Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation. Our machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils revealed the significant influence of climate, elevation, industrial activities, soil properties, railways, and population density on heavy metal accumulation. Yangxin County's central, northern, eastern edge, and southern edge regions exhibited high ecological risks from Cd and Hg pollution, necessitating immediate action. Prioritizing control in central, southern, and northern areas, adopting measures like eco-friendly railway materials, advanced wastewater treatments, waste sorting, using non-polluting fertilizers and pesticides, and strict industrial monitoring, are crucial for reducing pollution and safeguarding agricultural health.