## 1 Introduction  
Heavy metals (HMs) are recognized as ubiquitous elements in nature, present as trace constituents across various environmental media [1]. The predominant origins of HMs in river sediments have been identified as both natural accumulation processes (soil erosion, atmospheric deposition, rock weathering, etc.) and anthropogenic activities (agricultural practices, industrial operations, domestic discharges, etc.) [2, 3]. When concentrations exceed specific thresholds, HMs are known to undergo bioaccumulation within trophic networks, with subsequent transformation into more toxic organic species, ultimately posing persistent threats to aquatic ecosystems and human health [4, 5]. Upon entering the riverine environment, HMs are dispersed into runoff and sediments during the migration process. Within aquatic systems, sediments are recognized as the primary reservoirs for HMs, demonstrating dual functionality in their geochemical cycling by acting as both sinks and latent sources of metallic contaminants [6; 7]. These metallic species, characterized by their adsorption capacity, hydrolytic behavior, and precipitation potential, have been progressively concentrated through biogeochemical processes [8; 9; 10]. Furthermore, HMs in sediments are highly responsive to human activities; thus, their species and concentrations can reflect the characteristics of regional pollution and its potential sources [11; 12]. Therefore, the investigation of heavy metal (HM) pollution, health risks, and potential sources in river sediments is of significant importance for ensuring water security at the basin, national, and global levels. Currently, various methods have been employed to evaluate the pollution levels and risks associated with HMs in the environment, including the geoaccumulation index, contamination load index, and potential ecological risk index [13]; owing to their scope of application and assessment purposes, the application of any single method has notable drawbacks [14]. Even so, the majority of researchers have confirmed that an integrated analysis using multiple methods is more accurate and effective [15; 16]. Regarding the methods for HM source analysis, they primarily consist of multivariate statistical methods, geostatistical analysis, and isotopic tracing methods [17; 18]. Among these approaches, the positive matrix factorization (PMF) model endorsed by the United States Environmental Protection Agency (US EPA) has proven to be an effective and straightforward method for source analysis, as it reduces high-dimensional variables into a limited number of comprehensive factors. There has been widespread application of this model in environmental studies [19; 20]. Simultaneously, the four-step method for assessing environmental health risks suggested by the US EPA coupled with the Monte Carlo probabilistic risk evaluation method can quantify and mitigate the uncertainties associated with traditional assessment methods, thereby enhancing the accuracy and scientific rigor of HM contaminant risk assessment [21]. Systematic sediment surveys have become key to shaping transboundary water governance, and global sediment quality assessments are increasingly informing environmental policy-making [22]. The Rhine 2020 program demonstrated how lead (Pb) isotope fingerprinting in sediments enabled precise tracing of mining emissions, resulting in a 23% reduction in permitted emissions under the revised EU Water Framework Directive [23]. In the Mississippi River basin, the spatial correlation between sediment mercury (Hg) concentrations (0.08-1.45 mg/kg) and agricultural land use prompted a revision of the Hypoxia Action Plan, requiring 30 m riparian buffers in high-risk areas [24]. India's \"Namami Gange\" program used arsenic (As)/chromium (Cr) distribution patterns to establish a \"red zone\" industrial licensing system, which led to an 18.7 \(\pm\) 3.2% reduction in metal loads after implementation [25].