The United States Environmental Protection Agency’s Center for Computational Toxicology and Exposure
DioxanePresentation_080921.pdf (1.52 MB)

Developing scientific workflows for exposure assessments: a case study with 1,4-dioxane exposure

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posted on 2021-09-23, 19:06 authored by Daniel Dawson, Hunter Fisher, Abigail T. Noble, Qingyue Meng, Daniel Vallero, Rogelio Tornero-Velez, Elaine Cohen Hubal
A scientific workflow (“workflow”, hereafter) is designed to execute a series of data manipulation and computational steps to provide outputs tailored to decision-making contexts. For exposure assessments, they are useful tools for making robust, replicable estimates of exposure to chemicals, especially for complex exposure scenarios. For emerging contaminants of concern, workflow development may be hampered by the lack of available data with which to parameterize exposure models. In this situation, more data rich proxies may be useful for developing workflows with an eye towards generalization. We developed a workflow to estimate exposure to 1,4-dioxane, a persistent and mobile organic chemical that is considered a likely human carcinogen by the U.S. EPA. 1,4 Dioxane occurs in drinking water due to prior industrial use, and it occurs as an unintended byproduct in some ethoxylated personal-care products such as detergents and soaps. Both sources may contribute to human exposure and to wastewater contamination, potentially further contaminating down-stream drinking water sources. Within our workflow, we used the EPA-based simulation modeling tool SHEDS-HT to model exposure scenarios to 1,4 dioxane under several conditions, including differences in water source and assumed prevalence of 1,4 dioxane in consumer products. We found that human exposure is primarily driven by water consumption, and was highest for those exposed to contaminated ground water. Meanwhile, down-the-drain contamination is likely driven by consumer product use, with model estimates sensitive to a higher assumed prevalence of dioxane in products. These model-based results are bolstered by empirical 1,4-dioxane concentrations collected at wastewater plants that are within 1 order of magnitude of most down-the-drain estimates. Lastly, the workflow developed for this assessment can readily be adapted to additional exposure scenarios for 1,4 Dioxane and may serve as a starting template for modeling exposure to other chemicals of concern.

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