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Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling.
Citation:
Nelson, C., K. Li, D. Obenour, J. Miller, J. Misenheimer, K. Scheckel, A. Betts, A. Juhasz, D. Thomas, AND K. Bradham. Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH - PART A: CURRENT ISSUES. Taylor & Francis, Inc., Philadelphia, PA, 81(6):160-172, (2018).
Impact/Purpose:
Arsenic (As) is the highest ranked contaminant on the Agency for Toxic Substances and Disease Registry’s 2015 Priority List of Hazardous Substances, which prioritizes contaminants based on their occurrence at National Priorities List sites and potential threat to human health (ATSDR, 2015).1 Direct soil ingestion by children is the primary exposure route of concern for As-contaminated soils (Dudka and Miller, 1999).2 Accurate assessment of human health risks from ingestion of As-contaminated soil depends on estimating site-specific bioavailability, defined as the fraction of an ingested dose that crosses the gastrointestinal epithelium and becomes available for distribution to internal tissues (U.S. EPA, 2007a).3 In response, in vitro methods have been developed that reliably estimate soil As bioavailability by measuring the fraction of As in soil that dissolves upon exposure to gastric-like conditions (Brattin et al., 2013; Bradham et al., 2011; Bradham et al., 2015; Diamond et al., 2016)4-7; this surrogate measure is termed bioaccessibility.
Description:
Interest in improved understanding of relationships among soil properties and arsenic (As) bioaccessibility has motivated the use of regression models for As bioaccessibility prediction. However, limits in the numbers and types of soils included in previous studies restrict the usefulness of these models beyond the range of soil conditions evaluated, as evidenced by reduced predictive performance when applied to new data. In response, hierarchical models that consider variability in relationships among soil properties and As bioaccessibility across geographic locations and contaminant sources were developed to predict As bioaccessibility in 139 soils on both a mass fraction (mg/kg) and % basis. The hierarchical approach improved the estimation of As bioaccessibility in studied soils. In addition, the number of soil elements identified as statistically significant explanatory variables increased when compared to previous investigations. Specifically, total soil Fe, P, Ca, Co, and V were significant explanatory variables in both models, while total As, Cd, Cu, Ni, and Zn were also significant in the mass fraction model and Mg was significant in the % model. This developed hierarchical approach provides a novel tool to (1) explore relationships between soil properties and As bioaccessibility across a broad range of soil types and As contaminant sources encountered in the environment and (2) identify areas of future mechanistic research to better understand the complexity of interactions between soil properties and As bioaccessibility.
URLs/Downloads:
https://www.ncbi.nlm.nih.gov/pubmed/29336680
https://doi.org/10.1080/15287394.2018.1423798