Division approach of high-risk heavy metal(loid)s areas in metallurgical industrial regions: A practical inversion...

Metallurgical production is a global pillar industry, and poses significant health risks to surrounding residents. Rapid identification of risks is crucial for mitigating the severe impact of metallurgical activities on nearby residents. In this study, a modified CALPUFF model (M-CALPUFF) and a wind-corrected soil heavy metal(loid)s (HMs) prediction model based on Gaussian plume diffusion model (GWS model) were constructed to assess the environmental risks of HMs emissions from metallurgical enterprises. These models can identify high-risk areas using concise computational approach with limited raw data. The M-CALPUFF and the GWS model were used to quantify the concentration and spatial distribution of HMs in dustfall and soil respectively, and the results were consistent with empirical measurement. Validation against 156 dust samples demonstrated that M-CALPUFF achieved ≥ 70 % accuracy at > 60 % of sampling points. The GWS model simulation of 21 test sets showed over 95.2 % accuracy within a 95 % prediction interval for all tests. Finally, the HMs risk of the entire smelting area was evaluated. Spatial risk delineation revealed distinct patterns: children exhibited broader non-carcinogenic risk areas driven by frequent hand-to-mouth soil ingestion, whereas adults faced more extensive carcinogenic risks due to cumulative chronic exposure. This work provides a new method for predicting the HMs concentration in smelting areas and a new framework for identifying high-risk areas.