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A method based on regression modeling was developed to discern the contribution of component chemicals to the toxicity of highly complex, environmentally realistic mixtures of disinfection byproducts (DBPs). Chemical disinfection of drinking water forms DBP mixtures. Because of concerns about possible reproductive and developmental toxicity, a whole mixture (WM) of DBPs produced by chlorination of a water concentrate was administered as drinking water to Sprague–Dawley (S–D) rats in a multigenerational study. Age of puberty acquisition, i.e., preputial separation (PPS) and vaginal opening (VO), was examined in male and female offspring, respectively. When compared to controls, a slight, but statistically significant delay in puberty acquisition was observed in females but not in males. WM-induced differences in the age at puberty acquisition were compared to those reported in S–D rats administered either a defined mixture (DM) of nine regulated DBPs or individual DBPs. Regression models were developed using individual animal data on age at PPS or VO from the DM study. Puberty acquisition data reported in the WM and individual DBP studies were then compared with the DM models. The delay in puberty acquisition observed in the WM-treated female rats could not be distinguished from delays predicted by the DM regression model, suggesting that the nine regulated DBPs in the DM might account for much of the delay observed in the WM. This method is applicable to mixtures of other types of chemicals and other endpoints.

In assessing environmental health risks, the risk characterization step synthesizes information gathered in evaluating exposures to stressors together with dose-response relationships, characteristics of the exposed population, and external environmental conditions. This manuscript summarizes key steps of a cumulative risk assessment (CRA) followed by a discussion of considerations for characterizing cumulative risks. Cumulative risk characterizations differ considerably from single chemical- or single source-based risk characterization. CRAs typically focus on a specific population instead of a pollutant or pollutant source and should include an evaluation of all relevant sources contributing to the exposures in the population and other factors that influence dose-response relationships. Second, CRAs may include influential environmental and population-specific conditions, involving multiple chemical and nonchemical stressors. Third, a CRA could examine multiple health effects, reflecting joint toxicity and the potential for toxicological interactions. Fourth, the complexities often necessitate simplifying methods, including judgment-based and semiquantitative indices that collapse disparate data into numerical scores. Fifth, because of the higher dimensionality and potentially large number of interactions, information needed to quantify risk is typically incomplete, necessitating an uncertainty analysis. Three approaches that could be used for characterizing risks in a CRA are presented: the multiroute hazard index, stressor grouping by exposure and toxicity, and indices for screening multiple factors and conditions. Other key roles of the risk characterization in CRAs are also described, mainly the translational aspect of including a characterization summary for lay readers (in addition to the technical analysis), and placing the results in the context of the likely risk-based decisions.

When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, EPA guidance states that, toxicity data from a mixture considered “sufficiently similar” to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data rich and data poor cases. We construct a “similar mixtures risk indicator” (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose-response data examining acute neurobehavioral effects of pyrethroid mixtures in rats. Our method shows that the mixtures from 90% of the CCCs were sufficiently similar to the dose-response study mixture. Using exposure estimates for a hypothetical child, the 95th percentile of the (weighted) similar mixtures risk indicator (SMRI) for these sufficiently similar mixtures was 0.20 (i.e., where SMRI< 1, less concern; >1, more concern).