RESUMEN

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.