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    Rights statement: This is the author’s version of a work that was accepted for publication in Environment International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environment International, 92-93, 2016 DOI: 10.1016/j.envint.2016.04.009

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A meta-analysis to correlate lead bioavailability and bioaccessibility and predict lead bioavailability

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<mark>Journal publication date</mark>07/2016
<mark>Journal</mark>Environment International
Volume92-93
Number of pages7
Pages (from-to)139-145
Publication statusPublished
Early online date19/04/16
Original languageEnglish

Abstract

Defining the precise clean-up goals for lead (Pb) contaminated sites requires site-specific information on relative bioavailability data (RBA). While in vivo measurement is reliable but resource insensitive, in vitro approaches promise to provide high-throughput RBA predictions. One challenge on using in vitro bioaccessibility (BAc) to predict in vivo RBA is how to minimize the heterogeneities associated with in vivo-in vitro correlations (IVIVCs) stemming from various biomarkers (kidney, blood, liver, urinary and femur), in vitro approaches and studies. In this study, 252 paired RBA-BAc data were retrieved from 9 publications, and then a Bayesian hierarchical model was implemented to address these random effects. A generic linear model (RBA (%) = (0.87 ± 0.16) × BAc + (4.70 ± 2.47)) of the IVIVCs was identified. While the differences of the IVIVCs among the in vitro approaches were significant, the differences among biomarkers were relatively small. The established IVIVCs were then applied to predict Pb RBA of which an overall Pb RBA estimation was 0.49 ± 0.25. In particular the RBA in the residential land was the highest (0.58 ± 0.19), followed by house dust (0.46 ± 0.20) and mining/smelting soils (0.45 ± 0.31). This is a new attempt to: firstly, use a meta-analysis to correlate Pb RBA and BAc; and secondly, estimate Pb RBA in relation to soil types.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Environment International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environment International, 92-93, 2016 DOI: 10.1016/j.envint.2016.04.009