SCARCE 1 st ANNUAL CONFERENCE

Transcription

1 SCARCE 1 st ANNUAL CONFERENCE 2-3 December 21, Girona, Spain Quantitative characterization of mixture complexity of environmental chemical monitoring inventories: Tentative relationships with ecotoxicity and ecosystem variables Antoni Ginebreda 1, Aleksandra Jelić 1, Mira Petrović 2, Miren López de Alda 1, Damià Barceló 1,3, Marianne Köck 1, Marta Ricart 3,4, Helena Guasch 4, Rikke Brix 1, Anita Geiszinger 4, Julio C. López-Doval, Isabel Muñoz, Cristina Postigo 1, Anna M. Romaní 4, Marta Villagrasa 3, Sergi Sabater 3,4, Maria H. Conceição 6 1 Research, Barcelona, Spain 2 Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain. 3 Catalan Institute for Water Research, Girona, Spain 4 University of Girona, Girona, Spain University of Barcelona, Barcelona, Spain 6 Universidade de Brasília, Brasilia, Brazil

2 Outline Introduction & objectives Method development Preliminary Results (Case Studies) Conclusions

3 Introduction & objectives Use of chemicals by our technological society can be estimated in ~1, compounds, most of them organics [Schwarzenbach et al., 1994] and this number is continuously growing. Depending on their properties and extent of use these chemicals can potentially reach the environment, being their environmental and health effects unpredictable in long term. A simultaneous and huge progress on the analytical capabilities has taken place, mostly associated to the development of multiresidue analytical methods based on chromatographic techniques (GC-MS and LC-MS), capable to identify and quantify many of these compounds at trace levels of ng/l or pg/l Given these facts...

4 Introduction & objectives...three questions arise: 1) Is there any relationship between chemical pollution exposure and ecosystem impairment? 2) Exposure to multiple chemicals may result on any synergic effect ( cocktail effect )? 3) What to analyze? (prioritization of target compounds) not all measurable compounds are worth to be measured (this point is particularly relevant when routine monitoring control has to be implemented)

5 Introduction & objectives The basic environmental risk assessment approach: CHEMICAL EXPOSURE c i : concentrations Multivariate Analysis ANOVA... ECOLOGICAL STATUS Ecosystem variables: biofilm, macroinvertabrates... ecotoxicological multicomponent models Ex.: HQ E. V. = f ( HQ) ECOTOXICOLOGY Ecotoxicity variables: EC i, PNEC i, NOEC i...

6 Introduction & objectives Environmental exposure to multiple chemicals: Ecotoxicological assessment depending on Toxic Mode of Action Independent action or response addition model (IA): Concentration addition model (CA): HQ i = hq ij j HQ: hazard quotient of site i hq ij : hazard quotient of compound j at site i hq = ij c ij PNEC j PNEC = j EC j 1 c ij : concentration of compound j at site i PNEC j : Predicted No Effect Concentration of compound j Even though IA and CA models are conceptually very different, results are no so much. CA (expressed as HQ) is often preferred due to its simplicity.

7 Introduction & objectives Ecological effects caused by exposure to multiple chemicals Some published examples: CHEMICAL EXPOSURE ECOLOGICAL STATUS Pharmaceuticals vs. community structures of macroinvertebrates and diatoms [Muñoz et al. Environmental Toxicology and Chemistry 29; 28: ] Pesticides vs. Biological communities (macroinvertebrates, diatoms, biofilm metrics) [Ricart et al. J. Hydrology , 2 61] CHEMICAL EXPOSURE ECOTOXICOLOGY ECOLOGICAL STATUS SPEAR (species at risk index) log (HQ pesticides, daphnia) [Liess and von der Ohe. Environmental Toxicology and Chemistry 2; 24: ] [Schäfer et al. Science of Total Environment 27; 382: ] Macroinvertebrate Biodiversity (Shannon Index) log (HQ pharmaceuticals, daphnia) [Ginebreda et al. Environ. Intern , ] Multispecies potentially affected fraction (mspaf) f(hq, SSD) [De Zwart and Posthuma, Environmental Toxicology and Chemistry : ] [Carafa et al., Chemosphere. 21. submitted]

8 Atenolol Atorvastatin Bezafibrate Carbamazepine Chloramphenicol Cimetidine Clarithromycin Diazepam Diclofenac Enalapril Famotidine Fenofibrate Furosemide Gemfibrozil Glibenclamide Indomethacine Ketoprofen Lorazepam Mefenamic acid Metoprolol Metronidazole Mevastatin Nadolol Naproxen Pravastatin Ranitidine Salbutamol Sotalol Sulfamethazine Timolol Trimethoprim Method development Environmental sample characterized by its HQ: For a given analytical profile characterizing a site sample, HQ is readily computed from concentrations and PNEC s: HQ = hq 1 + hq 2 + hq n hq n = hq k k = 1 Is it possible to get additional information from HQ? Xk (%) hq

9 Method development Identification of relevant compounds (prioritization): For a given analytical profile characterizing a site a) Compute hq s for all identified compounds b) Normalize hq s to % c) Rank all compounds by decreasing hq d) Calculate h index (Hirsch) and others alike e) Identify H set of compounds (those comprised within h index) Hirsch index h = Example of a Pareto distribution Naproxen Ketoprofen Diclofenac Furosemide Mevastatin Carbamazepine Atenolol Bezafibrate Cimetidine Pravastatin Indomethacine Enalapril Trimethoprim Metoprolol Mefenamic acid Ranitidine Gemfibrozil Sotalol Clarithromycin Atorvastatin Lorazepam Metronidazole Salbutamol Famotidine Glibenclamide Fenofibrate Chloramphenicol Nadolol Timolol Diazepam Sulfamethazine H set Rank k Rank Applicable to any additive property such as concentrations, hq s etc. [Ginebreda et al. Sci. Tot. Env. (submitted)] hqk (%) hqk (%)

10 Method development Example of a Pareto distribution Vilfredo Pareto ( ) italian economist who stated in 196 the so called "8:2 rule" (Pareto Principle) 2 % of people own 8% of wealth 2 % of causes account for 8% of failures Few compounds are the responsible for most of the risk h index allows identifying the most relevant compounds (H set)

11 Method development Complexity embedded within HQ: Assuming valid the CA model Given a certain value of HQ, it may be obtained from different compound distributions hq A B C D hq A B C D HQ = = 1 HQ = = hq A B C D hq A B C D HQ = = 1 HQ = 2, + 2, + 2, + 2, = 1 All the above patterns are the same?

12 Method development Fitting a potential law (Zipf law) to a Pareto distribution: hq k = hq k α h index Zipf law: hq k = k WWTP1(Infl) Calculated (Zipf law; all compounds) Naproxen Ketoprofen Diclofenac Furosemide Mevastatin Carbamazepine Atenolol Bezafibrate Cimetidine Pravastatin Indomethacine Enalapril Trimethoprim Metoprolol Mefenamic acid Ranitidine Gemfibrozil Sotalol Clarithromycin Atorvastatin Lorazepam Metronidazole Salbutamol Famotidine Glibenclamide Fenofibrate Chloramphenicol Nadolol Timolol Diazepam Sulfamethazine hqk (%) Xk (%) H set Rank k

13 Method development Fitting a potential law (Zipf law) to a Pareto distribution: log log plot: log hq k = α log k + log hq 4 3 y = x R 2 = Ln hq k Ln k

14 Method development Breaking down the HQ structure (under the Zipf law): HQ = hq1 + hq2 + hq hq n = hq k n k = 1 HQ = n k = 1 hq k = n k = 1 hq k α = hq n k = 1 k α ξ(n, α) HQ = hq ξ(n, α) Intensity Complexity h index α exp. ξ(n, α).

15 Method development All the patterns are the same? NO Equal HQ Different power equation different INTENSITY and COMPLEXITY hq A B C D hq A B C D HQ = = 1 HQ = = 1 hq α = A 6 B hq α = C 4 D A B C D HQ = = 1 HQ = 2, + 2, + 2, + 2, = 1 MAX intensity MIN complexity MIN intensity MAX complexity

16 Preliminary Results Ecosystem variables: Biofilm metrics: Chl-a, EPS, Y max, Y eff, F1/F3 Macroinvertebrate biodiversity: Shannon-Wiener Index Llobregat River Basin: MODELKEY (Project GOCE) Chemical variables: Polar pesticides in water Herbicides (2): - Atrazine, Simazine, Cyanazine, Desethylathrazine, Terbutylazine, Deisopropylatrazine, Diuron, Isoproturon, Linuron, Chlortoluron, Mecoprop, 2,4-D, Bentazone, MCPA, Molinate, Propanil, Alachlor, Metolachlor Insecticides (4): - Diazinon, Dimethoate, Fenitrothion, Malathion Ecotoxicity: HQ vs. algae and daphnia [Muñoz et al. Env. Tox. Chem , ] [Ricart et al. J. Hydrology , 2 61] [Ginebreda et al. Environ. Intern , ]

17 Preliminary Results Compound prioritization based on h (Hirsch) indexes: HQ (pesticides, algae) Point # h index H-compounds % of HQ explained A1 4 Isoproturon, Linuron, Diuron, Terbutylazine 96.7 A2 2 Diuron, Linuron 98. A3 2 Diuron, Linuron 98.1 LL1 3 Diuron, Terbuthylazine, Linuron 96.9 LL2 3 Diuron, Terbuthylazine, Linuron 98. LL3 3 Diuron, Terbuthylazine, Linuron 98. LL4 1 Diuron 97. Isoproturon, Linuron, Diuron, Terbutylazine 22 4 HQ (pesticides, daphnia) In the example studied: Point # h index H-compounds % of HQ explained A1 2 Fenitrothion, Linuron 99. A2 1 Diazinon 99.8 A3 1 Diazinon 99.3 LL1 3 Diazinon, Fenitrothion, Linuron 99.7 LL2 2 Diazinon, Fenitrothion 99. H compounds account for most of the risk H set is efficient in the prioritization of compounds LL3 1 Diazinon 98.6 LL4 1 Diazinon 99.1 Diazinon, Fenitrothion, Linuron 22 3 H sets based on different species ecotoxicity reflect well its specific sensibility

18 Preliminary Results Some Correlations between ecosystem variables and HQ(pesticides): Ecosystem Variable HQ (ecotoxicity sp.) R Shannon Diversity algae F1/F3 algae -.66 Yeff algae.41 Ymax algae.449 Shannon Diversity daphnia F1/F3 daphnia Shannon Biodiversity Index (macroinvertebrates) 2, 2, 1, 1,, y = -,661x + 2,764 R = -,933 F1/F3,8,7,6,,4,3,2,1 y = -,87x +,6733 R = -,66,,, 1, 1, 2, 2, HQ (algae),,, 1, 1, 2, 2, HQ (algae)

19 Preliminary Results Complexity effects on HQ (Zipf law): HQ = hq complexity Pesticides (algae) Contribution of "Intensity" and "Complexity" Pesticides (daphnia) Contribution of "Intensity" and "Complexity" 1, 1, Hazard Quotient 1, 1, hq complexity HQ Hazard Quotient 1, 1, 1,,1 hq complexity HQ,1 A1 A2 A3 LL1 LL2 LL3 LL4 hq,11 1,74 4,373,6 1,8,68 2,1 complexity 2,81 1,362 1,171 1,847 1,46 1,166 1,31 HQ,424 21,28,121 1,117 1,82,99 2,644, A1 A2 A3 LL1 LL2 LL3 LL4 hq,26 31,12 22,37,28 1,21 9,378 16,973 complexity 1,496 1,2 1,7 1,271 1,9 1,14 1,9 HQ,4 31,884 22,39,36 1,32 9,12 17,123 In the studied cases: 1. intensity seems to have in general greater weight than complexity in the resulting HQ 2. Complexity values show low differences among the different points

20 Snannon Biodiversity Index (macroinvertebrates) Preliminary Results Complexity effects on Ecosystem variables: Pesticides (daphnia) 3, 2, 2, 1, 1,, y = -,41x + 1,7827 R = -,86 In the studied cases:, -2, -1,, 1, 2, 3, log (h) + - Snannon Biodiversity Index (macroinvertebrates) 3, 2, 2, 1, 1, Pesticides (daphnia) y = 6,34x + 1,29 R =,691,,,1,1,2,2 log (complexity) intensity complexity Pesticides (algae) Photosynthesis h (intensity) efficiency vs. complexity Yeff and capacity Similar 1 behaviour is observed when Zipf h Biodiversity There is an inverse trend between, intensity and complexity - Shannon biodiversity and F1/F3 show a negative (inverse)correlation to complexity Ymax 1 show a positive correlation to complexity exponent (α exponent) is used as measure of complexity complexity

21 Conclusions 1) Is there any relationship between chemical pollution exposure and ecosystem impairment? Ecosystem variables may be correlated to ecotoxicological multicomponent exposure models such as CONCENTRATION ADDITION (CA), expressed as hazard quotients (HQ) 2) What to analyze? (prioritization of target compounds) Under the assumption of CA model, compounds may be ranked in descending order according to its normalized hazard quotient (hq). On the so obtained (Pareto type) distribution, appropriate indexes, such as h (Hirsch) well known in other scientific domains can be applied in order to identify and prioritize relevant compounds for the scenario under study. 3) Exposure to multiple chemicals may result on any synergic effect (mixture effects)? Rank lists can be numerically represented according to a potential law equation (Zipf law), which allows: (a) To break down HQ in two parts, corresponding respectively to the effects of intensity and complexity of the mixture (b) Zipf exponents can serve also as a measure of complexity 4) Preliminary illustrative examples of the above concepts have been shown for the Llobregat River case study (pesticides in water vs. biofilm metrics or macroinvertebrates diversity). 1) More work is needed in the interpretation of results We are far from solving the question, but.

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23 Francesc Casademont ( ). El Ter a Girona Thank you for your attention!