Deciphering the drivers and mechanisms of soil heavy metal(loid) pollution through deep learning and causal modeling

Understanding the formation and development mechanisms of soil heavy metal(loid)s (HMs) is essential for targeted prevention and sustainable soil management. However, these mechanisms remain incompletely resolved because pollution reflects diverse inputs and complex environmental interactions. A deep learning model (HMNet) was developed to adaptively integrate multi-source open-access datasets and delineate contamination patterns at fine spatial resolution, thereby reflecting the integrated effects of diverse inputs on contamination distribution. The result demonstrates that HMNet has robust performance in both monitoring (average Kappa of 0.777, Recall of 0.815, and overall accuracy of 82.82%) and generalization (average Kappa of 0.762, Recall of 0.799, and overall accuracy of 81.61%). Driver attribution using DeepSHAP, combined with correlation analysis, revealed a hierarchical structure of controls, with anthropogenic activities acting as the dominant external inputs, soil physicochemical properties governing HM retention and mobility, and vegetation, topography and climate modulating spatial heterogeneity. Structural equation modeling was further employed to quantify direct and indirect interaction pathways, indicating that anthropogenic activities elevate HM accumulation risk primarily by weakening soil-vegetation buffering capacity, whereas climate-weathering processes indirectly enhance HM stabilization by promoting parent-material alteration and vegetation recovery. Building on these findings, management strategies were formulated that prioritize interventions according to regulatory risk class and dominant process controls. Recommended strategies include source identification and industrial or traffic abatement, fertilizer-quality control and precision nutrient management, erosion control, sediment interception and drainage design, and site-specific soil amendments and vegetation restoration, implemented through pilot testing, targeted monitoring, and adaptive evaluation. Overall, this study establishes an integrated framework linking monitoring, mechanism analysis, and management guidance, providing a process-based paradigm for refined understanding and control of soil HM pollution.