Suchergebnisse

A third generation of environmental policy making and risk management will increasingly impose environmental measures, which may give rise to analyzing countervailing risks. Therefore, a comprehensive analysis of all risks associated with the decision alternatives will aid decisionmakers in prioritizing alternatives that effectively reduce both target and countervailing risks. Starting with the metaphor of the ripples caused by a stone that is thrown into a pond, we identify 10 types of ripples that symbolize, in our case, risks that deserve closer examination: direct, upstream, downstream, accidental risks, occupational risks, risks due to offsetting behavior, change in disposable income, macro‐economic changes, depletion of natural resources, and risks to the manmade environment. Tools to analyze these risks were developed independently and recently have been applied to overlapping fields of application. This suggests that either the tools should be linked in a unified framework for comparative analysis or that the appropriate field of application for single tools should be better understood. The goals of this article are to create a better foundation for the understanding of the nature and coverage of available tools and to identify the remaining gaps. None of the tools is designed to deal with all 10 types of risk. Provided data suggest that, of the 10 types of identified risks, those associated with changes in disposable income may be particularly significant when decision alternatives differ with respect to their effects on disposable income. Finally, the present analysis was limited to analytical questions and did not capture the important role of the decision‐making process itself.

AbstractIn 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 article 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 semi‐quantitative 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, Environmental Protection Agency 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"(SMRI) (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) SMRI for these sufficiently similar mixtures was 0.20 (i.e., where SMRI <1, less concern; >1, more concern).