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Is a mixture assessment factor (MAF) the right way forward? Thomas Backhaus University of Gothenburg

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Presentation on theme: "Is a mixture assessment factor (MAF) the right way forward? Thomas Backhaus University of Gothenburg"— Presentation transcript:

1 Is a mixture assessment factor (MAF) the right way forward? Thomas Backhaus University of Gothenburg thomas.backhaus@gu.se

2 AreaSizeReference Environment100 Janssen, 2004; van Vlaardingen, 2007 Environment100Tørsløv, 2013 Human health100Muilerman, 2011 Human health10Tørsløv, 2013, Petersen, 2014 Suggested MAFs No explicit justification given

3  easy to implement  no additional data needed  increased level of protection CONSPROS  blanket type of approach  hard to justify size

4 Conservatism in Hazard Assessment REACH Guidance Document, Chapter R10, 2008

5  No biotransformation  No abiotic transformation  High production volumes, resp. market share  High emission rates  Emission takes place in a confined space  Emission happens suddenly (peak concentrations) Conservatism in Exposure Assessments

6 Hazard Assessment  Assessment Factors Exposure Assessment  Conservative Assumptions Consideration of Uncertainties

7 Assessment of Single Substances Exposure AssessmentHazard AssessmentRisk Assessment

8 Assessment of Mixtures Exposure AssessmentHazard AssessmentRisk Assessment

9 A Mixture Assessment Factor needs to cover exposure and hazard assessment Assessment of Mixtures

10  The simultaneous presence of compounds as mixtures is ignored  Not all components included  Insufficient (eco)toxicological knowledge on the mixture components  Sole use of CA  Interactions (synergistic, antagonistic) Mixture-specific uncertainties

11 Equals the number of expected compounds in a mixture MAF based on Concentration Addition if

12 Equals the number of expected compounds in a mixture E.g.: the CA-expected effect of a mixture is lower than 10%, if all components of a 100-compound mixture are below a concentration of EC10/100 MAF based on Concentration Addition

13  Pesticide Mixture  Result from the Swedish pesticide monitoring program  n = 42 . Example

14 Distribution of Risk Quotients 136

15  Several individual compounds are present at concentrations above their EQS.  Need for regulatory action already on the single substance level  Assumption: successful risk mitigation, i.e. all compounds are present at a concentration below their respective EQS. Environmental Risk of the pesticide mixture

16 Distribution of Risk Quotients after risk mitigation 16.0

17 Maximum Cumulative Ratio Equitoxic Mixture:

18 4 different scenarios Mixture Typen Prior adjustment After risk mitigation for single compounds Risk quotient MCR Risk quotient MCR Pesticides421363.616.8017 Pharmaceuticals18 481.2 4.65 4.7 Anti-androgens15 2.01 2.00 Organic air pollutants 29 4.332.9 3.78 3.9

19 Maximum exceedance of a safe level in various situations ‘n’ known Individual RQ’s < 1 RQ’s quantified Info. on interactions? MAF nounknownno arbitrary value yesunknownno arbitrary value yes no number of mixture components*IF yes no interactions unlikely number of mixture components yes noMCR*IF yes interactions unlikely MCR yes case-by-case based on weight of evidence

20 Maximum exceedance of a safe level in various situations ‘n’ known Individual RQ’s < 1 RQ’s quantified Info. on interactions? MAF nounknownno arbitrary value yesunknownno arbitrary value yes no number of mixture components*IF yes no interactions unlikely number of mixture components yes noMCR*IF yes interactions unlikely MCR yes case-by-case based on weight of evidence

21 Maximum exceedance of a safe level in various situations ‘n’ known Individual RQ’s < 1 RQ’s quantified Info. on interactions? MAF nounknownno arbitrary value yesunknownno arbitrary value yes no number of mixture components*IF yes no interactions unlikely number of mixture components yes noMCR*IF yes interactions unlikely MCR yes case-by-case based on weight of evidence

22 Maximum exceedance of a safe level in various situations ‘n’ known Individual RQ’s < 1 RQ’s quantified Info. on interactions? MAF nounknownno arbitrary value yesunknownno arbitrary value yes no number of mixture components*IF yes no interactions unlikely number of mixture components yes noMCR*IF yes interactions unlikely MCR yes case-by-case based on weight of evidence

23 Maximum exceedance of a safe level in various situations ‘n’ known Individual RQ’s < 1 RQ’s quantified Info. on interactions? MAF nounknownno arbitrary value yesunknownno arbitrary value yes no number of mixture components*IF yes no interactions unlikely number of mixture components yes noMCR*IF yes interactions unlikely MCR yes case-by-case based on weight of evidence

24  Mixture hazard assessment not possible without prior /parallel exposure assessment  Transparent use of AF’s: which uncertainty is covered?  Uncertainties in mixture assessment –incomplete exposure data –incomplete hazard data –synergism, antagonism Summary & Conclusions

25  n is a sufficiently protective MAF under the assumption of CA  MCR / STU seems a good descriptor for a MAF for coincidental mixtures, if no single compound has a RQ>1.  Only applicable to well characterized mixtures Summary & Conclusions

26  The specific uncertainty of coincidental mixtures cannot be lowered by “the actor” (chemical producer, importer, down-stream user)  Task for regulatory authorities! Summary & Conclusions

27  A MAF basically lowers the critical threshold for regulatory action from a risk quotient of 1 to a lower value.  Needed, because a risk quotient below 1 implies a ‘safe situation’ (no need for action).  Wrong conclusion. A step in a different direction…

28  We need to overcome the notion that a risk quotient below one indicates ‘no risk’ and no need for action.  A risk quotient should not be taken as an indication of risk per se, but as the contribution of a compound to the total risk in a given scenario. A step in a different direction…

29 Is a mixture assessment factor (MAF) the right way forward? Thomas Backhaus University of Gothenburg thomas.backhaus@gu.se ThoBaSwe @Twitter Thomas Backhaus, Mikael Gustavsson, Anke Hartmann, University of Gothenburg, Sweden Michael Faust, F&B Environmental Consulting, Germany Markus Klar, Henrik Sundberg, Stefan Gabring, Gunilla Ericson, Sten Flodström, Swedish Chemicals Agency, Sweden


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