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Using Machine Learning To Predict Soil Lead Relative Bioavailability.

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Using Machine Learning To Predict Soil Lead Relative Bioavailability.

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Using machine learning to predict soil lead relative bioavailability.

Shuang Zhang¹, Xiaoping Li², Tunyang Geng¹

¹Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi 710062, PR China; International Joint Research Centre of Shaanxi Province for Pollutant Exposure and Eco-environmental Health, Xi'an, Shaanxi 710062, PR China.

Journal of Hazardous Materials

|November 26, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study estimates lead relative bioavailability (Pb-RBA) from various soil types and biological targets using machine learning. Findings provide more accurate Pb-RBA data for lead risk assessment, differing from current EPA estimates.

Keywords:

Bioaccessibility (Pb-BAc)In vivo-in vitro correlations (IVIVCs)Machine learning Pb Relative bioavailability (Pb-RBA)

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Area of Science:

Environmental Science
Toxicology
Computational Biology

Background:

Lead relative bioavailability (Pb-RBA) is crucial for assessing human health risks from lead exposure.
Existing data lacks specific Pb-RBA values for diverse soil sources and biological endpoints in vivo.
Current United States Environmental Protection Agency (USEPA) Pb-RBA estimates for soils may not be universally applicable.

Purpose of the Study:

To estimate Pb-RBA from different soil sources and biological endpoints in vivo using machine learning.
To provide more accurate Pb-RBA data for improved soil lead risk assessment.
To define Pb-RBA across various soil types and biological targets.

Main Methods:

Utilized machine learning, specifically the Random Forest (RF) model, to predict Pb-RBA.

Estimated Pb-RBA for multiple biological endpoints: blood, kidney, liver, and femur.

Analyzed Pb-RBA across various soil sources including shooting ranges, agricultural, urban, mining, industrial, and certified reference materials.

Main Results:

Predicted mean Pb-RBA values varied by endpoint: blood (49.94±18.65%), kidney (60.15±26.62%), liver (60.90±21.51%), femur (50.70±17.56%), and combined (62.89±16.64%).
Pb-RBA from shooting range soils was highest (88.21±16.92%), while industrial soils showed the lowest (47.71±20.35%).
Estimated Pb-RBA values generally ranged from 20-80%, differing from the USEPA's 60% soil Pb-RBA.

Conclusions:

This study provides the first definition and in vivo data for Pb-RBA across diverse soil sources and biological endpoints.
Machine learning models offer a robust approach for estimating Pb-RBA, improving upon generic estimates.
The findings necessitate a re-evaluation of current lead risk assessment protocols using more specific Pb-RBA data.