Citation:

Wood, M., K. Plourde, S. Larkin, P. Egeghy, A. Williams, V. Zemba, I. Linkov, AND D. Vallero. Advances on a Decision Analytic Approach to Exposure‐Based Chemical Prioritization. RISK ANALYSIS. Blackwell Publishing, Malden, MA, 40(1):83-96, (2020). https://doi.org/10.1111/risa.13001

Impact/Purpose:

This work grew out of the need to prioritize chemicals, especially those that may enter the marketplace, on the basis of exposure potential. This is designed to complement research on toxicity to support human health and environmental risk assessments, including screening level risk determinations for new chemicals by EPA’s OCSPP. The U.S. Army has conducted multicriteria decision analysis and expert elicitations that are consistent with EPA’s prioritization needs, so the agencies have collaborated under an interagency agreement to identify and weight the importance of factors that drive exposure potential, especially physicochemical inherent properties and product use. The article reports the results of these analyses.

Description:

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high‐throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high‐level chemical prioritization tool for risk‐based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

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