Pollution characteristics and quantitative source apportionment of heavy metals within a zinc smelting site by GIS-based PMF and APCS-MLR models

Huagang Lv, Zhihuang Lu, Guangxuan Fu, Sifang Lv,Jun Jiang, Yi Xie,Xinghua Luo,Jiaqing Zeng,Shengguo Xue

JOURNAL OF ENVIRONMENTAL SCIENCES(2024)

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摘要
The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater. In this study, 63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics, ecological risks, and source apportionment of heavy metal(loid)s (HMs). The results revealed that the average contents of Zn, Cd, Pb, As, and Hg were 0.4, 12.2, 3.3, 5.3, and 12.7 times higher than the risk screening values of the construction sites, respectively. Notably, the smelter was accumulated heavily with Cd and Hg, and the contribution of Cd (0.38) and Hg (0.53) to ecological risk was 91.58%. ZZ3 and ZZ7 were the most polluted workshops, accounting for 25.7% and 35.0% of the pollution load and ecological risk, respectively. The influence of soil parent materials on pollution was minor compared to various workshops within the smelter. Combined with PMF, APCS-MLR and GIS analysis, four sources of HMs were identified: P1(25.5%) and A3(18.4%) were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter; P2(32.7%) and A2(20.9%) were surface runoff of As-Pb foul acid; P3(14.5%) and A4(49.8%) were atmospheric deposition from the leach slag drying workshop; P4(27.3%) and A1(10.8%) were the smelting process of zinc products. This paper described the distribution characteristics and specific sources of HMs in different process workshops, providing a new perspective for the precise remediation of the smelter by determining the priority control factors. (c) 2024 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
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关键词
Smelter site,Heavy metal(loid)s,Source apportionment,Positive matrix factorization,Absolute principal component,score-multiple linear regression
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