Source Analysis and Health Risk Assessment of Heavy Metals in Farmland Soil Based on Receptor Models and Machine Learning in a Typical Province of Northeastern China

Heavy metal pollution in farmland soils threatens food security and human health, particularly in industrially developed regions. This study investigates the sources and health risks of heavy metals in farmland soils of Liaoning Province by integrating receptor models and machine learning. We collected 168 surface soil and measured Cd, Pb, Zn, Cu, Ni, As, Cr, and Co. Source apportionment used APCS-MLR and EF-PMF models. A random forest (RF) model with eight environmental covariates (road network, annual precipitation, soil clay content, LUCC, DEM, vegetation cover, GDP, and population) predicted spatial distribution. Health risks were assessed using the US EPA model and Monte Carlo simulation. The results showed that Cd and Pb were significantly enriched, with enrichment factors of 2.51 and 4.50 indicating substantial anthropogenic input. Spatially, higher concentrations occurred in western and southern areas near coal mines and industrial activities. EF-PMF identified industrial activities (66.3%), atmospheric deposition (17.3%), agricultural activities (13.5%), and traffic emissions (2.9%). RF showed the highest R2 for Cr, Pb, and As (R2 = 0.535, 0.484, and 0.474) and revealed that industrial and mining activities were the dominant drivers of spatial heterogeneity. Health risk assessment indicated that children faced higher risks than adults, and Cr and Ni were identified as the elements posing the greatest health risks. Monte Carlo simulation confirmed that Cr and Ni are priority pollutants requiring control. The EF-PMF–APCS-MLR, RF, and Monte Carlo framework improves source apportionment and spatial characterization, supporting targeted heavy metal control in industrial agricultural regions.