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

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 R2 of 0.95 and an RMSE
of 0.25. We further employed SHAP values in conjunction with quantitative
analysis to identify the key features that influence bioaccessibility.
By utilizing the developed integrated models, we carried out predictions
for 3002 data points across China, clarifying the bioaccessibility
of cadmium (Cd), lead (Pb), and arsenic (As) in the soils of various
sites and constructed a comprehensive spatial distribution map of
China using the inverse distance weighting (IDW) interpolation method.
Based on these findings, we further derived the soil environmental
standards for metallurgical sites in China. Our observations from
the collected data indicate a reduction in the number of sites exceeding
the standard levels for Cd, Pb, and As in mining/smelting sites from
5, 58, and 14 to 1, 24, and 7, respectively. This research offers
a precise and scientific approach for cross-regional risk assessment
at the continental scale and lays a solid foundation for soil environmental
management.