Identifying Key Drivers of Heavy Metal(loid)s Contamination in Farmland Soils Using Machine Learning with Source-Integrated Features

Accurate source identification is essential for the prevention and control of heavy metal(loid)s (HMs) contamination in farmland soils. Conventional source-apportionment approaches often rely on limited indicators and expert judgment, which can increase uncertainty in source interpretation. In this study, 800 topsoil samples collected from farmland in Ningxia, together with 24 environmental and anthropogenic variables, were used to develop element-specific machine learning models for Cd, Cr, Hg, Pb, and As. Five algorithms, including random forest (RF), Extra Trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and least absolute shrinkage and selection operator (LASSO-stacking), were compared, and Shapley additive explanations (SHAP) were used to interpret the variables statistically associated with the spatial variability of each metal. Positive matrix factorization (PMF) was further applied to identify potential source categories. Model performance varied among metals, indicating element-specific differences in predictability and controlling factors. SHAP analysis showed that precipitation, temperature, spatial coordinates (longitude/latitude), cropping intensity, and industrial-source fine particulate matter emissions were among the most important associated factors, although their relative importance differed across elements. PMF results suggested that 73.8% of Cd was associated with agricultural inputs, 87.6% of Hg with industrial atmospheric deposition, and 68.4% of Cr and 46.7% of As with natural sources, Pb showed relatively weak predictability, suggesting that its spatial variability may be influenced by unmeasured local or legacy inputs, while model-derived associations indicated possible links with spatial gradients, terrain-hydrological conditions, wind-related variables, and soil carrier properties. Overall, this study presents an integrated framework that combines machine learning-based associated-factor analysis with receptor-model source apportionment, providing a more nuanced understanding of HMs contamination in farmland soils and supporting targeted soil pollution prevention and control.