Quantitative assessment of Pb sources in urban–rural river sediments based on Pb isotopes and PMF and MixSIAR models

Abstract

The complex land use patterns in urban–rural rivers and the presence of diverse point and non-point source pollution pose significant challenges for tracing heavy metal(loid) sources in river sediments. This study employed a combined approach using lead (Pb) stable isotopes, positive matrix factorization (PMF), and a Bayesian mixture model (MixSIAR) to determine the concentrations of Cr, As, Cd, Mn, Cu, Zn, Ni, and Pb along with Pb isotope distribution characteristics in sediments from a typical urban–rural river (Yinghe River). Our investigation enabled the quantitative identification of heavy metal(loid) sources and revealed the contribution patterns of multi-source Pb pollution. The results showed that mean concentrations of all heavy metal(loid)s except Cr and Mn exceeded local soil background values. PMF analysis identified four potential sources: natural sources (19.6%) contributing primarily Cr and Mn; industrial sources (32.1%) associated with Cd, Pb, and Ni; agricultural sources (28.0%) linked to Pb, As, and Zn; and traffic sources (20.3%) related to Cu and Zn. Furthermore, by combining Pb stable isotopes with MixSIAR, the contributions of different Pb pollution sources were quantified as agricultural sources (32.1%), industrial sources (30.5%), traffic sources (27.2%), and natural sources (10.3%). The less-than-10% difference in contribution rates between PMF and MixSIAR for Pb source apportionment demonstrated model reliability. Based on the significant correlation between Pb pollution and land use patterns in the Yinghe River, corresponding pollution prevention strategies were proposed. These findings provide a novel perspective for quantitative source identification of heavy metal(loid) pollution in urban–rural river sediments, offering valuable support for river management and heavy metal(loid) pollution control.