1. occurrences, (eco)toxicity and relevance for water quality in Europe Chemical Mixtures Thomas Backhaus University of Gothenburg thomas.backhaus@dpes.gu.se

2.  Chemical Mixtures  Mixtures of toxic chemicals  Aquatic Ecosystems  Ecotoxicology Focus

3.  PREDICT  BEAM  ACE  EDEN  CONTAMED Inspired from work in the following EU projects

4. Monitoring Data Sweden Stina Adielsson, Sarah Graaf, Melle Andersson & Jenny Kreuger (2009) Resultat från miljöövervakningen av bekämpningsmedel (växtskyddsmedel). ISSN 0347-9307. Swedish University of Agricultural Sciences, Uppsala.

5.  Mixture effect is higher than the effect of each individual component  Individual quality targets not necessarily sufficient The ecotoxicology of chemical mixtures

6. Walter, H., et al. (2002) Mixture toxicity of priority pollutants at No Observed Effect Concentrations (NOECs). Ecotoxicology 11:299-310. Mixture of priority pollutants

7. Realistic pesticide mixture Junghans, et al. (2006) Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquatic Toxicology 76, 2006

8. Mixture toxicity concepts E Mix = Effect of the mixture of n compounds E i = Effect of substance i, when applied singly c i = Concentration of component i in the mixture (i = 1...n) ECx i = Concentration of substance i provoking a certain effect x when applied alone ECx (Mix) = Predicted total concentration of the mixture, that provokes x% effect. pi= relative fraction of component i in the mixture Similarly acting substances: Concentration Addition Dissimilarly acting substances: Independent Action

9. Mixture toxicity concepts E Mix = Effect of the mixture of n compounds E i = Effect of substance i, when applied singly Dissimilarly acting substances: Independent Action  Assumes that components affect the same endpoint,  but do that completely independent of each other

10. Biochemical networks

11. Ecological networks

12. Mixture toxicity concepts E Mix = Effect of the mixture of n compounds E i = Effect of substance i, when applied singly Dissimilarly acting substances: Independent Action  Assumes that components affect the same endpoint, but do that completely independent of each other  In contradiction to A.The physiological and ecological networking of life B.The notion of a “narcotic” mode of action, common to all organic chemicals.

13. Mixture toxicity concepts E Mix = Effect of the mixture of n compounds E i = Effect of substance i, when applied singly c i = Concentration of component i in the mixture (i = 1...n) ECx i = Concentration of substance i provoking a certain effect x when applied alone ECx (Mix) = Predicted total concentration of the mixture, that provokes x% effect. pi= relative fraction of component i in the mixture Similarly acting substances: Concentration Addition Dissimilarly acting substances: Independent Action

14. How? Nonylphenol River basin modeling of the expected consequences of chemical mixtures Sumpter, J., et al. (2006) Modeling Effects of Mixtures of Endocrine Disrupting Chemicals at the River Catchment ScaleE, Env. Sci. Techn. 40:5478-5489

15. Nonylphenol Natural estrogens (E1, E3), Pharmaceutical (EE2) River basin modeling of the expected consequences of chemical mixtures Sumpter, J., et al. (2006) Modeling Effects of Mixtures of Endocrine Disrupting Chemicals at the River Catchment ScaleE, Env. Sci. Techn. 40:5478-5489

16. “It is the opinion of the SCHER that the CA approach may be assumed as a temporary interim method for deriving EQs for mixtures.“ SCHER, Opinion on the Chemicals and the Water Framework Directive: Technical Guidance for Deriving Environmental Quality Standards (2010) How?

17.  Assumes similar mode of action of all mixture components  Only extrapolates from single substance to mixture toxicity  Assumes no interaction between the components in the mixture Properties of Concentration Addition

18.  CA and IA are based on mutually exclusive assumptions of the similarity, resp. dissimilarity  Differences between CA- and IA-predicted toxicities depend on  Effect level  Number of components  Mixture ratio Mode of Action

19. Mixture toxicity of 14 pharmaceuticals Mixture: 14 pharmaceuticals with dissimilar mechanisms of action Organism: Vibrio fischeri (marine bacterium) Red: CA Blue: IA Backhaus et al., (2000) Predictability of the toxicity of a mixture of dissimilarly acting chemicals to Vibrio fischeri, Env. Tox. Chemistry, 19(9): 2348-2356

20. Contribution of low, individually non-toxic concentrations Backhaus, Blanck, Sumpter, On the ecotoxicology of pharmaceutical mixtures, 2008

21. Possible ratios EC50(IA) / EC50(CA) Faust et al., (2000) Competing Concepts for the Prediction of Mixture Toxicity: Do the Differences Matter for Regulatory Purposes? Public Report of the BEAM Project

22. The maximum possible difference between IA- and CA- predicted toxicities  the maximum possible ratio between the CA- and the IA- predicted EC50 is n (the number of mixture components).  the more “imbalanced“ the mixture – in terms of TU contributions to the mixture - the smaller the maximum possible error. TU: Toxic Unit, ratio between conc and EC50 Junghans, et al. (2006) Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquatic Toxicology 76, 2006

23.  Assumes similar mode of action of all mixture components Differences to the toxicities predicted by the competing concept of IA are small and negligible Properties of Concentration Addition

24.  Assumes similar mode of action of all mixture components  Extrapolates from single substance to mixture toxicity  Assumes no interaction between the components in the mixture Properties of Concentration Addition √

25. Single Components Laboratory EQS (PNEC) Single Components Field Mixture Field

26. Vighi et al (2003) Water quality objectives for mixtures of toxic chemicals: problems and perspectives, Ecotox. Env. Safety, 54, 139-150 Applying Concentration Addition

27. Compound 1: PEC 1 =0.4*10 -4 EC50 Algae :1.0 EC50 Daphnids :0.1PNEC = 10 -4 EC50 Fish :1.0 Compound 2: PEC 2 = 0.8*10 -4 EC50 Algae :0.1 PNEC = 10 -4 EC50 Daphnids :1.0 EC50 Fish :1.0

28. ? Compound 1Compound 2 0.40.8

29. Single Components Laboratory EQS (PNEC) Concentration Addition Single Components Field Mixture Laboratory EQS (PNEC) Mixture Field

30. EC50’s Algae Daphnids FishConc Comp1 1.0 0.11.00.4*10 -4 Comp2 0.1 1.01.00.8*10 -4 Mix 0.15 0.721.01.2*10 -4 RQ=0.8

31.  Extrapolates from single substance to mixture toxicity Extrapolation Lab->Field can be achieved either by summing up PEC/PNECs or by summing up TUs Properties of Concentration Addition

32.  Assumes similar mode of action of all mixture components  Extrapolates from single substance to mixture toxicity  Assumes no interaction between the components in the mixture Properties of Concentration Addition √ √

33. A synergistic mixture Laetz et al., EHP, 2008 S ynergistic effects of binary mixtures of carbamate (CB) and organophospate (OP) pesticides DZN: Diazinon; MLN: Malathion; CRL: Carbaryl CBN: Carbofuran; CFS: Clopyrifos

34. Belden, J.B. et al. (2007) How well can we predict the toxicity of pesticide mixtures to aquatic life? Integrated Environmental Assessment and Management. 3:364-372.

35. TU Distribution for a realistic pesticide mixture Junghans, et al. (2006) Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquatic Toxicology 76, 2006 Sum of TU’s = 0.98

36. What happens if a compound is synergized? SynFactor = 2 SynFactor = 10 Rank of synergised compound Sum of Toxic Units

37. Average Synergy Factor What happens if n compounds are synergized?

38.  Rare  Limited to mixtures with few compounds  Multi-component mixtures dampen quantitative consequences Interactions, Synergisms, Antagonisms

39.  Assumes similar mode of action of all mixture components  Extrapolates from single substance to mixture toxicity  Assumes no interaction between the components in the mixture Properties of Concentration Addition √ √ √

40.  Mode of Action –Start with CA –Accompany by an error estimations –IA only if (i) indications of risk and (ii) the error estimates indicate that IA may indeed predict a lower mixture toxicity Options for overcoming the limitations of CA/IA

41.  Extrapolation Lab -> Field –Start with summing up PEC/PNECs (scientifically problematic, but easily applied and cautious) –Analyse sum of toxic units (scientifically sound, potentially problematic to apply) Options for overcoming the limitations of CA/IA

42.  Interactions –Need for a case-by-case consideration –Multi-component mixtures are comparatively robust Options for overcoming the limitations of CA/IA

43.  Mixtures matter. They are there and they are toxic.  Quality standards and risk quotients for individual compounds form the basis, but are insufficient alone.  The science on mixture ecotoxicology provides regulatory tools and options (mainly based on CA, accompanied by error estimations) Summary and Conclusions

44. occurrences, (eco)toxicity and relevance for water quality in Europe Chemical Mixtures Thomas Backhaus University of Gothenburg thomas.backhaus@dpes.gu.se