Validation of an In Vitro Bioaccessability Test Method for the Estimation of the Bioavailability of Arsenic from Soil and Sediment

Abstract

Accurate evaluation of the human health risk from ingestion of arsenic in soil or soil-like media requires knowledge of the relative bioavailability (RBA) of arsenic in the soil or soil-like material. In general, studies to date have indicated that the RBA of arsenic in soil is lower than the U.S. Environmental Protection Agency (EPA) default value of 100%. Consequently, estimation of site-specific RBA values can often save substantial costs during site cleanup. Although RBA can be measured using studies in animals, such studies are generally slow and costly. An alternative strategy is to perform measurements of arsenic solubility in the laboratory. Typically, a sample of soil or sediment is extracted using a fluid that has properties that resemble a gastrointestinal fluid, and the amount of arsenic solubilized from the sample into the fluid under a standard set of extraction conditions is measured. The fraction of arsenic that is solubilized is referred to as the in vitro bioaccessibility (IVBA). The IVBA is then utilized to predict the in vivo RBA of arsenic in that sample, usually through an empiric correlation model. The objective of this demonstration project was to develop, optimize, and validate an IVBA-based method to accurately predict the RBA of arsenic in soil and soil-like materials.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2012
Accession Number
ADA582226

Entities

Organizations

  • Environmental Security Technology Certification Program

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Arsenates
  • Calibration
  • Chemical Synthesis
  • Chemistry
  • Cost Analysis
  • Data Sets
  • Electronic Mail
  • Environmental Pollutants
  • Environmental Protection
  • Health Services
  • Materials
  • Measurement
  • Risk Analysis
  • Sediments
  • Test And Evaluation
  • Test Methods
  • Validation

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  • Computational Modeling and Simulation
  • Data Mining and Knowledge Discovery.
  • Environmental Engineering.