文献类型：期刊文献

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

作者：Dong, Shiwei[1] Pan, Yuchun[1] Guo, Hui[2] Gao, Bingbo[3] Li, Mengmeng[4]

第一作者：Dong, Shiwei

通信作者：Li, MM[1]

机构：[1]Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China;[2]Chinese Acad Forestry, Forestry Expt Ctr North China, Beijing 102300, Peoples R China;[3]China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China;[4]Fuzhou Univ, Acad Digital China Fujian, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350108, Peoples R China

年份：2021

卷号：10

期号：10

外文期刊名：LAND

收录：;Scopus(收录号：2-s2.0-85116135359);WOS:【SSCI(收录号:WOS:000713187900001)】；

基金：This research was funded by the Beijing Natural Science Foundation (Grant Number 8192015) and the National Natural Science Foundation of China (Grant Number 41801276).

语种：英文

外文关键词：soil sample; natural and anthropogenic factors; identification; multi-object; spatial analysis; agricultural land; principal component analysis

摘要：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.