Spatial distribution, contamination characteristics and ecological-health risk assessment of toxic heavy metals in soils near a smelting area

Abstract

Soil heavy metals (HMs) contamination stemming from smelting and mining activities is becoming a global concern due to its devastating impacts on the environment and human health. In this study, 128 soil samples were investigated to assess the spatial distribution, contamination characteristics, ecological and human health risk of HMs in soils near a smelting area by using BP artificial neural network (BP-ANN) and Monte Carlo simulation. The results showed that the concentrations of all five HMs in the soil greatly exceeded the background value of study area with a basic trend: Pb > As > Cr > Cd > Hg, indicating a high pollution level. Arsenic and lead were the major pollutants in the study area with an exceedance rate of 78.95% and 28.95%, respectively. The toxic fume and dust emitted during the smelting process were identified as the major sources of HMs pollution in soil, while Cd pollution was mainly caused by agricultural activities near the study area. The probabilistic risk assessment suggested that the average HQ values of five HMs for children and adults exceeded the acceptable threshold with a trend: As > Pb > Cr > Cd > Hg. The average CR values of As, Cr and Pb for all population were greatly larger than the acceptable threshold (CR ≥ 1), indicating a high cancer risk. However, the CR values of Cd for adults and children were within the acceptable threshold (CR < 1), implying no cancer risk. The results of the present study can provide some insight into the contamination characteristics, ecological and human health risk of HMs in contaminated soils by mining and smelting activities, which can help prevent and control soil pollution and environmental risk.

Keywords: BP artificial Neural network; Ecological risk; Health risk assessment; Heavy metals; Monte Carlo simulation; Soil contamination.