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Identification and Prioritization of Chemical Mixtures from Environmental Residue Data
Citation:
Holm, K., Rocky Goldsmith, D. Chang, Chris Grulke, M. Phillips, C. Tan, AND R. Tornero-Velez. Identification and Prioritization of Chemical Mixtures from Environmental Residue Data. Presented at SOT Annual Meeting, Phoenix, AZ, March 23 - 27, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
High throughput toxicity testing has greatly improved the speed at which single chemicals can be screened using in vitro methods. However, people are not exposed to a single chemical at a time, rather to a mixture of chemicals. Even with the increased speed of these methods, testing every possible mixture is not feasible. Indeed, under purely random conditions the possible number of chemical combinations scales geometrically as the pool of chemicals increases. However, in analogy with observations made on communities of flora and fauna in an ecological niche, anthropogenic chemicals in the environment arise through a myriad of deliberate technical, social and economic processes, which introduce structure and influence chemical co-occurrence. In this study, we i) test structural features in pesticide residue floor wipe samples from two comprehensive surveys, the Childcare Center (CCC) Survey, and the American Healthy Homes Survey (AHHS), , each serving as a distinct quasi-ecological niche; and ii) develop an approach to identify mixtures for testing. Although structuring (non-randomness) reduces co-occurrence additional filtering is needed to pair down the candidate set of mixtures for testing. The approach we describe involves filtering the chemical list based on a risk threshold (a product of the observed chemical surface load (ng/cm2)) and a relative potency index). Factor analysis is then conducted for the exposure data limited toon the filtered higher-risk chemical list. The mixtures identified through the factor analysis are evaluidated against a previously published co-occurrence method that uses the observed presence/absence matrix (locations by chemicals), and are found to be in close agreement . The identified chemical mixtures are based on observed environmental chemical residue data, and are therefore relevant for future toxicity testing for synergism.