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Spatial Distribution, Source Apportionment, and Pollution Assessment of Toxic Metals Around Agricultural Soils Based on APCS-MLR Receptor Modelling: A Case Study of the Northern Slope of Tianshan Mountains

Author

Listed:
  • Buasi Nueraihemaiti

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    These authors contributed equally to this work.)

  • Halidan Asaiduli

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    These authors contributed equally to this work.)

  • Abudugheni Abliz

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Panqing Ye

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Xianhe Liu

    (College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
    Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

Abstract

To investigate the contamination status and analyze the sources of soil toxic metal contamination on the northern slopes of the East Tianshan mountain industrial belt in Xinjiang, northwest China, this study measured the contents of six common toxic metals such as Zn, Cu, Cr, Pb, Hg and As in 82 surface soil (0–20 cm), and using the ground accumulation index, pollution load index, and improved weighted index assessed the contamination characteristics of the soil and using a semi-variance function and APCS-MLR model the identified the potential sources of contamination. The results indicate that the average concentrations of Pb, Hg, and As are significantly higher than the background values in Xinjiang. The average ranking of toxic metal content is as follows: Zn > Cr > Pb > Cu > Hg > as. A single-factor pollution index analysis shows that As and Pb pollution are severe, while Hg and Cu pollution are moderate. The improved weighted index shows that moderate lead pollution accounts for 6.1% and severe lead pollution accounts for 54.88%; 98.88% of arsenic is severely contaminated. The APCS-MLR model identified three main sources of heavy metals: Cu and Cr as industrial production sources, Pb and Zn as transportation and agricultural activity sources, and As, Cr, Cu, Hg, and Zn as natural and mixed pollution sources. This study provides a solid scientific basis for the prevention and control of toxic metal pollution in agricultural soils, thus ensuring food security and sustainable development in the region.

Suggested Citation

  • Buasi Nueraihemaiti & Halidan Asaiduli & Abudugheni Abliz & Panqing Ye & Xianhe Liu, 2024. "Spatial Distribution, Source Apportionment, and Pollution Assessment of Toxic Metals Around Agricultural Soils Based on APCS-MLR Receptor Modelling: A Case Study of the Northern Slope of Tianshan Moun," Land, MDPI, vol. 13(12), pages 1-21, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2067-:d:1534537
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    References listed on IDEAS

    as
    1. Guangyang Wu & Lanhai Li & Sajjad Ahmad & Xi Chen & Xiangliang Pan, 2013. "A Dynamic Model for Vulnerability Assessment of Regional Water Resources in Arid Areas: A Case Study of Bayingolin, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3085-3101, June.
    2. Huanhuan Shi & Min Zeng & Hongxia Peng & Changsheng Huang & Huimin Sun & Qingqin Hou & Pengcheng Pi, 2022. "Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model," IJERPH, MDPI, vol. 19(13), pages 1-18, June.
    3. Shunqi Nie & Honghua Chen & Xinxin Sun & Yunce An, 2024. "Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model," Sustainability, MDPI, vol. 16(11), pages 1-14, May.
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