, copper, and nickel exposure to larvae and juveniles of the Australian sand crab, Portunus pelagicus. This study was evaluated for reliability and relevance, and it was rejected for the following reasons: Limited number of nickel concentrations: The test design involved exposure to 0, 10, and 100 µg Ni/L. This limited number of test concentrations makes it difficult to calculate reliable EC10 values. No replication of nickel concentrations: The tests were conducted in a single test chamber for each nickel concentration, and the organisms within the test chamber were considered to be replicate observations. This is known as pseudoreplication, and does not replicate the treatment effect, which is the nickel concentration. Without replication, the treatment variability can not be quantified, and therefore ANOVA analysis can not be performed. The NOECs that are reported by Mortimer and Miller (1994) are therefore invalid. concentrations were not measured: The nickel concentrations reported in the paper are based on nominal, as opposed to measured, concentrations. The Aquatic Effects Assessment of used nominal nickel concentrations under certain circumstances, including when the test was otherwise considered to be reliable and relevant. As indicated above, this is not the case for Mortimer and Miller (1994), and the absence of measured nickel concentrations becomes a more critical factor. 2: Reason for rejection: Petrich and Reisch (1979) measured acute (4 and 7 days exposure) toxicity of aluminum and nickel to the polychaetes Capitella capitata, Ctenodrillus serratus, and Neanthes arenacereodentata, and chronic toxicity (28 days exposure) of aluminum and nickel to C. serratus. This study was evaluated for reliability and relevance, and it was rejected for the following reasons: No information on life stage of test organisms: The life stage of the test organisms is not provided, nor is information on whether or not the life stage was uniform within the test (i.e., whether or not the organisms were larvae, juveniles, adults, or a mixture of life stages). No information on administration of test substance: The methods do not indicate whether or not the test was performed under static or static-renewal conditions. Limited information on water quality parameters over course of test: The only information on the water quality parameters for this test are for ph at the beginning of the test, where it was reported that the ph range was set between 7.6 to 8.0. There is no information on ph at the end of the test, nor is there any information on seawater salinity or dissolved organic carbon. If the test was conducted under static-conditions, then it would be important to demonstrate that water quality parameters were acceptable at the end of the 28 d exposure. Feeding of test animals: Test animals were fed only twice over the course of the experiment (days 0 and 14). There is no supporting information to demonstrate that this feeding frequency supports the health of the test organisms. No information on variability of test results: Variability among replicates within treatments is not reported. 3 NM: Not Measured 4 NA: Not available in report 133

148 Chronic toxicity to marine fish Accepted data on chronic single-species toxicity tests resulting in accepted high quality reliable NOEC or L(E)C10 values (expressed as Ni) for fish are summarized in Table From the database, two EC10 values are reported, ranging from 3599 µg Ni/L for growth of the topsmelt, Atherinops affinis, to µg Ni/L for growth of the sheepshead minnow, Cyprinodon variegatus. 134

149 Table Overview of the accepted high quality nickel chronic NOEC values for marine fish. Golder 2007 Species Age and/or size of test organism Test duration Effect parameter Endpoint Value (µg/l) (95% CI) Conc. response Analysis of concentrations Administration of test substance Cyprinodon variegatus Larval (<48 h) 28 d Growth EC10 20,760 (20,350-21,170) Yes Yes Flow-through NM 2 6 Natural seawater Hunt et al Atherinops affinis Larval 40 d Survival EC10¹ 3,599 (2,283-5,673) Yes Yes Flow-through NM 2 Natural water ¹ EC10 values calculated from original data provided from author(s). 2 NM: Not measured Temp. ( C) Salinity (PSU) DOC (mg/l) Nicb (µg/l) Test water 135

150 Summaries of literature Golder 2007 Test species: Macrocystis pyrifera. The 48-h spore germination and germ tube growth toxicity test using the giant kelp (Macrocystis pyrifera) was conducted according to procedures described in US EPA (1995). The test involved exposing giant kelp zoospores and embryonic gametophytes to nickel-spiked test solutions for 48 h in a static system. Sublethal toxicity of nickel was assessed by measuring the percentage of zoospore germination and the germination tube length at the end of the 48 h exposure period. Natural seawater (typically 28 psu, and adjusted to 34 psu with hypersaline brine) was used as the dilution water. Total and dissolved nickel concentrations were measured in each treatment at the start and end of the definitive test. In the negative control, the mean percentage germination was 85.0%, and mean germ tube length was 13 µm. The nickel-spiked test solutions showed statistically significant decreases in mean germination and mean germ tube length in all test solutions, except for mean germination in the lowest nickel concentration (10 µg Ni/L). The EC10s for spore germination and germ tube growth reported by Golder (2007) were 20 and 10 µg Ni/L, respectively. However, the data were recalculated by Parametrix using the mean nickel concentrations of all of the samples taken during the study rather than just the initial concentrations. EC10s for spore germination and germ tube growth were 96.7 and 494 µg Ni/L, respectively. These recalculated EC10 values has been used and the EC10 of the most sensitive endpoint spore germination has been carried forward into the PNEC derivation. Test species: Cyprinodon variegatus. A 28-d sheepshead minnow survival and growth toxicity test was conducted according to procedures described by ASTM (2004), using a flow-through exposure system. The flow through system provided continuous delivery of fresh test solutions at a flow rate of 6 ml/min. The test involved exposing larval minnows (<48 h old at test initiation) to nickel-spiked test solutions. toxicity was assessed by measuring effects on survival and growth (dry weight) of exposed minnows at the end of the exposure period. Natural seawater (28 psu) was used as the dilution water. Total and dissolved nickel concentrations were measured daily for the first four days of the test, and then twice per week for the balance of the exposure period. Mean percent survival and dry weight in the negative control were 100% and 3.2 mg/fish, respectively. Adverse effects on survival and dry weight of larval minnows were only observed in the highest test concentration (40.6 mg Ni/L), where mean survival was 1.7 % and mean dry weight was 0.01 mg/fish. There were no statistical decreases in either survival or dry weight in any of the other nickel concentrations. The laboratory reported a LC50 value for survival of 27.8 mg Ni/L, and an EC10 for growth of 20.3 mg Ni/L. However, the data were recalculated by Parametrix using the mean nickel concentrations rather than initial concentrations. EC10s for survival and growth were and µg Ni/L, respectively. These recalculated EC10 values has been used and the EC10 of the most sensitive endpoint growth inhibition has been carried forward into the PNEC derivation. Parametrix 2007a Test species: Champia parvula. The chronic static reproduction tests with C. parvula were conducted by exposing male and female branches of the macroalga to various concentrations of nickel for 48 h followed by a recovery period of 5-8 days. Chronic test procedures were performed in accordance with the EPA guidance document entitled "Short-term Methods for Estimating the Chronic Toxicity of Effluents and Receiving Waters to Marine and Estuarine Organisms" (2nd edition. 1994, EPA , and 3rd edition, 2002 EPA-82 1-R ). At test completion (48-h exposure period and 5-day recovery period), no cystocarp 136

151 production was measured in the 2500, 5000, and µg/l nominal nickel concentrations; cystocarp production in the 156, 312, 625, and 1250 µg/l nickel concentration averaged 19.35, 12.25, 5.25, and 2.5 cystocarps per flask, respectively. For dissolved nickel, the IC10, IC25 and IC50 values were 144, 197, and 456 µg/l, respectively. There was no control mortality in the AS W/GP2 (dilution water) control group with cystocarp production averaging for the test diluent (ASW/GP2); production was above the EPArecommended criterion of 10 cystocarps per flask. The IC10 value of 144 µg Ni/L for inhibition of reproduction has been carried forward into the PNEC derivation. Parametrix 2007b Test species: Skelatonema costatum. A static 72-hour toxicity test was conducted with the diatom, Skelatonema costatum, following procedures described by OECD (2006) Test Guideline 201. Four tests were conducted with separate sources of natural seawater without EDTA that varied in dissolved organic carbon from 1.2 to 2.7 mg/l. The were no apparent correlation between DOC and ECx levels. One additional test was performed using synthetic seawater without EDTA (DOC = 0.2 mg/l). Salinity ranged from 29.5 to 30.1 psu. Test performance was acceptable according to all OECD acceptability criteria including control cell density increase (> 16x), section by section control growth rate variability (< 35%), and replicate control growth rate variability (< 7%). EC10 values based on average specific growth rate ranged from µg Ni/L (where DOC = 2.5 mg/l) to µg Ni/L (where DOC = 1.6 mg/l). The geometric mean of the five tests (four natural seawater and one synthetic seawater) was µg Ni/L. This geometric mean has been carried forward into the PNEC derivation. Parametrix 2007c Test species: Dunaliella tertiolecta. A static 72-hour toxicity test was conducted with the dinoflagellate, Dunaliella tertiolecta, following procedures described by OECD (2006) Test Guideline 201. Dinoflagellate cultures were exposed to NiCl2 6H 2 O at concentrations ranging from 0 to 42,989.9 µg Ni/L in enriched Yaquina Bay (Newport, Oregon, USA) natural seawater (29 psu salinity). Test performance was acceptable according to all OECD acceptability criteria including control cell density increase (> 16x), section by section control growth rate variability (< 35%), and replicate control growth rate variability (< 7%). At test completion, NOEC and LOEC values (specific growth rate) were and µg Ni/L, respectively, and EC10 and EC20 values based on specific growth rate were and µg Ni/L, respectively. The EC10 of µg Ni/L based on specific growth rate was carried forward to the PNEC derivation. Parametrix 2007d Test species: Strongylocentrotus purpuratus. A static 48 hour toxicity test was conducted with the sea urchin, Strongylocentrotus purpuratus, following procedures described by ASTM E1563. The test was conducted with natural seawater from Yaquina Bay (Newport, Oregon, USA; 30 psu salinity). Sea urchin embryos were exposed to NiCl2 6H 2 O at concentrations ranging from 0 to µg Ni/L. Based on percentage of normal shell development, NOEC and LOEC values at test termination were 286 and 512 µg Ni/L, respectively. EC10, EC25, and EC50 values based on percentage normal shell development were 335, 409, and 561 µg Ni/L, respectively. All test acceptability criteria were met. The EC10 of 335 µg Ni/L based on inhibition of development was carried forward for PNEC derivation. Test species: Dendraster excentricus. A static 48 hour toxicity test was conducted with the echinoderm, Dendraster excentricus, following procedures described by ASTM E1563. The test was conducted with natural seawater, from Yaquina Bay (Newport, Oregon, USA; 30 psu salinity). Echinoderm embryos were exposed to NiCl2 6H 2 O at concentrations ranging from 137

152 0 to µg Ni/L. Based on percentage of normal shell development, NOEC and LOEC values at test termination were 153 and 540 µg Ni/L, respectively. EC10, EC25, and EC50 values based on percentage normal shell development were 191, 330, and 686 µg Ni/L, respectively. All test acceptability criteria were met. The EC10 of 191 µg Ni/L based on inhibition of development was carried forward for PNEC derivation. Parametrix 2007e Test species: Mytilus galloprovincialis. A static 48 hour toxicity test was conducted with the bivalve mollusc, Mytilus galloprovincialis, following procedures described by ASTM E724. Four tests were conducted with separate sources of natural seawater that varied in dissolved organic carbon from 1.2 to 2.7 mg/l. No apparent correlation between DOC and ECx level was observed. Salinity ranged from 29.5 to 30.1 psu. Development in negative control treatments was > 90% for each test. EC10 values based on percentage of normal shell development ranged from 228 µg Ni/L (where DOC = 1.6 mg/l) to 350 µg Ni/L (where DOC = 2.7 mg/l). The geometric mean of these EC10 values was 270 µg Ni/L, and this value was taken forward for PNEC derivation. Parametrix 2007f Test species: Neanthes arenaceodentata. A chronic life cycle test (127 d) was performed on the annelid Neanthes arenaceodentata, following procedures described by ASTM E1562. Organisms were exposed to NiCl2 6H 2 O at concentrations ranging from 0 to µg Ni 2+ /L in reconstituted seawater (30 psu salinity). All test acceptability criteria were met. At test completion, EC10 and EC20 values (based on the most sensitive endpoint number of emergent juveniles) were 22.5 and 35.0 µg Ni/L, respectively. The EC10 of 22.5 µg Ni/L was taken forward for PNEC derivation. Parametrix 2007g Test species: Crassostrea gigas. A static 48 hour toxicity test was conducted with the bivalve mollusc, Crassostrea gigas, following procedures described by ASTM E724. The test was conducted with natural seawater from Yaquina Bay (Newport, Oregon, USA; 30 psu salinity). Oyster embryos were exposed to NiCl2 6H 2 O at concentrations ranging from 0 to µg Ni/L. Based on percentage of normal shell development, NOEC and LOEC values at test termination were 214 and 478, respectively. EC10, EC25, and EC50 values based on percentage normal shell development were 430.8, 479.4, and µg Ni/L, respectively. All test acceptability criteria were met. The EC10 of µg Ni/L based on inhibition of development was carried forward for PNEC derivation. Bielmeyer et al Test species: Diadema antillarum. A static 40 h developmental test was conducted on embryos of the long-spined sea urchin, Diadema antillarum. The test was performed following ASTM guidelines (ASTM E1563). Tests were performed in natural seawater (T = 20 C, salinity = 33 psu). Measured nickel concentrations were 0, 8, 11, 37, 74, and 101 µg Ni/L. Exposure to low nickel concentrations resulted in abnormal development of the pluteus stage. The EC50 reported by the authors in the paper was 15 µg Ni/L. Parametrix obtained the study raw data from the authors and calculated EC10 and EC50 values of 2.9 and 13.1 µg Ni/L, respectively. The EC10 of 2.9 µg Ni/L was considered to be accepted for PNEC derivation; it was however noted that this species seemed to be extremely sensitive to nickel Although the Bielmeyer et al. (2005) study appears to have been conducted according to standard test methodology (i.e., ASTM E1563), questions arise about the reliability of the data. These questions of relevance are based on general aspects of the ecology and natural history of this organism, as opposed to the way in which the test was performed. 138

153 Diadema antellarum was subject to a dramatic population decline in the mid 1980s as a consequence of a widespread disease (Lessios 1988). The nature of the disease was apparently a pathogen, although its identity has not been identified. Populations throughout the natural range of D. antellarum exhibited drastic declines, and in most areas the populations remain at very low levels compared to pre-disease periods (Chiappone et al. 2002; Lessios 2005). The occurrence of disease in D. antellarum is significant because this species exhibited an EC10 for Ni that was 115 times lower than another sea urchin species in the Ni database, i.e., Strongylocentrotus purpuratus (EC10 for S. purpuratus = 335 µg Ni/L) and 31 times lower than a second sea urchin species Paracentrotus lividus (EC10 (provisional) = 89.0 µg Ni/L - see below). Another echinoderm in the Ni ectoxicity database, the sand dollar Dendraster excentricus, also exhibited a much higher EC10 (191 µg Ni/L) than D. antellarum. It is therefore not unlikely that the apparent high sensitivity shown by D. antellarum to nickel was due to the added stress of a pathogen. It is not possible to test the combined effects of disease and Ni on this species. It is however felt that using the results form this species would introduce uncertainty into the marine effect assessment of Ni because the data more likely represent the combined effect of nickel exposure and disease. For these reasons, data from Bielmeyer et al. (2005) have been rejected for use in the SSD. The impact of excluding data from Diadema antellarum on the outcome of the HC5 is presented in Appendix H.1. Hunt et al Test species: Mysidopsis intii. A full life cycle test was performed on Mysidopsis intii, following methods described by US EPA (1985). Tests were initiated with 2 d old M. intii neonates in natural seawater (T = 20, salinity = 34 psu), and were conducted using a flowthrough exposure system. Survival, growth, and reproduction were measured. Survival did not exhibit a consistent concentration response relationship, but growth did. The 28 d chronic value (geometric mean of upper and lower chronic limit) reported by the authors for growth was 22.1 µg Ni/L. Parametrix obtained the study raw data from the authors and calculated EC10 and EC50 values of 45.2 and 64.4 µg Ni/L, respectively. The EC10 of 45.2 µg Ni/L was taken forward for PNEC derivation. Although the chronic test was designed as a full life-cycle test inclusive of reproductive endpoints, there were no juveniles produced over the duration of the study and the number of gravid adults was low in the controls, thus precluding any statistical evaluation. Test species: Haliotus rufescens. Two chronic tests were conducted with embryos (< 1 h postfertilization) of the abalone, H. rufescens. The tests covered the entire larval life-cycle through metamorphosis and into the juvenile form. The tests lasted for 14 and 22 d. Larval/juvenile metamorphosis was the test endpoint. The 22d chronic value (geometric mean of upper and lower chronic limit) reported by the authors for growth was 26.4 µg Ni/L. Parametrix obtained the study raw data from the authors and calculated EC10 and EC50 values of 36.4 and µg Ni/L, respectively. The EC10 of 36.4 µg Ni/L was taken forward for PNEC derivation. Test species: Atherinops affinis. A 40d early life stage test was performed with larval (grastula stage) topsmelt, A. affinis. The test was performed under flow-through conditions following guidelines described by US EPA (1985). Natural seawater (T = 20 C, salinity = 34 psu) served as the control water. Endpoints included hatching success (measured after 12 d), larval survival, length, and weight (after 40d). The 40d chronic value (geometric mean of upper and 139

154 lower chronic limit) reported by the authors for survival, the most sensitive endpoint was 4,270 µg Ni/L. Parametrix obtained the study raw data from the authors and calculated EC10 and EC50 values of 3,599 and 8,363 µg Ni/L, respectively. The EC10 of 3,599 µg Ni/L was taken forward for PNEC derivation. Gentile et al. (1982) Test species: Mysidopsis bahia. A 36 d flow-through test was performed on neonates (<24 h old) of the mysid M. bahia. Natural seawater (T = 21, salinity = 30 psu) was used. Endpoints included time to sexual maturity, duration of embryonic development, and brood size. Survival, numbers of juveniles, and total broods released were the most sensitive endpoints, all with a NOEC of 61 µg Ni/L. The NOEC of 61 µg Ni/L was taken forward for PNEC derivation. Novelli et al. (2003) Test species: Paracentrotus lividus. A static 72 hour toxicity test was conducted with the Mediterranean sea urchin Paracentrotus lividus. The test was conducted with artificial seawater (Ocean Fish, Prodac International, Padua, Italy). Echinoderm embryos were exposed to NiNO 3 over a nominal concentration range from 0 to 5000 µg Ni/L. Based on percentage of normal shell development and nominal test concentrations, the NOEC at test termination was 50 µg Ni/L. The EC10 provided by authors upon request was 89.0 µg Ni/L and carried forward for PNEC derivation. Aside from having unmeasured exposure concentrations, all test acceptability criteria were met. Pagano (2007) Test species: Paracentrotus lividus. A static 72 h toxicity test was conducted with the Mediterranean sea urchin Paracentrotus lividus. The test was conducted in natural seawater that was collected at Mytilene Island, Greece. Urchins were also collected at Mytilene. Urchin embryos were exposed to NiCl 2 over a concentration range from 0.4 to 58,690 µg/l. Seawater temperature ranged from 15.8 to 16.8 degrees Celcius, and salinity was 38.6 to Measured nickel concentrations were within 20% of nominal concentrations. Larval development was monitored over 72 h, and the EC10 at test termination was 217 µg Ni/L PNEC Derivation for the marine compartment Evaluation of data available for the marine environment Chronic nickel toxicity data are available for fifteen marine species. (cf. further in section 5 on why the data are considered chronic effects data.) The database includes a broad representation of temperate marine organisms, including unicellular algae, macroalgae, invertebrates, and fish (Table ). Diversity also occurs within each of these groups and generally these are considered to be among the most sensitive to a variety of toxicants, in fact, organisms like Champia parvula, Skelatonema costatu, Mysidopsis bahia, and Atherinops affinis are commonly used as sentinel species due to their sensitivity and range of response to a varity of toxicants: Unicellular algae: Both diatoms (Skelatonema costatum) and flagellates (Dunaliella tertiolecta) are represented. Macroalgae: Both red (Champia parvula) and brown (Macrocystis pyrifera) are represented. 140

155 Invertebrates: Invertebrates include three echinoderms (Strongylocentrotus purpuratus, Dendraster excentricus, and Paracentrotus lividus), two crustaceans (Mysidopsis intii and Mysidopsis bahia), a gastropod mollusc (Haliotis rufescens), two bivalve molluscs (Mytilus galloprovincialis, Crassostrea gigas), and an annelid (Neanthes arenaceodentata). Fish: Two fish from cosmopolitan families are represented, including the topsmelt (Atherinops affinis, Family Atherinopsidae) and sheepshead minnow (Cyprinodon variegatus Family Cyprinodontidae). In terms of ecological diversity, the database includes primary producers, filter feeders, grazers, deposit- feeders, predators, and omnivores. Table Individual species covered in the Ni marine ecotoxicity database Number of species Individual species in Ni database 1 Macrocystis pyrifera (algae: red macroalgae) 2 Champia parvula (algae: brown macroalgae) 3 Dunaliella tertiolecta (algae: green flagellate) 4 Skelatonema costatum (algae: diatom) 5 Strongylocentrotus purpuratus (echinoderm: sea urchin) 6 Dendraster excentricus (echinoderm: sand dollar) 7 Mysidopsis intii (crustacean: mysid) 8 Mysidopsis bahia (crustacean: mysid) 9 Haliotis rufescens (mollusc: gastropod) 10 Mytilus galloprovincialis (mollusc : bivalve) 11 Neanthes arenaceodentata (annelid : polychaete) 12 Cyprinodon variegatus (fish: family Cyprinodontidae) 13 Atherinops affinis (fish: family Atherinopsidae) 14 Crassostrea gigas (mollusc: bivalve) 15 Paracentrotus lividus (echinoderm: sea urchin) 141

156 Table Individual families covered in the Ni marine ecotoxicity database Number of families Individual families in Ni database 1 Lessoniaceae (Macrocystis pyrifera) 2 Champiaceae (Champia parvula) 3 Chlorophyceae (Dunaliella tertiolecta) 4 Thallassiosipaceae (Skelatonema costatum) 5 Strongylocentrotideae (Strongylocentrotus purpuratus) 6 Dendrasterideae (Dendraster excentricus) 7 Mysidae (Mysidopsis intii and Mysidopsis bahia) 8 Haliotidaea (Haliotis rufescens) 9 Mytilidae (Mytilus galloprovincialis) 10 Nereidae (Neanthes arenaceodentata) 11 Cyprinodontidae (Cyprinodon variegatus) 12 Atherinopsidae (Atherinops affinis) 13 Echinidae (Paracentrotus lividus) 14 Ostreidae (Crassostrea gigas) The marine toxicity database used for calculation of the HC5 is presented in Table and includes 15 different organisms representing 6 different taxonomic groups (i.e., algae, crustaceans, echinoderms, mollusks, annelids, and fish) and covering a range of different life forms, feeding strategies and trophic levels. 142

157 Table Chronic Toxicity Data of to Aquatic Animals and Plants Test Substance Study Effect, duration Level µg Ni/L Nominal/ measured Salinity T C References Common name (95% CI)l FISH Cyprinodon Sheepshead minnow NiSO4 F EC10, 28 d 20,760 M 30 o/oo 25 Golder 2007 variegates (20,350 21,170) Atherinops affinis Topsmelt (marine, Ni-? F EC10 1, 40 d 3,599 M 34 o/oo 20 Hunt et al estuarin.) (2,283-5,673) CRUSTACEANS Mysidopsis intii Mysid, marine NiCl2 F EC10 1, 28 d 45.2 ( ) M 34 o/oo 20 Hunt et al Mysidopsis bahia Opossum shrimp, marine Ni-? F LOEC, 36 d 141 M 34 o/oo 21 Gentile et al NOEC, 36 d 61 ECHINODERMS Strongylocentrotus Purple sea urchin NiCl2 S EC10; 48 h 335 ( ) M 30 o/oo 15 Parametrix 2007d purpuratus Dendraster Sand dollar NiCl2 S EC10; 48 h 191 (46-280) M 30 o/oo 15 Parametrix 2007d excentricus Paracentrotus lividus Mediterranean sea urchin NiNO3 S EC10 1, 72 h 89.0 (57-121) N/M o/oo 18 Novelli et al 2003/Pagano 2007 NiCl2 217 ( ) Geomean: 139 MOLLUSCS Haliotis rufescens Abalone, gastropod, NiCl2 F EC d 36.4 ( ) M 34 o/oo 15 Hunt et al marine Mytilius Mussel NiCl2 S EC10; 48 h 259 ( ) M 30 o/oo 15 Parametrix 2007e galloprovincialis 228 ( ) 350 (98-386) 256 (82-354) Geomean: Crassostrea gigas Oyster NiCl2 S EC10, 48 h 431 ( ) M 30 o/oo 20 Parametrix 2007g 143

158 Common name Test Substance Study Effect, duration Level µg Ni/L (95% CI)l Nominal/ measured Salinity T C References ANNELIDS Neanthes Marine polychaete NiCl2 S EC M 29.5 o/oo 20 Parametrix 2007f arenaceodentata ( ) PLANTS Macrocystis pyrifera Giant kelp NiSO4 S EC10¹, 48 h 96.7 ( ) M 34 o/oo 14 Golder 2007 Dunaliella tertiolecta Flagellate NiCl2 S EC10; 72 h 17,891 M 29 o/oo 20 Parametrix 2007c (15,186-20,373) Champia parvula Red Macroalgae NiCl2 S EC10; 7 d 144 ( ) M 30 o/oo 23 Parametrix 2007a Skelatonema costatum Diatom NiCl2 S EC10; 72 h ( ) ( ) ( )122.7 ( )661.3 ( )Geomean: M 28 o/oo 20 Parametrix 2007b ¹ EC10 values calculated from original data provided from author(s). 144

159 Assessment Factor Approach Two ecotoxicological extrapolation methods are described in the TGD for deriving PNECs: 1) the assessment factor approach and 2) the SSD approach. In the assessment factor approach the PNEC is calculated from the lowest acute LC 50 or EC 50 or, preferably, from the lowest chronic EC10 or NOEC, value using assessment factors that depend on the available toxicity data (TGD - Chapter 3). Given that chronic NOECs are available for >3 species the TGD requires an assessment factor of 10 be applied to the lowest EC10 or NOEC value. In this case the lowest EC10 value (22.5 µg/l) was reported for the polychaete, Neanthes arenaceodentata; therefore, the calculated PNECmarine using the assessment factor approach would be 2.3 µg/l SSD Approach If the chronic database is sufficiently large, the PNEC should be calculated by means of statistical extrapolation, using all available chronic EC10 or NOEC values as input (TGD - Chapter 3, Appendix V). Several approaches were evaluated for analysis of the chronic data on marine pelagic organisms. These approaches included the log-normal distribution, using both the full marine data base, and a reduced data set (the motivation for evaluating a reduced data set is explained in greater detail below); a series of different parametric frequency distributions that are available in the BestFit software (c.f. the Aquatic and Terrestrial Effects Assessments) using the full data sets; and, a non-parametric approached called Kernel Density Estimation that was applied to the full data base. Log-normal distribution: Full database The first approach considered was the log-normal distribution, which should be used as long as it is not rejected by standard Goodness-of-Fit tests. The results of using log-normal distribution (RIVM ETX 2.0 software) are presented in Figure The HC5 from this distribution was determined to be 9.0 ( ) µg Ni/L (Fig. 2). The log-normal distribution was however rejected by both the Anderson-Darling (A/D = 0.81) and Kolmogorov-Smirnov Goodness of Fit tests (K/S = 0.94). Conclusion: The conclusion from this analysis is that the log normal distribution using the full data base should not be used. Therefore, it was necessary to evaluate other approaches. 145

160 1.0 Fraction Affected Cyprinodon variegatus (EC10 = µg Ni/L) Dunaliella tertiolecta (EC10 = µg Ni/L) Atherinops affinis (EC10 = 3599 µg Ni/L) Crassostrea gigas (EC10 = µg Ni/L) Strongylocentrotus purpuratus (EC10 = 335 µg Ni/L) Skelatonema costatum (EC10 = µg Ni/L) Mytilus galloprovincialis (EC10 = µg Ni/L) Dendraster excentricus (EC10 = 191 µg Ni/L) Champia parvula (EC10 = 144 µg Ni/L) Paracentrotus lividus (EC10 = µg Ni/L) Macrocystis pyrifera (EC10 = 96.7 µg Ni/L) Mysidopsis bahia (NOEC = 61.0 µg Ni/L) Mysidopsis intii (EC10 = 45.2 µg Ni/L) Haliotis rufescens (EC10 = 36.4 µg Ni/L) Neanthes arenaceodentata (EC10 = 22.2 µg Ni/L) HC5(50%) = 8.96 µg Ni/L log10 Toxicity Data Figure SSD relationship for chronic Ni marine test data without Diadema antellarum using a log-normal distribution. Relationship shown for median hazardous concentrations (solid line) and the lower (5%; upper dotted line) and upper (95%; lower dotted line) estimate of hazardous concentrations. HC5(50%) = 8.96 ( ). Horizontal error bars represent the lowest and highest 95% confidence limit for endpoints shown as the geometric mean of multiple tests for a given species. The intraspecies variability for the marine effects data set could not be assessed in the same way as done for the Aquatic Compartment since most of the points in the marine SSD are composed of single test results as opposed to the geometric mean from several repeated tests that were performed on a single species. However, a significant part of the variability of the Aquatic Effects database could be explained by the wide variability of abiotic factors in the test media that influence nickel bioavailability and hence toxicity (i.e., ph, hardness, and dissolved organic carbon). Given the reasonable uniform nature of typical physicochemical characteristics of seawater, and in particular the parameters that are known to affect nickel toxicity (i.e., ph, cation composition, DOC), it is expected that intraspecies variability would be less pronounced for marine organisms compared with freshwater organisms. Log-normal distribution using a reduced data base Three marine species were particularly less sensitive to nickel than the remaining species. Inclusion of EC10-values for these species in the dataset for curve fitting was obviously the reason for lack of acceptance of the GoF tests of the fit of the log normal distribution function. The species included the only two fish species represented in the database, i.e., Cyprinodon variegatus and Atherinops affinis, and the chlorophyte algae Dunaliella tertiolecta. Evidence was found to support the hypothesis that marine teleost fish may be less sensitive to nickel toxicity than freshwater fish or marine invertebrates (Smolders et al. 2006), and a plausible 146

161 mechanistic explanation for this observation was proposed 15. Furthermore, the low sensitivity of D. tertiolecta to chronic nickel toxicity can be explained by the documented extraordinary ability of this organism to produce intracellular sulfide, which acts to precipitate divalent metals like nickel, rendering the metals unavailable for toxic effects (Davies 1976). Even though this study focused on the sensitivity of Dunaliella tertiolecta to mercury, the underlying mechanism (reaction with sulfide rendering insoluble metal sulfide) is relevant to also nickel. Furthermore the insensitivity of Dunaliella tertiolecta to metal toxicity has been demonstrated in other metals (Fisher et al., 1984). Based on these mechanistic explanations for the particular low sensitivity of nickel observed for the fish and D. tertiolecta, the ecotoxicity data for these species were excluded from the data base, and the SSD was re-analyzed using the log-normal distribution. When data for fish and D. tertiolecta were removed from the database, the log-normal distribution was accepted based on Goodness of Fit testing, and yielded an HC5(50%) value of 23.7 µg Ni/L (5th percentile confidence interval = 8.6 µg Ni/L, 95th percentile confidence interval = 43.4 µg Ni/L) (Fig. 3). Fraction Affected Crassostrea gigas (EC10 = µg Ni/L) Strongylocentrotus purpuratus (EC10 = 335 µg Ni/L) Skelatonema costatum (EC10 = µg Ni/L) Mytilus galloprovincialis (EC10 = µg Ni/L) Dendraster excentricus (EC10 = 191 µg Ni/L) Champia parvula (EC10 = 144 µg Ni/L) Paracentrotus lividus (EC10 = µg Ni/L) Macrocystis pyrifera (EC10 = 96.7 µg Ni/L) Mysidopsis bahia (NOEC = 61.0 µg Ni/L) Mysidopsis intii (EC10 = 45.2 µg Ni/L) Haliotis rufescens (EC10 = 36.4 µg Ni/L) Neanthes arenaceodentata (EC10 = 22.2 µg Ni/L) HC5(50%) = µg Ni/L log10 Toxicity Data 15 Marine fish show low permeabilities to the major cations across the gills, which is necessary to limit the influx of the ions from the seawater across the otherwise strong concentration gradient between outside (seawater) and inside (blood). In addition the very high concentrations of major cations in seawater are likely to provide an effective protection of the fish for Ni uptake by competing with the metal for the same binding or uptake sites at the exchange surfaces. Most marine invertebrates do not posses such a regulatory system (there are some exceptions). They are more permeable and show rapid equilibration of the circulatory fluid with seawater after transfer from one salinity to another. The observation that nickel toxicity in the fish only occurs at levels at least ten times higher than in the invertebrates may also point to a different mode of action, since the high values at which effects are observed are of the same order of magnitude than these causing respiratory impairment in freshwater trout. 147

162 Figure SSD relationship for chronic Ni marine test data without fish (Atherinops affinis and Cyprinodon variegatus) or Dunaliella tertiolecta using a log-normal distribution. Relationship shown for median hazardous concentrations (solid line) and the lower (5%; upper dotted line) and upper (95%; lower dotted line) estimate of hazardous concentrations. HC5(50%) = 23.7 µg Ni/L ( µg Ni/L). Conclusion: Excluding fish and D. tertiolecta data means that the log-normal distribution is not rejected; The HC5(50%) value for this approach is 23.7 µg Ni/L. Evaluation of alternative non-rejected frequency distributions The third approach was to evaluate several other curve fitting functions for the speciessensitivity distribution (SSD). In order to select statistically significant distribution functions for the available data set, Goodness-of-Fit statistics (software BestFit, Palisade Inc.) were used. As was the case for the log-normal distribution, HC5(50%) values for marine water were derived from the full data base, i.e., the geometric species mean EC10 or NOEC values for all of the marine organisms listed in Table Several of the distributions considered by this analysis were not rejected by the Goodness of Fit tests (Table ). These are visually represented in Figure The Uniform, Triangular, Normal, Pareto and Beta distributions were rejected however. Of the distributions that were accepted by the Goodness of Fit tests, the HC5(50%) values ranged from 5.3 µg Ni/L for the logistic distribution to 25.4 µg Ni/L for the Weibull distribution. The 5 th and 95 th percentile confidence intervals for the distributions, as calculated using software (Version 5), were used as a means of estimating the uncertainty of the HC5(50%) predictions for the significant distributions. It is noted that software package does not make correction for a small sample size in its estimation of HC5(50%). It is however felt that this shortcoming is less important for the sample size of the nickel marine ecotoxicity database (n=15). The best fitting distribution was the Pearson V distribution 16, which yielded an HC5 (50%) of 23.2 µg Ni/L (Table ). Best fit means that the test statistics for Anderson-Darling and the Kolmogorov-Smirnov Goodness of Fit tests were the lowest of any of the distributions analyzed (Table ). The 5 th and 95 th confidence limits around this HC5 (50%) value were around a factor of 2. The lowest of the HC5(50%) values was calculated by the Logistic distribution. This distribution showed however higher Goodness of Fit statistics than the PearsonV distribution, indicating that the fit was not as accurate. In general the parametric distribution functions that were not rejected on a statistical basis yielded HC5 (50%) values ranged from 20.0 µg Ni/L (Extreme Value) to 25.4 µg Ni/L (Weibull), with the exception of the logistic distribution, which gave a value almost 4 to 5 times lower.. 16 The Pearson V distribution is also referred to as the Inverse Gamma or the Inverse Chi Square distribution 148

163 Marine Species Sensitivity Distribution for Ni 100% Cumulative probability 80% data 60% Pearson5 InvGauss 40% Weibull Logistic 20% ExtValue Expon 0% Ni conc (µg/l) Figure Summary of the statistically not rejected frequency distributions used to fit the nickel marine ecotoxicity data. 149

164 Table Summary of test statistics for the Anderson-Darling and Kolmogorov-Smirnov Goodness of Fit tests, and HC5(50%) values, for the distributions that are considered in Appendix 2 of the Aquatic Effects Assessment SSD fitted on logtransformed data A/D K/S HC5 at 50 th percentile confidence interval (5 th %ile and 95 th %ile confidence limits in parentheses) (µg Ni/L) Pearson V (also known as Inverse Gamma distribution) ( ) Inverse Gaussian ( ) Weibull ( ) Extreme Value ( ) Logistic ( ) Exponential ( ) Triangular# +Infinity Not calculated Normal# ** ( ) (using ETX2) Pareto# +Infinity Not calculated BetaGeneral# +Infinity Not calculated Uniform# Not calculated Gamma# N/A N/A N/A Pearson VI# N/A N/A N/A # These Distributions are significant at 5% confidence level according to the GoF tests. N/A: Not Available: For this data version 5 could not calculate the statistics. ** Different software packages produce different significance levels for the normal distribution, e.g., ETX Software calculates the normal distribution as significant does not. The normal distribution is rejected for further analysis because only the GoF tests are statistically significant. Note: The HC5 at the 50th percentile confidence limit was calculated using software, using 1000 simulations at 15 iterations each. In the evaluation of the outcome of the statistically significant distributions, it does not seem reasonable to use the best fitting distribution function, because of the inherent uncertainty of each of the available data points to which distribution functions are fitted. It does not appear that objective statistical or ecological criteria exist to support selection of one particular of the statistically significant distributions as the most favorable. Therefore, a holistic weight-of-evidence approach was taken. First, the arithmetic mean of HC5 (50 %) values of all statistically not rejected parametric distributions shown in Table was calculated. This value is 19.9 µg Ni/L (Table ). A second integrated approach considers a two-dimensional Monte Carlo analysis on all of the statistically significant distributions. For a detailed explanation of the approach taken for the 2-D Monte Carlo analysis, see Appendix II. The median HC5(50%) was 26.3 µg/l (5 th percentile confidence interval = 8.4 µg Ni/L; 95 th percentile interval = 49.2 µg Ni/L). Conclusion: The integrated approaches yielded HC5(50%) values of 19.9 µg Ni/L for the arithmetic mean and 26.3 µg Ni/L for the Monte-Carlo analysis. 150

165 Kernel Density Estimation A fourth option is finally considered. This approach involves the use of a non-parametric approach called flexible kernel density estimation. This is a non-parametric approach that attempts to fit a distribution to empirically-derived data. The approach is to fit a curve to the data points locally. The underlying assumption is a log-normal distribution determining the influence of the nearest points. The non-parametric nature of the approach means that classical Goodness-of-Fit tests are not relevant for evaluating the distribution. The outcome of this analysis is dependent upon the choice of bandwidth and smoothing method. For details on the method, see Appendix H.2. For the nickel marine ecotoxicity dataset, two bandwidths were identifed as relevant: 1) the Normal automatic kernel density estimate (bandwidth 0.44, HC5(50%) = 13.3 ug Ni/L), and 2) the Normal robust kernel density estimate (bandwidth 0.35, HC5(50%) = 14.5 ug Ni/L). The second estimate is considered the most preferred option because of the balance between log-normal and parametric distribution (see Appendix H.2). This would lead to a HC5(50%) 14.5 µg Ni/L. Conclusion: Kernel Density Estimation yields HC5(50%) values ranging from 13.3 to 14.5 µg Ni/L The preferred estimate yields an HC5(50%) of 14.5 µg Ni/L. Summary The following conclusions were reached in these analyses: The log-normal distribution is rejected when the full database is used; When three insensitive species are excluded from the database, the log-normal distribution is statistically accepted, and the HC(50%) is 23.7 µg Ni/L; The arithmetic mean of the non-rejected parametric distributions is 19.9 µg Ni/L; The outcome of the 2-D Monte Carlo analysis of the non-rejected parametric distributions is 26.3 µg Ni/L; The preferred Kernel Density Estimation approach yields an HC5(50%) value of 14.5 µg Ni/L. There are no clear statistical or ecological indicators that favor one of these approaches over the other and therefore all valid approaches are taken into account below PNEC derivation The results of the different calculations, i.e. using an assessment factor of 10 according to the TGD (Chapter 3-3.3) and the statistical extrapolation approach, are shown in Table Applying the assessment factor on the lowest EC10 value for Neanthes anreaceodentata results in the total risk approach in a PNEC marine of 2.3 µg/l.. 151

166 Table Comparison of Assessment Factor approach (i.e., application of lowest chronic EC10 value with an assessment factor of 10) with HC5 values derived from a variety of statistically-based approaches Approach PNEC (µg Ni/L) HC5(50%) (µg Ni/L) 5 th 95 th Percentile Confidence Intervals (µg Ni/L) Assessment factor = A NA B NA B Log-normal SSD, fish and D. tertiolecta excluded Arithmetic mean of all statistically significant parametric distributions full dataset Kernel density estimation, full data set NA B NA B 19.9 (without MC) 26.3 (with MC) SD = 7.5 D NA B 14.5 (0.35 bandwidth) C A : An assessment factor of 10 was applied to the lowest EC10 in the data base, which was for Neanthes arenaceodentata (EC10 = 22.5 µg Ni/L). B : Not appropriate C : The outcome of the KDE analysis is dependent on the bandwidth that is chosen. The results shown are based on bandwidths between 0.35 (HC5 = 17.2 µg Ni/L) and 0.44 (HC5 = 13.9 µg Ni/L). D It was not possible to calculate 95% Confidence Intervals for the arithmetic mean of all statisticall significant parametric distributions. However, the standard deviation was calculated, and this value was 7.5. Conclusion The log normal SSD for the restricted data set (i.e., with data for fish and D. tertiolecta removed) could be considered, but an Assessment Factor of 3 would be applied because not all of the high quality data were used, i.e. three data points on particularly insensitive species were excluded from the SSD curve meaning that only the data from 12 species were used. Both the smaller number of data points and the uncertainty related to the mechanistic hypothesis behind exclusion of the data on the three data points (see below) could justify the use of an AF of 3. Applying an Assessment Factor of 3 to the HC5(50%) of 23.7 µg/l would result in a PNEC of = 7.9 µg/l. An alternative for calculating the PNEC is to use the same approach as above but with an AF of only 2 based on the mechanistic hypothesis that the particular insensitive species belong to another species sensitivity distribution than that for the other organisms, meaning that in fact all 15 data points are being used (i.e. leaving out the three data points in question as basis for the HC5-calculation but not out of the assessment). Whether this approach is regarded as more reasonable than the one above depends on whether or not the the mechanistic basis for exclusion of the data on the three insensitive species is accepted as fully valid, (i.e. without significant uncertainty), and whether the smaller number of data points used for HC5 derivation should lead to a larger Assessment Factor. A third option is to use the arithmetic mean of all statistically valid parametric distributions (as shown in Table ) and applying an Assessment Factor of 2, which can be justified because all species were included in the HC5(50%) estimate (see below for further discussion). Applying the Assessment Factor of 2 to the arithmetic mean of the 152

167 HC5(50%) values (i.e., 19.9 µg Ni/L) would result in a PNEC of 10.0 µg/l, whereas the use of the HC5(50%) from the 2-D Monte Carlo analysis (HC5(50%) = 26.3 µg Ni/L) would result in a PNEC of 13.1 µg Ni/L. The non-parametric Kernel Density Estimation approach was introduced as a fourth option because of the occurrence of particularly insensitive species (which lead to an anomaly in the SSD distribution). Applying an Assessment Factor of 2 to the HC5(50%) of 14.5 µg/l (the preferred HC5 (50%)-value of the Kernel Density Estimate Approach) would result in a PNEC of 7.3 µg/l. A thorough discussion of these possible approaches took place by TC NES. Justification for several different approaches was provided by different countries and IND at the meeting. It was concluded that each of the above approaches had advantages and disadvantages (pros and cons), that no approach could be said to be scientifically and indisputably superior, and that all approaches should be considered. IND and Rapporteur therefore together suggested a consensus compromise, namely to use the mean value of HC5 and PNEC estimated by the two latter approaches. TCNES accepted this compromise proposal also because the final result would not deviate much from the result of employing the other considered approaches. In conclusion it was agreed to take the mean from the most cautious approach of option 3 (i.e. the arithmetic mean HC5 (50%) value of 19.9 μg/l for all statistically valid parametric distributions) and the outcome of option 4 (i.e. the HC5 (50%) of 14.5 μg/l of the Kernel Density Estimation approach with optimal band width). Therefore an HC5 (50%) value of 17.2 μg/l is taken forward for the PNEC determination. The choice of the Assessment Factor is discussed in greater detail below Bioavailability It should be noted that no incorporation of the potential modifying effect of dissolved organic carbon was included in this analysis. Although DOC has been shown to be an important factor controlling the toxicity of nickel in the freshwater environment, and also may be important in the marine environment, the range of DOC concentrations present in the natural waters tested as part of this program (0.22 to-2.7 mg/l cf. the algae data!) was probably too narrow with the obtained precision and accuracy of the ECx results to accurately quantify any effect in toxic response attributable to DOC. Other parameters are known to affect the toxicity of nickel to freshwater, namely ph and the concentrations of Mg 2+ and Ca 2+. In marine waters, the ph is typically stable in the range of 8.1 to 8.4. The stability of the ph is due to the high buffering capacity of seawater. The marine concentrations of Mg 2+ and Ca 2+ are also stable, and are typically 800 and 2200 mg/l, respectively. The stability of ph, Ca 2+, and Mg 2+ reduces the need to normalize marine toxicity data, as long as the toxicity tests were performed on seawater at salinities between 28 and 34 ppt, and as long as the DOC concentrations were not excessively high. Some correction might be warranted if the DOC concentrations at a specific site or within a specific region of the EU are in excess of the ranges used in the experiments on which the nickel PNECmarine is based, i.e., > 2.7 mg/l. However, the development of a relationship between DOC concentration and nickel toxicity to marine organisms has not yet been established and remains a research need at this point. 153

168 Biotic ligand models (BLMs) are available for freshwater organisms, and these were used in the Aquatic Effects Assessment to normalize the toxicity data for freshwater organisms to a series of standard freshwater scenarios. The freshwater BLMs normalize nickel toxicity based on ph, Ca 2+ and Mg 2+ concentrations, and DOC concentration. Arnold et al. (2005) 17 used a BLM developed for the freshwater invertebrate Daphnia magna to predict copper toxicity to the marine bivalve Mytilus edulis. This approach has been utilized in the Marine Effects Assessment of Copper. To explain the possible relevance of the freshwater nickel BLMs in the Marine Effects Assessment of nickel, a marine scenario was developed in which the water quality parameters were set as close to marine conditions as possible without exceeding the geochemical boundaries of the BLMs. Specifically, ph was set to a typical marine value of 8.0, which was the mean of the values from the marine toxicity tests. DOC was set at 2.6 mg/l, which is the highest value from any of the marine ecotoxicity tests. Hardness was set at 310 mg CaCO 3 /L, which is the upper limit of the BLMs for hardness. Ca 2+ and Mg 2+ concentrations at this hardness concentration are still quite below what they would be in seawater. Other anions (sulfate and ), which affect Ni speciation but not Ni toxicity per se, were set at both typical freshwater conditions and typical marine conditions. The anions that were considered include sulfate and. The concentrations used were mg/l Cl and 3528 mg/l SO4 for seawater, and 250 mg/l Cl and 126 mg/l SO4 for freshwater. The HC5(50%) values that arose from the scenario using typical FW anionic concentrations was 10.0 µg Ni/L, whereas the HC5(50%) using marine anionic concentrations was 13.0 µg Ni/L. Both HC5 (50%) values were derived using the log-normal distribution, and both were accepted for statistical significance at the 0.05 level. This range suggests that that the sensitivity ranges shown by freshwater organisms appear to be lower than marine organisms when bioavailability considerations are applied. It should, however, be strongly emphasized that little confidence should be placed on these estimates because of the uncertainties involved in estimating nickel speciation within the current freshwater BLM, differences between physiologies of freshwater and marine organisms, and the taxonomic differences between the freshwater and marine ecotoxicity databases Uncertainty analysis The use of statistical extrapolation is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC/L(E)C10. Based on uncertainty considerations, the TGD (2003) recommends, for the freshwater compartment, to apply an additional assessment factor on the 50% confidence value of the 5th percentile value (thus PNEC = 5th percentile value (50th c.i.)/af), with an AF between 1 and 5, to be judged on a case-by-case basis. 1. The overall quality of the database and the end-points covered, e.g., if all the data are generated from true chronic studies (e.g., covering all sensitive life stages; real chronic exposure time): The marine nickel toxicity database is comprised nearly entirely of data from standardized test methodologies. Some of these tests are relatively short in terms of duration, e.g., the Mytilus edulis developmental test was conducted for 48 h. However, this test and other tests (e.g., developmental tests on Macrosystis pyrifera, Strongylocentrotus purpuratus, Dendraster excentricus, Crassostrea gigas, Paracentrotus lividus and Haliotis rufescens) measuring 17 Arnold, WR, RC Santore, and JS Costifas Predicting copper toxicity in estuarine and marine waters using the Biotic Ligand Model. Marine Pollution Bulletin. 50:

169 effects of development were performed on the early life stage of the organism, and assessed effects of nickel exposure on critical processes, e.g., development and metamorphosis. Other tests measured significant portions of the organisms life cycle. For example, the two fish species, Cyprinodon variegates and Atherinops affinis were initiated with larvae and continued for 28 and 40 days, respectively. The test on the polychaete worm Neanthes arenaceodentata began with 2-3 week old juveniles, continued through sexual maturation, and measured reproduction. The total exposure period for this test was 127 days. Sexual reproduction was also measured in the red algae, Champia parvula. Finally, growth endpoints for algae were measured after 72 hours, which is in accordance with the standard test methodology for algal tests. In summary, the tests reported ecologically relevant endpoints and chronic exposure durations. 2. The diversity and representativeness of the taxonomic groups covered by the database From the extracted data, it seems that the Ni-database fulfills the requirement of different NOEC values. Indeed, the database is comprised of 15 different species mean NOEC or EC10 values for 14 different families, including 23 individual high quality NOEC or EC10 (9 individual NOEC for algae and plants; 12 for invertebrates; 2 for fish. In addition, organisms belonging to different ecological functional groups (e.g., primary producers, herbivores, omnivores, predators) and feeding behaviors (filter feeders, deposit feeders) are found in the Ni-database. The database does not represent all of the major groups of marine organisms. Many groups of marine organisms, for example, coelenterates, are missing. However, the database does represent typical marine organisms that are encountered in temperate marine systems. Furthermore, the database includes the majority of standardized tests that are currently available. 3. Statistical uncertainties around the 5 th percentile estimate, e.g., reflected in the goodnessof-fit or the size of confidence interval around the 5 th percentile The log-normal distribution was rejected by both the Anderson-Darling (A/D = 0.81) and Kolmogorov-Smirnov Goodness of Fit tests (K/S = 0.94) for the distribution that includes all 15 species. This indicates that the species sensitivities may not be from the same distribution. Possible reasons for this include different modes of action for nickel toxicity among the tested organisms, differences in test conditions, and differences in nickel bioavailability. As the tests were all conducted in artificial or natural seawater at salinities ranging from 28 to 34 parts per thousand, and with dissolved organic carbon concentrations below 2 mg/l, the two latter possibilities being the reason are not supported. Thus the other reason has to be considered. Different types of SSD curve fitting functions and goodness of fit approaches were investigated when estimating the HC5(50%) for the marine database. Additionally, different approaches for aggregating the individual HC5(50%) values were also considered. As described in Section 4.3, the HC5(50%) values from the aggregation options ranged from 19.9 µg Ni/L to 26.3 µg Ni/L. According to the TGD (2003) the PNEC should be derived from the HC 5 at 50 th % confidence limit (µg/l) and considering the application of an additional assessment factor. The preferred HC5(50%) was 17.2 µg Ni/L, based on the arithmetic mean of two approaches, 1) the mean of all statistically significant parametric approaches, i.e., 19.9 µg Ni/L, and 2) the optimal value from the Kernel Density Estimation, i.e., 14.5 µg Ni/L. The statistical uncertainties for each of the statistically significant parametric distributions that yielded the value of 19.9 µg 155

170 Ni/L are shown in Table In general, the 95% confidence intervals for each of the statistically significant distributions was within a factor of 2 of the HC5(50%) value. The reason for choosing the arithmetic mean HC5 (50%)-value of the values estimated by all statistically valid parametric distributions and not the higher HC5 (50%) value obtained by the Monte Carlo Analysis was that a lower value was supported by the HC5 (50%) value from a significant log-normal distribution fitted to data excluding data from the three particularly insensitive species and also by employing the preferred semi non parametric method on all available data. It is however noted that using the arithmetic mean HC5 (50%) value for the parametric distributions includes the influence of using also the logistic distribution which gave a particularly low HC5 (50%) value (c.f., Table ). Goodness-of-Fit testing is not performed for Kernel Density Estimation, as this is a non-parametric approach. However, the 5 th to 95 th confidence intervals for this approach (i.e., µg Ni/L) are within the range of the other approaches outlined in Table 9. Also, the bandwidth used was chosen after careful evaluation of possible alternatives (for details, see Annex 2). Including the optimal value from the non-parametric Kernel Density Estimation approach of 14.5 µg Ni/L increased the cautiousness of the final approach taken. Thus, overall this preferred HC5(50%) value for marine organisms of 17.2 μg Ni/L is regarded as being appropriately representative of the values obtained by using different but acceptable approaches in a cautious way. In conclusion the confidence in this HC5(50%) value is high. 4. Comparisons between field and mesocosm studies and the 5 th percentile and mesocosm/field studies to evaluate the laboratory to field extrapolation. No field data were available that allow deriving threshold concentrations of Ni in marine waters at the field scale. 5. Species sensitivity below HC 5./ sensitivity analysis There are no species that show EC10 or NOEC values below the HC5 of 17.2 µg Ni/L. The calculated EC10 for Diadema antellarum, which was rejected over concerns that disease in this organism may have affected the outcome of the toxicity test, was 2.8 µg Ni/L. This value was nearly three times below the HC5(50%). The impact of including data of this species in the SSD are shown in Appendix H.1. An HC5(50%) value of 4.5 µg Ni/L results from the SSD using a non-rejected parametric distribution function, indicating that including the D. antellarum data would have a significant impact on the HC5(50%) and the PNEC. Because of the concerns over the effect of disease, and the observation that other echinoderms (including sea urchins) exhibit much higher tolerances to Ni than the tropical reef dwelling D. antellarum, data for this organism was not used in the SSD curve fitting and HC5(50%) calculation. The most sensitive organism in the database is the polychaete Neanthes arenaceodentata, with an EC10 of 22.5 µg Ni/L. This EC10 value is slightly higher than the HC5(50%) value of 17.2 µg Ni/L. 6. Range of species sensitivity/sensitivity analysis. Chronic nickel toxicity ranged by a factor of greater than 900, with the lowest EC10 of 22.2 µg Ni/L for Diadema antellarum and the highest EC10 of µg Ni/L for the fish 156

171 Cyprinodon variegatus. Unusually high toxicity values can affect the outcome of the SSD analysis as much as low toxicity values. Two groups of organisms were identified as being significantly more tolerant of nickel exposure than the other organisms in the database, and these included fish and flagellate algae. The two fish in the database, Cyprinodon variegatus and Atherinops affinis, exhibited EC10 values of and 3599 µg Ni/L. The flagellate Dunaliella tertiolecta showed an EC10 of µg Ni/L. The impact of these data on the outcome of the SSD analysis was evaluated by determining the HC5 without data from fish and the flagellate. As indicated earlier, evidence exists to suggest that marine fish are less sensitive to metals than freshwater fish and other marine invertebrates. Smolders et al. (2006) 18 compared the sensitivity of metals among freshwater and marine plants, invertebrates, and fish. Insufficient data were available to compare chronic toxicity among these groups, but in terms of acute toxicity, marine fish were consistently the least sensitive group for Cd, Cu, and Zn. For example, marine fish were more tolerant than freshwater fish for Cd, Cu, and Zn by factors of 25, 2, and 5, respectively. Marine fish were more tolerant than marine invertebrates for Cd, Cu, and Zn by factors of 17, 6, and 15, respectively. Finally, marine fish were more tolerant that marine plants for Cd, Cu, and Zn by factors of 28, 5, and 60, respectively. Ni appears to be less chronically toxic to marine organisms compared to similar freshwater organisms. In fact, the observed chronic Ni toxicity concentrations for marine fish are similar to those observed for acute toxicity to Oncorhnychus mykiss by Pane et al. (2003) 19. They observed acute Ni toxicity of mg Ni/L in freshwater, which is between the chronic EC10 values for the marine species A. affinis and C. variegatus tested in sea water. Pane et al. (2003) concluded that the mechanism for acute nickel toxicity was due to damage to the respiratory epithelium of the gill, as opposed to the chronic effects of nickel, which appear to be related to ion regulatory effects, and specifically the uptake and metabolism of Mg 2+ (Pane et al. 2006) 20. Marine teleost fish are active regulators with an internal major ion composition and osmolarity that is considerably lower and independent of that of seawater. This is achieved by the combined action of the gills, digestive system and kidneys which are all involved in the regulatory processes. In contrast most marine invertebrates do not regulate the composition of the circulatory fluid very strongly and are in near equilibrium with seawater. These important differences in ion and osmo-regulation may explain the differences in nickel sensitivity since the two physiological strategies (fish versus invertebrates) results in totally different situations. Marine fish show low permeabilities to the major cations across the gills, which is necessary to limit the influx of the ions from the seawater across the otherwise strong concentration gradient between outside (seawater) and inside (blood). In addition the very high concentrations of major cations in seawater are likely to provide an effective protection of the fish for Ni uptake by competing with the metal for the same binding or uptake sites at the exchange surfaces. Most marine invertebrates (there are some exceptions) do not posses 18 Smolders, R. A. Vlaeminck and R. Blust Comparative Toxicity of Metals to Freshwater and Saltwater Organisms. Report to the Industry Sponsors of the Ecotoxicology Technical Advisory Panel. January pp. 19 Pane, E. F., A. Haque, and C.M. Wood Mechanistic analysis of acute, Ni-induced respiratory toxicity in the rainbow trout (Oncorhynchus mykiss): an exclusively branchial phenomenon. Aquatic Toxicology. 69: Pane, E. F., M. Patel, and C.M. Wood Chronic, sublethal nickel acclimation alters the diffusive properties of renal brush border membrane vesicles (BBMVs) prepared from the freshwater rainbow trout. Comparative Bioachemistry and Physiology. Part C: Toxicology and Pharmacology. 143:

172 such a regulatory system, are more permeable and show rapid equilibration of the circulatory fluid with seawater after transfer from one salinity to another. The observation that nickel toxicity in the fish only occurs at levels at least ten times higher than in the invertebrates may also point to a different mode of action, since the high values at which effects are observed are of the same order of magnitude than these causing respiratory impairment in freshwater trout. One possibility is that the cationic competition under marine conditions greatly reduces the toxicity of nickel as an ion regulatory toxicant, and that nickel concentrations must be sufficiently high to cause epithelial damage to the gills of marine fish before they manifest toxicity. It should be noted that the fish in the marine nickel database are not necessarily tolerant species. Specifically, A. affinis is used in effluent toxicity test programs because of its sensitivity to toxicants 21. Without the fish data, the least sensitive species is Dunaliella tertiolecta. The EC10 for D. tertiolecta was µg Ni/L, which is > 40 times higher than the next most sensitive organism, which is the oyster C. gigas (EC10 = µg/l). This phytoplankton species has been demonstrated to be very tolerant of metal exposure (Davies, 1976; Fisher et al 1984). The mechanism for the metal tolerance shown by D. teriolecta was identified by Davies (1976), who demonstrated that this species produces an extraordinary quantity of intracellular sulfide compared with other marine organisms. Sulfide within the cell acts to precipitate metals within the cell and renders the metals unavailable for toxic effects. The identification of a mechanism within this species that detoxifies metals suggests that this species could be excluded from the database, as none of the remaining organisms are as tolerant or have been shown to have detoxicification mechanisms. Excluding otherwise acceptable chronic ecotoxicity data from the data base for the calculation of an HC5(50%) should only be considered under extraordinary circumstances. In this particular case, two compelling factors indicate that excluding data for the fish and D. tertiolecta might be reasonable considering several factors. First, the log-normal distribution was rejected when these data are included in the analysis, apparently because these species are very insensitive compared to the other species in the data base. Second, mechanistic reasons have been identified to explain the insensitivity of these particular species. Reasons include that for these species nickel has a different mode of toxicity than for the remaining species. An evaluation of the exclusion of the fish and D tertiolecta data on the calculation of the HC5(50%) is therefore warranted as part of the weight-of-evidence approach for selecting the most appropriate approach for calculating the HC5(50%). It is difficult to determine the impact of removing the EC10s for the insensitive fish species and D. tertiolecta because of the impact of these data on the distribution. In other words, the HC5(50%) that is calculated with and without these data are derived from different frequency distributions. The data base minus fish and D. tertiolecta and application of the log normal distribution function yielded an HC5(50%) of 23.7 µg Ni/L, whereas the SSD for the full data base yielded an HC5(50%) of only 9.0 µg Ni/L, which is nearly three times lower. However, this is not a valid comparison because the log-normal distribution for the full data base was not significant according to Goodness of Fit tests in the latter case. Nevertheless, this comparison supports the approach taken to derive the HC5(50%), which was to consider all statistically significant parametric distributions, but interpreted in light of the results 21 A. affinis is the prefered marine fish test of the California State Water Resources Control Board for evaluating water quality ( ( 158

173 employing semi non parametric methods but also the result of using the classical log-normal distribution function excluding data from the three particularly insensitive species. An evaluation of the weight of evidence shows that the nickel marine toxicity database contains a broad diversity of marine taxonomic groups, feeding habits, and ecological niches. Practically speaking, the full array of currently available standardized marine toxicity tests have been used to generate the nickel toxicity database, meaning that tests in non-standard testing methods would be required to broaden the database even more. The HC5(50%) from the preferred approach was 17.2 µg Ni/L, which is lower than the EC10 for the most sensitive organism in the nickel ecotoxicity database, i.e., Neanthes arenoceodentata with an EC10 of 22.5 µg Ni/L. Therefore, this HC5(50%) provide protection against the most sensitive endpoint in the database. The bioavailability of nickel in marine systems is not expected to be significantly affected by factors other than DOC. Concentrations of all other constituents of seawater than may affect Ni toxicity, such as ph, Ca 2+ and Mg 2+, are nearly uniform in coastal marine waters. None of the laboratory tests were conducted with DOC values greater than 2.6 mg/l. It is plausible that higher DOC values will ameliorate Ni toxicity via sorption of Ni 2+. The toxicity data in the Ni ecotoxicity database have therefore been generated under conditions that maximize bioavailability of nickel in marine waters. In other words, relatively few or no natural marine waters should be expected to exhibit higher sensitivities than the waters used to generate the toxicity data. Finally, the two approaches used to derive the HC5(50%) of 17.2 µg Ni/L were statistically sound. First, all of the parametric distributions that the arithmetic mean value of 19.9 µg Ni/L is based on gave an accurate fit of the marine nickel toxicity data, i.e. none of these distributions were statistically rejected. (Fig. 4). Also, the Kernel Density Estimation value of 14.5 µg Ni/L was based on the bandwidth considered by statistical experts to be the optimal bandwidth, meaning the statistically optimal parameters were used. Based on this evaluation of the weight of evidence, it is proposed to use HC5(50%) derived from the arithmetic mean of 1) the mean of all statistically significant parametric approaches, i.e., 19.9 µg Ni/L, and 2) the optimal value from the Kernel Density Estimation, i.e., 14.5 µg Ni/L. An Assessment Factor of 2 is furthemore proposed to be applied to the HC5(50%) for the derivation of a PNECmarine value. Using the HC5(50%) of 17.2 µg Ni/L, the PNECmarine would therefore be 8.6 µg Ni/L Arguments for using an AF of 2 includes besides the large margin to the lowest available measured EC10 value for marine organisms (a factor of more than 2) and that the selected HC5 (50%) value is set based on a cautious weight of evidence analysis also the lack of attempts to normalize the EC10 values for marine organisms according to the potential influence of DOC. Whereas this might be reasonable, this is not done because such normalization cannot currently be backed up with quantifiable evidence. However general knowledge regarding the impact of DOC on nickel bioavailability makes it plausible that this lack of normalization implies the use of a cautious approach. Arguments for using an AF of 3 instead of an AF of 2 is that chronic toxicity database for marine organisms contains fewer data than that of the freshwater database. Furthermore, the PNEC derived from the use of an AF of 3 would be 5.7 µg Ni/L, which would provide a large margin to the lowest measured EC10 for marine organisms (a factor of more than 3). This may be especially reassuring of the appropriateness of this choice, because when evaluating the number of data points in the marine database, it has to be recalled that this database is used 159

174 for protection of a type of ecosystem, the marine environment, which in general has a greater taxonomic diversity than that of the freshwater environment. Both of the above derived PNECmarine values are within the range of FW PNEC values derived using the eco-region approach. Specifically, the freshwater PNEC values, which are based on an Assessment Factor of 2, range from 3.6 µg Ni/L to 21.8 µg Ni/L, depending on water chemistry. The PNEC values for fresh water and marine water are difficult to directly compare because bioavailability correction is used in the derivation of the freshwater PNECs but not for the marine PNEC. However, the competitive effects of Ca 2+ and Mg 2+ in seawater are many times higher than in freshwater meaning that everything else being equal individual marine organisms would be assumed to be protected towards nickel toxicity by the increase in competitive interaction and would therefore be apparently less sensitive to nickel toxicity. Another point to consider is the fact that the DOC concentration in the marine tests was <2.6 mg/l, whereas the DOC range in the freshwater ecoregions is from 2.5 to 12 mg/l. The lack of being able currently to quantifying the protective effect of DOC in marine waters makes this difference difficult to take into account, but the low DOC content of in the marine toxicity tests does mean that the conditions were not favorable for nickel speciation to be impacted by sorption of soluble nickel to DOC. Therefore, it may not be unexpected that the PNECmarine using either an AF of 2 or 3 is within the range of the freshwater PNEC values. However in conclusion on the subject on the choice of the assessment factor and considering all arguments above including the particular cautious approach taken for derivation of HC5(50%) it is felt that the most appropriate AF would be 2. Therefore, hence the PNECmarine is proposed to be 8.6 µg Ni/L, which will be carried over to the risk characterization. 160

175 3.2.3 Terrestrial Effects Assessment Sources and selection of ecotoxicological data Sources of ecotoxicological data The ecotoxicological data in this report are derived from ongoing research activities and from original papers on the subject, published in international journals. Databases searched for literature were NiPERA CAB abstracts ( ), Current Contents, Science Direct, IS Web of Science, and AGRICOLA. Review articles covering in the environment were also searched for data sources. Only original literature was quoted Selection of ecotoxicological data The assessment of data adequacy involves a review of individual data elements with respect to how the study is conducted and how the results are interpreted in order to accept (or reject) a study in accordance with the purpose of the assessment. The term adequacy covers both the reliability of the available data and the relevance of the data for environmental hazard and risk assessment. These two basic elements have been defined by the TGD as follows: Reliability: covering the inherent quality of a test relating to test methodology and the way that the performance and results of a test are described. Relevance: covering the extent to which a test is appropriate for a particular risk assessment. Only ecotoxicity data that comply with all of the above-mentioned criteria can be considered valid and may be used in the risk assessment. The toxicity data on invertebrates and plants are from single-species tests that study common ecotoxicological parameters such as survival, growth and/or reproduction. The toxicity data on micro-organisms are from tests in which microbe-mediated soil processes, such as C- and N- mineralization were studied. These microbial toxicity tests are multiple species tests because these microbe-mediated processes reflect the action of many species in soil microbial communities. Relevance Biological relevancy The toxicity data on terrestrial organisms are from ecotoxicity tests that study relevant ecotoxicological parameters such as survival, growth, reproduction, litter breakdown, and abundance. Relevant endpoints for soil micro-organisms focussed on functional parameters (such as respiration, nitrification, mineralization) and microbial growth. Other endpoints, e.g., changes in metabolism, chlorosis, cell membrane stability are considered less relevant as they are more difficult to interpret on a community level (for population effects) and are therefore not selected for the effects assessment. The ecological relevance of soil enzymatic assays is a subject of current debate. The debate centers on several factors, which are elaborated on in Appendix I.1 for the effects assessment, studies on enzyme activity were screened using the same reliability and relevance criteria as other microbial processes, with one exception: soil 161

176 enzyme tests were rejected if they were performed in buffered soil suspensions at a ph value that was greater than 0.5 ph units different from that in the undisturbed soil because the change in ph should affect the cation exchange capacity and subseuqnetly the toxicity of nickel. The impact of those studies that pass relevance and reliability criteria on the PNEC was assessed in a sensitivity analysis, which is also presented in Appendix I.1. Relevancy of the test media Only data from observations in natural and artificial (OECD) soil media have been used in this report, tests performed in substrates that were judged as not representative for soils (e.g. nutrient solution, agar, pure quartz sand and farmyard manure) were not included in this effects assessment. Site of origin of soil and the basic soil variables was generally not used as exclusion parameters, although studies performed in the lower soil horizon were not included due to the generally very low organic content. First of all, given the lack of knowledge for which soil parameters that are important for soil toxicity there is little or no reason to prior differentiate between site of origin, e.g. different continents. Ideally, the data used in the effect assessment should ideally be based on organisms and exposure conditions relevant for Europe. This would, however, considerably reduce the amount of data to be used. Therefore, also data based on soils collected outside Europe have been used, excluding data from tropical or subtropical regions. Relevancy of the physico-chemical characteristics of the test media The main parameters driving the bioavailability of nickel (see Section 2.7.3), i.e. OM, CEC, ph and clay, were used for data selection. The conclusion (i) research projects demonstrated that ph (for accounting of ageing effects) and cation exchange capacity (CEC) (for accounting of the soil type effects) were the main variable driving the bioavailability/aging and therefore toxicity of nickel towards terrestrial organisms. Therefore both the ph and CEC were used for data selection purposes. If the CEC was missing from a test with plants/invertebrates/microorganisms, then it was estimated from % clay, ph and % organic matter (OM) using an experimentally derived regression model: CEC=( ph)*clay/100+( ph)*om/100; the clay is the % clay in the soil (Helling et al., 1964; regression based on CEC measured at various ph values on 60 different soils; CEC refers to the soil ph). Practically this means that only tests were selected for which the % OM, ph and % clay (or CEC if % OM and % clay missing) of the test soil were reported. Other regression models for the prediction of the CEC content in soils are described in the literature. For example, Krogh et al. (2000) showed that for the description and prediction of CEC in Danish soils, the organic matter and clay content of the soils (i.e. CEC= %OM for organic soils; CEC= %OM+0.42%clay for calcareous soils) seemed to be sufficient. The Helling et al. (1964) model was chosen because it was based on a comprehensive and broad set of soils. Furthermore, the range of the physico-chemistry of the test media affecting the bioavailability of Ni, i.e. CEC, should be within the range of the developed/validated terrestrial regression models. The conclusion (i) research program shows that all terrestrial bioavailability regression models were developed in soils with: a CEC range between 1.8 and 52.8 cmol/kg, a ph range between 3.6 and 7.7, a clay content between 0.4 and 55.5 %. Test duration 162

177 What comprises chronic exposure is a function of the life cycle of the test organisms. A priori fixed exposure durations are therefore not relevant. The duration should be related to the typical life cycle and should ideally encompass the entire life cycle or, for longer-lived species the most sensitive life stage. Retained exposure durations should also be related to recommendations from standard ecotoxicity (e.g. ISO, OECD, ASTM) protocols. Typically test duration for the higher plants are within the range of 4 (e.g. the barley root elongation test based on ISO (1995)) and 21 days (e.g. the tomato shoot yield test based on ISO (1995)). OECD n 208 (plant seedling emergence and growth test, 1984) recommended a test duration of at least 14 days after emergence of the seedlings. Testing with soil invertebrates have a typical acute exposure duration of 7 to 14 days for the oligochaetes Eisenia fetida/eisenia andrei. Assessing the chronic effects of substances on sub-lethal endpoints such as reproduction on oligochaetes has a typical exposure duration of 3 to 6 weeks for the standard organism Enchytraeus albidus (OECD, 2000; ISO 16387). For another standard species Folsomia candida survival and reproduction is typically assessed after 28 days of exposure (ISO 11267, 1999). Reported test duration using soil microorganisms vary and last 28 days for the carbon transformation test (OECD n 216) and for the nitrogen transformation test (OECD n 217). Reliability All articles obtained were reviewed and if of sufficient quality then the necessary data were extracted. With regard to sufficient quality this was assessed for each individual article. Following reliability indices were used: 1) little or no control mortality or seed emergence and weight loss; 2) appropriate zero controls; 3) random distribution or organism in exposure containers (ensuring homogeneity); 4) constant test conditions during experiment; and 5) sufficient replicates and concentrations for statistical analysis; 6) no mixed metal contamination along with increasing Ni concentration. The list should also include a detailed description of soil parameters and other experimental conditions. Type of test Both standard test organisms and non-standard species can be used in the framework of a risk assessment. In general, toxicity data generated from standardized tests, as prescribed by organizations such as OECD and USEPA will need less scrutiny than non-standardized test data, which will require a more thorough check on their compliance with reliability criteria before being used. GLP and non-glp tests can be used provided that the latter fulfill the stipulated requirements. Concentration-effect relationships Because effect concentrations are statistically derived values, information concerning the statistics should be used as a criterion for data selection. In that respect L(E)C 10 values are considered as equivalent to NOEC. If no methodology is reported or if values are visually derived, the data were considered unreliable. Effect levels derived from toxicity tests using only 1 test concentration always results in unbounded and therefore unreliable data. Therefore, only the results from toxicity tests using 1 control and at least 2 Ni concentrations were retained. Tests that do not comply with the above-mentioned stipulations are not used in this risk assessment. However, the use of unbounded NOEC values could be justified on a case by case basis, e.g. when no other toxicity values are available for a particular species or when scientific evidence exist that the true toxicity towards a specific organism would be biased if such data were not taken into account. In the latter case a justification will be given. Test substance 163

178 Terrestrial toxicity data in this report cover nearly all nickel compounds found in the literature. It is at present not justifiable to differentiate among soluble the nickel compounds (Ni 2+ salts) when using the general data set, simply because other factors are more important for the NOEC than the actual nickel compound used. In single studies with higher plants (Figure ) it was possible to observe different NOECs for different nickel compounds, most notably NiO and Ni show much higher NOEC than the more soluble compounds (see, for example, Smolders, 2000). At the moment, there are too few data enabling an effect assessment for each nickel compound. Finally, it is doubtful whether discrimination between most nickel compounds would have an ecological relevance, as the compound will change with time and under various environmental conditions 22. In summary, this assessment has pooled results of studies that have been conducted with soluble nickel compounds. These includes nickel and nickel sulfate, two of the nickel salts that are addressed by the ESR Risk Assessment of nickel. Nevertheless, it should be noted that studies involving nickel compounds with a very low solubility, especially NiO and metallic nickel, are not included in this report NOEC (mg Ni/kg) Oat-Ni-acetate Oat-NiCl2Oat-NiSO4 Alf-NiCl2 Alf-NiSO4Alf-Ni(NO3)2 Species / compound Figure Mean (± Stdev) NOECs or EC10s for various nickel compounds for two plant species, Oat (Avena sativa) and Alfalfa (Medigo sativa). Note: Data for this Figure are derived from de Haan et al 1985, Halstead et al 1969, Poulik et al. 1997, Dang et al. 1994, Webber 1972, Liang and Schoenau (1995), Taylor and Allison (1981)). Formatted: English (U.K.) Chemical analysis The toxicity data have been related to the total concentration of Ni (defined as strong acid extraction without further confinement) rather than more easily extractable fractions, by use of e.g. CaCl 2 or EDTA. It was reasoned that the uncertainty with regard to using various acid extraction procedures was much less than the variability between different techniques 22 compounds are in general relatively soluble at ph values below 6.5, whereas nickel can exists as insoluble nickel hydroxides at ph above 6.7 which are slowly mobilized (Schmitt, 1992). Acid rain, therefore, has a pronounced tendency to mobilize nickel from soils and causes increased levels in the ground water (Sunderman, 1992 ). The divalent ion, Ni(II), seems to be the most stable form in soil solutions (Uren, 1992). In the soil solution it may occur in the ionic form or complexes with either organic or inorganic ligands. In acidic soil the most dominant soil solution form seems to be Ni 2+ or NiSO 4, whereas in alluvial soils NiCO 3, NiHCO 3 and Ni 2+ seem to be the most stable forms (Uren, 1992). 164

179 extracting the easily extractable fractions. Furthermore, a large part of terrestrial Ni concentrations are only reported as totals based to the known addition (added concentration) rather than measured concentrations (actual concentrations) with the latter including background concentrations. All concentrations reported are based on soil dry weight, and reported as mg Ni/kg dry weight soil. There is a preference for using measured data. The data used in the effect assessment should therefore ideally be based on measured concentrations. This would however considerably reduce the amount of data to be used. Therefore, in this effects assessment, both nominal and actual (measured) effect concentrations were selected for PNEC derivation. If it is not mentioned whether the NOEC/L(E)C 10 values are based on measured or nominal concentrations, they were considered as nominal concentrations. Tests that do not comply with the above-mentioned stipulations are rated as not reliable and are not recommended for use in the risk assessment exercise. Missing background concentrations For many studies the pre-test Ni concentrations (concentrations in the controls) of the test soils used were not reported. In these cases only the added concentrations were reported. Two different approaches for estimating the missing background Ni concentrations in the test media were investigated. Regression approach Assuming that soil Ni background concentration closely correlates to the soil texture and % organic matter then it should be possible to predict this pre-test Ni concentration. Two equations correlating the background Ni concentrations with the soil texture have been published. One is based on Danish soils (Equation 1) and the second on Dutch soils (Equation 2). A test of the ability to predict the pre-test Ni concentrations can be performed by applying these equations to the studies already having information on pre-test Ni concentrations and then comparing predicted with measured values. The comparison between the predicted and measured concentrations yields a very poor correlation (Figure ). This makes the prediction of background Ni concentrations based on soil texture not justifiable. Thus in conclusion, estimations of the missing background concentrations were not included as it was regarded not to improve the data reliability. Equation 1, Danish data: Ni concentration = Clay (%)*1.095-Silt (5)*0.305-Humus (%)* (r 2 =0.88) Equation 2, Dutch data: Ni concentration = 10 + Clay (<2um) (%) 165

180 Predicted soil concentrations (mg Ni/kg) Ni 2 = 1.095*Clay *Silt-0.203*OM (Danish) Ni b = Clay +10 (Dutch) Dutch (95% conf. limits) 1:1 Danish (95% confidence limits) Measured concentrations (mg Ni/kg) Figure : Predicted of soil background concentration based on two correlations obtained for Danish (Bak, 1997) (n=72) and Dutch soil (n=108) (Slooff, 1992). Measured values approach Estimating the missing background Ni concentrations in the test media was performed using the following approach: predictions of Ni background concentrations for natural soils (other than artificial soils, e.g. OECD soils) with missing background concentration were based on the median ambient background value reported for topsoils in Europe (see FOREGS database, 2005), i.e. 14 mg Ni/kg. For OECD soils, no measured data for the background concentrations are available. In that particular case, as a worst case assumption, the total NOEC was set equal to the added NOEC Derivation of NOEC or L(E)Cx values General approach The toxicological variables are estimated based on NOEC (No Observed Effect concentration) or L(E)C 10 values. The methods that have been used for the derivation of NOEC values, being real NOEC values or NOEC values derived from effect concentrations, are based on the recommendations outlined in the revised TGD (2003). If L(E)C 10 data are reported or if both NOEC and L(E)C 10 data are available (as is often the case in research projects), the L(E)C 10 value was used in the effects assessment. If L(E)C 10 data are not reported, they were estimated from the raw data whenever possible. To determine EC 10 values, data from individual studies were subjected to a log-logistic model (CSIRO Australia 2001). EC 10 values were determined from the fitted curves. Calculations of ECx values were restricted to studies with 4 or more Ni dosing levels (not including controls). All EC 10 values included in the PNEC derivation should preferably be above the lowest added (first concentration above control) test concentration for each test. Estimation of L(E)C 10 values below the lowest added concentration tested has been asserted to introduce uncertainty (ISO, 2004). On the other hand, it is noted that toxicity tests always also include a control 166

181 with no added test substance and this is also used in the model for the ECx estimation. When testing naturally occurring substances like, this control often will include either background concentrations of nickel (tests in natural water) or traces of nickel (tests in artificial media), which may reduce concerns about estimating L(E)C 10 values that are below the lowest added test concentration (because the control is effectively the lowest test concentration)..the EC 10 values below the lowest applied test concentration are rejected if the lowest test concentration resulted in 20% inhibition or more and/or if the EC 10 is more than twofold below the lowest applied (non-zero) test concentration. This data selection procedure is not a strict one used without further consideration. The robustness of the NOEC derivation is also relevant to consider in the regard, including for example the difference between the NOEC and the LOEC value. These extrapolated EC 10 values are not underlined in the tables with effects data but are marked with *. The impact of excluding or including EC 10 values below the lowest tested concentration on the PNEC was assessed in a sensitivity analysis, which is also presented in Appendix I.2. If no reliable L(E)C 10 values are available, real NOEC values should be derived from the data reported, i.e. the NOEC is one of the concentrations actually used in the test and should be derived using appropriate statistics (significance level usually: p = 0.05 (optional: the p = 0.01 level if reported instead of the p = 0.05 level)). The use of LOEC or MATC or unbounded NOEC or LOEC values could also be considered in specific cases, e.g. if other toxicity values are not available for a particular species. For example, if no effects were observed at the highest or the only tested concentration, then this concentration can be used as a conservative estimate for the real NOEC Averaging thresholds for same process/species In this report averaging of the NOEC or L(E)C10 values for higher plants, invertebrates and microbial processes was used to avoid over-representation of ecotoxicological data from one particular species or bacterial process. The approach used is outlined hereunder: If for one process/species several chronic NOEC or L(E)C10 values based on the same toxicological endpoint are available, these values are averaged by calculating the geometric mean, resulting in the species mean NOEC or L(E)C10, if performed under comparable conditions or after normalisation. For each individual study the NOEC or L(E)C10 included in the database is the most sensitive NOEC or L(E)C10 identified for the test species/process on the given substrate. That is, if multiple exposure/incubation times were examined, and NOEC or L(E)C10s were determined for each time, then the most sensitive NOEC or L(E)C10 for the test species or process was selected for inclusion. If for one species several chronic NOEC or L(E)C10 values based on different toxicological endpoints are available (e.g., mortality, growth, reproduction), the lowest value is selected. The lowest value is determined on the basis of the geometric mean if more than one value for the same endpoint is available. 167

182 If several life stages (e.g., juveniles and adults) have been studied for an organism then the most sensitive life stage has been considered. For some species or processes both acute and long-term data are available. The short- and long-term data may provide widely different toxicity data for a given toxic substance. This is for example observed for microbial processes (Doelman and Haanstra 1989). If more data are present for the same species in an experiment then the most sensitive variable and the longest exposure time were in general used. The primary aim was to get truly chronic data, however, for soil organisms this has often not been possible except for microorganisms where many life- cycles often are included in the test. In a microbial test adaptation or acclimation may take place (see above), but for all tests the lowest NOEC or EC10 was chosen independently of the exposure time. It may further be noted that for the SSD approach this does not necessarily lead to lower PNEC values. If species data were dependent of each other, e.g., toxicity data for more species were obtained in a multispecies system, then the data were not used in the SSD approach as they were not independent as required for the this approach Derivation of PNEC values using statistical extrapolation (methods) The PNEC values were derived from the ecotoxicity data (either NOEC values or EC 10 values from laboratory tests), using the ecotoxicological statistical extrapolation method (also referred to as the Species Sensitivity Distribution (SSD-) method), which is described in the TGD (Chapter 3, Appendix V). To evaluate the toxicity data, the statistical extrapolation method was used, calculating the median fifth percentile (HC5(50%)) of the species sensitivity distribution. The log-normal distribution (e.g. the methods of Wagner & Løkke (1991) and Aldenberg & Jaworska (2000)) and the log-logistic distribution (Aldenberg & Slob, 1993) are pragmatic choices because of their mathematical properties (methods exist that allow for most in-depth analysis of various uncertainties). However, several other SSD curve fitting functions could be used in order to derive variability distributions (i.e. species-sensitivity distributions, SSD) and percentiles from parametric (e.g. Log-normal, Weibull distributions, ) and / or even from nonparametric methods. To avoid overfitting it was recommended that the selected SSD functions should not be too complex (2-3 paremeters functions are preferred over multiparameters functions). Indeed, a perfect fit can always be obtained by using for example a high degree polynomial distribution. Fitting of the normalised chronic Ni toxicity data is assessed towards the classical log normal distribution function or by selecting the fitting function giving the best goodness of curve fit in individual cases by selecting among 9 different frequency distributions which could be described by 2 parameters:, i.e. Erlang, Normal, Logistic, Inverse Gaussian, Extreme Value, Weibull, Pearson V, Uniform, Pareto, and to 2 distributions which could be described using 3 parameters, i.e. Triangular and Pearson VI. The main characteristics desribing these SSD curve fitting functions are reported in Appendix I.3. The selection of the distributions is based on the available distributions in the BestFit software. In order to evaluate the fit of various distribution functions for a given data set, goodness-offit statistics (software BestFit, Palisade Inc.) were used for evaluating the SSD curve fit for these curve fit functions. Goodness-of-fit tests are formal statistical tests of the hypothesis that the data represent an independent sample from an assumed continuous distribution. These tests involve a comparison between the actual data and the theoretical distribution (i.e. curve 168

183 fit function) under consideration. The Andersen-Darling (A-D) test places most emphasis on tail values whereas the Kolmogorov-Smirnov (K-S) test investigates the data fit for the middle of the distribution curve. Appendix I.3 provides background information on the goodness of fit statistics used in this report. Differences in the measure of goodness of fitting of the tails of the distribution curves may have an impact on the goodness of fit evaluations of the curves employed. If the approach is used to select the fitting function, which has the best goodness of fit to the particular data in each case, the actual choice of the goodness of fit function may determine the selection of the particular fitting function and hence the derivation of the HC5(50%). The A-D and the K-S tests belong to the wide class of quadratic statistics measuring vertical discrepancy in a cumulative distribution function-type probability plot (Stephens, 1982). The calculated goodness-of-fit statistic measures how good the fit is: critical values are calculated and used in order to determine whether a fitted distribution should be accepted or rejected at a specific level of confidence. Typically, these values depend on the type of distribution fit, the number of data points and the confidence interval. The level at which one distinguishes between likely and unlikely values of the test statistic is a matter of judgement. Typically a significance level of 0.05 is used, implying that a value of the test statistic below the 95 th percentile of the distribution for the statistic is acceptable and leads to the inability to reject the hypothesis. In other words, the lower the Goodness of Fit statistics, the better the distribution under consideration fits the data.a value of the calculated A-D statistic above the 95 th percentile of the distribution leads to the rejection of the null hypothesis, i.e. the distribution is not a good fit (Cullen & Frey, 1999). Additional guidance on the application of SSD statistics on small data sets is provided in Section 4 (Marine Effects Assessment of ), Annex II of the EU Risk Assessment of. If the background concentration is not reported, they are estimated based on the rules outlined in Results Toxicity to higher plants Data on chronic single-species toxicity tests resulting in L(E)C 10 /NOEC values for plants are summarised in Table Values selected for the effects assessment are underlined. In the total risk approach 68 individual high quality L(E)C 10 /NOEC values (for 11 different species) are selected ranging from 11.0 mg/kg for Lycopersicum esculentum to 1,127 mg/kg for Hordeum vulgare (Rothamsted research, 2005). Table gives an overview of the rejected data and the reason(s) why they were rejected. 169

184 Table Overview of the accepted NOEC/EC10 values for higher plants (estimated background nickel concentrations and CEC** are indicated in italics) Added NOEC Total NOEC Test subst. Organism Medium ph OC clay Cb CEC Equil. Period Duration Endpoint NOEC or EC10 Unbounded NOEC NOEC or EC10 Unbounded NOEC % % mg/kgdw cmol/kg d D mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Hordeum vulgare Loamy sand EC10rl Houthalen NiCl2 Hordeum vulgare Sandy clay loam EC10rl Zegveld NiCl2 Hordeum vulgare Loamy sand EC10rl Montpellier NiCl2 Hordeum vulgare Loamy sand EC10rl Rhydtalog NiCl2 Hordeum vulgare Jyndevad EC10rl NiCl2 Hordeum vulgare Sandy loam EC10rl Kovlinge II NiCl2 Hordeum vulgare Clay Aluminosa EC10rl NiCl2 Hordeum vulgare Borris EC10rl NiCl2 Hordeum vulgare Sandy clay loam EC10rl Woburn NiCl2 Hordeum vulgare Silt loam EC10rl Ter Munck NiCl2 Hordeum vulgare Clay EC10rl Souli NiCl2 Hordeum vulgare Silt loam EC10rl Marknesse NiCl2 Hordeum vulgare Clay EC10rl Brecy NiCl2 Hordeum vulgare Cordoba EC10rl NiCl2 Hordeum vulgare Cordoba EC10rl NiCl2 Hordeum vulgare Loam EC10rl Guadalajara NiCl2 Lycopersicon Loamy sand EC10y(s) esculentum Houthalen NiCl2 Lycopersicon Sandy clay loam EC10y(s) esculentum Zegveld NiCl2 Lycopersicon Loamy sand EC10y(s) esculentum Montpellier 170

185 Added NOEC Total NOEC Test subst. Organism Medium ph OC clay Cb CEC Equil. Period Duration Endpoint NOEC or EC10 Unbounded NOEC NOEC or EC10 Unbounded NOEC % % mg/kgdw cmol/kg d D mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Lycopersicon Loamy sand EC10y(s) esculentum Rhydtalog NiCl2 Lycopersicon Jyndevad EC10y(s) esculentum NiCl2 Lycopersicon Sandy loam EC10y(s) esculentum Kovlinge II NiCl2 Lycopersicon Clay Aluminosa EC10y(s) 28* 47* esculentum NiCl2 Lycopersicon esculentum Borris EC10y(s) NiCl2 Lycopersicon Sandy clay loam EC10y(s) esculentum Woburn NiCl2 Lycopersicon Silt loam EC10y(s) esculentum Ter Munck NiCl2 Lycopersicon Clay EC10y(s) esculentum Souli NiCl2 Lycopersicon Silt loam EC10y(s) esculentum Marknesse NiCl2 Lycopersicon Clay EC10y(s) esculentum Brecy NiCl2 Lycopersicon Cordoba EC10y(s) esculentum NiCl2 Lycopersicon Cordoba EC10y(s) esculentum NiCl2 Lycopersicon esculentum Loam Guadalajara EC10y(s) Rothamsted research, 2005 [1] NiSO4 Spinach Sandy NOECy NiSO4 Spinach Heavy clay NOECy Willaert & Verloo, 1988 [2] NiCl2 Avena sativa Grenville sandy EC10y(g) loam + P NiCl2 Medicago sativa Grenville sandy EC10y(t) loam NiCl2 Medicago sativa Grenville sandy loam + P EC10y(t)

186 Added NOEC Total NOEC Test subst. Organism Medium ph OC clay Cb CEC Equil. Period Duration Endpoint NOEC or EC10 Unbounded NOEC NOEC or EC10 Unbounded NOEC % % mg/kgdw cmol/kg d D mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Avena sativa Uplands sand EC10y(g) NiCl2 Avena sativa Uplands sand 3 + P EC10y(g) NiCl2 Avena sativa Uplands sand 3 + L EC10y(g) NiCl2 Avena sativa Uplands sand 3 + P EC10y(g) L NiCl2 Medicago sativa Uplands sand 3 + P EC10y(t) NiCl2 Medicago sativa Uplands sand 3 + L EC10y(t) NiCl2 Medicago sativa Uplands sand 3 + P EC10y(t) L NiCl2 Avena sativa Uplands sand EC10y(g) NiCl2 Avena sativa Uplands sand 4 + P EC10y(g) NiCl2 Avena sativa Uplands sand 4 + L EC10y(g) NiCl2 Avena sativa Uplands sand 4 + P EC10y(g) L NiCl2 Medicago sativa Uplands sand EC10y(t) NiCl2 Medicago sativa Uplands sand 4 + P EC10y(t) NiCl2 Medicago sativa Uplands sand 4 + L EC10y(t) NiCl2 Medicago sativa Uplands sand 4 + P + L EC10y(t) Halstead et al., 1969 [3] The EC10 values are not reported values but calcualted from original data. NiSO4 Avena sativa Light (9% clay) To maturity NOECy NiSO4 Raphanus sativus Light (9% clay) NOECy NiSO4 Lactuca sativa Heavy (45% clay) NOECy Liang & Schoenau, 1995 [4] NiSO4 Allium cepa EC10y NiSO4 Trigonella poenumgraceum EC10y Dang et al., 1990 [5] The EC10 values are not reported values but calcualted from original data. 172

187 Added NOEC Total NOEC Test subst. Organism Medium ph OC clay Cb CEC Equil. Period Duration Endpoint NOEC or EC10 Unbounded NOEC NOEC or EC10 Unbounded NOEC % % mg/kgdw cmol/kg d D mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiSO4 Lolium perenne Sandy loam EC10y Frossard et al., 1989 [6] NiSO4 Lactuca sativa Steinhof EC10y(l) NiSO4 Lactuca sativa Gansemos EC10y(l) NiSO4 Lactuca sativa Erlach EC10y(l) NiSO4 Lactuca sativa Gasel EC10y(l) Gupta et al., 1987 [7] The EC10 values are not reported values but calcualted from original data. Ni-acetate Avena sativa EC10y(g) Ni-acetate Avena sativa EC10y(g) Ni-acetate Avena sativa EC10y(g) Ni-acetate Avena sativa EC10y(g) Ni-acetate Avena sativa EC10y(g) De Haan et al., 1985 [8] The EC10 values are not reported values but calcualted from original data. NiCl2 Zea mays Giza alluvial loam EC NiCl2 Zea mays Nobaria sandy loam EC Metwally & Rabie, 1989 [9] The EC10 values are not reported values but calcualted from original data. *EC10 below lowest tested (no zeo control); the impact of this value is used in sensitivity analysis ** If the CEC was missing from a test with plants/invertebrates/micro-organisms, then it was estimated from % clay, ph and %organic matter using an experimentally derived regression model: CEC=( ph)*clay/100+( ph)*om/100; the clay is the % clay in the soil (Helling et al., 1964; regression based on CEC measured at various ph values on 60 different soils; CEC refers to the soil ph). Note: Values selected for the effects assessment are underlined. NOEC/EC10 indices: m = mortality; y = yield (based on root (r), shoot (s), leaves (l), grain (g), tops (t), tubers (tub) or total plant (tp) dry weight); rl = root length. 173

188 Table : Reported higher plant studies NOT used in the effects assessment Author Soil Group Main Exclusion Factor Aggangan et al 1998 Yellow Sand Eucalyptus europhylla Ni dose ambiguous. Reported as mmole /kg in methods but as μmole /kg in results. Ahonen-Jonnarth & Finlay 2001 B Horizon Sand Pinus sylvestris Ni added in nutrient solution Ahonen-Jonnarth et al unspecified Pinus sylvestrus Ni dose failed to produce a bound toxic threshold Allinson & Dzialo 1981 Paxton fine sandy loam Avena sativa Single dose level (with no sig response) Allinson & Dzialo 1981 Paxton fine sandy loam Lolium hybridum Single dose level (with no sig response) Atta-Aly 1999 Nile Delta Clay Petroselinum crispus Topical Ni application (& No negative yield response) Berry & Wallace 1989 Lactuca sativa Nutrient culture Bingham et al 1979 Redding fine sandy loam Plant Ni added in sewage sludge Burd et al 1998 Pro-mix BT Brassica campestnis Topical Ni application in nutrient solution only Dahiya et al 1994 Sandy (Typic Torripsamments) Triticum aestivum Unbounded NOEC (no significant effect at 20mg/kg only non-n amended soil considered) Dixon, 1988 Sandy loam Quercus ruba CEC was not reported & could not be estimated (no clay content) Elmosly & Abdel-Sabour 1997 Sandy Loam Plant No negative yield response Elmosly & Abdel-Sabour 1997 Calc. Silt Loam Plant No negative yield response Elmosly & Abdel-Sabour 1997 Sandy Loam Plant No negative yield response Gorlach & Gambus 1992 Brown soil Avena sativa Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Cannabis sativa Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Zea mays Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Raphanus sativus Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Trifolium pratense Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Triticum aestivum Metals (Cd,Cu,Ni,Pb,Zn) added in combination Gorlach & Gambus 1992 Brown soil Helioanthus annuus Metals (Cd,Cu,Ni,Pb,Zn) added in combination Root et al 2000 Loamy Sand Echinochloa colona No Ni application, Field contaminated soil (multiple metals) Halstead et al., 1969 Granby sandy loam + P + L Avena sativa Halstead et al., 1969 Granby sandy loam Medicago sativa Halstead et al., 1969 Granby sandy loam + P Medicago sativa CEC (61.7 meq/100 g) of the soil is above the P90 value for EU soils and is outside the boundaries of the regression model CEC (61.7 meq/100 g) of the soil is above the P90 value for EU soils and is outside the boundaries of the regression model CEC (61.7 meq/100 g) of the soil is above the P90 value for EU soils and is outside the boundaries of the regression model 174

189 Author Soil Group Main Exclusion Factor Halstead et al., 1969 Granby sandy loam + L Medicago sativa CEC (61.7 meq/100 g) of the soil is above the P90 value for EU soils and is outside the boundaries of the regression model Halstead et al., 1969 Granby sandy loam + P + L Medicago sativa CEC (61.7 meq/100 g) of the soil is above the P90 value for EU soils and is outside the boundaries of the regression model CEC (61.7 meq/100 g) of the soil is outside the boundaries of the regression model Halstead, Finn & MacLean 1969 Uplands sand 3 Medicago sativa Unbounded LOEC (40% effect) Halstead, Finn & MacLean 1969 Grenville Sandy Loam Avena sativa Unbounded NOEC >500 Halstead, Finn & MacLean 1969 Granby sandy loam Avena sativa Unbounded NOEC >500 Heale & Ormrod 1983 Silica sand Acer ginnala Ni added in nutrient solution (topical application) Heale & Ormrod 1983 Silica sand Betula papyrifera Ni added in nutrient solution (topical application) Heale & Ormrod 1983 Silica sand Picea abies Ni added in nutrient solution (topical application) Heale & Ormrod 1983 Silica sand Pinus banksiana Ni added in nutrient solution (topical application) Hunter & Vergnano 1953 Quartz Sand Avena sativa Topical Ni application in nutrient solution only Khalid & Tinsley, 1980 Seaton loam Lolium perenne CEC was not reported & could not be estimated (no clay/om content) Kukkola et al 2000 Vaccinium type, mineral forest soil. Pinus sylvestris Unbounded NOEC for yield, defined NOEC only for root:shoot ratio. Leon et al None (hydroponic exposure) revilla exul Hydroponic exposure Liang & Schoenau 1995 Heavy (45% clay) Avena sativa Unbounded NOEC >160 Liang & Schoenau 1995 Light (9% clay) Lactuca sativa LOEC>20% effect MacLean & Dekker, 1978 Grenville loam Zea mays LOEC>20% effect MacLean & Dekker, 1978 Grenville loam + P Zea mays LOEC>20% effect Metwally & Rabbie 1989 Giza Alluvial Loam Vivacia faba No significant dose response Metwally & Rabbie 1989 Nobaria Sandy Loam calcareous Vivacia faba No significant dose response Molas and Bahren 2004 Medium clay soil Hordeum vulgare Ni dose failed to produce a bound toxic threshold Molas and Bahren 2004 Heavy clay soil Hordeum vulgare Only one nickel dose tested Murch et al None (hydroponic exposure) Hypericum perfornatum Hydroponic exposure Nieminen 1998 Quartz Sand Pinus sylvestrus Little information given: dose in mg/container, no sand mass stated. Nieminen 2004 Quartz sand Pinus sylvestrus ph not reported for each treatment (only range prrovided) Palacios et al 1999 Calcareous regosol Lycopersicon esculentum Ni added to sewage sludge then mixed into soil Pandy and Sharma 2002 Quartz sand Brassica oleracea Ni dose failed to produce a bound toxic threshold Papazoglou et al Uncultivated surface soil Arundo donax Ni dose failed to produce a bound toxic threshold; Discontinuous exposure Parida et al Sandy loam Trigonella corniculata Soil Ni concentration not measured; No texture indication provided; Some exposure details missing, i.e., temp., photoperiod 175

190 Author Soil Group Main Exclusion Factor Patterson & Olson 1983 Typic Borohemist Betula papyrifera Unbounded LOEC (60% effect) Patterson & Olson 1983 Typic Borohemist Pinus banksiana Unbounded LOEC (44% effect) Patterson & Olson 1983 Typic Borohemist Picea glauca No soil information. Considered unreliable Patterson & Olson 1983 Typic Dystrochrept Picea mariana No soil information. Considered unreliable Patterson & Olson 1983 Typic Borohemist Pinus resinosa No soil information. Considered unreliable Patterson & Olson 1983 Typic Dystrochrept Pinus resinosa No soil information. Considered unreliable Patterson & Olson 1983 Typic Borohemist Pinus strobus No soil information. Considered unreliable Filter paper test Porch & Kruegger 1999 Sandy Loam Lolium perenne unbounded NOEC (>100 mg/kg) Porch & Kruegger 1999 Sandy Loam Vigna radiata No significant dose response (>100 mg/kg) Porch & Kruegger 1999 Sandy Loam Raphanus sativus NOEC=10 and LOEC=100: 10 fold difference between NOEC and LOEC Poulik 1999 Orthic luvisol Lactuca sativa No negative yield response Poulik 1999 Orthic luvisol Lycopersicon esculentum No negative yield response Poulik, 1997 Sandy loam Avena sativa CEC was not reported & could not be estimated (no clay/om content) Prokipcak & Ormrod 1986 Silica sand Lycopersicon esculentum Ni added in nutrient solution (topical application) Prokipcak & Ormrod 1986 Silica sand Glycine max Ni added in nutrient solution (topical application) Rehab & Wallace 1978 Yolo Loam Gossypium barbadense Unbounded LOEC (46% effect) Rehab & Wallace 1978 Yolo Loam Gossypium hirsutum Unbounded LOEC (44% effect) Rehab & Wallace 1978 Yolo Loam Gossypium hirsutum Unbounded LOEC (59% effect) Rehab & Wallace, 1978 Yolo loam Gossypium barbadense LOEC>20% effect Roth et al 1971 Venice Peaty Muck Avena sativa Muck soil 33% OC Roth et al 1971 Venice Peaty Muck Glycine max Muck soil 33% OC Sadiq soils A-P Zea mays Single dose level, no yield data Sajwan, Ornes & Youngblood 1996 Orangeburg loamy sand Phaseolus vulgaris No sig effect (p>0.05) at top conc. 7.5mg/kg Sauerbeck & Hein 1991 Luvisol Plant Plant tissue concentration stated only. No toxicity data given. Sauerbeck & Hein 1991 Cambisol Plant Plant tissue concentration stated only. No toxicity data given. Simon et al., 1998 Ruzyne Raphanus sativus CEC was not reported & could not be estimated (no clay/om content) Simon et al., 1998 Lukavec Raphanus sativus CEC was not reported & could not be estimated (no clay/om content) Simon et al., 2000 Clay loam Raphanus sativus CEC was not reported & could not be estimated (no clay/om content); no details about mixing 176

191 Author Soil Group Main Exclusion Factor Simon et al., 2000 Sandy loam Raphanus sativus CEC was not reported & could not be estimated (no clay/om content); no detaisl about mixing Singh & Jeng 1993 Sandy Lolium perenne No significant dose response Singh et al None (hydroponic exposure) Pisum sativum Hydroponic exposure Sorteberg 1978 Clay Avena sativa Little information on soils, Ni doses not specified Sorteberg 1978 Peat Avena sativa Little information on soils, Ni doses not specified Sorteberg 1978 Sandy Avena sativa Little information on soils, Ni doses not specified Steyn et al 1996 unspecified Lactuca sativa Ni source was sewage sludge Steyn et al 1996 unspecified Phaseolus vulgaris Ni source was sewage sludge Steyn et al 1996 unspecified Triticum aestivam Ni source was sewage sludge Symeonidis et al 1985 Solution culture Agrostis capillaris Solution culture experiment Taylor & Allinson 1981 Paxton Medicago sativa Ni added in nutrient solution (topical application), not mixed Taylor & Allinson 1981 Merrimac Medicago sativa Ni added in nutrient solution (topical application), not mixed Vergnano & Hunter 1952 Quartz Sand Avena sativa Topical Ni application in nutrient solution only and no Yield data Vesper & Weidensaul 1978 Sand Glycine max Ni added as a soil soaking, not homogenised Wallace & Berry 1989 Solution culture Lactuca sativa Solution culture experiment Wallace et al Yolo Loam Glycine max Unbounded LOEC (32% effect) Wallace et al Yolo Loam Glycine max Unbounded LOEC (>30% effect) Wallace et al Yolo Loam Hordeum vulgare Unbounded LOEC (71% effect) Wallace et al Yolo Loam Phaseolus vulgaris Unbounded LOEC (64% effect) Wallace et al Yolo Loam Phaseolus vulgaris No sig. Dose response Wallace et al Yolo Loam Phaseolus vulgaris No sig. Dose response Wallace et al Yolo Loam Zea mays Unbounded NOEC Wallace et al., 1977 Yolo loam (ph 7.2) Glycine max CEC was not reported & could not be estimated (no clay/om content) Wallace et al., 1977 Yolo loam (ph 5.8) Phaseolus vulgaris CEC was not reported & could not be estimated (no clay/om content) Wallace et al., 1977 Yolo loam (ph 7.5) Phaseolus vulgaris CEC was not reported & could not be estimated (no clay/om content) Wallace et al., 1977 Yolo loam (ph 5.6) Zea mays CEC was not reported & could not be estimated (no clay/om content) Wallace et al., 1977 Yolo loam (ph 4.2) Zea mays CEC was not reported & could not be estimated (no clay/om content) Wallace et al., 1977 Yolo loam (ph 7.5) Zea mays CEC was not reported & could not be estimated (no clay/om content) Wang et al None (hydroponic exposure) Oryza sativa Hydroponic exposure 177

192 Author Soil Group Main Exclusion Factor Webber et al 1972 unspecified Avena sativa Ni added to sewage sludge Webber et al 1972 Silt loam Avena sativa very limited experimental details Webber et al 1972 Silt loam Mustard very limited experimental details Willaert & Verloo 1988 Sandy Loam Spinicia oleracea No significant dose response 178

193 Footnote: toxicity of nickel to higher plants [1] Rothamsted research (2005) (this research is part of the Conclusion (i) research project on the development of a predictive model of bioavailability and toxicity of nickel in soils ). 16 different European soils were amended with NiCl2 to obtain a range of 6 concentrations in a geometric series by spraying appropriate volumes of a NiCl2 solution. Two plant toxicity tests were performed: Hordeum vulgare (winter barley): pre-germinated barley is grown in 3 replicate pots (35 mm diameter with a soil depth of 100 mm) of each soil treatment for 4 days, after which the length of the longest root is measured The toxicity test follows the ISO method for measuring the inhibition of root growth. The number of barley seeds is 3 per pot. The soil moisture content is maintained at pf1.9. Statistics are specified, p < 0.05 (roots). Dose response results were fitted to a log-logistic model to derive threshold values. Lycopersicon esculentum (tomato): tomato plants are grown from seed in 3 replicate pots of each soil treatment for 21 days following emergence, after which the shoot dry matter yield is determined The toxicity test follows the ISO method for measuring the inhibition on emergence and growth of higher plants. The soil moisture content is maintained at pf1.9. Statistics are specified, p < 0.05 (roots). Dose response results were fitted to a log-logistic model to derive threshold values. The characteristics of the soils used in this research program are presented hereunder: Ni (backgr) ph Alox Feox Mnox Org C CEC at phsoil Silt Clay (mg/kg dry wt) (0.01M CaCl2) (mg/kg) (mg/kg) (mg/kg) (%) (cmol/kg) (%) (%) The Ni concentrations in the soils were determined by ICP-AES. All tests retained in this assessment show a clear dose response curve. Exposure parameter: root growth (barley), shoot yield (tomato) Equilibration time: 7 days Exposure time: 4 days (barley), 21 days (tomato) Study results (mg Ni/kg dw): added NOEC values between 31 and 1101 mg/kg for Hordeum vulgare; between 10 and 599 mg/kg for Lycopersicon esculentum. Total NOEC = added + background Ni 179

194 [2] Willaert, G. and Verloo, M. (1988). 3 different soil types sandy, sandy loam, and heavy clay were amended with NiSO 4 to obtain applied doses of 10, 20 and 40 ppm for the sandy soils, and 50, 100 and 200 ppm for the heavy clay soil. A pot experiment was conducted using Spinicia oleracea (spinach) as a test plant. One kilogram of air dried soil was mixed with NiSO4 in 3 replicate plastic pots and allowed to equilibrate for 10 days at field water capacity. Spinach was sown at a rate of 15 seeds per pot. Moisture content was maintained at 70% of the water holding capacity, and a 16-h day light cycle was used. Spinach plants were harvested 30 days after sowing, dried and weighed. Statistical analyses for plant yield, expressed as dry matter, were not specified. No significant dose response reported. Insufficient dose levels for calculation of effects concentrations. The characteristics of the soils used in this study are: Soil type ph C Background Ni CEC % mg/kg dw meq/100 g Sandy Sandy loam Heavy clay The Ni concentrations in the soils were determined by atomic absorption spectroscopy. Exposure parameter: shoot yield Equilibration time: 10 days Exposure time: 30 day Study results (mg Ni/kg dw): added NOEC values of 10 mg/kg for sandy soil; 100 mg/kg for the heavy clay soil. Total NOEC = added + background Ni [3] Halstead, R. L., Finn, B. J. and MacLean, A. J. (1969). Four soil types Grenville sandy loam, Granby sandy loam, and Uplands sand of varying ph were used to assess the effect of added NiCl2 on plant yield in pot tests. Oats (Avena sativa) and alfalfa (Medicago sativa) were used as test plants. (NiCl 2 ) was mixed with 2268 grams of air-dry soil in plastic pots at rates of 0, 20, 50, 100, and 500 Ni. After 1 month of incubation, treatments of lime (L), phosphorous (P), and lime + phosphorous (L+P) were added to the soils. Phosphorous was added at a rate of 500 ppm, and lime was added at a rate of 3750, 1000, or 3000 ppm. Soils were kept moist at field capacity for another month, after which 500 ppm of a fertilizer was mixed with the soil in all pots prior to seeding oats. The treatments were randomized and replicated four times. Oat grains, tops, and roots were harvested after 110 days. Then alfalfa was seeded and alfalfa tops and roots were harvested after 83 days. The soil ph was determined by glass electrode, and phosphorous was determined by the NaHCO3 method. Statistical analyses for plant yield were not specified. 180

195 Soil characteristics: Property Greenville Sandy Loam Granby Sandy Loam Uplands Sand (1) Uplands Sand (2) ph Total N (%) Organic matter (%) CEC (meq/100 g) Base saturation (%) The Ni concentrations in the soils were determined by atomic absorption spectrometry. Exposure parameter: grain yield (oats), top yield (alfalfa), straw yield (oats) Equilibration time: 60 days Exposure time: 83 days (alfalfa), 110 days (oats) Study results (mg Ni/kg dw): The derived no-observed-effect concentrations (NOECs) were based on the dose below that causing yield below 2 standard deviations of the control (Oliver and McLaughlin 2003). Effects concentrations (ECx) are not reported values, but were extrapolated from original data (Oliver and McLaughlin 2003). For Avena sativa the added NOEC/EC10 values varied between 43 and 453 mg/kg; for Medicago sativa between 34 and 383 mg/kg. Total EC10 = added + background Ni (Oliver and McLaughlin 2003) [4] Liang, J. and Schoenau, J. J. (1995). Two soil types low clay and high clay content were used in a study to assess the bioavailability of Ni in a plant growth study. Oats (Avena sativa), radishes (Raphanus sativus), and lettuce (Lactuca sativa) were used as test plants. Air-dried soils were spiked with a nickel solution (applied as NiSO 4 ) to achieve concentrations of 0, 40, 80, and 160 mg Ni/kg, air-dried again and thoroughly mixed. Prior to treatment, the soils were watered to field capacity and incubated for two months. Macro- and micro-nutrients were added to each pot. Each of 3 replicate pots contained 500 g of Ni-spiked soil. All pots were watered daily to keep the soils at 85-90% of field capacity and maintained at 25 C during the day and 12 C at night. Oats (Avena sativa): 8 seeds were added to each of 3 replicate pots, thinned to 4 seedlings, and allowed to mature. At harvest, oats were cut 5 mm above soil surface and the grain and straw were separated and dried. Radishes (Raphanus sativus): 5 seeds were added to each of 3 replicate pots, thinned to 3 seedlings, and allowed to grow for 30 days. At harvest, plants were separated by leaves and edible bulbs, and the roots discarded. Lettuce (Lactuca sativa): seeds were added to each of 3 replicate pots, thinned to 1 seedling, and allowed to grow for 40 days. At harvest, leaves were cut above soil surface and washed. Replicated treatments were evaluated using analysis of variance. Means were compared using Fisher s PLSD test (p = 0.05 and 0.01). Soil characteristics: Soil ph Organic matter Clay CEC Ni Cd Cr Pb % % cmol/kg mg/kg mg/kg mg/kg mg/kg Light (9% clay) Heavy (45% clay) The Ni concentrations in soils and plant material were determined by atomic adsorption spectrometry. Exposure parameter: dry matter yield Equilibration time: 60 days 181

196 Exposure time: 30 days (radish), 40 days (lettuce), to maturity (oats) Study results: Effects concentrations (ECx) were not calculated; only 3 dose levels were evaluated. For Avena sativa the added NOEC was 80 mg/kg; for Raphanus sativus 80 mg/kg and for Lactuca sativa 40 mg/kg. Total NOEC = added + background Ni (Oliver and McLaughlin 2003) [5] Dang, Y. P., Chabra, R. and Verma, K. S. (1990). A clay-loam soil was used in a greenhouse pot study to evaluate the effects of added Ni (amended as NiSO4) on plant yield. Onion (Allium cepa) and fenugreek (Trigonella poenumgraccum) were used as test plants. Air-dried soil (4 kg) was added to earthen pots lined with polyethylene. (NiSO4) was applied at a rate of 0, 50, 100, 200, or 400 mg/kg and replicated three times in a randomized block design. Initial doses of N, P and K were 60, 50, and 60 mg/kg soil, respectively. After 20 days an additional 25 mg/kg N was added. Allium cepa (onion): pre-germinated seedlings were added to the treated soils after 1 week of equilibration and grown to maturity. Fresh and dry matter yield were determined. Statistics not specified, p = 0.05 and Fenugreek (Trigonella poenum-graccum): seeds were sown directly into pots after equilibration, thinned to 4 plants one week after emergence and grown for 8 weeks. Fresh and dry matter yield were determined. Statistics not specified, p = 0.05 and Soil characteristics: Organic C Sand Silt P Zn (mg/kg) ph (%) CEC (%) (%) Clay (%) EC (mg/kg) concentrations were analyzed using atomic absorption spectrophotometer. Exposure parameter: fresh and dry matter yield Equilibration time: 7 days Exposure time: 56 days Study results (mg Ni/kg dw): The effect concentrations (ECx) were derived from original data (Oliver and McLaughlin 2003). The no-observed-effect concentrations (NOECs) were calculated and reported in Oliver and McLaughlin (2003). An added EC10 of 46 mg/kg was observed for Allium cepa and 84 mg/kg for Trigonella poenum. [6] Frossard, R., Stadelmann, F. X. and Niederhauser, J. (1989). A sandy loam soil was used in a pot study to evaluate the effects of Ni on the fructan, sugar and starch content of ryegrass. Lolium perenne (perennial ryegrass): Ryegrass was grown in 7.6 kilograms (dry weight) of sandy loam soil in plastic pots. Fertilization of the soil (200 mg N, 97 mg P, 300 mg K, 48 mg Mg, 10 mg Na2B4O7-H20) was conducted after each of three harvests. NiSO4 solutions were added 4 14 days before seeds were sown (0.5 g/pot). The NiSO 4 applied doses were 0, 5, 50, 100, and 250 mg/kg. Pots were kept outdoors during rainy weather and moved into the greenhouse at night. Pots were irrigated with demineralized water. The daily mean temperature for the experiments was 16.6 C. The first harvest was made 6-8 weeks after sowing and successive harvests were made every 4 weeks. Shoots were collected 4-6 cm above the soil and dry matter yield was evaluated. Dry weights were evaluated using analysis of variance and Duncan s multiple range test (p < 0.05). Soil characteristics: 182

197 Organic C Cd (mg/kg Soil ph (%) CEC dw) Ni (mg/kg dw) Cu (mg/kg dw) Zn (mg/kg dw) Sandy loam concentrations were analyzed using flame atomic absorption spectrophotometry. Exposure parameter: dry matter yield Equilibration time: 4-14 days Exposure time: days Study results (mg Ni/kg dw): Effect concentrations (ECx) were derived and reported in Oliver and McLaughlin (2003). An added EC10 value of 110 mg/kg was observed. Total NOEC/ECx = added + background Ni (Oliver and McLaughlin 2003) [7] Gupta, S. K., Hani, H., Santschi, E. and Stadelmann, F. X. (1987). Four different soil types Steinhof, Gänsemos, Erlach, and Gasel were amended with increasing doses of NiSO 4 to obtain applied doses ranging from 29 to 503 mg/kg. A greenhouse pot experiment was conducted using Lactuca sativa (lettuce) as a test plant. Dry matter plant yield was evaluated after 63 days. Statistical analyses for plant yield were not specified Soil characteristics: Soil ph Background Ni CEC mg/kg dw mmol/kg Steinhof Gänsemos Erlach Gasel Analysis of nickel concentrations was not described. Exposure parameter: leaf yield Equilibration time: not described Exposure time: 63 days Study results (mg Ni/kg dw): Added EC10 values between 18 and 422 mg/kg were found for Lactuca sativa.total NOEC/ECx = added + background Ni (Oliver and McLaughlin 2003) The NOEC was calculated from pooled results of three exposure times, and is expressed as the highest dose causing < 10% effect (Oliver and McLaughlin 2003). The ECx values were not reported, but were extrapolated from original data and may extend beyond the data range (Oliver and McLaughlin 2003) [8] de Haan, S., Rethfeld, H. and van Driel, W. (1985). Six different soil types 3 loamy (C1, c2, and c3) and 3 sandy (s1, s2, and s3) were used in a greenhouse pot study to determine the loading rate of Ni and its effect on crop yield and quality. Oats (Avena sativa) was used as the test plant. Ni (as nickel acetate) was applied to the soils to obtain doses of 0, 6.25, 12.5, 25, 50, and 100 mg Ni/kg. All soils contained no lime and were slightly acidic. Avena sativa (oats): Potted soils were fertilized (1.2 g N/pot, 1.2 g P/pot, 1.2 g K/pot, and 1 g Mg/pot). Later, soils c2, s1, s2, and s3 received an additional 183

198 application of 0.3 g N/pot and oat seeds were planted at a rate of 36 seeds/pot, with 3 replicates. After 2 weeks the seedlings were thinned to 24 plants per pot. Grain and straw yields were evaluated at the end of the experiment. The Student-Newman-Keuls multiple range test was used to evaluate significant differences (p = 0.05). Soil characteristics: Soils Loamy soils Sandy soils Properties C1 C2 C3 S1 S2 S3 ph KCl Clay (%) Organic matter (%) CEC (meq/100 g DM) Cd (mg/kg) Cr (mg/kg) Cu (mg/kg) Ni (mg/kg) Pb (mg/kg) Zn (mg/kg) concentrations were analyzed by X-ray spectrometric analysis. Exposure parameter: grain yield Equilibration time: not described Exposure time: 150 days Study results (mg Ni/kg dw): Added EC10 values between 16 and 66 mg/kg were found for Avena sativa. The ECx values were not reported, but were extrapolated from original data and may extend beyond the data range (Oliver and McLaughlin 2003). Total NOEC/ECx = added + background Ni. Ni was phytotoxic at 100 mg/kg, and reductions in grain and straw yields were greatest in oats grown in sandy soils. [9] Metwally, A. I. and Rabie, M. H. (1989). Two different soil types Giza alluvial loam and Nobaria sandy loam - were used in a greenhouse pot study to assess the essentiality of Ni on plant growth and uptake. Corn (Zea mays) and faba beans (Vicia faba) were used as test plants. Air-dried, sieved soils were amended with nickel (NiCl 2 ) to obtain applied doses of 0, 10, 20, 30, 40, or 50 mg/kg for faba beans, and 0, 40, 80, 120, or 200 mg/kg for corn. Faba beans (Vicia faba): One kg of soil was mixed with 100 g of washed sand and placed in plastic pots. Each pot was fertilized with 15 mg N, 30 mg P 2 O 5, and 24 mg K 2 O. Three replicate pots containing 5 bean seeds were arranged in a complete block design. Pots were watered daily with deionized water to maintain soil moisture at 60% of the water holding capacity. After germination, seedlings were thinned to 3 plants. The plants were harvested (1 cm above soil) 50 days after planting. Dry matter plant yield was determined at the end of the study. Statistical significance was evaluated using a least significant difference method (p < 0.05). 184

199 Corn (Zea mays). Five kg of soil was placed in plastic pots and mixed with nutrients (90 ppm N, 30 ppm P, and 24 ppm K). Three replicate pots containing 5 corn seeds were arranged in a complete block design. Plants were thinned to 3 plants one week after germination. Pots were watered daily with deionized water to maintain soil moisture at 60% of the water holding capacity. Plants were harvested 45 days after seeding. Dry matter plant yield was determined at the end of the study. Statistical significance was evaluated using a least significant difference method (p < 0.05). Soil characteristics: Medium ph CaCo3 OM OC clay silt sand Background Ni CEC % % % % % % mg/kg dw cmol/kg Giza alluvial loam Nobaria sandy loam concentrations were determined colorimetrically using the dimethyleglyoxime method. Exposure parameter: above ground yield Equilibration time: not described Exposure time: days Study results (mg Ni/kg dw): added EC10 values of 19 and 119 mg/kg were observed for Zea mays. No significant dose response for faba beans (Vicia faba) was found for Ni additions of ppm in both soil types. The ECx values for corn (Zea mays) were not reported, but were extrapolated from original data (Oliver and McLaughlin 2003) and reported below. Total NOEC/ECx = added + background Ni Toxicity to soil invertebrates Data on chronic single-species toxicity tests resulting in NOEC/L(E)C 10 values for soil invertebrates are summarised in Table Values selected for the effects assessment are underlined. In the total risk approach 37 individual NOECs (for 6 different species) are selected ranging from 36.4 mg/kg for Folsomia candida reproduction to 1,140 mg/kg for Eisenia fetida reproduction (University of Ghent, 2005). Table gives an overview of the rejected data and the reason(s) why they were rejected. An EC10 value was calculated for mortality of Lumbriculus rubellus from data provided in Ma (1982), who provided mortality data in tabular form. 185

200 Table Overview of the accepted EC10/NOEC values for soil invertebrates (estimated background nickel concentrations and CEC** are indicated in italics). Values selected for the effects assessment are underlined. EC10/NOEC indices: m: mortality, r: reproduction Added NOEC Total NOEC Test substance Organism Medium ph OC clay Cb CEC Equil. Period Durat. Endpoint NOEC or EC10 Unbound ed NOEC NOEC or EC10 Unbounded NOEC % % Mg/kgdw cmol/k g d d mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Folsomia candida Loamy sand EC10r Houthalen NiCl2 Folsomia candida Sandy clay loam Zegveld EC10r NiCl2 Folsomia candida Loamy sand EC10r Montpellier NiCl2 Folsomia candida Loamy sand EC10r Rhydtalog NiCl2 Folsomia candida Jyndevad EC10r NiCl2 Folsomia candida Sandy loam EC10r 20.2* 20.9* Kovlinge II NiCl2 Folsomia candida Clay Aluminosa EC10r NiCl2 Folsomia candida Borris EC10r NiCl2 Folsomia candida Sandy clay loam EC10r Woburn NiCl2 Folsomia candida Silt loam EC10r Ter Munck NiCl2 Folsomia candida Clay Souli EC10r NiCl2 Folsomia candida Silt loam EC10r Marknesse NiCl2 Folsomia candida Clay NOECr Brecy NiCl2 Folsomia candida Cordoba EC10r NiCl2 Folsomia candida Cordoba EC10r NiCl2 Folsomia candida Loam Guadalajara EC10r NiCl2 Eisenia fetida Loamy sand EC10r Houthalen NiCl2 Eisenia fetida Sandy clay loam Zegveld EC10r

201 Added NOEC Total NOEC Test substance Organism Medium ph OC clay Cb CEC Equil. Period Durat. Endpoint NOEC or EC10 Unbound ed NOEC NOEC or EC10 Unbounded NOEC % % Mg/kgdw cmol/k g d d mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Eisenia fetida Loamy sand EC10r Montpellier NiCl2 Eisenia fetida Loamy sand EC10r Rhydtalog NiCl2 Eisenia fetida Jyndevad EC10r NiCl2 Eisenia fetida Sandy loam EC10r Kovlinge II NiCl2 Eisenia fetida Clay Aluminosa EC10r NiCl2 Eisenia fetida Borris EC10r NiCl2 Eisenia fetida Sandy clay loam EC10r Woburn NiCl2 Eisenia fetida Silt loam EC10r Ter Munck NiCl2 Eisenia fetida Clay Souli NOEC NiCl2 Eisenia fetida Silt loam EC10r Marknesse NiCl2 Eisenia fetida Clay EC10r Brecy NiCl2 Eisenia fetida Cordoba NOEC NiCl2 Eisenia fetida Cordoba EC10r NiCl2 Eisenia fetida Loam Guadalajara EC10r University of Ghent & Euras, 2005 [1] NiCl2 Eisenia fetida OECD NOECr NiCl2 Enchytraeus albidus OECD NOECr NiCl2 Folsomia candida OECD NOECr Lock & Janssen, 2002 [2] NiCl2 Eisenia veneta Loamy sand EC10r (LUFA 2.2) Scott-Fordsmand et al., 1998 [3] NiCl2 Folsomia fimetaria Loamy sand EC10r (LUFA 2.2) 187

202 Test substance Added NOEC Total NOEC Organism Medium ph OC clay Cb CEC Equil. Durat. Endpoint NOEC or Unbound NOEC or Unbounded Period EC10 ed NOEC EC10 NOEC % % Mg/kgdw cmol/k d d mg/kgdw mg/kgdw mg/kgdw mg/kgdw g Scott-Fordsmand et al., 1999 [4] NiCl2 Lumbricus rubellus Sandy loam EC10m Ma, 1982 [5] *EC10 below lowest tested (no zeo control); the impact of this value is used in sensitivity analysis **If the CEC was missing from a test with plants/invertebrates/micro-organisms, then it was estimated from % clay, ph and %organic matter using an experimentally derived regression model: CEC=( ph)*clay/100+( ph)*om/100; the clay is the % clay in the soil (Helling et al., 1964; regression based on CEC measured at various ph values on 60 different soils; CEC refers to the soil ph). 188

203 Table Reported higher invertebrate studies NOT used in the effects assessment Author Soil Group Main Exclusion Factor Boyd and Williams 2003 ASTM; High (74) Sand; High (98) Sand; Nematode (C. elegans) Data not available to determine NOEC/LC10 (only LC50s reported); Short exposure period (24 h); Acute lethality was only endpoint Brown 2004 None (agar gel matrix) Nematode (C. elegans) Hydroponic exposure Hartenstein et al 1980 Activated sludge Invertebrate (earthworm) Activated sludge, not soil medium Hartenstein et al 1981 Silt loam Earthworm growth (Eisenia foetida) Ni added to sewage sludge before being introduced to worms Korthals et al 1996 Sandy Nematode (Acrobeles) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Acrobeloides) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Alaimus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Aphelenchoides) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Aporcelaimellus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Clarkus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Eucephalobus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Filenchus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Plectus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Pratylenchus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Prismatolaimus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Pseudhalenchus) EC50 stated only, thus no NOEC Korthals et al 1996 Sandy Nematode (Rhabditidae) EC50 stated only, thus no NOEC But this study could be used as support evidence Korthals et al 1996 Sandy Nematode (Tylenchorhynchus) EC50 stated only, thus no NOEC Malecki, Neuhauser & Loehr 1982 Earthworm growth & reproduction (Eisenia fetida) Ni added to manure and then introduced to worms as food (8wk and 20wk studies) Neuhauser et al 1985 Artificial (EEC 1982) Earthworm growth & reproduction (Eisenia fetida) Ni doses not specified, only LC 50 value cited Neuhauser, Malecki & Loehr 1984 Earthworm growth & reproduction (Eisenia fetida) Ni added to manure and then introduced to worms as food. Peredney & Williams 2000 ASTM artificial soil Invertebrate (nematode?) LC50 stated only 189

204 Footnote: toxicity of nickel to terrestrial invertebrates [1] University of Ghent, (this research is part of the Conclusion (i) research project on the development of a predictive model of bioavailbility and toxicity of nickel in soils ). 16 different European soils were amended with NiCl 2 to obtain a range of 4 concentrations per order of magnitude in a geometric series. Folsomia candida: tests were performed using 4 replicates for each treatment with 10 synchronised juvenile organisms (age between 10 and 12 days). The tests are conducted at a temperature of 20 ± 1 C in pots containing 30 g soil. Soil moisture was kept constant through addition of distilled water. At test initiation, the organisms were fed with 2 mg of yeast. After 28 days of exposure, the total produced and dead juveniles are counted. A detailed description of the test is given in the ISO protocol Eisenia fetida: Tests were performed using 4 replicates per soil treatment. The moisture content of the soil is adjusted to % of the maximum WHC by the addition of distilled water. Adult worms between two months and one year old and with a clitellum were used to start the test. A loading of 10 earthworms in 750 g dry mass of soil is used. The test temperature is 20 ± 2 C. After 28 of incubation, the living adult worms are observed and counted the end of the test, the number of juveniles produced over the 8-week test period is determined. The characteristics of the soils selected for this research are mentioned in Section Statistics are specified, p < 0.05 (roots). Dose response results were fitted to a log-logistic model to derive threshold values. The Ni concentrations in the soils were determined by AAS. All tests retained in this assessment show a clear dose response curve. Equilibration time: 7 Exposure time: 28 days Study results (mg Ni/kg dw): EC10/NOEC concentrations between 46.5 and 1110 mg/kg for Eisenia fetida, and between 20.2 and 1100 mg/kg for Folsomia candida. [2] Lock, K. and Janssen, C. R. (2002). Artificial soils (OECD Guideline 207) were amended with a logarithmic series of four concentrations of Ni solution (as NiCl 2 ) per order of magnitude to obtain concentrations of 100, 180, 320, 560, 1000 mg Ni/kg dry wt. Enchytraeus albidus: six-week chronic tests were carried out according to OECD Guideline 220 (1999). Ten adult worms with fully developed clitellum were added to 20 g of wet soil. Tests were conducted at a moisture content of 55% of the water holding capacity. Test vessels were kept at C and a light:dark cycle of 16:8 hours. Soil moisture content was adjusted twice a week to replenish weight loss. Rolled oats were placed on the soil surface weekly as a food source. After three weeks exposure, the adults were removed; juveniles were counted after another three weeks exposure. Folsomia candida: reproduction tests were performed using 10 synchronised juvenile organisms (age between 10 and 12 days). The test followed the protocol of ISO protocol (1994).The tests are conducted at a temperature of 20 ± 1 C and constant illumination in pots containing 30 g soil. Soil moisture was kept constant through addition of distilled water. At test initiation, the organisms were fed with dry yeast. The moisture content of the soil was adjusted to 55% of the maximum water holding capacity by the addition of distilled water. After four weeks of exposure, the total number of juveniles was counted. Eisenia fetida: reproduction tests were conducted using adult worms between two months and one year old and with fully developed clitellum. At test initiation, 10 earthworms were placed in 750 g dry mass of soil, and finely ground cow dung (2% dry weight) was supplied as a food source. The moisture content of the soil was adjusted to 55% of the maximum water holding capacity by the addition of distilled 190

205 water. Test vessels were maintained at 20 ± 1 C and a light:dark cycle of 16:8 hours After three weeks of incubation, the number of cocoons was determined. Effects concentrations (EC50s) were calculated using the probit method, and lethal concentrations (LC50s) were calculated using the moving average method (p < 0.05). The Kruskal-Wallis ANOVA followed by posthoc multiple comparisons were used to determine no-observable-effects concentrations (NOECs). Control mortality was < 10%. No mortality occurred at 1000 mg/kg dw for E. fetida, and mortality of F. candida was < 10% at this dose level. Soil characteristics: Medium ph sand clay peat Background Ni CEC % % % mg/kg dw cmol/kg Artificial soil concentrations were measured using ICP-AES. Measured Ni concentrations in the soil were within % of nominal. Equilibration time: not described Exposure time: 21 (Eisenia fetida), 28 (Folsomia candida), and 42 (Enchytraeus albidus) days Study results (mg Ni/kg dw): NOEC concentrations of 180 mg/kg for Eisenia fetida, 320 mg/kg for Folsomia candida, and 180 mg/kg for Enchytraeus albidus. [3] Scott-Fordsmand, J. J., Weeks, J. M. and Hopkin, S. P. (1998). A loamy sand soil (LUFA-Speyer 2.2) was used in a study to assess the acute and subacute toxicity of Ni to the earthworm (Eisenia veneta). A Ni solution (as NiCl 2 ) was added to ovendried soil to obtain concentrations of 50, 100, 300, 500, 700, and 1000 mg Ni/kg dry wt. The test was conducted in 4 replicate plastic containers containing 610 g moist soil (50% of the water holding capacity). The soil ph was adjusted to , and test containers were maintained at a temperature of 20 C and a 12:12 light:dark cycle. Eisenia veneta: 10 adult worms ( g live weight) per Ni concentration tested were placed in each of 4 replicate pots for 4 weeks. Food was supplied as 3 g of horse manure weekly. At the end of the 28-day experimental period, survival, growth, and reproduction were determined. The noobservable-effects concentrations (NOECs) and lowest-observable-effects concentrations (LOECs) were estimated using Dunnett s multiple comparison procedure (p < 0.05). Effect concentrations (EC10, EC50) and bootstrapping intervals were estimated by fitting a logistic model to the data. Soil characteristics: Medium ph OC Clay Silt Sand (%) (%) (%) (%) Loamy sand (LUFA-Speyer 2.2) measurements were not described. Equilibration time: not described Exposure time: 28 days Study results (mg Ni/kg dw): Added EC10 values of 85 mg/kg (cocoon production) and 247 mg/kg (survival/growth) were observed for Eisenia veneta.adult mortality increased with increasing Ni concentrations, with 100% mortality at 1000 mg Ni/kg soil. Cocoon production (reproduction) was the most sensitive endpoint evaluated, with reductions at soil Ni 191

206 concentrations above 85 mg/kg. Mean adult and cocoon weights were not adversely affected. Lysosomal membrane stability also was evaluated on the basis of neutral-red retention time. [4] Scott-Fordsmand, J. J., Krogh P.H. and Hopkin, S. P. (1999). Folsomia fimetaria Scott-Fordsmand, J. J., Krogh, P. H., and S. P. Hopkin. (1999). Toxicity of to a Soil- Dwelling Springtail, Folsomia fimetaria (Collembola: Isotomidae). Ecotoxicology and Environmental Safety, 43, Deleted: Fimetaria Formatted: Bullets and Numbering A loamy sand soil (LUFA-Speyer 2.2) was used in a microcosm study to assess the acute and subacute toxicity of Ni to the springtail (Folsomia fimetaria). A Ni solution (as NiCl 2 ) was added to oven-dried soil to obtain concentrations of 0, 100, 300, 500, 700, and 1000 mg Ni/kg dry wt. The test was conducted in 4 replicate microcosms containing 30 g moist soil (25.5 g dry soil and 4.5 ml demineralised water). The soil ph was adjusted to with CaCO 3, and test containers were maintained at a temperature of 20 C and a 12:12 light:dark cycle. Folsomia fimetaria: 20 adult worms (10 male and 10 female, days old) per Ni concentration were incubated for 21 days. Food was supplied on days 0 and 14 as 15 mg dry-wt of baker s yeast. At the end of the 21-day experimental period, adult survival, growth, and reproduction were determined. Juvenile survival and body size were measured by exposing 20 juveniles (0-3 days old) to Ni concentrations for 21 days. The no-observable-effects concentrations (NOECs) and lowest-observable-effects concentrations (LOECs) were estimated using Tukey s Studentized range test (p < 0.05). Effect concentrations (EC10, EC50) and confidence intervals were estimated by fitting a logistic model to the data. Soil characteristics: Medium ph TOC (%) Clay (%) Silt (%) Sand (%) Loamy sand (LUFA-Speyer 2.2) Soil nickel measurements not performed. Equilibration time: not described Exposure time: 21 days Study results (mg Ni/kg dw): Response NOEC LOEC LC10/EC10 LC50/EC50 Adult survival (females) ( ) 786 ( ) Adult survival (males) ( ) 922 ( ) Adult reproduction (16-332) 450 ( ) Juvenile survival ( ) 859 ( ) Adult growth (female) >1000 >1000 >1000 >1000 Adult growth (male) >1000 >1000 >1000 >1000 Juvenile growth >

207 Exposure of F. fimetaria to Ni caused significant toxiclogical effects at concentrations above 173 mg Ni/kg. Reproduction was the most sensitive parameter at soil Ni concentrations above 173 mg Ni/kg. A 10% decrease in adult female survival occurred at 427 mg Ni/kg and at 645 mg Ni/kg for adult males. [5] Ma, W. (1982). A sandy loam soil was used in a laboratory experiment to assess the bioavailability of Ni to earthworms (Lumbricus rubellus). The soil was amended with NiCl 2 to obtain nominal concentrations of 20, 150, 1000, or 3000 mg Ni/kg dry wt. Lumbricus rubellus: Groups of 5 adult worms (700 mg live weight) were placed in each 5 or 6 replicate nylon-mesh bags embedded in uncontaminated soil. Each bag contained 5 liters of treated soil. The soil moisture was maintained at 35 40% of the water holding capacity. Air-dried alder leaves were added to the soil surface as food. The containers were maintained for 6 12 weeks at C and 85 90% relative humidity. Growth and mortality was measured at the end of 6 and 12 weeks. The statistical method was not specified. Soil characteristics: Medium ph OC clay OM CaCO3 Background Ni CEC % % % % mg/kg dw cmol/kg Sandy loam concentrations were measured using atomic absorption spectrophotometry. Equilibration time: not described Exposure time: 6-12 weeks Study results (mg Ni/kg dw): The derived NOEC was determined as the highest dose causing < 10% effect (Oliver and McLaughlin 2003). Effect concentrations (ECx) were calculated from the original data (Oliver and McLaughlin 2003). The derived EC10 value for Lumbricus rubellus was 842 mg/kg Toxicity to soil micro-organisms Data on microbial toxicity tests resulting in NOEC values are summarised in Table Tests on microbial processes are multi-species test, in which the native soil microbial community is exposed. The selected NOEC or EC 10 values comprise functional parameters (n=39), and microbial species (n=13). The functional parameters are based on the carbon cycle (n=27), nitrogen cycle (n=12), including denitrification and mineralization of specific substrates. Enzymatic parameters are also further considered in the effects assessment. 6 different enzymatic processes were compiled in the database. In the total risk approach, NOEC or EC 10 values range from 28 mg/kg (nitrificationmineralisation; Smolders, 2000) to 2,491 mg/kg (respiration; Doelman & Haanstra, 1984). Table gives an overview of the rejected data and the reason(s) why they were rejected. EC 10 values were calculated from data provided in journal articles for Babich and Stotzky (1982b), who reported only NOEC values for a range of Eubacteria, actinomycetes, yeasts, and filamentous fungi 193

208 194

209 Table Overview of the accepted EC10/NOEC values for microbial processes/species. Values selected for the effects assessment are underlined (estimated background nickel concentrations and CEC** are indicated in italics). EC10 in italics refer to enzymatic processes and are included in a sensitivity analysis of the PNEC calculation. Test substance Process/species Medium ph %OC %clay Cb CEC Duration EC10 (NOEC when indicated) Added NOEC Unbounded NOEC EC10 (NOEC when indicated) Total NOEC Unbounded NOEC mg/kgdw cmol/kg mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Nitrification Loamy sand / / Houthalen NiCl2 Nitrification Sandy clay loam Zegveld NiCl2 Nitrification Loamy sand * 36* Montpellier NiCl2 Nitrification Loamy sand Rhydtalog NiCl2 Nitrification Jyndevad <21.5 <22.5 NiCl2 Nitrification Sandy loam * Kovlinge II NiCl2 Nitrification Clay Aluminosa NiCl2 Nitrification Borris NiCl2 Nitrification Sandy clay loam * 101* Woburn NiCl2 Nitrification Silt loam Ter Munck NiCl2 Nitrification Clay Souli NiCl2 Nitrification Silt loam * 66* Marknesse NiCl2 Nitrification Clay Brecy NiCl2 Nitrification Cordoba NiCl2 Nitrification Cordoba NiCl2 Nitrification Loam Guadalajara NiCl2 Glucose respiration Loamy sand h <21.5 <22.5 Houthalen NiCl2 Glucose respiration Loamy sand

210 Test substance Process/species Medium ph %OC %clay Cb CEC Duration EC10 (NOEC when indicated) Montpellier Added NOEC Unbounded NOEC EC10 (NOEC when indicated) Total NOEC Unbounded NOEC mg/kgdw cmol/kg mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Glucose respiration Jyndevad h <21.5 <22.5 NiCl2 Glucose respiration Sandy loam h 35* 37* Kovlinge II NiCl2 Glucose respiration Clay Aluminosa h NiCl2 Glucose respiration Borris h 13* 16* NiCl2 Glucose respiration Sandy clay loam h Woburn NiCl2 Glucose respiration Silt loam h Ter Munck NiCl2 Glucose respiration Clay Souli h NiCl2 Glucose respiration Silt loam h Marknesse NiCl2 Glucose respiration Clay h Brecy NiCl2 Glucose respiration Cordoba h NiCl2 Glucose respiration Cordoba h NiCl2 Glucose respiration Loam h Guadalajara NiCl2 Maize respiration Loamy sand Houthalen NiCl2 Maize respiration Loamy sand Rhydtalog NiCl2 Maize respiration Jyndevad <25 <26 NiCl2 Maize respiration Sandy loam <42 <44 Kovlinge II NiCl2 Maize respiration Borris

211 Test substance Process/species Medium ph %OC %clay Cb CEC Duration EC10 (NOEC when indicated) NiCl2 Maize respiration Sandy clay loam Woburn NiCl2 Maize respiration Silt loam Ter Munck NiCl2 Maize respiration Clay Souli NiCl2 Maize respiration Clay Brecy Added NOEC Unbounded NOEC EC10 (NOEC when indicated) Total NOEC Unbounded NOEC mg/kgdw cmol/kg mg/kgdw mg/kgdw mg/kgdw mg/kgdw * 41* NiCl2 Maize respiration Cordoba NiCl2 Maize respiration Cordoba * 45* NiCl2 Maize respiration Loam Guadalajara University of Leuven, 2005 [1] NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 NiCl2 Aspergillus flavipes (hyphal growth) Aspergillus flavus (hyphal growth) Aspergillus clavatus (hyphal growth) Aspergillus niger (hyphal growth) Penicillium vermiculatum (hyphal growth) Rhizopus stolonifer (hyphal growth) Trichoderma viride (hyphal growth) Gliocladium sp. (hyphal growth) Serratia marcescens (colony count) Proteus vulgaris (colony count) Bacillus cereus (colony count) Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d Kitchawan soil Several d

212 Test substance Process/species Medium ph %OC %clay Cb CEC Duration EC10 (NOEC when indicated) Added NOEC Unbounded NOEC EC10 (NOEC when indicated) Total NOEC Unbounded NOEC mg/kgdw cmol/kg mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 Nocardia rhodochrous Kitchawan soil Several d (colony count) NiCl2 Rhodotorula rubra (colony Kitchawan soil Several d count) Babich & Stotzky, 1982b [2] The EC10 values are not reported values but calcualted from original data. NiCl2 Respiration (CO2 release) Sandy loam w (NOEC) NiCl2 Respiration (CO2 release) Clay w NiCl2 Respiration (CO2 release) Sandy peat w Doelman & Haanstra, 1984 [3] NiCl2 Respiration (CO2 respiration) Typic Xerochrept d 27 (NOEC) Saviozzi et al., 1997 [4] NiSO4 ATP content Sandy cambisol y 77 (NOEC) Wilke, 1988 [5] NiCl2 Glutamate respiration (CO2 Sand y 55 release) (NOEC) NiCl2 Glutamate respiration (CO2 Sandy Peat y 55 release) (NOEC) NiCl2 Glutamate respiration (CO2 Clay y 55 release) (NOEC) NiCl2 Glutamate respiration (CO2 Sandy Loam y 55 release) (NOEC) Haanstra & Doelman, 1984 [6] 41 (NOEC) 86 (NOEC) 63 (NOEC) 59 (NOEC) 94 (NOEC) 57 (NOEC) NiCl2 urease sand y NiCl2 urease sandy loam y NiCl2 urease silty loam d NiCl2 urease clay y NiCl2 urease sandy peat y Doelman&Haanstra, 1986 [7] 198

213 Test substance Process/species Medium ph %OC %clay Cb CEC Duration EC10 (NOEC when indicated) Added NOEC Unbounded NOEC EC10 (NOEC when indicated) Total NOEC Unbounded NOEC mg/kgdw cmol/kg mg/kgdw mg/kgdw mg/kgdw mg/kgdw NiCl2 phosphatase sandy loam y NiCl2 phosphatase silty loam y NiCl2 phosphatase clay d Doelman&Haanstra, 1989 [8] NiCl2 arylsulfatase sand d NiCl2 arylsulfatase sandy loam d NiCl2 arylsulfatase silty loam d NiCl2 arylsulfatase clay y NiCl2 arylsulfatase sandy peat y Doelman&Haanstra, 1991 [9] NiCl2 dehydrogenase haplic luvisol Welp, 1999 [10] NiSO4 saccharase Sandy cambisol y 77 (NOEC) 86 (NOEC) NiSO4 protease Sandy cambisol y 77 (NOEC) 86 (NOEC) Wilke, 1988 [5] NiSO4 N-mineralisation Nethen d NiSO4 N-mineralisation Nethen/NH d Smolders, 2000 [11] *EC10 below lowest tested (no zeo control); the impact of this value is used in sensitivity analysis **If the CEC was missing from a test with plants/invertebrates/micro-organisms, then it was estimated from % clay, ph and %organic matter using an experimentally derived regression model: CEC=( ph)*clay/100+( ph)*om/100; the clay is the % clay in the soil (Helling et al., 1964; regression based on CEC measured at various ph values on 60 different soils; CEC refers to the soil ph). 199

214 Table Reported microbial/enzymatic studies NOT used in the effects assessment Author Soil Group Main Exclusion Factor Al-Khafaji & Tabatabai 1979 Harps Microbe process (Arylsulfatase) Unbounded NOEC (>1468) Al-Khafaji & Tabatabai 1979 Okoboji Microbe process (Arylsulfatase) Unbounded NOEC (>1468) Al-Khafaji & Tabatabai, 1979 Nicollet Arylsulphatase activity ph test >0.5 ph units different from that in soil or NOEC cannot be derived Al-Khafaji & Tabatabai, 1979 Webster Arylsulphatase activity ph test >0.5 ph units different from that in soil or NOEC cannot be derived Babich & Stotzky 1982 Kitchawan Microbe pop. (Agrobacterium radiobacter) Unbounded LOEC (85% effect) Babich & Stotzky 1982 Mopala Microbe pop. (Agrobacterium radiobacter) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982 Kitchawan Microbe pop. (Bacillus megaterium) Unbounded LOEC (>99% effect) Babich & Stotzky 1982 Mopala Microbe pop. (Bacillus megaterium) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982 Kitchawan Microbe pop. (Cryptococcus terreus) Unbounded LOEC (72% effect) Babich & Stotzky 1982 Mopala Microbe pop. (Cryptococcus terreus) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982 Mopala Microbe pop. (Nocardia rhodochrous) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982 Mopala Microbe pop. (Rhodotorula rubra) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982 Kitchawan Microbe pop. (Torulopsis glabrata) Unbounded LOEC (84% effect) Babich & Stotzky 1982 Mopala Microbe pop.(serratia marcescens) Unbounded NOEC, No sig Effect (p>0.05) Babich & Stotzky 1982a Nutrient Agar Mycelial Growth Agar medium Beck, 1981 Puch (Kalk) Nitrification CEC was not reported & could not be estimated (no clay/om content) Bhuiya & Cornfield 1972 Bagshot sand Microbe process (respiration) Single dose level Bremner & Douglas 1971 Fayette silty clay Microbial enzyme ph test >0.5 ph units different from that in soil or NOEC cannot be derived Bremner & Douglas 1971 Webster clay loam Microbial enzyme ph test >0.5 ph units different from that in soil or NOEC cannot be derived Chander & Brookes Hamble series Microbial population Ni added in sewage sludge Chaudri, McGrath & Giller 1992 Sandy Loam Microbe population (Rhizobium leguminosarum) No significant Dose response Cornfield, 1977 Loamy soil Respiration (CO2 respiration) Unbounded NOEC An 18% effect was recorded at 10 mg/kg decatanzaro & Hutchinson 1985 Windy Lake Microbe Nitrification Unbounded NOEC >100 decatanzaro & Hutchinson 1985 Windy Lake Microbe population (Nitrobacter) No consistent dose response decatanzaro & Hutchinson 1985 Burt Lake Microbe population (Nitrobacter) No consistent dose response 200

215 decatanzaro & Hutchinson 1985 Sudbury Microbe population (Nitrobacter) Contaminated soil (Smelter emissions) decatanzaro & Hutchinson 1985 Windy Lake Microbe population (Nitrosomonas) No consistent dose response decatanzaro & Hutchinson 1985 Sudbury Microbe population (Nitrosomonas) Contaminated soil (Smelter emissions) decatanzaro & Hutchinson 1988 Sudbury Microbe Nitrification Contaminated soil (Smelter emissions) decatanzaro & Hutchinson, 1985 Burt lake Nitrification CEC was not reported & could not be estimated (no clay content) decatanzaro & Hutchinson, 1985 Windy lake Nitrification CEC was not reported & could not be estimated (no clay content) decatanzaro & Hutchinson, 1985 Burt lake Nitrosomas (MPN) CEC was not reported & could not be estimated (no clay content) Deng & Tabatabai 1995 Weller Microbe process (cellulase activity) Single dose level (with no sig response) Deng & Tabatabai 1995 Pershing Microbe process (cellulase activity) Single dose level (with no sig response) Deng & Tabatabai 1995 Grundy Microbe process (cellulase activity) Single dose level (with no sig response) Doelman & Haanstra 1984 Silty loam Microbe process (respiration) Unbounded NOEC (>8000) Doelman & Haanstra 1984 Sand Microbe process (respiration) Unbounded NOEC (no significant advers effect, p>0.05, at top conc. 8000mg/kg) Doelman & Haanstra 1984 Silty loam Microbe process (respiration) Unbounded NOEC (no significant advers effect, p>0.05, at top conc. 8000mg/kg) El-Sharouny et al 1988 Clay Microbe pop. (Aspergillus sp.) Ni dose cannot be calculated from data El-Sharouny et al 1988 Clay Microbe pop. (Chaetomium sp.) Ni dose cannot be calculated from data El-Sharouny et al 1988 Clay Microbe pop. (Penicillium sp.) Ni dose cannot be calculated from data El-Sharouny et al 1988 Clay Microbe population (Rhizopus sp.) Ni dose cannot be calculated from data Frankenberger & Tabatabai 1981 Muscatine Microbial enzyme (amidase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1982 Harps Microbial enzyme (amidase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1983 Okoboji Microbial enzyme (amidase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Harps Enzyme activity (L-asparaginase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Muscatine Enzyme activity (L-asparaginase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Okoboji Enzyme activity (L-asparaginase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Harps Enzyme activity (L-glutaminase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Muscatine Enzyme activity (L-glutaminase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frankenberger & Tabatabai 1991 Okoboji Enzyme activity (L-glutaminase) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Frostegard et al 1993 Sandy loam Microbe pop. (PLFA composition) Response variable PLFA composition indicates shift in microbial population only (not readily comparable to other End points) Frostegard et al 1993 Forest soil Microbe pop. (PLFA composition) Forest soil is more humus than soil (80%OM) Frostegard et al 1993 Forest soil Microbe process (ATP content) Forest soil is more humus than soil (80%OM) 201

216 Frostegard et al., 1993 Sandy loam ATP content CEC was not reported & could not be estimated (no clay content) Fu & Tabatabai 1989 Ames Microbial Process (Nitrate reductase activity) Unbounded LOEC (63% effect) Fu & Tabatabai 1989 Canisteo Microbial Process (Nitrate reductase activity) Unbounded NOEC Fu & Tabatabai 1989 Okoboji Microbial Process (Nitrate reductase activity) Two dose levels 10x apart, plus details lacking 15% effect at 147 mg/kg (not unbounded), but agree only to test concentratoons Giashuddin & Cornfield 1979 Bagshot sand Microbial mineralisation/nitrification NiO used as Ni source (only 3% soluble). Giashuddin & Cornfield, 1978 Bagshot sand N-accumulation (mineral N conc.) Unbounded NOEC Giashuddin & Cornfield, 1978 Bagshot sand NO3-N concentration Unbounded NOEC Giashuddin & Cornfield, 1978 Bagshot sand Respiration (CO2 respiration) Unbounded NOEC A 22% effect was recorded at 10 mg/kg (two test concentrations higher also used) Gupta et al 1987 Steinhof Microbe process (Respiration) NOEC is calculated from pooled results of 3 exposure times Haanstra & Doelman 1984 Silty loam Microbe process Which? Cant find out what expreiment this relates to. Lowest dose produced effect. The percent (and so NOEC) could not be calculated as response variable was a relative measure of lag-time. Hattori 1992 Gley soil Microbial Activity Sewage sludge mixed into soil Hattori 1992 Andosol Microbial Activity Sewage sludge mixed into soil Juma & Tabatabai 1977 Webster Microbe process (acid phosphatase activity) ph test >0.5 ph units different from that in soil or NOEC cannot be derived Juma & Tabatabai 1977 Okoboji Microbe process (acid phosphatase activity) Two dose levels 10x apart, plus details lacking Juma & Tabatabai 1977 Harps Microbe process (alkaline phosphatase activity) Two dose levels 10x apart, plus details lacking Liang & Tabatabai 1977 Webster Microbial N mineralisation Single dose level Liang & Tabatabai 1977 Judson Microbial N mineralisation Single dose level Liang & Tabatabai 1977 Harps Microbial N mineralisation Single dose level Liang & Tabatabai 1977 Okoboji Microbial N mineralisation Single dose level Liang & Tabatabai 1978 Webster Microbial Nitrification Single dose level, effect >60% Liang & Tabatabai 1978 Harps Microbial Nitrification Single dose level, effect >60% Liang & Tabatabai 1978 Okoboji Microbial Nitrification Single dose level, effect >60% Lighthart, Baham & Volk 1983 Crider Microbial Process (respiration) Inhibition level by Ni doses could not be accurately interpreted from graphical display (no numerical data given) Lighthart, Baham & Volk 1983 Rifle Microbial Process (respiration) Inhibition level by Ni doses could not be accurately interpreted from graphical display (no numerical data given) Inhibition level by Ni doses could not be accurately interpreted from graphical display (no numerical data Lighthart, Baham & Volk 1983 Sharpsburg Microbial Process (respiration) given) 202

217 Lighthart, Baham & Volk 1983 Toledo Microbial Process (respiration) Inhibition level by Ni doses could not be accurately interpreted from graphical display (no numerical data given) Lighthart, Baham & Volk 1983 Walla Walla Microbial Process (respiration) Inhibition level by Ni doses could not be accurately interpreted from graphical display (no numerical data given) Simon 1999 Ruzyne Microbe pop. (Azotobacter sp.) Unbounded LOEC (60% effect) Simon et al 1998 Ruzyne Microbe biomass Unbounded NOEC Simon et al 1998 Lukavec Microbe biomass Unbounded NOEC Simon et al 1998 Ruzyne Microbe pop. (bacteria count.) Unbounded LOEC (83% effect) Simon et al 1998 Lukavec Microbe pop. (bacteria count.) Unbounded NOEC Simon et al 1998 Ruzyne Microbe population (Micromycetes) Unbounded LOEC (60% effect) Simon et al 1998 Ruzyne Microbe population (Oligotrophs) Unbounded LOEC(37% effect) Simon et al 1998 Ruzyne Microbe process (PNR) Unbounded NOEC Simon et al 1998 Lukavec Microbe process (pot. Respiration) Unbounded LOEC (45% effect) Simon, 1998 Lukavec soil Micromycetes (total number) CEC was not reported & could not be estimated (no clay/om content) Simon, 1998 Lukavec Nitrification (TPNIT) CEC was not reported & could not be estimated (no clay/om content) Simon, 1998 Ruzyne Respiration (CO2 release) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Ruzyne soil Bacteria (Total number) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Lukavec soil Bacteria (Total number) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Ruzyne Dehydrogenase activity ph test >0.5 ph units different from that in soil or NOEC cannot be derived Simon, 1999 Lukavec Dehydrogenase activity ph test >0.5 ph units different from that in soil or NOEC cannot be derived Simon, 1999 Ruzyne soil Microbial population (microbial C) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Lukavec soil Microbial population (microbial C) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Ruzyne Nitrification (TPNIT) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Lukavec Nitrification (TPNIT) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Ruzyne Respiration (CO2 release) CEC was not reported & could not be estimated (no clay/om content) Simon, 1999 Lukavec Respiration (CO2 release) CEC was not reported & could not be estimated (no clay/om content) Spalding 1979 None Microbe process No soil. Experiment performed in needle litter. Stott, Dick & Tabatabai 1985 Clarion Microbe process (phosphatase activity) Unbounded NOEC (No significant effect, p>0.05, at top conc. 1445mg/kg) Stott, Dick & Tabatabai 1985 Nicollet Microbe process (phosphatase activity) Unbounded NOEC (No significant effect, p>0.05, at top conc. 1445mg/kg) Stott, Dick & Tabatabai 1985 Okoboji Microbe process (phosphatase activity) Unbounded NOEC (No significant effect, p>0.05, at top conc. 1445mg/kg) 203

218 Tabatabai 1977 Weller Microbe process (Urease) Unbounded LOEC (33% effect) Tabatabai 1977 Nicollet Microbe process (Urease) Two dose levels 10x apart, plus details lacking Tabatabai 1977 Webster Microbe process (Urease) Two dose levels 10x apart, plus details lacking Tabatabai 1977 Harps Microbe process (Urease) Two dose levels 10x apart, plus details lacking Tabatabai 1977 Luton Microbe process (Urease) Two dose levels 10x apart, plus details lacking Tabatabai 1977 Okoboji Microbe process (Urease) Two dose levels 10x apart, plus details lacking University of Leuven, 2005 [1] Sandy clay (Zegveld) Loamy sand Glucose respiration Unbounded NOEC Unbounded NOEC (Rhydtalog)) University of Leuven, 2005 [1] Sandy clay (Zegveld) Loamy sand (Montpellier) Aluminosa clay Maize respiration Unbounded NOEC Unbounded NOEC Unbouded NOEC Wike, 1988 Sandy cambisol Alkaline phosphatase activity LOEC>20% effect Wike, 1988 Sandy cambisol Dehydrogenase activity LOEC>20% effect Wilke 1991 Sandy Luvisol Microbe process (Dehydrogenase and Respiration) Single dose level, no effect estimate 204

219 Footnote: toxicity of nickel to terrestrial processes (microbe/enzyme-mediated processes) [1] University of Leuven- - (this research is part of the Conclusion (i) research project on the development of a predictive model of bioavailbility and toxicity of nickel in soils ). 16 different European soils were amended with NiCl2 to obtain a range of 6 concentrations in a geometric series. Nitrification: 7 days after metal spiking, duplicate 100 g subsamples of each Ni concentration of each soil were amended with 100 mg NH4-N kg-1 fresh soil using a stock solution containing 47 mg (NH4)2SO4 ml-1. The Potential Nitrification Rate (PNR, mg NO3-N kg-1 fresh soil day-1) was calculated from the linear increase in soil NO3-N in the period after substrate addition (up to 28 days in acid soils). The soil nitrate was measured colorimetrically in a centrifuged soil extract (1M KCl, 10g subsample, L/S = 2.5, 2 h end-over-end shaking, n = 2). The PNR was calculated as the slope of the regression of soil nitrate concentration against time. This test is most sensitive to Ni in the initial period after NH4+ addition, i.e. as long as the substrate is still abundantly present. The nitrification rate typically increases with increasing soil ph. Therefore, the test duration must be restricted at high ph whereas the test must be long enough in acid soils to identify a significant increase in soil NO3-. The incubation time was therefore varied between soils, i.e. between 4 and 28 days. Glucose respiration: 7 days after metal spiking, duplicate 40 g subsamples of each Ni concentration from each soil were placed in separate 200 ml glass jars. Soils were then amended with 1 ml of 14-C labelled glucose solution (40 mg ml-1 glucose, specific activity = KBq mg-1 glucose-c) and mixed thoroughly. Each glass jar was immediately placed and sealed inside a 1.5 l preserving pot containing 20 ml 1.0 M NaOH. Each sample was then incubated in the dark at 20 C for 24 hours, after which 1 ml of the NaOH was removed and added to 10 ml scintillation cocktail (XT Gold) for activity determination by beta counting. The percent of added glucose-c respired was calculated from sample radioactivities, and the data used to derive Ni dose-response curves based on soil (mg Ni kg-1) concentrations. Maize respiration: 7 days after metal spiking, duplicate 40 g subsamples of each Ni concentration (of each soil) were placed in separate 200 ml glass jars. To each 40 g subsample, 40 mg of ground, 14C labelled maize root material was added and mixed thoroughly. This plant material was derived from soil grown maize plants continuously exposed to a 14CO2 labelled atmosphere that ensured homogeneous labelling of the plant carbon. The carbon content of the maize root substrate was determined as 22% with a specific activity of MBq g-1 C. A scintilation vial containing 15 ml 1.0 M NaOH was secured to each glass jar with parafilm, and the bound vessels were then placed inside 1.5 l preserving pots containing 15 ml water. The preserving pots were sealed (airtight) and incubated in the dark at 20 C for 28 days. Upon completion of the incubation period NaOH traps were removed and 1.0 ml subsamples were taken and added to 10 ml scintilation cocktail (XT Gold), with the solutions then shaken and activity determined by beta counting. The percent of added plant-c respired was determined, and Ni dose response curves were derived based on soil (mg Ni kg-1) concentrations. For more than half of the soils, 50% effect (EC50) was not reached within the tested concentrations, due to low slope of the dose-response curves. Therefore, models for incorporation of bioavailability are based on EC20 instead of EC50. Endpoint: nitrification and respiration (glucose and maize). 205

220 Exposure time: between 4 and 28 days (nitrification); 1 days (glucose respiration); 28 days (maize respiration). Study results (mg Ni/kg dw): EC10/NOEC values for nitrification vary between 20 and 439 mg/kg; between 13 and 376 mg/kg for glucose respiration and between 28 and 446 mg/kg for maize respiration. [2] Babich, H. and Stotzky, G. (1982b). Soil obtained from Kitchawan Research Laboratory (New York, USA) was used to assess the toxicity of Ni to a variety of soil microbes. The soil was amended with Ni (as NiCl2) to obtain dosing levels of 10, 50, 100, 250, 500, 750, or 1000 ppm Ni. Filamentous fungi: Fungal species tested included Aspergillus flavipes, Aspergillus flavus, Aspergillus clavatus, Aspergillus niger, Penicillium vermiculatum, Rhizopus stolonifer, Trichoderma viride, and Gliocladium sp. The soil-plating technique (Stotzky 1965, 1973) was used to study fungal growth rates in unamended or Ni-amended soils. Moist (2% above the 1/3-bar tension water content) soil was placed in polyethylene bags, mixed with a NiCl2 solution, stored overnight at 4 C, and then passed through a 2-mm sieve. Approximately g of Ni-treated soil was placed in each of 3 replicate Petri dishes and leveled. The soil plates were sterilized by autoclave for 15 minutes, and then slants containing fungi were placed in a central depression in the sterilized plates. After inoculation, the soil plates were placed in an incubator at 25 C. The experiment was performed twice. Soil plates of R. stolonifer and T. viride were replicated on days 1 and 5 days after inoculation, and soil plates for A. flavus, A. flavipes, A. clavatus, A. niger, P. vermiculatum, and Gliocladium sp. were replicated on days 1 and 8 after inoculation. The arithmetic means were evaluated for significant differences using the Student s t test (p < 0.05). Unicellular microbes: Microbes tested included Eubacteria (Agrobacterium radiobacter, Serratia marcescens, Proteus vulgaris, Bacillus cereus, Bacillus megaterium), an actinomycete (Nocardia rhodochrous), and several yeasts (Cryptococcus terreus, Rhodotorula rubra, and Torulopsis glabrata). Microbial suspensions were grown in each of 3 replicate tubes containing 1 g sterilized dry soil amended with varying concentrations of NiCl2. Tubes were incubated for 1 week at 25 C with 2 ml saline. The soils were serially diluted in saline and pour plates were made with nutrient agar. The plates were inverted, incubated at 25 C for 1-3 days and colonies were counted. The experiments were performed twice. The arithmetic means were evaluated for significant differences using the Student s t test (p < 0.05). Soil characteristics: ph: 4.9; OM (%): 5.75; CEC: 8.15 meq/100 g; clay content: 9.4 % and Ni background: 19.7 mg/kg. Ni concentrations were determined by atomic absorption spectroscopy. Soil ph was not regulated. Endpoint: Fungal growth Exposure time: 4-7 days. Study results (mg Ni/kg dw): EC10/NOEC values for individual species varied between 13 and 530 mg/kg. The no-observed-effect concentration (NOEC) is expressed as the highest dose that resulted in < 10% effect (Oliver and McLaughlin 2003). The derived effective concentration (ECx) values were calculated from original data (Oliver and McLaughlin 2003). [3] Doelman, P. and Haanstra, L. (1984). Five Dutch soil types (sand, sandy loam, silty loam, clay and sandy peat) were used in a study to assess the inhibitory effect of Ni on soil respiration, measured as CO2 evolution. Soils were 206

221 amended with Ni (as NiCl2) to obtain concentrations of 0, 150, 400, 1000, 3000, and 8000 ug Ni/g. CO2 Respiration: Field-collected soil samples were sieved (< 2 mm) and homogenized to which NiCl2 powder was added. An untreated soil served as a control. Soil samples were stored in polyethylene bags and incubated at 20ºC and a moisture content of 50-80% of the water holding capacity. Duplicate soil respiration measurements were made for each soil type for a period of 6 10 weeks starting 2 7 days following the application of NiCl2, and again after 18 months. Respiration was measured by estimating the CO2 produced in 750 ml vessels with soil samples of g. Statistical analyses were performed using a t test (p < 0.05 or 0.10). Soil characteristics: Characteristic Sand Sandy Loam Silty Loam Clay Sandy Peat ph-h Organic matter (%) Clay, < 2um (%) Silt, 2-50 um (%) Sand, > 50 um (%) CEC (meq/100 g) Ni mg/kg Soil Ni concentrations were not measured. Endpoint: Respiration (CO 2 respiration) Exposure time: 2, 4, 8, and 70 weeks. Study results (mg Ni/kg): Respiration results in NOEC/EC10 vamues varying between 291 and 2452 mg/kg. The no-observed-effect (NOEC) and lowest-observed-effect (LOEC) values for CO2 respiration at the longest equilibration period are summarized below. Derived effective concentrations (ECx) were calculated from original data (Oliver and McLaughlin 2003). The inhibition of CO2 respiration was greatest in sandy soil and least in the clay soil. Soil ph was reported as the primary abiotic factor affecting inhibition for Ni. [4] Saviozzi, A., Levi-Minzi, R., Cardelli, R. and Riffaldi, R. (1997). An Italian soil (Typic Xerochrept) was used in a study to assess the inhibitory effect of nickel (applied as NiCl 2 ) on soil respiration. The soil was amended with NiCl 2 solutions to produce soil concentrations of 50, 100, 250, 500, and 1000 ug/g-dry weight. CO 2 Respiration: Soil (100 g) was mixed in 300 ml cylindrical glass containers with solutions of NiCl 2 to bring the soil to 60% of its maximum water holding capacity. Ground (< 1 mm) soybean straw was mixed with the Ni-treated soils at a rate of 1% of dry matter to promote microbial activity. CO 2, samples were collected and incubated at 22ºC, and carbon dioxide measurements were collected periodically over 28 days. CO 2 - free water was added to maintain the original moisture level. The experiment was performed in two replicates and the ph was measured at the beginning and end of the incubation period. Heavy metals analysis was conducted by atomic absorption spectroscopy. Effect concentrations (20% and 50%) for inhibition of CO 2 production were calculated by fitting a smooth curve to the data. Soil characteristics: ph: 5.2; organic carbon content: 1.4%; clay content: 8%; CEC: 13.1 meq/100 g; Ni background: 14 mg/kg. Extracted Ni was analyzed by atomic absorption spectroscopy. Endpoint: Soil respiration (CO 2 respiration) 207

222 Exposure time: 28 days. Study results (mg Ni/kg dw): an EC10 value of 27 mg/kg was obtained for CO2 respiration. The derived no-observed-effect concentration (NOEC) was reported in Oliver and McLaughlin (2003), and is expressed as the EC10 calculated using a log-logistic model. The reported effective concentrations (ECx) were re-calculated from a curve fitted to the authors stated ECx values, and may be extrapolated beyond the data range (Oliver and McLaughlin 2003). [5] Wilke, B-M. (1988). A sandy cambisol soil was used to assess the long-term effects of Ni on soil dehydrogenase, alkaline phosphatase, saccharase, and protease activity, as well as on microbial biomass and ATP-content. Ni (applied dry as NiSO 4 ) was added to the soil to obtain concentrations of 0, 100, and 400 mg/kg. Significant differences were evaluated using t tests (p < 0.05). Soil characteristics: ph: 6.0; organic carbon content: 1.2%; clay content: 9%; CEC: 10.3 cmol/kg; Ni background: 9 mg/kg. Endpoint: Soil microbial activities (dehydrogenase, alkaline phosphatase, saccharase, protease activity, ATP-content). Exposure time: 9 years. Study results (mg Ni/kg): ATP content, sacchrarase activity and protease activity resulted in NOEC value of 77 mg/kg. The original study language is German; this study has not been translated. The no-observedeffect concentration (NOEC) was expressed as the EC10 calculated using a log-logistic model (Oliver and McLaughlin 2003). Effective concentrations (ECx) could not be calculated; only 3 dose levels were evaluated in the study. [6] Haanstra, L. and Doelman, P. (1984). Five Dutch soil types (sand, sandy loam, silty loam, clay and sandy peat) were used in a study to assess the effect of Ni on glutamate respiration as measured by CO 2 release. Soils were amended with Ni (as NiCl 2 ) to obtain concentrations of 55, 400, and 1000 mg Ni/kg. Glutamic acid decomposition: Duplicate field-collected soil samples were sieved (< 2 mm) and homogenized to which NiCl 2 and glutamic acid (10 mmol/kg) were applied as finely ground powders. Samples were stored in polyethylene bags at 20ºC and maintained at 60 75% of the water holding capacity. The decomposition time was defined as the time from mixing the soil with the amino acid until the maximum respiration rate is reached. Glutamic acid decomposition time was measured at 6 months. The respiration rate was determined by mixing 15 g of soil with 2000 mg/kg glutamic acid and measuring CO 2 production. Respiration was monitored every 2 hours for hours. The maximal decomposition time was less than 75 hours. Student s t test was used to evaluate differences in mean glutamic acid decomposition (p < 0.05 or 0.10). Significance was defined as a difference of 2.13 times the standard deviation. The ph of the soils varied between 4.4 and 7.5. Endpoint: Glutamate respiration Exposure time: 18 months. The soil characteristics used in this research are described in Doelman and Haanstra (1984). Soil Ni concentrations were not measured, and soil ph was not regulated. 208

223 Study results (mg Ni/kg): NOEC values for the endpoint glutamic acid decomposition resulted in a NOEC value of 55 mg/kg. Effective concentrations (ECx) could not be calculated; only 3 dose levels were evaluated in the study. [7] Doelman, P. and Haanstra, L. (1986). Five Dutch soil types (sand, sandy loam, silty loam, clay, and sandy peat) were used in a study to assess the effect of Ni on urease activity. Soils were amended with Ni (as NiCl 2 ) to obtain concentrations of 55, 150, 400, 1000, 3000, or 8000 mg/kg-dry weight. Urease activity: Field-collected soil samples were sieved (2-mm) and homogenized to which Ni (as NiCl 2 powder) and urea (10 mmol/l) were added. An untreated soil served as a control. Soil samples were stored in the dark at 20ºC and moistures of 12 30% of the water holding capacity. The ph of the soils varied between 4.4 and 7.7. Urease activity, defined as millimoles of urea hydrolyzed at 37ºC/kg dry soil/hour, was determined by a procedure similar to Zantua and Bremner (1975). Non-hydrolyzed urea was colorimetrically determined after 6 weeks and 18 months of incubation. All measurements were made in triplicate. The 6- week and 18-month ecological dose-50% (ED50) and ecological dose range (EDR) were calculated and reported for each soil type. The ED50 was calculated using the logistic dose response model and was defined as the Ni concentration at which urease activity is half of the uninhibited level. The EDR was defined as the Ni concentration range at which urease activity decreased from 90% to 10%. The soil characteristics used in this research are described in Doelman and Haanstra (1984). Soil Ni concentrations were not measured. Endpoint: Urease activity. Exposure time: 6 weeks and 18 months. Study results (mg Ni/kg): NOEC/EC10 values for urease activity between 90 and 2300 mg/kg. The ED50 and EDR (=ED10-ED90) values were reported by the study authors. The effective concentrations (ECx) were re-calculated from a curve fitted to the authors stated values (Oliver and McLaughlin 2003). The selected EC10 data refer to longest equilibration time unless ED10 is >2-fold below lowest tested concentration. [8] Doelman, P. and Haanstra, L. (1989). Five Dutch soil types (sand, sandy loam, silty loam, clay, and sandy peat) were used in a study to assess the effect of Ni on phosphatase activity. Soils were amended with Ni (as NiCl 2 ) to obtain concentrations of 55, 150, 400, 1000, 3000, or 8000 mg/kg-dry weight. Phosphatqse activity: Field-collected soil samples were sieved (2-mm) and stored in the dark at 20ºC at moistures of 12-30% to which Ni (as NiCl 2 powder, 57.8 g/mol) and p- nitrophenylphosphate (p-npp) were added. Phosphatase activity was determined by a procedure similar to Tabatabai and Bremner (1969). Each soil type (0.5 grams) was incubated with 5 ml 10 mmol/l p-npp. Triplicate measurements of phosphatase activity were made after 6 weeks and 18 months incubation. Phosphatase activity was expressed as mmol p-npp formed per kg dry soil per hour. The ED50 was defined as the Ni concentration at which phosphatase activity is half of the uninhibited level and were calculated using the logistic dose response model. The EDR was defined as the Ni concentration range at which phosphatase activity decreased from 90% to 10%. The soil characteristics used in this research are described in Doelman and Haanstra (1984). Soil Ni concentrations were not measured, and the soil ph was not regulated. Endpoint: phosphatase activity. 209

224 Exposure time: 6 weeks and 18 months. Study results (mg Ni/kg): EC10 values for phosphatase activity varied between 276 and 7023 mg/kg. The ED50 and EDR (=ED10-ED90) values were reported by the study authors. The effective concentrations (ECx) were re-calculated from a curve fitted to the authors stated values (Oliver and McLaughlin 2003). The selected EC10 data retained refer to longest equilibration time unless ED10 is >2-fold below lowest tested concentration. ED50 and ECx values for sandy peat were not calculated due to the excessive spread of the data. [9] Haanstra, L. and Doelman, P. (1991). Five Dutch soil types (sand, sandy loam, silty loam, clay, and sandy peat) were used in a study to assess the inhibitory effect of Ni on arylsulphatase activity. Soils were amended with Ni (as NiCl 2 powder) to obtain concentrations ranging from mg/kg-dry weight. Arylsulfatase activity: Field-collected soil samples were sieved (2 mm) and stored in the dark at 20ºC at moistures representing 55-70% of the water holding capacity. Ni (as NiCl 2 powder) was added to the soils. Arylsulfatase activity was measured by the method of Tabatabai and Bremner (1970) using p-nitrophenylsulphate as the substrate. Moist soil (12.5 g) samples were mixed with 5ml of 10 mmol/l p-nitrophenylsulphate and incubated in a shaker for 2 hours at 30ºC. Triplicate arylsulfatase activity measurements were made, along with an untreated soil control, and expressed as mmol p-nitrophenol formed per kg dry soil per hour. Measurements were made at six weeks and at 18 months. The ED50 was calculated using the logistic dose response model. The EDRs, defined as Ni concentration range in which arylsulfatase activity declines from 90% to 10%, are reported for each soil type. The soil characteristics used in this research are described in Doelman and Haanstra (1984). Soil Ni concentrations were not measured, and the soil ph was not regulated. Endpoint: Arylsulfatase activity. Exposure time: 6 weeks and 18 months. Study results (mg Ni/kg): EC10 values for arylsulfatase activity varied between 311 and 7084 mg/kg. The ED50 and EDR (=ED10-ED90) values were reported by the study authors. The effective concentrations (ECx) were re-calculated from a curve fitted to the authors stated values (Oliver and McLaughlin 2003). The selected EC10 data retained refer to longest equilibration time unless ED10 is >2-fold below lowest tested concentration. [11] Smolders, E. (2000). A Belgium soil (Nethen) collected from an agricultural site was used to evaluate the effect of Ni on soil nitrogen mineralization (OECD Test Guideline 216). The soil sample was preincubated in the dark for 8 days at 20ºC in polyethylene bags. Ni, NiO and NiSO 4 6 H 2 O were added to soil at concentrations of 10, 33, 100, 330, and 1000 mg/kg dry weight. The final soil weight of each treatment was 300 g. Soils were maintained at 50% of the water holding capacity. After days 0, 7, 14, and 28 of incubation, soils samples were extracted in triplicate. Soil nitrate content at day 28 was compared to controls using a t-test (p < 0.05). The ED 50 values and 95% confidence intervals were calculated according to Doelman and Haanstra (1989), p < concentrations were measured by atomic adsorption spectrometry. A NH 4 + -induced nitrification test of was conducted using ammonium (NH 4 ). Soil was preincubated for 10 days at 20ºC. An ammonium sulfate solution was added to 100 ug NH 4 -N/g dry weight along with varying nickel concentrations. Soils were maintained at 50% of the 210

225 water holding capacity. Nitrate analyses were made after days 0, 4, and 7. Soil characteristics: ph: 7.0; clay content: 10%; CEC: 6.5 mmol/kg; Ni background: 8 mg/kg. Endpoint: Nitrate transformation. Exposure time: 7 or 28 days. Study results (mg Ni/kg): added EC10 values varied between 20 and 257 mg/kg. The effective concentrations (ECx) were re-calculated from a curve fitted to the authors stated values (Oliver and McLaughlin 2003). The no-observed-effect concentration (NOEC) for the NH 4 + -induced nitrification test was expressed as the LOEC divided by 2 (12% effect) (Oliver and McLaughlin 2003). Nitrogen transformation to nitrate was inhibited only for the treatment with NiSO 4 6H 2 O, with a reported LOEC of 300 mg Ni/kg. The addition of 1000 mg Ni/kg resulted in 91% inhibition. Treatment with Ni or NiO had a marginal stimulating effect. The NH 4 + -induced nitrification test showed that Ni reduces the nitrification rate at all NiSO 4 6H 2 O additions. [10] Welp, G. (1999). A loess soil (FAO: haplic luvisol) was used in a study to evaluate the inhibitory effect of Ni on microbial dehydrogenase activity. The soil was amended with a nickel suspension (as NiCl 2 ) to obtain concentrations of mg/kg-dry weight. Deydrogenase activity: The dehydrogenase activity assay was conducted following the method of Trevors (1984). Moist soil (equivalent to 1 g oven dried) was added to 20-ml test tubes and mixed with 0.05 ml of 1.0% (w/v) glucose solution, 0.2 ml of 0.4% (w/v) INT solution, and 0.5 ml of varying amounts of an aqueous NiCl 2 suspension. The tubes were stoppered and incubated in the dark at 21ºC for 24 hours. Dehydrogenase activity was expressed as µg INF (iodonitrotetrazolium ) produced per g of soil over 24 hours, and calculated by comparing absorpance values to INF standards. Effective dose 10, 25, 50, 75, and 90% were calculated using a mathematical model (p < 0.05). Dose-response curves were analyzed by goodness-of-fit, with coefficient of determination of Soil characteristics: ph: 7.02; clay content: 15.2 %; CEC: 12.4 meq/100 g; Ni background: 19.4 mg/kg. Endpoint: dehydrogenase activity. Exposure time: 24 hours. Study results (mg Ni/kg): EC10 of 7.9 was calculated for deydrogenase activity. ECx values were recalculated from a curve fitted to the author-stated ECx values (Oliver and McLaughlin 2003) and are summarized below Essentiality and Toxicity is ubiquitous in the terrestrial environment, present both due to natural causes and anthropogenic distribution. is an essential element for at least some organisms and toxic to all given the right dose (Hopkin, 1989). Besides, being an essential metal it is likely to be regulated within the soil organism, but this has not been shown for nickel. has been shown essential for certain microorganisms, plants and higher animals, and is recognized by the U.S. Department of Agriculture and the Association of American Plant Food Control Officials as an essential plant nutrient (Frantz, 2005; AAPFCO, 2005). No 211

226 information was found on the possible essentiality of Ni for soil invertebrates. Within the organism nickel is a catalyst and constituent of certain enzymes and proteins (Eskew, 1983; Goubeaud, 1997; Hausinger, 1987; Hausinger, 1992; Hausinger, 1994; Nies, 1999; Thauer, 1980; Thauer, 1983; Thauer, 1999; Thauer, 2001; Welch, 1975; Welch, 1981; Welch, 1992; Welch, 1995). These enzymes include the urease enzyme which hydrolyses urea to form ammonia and carbamate, the methyl coenzyme M reductase which is involved in energyyielding, enzymes in the methane-producing pathway of all methanogenic bacteria, the hydrogenase which catalyses the reversible oxidation of hydrogen gas and carbon monoxide dehydrogenase oxidising carbon monoxide to carbon dioxide. The best known catalytic functions are the NiFe hydrogenases, which split molecular hydrogen into protons and electrons, the ureases which splits urea into carbon dioxide and ammonia, the cofactor F 430 (in methanogenic bacteria) which releases methane from a methyl group, and the actyl-scoa synthase (in anaerobic bacteria) in which nickel accepts a methyl group from B 12 and fuses it together with CO and HSCoA to acetyl-scoa. In addition, some plants and possibly invertebrates utilise high nickel accumulation as a chemical defence against predators (Martens, 1994; Boyd, 1994; Wild, 1975). No information was found on the internal, and ambient, concentrations necessary to maintain these essential functions, but it is likely that this is species dependent displaying a log normal log-normal distribution. The toxic modes of nickel are probably primarily due to its ability to replace other metal ions in enzymes and proteins, or to bind to cellular compounds containing O-, S- and N-atoms, e.g., enzymes and nucleic acid, which are then inhibited. According to the definition by (Nieboer, 1980) nickel, as do chromium and zinc, belong to the group of Borderline metals, which indicates that Ni may replace Class A and other Borderline metals (e.g., Mg 2+, Ca 2+, Zn 2+, Mn 2+, Ti 2+ ). Such substitution may cause malfunctions of both metal containing and metal activated enzymes. Further, Ni is further often observed especially to affect the iron metabolism (Nielsen et al. 1984, 1987). Low concentrations of nickel in soil (< 2 kg /hectare) have been documented to cause adverse effects in certain plants. For example, pecan trees (Carya illinoinensis) that have developed mouse ear, in which leaves and leaflets of affected trees are dwarfed compared with normal leaves, has been attributed to nickel deficiency (Bai et al. 2006). Summary Being both essential and toxic there is a window of essentiality for each species below which deficiency occurs and above which toxicity is induced. Hence, reduced growth of an organism may both be the caused by insufficient and excessive uptake of nickel. This window is almost certainly species specific. The fact that Ni is both essential and toxic indicates that this may have implications for the derivations of the PNEC, indicating the possibility of deriving a PNEC below essential requirements for certain organisms. However, at present there is an insufficient knowledge of the required ambient levels for organisms Soil characteristics of the test media In order to better (1) understand the observed differences in intraspecies sensitivity for nickel and (2) investigate the quality of the database (i.e. range of soil characteristics covered), the physico-chemical characteristics encountered in the toxicity tests were assessed thoroughly as follows: Background nickel concentrations 212

227 In order to calculate the PNEC for the terrestrial compartment, the background concentrations from the reported toxicity data has to be taken into account. An overview of the reported Ni background concentration encountered in the soil media used for toxicity tests is presented in Figure cumulative probability 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 0, background Ni (mg/kg) microbial processes invertebrates higher plants Figure Distribution of the observed background Ni concentration in the ecotoxicity tests In general, similar Ni background concentrations are observed between the soil media used for toxicity tests with plants and microbial processes. The median Ni background concentrations observed in the ecotoxicity tests with higher plants, invertebrates and microbial processes were respectively 14 mg/kg (10th % 2 mg/kg, 90th % 40 mg/kg for the higher plants), 11 mg/kg (10th % 1 mg/kg, 90th % 60 mg/kg for the invertebrates) and 18 mg/kg (10th % 2 mg/kg, 90th % 39 mg/kg for the microbial processes). ph In general, similar ph values are found in the soil media used for toxicity tests with plants, invertebrates microbial processes (Figure ). The median ph values from the plants was 6.1 (10 th % 4.2, 90 th % 7.7), for microbial processes 5.2 (10 th % 4.1, 90 th % 7.6) and for the invertebrates a median ph value of 6.0 was observed (10 th % 4.1, 90 th % 7.6). The ph range observed in the test media are mostly covered by the boundaries of the regression models, i.e. ph range between 3.6 and 7.7. Some higher plant tests were performed at ph values outside the boundaries of the regression models. However, it must be emphasized that ph is not the main driver for bioavailability for Ni in the soil environment. But soil ph also affects Ni bioavailability in soils, because the main driver for Ni bioavailability is CEC and CEC is amongst others dependent on ph (increasing CEC with increasing ph). Also leaching and ageing reactions seem to be dependent on ph (see section 2.7.2). 213

228 ph range regression models: 3,6-7,7 cumulative probability 1 0,8 0,6 0,4 0,2 0 microbial processes invertebrates higher plants ph Figure Distribution of the observed phs in the ecotoxicity tests & boundaries of regression models Organic matter content (OM) In general, similar organic matter contents are found in the soil media used for toxicity tests with plants, invertebrates microbial processes (Figure ). The median organic matter values from the invertebrates database was 2.3% (10 th % 0.7%, 90 th % 15.7%), for the microbial processes was 2.3% (10 th % 0.8%, 90 th % 12.8%) and for the higher plants 2.3% (10 th % 0.8%, 90 th % 6.8%). The OM range observed in the test media are covered by the boundaries of the regression models, i.e. OM range between 0.4 and 56.8%. 1 OM content regression models: 0,4-56,8% cumulative probability 0,8 0,6 0,4 microbial processes invertebrates 0,2 higher plants 0 0, OM content (%) Figure Distribution of the observed organic matter content in the ecotoxicity tests & boundaries of regression models - clay content In general, similar clay contents are found in the soil media used for toxicity tests with plants and invertebrates while the tests performed with microbial processes are done with soils with lower clay contents (Figure ). The median clay content value for the plants was 19.8% (10 th % 3.9, 90 th % 49.2%), for the invertebrates 19.8% (10 th % 2.7%, 90 th % 48.1% for the 214

229 invertebrates) while for the microbial processes a median clay content of 10% was observed (10 th % 3.0%, 90 th % 49.2%). The clay content range observed in the test media are covered by the boundaries of the regression models, i.e. clay content range between 0.4 and 55.4%. Some microbial processes & higher plant tests were performed at clay contents outside the boundaries of the regression models. However, it must be emphasized that the clay content is not the primary driver for bioavailability for Ni in the soil environment. cumulative probability 1 0,8 0,6 0,4 0,2 clay content regression models: 0,4-55,4 % microbial processes invertebrates higher plants 0 0, Clay content (%) Figure Distribution of the observed clay content in the ecotoxicity tests & boundaries of regression models Cation exchange capacity (CEC) In general, similar CEC are found in the soil media used for toxicity tests with plants, invertebrates microbial processes (Figure ). The median CEC content values for the microbial processes, plants and invertebrates were respectively 11.0, 12.6 and 13.3 cmol/kg (10 th % 4.9 cmol/kg, 90 th % 33 cmol/kg for the higher plants; 10 th % 3.1 cmol/kg, 90 th % 32.1 cmol/kg for the invertebrates; 10 th % 3.1 cmol/kg, 90 th % 35.3 cmol/kg for the microbial processes). The CEC range observed in the test media are covered by the boundaries of the regression models, i.e. CEC range between 1.8 and 7.7 cmol/kg. cumulative probability 1 0,8 0,6 0,4 0,2 0 CEC range regression models: 1,8-52,8 cmol/kg CEC (cmol/kg) microbial processes invertebrates higher plants Figure Distribution of the observed CEC in the ecotoxicity tests & boundaries of regression models. 215

230 Comparison with soil characteristics for European soils The values of the soil parameters in the ecotoxicity tests were compared with ranges reported for European soils (Table ). Data on the EU soil characteristics were obtained for the following regions and sources: Scattered world soil properties data from the International Soil Reference and Information Center (ISRIC) World soil types and soil properties data from the Food and Agriculture Organization (FAO) of the United Nations European soil properties and (heavy) metal concentration data from: Agricultural Soils in Northern Europe: A Geochemical Atlas (Clemens, 2003) European soil properties and heavy metal concentration data from Dick Brus of the Alterra Research Institute for the Green World, Soil Research Center ICP Forest database. The estimation of the frequency distribution of soil bioavailability parameters throughout the EU was performed using the area-based kriging analysis wherein the discrete data were used to interpolate a continuous surface for each parameter. The resulting EU soil surface was then used to define the frequency distributions based on area. All kriging analyses were conducted using ArcGIS Geostatistical Analyst. Because each dataset was accounted for in a comparable way using the area-based approach, this method was considerably more robust and the resulting frequency distributions are far less biased by individual datasets. The gathered data were further weighted on surface area and distribution functions for each soil variable known to mitigate Ni toxicity in soils (i.e. CEC, OM, ph and clay content) in EU soils were constructed as shown in Figure , , and From each distribution specific percentiles could be derived (these data were retrieved from the Cu RAR, 2005). Maps and range of the abiotic factors (ph, OM and CEC) in the EU soils according to the Parametrix database are provided in Appendix I.4. The maps are to some extend different from the maps provided from the FOREGS sampling programme. - Cation exchange capacity (CEC) The CEC content in EU soils ranges between 12.8 and 46.5 cmol/kg (range as 10 th and 90 th percentiles) as shown in Figure A typical CEC concentration (50 th %) of 20.5 cmol/kg was estimated in EU soils. On the other hand, the FOREGS database shows lower CEC, organic matter and clay content as compared to the data in the Parametrix database (see Table ). cumulative distribution CEC (cmol/kg) 216

231 Figure Distribution of the observed CEC content in EU soils. - ph The ph in EU soils ranges between 4.6 and 6.2 (range as 10 th and 90 th percentiles) as shown in Figure A typical ph value (50 th %) of 5.3 was estimated in EU soils. cumulative distribution ph Figure Distribution of the observed ph in EU soils Organic matter content The organic matter content in EU soils ranges between 2.7 and 26.7 % (range as 10 th and 90 th percentiles) as shown in Figure A typical OM concentration (50 th %) of 9.4 % was estimated in EU soils. cumulative distribution OM (%) Figure Distribution of the observed organic matter content in EU soils Clay content 217

232 The clay content in EU soils ranges between 17.2 and 29.2% (range as 10 th and 90 th percentiles) as shown in Figure A typical clay content (50 th %) of 23.3 % was estimated in EU soils. cumulative distribution clay content (%) Figure Distribution of the observed clay content in EU soils Comparison between the soil properties used in the ecotoxictiy tests,and those encountered in EU soils revealed similar ranges for ph, OM content and Ni background (Table ; Figure ). For the CEC (the main mitigating parameter) and OM content it seems that the ecotoxicity database is skewed towards lower values. In other words, more sensitive soils are included in the effects database compared to those encountered in the EU. Table Soil parameters of the selected toxicity studies (min-max values) and European soils (reported as 10 th and 90 th %) Parameter Plants Invertebrates Microbial tests ph Toxicity studies European soils (Parametrix) European soils (Foregs) 4.2*-7.1 OM (%) Toxicity studies European soils (Parametrix) European soils (Foregs) 1.2*-7.9 CEC (cmol/kg) Toxicity studies European soils (Parametrix) European soils (Foregs) ** Clay (%) Toxicity studies European soils (Parametrix) European soils (Foregs) 0.7*-8.0 Ni background Toxicity studies concentration (mg/kg) European soils no data *: estimated value from graphical interpolation; **: estimated value from the clay, OM and ph from the Foregs database according to the Helling et al. (1964) equation: CEC=( ph)*clay/100+( ph)*om/

233 Compared to the Foregs database (see Appendix I.5 for maps and range in abiotic factors across EU soils), a similar ph range was observed from the data collected by Parametrix. On the other hand, the Foregs database shows substantially lower CEC, organic matter and clay content as compared to the data collected by Parametrix. This observed difference in physicochemical boundaries of both database could be explained by the difference in sampling strategy. Indeed, the databases collected by Parametrix contain soil properties from both natural and agricultural soils (where higher organic matter and therefore CEC content are expected) while the Foregs database only contains soils sampled at locations that had no visible or known contamination (priority for site selection was given to forested and unused lands; greenland and pastures; and non-cultivated parts of agricultural land). Table and Figure suggests that the Foregs database may be somewhat skewed towards soils with a low organic matter and CEC content. The boundaries of the developed regression models cover the physico-chemical range for ph, OM, CEC and clay content for both databases, including the Foregs database soils. Moreover, the developed regression models cover the range of the physico-chemistry of the toxicity tests, especially for the most important factor mitigating Ni toxicity towards soil organisms, i.e. the CEC content of the soils. 219

234 Organic matter range (% ) EU soils (Parametrix) EU soils (Foregs) Toxicity data Regression models Organic matter (%) ph range EU soils EU soils (Foregs) Toxicity data Regression models ph Clay content range (% ) EU soils EU soils (Foregs) Toxicity data Regression models Clay content (%) CEC content range (cmol/kg) EU soils EU soils (Foregs) Toxicity data Regression models CEC content (cmol/kg) Figure : Soil parameters of the selected toxicity studies (min-max values), boundaries of the regression models (min-max values) and for the European soil programme programme and the FOREGS data (reported as 10 th and 90 th %). 220

235 Implementation of bioavailability IntroductionConsidering the bioavailability of nickel in soils, at least two phenomena on the ecotoxicity of nickel to soil organisms are apparent: 1) the toxicity response is highly dependent on soil type, and 2) the toxicity response is highly dependent on the time between the addition of soluble nickel to soils and the measurement of toxicity: in general nickel toxicity under field conditions is only observed at higher doses than under laboratory conditions. Further, it could be mentioned that the exposure time, the interaction with other compounds are also important. It is relevant for the risk assessment to account for those observed differences in bioavailability and hence to quantify the relationship between nickel speciation and bioavailability for different soils species and/or processes. The latter can be used to introduce a correction factor for bioavailability of nickel in soils. Recently these relationships, as further can be seen in Sections and 2.7.3, have been developed in the framework of an extensive conclusion (i) research programme (NiPERA, 2005). The quantifiers developed in this programme give the opportunity to address both the role of soil properties and the differences in nickel toxicity between laboratory spiked soils and soils contaminated in the field. The general approach used for implementing nickel bioavailability in soils is summarized in Figure Figure General approach used for the incorporation of Ni bioavailability in soils. 221