Teng-Jiao Xie

With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy metal concentrations and assessment of pollution risks are urgent tasks. This study selected the affected area of a non-ferrous metal smelting slag yard in Gejiu City as the research subject. Farmland soil samples were collected from this area, and the basic physicochemical properties of the soil, along with the contents of Pb and Cd, were analyzed. Models for quantitatively inverting Pb and Cd contents based on soil physicochemical parameters were established using multiple linear regression (MLR), backpropagation neural network (BPNN), and genetic algorithm-optimized BPNN (GA-BPNN). Among the three inversion models, the GA-BPNN model demonstrated the highest accuracy, with inversion precision R² reaching 0.8980 and 0.9013 for Pb and Cd, respectively; the corresponding root mean square error (RMSE) values were 0.0001868 and 0.0001821. The inversion model established a nonlinear relationship between heavy metals and basic soil physicochemical properties, enabling further heavy metal pollution assessment based on the inversion results. This method provides a novel approach for inverting heavy metal content in soil and holds practical value for heavy metal remediation.

Industrial development has caused significant environmental damage, especially through potentially toxic element (PTE) pollution. Combining pollution indices, health risk assessment, spatial autocorrelation (Moran’s I), and receptor modeling (APCS/MLR), this study quantified sources and risks of heavy metals in smelting-adjacent farmland soils, facilitating targeted PTE pollution mitigation. Soil analysis revealed significantly elevated mean concentrations of As (326 mg/kg), Cd (23 mg/kg), Cr (104 mg/kg), Cu (106 mg/kg), Ni (73 mg/kg), Pb (274 mg/kg), and Zn (660 mg/kg), all exceeding Yunnan provincial background values. The average total non-carcinogenic risk index (HIadult = 2, HIchild = 11) and total carcinogenic risk index (TCRadult = 5.52 × 10–4, TCRChild = 6.44 × 10–4) for both adults and children exceeded the threshold (HI = 1, TCR = 1 × 10–04). The results of environmental pollution evaluation show that the overall pollution in the study area is a heavy pollution level. The ACPS-MLR model showed that Cd and Zn in soil mainly came from industrial activities (37%). Cu and Pb were derived from motor vehicle emissions and agricultural activities (20%). As may be derived from agricultural and industrial activities. Furthermore, based on the combination of source apportionalization and the spatial distribution of environmental pollution, the northeastern part of the study area and transportation hubs are the key pollution areas and need to be given priority for treatment. PTEs accumulate in the soil, will be enriched through the food chain, and seriously threaten human health and soil ecological environment. Therefore, this study can provide a basis for identifying, preventing, and controlling the risk of PTEs pollution in soil.