Exposure risk assessment of polycyclic aromatic hydrocarbons (PAHs) in soil around coking sites at regional scale...

Coking industrial activities contribute to approximately one-quarter of soil polycyclic aromatic hydrocarbons (PAHs) pollution in the Beijing-Tianjin-Hebei region, posing risks to soil quality and human health. High-resolution, multi-site prediction remains challenging because conventional models seldom incorporate source-explicit drivers. Here, we developed a regional risk-mapping framework that integrates coking enterprise source strength (SBI) and modeled atmospheric deposition flux (CPES) into a stacked-ensemble learning pipeline. A total of 320 soil samples collected around 20 representative plants were used for model training and validation; the optimized model was then applied to predict ∑PAHs within 3-km buffers of 130 coking sites across the region. We constructed three progressively enhanced covariate sets-environmental factors (EF), EF+SBI, and EF+SBI+CPES-to test performance gains and driver attribution. Incorporating SBI and CPES raised R² from 0.41 to 0.63 and reduced RMSE from 0.42 to 0.33, while identifying distance to site, atmospheric deposition, and NDVI as dominant predictors. Operational plants had higher mean ∑PAHs than closed plants (1028 vs 644 ng/g). Closed plants still surpassed background levels by about 1.3 times. Scenario analysis showed a clear greening effect. In the priority management zone, increasing NDVI by 0.025 decreased the predicted mean from 541.7 ng/g to 489.5 ng/g, halving moderately-heavily polluted farmland, and reducing the potentially exposed population by over 40 %. The source-aware framework improves accuracy and transferability of regional PAH exposure risk assessment and supports tiered, site-adjacent zoning and mitigation around industrial point sources.