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Causal inference-driven framework for source verification: Integrating PMF, health risk assessment, and spatialized emission inventories, and in multi-source composite heavy metal soil pollution

文献类型：期刊论文

作者Xu, Minke2,3; Lei, Mei2,3; Ju, Tienan2,3; Guo, Guanghui2,3; Xie, Yunfeng1; Shi, Peili1
刊名JOURNAL OF HAZARDOUS MATERIALS
出版日期2026-01-15
卷号502页码:140873
关键词Source identification Soil contamination Geographical Convergent Cross Mapping Anthropogenic inputs Source-receptor relationship
ISSN号0304-3894
DOI10.1016/j.jhazmat.2025.140873
产权排序1
文献子类Article
英文摘要Soil heavy metal pollution in industrialized regions poses significant health risks and management challenges due to its complex source composition and nonlinear source-receptor relationships. This study establishes a novel framework with GCCM-driven causal inference as the core, constructs explicit causal links between high-risk sources' characteristic pollutants (from PMF-health risk assessment) and emission source spatiotemporal features (from spatialized inventories) to prioritize soil heavy metal pollution control in complex industrial regions. Focusing on a mining city in Yunnan, China, 460 soil samples were analyzed for As, Pb, Cd, Zn, and Ni. PMF identified 4 pollution sources, with health risk assessment highlighting As as the priority pollutant. Highresolution spatialized emission inventories reveal distinct spatiotemporal patterns among different pollution sources, which indicates a multi-source composite pollution pattern and the presence of complex pollution formation mechanisms in the study area. The GCCM analysis identified unidirectional causal relationships between smelting emissions and soil arsenic (As) concentrations (rho=0.456, p < 0.01), surpassing the effectiveness of Pearson correlation and geographically weighted regression in capturing nonlinear source-receptor linkages. This study demonstrates causal emission-contamination links, enabling accurate, economically scalable soil pollution remediation for industrial regions.
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WOS关键词POLICY ; MODEL
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001660112100001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/219720]  
专题资源利用与环境修复重点实验室_外文论文
通讯作者Lei, Mei
作者单位1.Minist Ecol & Environm, Tech Ctr Soil Agr & Rural Ecol & Environm, Beijing 100012, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
推荐引用方式
GB/T 7714		 Xu, Minke,Lei, Mei,Ju, Tienan,et al. Causal inference-driven framework for source verification: Integrating PMF, health risk assessment, and spatialized emission inventories, and in multi-source composite heavy metal soil pollution[J]. JOURNAL OF HAZARDOUS MATERIALS,2026,502:140873.
APA Xu, Minke,Lei, Mei,Ju, Tienan,Guo, Guanghui,Xie, Yunfeng,&Shi, Peili.(2026).Causal inference-driven framework for source verification: Integrating PMF, health risk assessment, and spatialized emission inventories, and in multi-source composite heavy metal soil pollution.JOURNAL OF HAZARDOUS MATERIALS,502,140873.
MLA Xu, Minke,et al."Causal inference-driven framework for source verification: Integrating PMF, health risk assessment, and spatialized emission inventories, and in multi-source composite heavy metal soil pollution".JOURNAL OF HAZARDOUS MATERIALS 502(2026):140873.

入库方式： OAI收割

来源：地理科学与资源研究所

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