ORIGINAL RESEARCH
Integrating PCA-PMF Models for Source
Apportionment of Heavy Metals in Urban
River Soils: A Case Study of Suzhou, China
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1
School of Environment and Surveying and Mapping Engineering, Suzhou University, Suzhou, 234000, Anhui, China
2
School of Resources and Civil Engineering, Suzhou University, Suzhou, 234000, Anhui, China
Submission date: 2025-02-28
Final revision date: 2025-04-29
Acceptance date: 2025-05-13
Online publication date: 2025-07-06
Corresponding author
Jiying Xu
School of Resources and Civil Engineering, Suzhou University, Suzhou, 234000,Anhui, China
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ABSTRACT
This study integrates principal component analysis (PCA) and positive matrix factorization
(PMF) models to investigate the source apportionment of heavy metal contamination in urban river
soils of Suzhou City, China, a coal-resource-based region experiencing rapid industrialization. Soil
concentrations of Cu, Pb, Zn, Cd, Ni, Cr, and As were quantified, revealing elevated levels exceeding
regional background values for all elements except Zn. A comprehensive pollution assessment was
conducted through single-factor pollution index, Nemerow comprehensive pollution index, geoaccumulation
index, and potential ecological risk index analyses. Results identified Cd, Cu, and As
as predominant contaminants, with spatial heterogeneity showing higher pollution levels on the river's
right bank. Ecological risk assessment indicated moderate contamination by Cu and As, and severe
contamination by Cd, with an overall slight ecological risk. The PCA-PMF integrated approach
extracted three principal components explaining 70.16% of total variance and quantified four primary
sources: industrial emissions (31.00%), mixed light industrial and traffic sources (12.11%), anthropogenic
activities (26.67%), and combined atmospheric deposition and mining activities (31.63%). The findings
demonstrate that industrial and mining operations constitute the predominant contamination sources,
providing critical data for developing targeted soil remediation strategies in urbanized coal-resource
regions.