Risk assessment of environmental multichemical exposure: Tentative relationships with ecotoxicity and ecosystem variables

Transcription

1 ADVANCED COURSE ON ANALYSIS, FATE AND RISKS OF ORGANIC CONTAMINANTS IN RIVER BASINS UNDER WATER SCARCITY. 7-8 february, Valencia, Spain Risk assessment of environmental multichemical exposure: Tentative relationships with ecotoxicity and ecosystem variables Antoni Ginebreda, Aleksandra Jelić, Mira Petrović, Miren López de Alda, Damià Barceló,, Marianne Köck, Marta Ricart,, Helena Guasch, Rikke Brix, Anita Geiszinger, Julio C. López-Doval, Isabel Muñoz, Cristina Postigo, Anna M. Romaní, Marta Villagrasa, Sergi Sabater,, Maria H. Conceição 6 Research, Barcelona, Spain Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain. Catalan Institute for Water Research, Girona, Spain University of Girona, Girona, Spain University of Barcelona, Barcelona, Spain 6 Universidade de Brasília, Brasilia, Brazil Outline. Introduction. Models for multichemical exposure ecotoxicology. Method development (synergistic effects and compound prioritisation). Preliminary field results (Case Studies). Conclusions

2 . Introduction Introduction RISK ASSESSMENT Definition: Procedures aiming to identify hazards and to quantify the associated risk (in our case, related to contaminants) concerning: Human health Ecosystems

3 Introduction RISK ASSESSMENT Introduction RISK ASSESSMENT HAZARD IDENTIFICATION EXPOSURE ASSESSMENT EFFECT ASSESSMENT RISK CHARACTERISATION Risk = Expossure Adverse Effects Risk = Expossure Adverse Effects

4 Introduction Use of chemicals by our technological society can be estimated in ~, compounds, most of them organics [Schwarzenbach et al., 99] 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... Introduction...Three questions arise: ) Is there any relationship between chemical pollution exposure and ecosystem impairment? ) Exposure to multiple chemicals may result on any synergic effect ( cocktail effect )? ) 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 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.... Models for multichemical exposure ecotoxicology

6 Models for multichemical exposure ecotoxicology Environmental exposure to multiple chemicals: Ecotoxicological assessment depending on Toxic Mode of Action Independent action or response addition model (IA): Concentration addition model (CA): Models for multichemical exposure ecotoxicology Concentration Addition model (CA): All components are assumed to share the same action mechanisms (Loewe and Muinschnek, 96) HQ i = ij j HQ: ij : hazard quotient of site i (also called TU s TOXIC UNITS ) hazard quotient of compound j at site i cij ij = PNEC j EC j PNEC j = c ij : concentration of compound j at site i PNEC j : Predicted No Effect Concentration of compound j

7 Models for multichemical exposure ecotoxicology Independent Action (IA) : All components are assumed to act by dissimilar mechanisms Response (i.e., effects) addition (Bliss, 99) Toxic mode of action is calculated analogously to probability calculus laws For two compounds A and B, their joint response is: P( A B) = P( A) + P( B) P( A B) For a mixture of n components: E( mixture) = n [ E( c i )] i= E(c i ) : Effect caused by component i E(mixture): Effect caused by the mixture of n components Models for multichemical exposure ecotoxicology Environmental exposure to multiple chemicals: Ecotoxicological assessment depending on Toxic Mode of Action Independent action or response addition model (IA): Concentration addition model (CA): Even though IA and CA models are conceptually very different, results are no so much. IA predicts lower mixture toxicity than CA. When compared to experimental values, IA tends to underestimate whereas CA tend to overestimate toxicity CA (expressed as HQ) is often preferred due to its simplicity.

8 . Method development (synergistic effects and compound prioritisation) 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...

9 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 9; 8: 76-7 ] Pesticides vs. Biological communities (macroinvertebrates, diatoms, biofilm metrics) [Ricart et al. J. Hydrology.. 8, 6] CHEMICAL EXPOSURE ECOTOXICOLOGY ECOLOGICAL STATUS SPEAR (species at risk index) log (HQ pesticides, daphnia) [Liess and von der Ohe. Environmental Toxicology and Chemistry ; : 9-96 ] [Schäfer et al. Science of Total Environment 7; 8: 7-8] Macroinvertebrate Biodiversity (Shannon Index) log (HQ pharmaceuticals, daphnia) [Ginebreda et al. Environ. Intern.. 6, 6] Multispecies potentially affected fraction (mspaf) f(hq, SSD) [De Zwart and Posthuma, Environmental Toxicology and Chemistry.. : ] 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: 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 Xk (%) Sulfamethazine Timolol Trimethoprim HQ = n = k n k = Is it possible to get additional information from HQ?

10 Method development Identification of relevant compounds (prioritization): For a given analytical profile characterizing a site a) Compute s for all identified compounds b) Normalize s to % c) Rank all compounds by decreasing 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 k (%) k (%) Naproxen Ketoprofen Diclofenac Furosemide H set Mevastatin Carbamazepine Atenolol Bezafibrate Cimetidine Pravastatin Indomethacine Enalapril Trimethoprim Metoprolol Mefenamic acid Ranitidine Gemfibrozil Sotalol Rank k Rank Clarithromycin Atorvastatin Lorazepam Metronidazole Salbutamol Famotidine Glibenclamide Fenofibrate Chloramphenicol Nadolol Timolol Diazepam Sulfamethazine Applicable to any additive property such as concentrations, s etc. Method development Example of a Pareto distribution Vilfredo Pareto (88-9) italian economist who stated in 96 the so called "8: rule" (Pareto Principle) % of people own 8% of wealth % 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 A B C D 7 6 A B C D HQ = = HQ = = A B C D 7 6 A B C D HQ = = HQ =, +, +, +, = All the above patterns are the same? Method development Fitting a potential law (Zipf law) to a Pareto distribution: k = k α h index Xk (%) k (%) Zipf law: k =. k -.98 WWTP(Infl) Calculated (Zipf law; all compounds) Naproxen Ketoprofen Diclofenac Furosemide Mevastatin H set Carbamazepine Atenolol Bezafibrate Cimetidine Pravastatin Indomethacine Enalapril Trimethoprim Metoprolol Mefenamic acid Ranitidine Gemfibrozil Sotalol Clarithromycin Rank k Atorvastatin Lorazepam Metronidazole Salbutamol Famotidine Glibenclamide Fenofibrate Chloramphenicol Nadolol Timolol Diazepam Sulfamethazine

12 Method development Fitting a potential law (Zipf law) to a Pareto distribution: log log plot: log k = α log k + log y = -.978x +.9 R =.997 Ln k Ln k Method development Breaking down the HQ structure (under the Zipf law): HQ = n = k HQ = n k = k = n k = k α = n k = n k k = α ξ(n, α) HQ = ξ(n, α) Intensity Complexity h index α exp. ξ(n, α).

13 Method development All the patterns are the same? NO Equal HQ Different power equation different INTENSITY and COMPLEXITY A B C D 7 6 A B C D HQ = = HQ = = A 6 B α = α = C D A B C D HQ = = HQ =, +, +, +, = MAX intensity MIN complexity MIN intensity MAX complexity. Preliminary field results (Case Studies)

14 Preliminary Results Ecosystem variables: Biofilm metrics: Chl-a, EPS, Y max, Y eff, F/F Macroinvertebrate biodiversity: Shannon-Wiener Index Llobregat River Basin: MODELKEY (Project 7- GOCE) Chemical variables: Polar pesticides in water Herbicides (): - Atrazine, Simazine, Cyanazine, Desethylathrazine, Terbutylazine, Deisopropylatrazine, Diuron, Isoproturon, Linuron, Chlortoluron, Mecoprop,,-D, Bentazone, MCPA, Molinate, Propanil, Alachlor, Metolachlor Insecticides (): - Diazinon, Dimethoate, Fenitrothion, Malathion Ecotoxicity: HQ vs. algae and daphnia [Muñoz et al. Env. Tox. Chem. 9. 8, 76 7] [Ricart et al. J. Hydrology.. 8, 6] [Ginebreda et al. Environ. Intern.. 6, 6] Preliminary Results Compound prioritization based on h (Hirsch) indexes: HQ (pesticides, algae) Point # h index H-compounds % of HQ explained A Isoproturon, Linuron, Diuron, Terbutylazine 96.7 A Diuron, Linuron 98. A Diuron, Linuron 98. LL Diuron, Terbuthylazine, Linuron 96.9 LL Diuron, Terbuthylazine, Linuron 98. LL Diuron, Terbuthylazine, Linuron 98. LL Diuron 97. Isoproturon, Linuron, Diuron, Terbutylazine HQ (pesticides, daphnia) In the example studied: H compounds account for most of the risk Diazinon, Fenitrothion, Linuron H set is efficient in the prioritization of compounds H sets based on different species ecotoxicity reflect well its specific sensibility Point # h index H-compounds % of HQ explained A Fenitrothion, Linuron 99. A Diazinon 99.8 A Diazinon 99. LL Diazinon, Fenitrothion, Linuron 99.7 LL Diazinon, Fenitrothion 99. LL Diazinon 98.6 LL Diazinon 99.

15 Preliminary Results Some Correlations between ecosystem variables and HQ(pesticides): Ecosystem Variable HQ (ecotoxicity sp.) R Shannon Diversity algae -.9 F/F algae -.66 Yeff algae. Ymax algae.9 Shannon Diversity daphnia -.77 F/F daphnia Shannon Biodiversity Index (macroinvertebrates),,,,, y = -,66x +,76 R = -,9 F/F,8,7,6,,,,, y = -,87x +,67 R = -,66,,,,,,, HQ (algae),,,,,,, HQ (algae) Preliminary Results Complexity effects on HQ (Zipf law): HQ = complexity Pesticides (algae) Contribution of "Intensity" and "Complexity" Pesticides (daphnia) Contribution of "Intensity" and "Complexity",,, Hazard Quotient,, complexity HQ Hazard Quotient,,, complexity HQ, A A A LL LL LL LL, A A A LL LL LL LL,,7,7,6,8,68, complexity,8,6,7,87,6,66, HQ,,8,,7,8,99,6,6,,7,8, 9,78 6,97 complexity,96,,7,7,9,,9 HQ,,88,9,6, 9, 7, In the studied cases:. intensity seems to have in general greater weight than complexity in the resulting HQ. Complexity values show low differences among the different points

16 . Conclusions Conclusions () ) 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) ) 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 (). 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.

17 Conclusions () ) Exposure to multiple chemicals may result on any synergistic effect (mixture effects)? Rank lists can be numerically represented according to a potential law equation (Zipf law), which allows: (a) (b) To break down HQ in two parts, corresponding respectively to the effects of intensity and complexity of the mixture Zipf exponents can serve also as a measure of complexity ) 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). More work is needed on the interpretation of results We are far from solving the question, but.