title: Predicting arsenic relative bioavailability in contaminated soils using meta analysis and relative bioavailability-bioaccessibility regression models.
abstract: A number of in vitro assays are available for the determination of arsenic (As) bioaccessibility and prediction of As relative bioavailability (RBA) to quantify exposure for site-specific risk assessment. These data are usually considered in isolation; however, meta analysis may provide predictive capabilities for source-specific As bioaccessibility and RBA. The objectives of this study were to predict As RBA using previously published in vivo/in vitro correlations and to assess the influence of As sources on As RBA independent of geographical location. Data representing 351 soils (classified based on As source) and 514 independent bioaccessibility values were retrieved from the literature for comparison. Arsenic RBA was predicted using published in vivo/in vitro regression models, and 90th and 95th percentiles were determined for each As source classification and in vitro methodology. Differences in predicted mean As RBA were observed among soils contaminated from different As sources and within source materials when various in vitro methodologies were utilized. However, when in vitro data were standardized by transforming SBRC intestinal, IVG, and PBET data to SBRC gastric phase values (through linear regression models), predicted As RBA values for As sources followed the order CCA posts ≥ herbicide/pesticide > mining/smelting > gossan soils with 95th percentiles for predicted As RBA of 78.0, 78.4, 67.0, and 23.7%, respectively.
authors: Centre for Environmental Risk Assessment and Remediation, University of South Australia, Mawson Lakes, South Australia 5095, Australia. Albert.Juhasz@unisa.edu.au
authors: Albert L
authors: AL
authors: Juhasz
authors: John
authors: J
authors: Weber
authors: Euan
authors: E
authors: Smith
mesh_terms: D001151:Arsenic / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism; D004787:Environmental Pollution / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D018570:Risk Assessment / Q000379:methods; D012989:Soil Pollutants / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism
chemical_list: D012989:Soil Pollutants; D001151:Arsenic