Predicting the Bioaccessibility of Soil Cd, Pb, and As with Advanced Machine Learning for Continental-Scale Soil Environmental Criteria Determination in China.

ABSTRACT: Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil. Traditional batch experimental methods and linear models, however, are time-consuming and often fall short in precisely quantifying bioaccessibility. In this study, using 937 data points gathered from 56 journal articles, we developed machine learning models for three harmful inorganic elements, namely, Cd, Pb, and As. After thorough analysis, the model optimized through a boosting ensemble strategy demonstrated the best performance, with an average R ² of 0.95 and an RMSE of 0.25. We further employed SHAP values in conjunction with quantitative analysis to identify th

SUBMITTER: Xie K 

PROVIDER: S-EPMC11540114 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature