GYIG OpenIR  > 环境地球化学国家重点实验室
Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest
Xiaohang Xu; Zhidong Xu; Longchao Liang; Jialiang Han; Gaoen Wu; Qinhui Lu; Lin Liu; Pan Li; Qiao Han; Le Wang; Sensen Zhang; Yanhai Hu; Yuping Jiang; Jialin Yang; Guangle Qiu; Pan Wu
2024
Source PublicationScience of The Total Environment
Volume954
Abstract

Heavy metal(loid)s (HMs) in agricultural soils not only affect soil function and crop security, but also pose health risks to residents. However, previous concerns have typically focused on only one aspect, neglecting the other. This lack of a comprehensive approach challenges the identification of hotspots and the prioritization of factors for effective management. To address this gap, a novel method incorporating spatial bivariate analysis with random forest was proposed to identify high-risk hotspots and the key influencing factors. A large-scale dataset containing 2995 soil samples and soil HMs (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn) was obtained from across Henan province, central China. Spatial bivariate analysis of both health risk and ecological risks revealed risk hotspots. Positive matrix factorization model was initially used to investigate potential sources. Twenty-two environmental variables were selected and input into random forest to further identify the key influencing factors impacting soil accumulation. Results of local Moran's I index indicated high-high HM clusters at the western and northern margins of the province. Hotspots of high ecological and health risk were primarily observed in Xuchang and Nanyang due to the widespread township enterprises with outdated pollution control measures. As concentration and exposure frequency dominated the non-carcinogenic and carcinogenic risks. Anthropogenic activities, particularly vehicular traffic (contributing ∼37.8 % of the total heavy metals accumulation), were the dominant sources of HMs in agricultural soils. Random forest modeling indicated that soil type and PM2.5 concentrations were the most influencing natural and anthropogenic variables, respectively. Based on the above findings, control measures on traffic source should be formulated and implemented provincially; in Xuchang and Nanyang, scattered township enterprises with outdated pollution control measures should be integrated and upgraded to avoid further pollution from these sources.

 

 

DOI10.1016/j.scitotenv.2024.176359
URL查看原文
Indexed BySCI
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.gyig.ac.cn/handle/42920512-1/15630
Collection环境地球化学国家重点实验室
Affiliation1.Key Laboratory of Karst Georesources and Environment, Ministry of Education, College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
2.State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
3.School of Chemistry and Materials Science, Guizhou Normal University, Guiyang 550001, China
4.The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Provincial Engineering Research Center of Ecological Food Innovation, School of Public Health, Guizhou Medical University, Guiyang 550025, China
5.Henan Academy of Geology, Zhengzhou 450016, China
6.No.6 Geological Unit Team, Henan Provincial Non-ferrous Metals Geological and Mineral Resources Bureau, Luoyang 471002, China
Recommended Citation
GB/T 7714
Xiaohang Xu,Zhidong Xu,Longchao Liang,et al. Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest[J]. Science of The Total Environment,2024,954.
APA Xiaohang Xu.,Zhidong Xu.,Longchao Liang.,Jialiang Han.,Gaoen Wu.,...&Pan Wu.(2024).Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest.Science of The Total Environment,954.
MLA Xiaohang Xu,et al."Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest".Science of The Total Environment 954(2024).
Files in This Item:
File Name/Size DocType Version Access License
Risk hotspots and in(20036KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xiaohang Xu]'s Articles
[Zhidong Xu]'s Articles
[Longchao Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xiaohang Xu]'s Articles
[Zhidong Xu]'s Articles
[Longchao Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xiaohang Xu]'s Articles
[Zhidong Xu]'s Articles
[Longchao Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.