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Pollution Characteristics, Spatial Patterns, and Sources of Toxic Elements in Soils from a Typical Industrial City of Eastern China

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  • Fang Xia

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Youwei Zhu

    (Protection and Monitoring Station of Agricultural Environment, Bureau of Agriculture of Department of Rural and Agriculture of Zhejiang Province, Hangzhou 310020, China)

  • Bifeng Hu

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Xueyao Chen

    (Protection and Monitoring Station of Agricultural Environment, Bureau of Agriculture of Department of Rural and Agriculture of Zhejiang Province, Hangzhou 310020, China)

  • Hongyi Li

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Kejian Shi

    (School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Liuchang Xu

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China)

Abstract

Soil pollution due to toxic elements (TEs) has been a core environmental concern globally, particularly in areas with developed industries. In this study, we sampled 300 surface (0–0.2 m) soil samples from Yuyao City in eastern China. Initially, the geo-accumulation index, potential ecological risk index, single pollution index, and Nemerow composite pollution index were used to evaluate the soil contamination status in Yuyao City. Ordinary kriging was then deployed to map the distribution of the soil TEs. Subsequently, indicator kriging was utilized to identify regions with high risk of TE pollution. Finally, the positive matrix factorization model was used to apportion the sources of the different TEs. Our results indicated that the mean content of different TEs kept the order: Zn > Cr > Pb > Cu > Ni > As > Hg ≈ Cd. Soil pollution was mainly caused by Cd and Hg in the soil of Yuyao City, while the content of other TEs was maintained at a safe level. Regions with high TE content and high pollution risk of TEs are mainly located in the central part of Yuyao City. Four sources of soil TEs were apportioned in Yuyao City. The Pb, Hg, and Zn contents in soil were mainly derived from traffic activities, coal combustion, and smelting. Meanwhile, Cu was mainly sourced from industrial emissions and atmospheric deposition, Cr and Ni mainly originated from soil parental materials, and Cd and As were produced by industrial and agricultural activities. Our study provides important implications for improving the soil environment and contributes to the development of efficient strategies for TE pollution control and remediation.

Suggested Citation

  • Fang Xia & Youwei Zhu & Bifeng Hu & Xueyao Chen & Hongyi Li & Kejian Shi & Liuchang Xu, 2021. "Pollution Characteristics, Spatial Patterns, and Sources of Toxic Elements in Soils from a Typical Industrial City of Eastern China," Land, MDPI, vol. 10(11), pages 1-20, October.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1126-:d:662952
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    References listed on IDEAS

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    1. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    2. Modian Xie & Hongyi Li & Youwei Zhu & Jie Xue & Qihao You & Bin Jin & Zhou Shi, 2021. "Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China," Land, MDPI, vol. 10(6), pages 1-17, May.
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    Cited by:

    1. Shuaiwei Shi & Meiyi Hou & Zifan Gu & Ce Jiang & Weiqiang Zhang & Mengyang Hou & Chenxi Li & Zenglei Xi, 2022. "Estimation of Heavy Metal Content in Soil Based on Machine Learning Models," Land, MDPI, vol. 11(7), pages 1-19, July.
    2. Shiyi Wang & Yanbin Zhang & Jieliang Cheng & Yi Li & Feng Li & Yan Li & Zhou Shi, 2022. "Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China," IJERPH, MDPI, vol. 19(6), pages 1-19, March.
    3. Feng Li & Mingtao Xiang & Shiying Yu & Fang Xia & Yan Li & Zhou Shi, 2022. "Source Identification and Apportionment of Potential Toxic Elements in Soils in an Eastern Industrial City, China," IJERPH, MDPI, vol. 19(10), pages 1-19, May.
    4. Ziwei Feng & Wende Chen & Yichen Meng & Haixia Lu & Xinyi Shi & Jiajun Zhang, 2024. "Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-24, May.
    5. Yingfan Zhang & Tingting Fu & Xueyao Chen & Hancheng Guo & Hongyi Li & Bifeng Hu, 2022. "Modeling Cadmium Contents in a Soil–Rice System and Identifying Potential Controls," Land, MDPI, vol. 11(5), pages 1-13, April.
    6. Ahmed Saleh & Yehia H. Dawood & Ahmed Gad, 2022. "Assessment of Potentially Toxic Elements’ Contamination in the Soil of Greater Cairo, Egypt Using Geochemical and Magnetic Attributes," Land, MDPI, vol. 11(3), pages 1-19, February.

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