Citation:

Isaacs, K., C. Ring, AND L. Li. Everything Everywhere All at Once: Characterizing Complex Chemical Exposure Pathways Using Computational Modeling. ISES, Chicago, IL, August 27 - 31, 2023.

Impact/Purpose:

This abstract describes an abstract for a proposed symposium at the 2023 International Society for Exposure Science meeting in Chicago, IL. 

Description:

Characterization of exposure pathways associated with human and ecological receptors is a key step in prioritizing and assessing chemicals based on the risks they pose to public health and the environment.  Simply stated, an exposure pathway is the route a chemical takes from its source to a receptor. Information required to characterize pathway (and the ultimate internal and external exposure in a receptor) for even a single chemical includes chemical use, emission, environmental transport and partitioning to media (e.g., air, dust, water), exposure routes, and toxicokinetic data. In many countries information on exposure pathway identifies the relevant safety laws and even the relevant regulatory body. When one considers the tens to hundreds of thousands of chemicals in commerce, understanding the complex network of overlapping, and potentially interacting, exposure pathways requires novel approaches that pivot away from a one-chemical-at-a-time mindset towards methods that consider more holistically the relationships among chemical structures (and properties), patterns of chemical use, and observed occurrence of chemicals in different geospatial locations and in different media. New approaches that make use of computational modeling can identify and quantify these relationships, and ultimately form the basis for predictive models for estimating chemical concentrations (within the home, a workplace, or natural environment) and associated receptor exposures. This session will address the use of computational modeling approaches to characterize exposure pathways in either a qualitative or quantitative manner. Abstracts of interest to this session would address the use of modeling approaches, including data-driven machine learning approaches and theory-driven mechanistic modeling approaches, to: 1) estimate pathway-specific chemical exposures from minimal information, especially for under-studied sources, chemical categories, pathways, or receptors; 2) interpret chemical monitoring and biomonitoring data, including non-targeted data, with the goal of identifying, quantifying, or excluding chemical sources and pathways;  3) extrapolate and curate existing chemical use or release information to fill gaps in the data required to characterize exposure pathways; and 4) characterize or predict patterns of chemical co-occurrence in the environment. 

Record Details: