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Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China

Author

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  • Shiwei Dong

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yuchun Pan

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Hui Guo

    (Forestry Experiment Center of North China, Chinese Academy of Forestry, Beijing 102300, China)

  • Bingbo Gao

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Mengmeng Li

    (Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China)

Abstract

Identifying influencing factors of heavy metals is essential for soil evaluation and protection. This study investigates the use of a geographical detector to identify influencing factors of agricultural soil heavy metals from natural and anthropogenic aspects. We focused on six variables of soil heavy metals, i.e., As, Cd, Hg, Cu, Pb, Zn, and four influencing factors, i.e., soil properties (soil type and soil texture), digital elevation model (DEM), land use, and annual deposition fluxes. Experiments were conducted in Shunyi District, China. We studied the spatial correlations between variables of soil heavy metals and influencing factors at both single-object and multi-object levels. A geographical detector was directly used at the single-object level, while principal component analysis (PCA) and geographical detector were sequentially integrated at the multi-object level to identify influencing factors of heavy metals. Results showed that the concentrations of Cd, Cu, and Zn were mainly influenced by DEM ( p = 0.008) and land use ( p = 0.033) factors, while annual deposition fluxes were the main factors of the concentrations of Hg, Cd, and Pb ( p = 0.000). Moreover, the concentration of As was primarily influenced by soil properties ( p = 0.026), DEM ( p = 0.000), and annual deposition flux ( p = 0.000). The multi-object identification results between heavy metals and influencing factors included single object identification in this study. Compared with the results using the PCA and correlation analysis (CA) methods, the identification method developed at different levels can identify much more influencing factors of heavy metals. Due to its promising performance, identification at different levels can be widely employed for soil protection and pollution restoration.

Suggested Citation

  • Shiwei Dong & Yuchun Pan & Hui Guo & Bingbo Gao & Mengmeng Li, 2021. "Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China," Land, MDPI, vol. 10(10), pages 1-15, September.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:10:p:1010-:d:643695
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    References listed on IDEAS

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    1. Shiwei Dong & Hong Li & Danfeng Sun, 2017. "Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    2. Pengwei Qiao & Mei Lei & Guanghui Guo & Jun Yang & Xiaoyong Zhou & Tongbin Chen, 2017. "Quantitative Analysis of the Factors Influencing Soil Heavy Metal Lateral Migration in Rainfalls Based on Geographical Detector Software: A Case Study in Huanjiang County, China," Sustainability, MDPI, vol. 9(7), pages 1-13, July.
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    Cited by:

    1. 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.
    2. Teng Zhang & Yixuan Sun & Mei Guan & Jieming Kang & Baolei Zhang, 2022. "Human Activity Intensity in China under Multi-Factor Interactions: Spatiotemporal Characteristics and Influencing Factors," Sustainability, MDPI, vol. 14(5), pages 1-16, March.

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