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

Shiwei Dong, Yuchun Pan, Hui Guo, Bingbo Gao and Mengmeng Li
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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

Land, 2021, vol. 10, issue 10, 1-15

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.

Keywords: soil sample; natural and anthropogenic factors; identification; multi-object; spatial analysis; agricultural land; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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