4.6 Article

Assessment of Anthropogenic Sources of Potentially Toxic Elements in Soil from Arable Land Using Multivariate Statistical Analysis and Random Forest Analysis

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SUSTAINABILITY
卷 12, 期 20, 页码 -

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MDPI
DOI: 10.3390/su12208538

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arable land; potentially toxic elements; anthropogenic source identification; multivariate statistical analysis; random forest analysis

资金

  1. Classification project of soil environmental quality of cultivated land in Xiangzhou

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In order to study the spatial distribution and anthropogenic sources of potentially toxic elements in Xiangzhou, soil samples were collected from arable land and were analyzed for five different potentially toxic elements: Cd, Hg, As, Pb, and Cr. Inverse distance weighting (IDW) was used to study the spatial distribution of potentially toxic elements in the soil, while principal component analysis (PCA) and random forest analysis (RFA) were applied to examine the anthropogenic sources. It was shown that the combination of multiple analysis tools provides an effective way of delineating multiple potentially toxic elements from anthropogenic sources. The results showed that the average contents of Cd, Hg, and Cr in soils were lower than the background values of Hubei, whereas the average concentrations of As and Pb in soils were higher than the background values of Hubei. Through PCA, it was concluded that human activities contributed more than 60% of the As, Pb, and Cr concentrations in Xiangzhou soils, which was verified by a random forest simulation methodology. Through random forest analysis, Pb, As, and Cr in the soil were found to originate from factories and enterprises, livestock farms, mining areas, and traffic; Cd in the soil was found to originate from mining and the processing of minerals, human production and construction activities, and agricultural irrigation; and Hg in the soil was found to originate from livestock manure, mining and processing of minerals, and human industrial production. The results of this study could provide support for better management of soil pollution through prevention practices such as specific industrial governance and layout optimization.

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