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Bayesian metamodel to estimate risk for thousands of chemicals in surface water
Citation:
Sayre, R., J. Arnot, K. Isaacs, P. Fantke, M. Serre, AND J. Wambaugh. Bayesian metamodel to estimate risk for thousands of chemicals in surface water. American Chemical Society Fall Meeting 2020, Virtual, NC, August 17 - 20, 2020. https://doi.org/10.23645/epacomptox.18817952
Impact/Purpose:
Poster presented to the American Chemical Society Fall meeting August 2020
Description:
Background: With thousands of chemicals in commerce and the environment, efficient tools are needed to support risk prioritization and evaluation. Knowledge gap: Inconsistent data availability for concentrations in surface water to develop exposure estimates. Proposed solution: Development of an open, reproducible workflow to: 1. Determine representative surface water concentrations for hundreds of organic chemicals in the United States based on already available monitoring data. 2. Calibrate a metamodel to predict representative surface water concentrations for thousands of non-monitored organic chemicals. 3. Prioritize organic chemicals based on the relationship between concentration ranges and predicted no-effect concentrations for freshwater standard test species. This abstract does not represent US EPA policy.
URLs/Downloads:
DOI: Bayesian metamodel to estimate risk for thousands of chemicals in surface water
RRSAYRE_ACSPOSTER_2020_FORSTICS.PDF (PDF, NA pp, 281.134 KB, about PDF)