IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p511-d476296.html
   My bibliography  Save this article

Identification of Soil Heavy Metal Sources in a Large-Scale Area Affected by Industry

Author

Listed:
  • Yuan Xu

    (Institute of Soil and Solid Waste Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Huading Shi

    (Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Yang Fei

    (Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Chao Wang

    (Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Li Mo

    (Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China)

  • Mi Shu

    (School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China)

Abstract

Heavy metals (HMs) in soil are some of the most serious pollutants due to their toxicity and nonbiodegradability. Especially across large-scale areas affected by industry, the complexity of pollution sources has attracted extensive attention. In this study, an approach based on zoning to analyze the sources of heavy metals in soil was proposed. Qualitative identification of pollution sources and quantification of their contributions to heavy metals in soil are key approaches in the prevention and control of heavy metal pollution. The concentrations of five HMs (Cd, Hg, As, Pb and Cr) in the surface soil of the Chenzhou industrial impact area were the research objects. Multiple methods were used for source identification, including positive matrix factorization (PMF) analysis combined with multiple other analyses, including random forest modeling, the geo-accumulation index method and hot spot analysis. The results showed that the average concentrations of the five heavy metals were 9.46, 2.36, 2.22, 3.27 and 1.05 times the background values in Hunan soil, respectively. Cd was associated with moderately to strongly polluted conditions, Hg, As and Pb were associated with unpolluted to moderately polluted conditions and Cr was associated with practically unpolluted conditions. The mining industry was the most significant anthropogenic factor affecting the content of Cd, Pb and As in the whole area, with contribution rates of 87.7%, 88.5% and 62.5%, respectively, and the main influence area was within 5 km from the mining site. In addition, we conducted hot spot analysis on key polluting enterprises and identified hot spots, cold spots, and areas insignificantly affected by enterprises, used this information as the basis for zoning treatment and discussed the sources of heavy metals in the three subregions. The results showed that Cd originated mainly from agricultural activities, with a contribution rate of 63.6%, in zone 3. As originated mainly from sewage irrigation, with a contribution rate of 65.0%, in zone 2, and the main influence area was within 800 m from the river. This element originated mainly from soil parent materials, with a contribution rate of 69.7%, in zone 3. Pb mainly originated from traffic emissions, with a contribution rate of 72.8%, in zone 3, and the main influence area was within 500 m from the traffic trunk line. Hg was mainly derived from soil parent materials with a contribution rate of 92.1% in zone 1, from agricultural activities with a contribution rate of 77.5% in zone 2, and from a mixture of natural and agricultural sources with a contribution rate of 74.2% in zone 3. Cr was mainly derived from the soil parent materials in the whole area, with a contribution rate of 90.7%. The study showed that in large-scale industrial influence areas, the results of heavy metal source analysis can become more accurate and detailed by incorporating regional treatment, and more reasonable suggestions can be provided for regional enterprise management and soil pollution control decision making.

Suggested Citation

  • Yuan Xu & Huading Shi & Yang Fei & Chao Wang & Li Mo & Mi Shu, 2021. "Identification of Soil Heavy Metal Sources in a Large-Scale Area Affected by Industry," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:511-:d:476296
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/511/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/2/511/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shih, Yu-Shan & Tsai, Hsin-Wen, 2004. "Variable selection bias in regression trees with constant fits," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 595-607, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dexian Li & Guannan Liu & Xiaosai Li & Ruiping Li & Juan Wang & Yuanyi Zhao, 2022. "Heavy Metal(loid)s Pollution of Agricultural Soils and Health Risk Assessment of Consuming Soybean and Wheat in a Typical Non-Ferrous Metal Mine Area in Northeast China," Sustainability, MDPI, vol. 14(5), pages 1-15, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. S. H. C. M. van Veen & R. C. van Kleef & W. P. M. M. van de Ven & R. C. J. A. van Vliet, 2018. "Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1-12, February.
    3. Postiglione, Paolo & Benedetti, Roberto & Lafratta, Giovanni, 2010. "A regression tree algorithm for the identification of convergence clubs," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2776-2785, November.
    4. Xiaogang Su & George Ekow Quaye & Yishu Wei & Joseph Kang & Lei Liu & Qiong Yang & Juanjuan Fan & Richard A. Levine, 2024. "Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees," Mathematics, MDPI, vol. 12(20), pages 1-28, October.
    5. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    6. Alvarez-Iglesias, Alberto & Hinde, John & Ferguson, John & Newell, John, 2017. "An alternative pruning based approach to unbiased recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 90-102.
    7. Gerhard Tutz & Moritz Berger, 2016. "Item-focussed Trees for the Identification of Items in Differential Item Functioning," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 727-750, September.
    8. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    9. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:511-:d:476296. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.