Selection of Scenarios for Exposure of Soil Organisms to Plant Protection Products 1

Transcription

1 SCIENTIFIC REPORT OF EFSA Selection of Scenarios for Exposure of Soil Organisms to Plant Protection Products 1 In support of Revision of the Guidance Document on Persistence in Soil under Council Directive 91/414/EEC and Council Regulation 11/07/2009 (SANCO/9188/VI/97 rev. 8, ) This scientific output, published on 07 April 2011 replaces the earlier version published on 16 June European Food Safety Authority 3, 4 European Food Safety Authority (EFSA), Parma, Italy 1 At the request of EFSA, Question No EFSA-Q , issued on 8 June Only minor editorial changes were made. These changes do not change the overall conclusions of the opinion. 3 Correspondence: PPR@efsa.europa.eu 4 Acknowledgement: EFSA wishes to thank the members of the sub-wg of the Persistence in Soil/fate WG for the preparation of this EFSA scientific report: Aaldrik Tiktak, Jan Vanderborght, Michael Klein and Ciro Gardi and EFSA s staff member from AMU Olaf Mosbach-Schulz and PPR Mark Egsmose for the support provided to this scientific output. EFSA also wishes to thank the hearing expert Ian Hardy for preparing appendix D of the report. The scientific report was reviewed by Jos Boesten, Richard Bromilow and Theo Brock. Suggested citation: European Food Safety Authority; Selection of Scenarios for Exposure of Soil Organisms to Plant Protection Products. [82pp.]. doi: /j.efsa Available online: European Food Safety Authority,

2 ABSTRACT European scenarios for exposure of soil organisms to plant protection products are currently not available. As part of the revision of the Guidance Document on Persistence in Soil (9188/VI/97 rev 8 published in 2000), the PPR-panel was therefore asked to start the development of tiered exposure-assessment approaches for soil organisms in which European exposure scenarios play an important role. This report contributes to this revision by developing a systematic approach to the selection of realistic worst-case scenarios for exposure of soil organisms. Realistic worst-case conditions are defined as the 90 th spatial percentile of the exposure concentration (the maximum over time) in the intended area of use in each of three regulatory zones defined in Council regulation (EC) 1107/2009. Separate scenarios were developed for the concentration in total soil (mg kg -1 ) and for the concentration in the liquid phase (mg l -1 ), giving six scenarios to be developed. The scenario selection began with the compilation of a coherent database. Then, a simplified model was selected to generate maps of the concentration in total soil and the concentration in the liquid phase over the entire area of annual crops in the three zones. In the subsequent two steps, procedures were applied to account for parameter uncertainty and scenario uncertainty (i.e. the likelihood that a scenario that is derived for one substance is not conservative enough for another). In the final step, the six scenarios were selected by defining their air temperature, soil organic matter content and their soil textural class. The concentration in total soil was shown to decrease in the order North>Central>South, whereas the concentration in the liquid phase showed an opposing trend with the highest concentration in the Southern European scenario. The scenario development was based on the total area of annual crops in the EU-27, but the endpoint of the exposure assessment is the 90 th percentile of the intended use area. The scenarios may therefore not be conservative enough. To ensure that the selected scenarios are sufficiently conservative, safety factors were derived. KEY WORDS Exposure assessment, exposure scenarios, scenario development, soil, parameter uncertainty, scenario uncertainty, plant protection product 2

3 SUMMARY European scenarios for exposure of soil organisms to plant protection products are currently not available. As part of the revision of the Guidance Document on Persistence in Soil (9188/VI/97 rev 8 published in 2000), the PPR-panel was therefore asked to start the development of tiered exposureassessment approaches for soil organisms in which European exposure scenarios play an important role. This report contributes to this revision by developing a systematic approach to the selection of realistic worst-case scenarios for exposure of soil organisms to substances in soil. Realistic worst-case conditions are defined as the 90 th spatial percentile of the exposure concentration (the maximum over time) in the intended area of use in each of the three regulatory zones described in Annex 1 of Council Regulation (EC)1107/2009 concerning the placing of plant protection products on the market. Separate scenarios were developed for the concentration in total soil (mg kg -1 ) and for the concentration in the liquid phase (mg l -1 ), so that the total number of scenarios developed was six. The scenarios are part of a tiered approach. The aim of this tiered approach is to assess this 90 th spatial percentile resulting from the use of the plant protection product (assuming a market share of 100%) and considering the population of agricultural fields (in one of the three regulatory zones) where the crop is grown and in which this plant protection product is applied. Tier-1 is proposed to be based on a simple analytical model. Tier-2 is to be based on simulations with numerical fate models. The Tier-1 and Tier-2 scenarios apply to the 90 th percentile of the entire area of annual crops, and are to be used for all plant protection products. In Tier-3 and Tier-4, specific crops and/or specific plant protection products with specific properties may be considered. The selection of the Tier-1 and Tier-2 scenarios consists of five steps. In the first step, a coherent database is created for the entire area of annual crops in the EU-27. Then, a model is selected to generate maps of the concentration in total soil and the concentration in the liquid phase in the entire surface area of annual crops in the three zones. In the subsequent two steps, procedures are applied to account for parameter uncertainty and scenario uncertainty (i.e. the likelihood that a scenario that was selected for one single substance is not conservative for another). In the final step, the scenarios are selected by defining their air temperature and the organic matter content and the textural class of the topsoil. The selection of the Tier-3 and Tier-4 scenarios follows in principle the same steps, but instead of using the total area of annual crops, the area may be limited to the intended area of use and the selection is made only for the substance under consideration. All data for the scenario selection were made available at a resolution of 1x1 km 2. Information about the subsoil was not available for the entire area of the EU-27, and so we carried out the scenario selection with topsoil properties only. We considered this acceptable, because the end-point for the exposure assessment is the 1-20 cm soil layer. The exposure scenarios apply to annual crops only, so a land-use mask was applied to remove other land-uses from the dataset. Because soil-profile data were not available, running a spatially distributed version of an existing fate model was not feasible. Therefore we developed the PERsistence in Soil Analytical Model (PERSAM), which uses four spatially distributed input parameters, viz.. the organic matter content; the bulk density; an Arrhenius weighted mean annual temperature; and the volume fraction of water at field capacity. The model calculates the steady-state concentration after infinite time (the background concentration). The peak concentration is calculated by taking the sum of this background concentration and the concentration immediately after application. Degradation is the only loss process considered from the top 20 cm layer. For calculating the background concentration, PERSAM assumes that the soil in the top 20 cm is perfectly mixed due to annual ploughing. Simulations from PERSAM showed good correlation with those from the Tier-2 fate model PEARL, and so its use for scenario selection was justified. Uncertainty analysis with PERSAM revealed that uncertainty about substance-fate parameters and soil parameters (in particular the bulk density) leads to higher substance concentrations at the 90 th 3

4 percentile of the concentration distribution than when uncertainty is not taken into consideration. A scenario that is derived without considering uncertainty is therefore not sufficiently conservative. Uncertainty about substance and soil properties has therefore explicitly been included in the scenarioselection procedure. Given that most registration dossiers contain only four values for DegT50 and K om, the best way to deal with parameter uncertainty is to use the median values of the substance property distributions in combination with a higher (95 th ) percentile of the distribution that does not consider uncertainty but covers only the effect of spatial variation in soil and climate properties. The Tier-2 scenarios should be sufficiently conservative for all relevant substances and for all evaluation depths. Due to the non-linearity of the relation between soil parameters, substance fate parameters and the predicted concentrations, this is not necessarily the case. The problem of this scenario uncertainty was tackled by overlaying maps of locations that are close to the 95 th spatial percentile for a large number of substances. From the common locations, the Tier-2 scenario was selected. Application of this procedure resulted in the selection of a single Tier-2 scenario for each zone and for each ecotoxicologically relevant type of concentration. The North-South gradient of organic matter in Europe leads to a North-South gradient of the soil bulk density. Consequently, the organic matter content of the selected concentration-in-total-soil scenarios increases and the bulk density decreases in the order South-Central-North. The peak concentration in total soil is primarily affected by the bulk density, so this predicted concentration increases when going from the Southern scenario to the Northern scenario. An opposing trend is found for the scenarios for the concentration in the liquid phase where, due to the decreasing organic matter content from North to South, the highest pore water concentration is predicted for the Southern European scenario. For both types of concentrations, the background concentration is generally small compared to the maximum concentration immediately after application (except for persistent substances where the background concentration is 50% of the concentration directly after application). As a consequence, the variation of the peak concentration comes mainly from the variation of the soil properties and rather little from the substance properties. The differences of the predicted concentrations between the scenarios were small: a factor of 1-4 for the concentration in total soil scenarios and a factor of for the pore-water scenarios, which is caused by the opposing spatial trends of organic matter and temperature in the European Union. The Tier-1 and Tier-2 scenarios apply to the total surface area of annual crops. However the end-point for the exposure assessment is, the 90 th percentile of the exposure concentration within the intended area of use of a plant protection product (this is also the target for the Tier-3 and Tier-4 scenarios). The area of the selected crop (or combination of crops) will have an effect on the 90 th percentile exposure concentration, so the Tier-1 and Tier-2 scenarios may not be conservative enough. To ensure that the selected scenarios are sufficiently conservative, we derived a series of safety factors. An assessment with 17 different crops and groups of crops (together covering all the area of annual crops within the EU-27) showed that the safety factors are generally small ( for the concentration in porewater scenarios and for the concentration in total-soil scenarios). These safety factors can be used if registration is needed for (groups of) these annual crops. We do not, however, guarantee these safety factors for sub-populations of these major crops (e.g. leek as a sub-population of vegetables). If a registration is needed for such a sub-population, the safety factor can always be based on the 100 th percentile of the entire population of annual crops within a zone. 4

5 TABLE OF CONTENTS ABSTRACT... 2 SUMMARY... 3 TABLE OF CONTENTS... 5 BACKGROUND AS PROVIDED BY EFSA... 6 TERMS OF REFERENCE AS PROVIDED BY EFSA... 6 ASSESSMENT INTRODUCTION Aim of study Targets for the exposure assessment Position of the realistic worst-case scenarios in the tiered assessment scheme Reading guidance SCENARIO DEVELOPMENT: A SYSTEMATIC APPROACH Introduction Overview of procedure Data compilation Model selection Derivation of the target spatial percentile with consideration of uncertainty about substance properties and soil properties Selection of scenarios that meet the target spatial percentile for multiple substances Scenario parameterisation Derivation of safety factors REFERENCES APPENDICES A. DESCRIPTION OF THE PERSISTENCE IN SOIL ANALYTICAL MODEL A1 Introduction A2 Mathematical model A3 Definition of an effective mean temperature A4 Spatial database used for evaluation A5 Comparison of PERSAM and PEARL A6 Listing of the Persistence in Soil Analytical Model B. ACCOUNTING FOR UNCERTAINTY IN THE SCENARIO SELECTION PROCEDURE B1 Introduction B2 Parameters considered uncertain B3 Sampling procedure B4 Effect of uncertainty on P90 concentrations B5 Contribution of spatial variability and uncertainty of substance and soil properties B6 Selection of areas where the overall 90 th percentile is exceeded C. EFFECT OF SUBSTANCE PROPERTIES ON THE SELECTED SCENARIO D. THE USE OF PEDOTRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY D1 Introduction D2 Correlation of bulk density with soil properties D3 Availability of pedotransfer functions D4 Variability in the estimated soil bulk density D5 Conclusion E. CROP COVER MAPS FOR THE CALCULATION OF SAFETY FACTORS

6 BACKGROUND AS PROVIDED BY EFSA During the review process of the substances of the second list, several concerns were raised regarding the Guidance Document on persistence in soil. A number of Member states have expressed interest in a revision of the current Guidance Document on persistence in soil during the general consultation of Member States on Guidance Documents in answer to the request by the Director of Sciences of EFSA in letter 3 July 2006 sent via the Standing Committee on the Food Chain and Animal Health. Further the EFSA PRAPeR Unit has noted that Guidance Document needs to be brought in line with the FOCUS degradation kinetics report (SANCO/100058/2005, version 2.0, June 2006). FOCUS (1997) developed the first guidance at EU level for exposure assessment in soil. This included a simple approach for estimating PEC SOIL but FOCUS (1997) did not develop first-tier scenarios (in contrast to subsequent FOCUS workgroups that developed such scenarios for surface water and groundwater) as development of soil scenarios was a lower priority at that time. FOCUS (2006) developed detailed guidance on estimating degradation rate parameters from laboratory and field studies, but did not develop exposure scenarios. Nevertheless there is need for such scenarios in view of ongoing discussions in PRAPeR experts groups regarding PECSOIL as current approaches at EU level just represent the range of climatic conditions covered by available field dissipation and or accumulation studies, and member states would like tools to be able to extrapolate to a wider range of climates occurring across the EU. The existing Guidance Document on Persistence in Soil (9188/VI/97 rev 8) published in 2000 does not include scenarios. The intention with the new guidance document is to update the existing Guidance Document on Persistence in Soil to include European exposure scenarios for soil and to provide guidance on best practice for using the results of field experiments and soil accumulation studies in the exposure assessment. TERMS OF REFERENCE AS PROVIDED BY EFSA The intention with this report is to provide the scientific methodology for the selection of the EU soil scenarios for estimating exposure of plant protection products to soil organisms. The report will provide scientific input to address the terms of references tasked by EFSA to the PPR Panel and approved on the 10 th of December 2007 and extended on the 3 rd of December 2009 by the EFSA Executive Director. The Scientific Panel on Plant Protection Products and their Residues (PPR Panel) of EFSA is asked to prepare a revision of the Guidance Document on persistence in soil (SANCO/9188VI/1997 of 12 July 2000). 6

7 ASSESSMENT 1. Introduction 1.1. Aim of study European scenarios for exposure of soil organisms are currently not available (EFSA, 2010a). There is, however, a need for such scenarios at the EU level in view of ongoing discussions in PRAPeR experts groups on PEC SOIL. Therefore, the PPR-Panel has started a revision of the existing Guidance Document on Persistence in Soil (SANCO/9188/VI/97 rev. 8, ) by developing tiered exposure-assessment approaches for soil organisms in which European exposure scenarios play an important role. The study reported here contributes to this revision by developing procedures for the selection of realistic worst-case scenarios for exposure of soil organisms to plant protection products. Here, a realistic worst-case scenario is defined as a combination of soil and climate properties within a certain region for which predicted concentrations (PECs) are equal to a certain percentile of the distribution of concentrations for all climate and soil property combinations within the region Targets for the exposure assessment FOCUS (2000) defined realistic worst-case conditions as the 90th percentile of PEC values within the agricultural area of use of the plant protection product in each of some ten climatic zones across the EU. The PPR-Panel checked with risk managers at Member State level whether a 90th percentile exposure concentration should also be used here, and their response confirmed this (EFSA, 2010a). In their reaction, several Member States also indicated that the exposure assessment procedure should be kept as simple as possible. Therefore, the PPR-Panel proposes to develop guidance for estimating 90th percentile values of PECSOIL for only the three zones described in Annex 1 of the new Regulation concerning the placing of plant protection products on the market (Figure 1). The exposure assessment is considered to be part of the terrestrial ecotoxicological effect assessment. This implies that it has to consider all types of concentration that are considered relevant for assessing the ecotoxicological effects. These concentrations are called Ecotoxicologically Relevant types of Concentration, abbreviated to ERC (Boesten et al., 2007). Based on EFSA (2009), the following types of concentrations are considered ecotoxicologically relevant: the concentration in total soil (adsorbed plus dissolved), expressed as mass of substance per mass of dry soil (mg kg-1) averaged over the top 1, 2.5, 5 or 20 cm of soil for various time windows: peak and time-weighted averages (TWA) for 7, 14, 21, 28 and 56 d; the concentration of substance in the liquid phase (mg l-1) averaged over the top 1, 2.5, 5 or 20 cm of soil for the same time windows. As described before, the maximum value in time (resulting from multiyear applications) will be the target for all types of concentration (Figure 2). So the 90th percentile will be based only on spatial aspects. The spatial 90th percentile PECSOIL within each of the three zones has to be based on a distribution of individual PECSOIL values. Each of these individual PECSOIL values is intended to be a correct estimate of the average value at the scale of individual agricultural fields to which the substance is applied. The assessment procedure will not account for the random spatial variability within such an individual field because the PPR-Panel considers this level of detail currently not sufficiently relevant for the risk-assessment schemes regarding ecotoxicological effects (EFSA, 2010a). The assessment procedure will account for systematic spatial variability (e.g. application of herbicides in orchards in strips below the trees, seed treatments). 7

8 Another aspect of the definition of the 90th percentile PECSOIL is the population of agricultural fields on which the percentile is based. The PPR-Panel proposes to base the definition of the population on the intended area of use e.g. for a plant protection product that is applied in potatoes, the population of fields is then defined as the fields on which potatoes are grown in a zone. Figure 1: Map with three regulatory zones described in Annex 1 of the new Regulation concerning the placement of plant protection products on the market (COM(2006) 388 final). Different scenarios have to be developed for each combination of crop type, tillage system and application technique, because the exposure assessment for the PEC SOIL depends strongly on (i) the type of crop (annual crops, grass, other permanent crops or rice), (ii) the tillage system, and (iii) the application technique of the plant protection product (EFSA, 2010a). This report is limited to the development of scenarios for annual crops in combination with conventional or reduced tillage and spray applications, because this combination comprises the largest surface area and the largest usage of plant protection products. 8

9 Concentration in total soil (mg/kg) 26 annual applications The peak Simulation with FOCUS_PEARL Figure 2: The target for all types of concentrations, i.e. the all time highest concentrations resulting from multi-year simulations. The realistic worst-case scenarios described in this report are part of tiered assessment schemes with five tiers (Figure 3). Two schemes will be developed, i.e. one for the concentration in total soil and one for the pore-water concentration. The schemes for the two types of ERCs are identical but the contents of the tiers differ so there are two parallel tiered assessment scheme. The tiered scheme applies to spray applications to annual crops under conventional or reduced tillage but may also be useful for other types of application or other tillage systems Position of the realistic worst-case scenarios in the tiered assessment scheme Tiers 1 and 2 are based on one scenario per zone for each of the two types of ERC, considering the total surface area of annual crops within a zone and assuming that substance parameters such as the DegT50 and the K om are not related to soil properties (e.g. the ph). In Tiers 3 and 4, the area of use can be restricted to specific crops and relationships between the K om and/or DegT50 and soil properties can be included. Tier 5 consists of an agreed spatially distributed modelling software tool at EU level that should be seen as a desirable future development. However, this tool is currently not available. The scenario-selection procedures in Tier-1 and Tier-2 are based on the total surface area of annual crops because this results in a robust procedure (Section 3.6). The end-point for the exposure assessment is, however, the 90 th percentile of the exposure concentration within the intended area of use of a plant protection product. The area of the selected crop (or combination of crops) will have an effect on the 90 th percentile exposure concentration, so the Tier-1 and Tier-2 scenarios as such may not be conservative enough. This conservatism problem is handled by introducing safety factors (Section 2.8). The scenario selection procedures in Tier-1 and Tier-2 further assume that the K om and DegT50 do not depend on soil properties. This is defensible for the majority of the plant protection products and their metabolites in soil. Therefore it is a logical starting point for these tiers. However, these tiers should also be conservative for substances whose K om and/or the DegT50 do depend on soil properties. This will be assured by using conservative values of the K om and/or the DegT50 of such substances in Tiers 1 and 2. 9

10 1 Conservative scenarios for analytical model (arable land and all substances) 2 Realistic worst-case scenarios for numerical models (arable land and all substances) 3 Crop- and/or substance specific scenarios for analytical model 4 Crop- and/or substance specific scenarios for numerical models 5 Spatially-distributed modelling with numerical models Figure 3: Tiered scheme for the exposure assessment for annual crops with conventional or reduced tillage and spray application. There are two identical assessment schemes, i.e. one for the concentration in total soil and one for the concentration in pore-water Reading guidance Chapter 2 gives an overview of the scenario development. A systematic approach consisting of five steps is presented, i.e. data compilation (Section 2.3), selection of a model for scenario selection (Section 2.4), correction for uncertainty in substance and soil properties (Section 2.5), the actual scenario selection based on a large number of substances (Section 2.6) and scenario parameterisation (Section 2.6). Chapter 3 provides the main conclusions and recommendations. A simplified spatially distributed version of the FOCUS fate models plays an important role in the scenario development. This model referred to as PERSAM (Persistence in Soil Analytical Model) is presented in Appendix A. Specific attention is given to the correspondence between PERSAM and one of the FOCUS fate models. Appendix B describes the role of parameter uncertainty in scenario development. A procedure is suggested in which the scenario selection is based on median substance properties and deterministic soil properties. Appendix C describes the effect of substance properties on the selected scenario. Appendix D gives background information on pedotransfer function for the soil bulk density, which is an important parameter because it directly affects the concentration in soil. Appendix E describes the maps of 17 major crops or crop groups that can be used for Tier-3 and Tier-4 assessments. 10

11 2. Scenario development: a systematic approach Selection of scenarios for exposure of soil organisms 2.1. Introduction This chapter describes the development of the Tier-2 scenarios for the two types of ERCs, namely the concentration in total soil (mg kg -1 ) and the concentration in pore water (mg l -1 ). As described in Chapter 2, the Tier-2 scenarios are combinations of soil and climatic properties within a zone, for which the predicted concentration is equal to the 90 th percentile of all concentrations within the area of annual crops. The end-point for the exposure assessment is, however, the 90 th percentile of the exposure concentration within the intended area of use of a plant protection product. The area of the selected crop (or combination of crops) will have an effect on the 90 th percentile exposure concentration, so the Tier-1 and Tier-2 scenarios as such may not be conservative enough. This conservatism problem is handled by introducing safety factors (Section 2.8). Geographical Information Systems (GIS) play an important role in the development of the Tier-2 scenarios. Soil information is combined with climate patterns and crop distribution by spatially overlaying basic maps using GIS. Such an overlay results in a large number of possible scenarios (in many cases more than 1000), which all need to be parameterised. If spatial information is available with sufficient detail, a spatially distributed model can be created (e.g. Tiktak et al., 2004). The soil end-points (the 90 th percentile concentrations) can be selected directly from the cumulative frequency distribution of the calculated concentrations. Data to fully parameterise comprehensive fate models are, however, not yet available for the entire EU-27. The EuroPEARL model, for example, covers only 75% of the total agricultural area of the former EU-15 (Tiktak et al., 2004). The reason is that the Soil Profile Analytical Database (SPADE) was not complete. Also the new version of SPADE (SPADE-8) is incomplete (Section 3.3). The alternative is to use a simple model with parameters that are available for the entire agricultural area of the EU-27 (e.g. Tiktak et al., 2006). So, the first challenge is to find a model that gives results comparable to the original model, but with parameters that are available for the entire EU-27. The derivation of Tier-2 scenarios is further complicated by the uncertainty about the soil and substance parameters that are used in the model. Analyses with the GeoPEARL model have shown that the overall 90 th percentile of the concentration of substance in leachate shifts towards higher values if uncertainty in substance properties is considered (Van den Berg et al., 2008; Heuvelink et al., 2008, 2010; Leterme et al., 2007). The consequence is that if scenarios are selected without consideration of uncertainty about substance properties, the selected scenario may not be sufficiently conservative. The second challenge is therefore to deal with uncertainty of substance parameters and soil properties in the scenario selection. Scenario selection is further complicated by the non-linearity of the relation between soil parameters, substance fate parameters and predicted environmental concentrations. This implies that the ranking of climate and soil property combinations is different for different substance properties. As a consequence, the percentile of the predicted environmental concentration of a certain substance using a soil and climate combination or scenario that was derived for other substance properties may differ from the intended percentile of the scenario. So, the third challenge is to find scenarios that are sufficiently conservative for all relevant substances Overview of procedure The development of the Tier-2 scenarios can be structured into five major steps, which are illustrated in the yellow box (Figure 4). The scenario selection begins with the compilation of a coherent database. Existing data are transferred to common formats and projections and quality checks are made. In the following step, a model is selected to generate maps of the concentration in total soil and the concentration in the liquid phase in the entire surface area of annual crops in the three zones. The selected model should be compatible with the available data, but should also give a good correlation 11

12 with the numerical models used in the Tier-2 scenarios. In the following steps, a procedure is applied to account for parameter uncertainty. This procedure comes down to selecting a spatial percentile that does not consider parameter uncertainty but assumes median parameter values. Finally, the scenarios are selected and parameterised. The development of Tier-3 and Tier-4 scenarios follows the same principles, but instead of using the entire area of annual crops, the area can be restricted to the intended use area and the selection is done with the substance under consideration only. Data compilation Selection of model for vulnerability mapping Development Derivation of the target spatial percentile with consideration of uncertainty about pesticide properties and soil properties Selection of scenarios that meet the target spatial percentile for multiple pesticides Scenario parameterization Simulation with the numerical fate models (e.g. PEARL and PELMO) End-points: Overall 90 th percentile of the concentration in total soil and the concentration in the liquid phase Figure 4: Illustration of the process to develop the Tier-2 exposure scenarios. The process has to be carried out for each combination of zone and ERC (i.e. six times) Data compilation The scenario-selection procedure starts with the compilation of a coherent database. A full description of the database can be found in Gardi (2010), here a brief summary is given. All of the geographical data in the database have been harmonised in the Lambert-Azimuthal Equal- Area (LAEA) ETRS 1989 projection system, which system preserves the area of individual polygons. The LAEA system is compliant with the European Commission s Infrastructure for Spatial Information in the Community (INSPIRE) guidelines (Annoni et al., 2001) and is used by the European Environment Agency s Corine land-cover map and the soil grids produced by the European Soil Bureau Network. The European Terrestrial Reference System (ETRS) is a geodetic reference system in which the Eurasian Plate as a whole is static, so that the co-ordinates are not subject to continental drift. All geographic data were converted to the ESRI ArcGrid raster format with a resolution of 1x1 km 2. The Tier-2 scenarios apply to 90 th percentile concentration in the total area of annual crops, and so a land-use mask is needed. This mask was created using the Corine (Coordinate Information on the 12

13 Environment) land-cover database. Corine land-cover classes 211 and 212 are identified as nonpermanent lands under a rotation system used for annually harvested plants and fallow lands, and so we included these two classes in the land-use mask. Land-cover data have been converted from the original 100x100 m 2 resolution to the 1x1 km 2 EFSA grids by taking the dominant land-use within each EFSA grid cell. Land-cover information was available for the years 1990 and 2000; only those grids that showed annual crops in both years were included in the mask (Figure 5). Figure 5: Non-permanent lands under a rotation system used for annually harvested plants and fallow lands within the EU-27. This map has been used to identify the area to which the Tier-2 scenarios apply. The fate of immobile substances is primarily affected by degradation and sorption. These two processes are primarily affected by organic matter content, bulk density, temperature and water content, so we need coherent maps of these four parameters (Figure 6). 13

14 Figure 6: Spatially-distributed input parameters used in the scenario selection procedure. The figure is limited to the area of annual crops (Figure 5). The map of organic matter content has been obtained by applying a conversion factor of 1.72 to the topsoil organic carbon map of Europe, which is described in detail by Jones et al., (2005). This map shows the distribution of organic carbon in European topsoils (0-30 cm). The map was obtained in two steps. First, for each combination of a soil typological unit and land-use type, an organic carbon class was predicted. Then, an area weighted average was calculated for each 1x1 km 2 grid cell (on the basis of the combinations present within a grid). This, in combination with a temperature correction, allowed the calculation of continuous values for each grid cell. Because the organic matter content map does not apply to arable land-use only, the frequency distribution of organic matter may be biased. 14

15 Soil bulk density has been calculated with a continuous pedotransfer approach. We evaluated a number of pedotransfer functions and concluded that pedotransfer functions using multiple parameters do not perform better than pedotransfer functions using only organic matter. The GeoPEARL pedotransfer function (Tiktak et al., 2002) was therefore considered appropriate for our purpose (Appendix D). Temperature has been derived from the WorldClim database (Hijmans et al., 2005). WorldClim is a set of global climate layers with a spatial resolution of 1x1 km 2. The data were aggregated to mean monthly averages for the period , which allowed the calculation of Arrhenius weighted long-term average temperatures (Appendix A3). The use of an Arrhenius weighted temperature leads to higher temperatures in climatic zones where the difference between winter and summer temperatures are high (continental and Mediterranean climates), so a clear west-east trend is visible (Figure 7). Figure 7: Difference between the Arrhenius weighted annual temperature as described in Appendix A3 and the mean annual temperature. The volume fraction of water at field capacity was derived from the soil textural class in the Soil Geographical Database of Europe, using the HYPRES pedotransfer rule (Wösten et al., 1999). The use of the volume fraction of water at field capacity as a substitute for the long-term average soil water content was justified by Tiktak et al. (2006), who showed that there was a good relationship with the long-term average water content simulated by EuroPEARL. 15

16 2.4. Model selection In this step, a model is selected to generate maps of the concentration in top soils in the entire area of annual crops in the EU-27. The most straightforward solution would be to run a spatially distributed version of one of the existing FOCUS fate models (e.g. Tiktak et al., 2004). Data to fully parameterise these fate models are, however, not yet available for the entire EU-27 because the Soil Profile Analytical Database (SPADE) is not yet complete (Figure 8). A simplified model, which is compatible with the available data, is therefore needed. The alternative would be to extrapolate the existing SPADE data to the entire EU-27 using a class matching approach (Centofanti et al., 2008), but this approach introduces additional uncertainty. The simplified model selected for scenario selection is the PERsistence in Soil Analytical Model (PERSAM). Analytical models are often used in risk assessment as an alternative to comprehensive numerical models, because they try to describe the most important processes with minimal effort and data requirements (e.g. Jury et al., 1983, 1987). In PERSAM, firstly the concentration in total soil immediately after application is calculated. In a second step, the model calculates the background concentration directly before the next application. Degradation is the only loss process considered during this step. The concentration in the liquid phase is calculated assuming linear equilibrium sorption (see Appendix A2 for the mathematical equations). Calculations for different substances showed that the background concentration is generally small compared to the maximum concentration directly after application (except for persistent substances and an evaluation depth of 20 cm where the background concentration is 50% of the concentration directly after application). As a consequence, the spatial variation but also the uncertainty about the peak concentration in total soil comes from the variation of the soil bulk density and hardly from substance properties. Since the uncertainty of the bulk density in the database is quite large compared to the spatial variation, the uncertainty of the peak concentration is also quite large compared with the spatial variation. For the pore water concentration, also the spatial variation of organic matter and the uncertainty about the K om leads to an additional variation in concentrations. PERSAM uses four spatially distributed model inputs available in the pan-european datasets described in Section 2.3, namely the Arrhenius weighted long-term average temperature, the organic matter content of the top soil, the bulk density of the top soil and the water content at field capacity of the top soil (Figure 6). The model uses an Arrhenius-weighed long-term average temperature instead of a normal long-term average temperature. This procedure guarantees that the same amount of decay over a time period is predicted as in the case that the temperature varies over time. The model simulates the concentration in total soil and the concentration in pore water, averaged over the top 1, 2.5, 5 or 20 cm of soil for various time windows: peak and time-weighted averages (TWA) for 7, 14, 21, 28 and 56 d. Different ploughing depths can be specified as well. We tested whether PERSAM gives comparable results to the PEARL model, which is one of the fate models that are to be used in combination with the Tier-2 scenarios. The EuroPEARL dataset (Tiktak et al., 2004) consisting of 1051 scenarios was used for this purpose. This dataset contains sufficient data to run both the comprehensive models and the analytical model. The correspondence between PEARL and PERSAM is generally good (Figures A3 and A4), so its use for vulnerability mapping is justified. 16

17 Figure 8: Coverage of the Soil Profile Analytical Database of Europe version 8 in land with annual crops. The peak concentration in total soil is generally high in Northern Europe and low in Southern Europe (Figure 9). This is caused by the inverse relationship between the peak concentration in total soil and the bulk density of the top soil (Figure 6). As mentioned before, the peak concentration comes from the build-up of two terms, i.e. the concentration directly after application and the background concentration. The background concentration is generally low compared to the concentration directly after application (Appendix B). Only for long-lived substances does the background concentration become important. For this reason, the third substance shows higher concentrations in total soil than the first two substances. So, substance properties do not have an effect on the concentration in total soil of short-lived substances. Due to this effect, the range of concentrations in total soil for the individual substances is generally small (approximately a factor of four between the lowest and the highest concentration encountered in the entire EU-27; e.g. Figure 10). The peak concentration in the liquid phase follows an opposite spatial trend, with high concentrations in Southern Europe and low concentrations in Northern Europe. This is caused by the inverse relationship between organic matter and the concentration in the liquid phase. It is because of this opposite spatial trend for the two ERCs that two sets of scenarios are needed. Note that the range for the concentration in pore water is much wider than the range for the concentration in total soil (up to a factor of 100 for the third substance). 17

18 Figure 9: Peak concentration of three example substances in total soil (left) and concentration in pore water (right) as simulated with the persistence in soil analytical model. Both the evaluation depth and the ploughing depth were set to 20 cm. 18

19 Figure 10: Spatial frequency distribution of the peak concentration in total soil (left hand side) and the peak concentration in pore water (right hand side) for the three example substances shown in Figure 8. 19

20 2.5. Derivation of the target spatial percentile with consideration of uncertainty about substance properties and soil properties As shown in the previous paragraph, PERSAM can be used to generate a concentration map for the entire area of annual crops within the three zones. The 90 th spatial percentile can be directly inferred from the cumulative frequency distribution of this map. However, the overall 90 th percentile of the substance concentration shifts towards higher values if uncertainty in substance properties and soil properties is considered (Appendix B). The consequence is that if scenarios are selected without consideration of uncertainty about substance and soil properties, the selected scenario may not be sufficiently conservative. Uncertainty about substance properties and soil properties has therefore been explicitly incorporated in the scenario-selection procedure. To quantify the effect of parameter uncertainty on the 90 th percentile concentration, an uncertainty analysis was carried out (Appendix B). This used the EuroPEARL dataset, which consists of 1051 scenarios (Tiktak et al., 2004). Two substance properties (the degradation half-life and the coefficient for sorption on organic matter) and one soil property (the soil bulk density) were assumed to be uncertain. Standard deviations and parameter distributions were obtained from the literature. The uncertainty analysis resulted in a cumulative probability density function (cpdf) showing the combined effect of variability and uncertainty. This cpdf can be compared with that resulting from a simulation with median substance properties and deterministic soil properties (i.e. the cumulative frequency distribution mentioned above). The 90 th percentile of the cpdf that includes substance and soil property uncertainty was confirmed (Figures B5 and B6) to correspond to higher concentrations than those of the 90 th percentile of the cpdf that considers spatial variability only. Figures B5 and B6 can also be used to derive a spatial percentile of the cpdf that does not consider parameter uncertainty but assumes median parameter values so that the concentration at this percentile corresponds with the overall 90 th percentile (see Figure 11 for procedure). The advantage of using this percentile is that in later steps, the scenario selection can be based on the cpdf resulting from median substance properties and deterministic soil properties. It was found that for the soil exposure endpoints (peak concentration in total soil and concentration in the liquid phase), the 90 th overall percentile corresponds to the 95 th percentile of the cpdf resulting from median substance properties and deterministic soil properties (Figure B14) Selection of scenarios that meet the target spatial percentile for multiple substances During this step, scenarios are selected that meet the target spatial percentile. As described in Section 2.5, the target percentile is the 95 th percentile in space if based on a simulation using median substance properties and deterministic soil properties. The scenarios should be sufficiently conservative for all relevant substances and for all evaluation depths. However, due to the non-linearity of the relation between soil parameters, substance fate parameters and predicted environmental concentrations, the ranking of climate and soil property combinations is different for different substance properties and different evaluation depths. As a consequence, a Tier-2 scenario derived for one substance or for one evaluation depth, may not be sufficiently conservative when applied to another substance or another evaluation depth ( scenario uncertainty, Appendix C1). To overcome this problem, the scenario selection procedure should be based on multiple substances and multiple evaluation depths. The following procedure was developed: Maps of the concentration in total soil and the concentration in the liquid phase were created for a set of 19 substances with different properties. Maps were created for two evaluation depths, i.e. 1 cm and 20 cm, so a total number of 38 maps were created. Properties of the 19 PPPs are shown in Figure 12; 20

21 All these maps were normalised: the grid cell with the highest concentration scored 100%, while the grid cell with the lowest concentration scored 0% (the vulnerability score); From each vulnerability map, the grid cells with a vulnerability score between 94 and 96% (target vulnerability ± 1%) were selected. We used a range of 95±1% for pragmatic reasons: with a smaller range it would have been impossible to find a sufficient number of scenarios in view of the available datasets; In a final step, the 95±1% vulnerability maps of all 19 PPPs and the two evaluation depths were overlain and those grid cells that were common in all maps were considered candidate scenarios. 1 zrel= 20 cm kom=1000 l/kg DT50 = 200 d 0.95 cum. freq spatial variability total Cl (mg/l) Figure 11: Procedure to derive the spatial percentile of the cpdf that does not consider substance and soil property uncertainty (red line) but predicts the same concentration as the 90th percentile of the overall cpdf (blue line). (This example shows the cpdf for the peak concentration in pore water, CL, and an ecologically relevant depth of 20 cm). 21

22 DegT50 (d) Kom (L kg -1 ) Not relevant, because these PPPs exceed 0.1 μg/l in groundwater Figure 12: Properties of the 19 substances included in the scenario selection. Substances included are shown in green. Results indicate that it is possible to find for all zones scenarios that are in the target vulnerability range for all substance-depth combinations (Figure 13). It is therefore possible to reduce the number of scenarios that should be developed to two for each climatic zone, i.e. one for the liquid phase concentration and one for the concentration in total soil. Figure 13: Geographical position of scenarios that conform the target vulnerability for all of the 38 substance-depth combinations. 22

23 The above described overlay procedure results in a number of possible scenarios. We further reduced the set of candidate scenarios to those scenarios that have organic matter contents and temperatures within 1% of the mean value of all candidate scenarios. This procedure avoids extreme scenarios to be selected. Mean properties of the candidate concentration in total soil scenarios are shown in Table 1, mean properties of the candidate concentration in liquid phase scenarios are shown in Table 2. The tables show that different scenarios are selected for the concentration in total soil than for the concentration in the liquid phase: the scenarios for the concentration in total soil generally have high organic matter contents whereas the scenarios for the concentration in the liquid phase generally have low organic matter contents. The tables further show that the organic matter content generally decreases in the order North > Central > South. These findings indicate that the scenario selection yields plausible scenarios. It can further be concluded that differences between the scenarios selected for the three zones are meaningful due to the North-South gradient of organic matter in the EU. Table 1: Mean properties of the concentration in total soil scenarios. Temperature T avg is the yearly average temperature at the location of the scenario. If calculations have to be done with PERSAM, the Arrhenius weighted temperature (T eff ) should be used. Zone T avg f om Texture T eff ( o C) (%) ( o C) Zone North Coarse 7.0 Zone Central Coarse 10.1 Zone South Medium fine 12.3 Table 2: Mean properties of the selected concentration in liquid phase scenarios. Temperature T avg is the yearly average temperature at the location of the scenario. If calculations have to be done with PERSAM, the Arrhenius weighted temperature (T eff ) should be used. Zone T avg f om Texture T eff ( o C) (%) ( o C) Zone North Medium 9.8 Zone Central Medium 11.2 Zone South Medium 14.7 We calculated the concentration in total soil and the concentration in the liquid phase for an evaluation depth of 20 cm and for all substances (Figure 12). The differences of the predicted concentrations between the three scenarios were small (Figure 14): a factor of 1-4 in the case of the concentration in total soil scenarios and a factor of in the case of the pore water scenarios. These small differences are caused by the opposite spatial trend of organic matter and temperature in the European Union. 23

24 Figure 14: 90 th percentile of the predicted concentration in total soil (top panel) and the concentration in the liquid phase (lower panel) for an evaluation depth of 20 cm as a function of substance properties. The lines refer to the three regulatory zones Scenario parameterisation Once a scenario location is selected, appropriate soil, weather and crop data need to be assigned. EFSA (2010b) describes the parameterisation procedure; here only a brief summary is given. Soil data The scenario selection was based on properties of the top soil (organic matter and texture). However, the fate models need information about subsoil properties as well. If the spatial coverage of European soil profile databases had been 100%, this information could have been directly extracted from this database. However, as this is not the case (Figure 8), we calculated average soil profiles, based on all arable soil profiles available in the SPADE-1 database (Table 3). Averages were calculated for the 0-30 cm soil layer, the cm soil layer, the cm soil layer and the cm soil layer (cf. 24

25 FOCUS, 2000). We judged the use of average soil profiles acceptable, because the evaluation depth for the exposure assessment is 1-20 cm. Soil texture was directly assigned to the actual scenario, using the soil textural class in Tables 1 and 2. Table 3 shows the organic matter content relative to the organic matter content of the top 30 cm. Only two depth profiles were calculated (one for mineral soils and one for organic soils), because differences between the soil textural classes of mineral soils were not significant. The actual organic matter content of the scenario was calculated by the equation: f = f f (1) om z, om om,0 where f om (kg kg -1 ) is the mass fraction of organic matter, f z,om (-) is the organic matter content relative to the topsoil organic matter content, and f om,0 (kg kg -1 ) is the organic matter content of the topsoil, which has been derived in the scenario selection procedure (Tables 1 and 2). Soil bulk density was derived from organic matter using the equation (Tiktak et al., 2002): ρ = f 2910 f ( r 2 = 0.91) (2) om om where ρ (kg m -3 ) is the dry bulk density and f om refers to the organic matter content of the scenario. The use of this pedotransfer function is justified in Appendix D. Soil hydraulic functions as required by PEARL were obtained from the soil textural class using the HYPRES pedotransfer rules (Wösten et al., 2004) and are also listed in Table 3. The water content at field capacity and the water content at wilting point required by PELMO were estimated with the analytical function proposed by van Genuchten (1980): θ θ θ ( h) = θ + (3) r s r n ( 1+ αh ) m where θ (m 3 m -3 ) is the volume fraction of water, h (cm) is the soil water pressure head, θ s (m 3 m -3 ) is the volume fraction of water at saturation, θ r (m 3 m -3 ) is the residual water content in the extremely dry range, α (cm -1 ) and n (-) are empirical parameters, and m (-) can be taken equal to: 1 m = 1 (4) n The depth of the soil profile was assumed to be 2 m. The lower boundary condition of the hydrological model is not expected to have a large effect on the predicted concentration in top soil. For pragmatic reasons, a free-drainage boundary condition was therefore assumed for all scenarios. For the top 1 cm, the thickness of the numerical compartments was set to 1 mm, because the evaluation depth of the exposure assessment can be as small as 1 cm. The layer thickness was 1 cm for the 1-30 cm soil layer, 2.5 cm for the cm soil layer, and 5 cm for the cm soil layer. The dispersion length was set to a value of 2.5 cm (Vanderborght et al., 2007). This value differs from the value used in FOCUS (2010), because the evaluation depth is 1-20 cm, whereas the evaluation depth for the FOCUS scenarios is 100 cm. All other soil parameters, including the depth dependence of transformation, were set to the default values published by FOCUS (2010). 25

26 Table 3: Mean soil profiles for the soil textural classes at the soil map of Europe. The soil profiles (totalling 534) were calculated using all arable soil profiles in the SPADE database. Coarse Depth a Sand Silt Clay b f z,om f z θ s θ r α n K s λ > Medium Depth Sand Silt Clay f z,om f z θ s θ r α n K s λ > Medium fine Depth Sand Silt Clay f z,om f z θ s θ r α n K s λ > Fine Depth Sand Silt Clay f z,om f z θ s θ r α n K s λ > Very fine Depth Sand Silt Clay f z,om f z θ s θ r α n K s λ > Organic Depth Sand Silt Clay f z,om f z θ s θ r α n K s λ > a) Depth (cm) is the depth, sand (%) is the sand content, silt (%) is the silt content, clay (%) is the clay content, f z,om (-) is the organic matter content relative to the topsoil organic matter content, f z (-) is the depth dependence of degradation in soil, θ s (m 3 m -3 ) is the volume fraction of water at saturation, θ r (m 3 m -3 ) is the residual water content in the extremely dry range, α (cm -1 ) and n (-) are empirical parameters of the van Genuchten equation, K s (m d -1 ) is the saturated hydraulic conductivity, and λ (-) is a shape parameter. b) Standard error of f z,om is 0 for the 0-30 soil layer, and 0.02 for the other soil layers. 26

27 Weather data The MARS climate database provides daily weather data for the entire EU-27 in a 25x25 km 2 grid. Therefore, daily weather data as needed by the fate models can be directly extracted for the selected scenario locations from the appropriate MARS grid. The MARS database contains all parameters that are required for simulation runs with the current fate models such as minimum and maximum temperature, rainfall, potential evapotranspiration and global radiation. A quality check was performed to check if the dataset contains unrealistic data (see EFSA (2010b) for details). We used MARS weather data for the period The weather data provided by MARS was converted to a 66- year time-series using the rules described in FOCUS (2000). We used the WorldClim dataset for scenario selection, because this dataset is available at a higher spatial resolution than the MARS climate database. To guarantee consistency in the scenario selection procedure, we scaled the daily temperature of the MARS time series (T day,mars ) so that the mean annual temperature equals the temperatures in Tables 1 and 2: T day, scenario Tday, MARS Tavg, MARS + Tavg, scenario = (5) where T day,scenario is the daily mean temperature in the scenario, T avg,scenario is the mean annual temperature of the scenario as listed in Tables 1 and 2, and T avg,mars is the mean annual temperature of the MARS time series. Crop data and irrigation The Tier-2 scenarios have been developed for a range of annual crops (typically some 15 crops for each scenario). Crop emergence and harvest dates for these crops were extracted for each 25x25 km 2 MARS grid cell from the MARS database. Crop development between emergence and harvest is described with phenological crop development stages having a value of 0 at emergence, 1 at flowering and 2 at maturity. These crop development stages are currently available for the FOCUS scenarios. We decided to use the crop development stages and crop parameters from these existing FOCUS scenarios (FOCUS, 2010). This was justified, because the FOCUS scenarios together represent the crop growth conditions in the EU-27 (FOCUS, 2010). The corresponding FOCUS scenario was selected from a map of FOCUS climatic zones (Figure 15). So, we used Jokioinen crop data for the CTN-scenario, Châteaudun crop data for the CLS, CTC and CLC scenarios, Kremsmünster crop data for the CTS scenario and Sevilla crop data for the CLS scenario (Figure 15). A crop that is irrigated in the corresponding FOCUS scenario is also assumed to be irrigated in the soil scenario. So, crops are potentially irrigated in the CTC, CLC and CTS scenarios. 27

28 Figure 15: Climatic zones according to the definition given in FOCUS (2000). The dots correspond to the position of the six Tier-2 scenarios Derivation of safety factors The scenario selection procedures in Tier-1 and Tier-2 are based on the total surface area of annual crops. The end-point for the exposure assessment is, however, the 90 th percentile of the exposure concentration within the intended area of use of a plant protection product. The area of the selected crop (or combination of crops) will have an effect on the 90 th percentile exposure concentration, and so the Tier-1 and Tier-2 scenarios as such may not be conservative enough. This conservatism problem is handled by introducing safety factors. The safety factors were derived from Tier-3 simulations for 17 annual crops or combinations of crops together covering 100% of the area of annual crops in the EU-27 and for three different substances (see Figure 10 for substance properties), and so 51 combinations were simulated. For each of these, the 90 th percentile concentration (approximated by the 95 th spatial percentile) within each regulatory zone was calculated. Crop distributions were based on CAPRI land-use maps (Leip et al., 2008), which were obtained by applying a downscaling procedure which uses CORINE land-cover data, census data and land-suitability classes (Appendix E). In the calculation of the frequency distributions, each grid was assigned a weighing factor based on fraction of land covered by each crop. Resulting frequency distributions and 95 th spatial percentiles are shown for one example substance (Figures 16 and 17). The range of frequency distributions is large (Figure 16), but the differences are relatively small at level of the 95 th spatial percentile (Figure 17). The safety factor for each Tier 2 scenario was obtained by comparing the 95 th spatial percentile of all 51 Tier-3 simulations with the Tier-2 scenario (which corresponds to the 95 th spatial percentile for the entire area of annual crops). We started with the calculation of the ratio between the 95 th spatial percentile for the crop-substance combinations with the 95 th spatial percentile for the entire area of annual crops: 28

29 f s, i, j, k P95i, j, k = (6) P95 annual, k where P95 i,j,k is the 95 th spatial percentile concentration for crop i, substance j and zone k, and P95 annual,k is the 95 th spatial percentile concentration for the total area of annual crops within zone k. The safety factor for each zone and for each type of concentration is the maximum of all 51 ratios obtained. The estimated safety factors are generally small (Table 4), except for the concentration in total-soil scenarios for zone North. These high safety factors are caused by two crops (oats and other fodder crops, Figure 17). A relatively large proportion of these crops is grown in areas high in organic matter (i.e. Norway and Finland, see Appendix E for crop maps). Table 4: Safety factors for the Tier-2 scenarios based on Eqn. 3. Safety factors in this table can be used if a registration is required for the 17 major crops. For each zone, the safety factor shown is the maximum of all ratios obtained with Eqn. 3. Figures in brackets show the range of all ratios obtained. Safety factor for C T (-) Safety factor for C L (-) North 1.79 ( ) 1.02 ( ) Central 1.16 ( ) 1.15 ( ) South 1.07 ( ) 1.13 ( ) The safety factors were derived for 17 major annual crops or groups of annual crops. We do not guarantee the safety factor for subpopulations of these major crops or groups of crops (e.g. leek as a subpopulation of vegetables). If a registration is required for such a subpopulation, the applicant should derive the safety factor based on the distribution of the subpopulation (this comes down to moving directly towards Tier-3 or Tier-4). If this information is not available, the safety factor can always be based on the 100 th percentile of the entire population of annual crops (Table 5). Table 5: Safety factors for the Tier-2 scenarios based on the 100 th percentile of the entire population of annual crops within each zone. Safety factors in this table have to be used if a registration is required for subpopulations of the 17 major crops and if information about the spatial distribution of the crops is not available. Safety factor for C T (-) Safety factor for C L (-) North 3.20 ( ) 1.41 ( ) a Central 2.13 ( ) 1.33 ( ) South 2.60 ( ) 1.39 ( ) a) Data points with organic matter content of zero were removed from the dataset 29

30 Figure 16: Spatial frequency distribution of the peak concentration of substance P3 (see Figure 10 for properties) in total soil (right hand side) and the peak concentration in pore water (left hand side) for the three regulatory zones. The red line shows the frequency distribution for the total area of annual crops, the gray area is composed of individual frequency diagrams for the 17 CAPRI crop types. 30

31 Figure 17: 95 th spatial percentile of the peak concentration of substance P3 in total soil (left hand side) and the concentration in pore water (right hand side) for the total area of annual crops and the 17 CAPRI crop types for the three regulatory zones. See Figure 10 for substance properties. 31

32 CONCLUSIONS 1. As part of the proposed revised assessment procedure of exposure of soil organisms to substances in soil, Tier-2 scenarios were developed that correspond to the 90 th spatial percentile of the exposure concentration (the maximum over time) in the intended area of use in each of the three regulatory zones described in Annex 1 of the new Regulation concerning the placing of plant protection products on the market. Separate scenarios were developed for the concentration in pore water and the concentration in total soil, giving a total of six developed scenarios. 2. A systematic approach was developed and successfully applied for the development of these Tier- 2 scenarios. The scenario selection begins with the compilation of a coherent database; a model is then selected to generate maps of the concentration in total soil and the concentration in the liquid phase for the entire surface area of annual crops in the three zones. In the next steps, procedures are applied to account for parameter uncertainty and scenario uncertainty. The selection of the Tier-3 and Tier-4 scenarios follows in principle the same steps but, instead of using the total area of annual crops, the area may be limited to the intended area of use and the selection is done only with the substance under consideration. 3. Application of a spatially distributed version of an existing fate model was not possible, because the soil-profile analytical database of Europe is not complete. For this reason, the PERsistence in Soil Analytical Model (PERSAM) was developed. This model uses only parameters that are available for the entire area of the EU, i.e. properties of the top 30 cm of the soil and an Arrhenius weighted mean annual temperature. The model calculates the steady-state concentration after infinite time (the background concentration). The peak concentration is calculated by taking the sum of this background concentration and the concentration directly after application. Degradation is the only loss process considered from the top 20 cm layer. For calculating the background concentration, PERSAM assumes that the soil in the top 20 cm is perfectly mixed due to annual ploughing. Simulations with PERSAM showed good correlation with those from the Tier-2 fate model PEARL, so its use for scenario selection was justified. 4. The background concentration is generally small compared to the maximum concentration immediately after application (except for persistent substances and an evaluation depth of 20 cm where the background concentration may be up to 50% of the concentration directly after application). As a consequence, the spatial variation but also the uncertainty about the peak concentration in total soil comes from the variation of the soil bulk density and rather little from the substance properties. For the pore-water concentration, the spatial variation of organic matter and the uncertainty about the K om also lead to additional variation in the concentrations. 5. Uncertainty about substance-fate parameters and soil parameters (DegT50, K om and the dry bulk density of the soil) leads to higher substance concentrations of the 90 th percentile of the concentration distribution than where uncertainty is not taken into account. As a consequence, uncertainty should be accounted for during the scenario-selection procedure. Given that most dossiers contain only four data points, the best way to deal with parameter uncertainty is to use the median values of the substance property distribution in combination with a higher percentile of the distribution that does not consider substance property uncertainty but covers only the effect of spatial variation in soil and climate properties. 6. The likelihood that a scenario selected for one substance would not be conservative for another substance was found to be large. The problem of this scenario uncertainty was tackled by overlaying maps of locations that are close to the target spatial percentile for a number of substances. From the common locations, the Tier-2 scenario was selected. Application of this 32

33 procedure resulted in the selection of a single scenario for each zone and each ecotoxicologically relevant type of concentration. 7. The organic matter content of the selected concentration in total-soil scenarios increases in the order South-Central-North. Consequently, the bulk-density shows an opposing trend. The peak concentration in total soil is primarily affected by the bulk density, and so the predicted peak concentration in total soil increases when going from the Southern European scenario to the Northern European scenario. An opposing trend is found for the concentration in pore-water scenarios where, due to the decrease of organic matter from North to South, the highest pore water concentration is predicted for the Southern European scenario. 8. The differences of the predicted concentrations between the three scenarios were small: a factor of 1-4 for the concentration in total-soil scenarios and a factor of for the pore-water scenarios. Differences for the pore-water scenarios are smaller than differences for the total concentration in soil scenarios, which is caused by the opposing spatial trend of organic matter and temperature in the European Union. 9. The scenario-selection procedures in Tier-1 and Tier-2 are based on the total surface area of annual crops. The end-point for the exposure assessment is, however, the 90 th percentile of the exposure concentration within the intended area of use of a plant protection product. The area of the selected crop (or combination of crops) will have an effect on the 90 th percentile exposure concentration, so the Tier-1 and Tier-2 scenarios as such may not be conservative enough. Safety factors can be used to deal with this conservatism problem. An assessment with 17 crops and groups of crops (together covering all the area of annual crops within the EU-27) showed that the safety factors are generally small ( for the concentration in pore-water scenarios and for the concentration in total-soil scenarios). These safety factors can be used if a registration is needed for (groups of) these annual crops. 10. We do not guarantee these safety factors for subpopulations of these major crops or groups of crops (e.g. leek as a subpopulation of vegetables). If a registration is required for such a subpopulation, the applicant should derive the safety factor based on the distribution of the subpopulation (this comes down to moving directly towards Tier-3 or Tier-4). If this information is not available, the safety factor can always be based on the 100 th percentile of the entire population of annual crops within each zone. RECOMMENDATIONS 1. A systematic approach for the development of scenarios has been applied to the persistence in soil end points. The principles behind this approach can also be applied to other fate end-points such as the concentration in groundwater and the concentration in ditch water resulting from drainage, overland flow and drift. We recommend re-evaluation of the existing groundwater scenarios and surface-water scenarios using the systematic approach described in this report in order to check if these scenarios meet the target vulnerability. 2. The development of this exposure-assessment methodology has demonstrated the importance of high-quality databases of soils, crop areas and climate with 100% coverage of the EU. To make the guidance operational, we recommend that the Commission ensures access to state-of-the-art databases of soils, crop areas and weather for the stakeholders. To ensure transparency, we further recommend that the description of the structure of the databases and of the data sources is made publicly available. 3. The organic matter content map of the European Union gives for each 1x1 km 2 grid cell average values for four types of land-use, so that it is not possible to calculate the frequency distribution of 33

34 organic matter in arable land alone. We recommend that the Commission ensures that separate organic matter maps are made available for the four major land-uses in the EU

35 REFERENCES Allen, R., and A. Walker, The influence of soil properties on the rates of degradation of metamitron, metazachlor and metribuzin. Pesticide Science (18): Alexander, E.B Bulk densities of California soils in relation to other soil properties. Soil Sci. Soc. Am. J. (44): Annoni A., Luzet C., Gubler E., Ihde J., 2001, Map Projections for Europe. Report EUR EN, JRC-Ispra (Italy), p 131. Boesten J.J.T.I., H. Köpp, P.I. Adriaanse, T.C.M. Brock, V.E. Forbes, Conceptual model for improving the link between exposure and effects in the aquatic risk assessment of pesticides. Ecotoxicology and Environmental Safety (66): Centofanti T., J.M. Hollis, S. Blenkinsop, H.J. Fowler, I. Truckell, I.G. Dubus, and S. Reichenberger (2008). Development of agro-environmental scenarios to support pesticide risk assessment in Europe. The Science of the Total Environment, 407: De Vries, F Karakterisering van Nederlandse gronden naar Fysisch-chemische kenmerken. Staring Centrum, Wageningen. Rapport 654. EFSA Opinion on a request from EFSA related to the default Q10 value used to describe the temperature effect on transformation rates of pesticides in soil. Scientific Opinion of the Panel on Plant Protection Products and their Residues (PPR-Panel). The EFSA Journal (622):1 32. EFSA The usefulness of total concentrations and pore water concentrations of pesticides in soil as metrics for the assessment of ecotoxicological effects. The EFSA Journal (922): EFSA 2010a. Scientific Opinion on outline proposals for assessment of exposure of organisms to substances in soil. The EFSA Journal (2010); 8(1):1442. EFSA. 2010b. Scenarios for exposure assessment in soil for terrestrial effect assessment. The EFA Journal (2010). In prep. FOCUS, Soil persistence models and EU registration. Report of the FOCUS Soil Modelling Workgroup. Available at FOCUS website FOCUS FOCUS groundwater scenario s in the EU review of active substances. Report of the work of the Groundwater scenarios working group of FOCUS, version 1 of 1 November EC Document Reference Sanco/321/2000rev. 2, 202 pp. FOCUS Guidance document on estimating persistence and degradation kinetics for environmental fate studies on pesticides in EU registration. Report of the FOCUS Work Group on degradation kinetics. EC Document reference Sanco/10058/2005 version 2.0, 434 pp. FOCUS Assessing potential movement of active substances and their metabolites to ground water in the EU. EC Document reference Sanco/xxx/2010. Available at FOCUS website Gardi, C Report on the Activities realised within the Service Licence Agreement between JRC and EFSA, as a support of the FATE Working Group of EFSA PPR. JRC Scientific Report (SLA/EFSA-JRC/2008/01). Hallett, S.H., Hollis, J.M., Keay, C.A. (1998). Derivation and evaluation of a set of empirically-based algorithms for predicting bulk density in British soils. In: Hallett, S.H. The development and application of spatial information systems for environmental science. PhD thesis, Cranfield University Heuvelink, G.B.M., F. van den Berg, S.L.G.E. Burgers, and A. Tiktak Uncertainty analysis of the GeoPEARL pesticide leaching model. In: J. Zhang and M.F. Goodchild (Ed.) Spatial Uncertainty. World Academic Press, Liverpool, UK, pp Heuvelink, G.B.M., S.L.G.E. Burgers, A. Tiktak, and F. van den Berg Uncertainty and sensitivity analysis of the GeoPEARL pesticide leaching model. Geoderma (155): Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones, and A. Jarvis Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatology (25):

36 Hollis, J.M., Jones, R.J.A., Marshall, C.J., Holden, A., Van de Veen, J.R. and Montanarella, L. (2006). SPADE- 2: The soil profile analytical database for Europe, version 1.0. European Soil Bureau Research Report No.19, EUR EN, 38pp. Office for Official Publications of the European Communities, Luxemburg. Jones, R.J.A, R. Hiederer, E. Rusco, P.J. Loveland, and L. Montanarella Estimating organic carbon in the soils of Europe for policy support. European Journal of Soil Science (56): Jury, W.A., D.D. Focht, and. W.J. Farmer Evaluation of pesticide groundwater pollution potential from standard indices of soil-chemical adsorption and biodegradation. J. Environ. Qual. (16): Jury, W.A., W.F. Spencer, and W.J. Farmer Behavior assessment model for trace organics in soil. I. Model description. J. Environ. Qual. (12): Leonaviciute N. (2000) Predicting soil bulk density and particle densities by pedotransfer functions from existing soil data in Lithuania, Geografijos metrastis, 33, pp Leip, A., G. Marchi, R. Koeble, M. Kempen, W. Britz, and C. Li Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen and carbon losses from arable soils in Europe. Biogeosciences (5): Leterme, B., M. Vanclooster, A.M.A. van der Linden, A. Tiktak and M.D.A. Rounsevell (2007). Including spatial variability in Monte Carlo simulations of pesticide leaching. Environmental Science and Technology (2007): Madsen-Breuning, H. and R.J.A. Jones The establishment of a soil profile analytical database for the European Union, p In: D. King et al. (ed.) EUR EN. Office for Official Publ. of the European Communities, Luxembourg. Manrique, L.A., and C.A. Jones Bulk-density of soils in relation to soil physical and chemical-properties. Soil Sci. Soc. Am. J. (55): Tiktak, A., J.J.T.I. Boesten, A.M.A. van der Linden, and M. Vanclooster Mapping Ground water vulnerability to pesticide leaching with a process-based metamodel of EuroPEARL. J. Environ. Qual. (35): Tiktak, A., D.S. de Nie, A.M.A. van der Linden, and R. Kruijne Modelling the leaching and drainage of pesticides in the Netherlands: the GeoPEARL model. Agronomie (22): Tiktak, A., D.S. de Nie, J.D. Piñeros Garcet, A. Jones, and M. Vanclooster Assessment of the pesticide leaching risk at the Pan-European level. The EuroPEARL approach. J. Hydrol. (289): Van den Berg, F., D.J. Brus, S.L.G.E. Burgers, G.B.M. Heuvelink, J.G. Kroes, J. Stolte, A. Tiktak, and F. de Vries Uncertainty and sensitivity analysis of GeoPEARL. Alterra report 1330, PBL report , Alterra, Wageningen, the Netherlands, pp Vanderborght, J., and H. Vereecken Review of dispersivities for transport modelling in soils. Vadose Zone Journal (6):1-28. Van Genuchten, M. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal (44): Walker A., and J.A. Thompson The degradation of simazine, linuron and propyzamide in different soils. Weed Research (17): Wösten, J.H.M., A. Nemes, A. Lilly, and C Le Bas Development and use of a database of hydraulic properties of European soils. Geoderma (90):

37 APPENDICES DESCRIPTION OF THE PERSISTENCE IN SOIL ANALYTICAL MODEL A1 Introduction This appendix describes the PERsistence in Soil Analytical Model (PERSAM), which is used for scenario selection. PERSAM is an analytical model, which calculates the degradation of substances in a single soil compartment. The mathematical model is described in section A2. The analytical model selected here uses four spatially distributed model inputs, namely the long-term average temperature, the organic matter content of the top soil, the bulk density of the top soil and the water content at field capacity of the top soil. The model uses an Arrhenius-weighed long-term average temperature instead of a normal long-term average temperature. This procedure is described in section A3. Scenario selection should be done with a model that gives comparable results as the fate models that are to be used in combination with the Tier-2 scenarios. For this reason, it was tested if the analytical model gives comparable results as the PEARL model, which is one of the fate models that are to be used. Results are given in Section A5. Comparison has been done using the EuroPEARL dataset, consisting of 1051 scenarios. This dataset is used, because it contains all the data that are needed to run both PERSAM and the PEARL model. The EuroPEARL dataset is shortly described in Section A4. A2 Mathematical model In PERSAM, first the initial concentration in total soil directly after application is calculated: C T, ini = DOSE ( ρ + Δρ) z (A1) rel where C T,ini (mg kg -1 ) is the initial concentration in total soil, DOSE is the annual application rate (kg ha -1 or mg dm -2 ), z rel (dm) is the ecologically relevant depth, ρ is the dry soil bulk density (kg.dm -3 ) and Δρ is a random term that accounts for the uncertainty in ρ. In the second step, the background concentration, C T,plateau (mg kg -1 ), just before the next application after an infinite number of applications is calculated: C T, plateau 365 f Tfθ fkkref zrel e = CT, ini (A2) 365 f Tfθ fkkref ztil 1 e where z til (dm) is the plough depth, and k ref (d -1 ) is the reference first-order rate coefficient at a reference temperature T ref and reference soil moisture content θ ref. f k is a random factor that accounts for the effect of the uncertainty of k ref. f T and f θ are factors that account for the effects of temperature and soil moisture on degradation. f T (-) is given by: E 1 1 f T = exp (A3) R T T ref 37

38 where E is the Arrhenius activation energy, (kj mol -1 ), R (kj mol -1 K -1 ) is the gas constant, T (K) is the temperature, and T ref (K) is the temperature at reference conditions (usually 20 deg C). The first-order rate coefficient is calculated from the degradation half-life ln(2) k ref = (A4) DegT 50 where DegT50 (d) is the degradation half-life in soil at reference temperature. The background concentration corresponds to the residue left, directly before the next application. The maximum concentration directly after application is calculated by: C T, peak CT, ini + CT, plateau = (A5) where C T,peak (mg kg -1 ) is the maximum concentration in total soil. For a linear sorption isotherm, the concentration in the liquid phase is calculated from the total concentration in the soil by: C L C = θ / + T ( ρ + Δρ ) f om f KomK om, ref (A6) where C L (mg L -1 ) is the concentration in the liquid phase, C T (mg kg -1 ) is the concentration in total soil, θ (m 3 m -3 ) is the volume fraction of liquid in soil, f om (kg kg -1 ) is the mass fraction of organic matter, K om,ref (dm 3 kg -1 ) is the reference coefficient for sorption on organic matter, and f Kom a random factor that accounts for the effect of substance property uncertainty. The concentration in the liquid phase can be calculated for the initial concentration (C L,ini ), the background concentration (C L,plateau ) and for the maximum concentration (C L,peak ). PERSAM can also be used to calculate TWA concentrations. A TWA concentration is defined as the concentration that is averaged over a certain time period since the application time: C TWA t avg 1 ' ( tavg ) C() t' dt = (A7) t avg 0 where t avg (d) is the time period since the application time over which concentrations are averaged. For a substance undergoing first-order decay, the TWA total soil concentration, C T,TWA for a certain period after the application, t avg, is calculated from: t avg 1 ' ( t ) C exp( f f k t ) dt CT = (A8) or: C, TWA avg T, peak T k ref ' tavg 0 C T, peak ( t ) = [ 1 exp( f f k t )] T, TWA avg T k ref avg (A9) tavg ft f kkref It should be noted that C T,TWA also depends on the decay rate. This is contrast to C T,peak, in which only the first term C T,plateau, depends on the decay rate. 38

39 A3 Definition of an effective mean temperature In PERSAM, an average temperature is taken to account for the temperature effect on the decay rate. The model can also be applied to account for temperature effects when the temperature varies with time. This, however, is only possible if an effective mean temperature is used instead of an arithmetic average of the monthly mean temperature. The following equations illustrate how this effective mean temperature can be derived. The starting point is the simple first-order degradation rate equation with a temperature and time dependent decay rate, k(t,t): dm dt or: = k( T, t)m (A10) dm M = k( T, t)dt (A11) Using the Arrhenius equation to rescale the reference first-order decay rate, k ref, to the decay rate at a certain temperature k(t) gives: dm M E act 1 1 = exp kref dt (A12) R T () t T ref After integration, the following relation is obtained between the mass at a certain time, t end, and the initial mass M 0 : ( t ) t M k end end ref Eact ln = M exp E RT () t dt (A13) 0 act 0 exp RTref An average temperature T mean, which stays constant with time and for which at time t end the same amount of mass is retained, can be defined: k ref E tend exp RT E act exp RTref act mean kref = E exp RT After rearrangement T mean is obtained from: act ref t end 0 Eact exp RT () t dt (A14) T mean = 1 R ln t end t end 0 E act E () act exp dt RT t (A15) Using T mean in the Arrhenius equation, the same amount of decay over the time period from t = 0 until t = t end is predicted as in the case that the temperature varies over time. 39

40 In the calculation of T mean, it is assumed that the Arrhenius equation can always be applied. However for temperatures smaller than 0 C (273 K), it is assumed that there is no decay. In this case, the formula for T mean can be given as: T mean if T ( t) > 273 then f else E = t 1 Rln tend f ( T, t) = 0 act end 0 f ( T, t) ( T, t) dt E = exp RT act () t (A16) Application of Eqn. A16 to the PERSAM dataset described in section 2.3 shows that the temperature shifts towards higher values (Figure A1). This effect is strongest in climatic zones where the difference between summer and winter temperatures is high (continental and Mediterranean climates see also Figure 7). Figure A1: Comparison of the mean annual temperature (left-hand side) and the Arrhenius weighted mean annual temperature (right-hand side). A4 Spatial database used for evaluation We tested if PERSAM gives comparable results as the PEARL model, which is one of the FOCUS fate models that are to be used in combination with the Tier-2 scenarios. Such a test is only possible if sufficient soil data is available to run also the FOCUS fate model. The EuroPEARL dataset (Tiktak et al., 2004) is one such dataset. This dataset consists of 1086 unique combinations of soil and climate, covering approximately 75% of the agricultural area of the EU-15. The four spatially-distributed parameters required by PERSAM were obtained from this dataset, so that both PEARL and PERSAM use the same data (Figure A2). 40

41 Figure A2: Basic soil and climatic information available in the EuroPEARL dataset. The figure shows organic matter in the top 1 m, in this study organic matter in the top 30 cm was used. The organic matter content of the top 30 cm was derived from the Soil Profile Analytical Database of Europe, version 1 (SPADE-1; Madsen-Breuning and Jones, 1995), using a horizon-thickness weighted averaging procedure. The bulk density of the top 30 cm of the soil (kg m -3 ) was derived from the organic matter content using the GeoPEARL pedotransfer function (Tiktak et al., 2002): ρ = f 2910 f ( r 2 = 0.91) (A17) om om where ρ (kg m -3 ) is the bulk density of the soil and f om (kg kg -1 ) is the organic matter content. See Appendix D for a justification of the use of the GeoPEARL pedotransfer function. The soil textural class was derived from the Soil Geographical Database of Europe, and was needed for the calculation of the volume fraction of water at field capacity (Wösten et al., 1999). The use of the volume fraction of water at field capacity was justified by Tiktak et al. (2006), who showed that there was a good relationship with the long-term average water content simulated by EuroPEARL. The mean annual temperature was derived from the MARS weather database. It was assumed that the mean annual temperature of the 0-30 cm soil layer equalled the mean annual air temperature. From the 1086 unique soil-climate combination in EuroPEARL, 1051 combinations, excluding the locations with peat soils (mainly grassland) were retained for further analyses. 41

42 A5 Comparison of PERSAM and PEARL To test the correspondence between PERSAM and EuroPEARL, both models were applied to the EuroPEARL dataset. The comparison was done for three substances (Table B1). EuroPEARL simulations were done for winter wheat, which implies that it was assumed that the total agricultural area of the EU-15 was covered with winter wheat. Substances were surface applied at a rate of 1 kg/ha annually. Application was one day after crop emergence in EuroPEARL (i.e. between October 18 in North-Western Europe and December 1 in Southern Europe). Tillage depth was assumed to be 20 cm. In EuroPEARL, tillage was done annually at September 15, right before substance application. The EuroPEARL soil profiles were ploughed to a depth of 20 cm, so the comparison applies to normal tillage agriculture. The output variable investigated was the all time highest peak (in other words the averaging time window was set to zero days). Two ecological relevant depths were considered, i.e. 1 cm and 20 cm. Both the concentration in total soil and the concentration in the liquid phase were compared. Results of the comparison are shown in Figures A3 and A4. Both figures show an excellent correlation between EuroPEARL results and results from PERSAM (R 2 > 0.97 for all end-points). The figures also show that in the case of the pore water concentration, the slope is different from 1. Using a Freundlich exponent of 1 instead of 0.9 (the value in EuroPEARL) eliminates also these differences (figure not shown). An additional analysis was carried out for averaging time-windows of 28 days and 365 days. Figure A5 shows an example of this analysis for an averaging time window of 28 days. The figure shows that the correlation is less than in the case of the peak concentration. Based on this analysis, we concluded that PERSAM can safely replace EuroPEARL in the scenario selection procedure if the peak concentration is considered. In the case of longer time-windows, the use of the analytical model becomes less defensible because dissipation processes like leaching and plant-uptake become more important. 42

43 Figure A3: Comparison of the peak concentration in total soil (left) and the peak concentration in the liquid phase (right) simulated with EuroPEARL and simulated with the analytical model for an ERD of 1 cm and a tillage depth of 20 cm. 43

44 Figure A4: Comparison of the peak concentration in total soil (left) and the peak concentration in the liquid phase (right) simulated with EuroPEARL and simulated with the analytical model for an ERD of 20 cm and a tillage depth of 20 cm. 44

45 Figure A5: Comparison of the concentration in total soil (left) and the concentration in the liquid phase (right) simulated with EuroPEARL and simulated with the analytical model for an ERD of 20 cm and a tillage depth of 20 cm. The averaging time window was set to 28 days. 45

46 A6 Listing of the Persistence in Soil Analytical Model The complete listing of the Persistence in Soil Analytical Model follows below. The script is written in awk, but any other programming language is suitable well. BEGIN { # User settings DegT50 = 50; Kom = 20; Zrel = 2; Ztil = 2; DOSE = 1; TWA = 28; } { } # Degradation half-life (d) # Sorption coefficient on organic matter (kg/l) # Evaluation depth (dm) # Tillage depth (dm) # kg/ha # Time window (d) kref = /DegT50; # Eqn. A4: Degradation constant (d-1) # Plot specific input ID = $1; # Plot ID Zone = $2; # Zone ID Area = $3; # Surface area (pixels) fom = $4/100; # Organic matter (kg.kg-1) Rho = $5/1000; # Bulk density (kg.dm-3) Teff = $6; # Mean annual effective temperature (deg C) WFC = $7/Rho; # Water content at field capacity (dm3.kg-1) TeffTWA = $8; # Mean effective temperature during TWA period (deg C) # Eqn. A1: Concentration after application (mg.kg-1) CTini = DOSE/(Zrel*Rho); # Eqn. A3: ft ft = exp(-7866*(1/(teff )-1/293.15)); # Exponent in Eqn. A2 exp365 = exp(-365.0*kref*ft); # Eqn. A2: Plateau concentration (mg/kg) CTplat = (Zrel/Ztil)*CTini*exp365/(1.0-exp365); # Eqn. A5: Peak concentration in total soil (mg/kg) CTpeak = CTplat + CTini; # Eqn. A6: Peak concentration in the liquid phase (mg/l) CLpeak = CTpeak/(WFC+fom*Kom); # ft during TWA period fttwa = exp(-7866*(1/(tefftwa )-1/293.15)); # Eqn. A9: Concentration in total soil after TWA days CTTWA = CTpeak/(TWA*ftTWA*kref)*(1-exp(-ftTWA*kref*TWA)); # Eqn. A6: Concentration in the liquid phase after TWA days CLTWA = CTTWA/(WFC+fom*Kom); # Output printf ("%4d %12.5f %12.5f %12.5f 12.5f\n",ID,CTpeak,CLpeak,CTTWA,CLTWA); 46

47 ACCOUNTING FOR UNCERTAINTY IN THE SCENARIO SELECTION PROCEDURE B1 Introduction PERSAM can be used to generate a concentration map for the entire agricultural area of the EU-27. The 90th percentile can be directly inferred from the cumulative frequency distribution of this map. However, the overall 90th percentile of the substance concentration shifts towards higher values if uncertainty in substance properties and soil properties is considered. The consequence is that if scenarios are selected without consideration of uncertainty about substance and soil properties, the selected scenario may not be sufficiently conservative. Uncertainty about substance properties and soil properties was therefore explicitly incorporated in the scenario selection procedure. This appendix describes how this was done. To quantify the shift of the 90th percentile concentration, an uncertainty analysis was carried out in which two substance properties (the degradation half-life and the coefficient for sorption on organic matter) and one soil property (the soil bulk density) were assumed to be uncertain (Section B2 and B3). The uncertainty analysis resulted in a cumulative probability density function (cpdf) showing the combined effect of variability and uncertainty. This cpdf can be compared with the cpdf resulting from a simulation with median substance properties and deterministic soil properties (i.e. the cumulative frequency distribution mentioned above). Results in Section B4 confirm that the 90th percentile of the cpdf that includes substance and soil property uncertainty corresponds with higher concentrations than the 90th percentile of the cpdf that considers spatial variability only. Section B5 shows that the persistence in soil end-points (peak concentration in total soil and concentration in the liquid phase), the 90th overall percentile corresponds to the 95th percentile of the cpdf resulting from median substance properties and deterministic soil properties. B2 Parameters considered uncertain Substance properties The degradation half-life (DegT50) and the coefficient for sorption on organic matter (K om ) were assumed to be uncertain. Although these properties are related to the physico-chemical properties of the substance, decay rates and sorption to organic matter also depend on the soil properties. The degradation half-life of a substance in soil differs between soil types. Allen and Walker (1987) and Walker and Thompson (1977) studied the effect of soil properties on the rate of degradation of various substance in 18 soils, with a clay percentage greater than 15%. The substances studied were metamitron, metazachlor, simazine, linuron and propyzamide. The average coefficient of variation was measured for simazine and the highest was measured for metazachlor. Figure B1 shows that the coefficient of variation was on average about 25%. 47

48 Figure B1: The coefficient of variation for the half-life for 5 substances, averaged over 18 UK soils (metamitron, metazachlor, simazine, linuron and propyzamide) as derived from Allen and Walker (1987) and Walker and Thompson (1977). Allen and Walker (1987) and Walker and Thompson (1977) also studied the effect of soil properties on the sorption coefficient on organic matter. The lowest coefficient of variation was measured for linuron, whereas the highest was measured for metamitron (Figure B2). On average the coefficient of variation was about 25%. Figure B2: The coefficient of variation for the sorption coefficient for 6 substances in 18 UK soils (metamitron, metazachlor, simazine, linuron, propyzamide and metribuzin) as derived from Allen and Walker (1987) and Walker and Thompson (1977). Based on this, we assumed that the CV of the two substance properties equals 25% and that the given mean properties of the substances correspond with the arithmetic means of the properties. The mean and standard deviation of the log transformed parameters μ and σ are related to the arithmetic mean E(X) and CV of the untransformed variable X as: 48

49 E( X ) μ = ln (B1) 2 CV + 1 σ = ln( CV 2 + 1) (B2) A log-normally distributed variable X can be written as: X = f X ξ (B3) where ξ = exp(μ) is the geometric mean of X and f X is a log-normally distributed variable with geometric mean equal to 1. k ref and K om,re therefore represent the geometric means of the degradation rate and sorption coefficients, respectively. We further assumed no correlation between substance properties and soil properties. Further, because of the wide grid spacing, we assumed no spatial correlation. In order to investigate the effect of substance property uncertainty on the distribution of the predicted concentrations, three dummy substances with different properties were considered. Mean values of the degradation half-life and the coefficient for sorption on organic matter are listed in Table B1.. The only other parameter in PERSAM is the Arrhenius activation energy, which was set to 65.4 kj mol -1 (EFSA, 2007) for all three substances. Table B1: Arithmetic mean of the substance properties and parameters of the log lognormal distribution. See equation B1 and B2 for the derivation of parameters μ and σ. Substance Arithmetic Mean CV μ σ DegT50 (d) (-) P P P K om (L/kg) P P P The resulting probability density functions are shown in Figure B3. 49

50 Figure B3: Probability density functions of the degradation half-life (d) and the coefficient for sorption on organic matter (L/kg) for substance P1, P2 and P3. Soil properties Some soil properties in the EuroPEARL dataset namely the soil bulk density and the water content at field capacity were predicted from other soil properties using pedotransfer functions. These predictions are afflicted with uncertainty. The soil bulk density is the only soil property that determines the total concentration in soil, CT. The pore water concentration, CL, depends also on the soil water content and the organic matter content. In this study, only the uncertainty about the soil bulk density was considered. This uncertainty was derived from the residuals of the pedotransfer functions that are reported in the literature. A detailed study about different pedotransfer functions and their performance to predict different datasets of bulk soil densities is given in Appendix D. The pedotransfer functions could predict only a part of the total variability of ρ b in the datasets that were used to parameterise the pedotransfer functions (which is quantified by the coefficient of determination, R²). When the transfer functions are used to predict a measured ρ b, Alexander (1980) found a standard error of ρ b of g/cm³ for upland soils and g/cm³ for alluvial soils. This is similar to the standard error of 0.19 g/cm³ that was found by Manrique and Jones (1991) using a pedotransfer function that uses only organic carbon. In order to show the magnitude of this uncertainty with respect to the variability of ρ b in the EuroPEARL database, the cumulative distribution of ρ b in 50

51 the database was plotted versus the cumulative normal distribution of ρ b with a mean of 1.25 g/cm³ (mean of the database) and a standard deviation of 0.15 g/cm³. This distribution represents the uncertainty of the prediction of ρ b by the pedotransfer function % 90.00% 80.00% APECOP database uncertainty of ptf 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% ρ b (kg/m³) Figure B4: Distribution of the bulk density in the EuroPEARL or APECOP database and the distribution around the mean bulk density in the database representing the uncertainty of the pedotransfer function prediction. To assess the effect of the uncertainty of the bulk density in the EuroPEARL database on the predicted concentrations, a random term Δρ was added to the bulk density. The random term was assumed to be normally distributed with a mean of 0 and a standard deviation of 0.15 g cm -3. If the value of ρ+δρ was smaller than the smallest value in the data base, the smallest value in the database was taken. B3 Sampling procedure For each random variable 20 values of f kom, f k, and Δρ were drawn at regular intervals of the lognormal cumulative density functions (for f kom, f k ) (cdfs) and of a normal cumulative density function (for Δρ). This implies that the samples were taken at the 2.5, 7.5, 92.5 and 97.5% percentiles. The use of this sampling procedure was justified, because the cdfs are continuous rising functions. Comparison with results from a full Monte Carlo analysis (results not shown) confirmed that this assumption was justified. The final result was a matrix with 20x20x20x1051 rows, so each location was characterised by 8000 parameter combinations. The same set of parameter combinations of f kom, f k and Δρ was used for each location. B4 Effect of uncertainty on P90 concentrations The effect of uncertain substance and soil properties on the cpdf is shown in Figures B5-B7. Each figure shows three lines: The full line shows the effect of the spatial variability of soil and climate properties. It results from an application of PERSAM to the 1051 locations with the substance properties set to the median values. 51

52 The line with long dashes shows the effect of uncertainty of substance properties and of the bulk density. It results from a simulation at the location where the median concentration is simulated using median substance properties. The line with short dashes shows the combined effect of variability and uncertainty. For an ecologically relevant depth of 1 cm, the background concentration after infinite time (Figure B7) is small compared to the maximum concentration after application (FigureB5). This is true for all three substances, even for the long-lived substance P3. Furthermore, the maximum total concentration in soil is almost not affected by the substance properties since the cpdfs of the three different substances are very similar Figure B5. Substance properties do have an effect on the cpdf of the background total concentration (FigureB7). The background concentration, however, is not considered a relevant ecotoxicological endpoint. Yet, for a larger ecotoxicologically relevant depth (e.g. 20 cm) and long lived substances (P3) the background concentration become more relevant compared with CT,in and substance properties (and their uncertainty) plays a role for CT,peak. For the long-lived substance P3 and an ecologically relevant depth of 20 cm (Figure B6), the background concentration is about 50% of the peak concentration. In this case, the peak total concentration in soil is affected by the substance properties. Since time window averaged concentrations depend on the degradation rate, it is evident that time window averages of pore water and total soil concentrations depend on the substance properties (Figure B8). For both concentration types (pore water and total concentration), both ecotoxicologically relevant depths (1 cm and 20 cm), and for the three substances, the uncertainty of the substance properties and the soil bulk density has an effect on the cpdf of the concentration distribution. The most obvious effect is the smoothing of the cpdf that considers both substance and soil property uncertainty and soil and climate variability (short dashes) when compared with the cpdf including only the spatial variation of climate and soil properties (full line). Due to this smoothing, the 90 th percentile of the cpdf that includes substance and soil property uncertainty corresponds with higher concentrations (P90 concentration) than the 90 th percentile of the pdf that considers spatial climate and soil variability only. Another effect of the substance and soil parameter uncertainty is the distribution of the concentration at one location (in this case the location where for the median soil and substance properties, the median of the concentration distribution for all locations is obtained) which is represented by the dashed line. For all cases, this distribution indicates that the concentrations that are predicted for a certain pixels are afflicted with quite some uncertainty. For the total concentrations, the distributions of concentrations at a certain location span almost the spatial distributions. The total peak concentrations are hardly depending on the substance properties (except for the long lived substances and an ecotoxicologically relevant depth of 20 cm). As a consequence, the spatial variation but also the uncertainty of the peak total concentrations comes from the spatial variation of the bulk soil density. Since the uncertainty of the bulk soil density in the data base is quite large compared with the spatial variation (Figure B4), the uncertainty of the peak concentrations is also quite large compared with the spatial variation. For the pore water concentrations, also the spatial variation of the organic matter content (which we assumed not to be afflicted by uncertainty) leads to an additional spatial variation in concentrations so that the spreading of predicted concentrations compared with the spreading due to substance and soil parameter uncertainty is larger. For time window averaged concentrations, we considered a window of 365 d, which is considerably larger than the largest time averaging window that will be considered for the soil PECs (i.e. 56 d). For smaller averaging time windows, the average temperature during the time window is different from the mean annual temperature which was available for our calculations. The results from our analysis can therefore not be extrapolated to smaller time windows in a quantitative way but rather illustrate qualitatively the effect of time averaging. The uncertainty of the predicted concentrations is for all substances, also the rapidly degrading ones, depending on the uncertainty of the degradation rate. As a consequence, there is an additional uncertainty factor for the time window averaged concentrations, which is not important for the peak concentrations. This may explain the larger effect of substance and soil property uncertainty on the cpdfs of time window averaged concentrations than on the cpdfs of peak concentrations. 52

53 The contribution of the parameter uncertainty on the P90 concentration and the contribution of the uncertainty of each individual parameter to the P90 concentration are shown in Figure B9 and B10 for an ecologically relevant depth of 1 and 20 cm, respectively. The ratio of the P90 concentration considering no uncertainty or considering uncertainty of only one parameter to the P90 concentration considering uncertainty of all parameters is used to quantify the effect of uncertainty and the contribution of each parameter to this effect. When the ratio of the P90 concentration considering no uncertainty to P90 concentration considering uncertainty is small, then parameter uncertainty has an important impact on the P90 concentrations. When the ratio of the P90 concentration considering uncertainty of only one parameter to the P90 concentration considering uncertainty of all parameters is close to 1, the uncertainty of this parameter plays an important role. Parameter uncertainty is more important for an ecologically relevant depth of 20 cm than a depth of 1 cm. For 1 cm relevant depth, the P90 of the total concentration is underestimated by 5% when uncertainty is not considered. This increases to 15% for more persistent substances (P3) and a relevant depth of 20 cm. Since pore water concentrations are influenced by an additional uncertain parameter, K om, the effect of neglecting soil and parameter uncertainty is larger for pore water concentrations than for total concentrations. For an ecologically relevant depth of 20 cm and a more persistent substance, the P90 peak pore water concentration is underestimated by 20% when uncertainty is not considered. Since time window averaged concentrations depend stronger on the uncertain degradation rate than peak concentrations just after application, the effect of neglecting uncertainty is also larger for time window averaged than for peak concentrations. This is more outspoken for substances that degrade rapidly (P1) (Figure B10). For the peak total concentrations, the effect of parameter uncertainty can almost completely be attributed to the uncertainty of the bulk density. Exceptions are long-lived substances for an ecologically relevant depth of 20 cm. For these cases, also the uncertainty about the decay rate plays a role. For the peak pore water concentrations, the uncertainty about the sorption coefficient K om seems to be the most important. For more persistent substances, the uncertainty about the decay rate and the soil bulk density becomes more important. For time window averaged concentrations, the impact of parameter uncertainty on the P90 concentration is even to the largest extent caused by the uncertainty about the degradation rate. 53

54 Figure B5: Cumulative distributions of peak concentrations in the pore water (C l mg l -1 : left) and the total concentration (C T mg kg -1 : right) for P1 (upper), P2 (middle) and P3 (lower), for a ploughing depth of 20 cm and an ecologically relevant depth of 1 cm. Dotted line includes the parameter uncertainty (DegT50, K om and ρ b ) and the spatial variation of soil and climate, the full line represents the effect of climate and soil variability and the dashed line represents the effect of the parameter uncertainty. 54

55 Figure B6: Cumulative distributions of peak concentrations in the pore water (C l mg l -1 : left) and the total concentration (C T mg kg -1 :right) for P1 (upper), P2 (middle) and P3 (lower), for a ploughing depth of 20 cm and an ecologically relevant depth of 20 cm. Dotted line includes the parameter uncertainty (DegT50, K om and ρ b ) and the spatial variation of soil and climate, the full line represents the effect of climate and soil variability and the dashed line represents the effect of the parameter uncertainty. 55

56 Figure B7: Cumulative distributions of background concentrations in the pore water (C l mg l -1 : left) and the total concentration (C T mg kg -1 : right) for P1 (upper), P2 (middle) and P3 (lower), for a ploughing depth of 20 cm. Dotted line includes the parameter uncertainty (DegT50, K om and ρ b ) and the spatial variation of soil and climate, the full line represents the effect of climate and soil variability and the dashed line represents the effect of the parameter uncertainty. 56

57 Figure B8: Cumulative distributions of time window averaged concentrations (time window average = 365 days) in the pore water (C l mg l -1 : left) and the total concentration (C T mg kg -1 : right) for P1 (upper), P2 (middle) and P3 (lower), for a ploughing depth of 20 cm. Dotted line includes the parameter uncertainty (DegT50, K om and ρ b ) and the spatial variation of soil and climate, the full line represents the effect of climate and soil variability and the dashed line represents the effect of the parameter uncertainty. 57

58 DT50=15 d,kom=15 cm³/g, eco depth= 1 cm P90/P90total Ctpeak Clpeak spatial DT50 rb kom DT50=50 d, kom=200 cm³/g, eco depth = 1cm P90/P90total Ctpeak Clpeak spatial DT50 rb kom DT50=200 d, kom=1000 cm³/g, eco depth = 1cm P90/P90total Ctpeak Clpeak spatial DT50 rb kom Figure B9: Ratio of the P90 concentration considering climate and soil spatial variation, climate and soil spatial variation and uncertainty of only DegT50, of only bulk density ρ b, and of only K om, to the P90 concentration considering spatial variation and the uncertainty of all parameters. Results are shown for peak total, C Tpeak, and peak pore water, C lpeak, concentrations and for an ecologically relevant depth of 1 cm. 58

59 DT50=15 d, kom=15 cm³/g, eco depth = 20cm P90/P90total CtTWA Ctpeak ClTWA Clpeak spatial DT50 rb kom DT50=50 d, kom=200 cm³/g, eco depth = 20 cm P90/P90total CtTWA Ctpeak ClTWA Clpeak spatial DT50 rb kom DT50=200 d, kom=1000 cm³/g, eco depth = 20 cm P90/P90total CtTWA Ctpeak ClTWA Clpeak spatial DT50 rb kom Figure B10: Ratio of the P90 concentration considering climate and soil spatial variation, climate and soil spatial variation and uncertainty of only DegT50, of only bulk density ρ b, and of only K om, to the P90 concentration considering spatial variation and the uncertainty of all parameters. Results are shown for different concentration types: total soil, C T, versus pore water, C l, and peak, C peak, versus time window averaged concentration (365 d), C TWA, and for an ecologically relevant depth zrel of 20 cm. 59

60 B5 Contribution of spatial variability and uncertainty of substance and soil properties Contour diagrams are used to present the overall percentiles as a function of two underlying frequency distributions (Figure B11 and B12). Contour lines are shown from the 10 th to 90 th percentile with an interval between two contour lines of 10% and for the 95 th percentile. The X-coordinate in the contour plots corresponds with the percentile of the cumulative distribution of the predicted concentrations due to spatial variability of soil (i.e. bulk density, organic carbon content, and soil moisture) and climate (i.e. temperature) that is obtained for the median substance properties. For instance, the 75 th percentile on the X-axis corresponds with the location at which for the median substance properties the 75% highest concentration is predicted. The Y-coordinate corresponds with the percentile of the cumulative probability density function resulting from uncertain substance and soil properties at a given location. For instance, the point X=75%, Y=80% corresponds with the substance parameter combination that leads to the 80% highest substance concentration at the location where for the median substance parameters, the 75% highest concentration is observed. For each point in the X-Y surface, the corresponding concentration is calculated for the specific location and parameter combination and the corresponding overall percentile is derived from this concentration using the overall cpdfs (dotted lines in Figure B11 and B12). It should be noted that the surface of the overall percentiles is not a continuous surface and the irregularities in the contour lines are not a consequence of an insufficient number of parameter combinations that are used to derive the cumulative pdfs. It rather reflects the interactions between the substance parameters and the soil parameters so that the sensitivity of the model towards changes in substance properties is not the same at each location. The X-axis corresponds with the cumulative pdf of climate and soil properties that is obtained for the median substance parameter combination. However, since the sensitivity of the model to substance properties depends also on the local soil and climate properties, the range of concentrations at two locations with similar concentrations for the median substance parameters may deviate considerably. The most important line is the 90 th percentile. This line shows all combinations of deterministic soil and climate properties on the one hand and the random or uncertain component of substance and soil properties on the other hand that would lead to an overall 90 th percentile. The crossing of a vertical line at the 90 th spatial percentile with the contour line of the overall 90 th percentile gives the percentile of the soil and substance property distribution that would lead to the 90 th overall substance percentile. The more contour lines that are crossed at by a vertical line at a certain spatial percentile, the wider the distribution or the more uncertain the predictions of the concentrations using deterministic soil, climate and substance properties are at a certain location. If the contour lines of the overall percentiles are more vertical, then the variability of concentrations that are predicted at a certain location considering the uncertainty of the soil and substance properties at that location is small as compared with the variability of the concentrations due to the spatial variation in soil and climate properties that is considered in the spatial database. More horizontal contour lines indicate that the variation of concentrations at a certain location becomes more important when compared with the variation of concentrations due to considered spatial variation. This indicates that the predicted spatial pattern of the concentrations is less certain and more blurred by uncertainty. As a consequence, the spatial delineation of regions with higher or lower predicted concentration becomes more uncertain. 60

61 Figure B11: Contour plot of the overall percentiles of the pore water (left) and total concentration (right) for P1 (upper), P2 (middle), and P3 (lower) for a ploughing depth of 20 cm and an ecologically relevant depth of 1 cm. The X-coordinate corresponds with the percentile of the cumulative spatial distribution and the Y-coordinate with the percentile of the cumulative distribution due to uncertain substance properties and soil properties. 61

62 Figure B12: Contour plot of the overall percentiles of the pore water (left) and total concentration (right) for P1 (upper), P2 (middle), and P3 (right) for a ploughing depth of 20 cm and an ecologically relevant depth of 20 cm. The X-coordinate corresponds with the percentile of the cumulative spatial distribution and the Y-coordinate with the percentile of the cumulative distribution due to uncertain substance properties and soil properties. The contour plots indicate that the predicted spatial variation due to considered spatial variation in soil and climate properties of the total concentration seems to be afflicted more by uncertainty than the pattern of the pore water concentrations. This seems to be counterintuitive since the effect of soil and substance property uncertainty on the overall P90 concentrations was found to be more important for the pore water concentrations, which depend on more uncertain parameters: K om, DegT50 and ρ b, than 62

63 for the total concentrations, which depend mainly on only one uncertain parameter, ρ b (except for the more persistent substances) (see Figure B11 and B12). The contour plots represent how important the effect of spatial variation is compared with the uncertainty. Since the total concentrations depend primarily on ρ b and since the uncertainty of ρ b is in the same order of magnitude as the spatial variation of ρ b, the uncertainty about the predicted total concentration at a certain location is of the same order of magnitude as the variation due to spatial variability. The spatial variation of pore water concentrations is also determined by the spatial variation in organic matter and soil water content. As a consequence, the ratio of the uncertainty to the predicted spatial variation of the pore water concentration may decrease even when the uncertainty leads to a larger shift of the P90 concentration. B6 Selection of areas where the overall 90 th percentile is exceeded The selection of a scenario is generally based on a spatial dataset of soil and climate properties, without considering the uncertainty about these properties, and using mean or median estimates of the substance properties. We made the arbitrary decision to use median substance properties. The effect of not considering soil and substance property uncertainty on the predicted P90 concentrations is shown in Figure B9 and B10. Instead of calculating the overall cpdfs, the overall P90 concentration may also be derived using the median substance and deterministic soil properties. A spatial percentile of the cpdf that does not consider substance and soil property uncertainty may be derived so that the concentration at this percentile corresponds with the overall P90 concentration that is derived from the overall cpdf (see Figure B13). 1 zrel= 20 cm kom=1000 l/kg DT50 = 200 d 0.95 cum. freq spatial variability total Cl (mg/l) Figure B13: Procedure to derive the spatial percentile of the cpdf that does not consider substance and soil property uncertainty (red line) but predicts the same concentration as the 90 th percentile of the overall cpdf (blue line). (This example shows the cpdf for the peak pore water concentrations, Cl, and an ecologically relevant depth of 20 cm) In Figure B14 the percentiles of the cumulative spatial distributions, which do not consider substance and soil property heterogeneity, for which the concentrations correspond with the 90 th percentile of the overall cpdfs are plotted. For the peak concentrations (both for total and pore water concentration and for the 1 and 20 cm ecologically relevant depth), the 90 th overall percentile is obtained from the 95 th percentile of the spatial cpdf. 63

64 ecologically relevant depth = 1 cm percentile DT50 15d; kom 15 g/cm³ DT50 50d; kom 200 g/cm³ DT50 200d; kom 1000 g/cm³ Ctpeak Clpeak ecologically relevant depth = 20 cm percentile DT50 15d; kom 15 g/cm³ DT50 50d; kom 200 g/cm³ DT50 200d; kom 1000 g/cm³ Ctpeak Clpeak Figure B14: Percentiles of spatial cumulative distributions of substance concentrations (peak total concentration, Ctpeak, and peak pore water concentration, Clpeak) for which the concentration is equal to the concentration of the 90 th percentile of the overall cumulative distribution. Results are shown for 3 different substances and two ecologically relevant depths. 64

65 EFFECT OF SUBSTANCE PROPERTIES ON THE SELECTED SCENARIO Due to the non-linearity of the relation between soil parameters, substance fate parameters and predicted environmental concentrations, the ranking of climate and soil property combinations may differ for different substance properties. As a consequence, the percentile of the predicted environmental concentration of a certain substance using a soil and climate combination or scenario that was derived for other substance properties may differ from the intended percentile of the scenario. In this appendix, this problem is evaluated using the EuroPEARL dataset. The parameters of all scenarios that lead for median substance properties to a concentration that is higher then the 90 th percentile of the total and pore water concentrations are shown in respectively Figure C1 and FigureC2, for the three substances P1, P2 and P3. These parameter combinations represent conservative scenarios in the sense that the predicted concentrations for these scenarios are higher than the 90 th percentile of the concentration distribution. Since the parameter combination set of one substance does not overlap perfectly with the set for another substance, there exist a number of parameter combinations or scenarios that are conservative for one substance but not for another. For instance, for the total concentrations, scenarios with a higher temperature and hence a higher degradation rate may be conservative for substances with a lower DegT50, e.g. P1 and P2, but not for substances with a higher DegT50, e.g. P3. For substances with a low degradation rate (P3), for which the background concentration before the substance is applied is substantial, scenarios with a high temperature and degradation rate may not be conservative. For substances with a high DegT50 (P3), scenarios with a low temperature and low organic matter content may be conservative. Because of the high bulk density that corresponds with these scenarios, these scenarios are not conservative for substances with a lower DegT50 (P1 and P2). For pore water concentrations, scenarios with low organic matter content are conservative. However, for substances with a substantial background concentration, i.e. substances with a high DegT50 (P3), a higher organic matter content in combination with lower temperature may still be conservative whereas scenarios with a lower organic matter content but a higher temperature may be conservative only for substances with a low DegT50 (P1 and P2). a) zrel = 1 cm T ( C) Kom 200 DegT50 50 Kom 15 DegT50 15 Kom 1000 DegT OM (g/g) 65

66 b) zrel = 20 cm T ( C) Kom 200 DegT50 50 Kom 15 DegT50 15 Kom 1000 DegT OM (g/g) Figure C1: Parameter combinations: corrected average annual temperature (T C) and organic matter content (OM g g -1 ), of scenarios that lead to a higher total concentration than the 90 th overall percentile for P1, P2 and P3 and for an ecologically relevant depth, zrel, of 1 cm (a) and 20 cm (b). a) zrel = 1 cm T ( C) Kom 200 DegT50 50 Kom 15 DegT50 15 Kom 1000 DegT OM (g/g) 66

67 b) zrel = 20 cm T ( C) Kom 200 DegT50 50 Kom 15 DegT50 15 Kom 1000 DegT OM (g/g) Figure C2: Parameter combinations: corrected average annual temperature (T C) and organic matter content (OM g g -1 ), of scenarios that lead to a higher pore water concentration than the 90 th overall percentile for P1, P2 and P3 and for an ecologically relevant depth zrel of 1 cm (a) and 20 cm (b) Since different parameter combinations are conservative for the three different substances, a scenario that is selected for one substance may be not conservative for another substance, i.e. result in a predicted concentration that is lower than the 90 th percentile of the concentration distribution for that substance. In order to evaluate the effect of the substance properties on the scenario selection, scenarios were derived for a set of 32 substances with different properties. A matrix of substance properties was established (Figure C3). 67

68 1000 Kom (l/kg) DegT50 (d) Figure C3: Set of substance parameters, DegT50 and K om, for which scenarios were derived. Substance properties that lead to a high leaching risk, i.e. high DegT50/ K om ratios, were not included in the matrix.. For each parameter combination shown in Figure C3, a P90 concentration considering the uncertainty of DegT50, K om and bulk density was derived. This was done for the peak total and pore water concentrations and for an ecologically relevant depth of 1 cm and 20 cm. For the obtained P90 concentration considering substance and soil property uncertainty, a scenario was derived from the spatial cpdf that does not consider soil and substance property uncertainty but predicts (almost) the same concentration. The soil and climate parameters for each scenario were then used to predict the peak concentrations of P2 and P3. The distributions of predicted concentrations of P2 and P3 for the derived set of scenarios are shown in Figure C4 (pore water concentration) and Figure C5 (total concentration). Since the total concentration does not depend on K om, the used matrix of substance properties led to only 6 scenarios (6 DegT50 values were considered in the matrix) or 6 points in Figure C5. Figure C5 shows for instance that for 20 % of the scenarios, the predicted total concentration is smaller than 0.55 mg/kg for P2 and smaller than 0.9 mg/kg for P3. These concentrations are lower than the P90 concentrations of P2, mg/kg, and P3, 1.25 mg/kg. This indicates that the concentration of a certain substance that is predicted using a scenario that was parameterised for another substance may deviate considerably from the 90 th percentile of the substance. In other words, if one scenario is used to predict concentrations of different substances, the percentiles to which these predicted concentrations correspond may deviate from the intended 90 th percentile and are uncertain. This deviation which is illustrated in Figures C4 and C5 is of the same order of magnitude as the effect of soil and substance property uncertainty on the 90 th concentration percentile. In order to be consistent, the scenario selection procedure should, besides considering the effect of soil and substance property uncertainty, also consider this scenario uncertainty. 68

69 a) zrel=20cm, Kom= 200 l/kg, DegT50= 50 d Cumulative percentile Cl (mg/l) b) zrel= 20cm, Kom = 1000 l/kg, DegT50 = 200 d Cumulative percentile Cl (mg/l) Figure C4: Distribution of pore water concentrations, C l, of P2 (a) and P3 (b) that are predicted by scenarios that were derived for a range of substance properties. The vertical red line represents the 90 th percentile of P2 (a) and P3 (b). 69

70 a) zrel = 20cm, Kom = 200 l/kg, DegT50 = 50 d Cumulative frequency CT (mg/kg) b) zrel = 20cm, Kom = 1000 l/kg, DegT50 = 200 d Cumulative frequency CT (mg/kg) Figure C5: Distribution of total concentrations, C T, of P2 (a) and P3 (b) that are predicted by scenarios that were derived for a range of substance properties. The vertical red line represents the 90 th percentile of P2 (a) and P3 (b). 70

71 THE USE OF PEDOTRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY D1 Introduction Soil bulk density is an important factor that directly affects the calculated PEC SOIL values, impacting both total soil and pore water concentrations. The calculated PEC SOIL values are inversely related to soil bulk density, so as the density increases, the PEC SOIL value decreases. SPADE-2 (Hollis et al., 2006) is the most comprehensive database of measured soil bulk densities for European soils, but this is not complete for the entire EU-27 and would therefore cause a problem for the scenario selection procedure. A number of pedotransfer functions (PTFs) were therefore evaluated in order to assess their suitability for use in calculating soil bulk densities for the scenario selection procedure. For the scenario selection procedure it is important to be able to robustly estimate soil bulk densities and also to understand the effect of variability within those estimates on the calculated PEC soil values. D2 Correlation of bulk density with soil properties Soil organic matter (OM) content is considered to be the predominant soil characteristic describing soil bulk density (Hollis et al., 2006) and this is supported by many other findings in the literature (Tiktak et al., 2002; Leonaviciute, 2000). Figure D1 shows the relationship between measured soil bulk density and organic matter content (data for over 850 soils taken from SPADE-2, with additional data from the Netherlands), illustrating a strong negative correlation. Correaltion of Bulk Density with OM Content Bulk Density (g/ml) y = e x R 2 = OM % Figure D1: Correlation of measured soil bulk density with organic matter content for a range of European soils Below 10% OM other soil properties such as texture may start to have more of an effect on the bulk density, but correlations with other soil properties remain low. Figures D2 - D4 show the correlation of soil bulk density with soil texture (same soils as in Figure D1), indicating much weaker correlations than with organic matter. 71

72 Correaltion of Bulk Density with Sand Content Bulk Density (g/ml) y = x R 2 = Sand % Figure D2: Correlation of measured soil bulk density with sand content for a range of European soils. Correaltion of Bulk Density with Silt Content y = x R 2 = Bulk Density (g/ml) Silt % Figure D3: Correlation of measured soil bulk density with silt content for a range of European soils. 72

73 Correaltion of Bulk Density with Clay Content y = x R 2 = Bulk Density (g/ml) Clay % Figure D4: Correlation of measured soil bulk density with clay content for a range of European soils. D3 Availability of pedotransfer functions A review of the literature gave a number of pedotransfer functions capable of calculating soil bulk density (For example: Hollis et al., 2006; Tiktak et al., 2002; Hallett et al., 1998; Leonaviciute, 2000) and these were evaluated in order to select an appropriate PTF for use in the scenario selection procedure. The PTFs ranged in complexity from those just considering one or two parameters to those containing more than four and a few examples for top soils are shown in Table D1 below. Table D1: Example pedotransfer functions for calculating topsoil bulk density Bulk density PTF Origin Reference ρ = xLN(Clay%) x A horizon arable UK soils Hallett et al., 1998 LN(Sand%) xLN(OC%) ρ = xf om xSQRT(f om ) Dutch soils Tiktak et al., 2002 ρ = x(Silt%) x (Clay%) x(OC%) Lithuanian top soils Leonaviciute, 2000 The PTFs shown in Table D1 were evaluated with the combined SPADE-2 and Netherlands data, with Figures D5 D7 showing the results. 73

74 SPADE-2 A horizon Arable soil PTF BD = *LN(Clay%) *LN(Sand%)-0.261*LN(OC%) R 2 = 0.52 Estimated bulk density (g/ml) Measured bulk density (g/ml) Figure D5: Correlation of estimated and measured soil bulk density using the A horizon UK topsoil PTF. PTF implemented in GeoPEARL Estimated bulk density (g/ml) R 2 = Measured bulk density (g/ml) Figure D6: Correlation of estimated and measured soil bulk density using the GeoPEARL PTF. 74

75 Lithuanian Topsoil PTF 2.0 Estimated bulk density (g/ml) R 2 = Measured bulk density (g/ml) Figure D7: Correlation of estimated and measured soil bulk density using the Lithuanian topsoil PTF. Calculations with all three PTFs resulted in a scatter of results. In spite of the PTF used within GeoPEARL only using organic matter as a single parameter, the scatter of results was not significantly worse than the other PTFs employing additional parameters. New multiple-regression evaluations were also conducted in an attempt to derive an improved PTF based on the combined SPADE-2 and Dutch soils. Parameters considered in the evaluation were, organic matter, sand, silt and clay contents with a number of transformation functions (square root, square and LN) also tested. No significant improvement in the correlation was obtained for the newly derived PTFs (Figure D8). 75

76 PTF - New (OM, SQRT OM, Sand, Silt and Clay) Estimated bulk density (g/ml) BD = *OM *SQRT(OM) *S *Si *C R 2 = Measured bulk density (g/ml) Figure D8: Correlation of estimated and measured soil bulk density using a new topsoil PTF derived from the SPADE-2 and Dutch soils database None of the tested PTFs were able to significantly improve upon the results of the simplified approach implemented in GeoPEARL based only on organic matter (Figure D6). Therefore, this approach was considered to be appropriate for the estimation of soil bulk density for the scenario selection procedure. D4 Variability in the estimated soil bulk density An important aspect of the estimation of soil bulk densities is to understand their variability and consequent impact on the calculated PECsoil values. Uncertainty in the estimated soil bulk density will directly impact on the calculated PECsoil via a negative correlation. Evaluation of any of the PTF correlations shown in Figures D5 D8 illustrates that a wide range of measured values are found for any given estimate of the soil bulk density. In order to assess the variability, the correlation data derived from the GeoPEARL PTF (Figure D6) were further evaluated for a range of different estimated soil bulk densities to determine the average and standard deviation of the corresponding measured values (Figure D9). Table D2 summarises the results of the evaluations for bulk densities of 1.0 and 1.4 g/ml. The results of the statistical evaluations of the data (Table D2; Figure D9) show that the GeoPEARL PTF is able to make a good estimate of the mean measured soil bulk density (the estimated bulk density is close to the average measured bulk density), with a standard deviation of

77 GeoPEARL PTF Estimated bulk density (g/ml) For an estimated BD of 1.4+/-0.02, the measured BD ranges from 0.83 to 1.63 Mean = Std. dev. = 0.18 For an estimated BD of 1.0+/-0.02, the measured BD ranges from 0.57 to 1.37 Mean = 1.03 Std. dev. = Measured bulk density (g/ml) Figure D8: Correlation of estimated and measured soil bulk density using the GeoPEARL PTF comparison of estimated and measured ranges. Table D2: Evaluation of the uncertainty in estimated soil bulk densities Estimated bulk density Measured bulk density (kg dm -3 ) (kg dm -3 ) Average Range Standard deviation 1.0± ± D5 Conclusion A simplified PTF based only on organic matter content currently used for the GeoPEARL model was shown to be able to robustly estimate soil bulk density. The calculated uncertainties in the estimations (standard deviation 0.18) should be taken into account in the scenario selection procedure. 77

78 CROP COVER MAPS FOR THE CALCULATION OF SAFETY FACTORS In Tier 3 and 4, the area of use can be restricted to specific crops. To make these tiers operational, land cover maps need to be available at the same spatial resolution as the fate models (1 x 1 km 2 ). Crop statistics, however, are usually only available at larger spatial scales (usually administrative units). Within these administrative units, the natural conditions are heterogeneous so that the assumption of identical cropping patterns cannot be maintained. For this reason, we suggest to use the CAPRI landuse maps (Leip et al., 2008). These maps were obtained using a downscaling procedure. This procedure has resulted in a land use map for 30 cropping activities and one aggregated nonagricultural land use class at a spatial resolution of 1 x 1 km 2 covering the entire EU-27. The CAPRI methodology combines remote sensing data, administrative crop data, land suitability data and statistical modelling. The CORINE land cover map serves as a starting point. Subdivisions within CORINE land cover classes were made based on a statistical model, regressing point observations of cropping activities on soil, relief and climate parameters (land suitability). Statistical data of agricultural production and land cover available for administrative regions were additionally used to scale the 30 land cover classes. The resulting land cover maps are shown in Figures E1 E3. Annual crops are shown only, because this report is limited to the development of scenarios for annual crops. Notice also that maps are available for major land-uses only, and that minor crops are combined into larger categories. 78

79 Figure E1: Crop cover maps (% of area) for barley, common wheat, fibres, floriculture, fodder crops and industrial crops. 79

80 Figure E2: Crop cover maps (% of area) for rye, soya, sugar beets, sun flowers, fresh vegetables and potatoes. 80

81 Figure E3: Crop cover maps (% of area) for rye, soya, sugar beets, sun flowers, fresh vegetables and potatoes 81