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Illustrative Case Using the RISK21 Roadmap and Matrix: Prioritization for Evaluation of Chemicals Found in Drinking Water
Citation:
Wolf, D., A. Bachman, G. Barrett, C. Bellin, J. Goodman, E. Jensen, A. Moretto, T. McMullin, T. Pastoor, R. Schoeny, B. Slezak, K. Wend, AND M. Embry. Illustrative Case Using the RISK21 Roadmap and Matrix: Prioritization for Evaluation of Chemicals Found in Drinking Water. CRITICAL REVIEWS IN TOXICOLOGY. CRC Press LLC, Boca Raton, FL, 46(1):43-53, (2016).
Impact/Purpose:
This is one of a series of publications resulting from work of the Health and Environmental Sciences Institute (HESI) committee on Risk 21.
Description:
The HESI-led RISK21 effort has developed a framework supporting the use of twenty first century technology in obtaining and using information for chemical risk assessment. This framework represents a problem formulation-based, exposure-driven, tiered data acquisition approach that leads to an informed decision on human health safety to be made when sufficient evidence is available. It provides a transparent and consistent approach to evaluate information in order to maximize the ability of assessments to inform decisions and to optimize the use of resources. To demonstrate the application of the framework’s roadmap and matrix, this case study evaluates a large number of chemicals that could be present in drinking water. The focus is to prioritize which of these should be considered for human health risk as individual contaminants. The example evaluates 20 potential drinking water contaminants, using the tiered RISK21 approach in combination with graphical representation of information at each step, using the RISK21 matrix. Utilizing the framework, 11 of the 20 chemicals were assigned low priority based on available exposure data alone, which demonstrated that exposure was extremely low. The remaining nine chemicals were further evaluated, using refined estimates of toxicity based on readily available data, with three deemed high priority for further evaluation. In the present case study, it was determined that the greatest value of additional information would be from improved exposure models and not from additional hazard characterization.
