Heterogeneity, nonlinearity, and multifactor interactions of polycyclic aromatic hydrocarbons in steelworks soils.

Industrial polycyclic aromatic hydrocarbons (PAHs) pollution threatens soil ecosystems worldwide, posing persistent risks due to their toxicity and intricate transport dynamics. In steelworks, a major PAH emitter, contaminant distribution arises from multifaceted interactions between production activities and geological features, complicating the elucidation of underlying mechanisms. Previous studies have largely overlooked the inherent heterogeneity in these influences, focusing instead on global relationships that may bias assessments of pollution drivers and PAH migration. Here we show heterogeneity, nonlinearity, and multifactor interactions in PAH contamination at a steelworks site using a multidimensional framework that integrates machine learning and spatial analysis. Applied to 3339 soil samples and nine influencing factors, the framework reveals distance to production facilities as the dominant driver, with a 60-m impact radius; production factors exert stronger effects on 2-3-ring PAHs than on 4-6-ring PAHs, particularly in deeper soil layers at depths of 9-20 m. Soil moisture and clay content synergistically control PAH mobility across strata, elevating the framework's explanatory power from 0.5 to 0.9 and enabling precise delineation of dynamics. This modular approach not only advances mechanistic insights into industrial PAH pollution but also provides scalable guidance for targeted prevention and remediation strategies across diverse contaminated sites.