Cookies?
Library Header Image
LSE Research Online LSE Library Services

Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression

Pan, Yujie, Sha, Anmeng, Han, Wenjing, Liu, Chang, Liu, Guowangchen, Welsch, Emily, Zeng, Min, Xu, Shasha, Zhao, Yi, Tian, Shang, Li, Yiyi, Deng, Rui, Zhang, Xin, Shi, Huanhuan, Cui, Yu, Huang, Changsheng and Peng, Hongxia (2025) Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression. Journal of Hazardous Materials, 485. ISSN 0304-3894

Full text not available from this repository.

Identification Number: 10.1016/j.jhazmat.2024.136841

Abstract

Soil heavy metal (HMs) contamination poses significant ecological and health risks, yet the spatial drivers of HMs pollution remain poorly understood. This study integrates pollution risk assessment, positive matrix factorization, machine learning, and multi-scale geographically weighted regression to develop a framework for identifying the spatial drivers of soil HMs contamination risk in Yangtze River New City, China. Analysis of 7152 samples revealed that although average HMs concentrations were below national standards, As, Cd, Cr, Cu, Hg, and Ni exceeded local background levels. Four key factors were identified as drivers of HMs contamination: natural sources (30.36 %, influenced by soil type), mixed agricultural and transportation sources (29.56 %, driven by cropland, aquaculture, and road density), human activities (12.68 %, including population density and community activities), and industrial sources (27.42 %, linked to factories and enterprises). Regional variations indicated that industrial activities, transportation, and human activities primarily influenced health risks, while agriculture and natural factors had a greater impact on ecological and environmental capacity risks. These findings underscore the importance of considering spatial heterogeneity in HMs pollution risk assessments and offer insights for developing targeted, region-specific policies to mitigate pollution risks of soil HMs.

Item Type: Article
Additional Information: © 2024 Elsevier B. V.
Divisions: Geography & Environment
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
G Geography. Anthropology. Recreation > G Geography (General)
Date Deposited: 08 Jan 2025 14:36
Last Modified: 15 Jan 2025 08:00
URI: http://eprints.lse.ac.uk/id/eprint/126675

Actions (login required)

View Item View Item