Spatial-machine learning framework for rapid identification of soil cadmium risk in high geochemical background areas

Rapid and accurate identification of cadmium (Cd) risk remains challenging in agricultural lands with high geochemical background levels. While predicting soil Cd mobility using the risk assessment code (RAC) is essential for evaluating ecological risks at regional scales, traditional prediction methods struggle to achieve high spatial prediction accuracy because of complex influencing factors and spatial heterogeneity. This study investigated the spatial distribution patterns of soil Cd mobility in karst regions under the influence of anthropogenic activities and natural background conditions. Our analysis revealed that areas of very high risk were predominantly concentrated in black shale formations and mining zones, reflecting the spatial heterogeneity of soil available Cd. Geographically weighted regression analysis demonstrated both negative and positive local correlation coefficients between soil properties and RAC values, suggesting complex spatial interactions. Incorporating these spatial relationships as covariates in the random forest model resulted in an enhanced prediction accuracy (R² = 0.96) compared to the non-spatial approach (R² = 0.80). The machine learning model with integrated spatial information developed in this study provides an improved framework for identifying soil Cd risks and understanding Cd geochemical behaviors, supporting the development of targeted pollution prevention and control strategies in areas with high geochemical background.