Citation:

Li, L., J. Westgate, L. Hughes, X. Zhang, B. Givehchi, L. Toose, J. Armitage, F. Wania, P. Egeghy, AND J. Arnot. A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 52(24):14235-14244, (2018). https://doi.org/10.1021/acs.est.8b04059

Impact/Purpose:

The large number of chemicals requiring evaluation compared to the dearth of exposure data challenges traditional single-chemical evaluation practice and thus warrants high-throughput (HT) and prioritization approaches. Although fate and transport models have been used to estimate environmentally-mediated exposures from industrial sources for thousands of organic chemicals, HT near-field human exposure models have notably been lacking. Recent advances have created the potential for rapid quantification of human exposures from consumer products and other indoor sources. These models, however, typically do not account for key toxicokinetic processes such as distribution, metabolism (biotransformation) and excretion in the human body and are unable to address the potential for adverse effects associated with the exposure using internal dose information. Physiologically-based toxicokinetic (PBTK) models support the calculation of internal doses but are less frequently applied because of their extensive data requirements. This paper introduces the Risk Assessment, IDentification And Ranking-Indoor and Consumer Exposure (RAIDAR-ICE) model, which was developed to quantitatively complete the “source-to-dose” continuum for a direct comparison of exposures with toxicological data. RAIDAR-ICE is a versatile, efficient tool for screening and prioritization of neutral organic chemicals based primarily on near-field direct and indirect exposure pathways and associated potential human health concerns. The model aims to balance the complexity of processes related to assessing human exposure and potential risk with data availability at a screening-level for thousands of chemicals. The paper describes RAIDAR-ICE and evaluates the model using a series of case study assessment applications.

Description:

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 near-field human exposure and the lack 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. The model is first evaluated with exposure estimates inferred from biomonitoring data for 37 chemicals with varying properties that are used in direct and indirect near-field exposure scenarios. The model is then applied to rank 131 chemicals based on different exposure- and risk-based metrics, demonstrating its capacity for differentiating low and high concern chemicals with data available for high-throughput evaluations. Finally, RAIDAR-ICE is combined with a far-field exposure model (RAIDAR) to demonstrate a comprehensive aggregate exposure pathway assessment. As demonstrated, RAIDAR-ICE is a useful tool for screening and prioritization of human exposure to neutral organic chemicals.

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