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Using machine learning to predict soil lead relative bioavailability
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DOI：10.1016/j.jhazmat.2024.136515.png)
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摘要
En 中文
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from different soil sources and endpoints based on machine learning. The Pb-BAc and Pb-RBA in soils were found to be mostly in the range of 20-80 %, which is different from the USEPA Pb-RBA of 60 % in soils. The mean Pb-RBA for different biological endpoints in vivo predicted using the RF model were 49.94 f 18.65 % for blood; 60.15 f 26.62 %, kidney; 60.90 f 21.51 %, liver; 50.70 f 17.56 %, femur; and 62.89 f 16.64 % as a combined measure. Pb-RBA of shooting range soils was 88.21 f 16.92 % (mean), spiked/aged soils 77.11 f 14.05 % and certified reference materials 73.70 f 20.31 %; agricultural soil 68.28 f 18.93 %, urban soil 64.36 f 21.82 %, mining/smelting soils 53.99 f 17.66 %, and industrial soils 47.71 f 20.35 %. This study is first to define the Pb-RBA according to various soil sources and endpoints in vivo with the objective of providing more accurate Pb-RBA data for soil lead risk assessment.
Keyword:
Pb
Relative bioavailability (Pb-RBA)
Bioaccessibility (Pb-BAc)
In vivo
in vitro correlations (IVIVCs)
Machine learning
期刊
IF:
11.3
论文数:
4.0W
被引数:
24.0W
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