Abstract
Convection and diffusion are key pathways for the migration of total petroleum hydrocarbons (TPH) and heavy metals (HMs) from soil to groundwater. However, the extent of their influence on pollutant migration, as well as the nonlinear relationships between these processes and pollutants, remains unclear. This study investigates the spatial distribution of TPH and HMs at a petrochemical site with complex hydrogeological conditions in southwestern China. In addition, machine learning (ML) was used to assess the effects of convection and diffusion on pollutant migration at the soil-groundwater interface. The analysis identifies and reveals TPH, Co, and Ni as the primary pollutants, with soil concentrations reaching 47.427, 7.024, and 4.766 times their respective screening values. Among various ML models, Random Forest (RF) was identified as the most effective, based on R (2), and RMSE performance metrics. The RF model demonstrates that the concentrations of TPH and As are closely related to soil depth. Furthermore, importance indices calculated by RF indicate that the significance of convection and diffusion varies across different soil-groundwater systems. Specifically, at the soil-perched water interface, convection plays a more significant role than diffusion in influencing the migration of TPH and As. However, at the soil-pore water interface, diffusion more significantly influences the migration of all pollutants compared to convection. Additionally, a threshold or saturation effect was observed for the impact of the convection factor on pollutant concentrations in groundwater. These findings highlight the distinct roles of convection and diffusion across various water interfaces, providing new insights into the mechanisms governing contaminant migration and fate.