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A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources

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Date 2018 Nov 9
PMID 30407800
Citations 18
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Abstract

Exposure- and risk-based assessments for chemicals used indoors or applied to humans (i.e., in near-field environments) necessitate an aggregate exposure pathway framework that aligns chemical exposure information from use sources to internal dose and eventually to their potential for health effects. Such a source-to-effect continuum is advocated to balance the complexity of human exposure and the insufficiency of relevant data for thousands of existing and emerging chemicals. Here, we introduce the Risk Assessment, IDentification And Ranking-Indoor and Consumer Exposure (RAIDAR-ICE) model, which establishes an integrated framework to evaluate human exposure due to indoor use and direct application of chemicals to humans. As a model evaluation, RAIDAR-ICE faithfully reproduces exposure estimates inferred from biomonitoring data for 37 chemicals with direct and indirect near-field sources. RAIDAR-ICE generates different rankings for 131 chemicals based on different exposure- and risk-based assessment metrics, demonstrating its versatility for diverse chemical screening goals. When coupled with a far-field RAIDAR model, the near-field RAIDAR-ICE model enables assessment of aggregate human exposure. Overall, RAIDAR-ICE is a powerful tool for high-throughput screening and prioritization of human exposure to neutral organic chemicals used indoors.

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References
1.
Zhang X, Arnot J, Wania F . Model for screening-level assessment of near-field human exposure to neutral organic chemicals released indoors. Environ Sci Technol. 2014; 48(20):12312-9. DOI: 10.1021/es502718k. View

2.
Czub G, McLachlan M . Bioaccumulation potential of persistent organic chemicals in humans. Environ Sci Technol. 2004; 38(8):2406-12. DOI: 10.1021/es034871v. View

3.
Shin H, McKone T, Bennett D . Intake fraction for the indoor environment: a tool for prioritizing indoor chemical sources. Environ Sci Technol. 2012; 46(18):10063-72. DOI: 10.1021/es3018286. View

4.
Ankley G, Bennett R, Erickson R, Hoff D, Hornung M, Johnson R . Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 2010; 29(3):730-41. DOI: 10.1002/etc.34. View

5.
Bonnell M, Zidek A, Griffiths A, Gutzman D . Fate and exposure modeling in regulatory chemical evaluation: new directions from retrospection. Environ Sci Process Impacts. 2017; 20(1):20-31. DOI: 10.1039/c7em00510e. View