Spatiotemporal Patterns and Driving Mechanisms of Heavy Metal Accumulation in China’s Farmland Soils Based on Meta-Analysis and Machine Learning

To elucidate the long-term spatiotemporal patterns and key drivers factors, this study employed a meta-analysis of data from soil containing Potentially Toxic Elements (Cd, As, Cr, Hg, and Pb) in Chinese farmland soils from 2003 to 2025. The geoaccumulation index, the potential ecological risk index, and standard deviation ellipses were used to assess the spatiotemporal evolution of heavy metal accumulation and ecological risk, while the Random forest–SHapley Additive exPlanations (RF-SHAP) method was employed to identify driving mechanisms. At the national scale, Cd and Hg are significantly enriched relative to the background values, whereas As, Cr, and Pb remained at relatively low levels, with enrichment ranked as Cd > Hg > Pb > Cr > As. Cd and Hg indicated mild pollution, but the Sichuan Basin emerged as a hotspot, where Cd reached moderate pollution and showed strong ecological risk, and Hg also exhibited high ecological risk. Over the past two decades, the contamination center shifted from coastal to southwestern inland regions, with an expanded and more dispersed distribution. Since 2017, Cd and Hg pollution levels have stabilized, suggesting that the aggravating trend has been preliminarily curbed. Industrial waste and wastewater discharge, irrigation and fertilization were identified as the primary anthropogenic factors of soil heavy metal accumulation, while climatic factors (temperature, precipitation, and solar radiation) and soil physicochemical properties (pH, clay content, and organic matter) played fundamental roles in spatial distribution and accumulation. Our findings call for targeted predictive research and policies to manage heavy metal risks and preserve farmland sustainability in a changing climate.