Extended application of diffusive gradients in thin films (DGT) in assessing arsenic bioavailability and human health risks in brownfield soils

The environmental and human health risks of arsenic (As) mainly depend on its bioavailability in the soil. However, precise determination of bioavailable As in situ is still challenging due to the complexity of soil types and hydrogeological conditions in polluted sites. Diffusive gradients in thin films (DGT) is one of the reliable analysis technology in the in situ analysis of bioavailable chemicals in waters, sediments and farmland soils. However, few studies have investigated the application of DGT in evaluating the bioavailability of heavy metals in brownfield soils. In this study, DGT was extended applied to evaluate the availability of As in 16 contaminated soil samples from seven brownfield sites. The correlation between the bioavailable As measured by DGT and the bioaccessible As determined by the Unified BRAGE method (UBM) assay was also analyzed. In addition, the human health risks were evaluated, and the DGT-based prediction model for bioavailable As was also developed (R2 = 0.852, P < 0.001). The results indicated that considering bioavailability in risk assessments can improve the accuracy of calculations for site-specific health risks and provide guidance for effective remediation. The study will demonstrate the effectiveness of DGT in characterizing bioavailable heavy metals in contaminated site soils and provide decision-making support for the soil remediation.(c) 2023 The Authors. Published by Elsevier B.V.

Automated summary

The environmental and human health risks of arsenic depend on its bioavailability in the soil. The Diffusive Gradients in Thin Films (DGT) technology is a reliable method for analyzing bioavailable chemicals in waters, sediments, and farmland soils. This study applied DGT to evaluate the availability of arsenic in contaminated soil samples and analyzed its correlation with the Unified BRAGE method (UBM) assay. The study also developed a DGT-based prediction model for bioavailable arsenic. The results showed that considering bioavailability in risk assessments can improve accuracy and provide guidance for remediation.