Impact of migration and prediction on heavy metals from soil to groundwater in an abandoned lead/zinc smelting site

Abstract

Long-term metal smelting activities can lead to enrichment and dispersion of heavy metals in the site soil and groundwater. The migration and prediction of heavy metals from soil to groundwater in an abandoned lead/zinc smelting site were studied using machine learning model. The results showed that heavy metals in site soil mainly accumulated in the fill layer, and vertically migrated to groundwater significantly. The mean of Pb, As, and Cd in site soils significantly exceeded the screening value of risk control standard for soil contamination of development land. The mean of Zn, Cd, Pb and As in groundwater exceeded the corresponding groundwater Class VI limit of standard for groundwater quality of China. Soil contamination of heavy metals was serious in the pyrometallurgical area, hydrometallurgical area and raw material storage area, and Cd and Pb in the upper soil layer had a strong migration potential downward with active and reducible state. The synergistic remediation for site soil and groundwater in smelting site was suggested when groundwater level was below 5 m and soil Cd concentration exceeded 344 mg/kg, or when the soil active Pb concentration exceeded 5425 mg/kg.

Keywords

lead/zinc smelting site / migration and transformation / machine learning / heavy metals / prediction

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