Potential driving forces and probabilistic health risks of heavy metal accumulation in the soils from an e-waste area, southeast China

Abstract

The integrated analysis of the distribution characteristics, health risks, and source identification of heavy metals is crucial for formulating prevention and control strategies for soil contamination. In this study, the area around an abandoned electronic waste dismantling center in China was selected as the research area. The probabilistic health risks caused by heavy metals were evaluated by the Monte Carlo simulation. Random forest, partial least squares regression, and generalized linear models were utilized to predict heavy metal distributions and identify the potential driving factors affecting heavy metal accumulation in soil. The relationships of spatial variation between the heavy metal contents and environmental variables were further visualized. The results revealed that cadmium (Cd) and copper (Cu) were the primary soil pollutants in the study area and caused high ecological risks. The probabilistic health risk assessment indicated that the non-carcinogenic and carcinogenic risks for all populations were acceptable. However, children are more susceptible to heavy metal soil contamination than adults. The sensitivity analyses indicated that the total contents of soil heavy metals and soil ingestion rate were the dominant factors affecting human health. The random forest model, with R2 values of 0.41, 0.65, 0.57, 0.71, and 0.58 for Cd, Cu, Ni, Zn, and Pb, respectively, predicted the heavy metal concentrations better than the other two models. The distance to the nearest industrial enterprise, industrial output, and agricultural chemical input were the main factors affecting Cd, Cu, Zn, and Pb accumulations in the soil, and soil pH and soil parent material were the primary factors influencing Ni accumulation in the soil. The visualization results of the geographically weighted regression model showed a significant relationship between soil heavy metal contents and industrial activity level. This study could be utilized as a reference for policymakers to formulate prevention and control strategies for heavy metal pollution in agricultural areas.