Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China.

Heavy metal(loid)s contamination is a constant issue at smelting sites. It is essential to investigate the spatial distribution and migration characteristics of heavy metal(loid)s in the soil for environmental management and remediation strategies of non-ferrous smelting sites. In this study, 203 soil samples from 57 sites were collected in a typical lead smelting site. The findings demonstrated that there were significant Pb, Zn, Cd, and As contamination in soil samples. The spatial distribution of heavy metal(loid)s showed strong spatial heterogeneity, the contaminated soil areas of Pb, As, Cd, and Zn were 99.5, 98.9, 85.3, and 72.4%, respectively. Pb, Cd, and As contamination of the soil reached a depth of 5 m, which migrated from surface to deep soil layers. The leaching contents of Zn, Pb, and As decreased obviously in 3–4 m soil layer, but the leaching content of Cd was still high, which indicated the high migration of Cd. With the increase of depth, the proportion of acid soluble fraction of heavy metal(loid)s decreased, and the residual fraction increased. The acid soluble fraction of Cd accounted for a higher proportion, and As mainly existed in reducible and residual fractions in soil. According to the calculation of the migration factor, the migration of heavy metal(loid)s in soils were order as Cd > Zn > Pb > As. The outcomes are advantageous for risk reduction and site remediation for non-ferrous metal smelting sites. 

Migration and distribution characteristics of soil heavy metal(loid)s at a lead smelting site

Antimony, beryllium, cobalt, and vanadium in urban park soils in Beijing: Machine learning-based source identification and health risk-based soil environmental criteria

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning.

Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis.

Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data.

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Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data.