| Autor: |
Guan XN; School of Land Engineering, Chang'an University, Xi'an 710054, China.; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China., Dong SW; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China., Liu Y; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China., Zhang XX; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China., Pan YC; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China., Lu C; Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. |
| Jazyk: |
čínština |
| Zdroj: |
Huan jing ke xue= Huanjing kexue [Huan Jing Ke Xue] 2024 Aug 08; Vol. 45 (8), pp. 4791-4801. |
| DOI: |
10.13227/j.hjkx.202308194 |
| Abstrakt: |
Identifying the influencing factors of soil heavy metal content changes is the basis for reducing or preventing soil heavy metal pollution. Taking an agricultural experimental field in Changping District of Beijing as an example, the heavy metal content changes in As, Cr, Cu, Ni, Pb, and Zn from 2012 to 2022 were firstly analyzed. Secondly, the influencing factors of the heavy metal content changes were detected based on the geographical detector at the single-target and multi-target levels, respectively. Finally, comparative experiments with the correlation analysis method and existing studies were set up to evaluate the effectiveness of the identification method of influencing factors developed in this study. The results showed that human activity factors have exacerbated the changes in soil heavy metal content in the study area as follows： ① At the single-target level, the land use type was the main influencing factor on the changes in Cr, Cu, and Zn contents, and the annual deposition flux influenced the changes in As. The results of the interaction detection showed that there was an enhancement effect among the factors, and the interaction of the human activity factors dominated for the factor identification. ② The results of the multi-target level detection covered the results of the single-target level detection, which could identify more influencing factors. The land use type affected the changes in Cu, Zn, Cr, Ni, and As, and the changes in As and Zn were influenced by the annual deposition fluxes. ③ The multi-target identification method coupled with geographical detector and principal component analysis could effectively identify the influencing factors of soil heavy metal content changes, which was much more effective than the single soil heavy metal correlation method. The developed multi-target identification method for influencing factors of heavy metal content changes can provide technical support for the regional pollution monitoring and macro-management of soil heavy metals. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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