New insights into pollution source analysis using receptor models: Effects of interaction between heavy metals and DOM on source identification and apportionment in rivers across industrial city

To effectively control pollution and protect the ecosystem, it is essential to accurately analyze the potential pollution sources of heavy metals (HMs) in rivers. However, the traditional source apportionment methods based on HMs disregard the interaction between HMs and dissolved organic matter (DOM). In this study, data of HMs and DOM was combined for tracing sources and assessing the effect of interaction between HMs and DOM on source apportionment in urbanized rivers that cross urban (URR), industrial (INR), and rural (RUR) regions. Four types of fluorescent substances were extracted from DOM: tryptophan-like (TRLF), microbial byproduct (MB), fulvic-like (FLF), and humic-like (HLF) fluorescence substances. Anthropogenic activities (42.3 %), microbial products (21.5 %), and geogenic origin (23.7 %) were respectively recognized as the dominant source in URR, INR, and RUR. Additionally, significant correlations were obtained between HMs and high molecular mass DOM. There was no direct effect pathway obtained between HMs and sources and distributional characteristics of HMs are influenced by both economic and social factors. HMs have been found to indirectly affect source apportionment through interaction with DOM. This work provided a comprehensive understanding of the effects mechanism of the interaction of HMs and DOM on source identification and offered an effective method for tracing pollution sources. [Display omitted] •Joining datasets of HMs and DOM increased the resolution of source identification.•HMs are more susceptible to complexation reactions with high molecular mass DOM.•HMs indirectly affect source apportionment through interaction with DOM.•Social and economic factors were the main factors that influenced pollution sources.