Integrating PCA-PMF Models for Source Apportionment of Heavy Metals in Urban River Soils: A Case Study of Suzhou, China

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.