Table 4 Properties of the different MAF classes
Factor | Ceiling | |||
|---|---|---|---|---|
MAFfactor | MAFexact | MAF80%, MAF90%, MAF95% | MAF2, MAF5, MAF10, MAF100 | |
Assumptions | - All relevant mixture components identified - Risk characterization ratios are unbiased estimates for the actual risk contribution of a substance - Summation of risk quotients adequate for estimating mixture risks - Risks of the individual chemicals are independent of each other | - All relevant mixture components identified - Risk characterization ratios are unbiased estimates for the actual risk contribution of a substance - Summation of risk quotients adequate for estimating mixture risks - Risks of the individual chemicals are independent of each other | - All relevant mixture components identified - Risk characterization ratios are unbiased estimates for the actual risk contribution of a substance - Summation of risk quotients adequate for estimating mixture risks - Risks of the individual chemicals are independent of each other - Pareto-distribution of RCR values | - All relevant mixture components identified - Risk characterization ratios are unbiased estimates for the actual risk contribution of a substance - Summation of risk quotients adequate for estimating mixture risks - Risks of the individual chemicals are independent of each other |
Regulatory suitability | - Ensures that the mixture risk, after the application of the MAF, is compatible with the European “zero-pollution ambition for 2050” - Similar impact (risk reduction requirements) for all substances, including low-risk compounds and compounds with low emission rates - Straight forward to implement into the workflow of safety assessment under REACH, after a regulatory MAF value is agreed upon - Concerns over equitable treatment of REACH registrants because registration requires demonstration of safety, not a precise assessment of risks. Registrants with different methodologies to demonstrate safe use would be affected differently by the MAF | - Ensures that the mixture risk, after the application of the MAF, is compatible with the European “zero-pollution ambition for 2050” - Impact proportional to (a) the relative contribution of a substance: high-risk substances(high emissions and/or high toxicity) are affected most, low-risk substances are not affected, and (b) the accuracy of the underlying risk estimates: substances with low-quality risk estimates (high assessment factors) are affected more - Straight forward to implement into the workflow of safety assessment under REACH, after a regulatory MAF value is agreed upon | - Residual risk, after the application of the MAF, is systematically higher than what is compatible with the European “zero-pollution ambition for 2050” - Impact proportional to (a) the relative contribution of a substance: high-risk substances are affected most, low-risk substances are not affected, and (b) the accuracy of the underlying risk estimates: substances with low-quality risk estimates (high assessment factors) are affected most - Simple to implement into the workflow of safety assessment under REACH, after a regulatory MAF value is agreed upon | - Unclear, how the residual risk, after the application of the MAF, relate to the European “zero-pollution ambition for 2050” - Impact proportional to (a) the relative contribution of a substance: high-risk substances are affected most, low-risk substances are not affected, and (b) the accuracy of the underlying risk estimates: substances with low-quality risk estimates (high assessment factors) are affected most - Simple to implement into the workflow of safety assessment under REACH, after a regulatory MAF value is agreed upon |
Risk of over-regulation | - None, as long as the basic assumptions are fulfilled | - None, as long as the basic assumptions are fulfilled | - If applied to mixtures with RQsum < 1.0, otherwise no risk of over-regulation | - Depends on the value chosen a priori - If applied to mixtures with RQsum < 1.0 - The exact amount of risk over-regulation is unpredictable and scenario-specific |
Risk of under-regulation | - None, as long as the basic assumptions are fulfilled | - None, as long as the basic assumptions are fulfilled | - Systematic risk of under-regulation for a sizeable fraction of relevant mixtures, even if the basic assumptions are fulfilled - The exact amount of risk under-regulation is unpredictable and scenario-specific | - Depends on the value chosen a priori - The exact amount of risk under-regulation is unpredictable and scenario-specific |
Robustness | - MAF estimate depends on the average data quality (exposure, hazard) of all mixture components | - Second highest robustness of all MAF alternatives - The actual numerical RCR value of the mixture components only matters insofar as it determines the proper classification of the substances into “risk drivers” (RCR > 1/MAF) and non- “risk drivers” (RCR < = 1/RCR) | - MAF estimate depends mainly on the accurate quantitative risk characterization of the “risk drivers”, i.e., those compounds that account for x % of the risk | - Highest robustness of all MAF alternatives - MAF value set a priori, which makes it independent of the data quality available for the mixture components |