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Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China

Author

Listed:
  • Bifeng Hu

    (Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China
    Unité de Recherche en Science du Sol, INRA, Orléans 45075, France
    InfoSol, INRA, US 1106, Orléans F-4075, France
    Sciences de la Terre et de l’Univers, Orléans University, Orleans 45067, France)

  • Ruiying Zhao

    (Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China)

  • Songchao Chen

    (InfoSol, INRA, US 1106, Orléans F-4075, France
    Unité Mixte de Rercherche (UMR) Sol Agro et hydrosystème Spatialisation (SAS), INRA, Agrocampus Ouest, Rennes 35042, France)

  • Yue Zhou

    (Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China)

  • Bin Jin

    (Ningbo Agricultural Food Safety Management Station, Ningbo 315000, China)

  • Yan Li

    (Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

  • Zhou Shi

    (Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China)

Abstract

Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.

Suggested Citation

  • Bifeng Hu & Ruiying Zhao & Songchao Chen & Yue Zhou & Bin Jin & Yan Li & Zhou Shi, 2018. "Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China," IJERPH, MDPI, vol. 15(4), pages 1-13, April.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:710-:d:140398
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    References listed on IDEAS

    as
    1. Bifeng Hu & Songchao Chen & Jie Hu & Fang Xia & Junfeng Xu & Yan Li & Zhou Shi, 2017. "Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    2. Bifeng Hu & Xiaolin Jia & Jie Hu & Dongyun Xu & Fang Xia & Yan Li, 2017. "Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China," IJERPH, MDPI, vol. 14(9), pages 1-18, September.
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    Cited by:

    1. Fang Xia & Bifeng Hu & Shuai Shao & Dongyun Xu & Yue Zhou & Yin Zhou & Mingxiang Huang & Yan Li & Songchao Chen & Zhou Shi, 2019. "Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China," IJERPH, MDPI, vol. 16(15), pages 1-15, July.
    2. 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.
    3. Sha Huang & Guofan Shao & Luyan Wang & Lin Wang & Lina Tang, 2018. "Distribution and Health Risk Assessment of Trace Metals in Soils in the Golden Triangle of Southern Fujian Province, China," IJERPH, MDPI, vol. 16(1), pages 1-17, December.
    4. Hua Wang & Wuyan Li & Congmou Zhu & Xiaobo Tang, 2021. "Analysis of Heavy Metal Pollution in Cultivated Land of Different Quality Grades in Yangtze River Delta of China," IJERPH, MDPI, vol. 18(18), pages 1-17, September.
    5. Shudi Zuo & Shaoqing Dai & Yaying Li & Jianfeng Tang & Yin Ren, 2018. "Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient," IJERPH, MDPI, vol. 15(10), pages 1-23, October.
    6. Jorge Paz-Ferreiro & Gabriel Gascó & Ana Méndez & Suzie M. Reichman, 2018. "Soil Pollution and Remediation," IJERPH, MDPI, vol. 15(8), pages 1-3, August.
    7. Zhaolin Du & Dasong Lin & Haifeng Li & Yang Li & Hongan Chen & Weiqiang Dou & Li Qin & Yi An, 2022. "Bibliometric Analysis of the Influencing Factors, Derivation, and Application of Heavy Metal Thresholds in Soil," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    8. Shuai Shao & Bifeng Hu & Zhiyi Fu & Jiayu Wang & Ge Lou & Yue Zhou & Bin Jin & Yan Li & Zhou Shi, 2018. "Source Identification and Apportionment of Trace Elements in Soils in the Yangtze River Delta, China," IJERPH, MDPI, vol. 15(6), pages 1-14, June.
    9. Zhiping Yang & Rong Zhang & Hongying Li & Xiaoyuan Zhao & Xiaojie Liu, 2022. "Heavy Metal Pollution and Soil Quality Assessment under Different Land Uses in the Red Soil Region, Southern China," IJERPH, MDPI, vol. 19(7), pages 1-15, March.

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