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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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Listed:
  • He Huang

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yong Zhou

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yujie Liu

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Ke Li

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Liang Xiao

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Mengyao Li

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yang Tian

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Fei Wu

    (Faculty of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

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.

Suggested Citation

  • He Huang & Yong Zhou & Yujie Liu & Ke Li & Liang Xiao & Mengyao Li & Yang Tian & Fei Wu, 2020. "Assessment of Anthropogenic Sources of Potentially Toxic Elements in Soil from Arable Land Using Multivariate Statistical Analysis and Random Forest Analysis," Sustainability, MDPI, vol. 12(20), pages 1-14, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8538-:d:428694
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    References listed on IDEAS

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    1. Josse, Julie & Husson, François, 2012. "Selecting the number of components in principal component analysis using cross-validation approximations," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1869-1879.
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    Cited by:

    1. Liang Xiao & Yong Zhou & He Huang & Yu-Jie Liu & Ke Li & Meng-Yao Li & Yang Tian & Fei Wu, 2020. "Application of Geostatistical Analysis and Random Forest for Source Analysis and Human Health Risk Assessment of Potentially Toxic Elements (PTEs) in Arable Land Soil," IJERPH, MDPI, vol. 17(24), pages 1-19, December.
    2. Li Wang & Yong Zhou & Qing Li & Tao Xu & Zhengxiang Wu & Jingyi Liu, 2021. "Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China," Agriculture, MDPI, vol. 11(1), pages 1-23, January.

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