A meta-analysis to correlate lead bioavailability and bioaccessibility and predict lead bioavailability.
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.
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Zhaomin
Z
Dong
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Kaihong
K
Yan
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Yanju
Y
Liu
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia. Electronic address: ravi.naidu@newcastle.edu.au.
Ravi
R
Naidu
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Luchun
L
Duan
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Ayanka
A
Wijayawardena
Lancaster Environment Centre, Lancaster University, LA1 4YQ Lancaster, United Kingdom.
Kirk T
KT
Semple
Global Center for Environmental Research (GCER), The Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia.
Mohammad Mahmudur
MM
Rahman
D001499:Bayes Theorem; D001682:Biological Availability; D004391:Dust / Q000032:analysis; D004781:Environmental Exposure; D006801:Humans; D007854:Lead / Q000737:chemistry / Q000493:pharmacokinetics* / Q000633:toxicity; D016014:Linear Models; D008906:Mining; D018547:Plant Stems / Q000737:chemistry; D012987:Soil; D012989:Soil Pollutants / Q000032:analysis / Q000493:pharmacokinetics* / Q000633:toxicity
D004391:Dust; D012987:Soil; D012989:Soil Pollutants; D007854:Lead
Bioaccessibility; Bioavailability; Lead; Meta-analysis; Soil