Contamination, Source Apportionment, and Health Risk Assessment of Heavy Metals in Farmland Soils Surrounding a Typical Copper Tailings Pond

Abstract

Tailings resulting from mining and smelting activities may cause soil heavy-metal pollution and harm human health. To evaluate the environmental impact of heavy metals from tailings on farmland soils in the surrounding area, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in tailings and farmland soils in the vicinity of a typical copper tailings pond were analyzed. Contamination status, potential sources, and health risks for farmland soils were investigated. The results showed that the tailings contained a high concentration of Cu (1136.23 mg/kg). The concentrations of Cd and Cu in the farmland soils exceeded the soil quality standard. The geoaccumulation index (Igeo) indicated that the soils were moderately polluted by Cu and Cd, and slightly polluted by Ni, Cr, and Zn. The absolute principal component scores–multiple linear regression (APCS-MLR) model was applied for source apportionment. The results showed that tailings release is the main source of soil heavy-metals contamination, accounting for 35.81%, followed by agricultural activities (19.41%) and traffic emission (16.31%). The health risk assessment suggested that the children in the study region were exposed to non-carcinogenic risks caused by As, while the non-carcinogenic risk to adults and the carcinogenic risk to both adults and children were at acceptable levels. It is necessary to take effective measures to control heavy-metal contamination from tailings releases to protect humans, especially children, from adverse health risks.

Source-based health risk assessment of heavy metal contamination in Soil: A case study from a polymetallic mining region in Southeastern Hubei, Central China

“…Moreover, various chemical additives used in metallurgical processes can result in tailings with elevated Cr concentrations . Subsequently, Cr from the tailings leached into the soil (Xiao et al, 2022). Similar ndings had been reported in the Dexing copper mine, where the accumulation of Cr and Cu has been attributed to mining, metallurgy activities, and tailings leakage (Ni et al, 2023).…”

“…Similar ndings have been reported in the Dexing copper mining area, where the main ecological risk was associated with mining and smelting activities and local development (Ni et al, 2023). In a copper tailings site in Jiangxi, researchers identi ed the main sources of local human health risks as tailings release, tra c emissions, and agricultural activities, given that the land in the area is primarily used for farming (Xiao et al, 2022). Furthermore, in the Wunugetushan copper-molybdenum mining area, risks were associated with natural sources, mining activities, and livestock manure (Wang et al, 2018).…”

“…Furthermore, in the Wunugetushan copper-molybdenum mining area, risks were associated with natural sources, mining activities, and livestock manure (Wang et al, 2018). These results collectively indicated that in addition to geological background, mining activities, and economic development characteristics can signi cantly in uence HM pollution in soil in polymetallic mining areas (Xiao et al, 2022;Xu et al, 2021).…”

“…Using these methods, previous research has highlighted signi cant environmental risks in mining areas, for instance, in a gold mining area in Shaanxi Province, As, Cd, Cr, and Hg were found to pose ecological and carcinogenic risks to humans (Chen et al, 2022b). Similarly, in soils near a copper tailings pond in Jiangxi Province, arsenic was identi ed as a non-carcinogenic risk for children (Xiao et al, 2022). To pinpoint potential sources of HMs, researchers have frequently employed techniques such as principal component analysis, cluster analysis, chemical mass balance, and positive matrix factorization (PMF) (Fang et al, 2021;Xiao et al, 2022;Zang et al, 2022).…”

“…Similarly, in soils near a copper tailings pond in Jiangxi Province, arsenic was identi ed as a non-carcinogenic risk for children (Xiao et al, 2022). To pinpoint potential sources of HMs, researchers have frequently employed techniques such as principal component analysis, cluster analysis, chemical mass balance, and positive matrix factorization (PMF) (Fang et al, 2021;Xiao et al, 2022;Zang et al, 2022). For example, in an opencast mine in Xinjiang, HMs were primarily attributed to transportation, industrial activities, and natural sources (Fan et al, 2022).…”