Ecological risk assessment and source identification of heavy metal in dredged sediments from the Gaoyou section of the Beijing- Hangzhou Grand Canal

Ecological risk assessment and source identification of heavy metal in dredged sediments from the Gaoyou section of the Beijing- Hangzhou Grand Canal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ecological risk assessment and source identification of heavy metal in dredged sediments from the Gaoyou section of the Beijing- Hangzhou Grand Canal Hao Jiang, Lichen Liang, Lili Wang, Yuanshun Xu, Houhu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7701802/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heavy metals demonstrate a marked tendency to accumulate in riverine sediments, primarily attributed to their chemical persistence and intrinsic toxicity, thereby posing latent long-term threats to both ecosystem integrity and human health. This study aimed to examine the occurrence of heavy metals (Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr) in dredged sediments obtained from the Gaoyou section of the Beijing-Hangzhou Grand Canal. The geoaccumulation index (Igeo) and potential ecological risk index (RI) were employed to evaluate the heavy metal pollution levels in the sediments and their associated ecological risks. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) models were used as analytical tools to identify and quantify the sources of the heavy metals. The findings revealed that Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr had mean concentrations of 0.289, 9.82, 0.051, 29.63, 70.33, 36.17, 32.54, and 53.54 mg/kg, respectively. Notably, the mean concentrations of Cu, Zn, Pb, and Ni exceeded the soil background values of Jiangsu Province. Results from I geo and RI analyses indicated slight enrichment of Hg, Pb, and Cu. The comprehensive ecological risk remained low, with Hg being the primary contributor. Based on PCA and PMF analyses, three primary sources of heavy metals were identified. Hg was primarily derived from industrial sources such as coal combustion; Pb, Ni, Zn, and Cu were mainly attributed to emissions from shipping and transportation; while As, Cr, and Cd occurred at low concentrations, indicating their predominantly natural origins. Sediment Heavy metal Risk assessment Source identification Beijing-Hangzhou Grand Canal Figures Figure 1 Figure 2 Figure 3 Introduction River sediment has the capacity to adsorb and accumulate pollutants, acting as a significant carrier of contaminants in aquatic systems and a primary internal pollution generator. Sediment-borne heavy metals are recognized as typical cumulative pollutants, with their concentrations in sediment exceeding those in the water phase by several orders of magnitude (Chettri et al., 2022 ; Dai et al., 2018 ), and they cannot be eliminated through natural decomposition processes. Therefore, sediment serves as a primary research target for monitoring heavy metal pollutants in aquatic ecosystems (Peng et al., 2009 ; Soares et al., 1999 ; Zhang et al., 2014 ). Heavy metals in the aquatic environment are deposited into sediments through adsorption, complexation, and other physicochemical processes (Huang et al., 2018 ; Miranda et al., 2022 ; Miranda et al., 2021 ). They exhibit significant ecotoxicity and persistence, accumulate in organisms, and progressively concentrate along the food chain (Qian et al., 2020 ; Yi et al., 2011 ). Variations in aquatic environmental parameters can induce the remobilization of heavy metals, facilitating their migration from sediments to the water body and thereby exerting long-term impacts on aquatic ecosystems (Duan et al., 2019 ; Miranda et al., 2021 ; Wang & Wang, 2017 ). Aquatic environments are rife with heavy metals, sourced from a myriad of places, encompassing natural geochemical weathering of soils and rocks, as well as anthropogenic inputs like the release of industrial wastewater, fertilization and irrigation activities in agricultural settings, pollution generated by shipping operations, atmospheric deposition processes, and operations associated with aquaculture (Fan et al., 2025 ; Hou et al., 2013 ; Liao et al., 2017 ). Consequently, Sediment quality indicators help evaluate the effects on ecosystems from both environmental changes and human-induced actions (Liao et al., 2017 ; Zhuang et al., 2018 ). Currently, for numerous rivers around the world, human activities have become the dominant contributor to heavy metal pollution. The analysis of heavy metals’ distribution patterns, concentration levels, and contamination degrees in riverbed sediments provides critical insights into human impacts on aquatic ecosystems and facilitates identifying potential pollution sources (Yan et al., 2024 ; Zhang et al., 2016 ). Such analysis is essential for assessing potential environmental risks in river systems and formulating targeted, effective environmental management strategies. The Beijing-Hangzhou Grand Canal, an extensive inland waterway in China, spans more than 1,700 km connecting northern Beijing with southern Hangzhou. As the world's ‌earliest excavated, largest-scale, longest-route, and most enduring‌ canal, it has exerted profound impacts on regional economic development and socio-cultural civilization along its course. It was listed as a UNESCO World Heritage Site in 2014 and now serves as the primary water conveyance channel for the eastern route of the South-to-North Water Diversion Project, supplying ecological and agricultural water to northern China. With accelerated urbanization and increased shipping traffic along the canal, ‌endogenous heavy metal contamination‌ in sediments has grown increasingly prominent. Previous studies have indicated varying degrees of heavy metal pollution in sediments along the urban Grand Canal sections (Shen et al., 2020 ; Wang et al., 2011 ; Yu et al., 2011 ; Zhuang et al., 2016 ), ‌yet systematic analysis of pollution sources remains limited‌. In this study, ‌dredged sediments‌ from the Gaoyou section of the Beijing-Hangzhou Grand Canal were analyzed, with the following objectives: (1) analyze the concentration characteristics of eight heavy metals (Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr)‌ in the sediments; (2) evaluate the pollution levels and potential ecological risks of the sediments using standardized indices; (3) identify the potential sources of heavy metals and quantify their respective contribution through multivariate statistical analysis.‌‌. Materials and methods Study area ‌ Situated in the central region of Jiangsu Province, the Gaoyou section of the Beijing-Hangzhou Grand Canal sits at the crossroads of the northern subtropical humid monsoon and the temperate monsoon climates, where the average annual precipitation reaches 1036.6 mm. As a vital component of the Grand Canal, this section is adjacent to Gaoyou Lake and Shaobo Lake on its eastern side (Fig. 1 ), stretching 43.6 km longitudinally through Gaoyou city, from the Ziyin Sluice at the Gaoyou-Baoying border in the north to Sanshilipu at the Jiangdu-Gaoyou border in the south. Currently classified as a Grade III inland waterway, it maintains a navigable depth of 3–4 m with a channel width of 45–55 m, serving as a key transport route for cargo between northern and southern Jiangsu. Eight culvert sluices are distributed along its course. The canal serves a crucial irrigation function, supplying water via a ‌three-tier canal system‌ to approximately 34,000 hectares of farmland‌ within the irrigation district. The Gaoyou section contains ‌one county-level drinking water source intake point‌. The water quality in this section consistently ‌attains Grade III or higher grade standards‌ as defined by China's Environmental Quality Standards for Surface Water (GB 3838 − 2002). Sampling and measurement ‌In October 2024, sediment sampling was conducted in a dredged sediment dewatering field that receives materials from the Gaoyou section after natural dewatering. A systematic grid sampling strategy (40 m × 40 m spacing) was implemented across the dewatering field, with 46 sampling points established. Using a Geoprobe automated sampling system, 46 sediment core samples were obtained from the mid-layers of dredged sediments at depths between 0.5 m and 1.0 m, with all sampling conducted ‌above the groundwater table‌. Each homogenized sample (approximately 0.5 kg) was immediately placed in polyethylene bags for sealing, followed by delivery to the laboratory for subsequent analytical procedures. The samples were treated to remove impurities, then air-dried, crushed, and finally sieved through a 0.15 mm nylon sieve for further use. A precise amount of 0.2–0.3 g was weighed from each sample, with the weighed sample subsequently placed into a digestion vessel. For Cd, Pb, Cu, Zn, Cr, and Ni, their concentrations were determined via microwave digestion with a HCl-HNO 3 -HF-HClO 4 mixed acid system; after digestion, the resulting solution was diluted to a specified volume, and their concentrations were then measured using a flame atomic absorption spectrophotometer (AA-7800G, Shimadzu, Japan). In contrast, Hg and As were digested in a water bath with aqua regia and analyzed using an atomic fluorescence spectrometer (AFS-8520, Haiguang, China). Quality control measures included blank samples, 10% parallel samples, and spiked samples. Detection results for blank samples were all below the method detection limits; the relative standard deviation (RSD) of all parallel samples was less than 10%; and recovery rates for all target heavy metals ranged from 96% to 109%. Evaluation method Geoaccumulation index As a widely accepted quantitative tool, the geoaccumulation index (Igeo) is frequently applied to assess the degree of heavy metal pollution in sediments and soils. (Cao et al., 2023 ; Peng et al., 2022 ). This index comprehensively considers background values arising from natural geological processes and the effects brought about by human activities, thus intuitively reflecting the extent of anthropogenic impact on the environment. The I geo is mathematically defined using the equation (Muller, 1969 ) presented below: $$Igeo={\log _2}\left( {\frac{{{C_n}}}{{K \cdot {B_n}}}} \right)$$ 1 Within this equation, C n stands for the measured concentration (mg/kg) of the target heavy metal in sediments, while B n represents the regional geochemical baseline level (mg/kg). This study employed the reported background concentrations of heavy metals in Jiangsu Province soils as the reference data (CMEMC, 1990 ). K represents a constant coefficient (typically set at 1.5) that addresses variations in regional soil background values induced by parent material differences. Sediment pollution grades were classified into seven levels based on calculated I geo values (Table 1 ). Potential ecological risk index Unlike I geo , which solely reflects individual metal contamination levels, the potential ecological risk index (RI) emphasizes differential toxicity effects of heavy metals on human health and ecosystems, with particular focus on Cd and Hg in aquatic systems, and quantifies ecological risks under synergistic multi-element interactions (Jiang et al., 2017 ; Sun et al., 2025 ). The established equation (Hakanson, 1980 ) was employed to calculate the RI. $$RI{\text{ }}={\text{ }}\sum\limits_{{i=1}}^{n} {E_{r}^{i}} =\sum\limits_{{i=1}}^{n} {T_{r}^{i}} \times C_{f}^{i}=\sum\limits_{{i=1}}^{n} {T_{r}^{i}} \times \frac{{C_{d}^{i}}}{{C_{r}^{i}}}$$ 2 Where \(E_{r}^{i}\) represents the potential ecological risk index corresponding to heavy metal i , \(T_{r}^{i}\) denotes the toxicity response factor associated with heavy metal i , with typical assigned values for Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr designated as 40, 10, 30, 5, 1, 5, 5, and 2, respectively (Dang et al., 2021 ; Hakanson, 1980 ). The parameter \(C_{f}^{i}\) refers to the contamination factor. For heavy metal i , its measured concentration in sediments (mg/kg) is denoted by \(C_{d}^{i}\) , and its geochemical baseline level (mg/kg) (CMEMC, 1990 ) is represented by \(C_{r}^{i}\) ​, which is equivalent to that applied in the I geo calculation‌. The criteria used to classify Er and RI are shown in Table 1 . Table 1 Criteria for classifying heavy metal pollution grades in sediments via I geo and RI I geo Grades E r Grades RI Grades I geo ⩽0 uncontaminated E r <40 Low risk RI < 150 Low risk 0 < I geo ⩽1 uncontaminated to moderately contaminated 40⩽ E r <80 Moderate 150⩽ RI < 300 Moderate 1 < I geo ⩽2 moderately contaminated 80⩽ E r <160 Considerable 300⩽ RI < 600 Considerable 2 < I geo ⩽3 Moderately to heavily contaminated 160⩽ E r <320 High 600 ⩽ RI High 3 < I geo ⩽4 Heavily contaminated 320⩽ E r Severe 4 < I geo ⩽5 Heavily to extremely contaminated 5 < I geo Extremely contaminated Statistical analysis ‌Data analysis of sediment heavy metal contamination was conducted using Microsoft Excel 2016, including calculations of I geo and RI to characterize basic properties and assess pollution status. ‌Origin 2021 was used for data validation and graphical visualization‌. IBM SPSS Statistics 26 was employed to perform principal component analysis (PCA) and Pearson correlation analysis, with the aim of elucidating the interrelationships among heavy metals and identifying their potential sources. ‌Source identification was further executed using EPA PMF 5.0 software‌ (Norris et al., 2014 ). Results and discussion Concentration analysis of heavy metals The mean concentrations of heavy metals in the sediments were 0.289 ± 0.260, 9.82 ± 1.47, 0.051 ± 0.019, 29.63 ± 4.48, 70.33 ± 11.99, 36.17 ± 10.61, 32.54 ± 5.85, and 53.54 ± 8.08 mg/kg for Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr, respectively (Table 2 ). Relative to Jiangsu Province’s corresponding soil background values, As, Cd, and Cr exhibited mean concentrations below the background values, whereas those of Cu, Zn, Pb, and Ni exceeded the background values by factors of 1.33, 1.12, 1.38, and 1.22, respectively. All measured heavy metal concentrations remained below the threshold limits specified in China's Soil Environmental Quality Standards (GB 15618 − 2018), implying that no significant pollution risk exists. Meanwhile, the coefficient of variation (CV) reflects the influence of external inputs and natural evolutionary processes on heavy metal concentrations (Zhang et al., 2024 ). This study found relatively low CVs for As, Cd, Cu, Zn, Pb, Ni, and Cr, with values of 15.0%, 37.3%, 15.1%, 17.0%, 29.3%, 18.0%, and 15.1%, respectively. In general, a small CV indicates limited influence from human activities, though it could also result from sediment mixing during dredging. The concentration of Hg present in the sediments varied between 0.068 and 0.958 mg/kg, showing significant fluctuations with a CV reaching 90%. The elevated mean concentration, extreme maximum value, and high variability of Hg collectively indicate that it is likely dominated by anthropogenic pollution sources (Anaman et al., 2022 ). Table 2 Statistical properties of sediment heavy metal data (n = 46) Elements Hg As Cd Cu Zn Pb Ni Cr Max (mg/kg) 0.958 13.20 0.104 41.00 111.00 60.00 49.00 71.00 Min (mg/kg) 0.068 7.16 0.015 22.00 53.00 18.00 23.00 39.00 Mean (mg/kg) 0.289 9.82 0.051 29.63 70.33 36.17 32.54 53.54 Median (mg/kg) 0.158 9.79 0.049 28.50 68.00 36.00 31.00 54.50 SD (mg/kg) 0.260 1.47 0.019 4.48 11.99 10.61 5.85 8.08 CV (%) 90.0 15.0 37.3 15.1 17.0 29.3 18.0 15.1 BV a (mg/kg) 0.289 10 0.126 22.3 62.6 26.2 26.7 77.8 SV b (mg/kg) 3.4 25 0.6 100 300 170 190 250 a BV: Background value in soil of Jiangsu Province b SV: Risk control standard in GB 15618–2018 Ecological risk assessment of heavy metals Figure 2 a presents the I geo results, which show that the mean I geo values of all heavy metals exhibited the following ranking: Pb (-0.18) > Cu (-0.19) > Ni (-0.32) > Zn (-0.44) > As (-0.63) > Hg (-1.04) > Cr (-1.14) > Cd (-1.98). According to I geo classification criteria, all sampling points for As, Cd, and Cr exhibited negative I geo values, indicating the absence of significant contamination. Most samples of the remaining metal elements also fell within the uncontaminated level. However, Pb (-0.13–0.61) and Cu (-0.6–0.29) exhibited a certain level of contamination, spanning from uncontaminated to moderately contaminated. The proportion of samples corresponding to this contamination range was 36.96% for Pb and 23.91% for Cu, respectively. Hg (-2.67–1.14), consistent with its significant spatial heterogeneity, had 4.35% of samples reaching the moderately contaminated level. For each individual heavy metal, the mean potential ecological risk index (Er) had the following ranking: Hg (39.95) > Cd (12.23) > As (9.82) > Pb (6.90) > Cu (6.64) > Ni (6.09) > Cr (1.38) > Zn (1.12), as displayed in Fig. 2 b. The mean E r values of all analyzed metals fell within the low ecological risk level, and this consistency matches the contamination levels assessed using the I geo method. For Hg, the E r values ranged from 9.41 to 132.60, with 21.74% of samples reaching a considerable ecological risk level. Furthermore, the RI values varied between 49.09 and 189.27 (mean = 84.14), with most sampling points in the low ecological risk category. Hg was determined to be the primary contributor to ecological risk, accounting for 47.49% of the total RI, while Cd and As contributed 14.53% and 11.67% to the total RI, respectively. This is because Hg poses a high ecological risk, as ‌it can affect human and ecosystem health even at extremely low concentrations (Hu et al., 2024 ). Therefore, attention to Hg pollution issues is crucial. Source identification of heavy metals Pearson correlation analysis Elemental correlations play a vital role in distinguishing specific categories of emission sources. Elements with high correlations may share a common source, exhibit similar transformation behaviors, or follow analogous transport pathways (Jiang et al., 2019 ). Correlation analysis was conducted to examine heavy metals in the study area (Table 3 ). The Pearson correlation coefficients revealed that Cu exhibited significant positive correlations with all other measured metals except Hg. A correlation coefficient of 0.829 (P < 0.05) was found between Cu and Ni, representing the strongest correlation and indicating that these two metals are affected by similar factors or share a common source. Ni showed significant moderate or weak positive correlations with Hg, As, and Zn (P < 0.05). Additionally, a weak negative correlation was observed between Hg and Cd (r = -0.308, P < 0.01). Further investigations are required to elucidate the associations among these heavy metals and their potential natural or anthropogenic sources. Table 3 Matrix of correlations among heavy metals in the sediments Hg As Cd Cu Zn Pb Ni Cr Hg 1 As -0.160 1 Cd -0.308* 0.145 1 Cu 0.133 0.519** 0.485** 1 Zn 0.030 0.366* 0.269 0.495** 1 Pb 0.175 0.117 0.099 0.412** -0.127 1 Ni 0.425** 0.422** 0.234 0.829** 0.496** 0.311* 1 Cr 0.048 0.347* 0.024 0.457** 0.247 0.360* 0.348* 1 ⁎⁎ Significant correlation coefficient at the 0.01 level ⁎ Significant correlation coefficient at the 0.05 level Principal component analysis (PCA) PCA reduces a dataset to a few key components while preserving the most significant variation in the data (Sheng et al., 2022 ). According to the test results for heavy metal concentration data (Kaiser-Meyer-Olkin (KMO) measure = 0.633 > 0.5; Bartlett's test of sphericity, p < 0.001), the first three principal components were selected based on eigenvalues exceeding 1, and their cumulative contribution rate to the total variance amounted to 71.97%, which confirms the effective performance of the PCA (Table 4 ). Specifically, PC1 exhibited relatively high positive loadings for Cu (0.94), Ni (0.86), As (0.63), Zn (0.62), Cr (0.59), and Cd (0.43), accounting for 39.51% of the total variance. Among these elements, Cu and Ni showed higher loading values compared to others and a significant correlation. However, when compared with their respective background values, only the mean concentrations of Cu, Ni, and Zn were found to be higher. Therefore, PC1 represents the combined influence of anthropogenic activities and natural sources. Characterized by Hg (0.83), PC2 accounted for 18.56% of the total variance. Prior discussions noted that Hg exhibits high concentration and variability. Additionally, the weak or non-significant correlations observed between Hg and other metal elements indicate that PC2 primarily originates from anthropogenic sources. PC3, accounting for 13.90% of the total variance, was primarily characterized by the dominance of Pb (0.67). Pb showed slight enrichment, indicating that PC3 likely reflects anthropogenic influences rather than geogenic or natural background sources. Table 4 Principal component loadings for the heavy metals Heavy metal Principal component PC1 PC2 PC3 Hg 0.18 0.83 -0.41 As 0.63 -0.28 0.03 Cd 0.43 -0.59 0.19 Cu 0.94 -0.03 0.04 Zn 0.62 -0.31 -0.54 Pb 0.43 0.44 0.67 Ni 0.86 0.24 -0.25 Cr 0.59 0.17 0.32 Percentage of variance (%) 39.51 18.56 13.90 Percentage of cumulative variance (%) 39.51 58.07 71.97 Positive Matrix Factorization (PMF) model PMF, a multivariate receptor modeling technique, quantifies the contribution of pollution sources by decomposing sample data into factor distributions and contributions (Paatero & Tapper, 1994 ). This method is particularly effective for identifying and apportioning heavy metal pollution sources, with demonstrated applications in sediment and particulate matter studies (Mousavi et al., 2018 ; Proshad et al., 2023 ). In this study, according to the number of components in PCA, PMF was performed with factor numbers set at 3, 4, and 5, with each configuration running 20 times. An optimal solution was determined when 3 factors were used, as evidenced by the smallest Q value and residuals predominantly falling within the − 3 to 3 range. Q (DISP% dQ) showed a change below 1%, accompanied by a matching rate of over 90% for Bootstrap (BS) factors. These results indicate that the model outputs demonstrate statistically valid decomposition and source apportionment capabilities for the heavy metal concentration dataset (Shi et al., 2024 ). Results obtained from the PMF analysis are illustrated in Fig. 3 Factor 1 contributed 34.60% of the total source contribution and was primarily driven by Cd (77.14%), significantly greater than that observed for other heavy metals. As an indicator element of agricultural activities, Cd is introduced in measurable amounts via the application of pesticides, phosphate fertilizers, and other related agricultural inputs (Hu et al., 2018 ; Liang et al., 2025 ; Lv et al., 2014 ). A fraction of this Cd is absorbed by crops, while the remainder is transported into rivers via rainfall and subsequently accumulates in riverbed sediments. However, Cd concentrations across the study area are consistently below background levels, ‌indicating minimal influence from anthropogenic activities. Consequently,‌ Cd concentrations in sediments remain relatively stable and ‌are‌ primarily derived from soil parent materials and rocks. (Islam et al., 2025 ). Factor 1 is classified as deriving from natural sources. Factor 2 accounted for 24.20% of the total variance and displayed a significant loading of Hg (76.16%). Hg contamination in the study area exhibited varying intensities. Studies have pinpointed the combustion of coal and other fossil fuels as the main source of Hg emissions, and this process facilitates long-range atmospheric transport of Hg (Lv & Wang, 2019 ; Shi et al., 2024 ; Xie et al., 2023 ). Prior to 2018, most industrial enterprises in Gaoyou city relied on scattered coal-fired boilers for steam supply, with the Hg-containing waste gas and coal smoke generated in this process accumulating in sediments through dry and wet deposition. Therefore, Factor 2 may originate from industrial sources. Factor 3 explained 47.45% of the total variance, and its characteristic feature was a descending order of Cr, As, Pb, Zn, Cu, and Ni, with their respective contribution rates being 54.79%, 53.64%, 46.03%, 45.48%, 43.87%, and 42.98%. This result is consistent with the significant correlations observed among these metal elements in the preceding analysis. Pb, Zn and Cu primarily originate from industrial processes and transportation-related emissions (Shi et al., 2008 ). Despite the ban on leaded gasoline in China since 2000, Pb enrichment continues due to the legacy of decades-long use (Fang et al., 2025 ; Han et al., 2017 ; Xiao et al., 2019 ). Several studies have demonstrated that the accumulation of Pb, Zn, and Cu can also be caused by tire wear, lubricating oil usage, bearing wear, and galvanized component corrosion (Fang et al., 2025 ; Huang et al., 2025 ; Wang et al., 2025 ). Additionally, Ni and Cu are extensively utilized as Ni-Cu alloys in the manufacturing of specific structural components for ship hulls, such as cooling systems and pipelines. When the protective coatings are compromised, this may result in the release of metal pollutants (Ma et al., 2025 ). The traffic along the Gaoyou section is well-developed, with a multitude of cross-river roads and riverside roads. In addition, according to the Statistical Yearbook of Gaoyou City (2024), approximately 29,000 ships of various types passed through this section throughout the year. Therefore, transportation activities are likely to act as a key contributor to the presence of Pb, Zn, Cu, and Ni in sediments. As primarily originates from the utilization of fertilizers, pesticides, aquaculture feeds, and fossil fuel combustion (Fang et al., 2025 ; Li et al., 2025 ; Sheng et al., 2022 ; Xiao et al., 2019 ), while Cr primarily originates from soil parent materials and accumulated weathering products (Lv et al., 2013 ; Panda et al., 2024 ; Xia et al., 2024 ). Table 2 illustrates that the concentrations of As and Cr are consistently below or close to the background values, indicating that natural processes serve as the main sources of As and Cr. Thus, Factor 3 is understood to be a mixed source combining transportation emissions and natural contributions. Conclusions The concentrations of eight heavy metals (Hg, As, Cu, Ni, Zn, Cr, Pb, and Cd) in the dredged sediments from the Gaoyou section of the Beijing-Hangzhou Grand Canal were determined and systematically analyzed in this study. The findings showed that although the overall concentrations of Cu, Zn, Pb, and Ni in the sediments remained low, their mean values were generally higher than the respective background levels. Multi-method pollution and risk assessments revealed slight enrichment of Hg, Pb, and Cu, among which Hg poses a relatively higher potential ecological risk due to its strong toxicity. In general, the sediments were characterized by a low comprehensive ecological risk. Source identification findings demonstrated that As, Cd, and Cr mainly originated from natural sources; Hg was primarily ascribed to industrial sources such as coal combustion; Pb, Cu, Zn, and Ni were mainly associated with anthropogenic activities, particularly shipping and transportation emissions. This study offers valuable perspectives on the heavy metal pollution status in the Gaoyou section and lays a scientific basis for formulating targeted strategies for pollution control. Declarations Competing interests: The authors have no financial or proprietary interests in any material discussed in this article. Funding This study was supported by the following funds: the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institute (No. GYZX240201), the Financial Budget Project of the Ministry of Ecology and Environment (Special Investigation and Remediation of Historically Accumulated Bulk Solid Waste), and the Financial Budget Project of the Ministry of Ecology and Environment (Technical Support for Environmental Risk Prevention and Control of Hazardous Waste in Key Regions). Author Contribution JH: Writing-original draft, Software, Formal analysis, Data curation. LL: Validation, Project administration, Investigation, Data curation. WL: Validation, Project administration, Investigation, Data curation. XY: Writing-review & editing, Supervision, Methodology, Investigation, Formal analysis. ZH: Writing-review & editing, Supervision, Project administration, Methodology, Investigation. Data Availability Data will be made available on request. References Anaman, R., Peng, C., Jiang, Z., Liu, X., Zhou, Z., Guo, Z., & Xiao, X. (2022). Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. Sci Total Environ , 823 , 153759. https://doi.org/10.1016/j.scitotenv.2022.153759 Cao, J., Guo, Z., Ran, H., Xu, R., Anaman, R., & Liang, H. (2023). Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine. Environ Pollut , 329 , 121713. https://doi.org/10.1016/j.envpol.2023.121713 Chettri, U., Chakrabarty, T. K., & Joshi, S. R. (2022). Pollution index assessment of surface water and sediment quality with reference to heavy metals in Teesta River in Eastern Himalayan range, India. Environmental Nanotechnology, Monitoring & Management , 18 , 100742. https://doi.org/10.1016/j.enmm.2022.100742 CMEMC. (1990). The Backgrounds of Soil Environment in China . China Environmental Science Press. Dai, L., Wang, L., Li, L., Liang, T., Zhang, Y., Ma, C., & Xing, B. (2018). Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China. Sci Total Environ , 621 , 1433–1444. https://doi.org/10.1016/j.scitotenv.2017.10.085 Dang, P., Gu, X., Lin, C., Xin, M., Zhang, H., Ouyang, W.,… Wang, B. (2021). Distribution, sources, and ecological risks of potentially toxic elements in the Laizhou Bay, Bohai Sea: Under the long-term impact of the Yellow River input. J Hazard Mater , 413 , 125429. https://doi.org/10.1016/j.jhazmat.2021.125429 Duan, L., Song, J., Liang, X., Yin, M., Yuan, H., Li, X.,… Yin, X. (2019). Dynamics and diagenesis of trace metals in sediments of the Changjiang Estuary. Sci Total Environ , 675 , 247–259. https://doi.org/10.1016/j.scitotenv.2019.04.190 Fan, W., Xia, W., Lin, L. S., Liu, H., Qu, X., Xin, W.,… Chen, Y. (2025). Heavy metal contamination in fish and human health risks from lakes of a mega inter-basin water diversion. Chemosphere , 379 , 144407. https://doi.org/10.1016/j.chemosphere.2025.144407 Fang, K., Xu, G., Wang, Y., Cheng, Y., Li, J., Chen, X., & Wang, B. (2025). Spatial Distribution, Source Identification, and Risk Assessment of Heavy Metals in Sediments of the Yellow River Basin, China. Applied Sciences , 15 (9). https://doi.org/10.3390/app15095188 Hakanson, L. (1980). An ecological risk index for aquatic pollution control.a sedimentological approach. Water Research , 14 (8), 975–1001. Han, L., Gao, B., Lu, J., Zhou, Y., Xu, D., Gao, L., & Sun, K. (2017). Pollution characteristics and source identification of trace metals in riparian soils of Miyun Reservoir, China. Ecotoxicol Environ Saf , 144 , 321–329. https://doi.org/10.1016/j.ecoenv.2017.06.021 Hou, D., He, J., Lu, C., Ren, L., Fan, Q., Wang, J., & Xie, Z. (2013). Distribution characteristics and potential ecological risk assessment of heavy metals (Cu, Pb, Zn, Cd) in water and sediments from Lake Dalinouer, China. Ecotoxicol Environ Saf , 93 , 135–144. https://doi.org/10.1016/j.ecoenv.2013.03.012 Hu, C., Ma, Y., Liu, Y., Wang, J., Li, B., Sun, Y., & Shui, B. (2024). Trophodynamics and potential health risk assessment of heavy metals in the mangrove food web in Yanpu Bay, China. Sci Total Environ , 920 , 171028. https://doi.org/10.1016/j.scitotenv.2024.171028 Hu, W., Wang, H., Dong, L., Huang, B., Borggaard, O. K., Bruun Hansen, H. C.,… Holm, P. E. (2018). Source identification of heavy metals in peri-urban agricultural soils of southeast China: An integrated approach. Environ Pollut , 237 , 650–661. https://doi.org/10.1016/j.envpol.2018.02.070 Huang, L., Jin, Q., Tandon, P., Li, A., Shan, A., & Du, J. (2018). High-resolution insight into the competitive adsorption of heavy metals on natural sediment by site energy distribution. Chemosphere , 197 , 411–419. https://doi.org/10.1016/j.chemosphere.2018.01.056 Huang, W., Liu, Y., Bi, X., Wang, Y., Li, H., Qin, J.,… Qiu, R. (2025). Source-specific soil heavy metal risk assessment in arsenic waste mine site of Yunnan: Integrating environmental and biological factors. J Hazard Mater , 486 , 136902. https://doi.org/10.1016/j.jhazmat.2024.136902 Islam, A. R. M. T., Rabbi, A. H. M. F., Anik, A. H., Khan, R., Masud, M. A. A., Nedjoud, G.,… Senapathi, V. (2025). Source distribution, ecological risks, and controlling factors of heavy metals in river sediments: Receptor model-based study in a transboundary river basin. International Journal of Sediment Research , 40 (1), 45–61. https://doi.org/10.1016/j.ijsrc.2024.10.001 Jiang, F., Ren, B., Hursthouse, A., Deng, R., & Wang, Z. (2019). Distribution, source identification, and ecological-health risks of potentially toxic elements (PTEs) in soil of thallium mine area (southwestern Guizhou, China). Environ Sci Pollut Res Int , 26 (16), 16556–16567. https://doi.org/10.1007/s11356-019-04997-3 Jiang, X., Xiong, Z., Liu, H., Liu, G., & Liu, W. (2017). Distribution, source identification, and ecological risk assessment of heavy metals in wetland soils of a river-reservoir system. Environ Sci Pollut Res Int , 24 (1), 436–444. https://doi.org/10.1007/s11356-016-7775-x Li, W., Liu, F., Zhang, W., & Wang, X. (2025). Spatial distribution, sources, and ecological risk assessment of heavy metals in Lianyungang coastal sediments of China. Environ Monit Assess , 197 (3), 280. https://doi.org/10.1007/s10661-025-13736-w Liang, H., Wang, G., Guo, H., Niu, L., & Yang, Q. (2025). Evaluation of heavy metal accumulation and sources in surface sediments of the Pearl River Estuary (China). Mar Environ Res , 204 , 106948. https://doi.org/10.1016/j.marenvres.2025.106948 Liao, J., Chen, J., Ru, X., Chen, J., Wu, H., & Wei, C. (2017). Heavy metals in river surface sediments affected with multiple pollution sources, South China: Distribution, enrichment and source apportionment. Journal of Geochemical Exploration , 176 , 9–19. https://doi.org/10.1016/j.gexplo.2016.08.013 Lv, J., Liu, Y., Zhang, Z., & Dai, J. (2013). Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. J Hazard Mater , 261 , 387–397. https://doi.org/10.1016/j.jhazmat.2013.07.065 Lv, J., Liu, Y., Zhang, Z., Dai, J., Dai, B., & Zhu, Y. (2014). Identifying the origins and spatial distributions of heavy metals in soils of Ju country (Eastern China) using multivariate and geostatistical approach. Journal of Soils and Sediments , 15 (1), 163–178. https://doi.org/10.1007/s11368-014-0937-x Lv, J., & Wang, Y. (2019). PMF receptor models and sequential Gaussian simulation to determine the quantitative sources and hazardous areas of potentially toxic elements in soils. Geoderma , 353 , 347–358. https://doi.org/10.1016/j.geoderma.2019.07.020 Ma, Y., Sun, Y., Wang, J., Liu, Y., Guo, M., Hu, C., & Shui, B. (2025). Analysis of heavy metal sources and potential ecological risk assessment of mangroves in Aojiang Estuary. Ecological Indicators , 173 . https://doi.org/10.1016/j.ecolind.2025.113343 Miranda, L. S., Ayoko, G. A., Egodawatta, P., & Goonetilleke, A. (2022). Adsorption-desorption behavior of heavy metals in aquatic environments: Influence of sediment, water and metal ionic properties. J Hazard Mater , 421 , 126743. https://doi.org/10.1016/j.jhazmat.2021.126743 Miranda, L. S., Wijesiri, B., Ayoko, G. A., Egodawatta, P., & Goonetilleke, A. (2021). Water-sediment interactions and mobility of heavy metals in aquatic environments. Water Res , 202 , 117386. https://doi.org/10.1016/j.watres.2021.117386 Mousavi, A., Sowlat, M. H., & Sioutas, C. (2018). Diurnal and seasonal trends and source apportionment of redox-active metals in Los Angeles using a novel online metal monitor and Positive Matrix Factorization (PMF). Atmospheric Environment , 174 , 15–24. https://doi.org/10.1016/j.atmosenv.2017.11.034 Muller, G. (1969). Index of geoaccumulation in sediments of the Rhine river. GeoJournal , 2 , 109–118. Norris, G., Duvall, R., Brown, S., & Bai, S. (2014). EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide . Paatero, P., & Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics , 5 (2). Panda, D., Ghosh, A. K., Ballesta, R. J., Espinosa, M. T. R., Pradhan, S. N., & Patra, A. (2024). Heavy Metals in the Sediments of the Chilika Lake, India: Contamination, Source and Toxicity Analysis. Water, Air, & Soil Pollution , 235 (7). https://doi.org/10.1007/s11270-024-07226-x Peng, J. F., Song, Y. H., Yuan, P., Cui, X. Y., & Qiu, G. L. (2009). The remediation of heavy metals contaminated sediment. J Hazard Mater , 161 (2–3), 633–640. https://doi.org/10.1016/j.jhazmat.2008.04.061 Peng, J. Y., Zhang, S., Han, Y., Bate, B., Ke, H., & Chen, Y. (2022). Soil heavy metal pollution of industrial legacies in China and health risk assessment. Sci Total Environ , 816 , 151632. https://doi.org/10.1016/j.scitotenv.2021.151632 Proshad, R., Dey, H. C., Khan, M. S. U., Baroi, A., Kumar, S., & Idris, A. M. (2023). Source-oriented risks apportionment of toxic metals in river sediments of Bangladesh: a national wide application of PMF model and pollution indices. Environ Geochem Health , 45 (9), 6769–6792. https://doi.org/10.1007/s10653-022-01455-x Qian, Y., Cheng, C., Feng, H., Hong, Z., Zhu, Q., Kolencik, M., & Chang, X. (2020). Assessment of metal mobility in sediment, commercial fish accumulation and impact on human health risk in a large shallow plateau lake in southwest of China. Ecotoxicol Environ Saf , 194 , 110346. https://doi.org/10.1016/j.ecoenv.2020.110346 Shen, L., Gan, Y., Li, C., & Wang, C. (2020). Pollution Level and Ecological Risk Rssessment of Heavy Metals in Riverside Sediments of the Grand Canal (Beijing, Tianjin and Hebei section). Bull Environ Contam Toxicol , 105 (3), 440–445. https://doi.org/10.1007/s00128-020-02957-z Sheng, D., Meng, X., Wen, X., Wu, J., Yu, H., & Wu, M. (2022). Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China. Sci Total Environ , 841 , 156733. https://doi.org/10.1016/j.scitotenv.2022.156733 Shi, G., Chen, Z., Xu, S., Zhang, J., Wang, L., Bi, C., & Teng, J. (2008). Potentially toxic metal contamination of urban soils and roadside dust in Shanghai, China. Environ Pollut , 156 (2), 251–260. https://doi.org/10.1016/j.envpol.2008.02.027 Shi, J., Yang, Y., Shen, Z., Lin, Y., Mei, N., Luo, C.,… Wang, D. (2024). Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation. J Hazard Mater , 478 , 135481. https://doi.org/10.1016/j.jhazmat.2024.135481 Soares, H. M., Boaventura, R. A., Machado, A. A., & Esteves da Silva, J. C. (1999). Sediments as monitors of heavy metal contamination in the Ave river basin (Portugal): multivariate analysis of data. Environ Pollut , 105 (3), 311–323. https://doi.org/10.1016/s0269-7491(99)00048-2 Sun, H., Ren, J., Wang, J., Yu, J., Zuo, L., Wu, X.,… Cheng, X. (2025). Spatial distributions, potential sources, and ecological risks of heavy metals in stream sediments in Zambia. Journal of Geochemical Exploration , 270 . https://doi.org/10.1016/j.gexplo.2024.107659 Wang, W., & Wang, W. X. (2017). Trace metal behavior in sediments of Jiulong River Estuary and implication for benthic exchange fluxes. Environ Pollut , 225 , 598–609. https://doi.org/10.1016/j.envpol.2017.03.028 Wang, X., Han, J., Xu, L., Gao, J., & Zhang, Q. (2011). Effects of anthropogenic activities on chemical contamination within the Grand Canal, China. Environ Monit Assess , 177 (1–4), 127–139. https://doi.org/10.1007/s10661-010-1623-0 Wang, Y., Zhang, J., Yan, Q., Guo, J., Liu, G., Hu, H., & Zhao, Y. (2025). Spatial distribution, sediment–water partitioning, risk assessment and source apportionment of heavy metals in the Golmud River-Dabson Salt Lake ecosystem. Environ Res , 268 , 120792. https://doi.org/10.1016/j.envres.2025.120792 Xia, F., Zhao, Z., Niu, X., & Wang, Z. (2024). Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil. J Hazard Mater , 465 , 133215. https://doi.org/10.1016/j.jhazmat.2023.133215 Xiao, R., Guo, D., Ali, A., Mi, S., Liu, T., Ren, C.,… Zhang, Z. (2019). Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China. Environ Pollut , 248 , 349–357. https://doi.org/10.1016/j.envpol.2019.02.045 Xie, Q., Ren, B., Deng, X., Yin, W., & Lu, Y. (2023). Quantitative source identification, risk assessment and pollution of heavy metals in soils around a typical Sb smelter in central and southern China. Stochastic environmental research and risk assessment , 37 (7), 2495–2511. https://doi.org/10.1007/s00477-023-02402-7 Yan, X., Chen, X., Zheng, W., Zhang, G., & Dong, A. (2024). Heavy Metals in the Sediments of Dongshan Bay, China: Sources, Distribution, and Controlling Factors. Water , 16 (6), 905. https://doi.org/10.3390/w16060905 Yi, Y., Yang, Z., & Zhang, S. (2011). Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze River basin. Environ Pollut , 159 (10), 2575–2585. https://doi.org/10.1016/j.envpol.2011.06.011 Yu, G., Liu, Y., Yu, S., & Li, G. (2011). Enrichment and potential ecological risk assessment of heavy metals in surface sediment from urban sections along the Grand Canal of China. Environmental Chemistry , 30 (11), 1906–1911. Zhang, C., Yu, Z. G., Zeng, G. M., Jiang, M., Yang, Z. Z., Cui, F.,… Hu, L. (2014). Effects of sediment geochemical properties on heavy metal bioavailability. Environ Int , 73 , 270–281. https://doi.org/10.1016/j.envint.2014.08.010 Zhang, Z., Huang, S., Chen, H., & Wang, J. (2024). Deciphering the pollution risks, sources and their links of heavy metals in soils. Sci Total Environ , 950 , 175331. https://doi.org/10.1016/j.scitotenv.2024.175331 Zhang, Z., Juying, L., Mamat, Z., & QingFu, Y. (2016). Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River, Northwest China. Ecotoxicol Environ Saf , 126 , 94–101. https://doi.org/10.1016/j.ecoenv.2015.12.025 Zhuang, Q., Li, G., & Liu, Z. (2018). Distribution, source and pollution level of heavy metals in river sediments from South China. Catena , 170 , 386–396. https://doi.org/10.1016/j.catena.2018.06.037 Zhuang, W., Liu, Y., Chen, Q., Wang, Q., & Zhou, F. (2016). A new index for assessing heavy metal contamination in sediments of the Beijing-Hangzhou Grand Canal (Zaozhuang Segment): A case study. Ecological Indicators , 69 , 252–260. https://doi.org/10.1016/j.ecolind.2016.04.029 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:54:10","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171554,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7701802/v1/cffe205f9e12317f302c4964.html"},{"id":96088472,"identity":"a153ea9f-d539-4f83-8b7a-9817a1d97fdd","added_by":"auto","created_at":"2025-11-17 12:54:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86348,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Gaoyou section of the Beijing-Hangzhou Grand Canal and sampling sites\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7701802/v1/1b41ffc3c6dc10d0edaa7577.png"},{"id":96088493,"identity":"0dde0e04-8877-4580-974a-d8803bf41654","added_by":"auto","created_at":"2025-11-17 12:54:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":663800,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot diagrams of geoaccumulation index (a) and potential ecological risk index (b) for heavy metals in the sediments\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7701802/v1/ff24087d54bbc6787b038513.jpeg"},{"id":96088466,"identity":"cbf82100-1078-4ce7-a97d-4f72d33997a3","added_by":"auto","created_at":"2025-11-17 12:54:08","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177268,"visible":true,"origin":"","legend":"\u003cp\u003eSource profiles and contributions of heavy metals based on PMF (a); Contribution of heavy metals to individual factors (b); Contribution of factors to respective heavy metals (c)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7701802/v1/5c95f5e09e43691aeee90a52.jpeg"},{"id":97139830,"identity":"2743c95d-beaa-4714-ab40-0a6e9cf82f91","added_by":"auto","created_at":"2025-12-01 10:02:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1711121,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701802/v1/10018df0-d75e-4ec8-a749-e77cd9245e1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ecological risk assessment and source identification of heavy metal in dredged sediments from the Gaoyou section of the Beijing- Hangzhou Grand Canal","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRiver sediment has the capacity to adsorb and accumulate pollutants, acting as a significant carrier of contaminants in aquatic systems and a primary internal pollution generator. Sediment-borne heavy metals are recognized as typical cumulative pollutants, with their concentrations in sediment exceeding those in the water phase by several orders of magnitude (Chettri et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and they cannot be eliminated through natural decomposition processes. Therefore, sediment serves as a primary research target for monitoring heavy metal pollutants in aquatic ecosystems (Peng et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Soares et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Heavy metals in the aquatic environment are deposited into sediments through adsorption, complexation, and other physicochemical processes (Huang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miranda et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miranda et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They exhibit significant ecotoxicity and persistence, accumulate in organisms, and progressively concentrate along the food chain (Qian et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Variations in aquatic environmental parameters can induce the remobilization of heavy metals, facilitating their migration from sediments to the water body and thereby exerting long-term impacts on aquatic ecosystems (Duan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Miranda et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang \u0026amp; Wang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAquatic environments are rife with heavy metals, sourced from a myriad of places, encompassing natural geochemical weathering of soils and rocks, as well as anthropogenic inputs like the release of industrial wastewater, fertilization and irrigation activities in agricultural settings, pollution generated by shipping operations, atmospheric deposition processes, and operations associated with aquaculture (Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, Sediment quality indicators help evaluate the effects on ecosystems from both environmental changes and human-induced actions (Liao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhuang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Currently, for numerous rivers around the world, human activities have become the dominant contributor to heavy metal pollution. The analysis of heavy metals\u0026rsquo; distribution patterns, concentration levels, and contamination degrees in riverbed sediments provides critical insights into human impacts on aquatic ecosystems and facilitates identifying potential pollution sources (Yan et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such analysis is essential for assessing potential environmental risks in river systems and formulating targeted, effective environmental management strategies.\u003c/p\u003e\u003cp\u003eThe Beijing-Hangzhou Grand Canal, an extensive inland waterway in China, spans more than 1,700 km connecting northern Beijing with southern Hangzhou. As the world's \u0026zwnj;earliest excavated, largest-scale, longest-route, and most enduring\u0026zwnj; canal, it has exerted profound impacts on regional economic development and socio-cultural civilization along its course. It was listed as a UNESCO World Heritage Site in 2014 and now serves as the primary water conveyance channel for the eastern route of the South-to-North Water Diversion Project, supplying ecological and agricultural water to northern China. With accelerated urbanization and increased shipping traffic along the canal, \u0026zwnj;endogenous heavy metal contamination\u0026zwnj; in sediments has grown increasingly prominent. Previous studies have indicated varying degrees of heavy metal pollution in sediments along the urban Grand Canal sections (Shen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhuang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), \u0026zwnj;yet systematic analysis of pollution sources remains limited\u0026zwnj;.\u003c/p\u003e\u003cp\u003eIn this study, \u0026zwnj;dredged sediments\u0026zwnj; from the Gaoyou section of the Beijing-Hangzhou Grand Canal were analyzed, with the following objectives: (1) analyze the concentration characteristics of eight heavy metals (Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr)\u0026zwnj; in the sediments; (2) evaluate the pollution levels and potential ecological risks of the sediments using standardized indices; (3) identify the potential sources of heavy metals and quantify their respective contribution through multivariate statistical analysis.\u0026zwnj;\u0026zwnj;.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy area\u003c/p\u003e\u003cp\u003e\u0026zwnj; Situated in the central region of Jiangsu Province, the Gaoyou section of the Beijing-Hangzhou Grand Canal sits at the crossroads of the northern subtropical humid monsoon and the temperate monsoon climates, where the average annual precipitation reaches 1036.6 mm. As a vital component of the Grand Canal, this section is adjacent to Gaoyou Lake and Shaobo Lake on its eastern side (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), stretching 43.6 km longitudinally through Gaoyou city, from the Ziyin Sluice at the Gaoyou-Baoying border in the north to Sanshilipu at the Jiangdu-Gaoyou border in the south. Currently classified as a Grade III inland waterway, it maintains a navigable depth of 3\u0026ndash;4 m with a channel width of 45\u0026ndash;55 m, serving as a key transport route for cargo between northern and southern Jiangsu. Eight culvert sluices are distributed along its course. The canal serves a crucial irrigation function, supplying water via a \u0026zwnj;three-tier canal system\u0026zwnj; to approximately 34,000 hectares of farmland\u0026zwnj; within the irrigation district. The Gaoyou section contains \u0026zwnj;one county-level drinking water source intake point\u0026zwnj;. The water quality in this section consistently \u0026zwnj;attains Grade III or higher grade standards\u0026zwnj; as defined by China's Environmental Quality Standards for Surface Water (GB 3838\u0026thinsp;\u0026minus;\u0026thinsp;2002).\u003c/p\u003e\u003cp\u003eSampling and measurement\u003c/p\u003e\u003cp\u003e\u0026zwnj;In October 2024, sediment sampling was conducted in a dredged sediment dewatering field that receives materials from the Gaoyou section after natural dewatering. A systematic grid sampling strategy (40 m \u0026times; 40 m spacing) was implemented across the dewatering field, with 46 sampling points established. Using a Geoprobe automated sampling system, 46 sediment core samples were obtained from the mid-layers of dredged sediments at depths between 0.5 m and 1.0 m, with all sampling conducted \u0026zwnj;above the groundwater table\u0026zwnj;. Each homogenized sample (approximately 0.5 kg) was immediately placed in polyethylene bags for sealing, followed by delivery to the laboratory for subsequent analytical procedures.\u003c/p\u003e\u003cp\u003eThe samples were treated to remove impurities, then air-dried, crushed, and finally sieved through a 0.15 mm nylon sieve for further use. A precise amount of 0.2\u0026ndash;0.3 g was weighed from each sample, with the weighed sample subsequently placed into a digestion vessel. For Cd, Pb, Cu, Zn, Cr, and Ni, their concentrations were determined via microwave digestion with a HCl-HNO\u003csub\u003e3\u003c/sub\u003e-HF-HClO\u003csub\u003e4\u003c/sub\u003e mixed acid system; after digestion, the resulting solution was diluted to a specified volume, and their concentrations were then measured using a flame atomic absorption spectrophotometer (AA-7800G, Shimadzu, Japan). In contrast, Hg and As were digested in a water bath with aqua regia and analyzed using an atomic fluorescence spectrometer (AFS-8520, Haiguang, China). Quality control measures included blank samples, 10% parallel samples, and spiked samples. Detection results for blank samples were all below the method detection limits; the relative standard deviation (RSD) of all parallel samples was less than 10%; and recovery rates for all target heavy metals ranged from 96% to 109%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation method\u003c/h2\u003e\u003cp\u003eGeoaccumulation index\u003c/p\u003e\u003cp\u003eAs a widely accepted quantitative tool, the geoaccumulation index (Igeo) is frequently applied to assess the degree of heavy metal pollution in sediments and soils. (Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This index comprehensively considers background values arising from natural geological processes and the effects brought about by human activities, thus intuitively reflecting the extent of anthropogenic impact on the environment. The I\u003csub\u003egeo\u003c/sub\u003e is mathematically defined using the equation (Muller, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) presented below:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Igeo={\\log _2}\\left( {\\frac{{{C_n}}}{{K \\cdot {B_n}}}} \\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin this equation, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e stands for the measured concentration (mg/kg) of the target heavy metal in sediments, while \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e represents the regional geochemical baseline level (mg/kg). This study employed the reported background concentrations of heavy metals in Jiangsu Province soils as the reference data (CMEMC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). \u003cem\u003eK\u003c/em\u003e represents a constant coefficient (typically set at 1.5) that addresses variations in regional soil background values induced by parent material differences. Sediment pollution grades were classified into seven levels based on calculated I\u003csub\u003egeo\u003c/sub\u003e values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePotential ecological risk index\u003c/p\u003e\u003cp\u003eUnlike I\u003csub\u003egeo\u003c/sub\u003e, which solely reflects individual metal contamination levels, the potential ecological risk index (RI) emphasizes differential toxicity effects of heavy metals on human health and ecosystems, with particular focus on Cd and Hg in aquatic systems, and quantifies ecological risks under synergistic multi-element interactions (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The established equation (Hakanson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) was employed to calculate the RI.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$RI{\\text{ }}={\\text{ }}\\sum\\limits_{{i=1}}^{n} {E_{r}^{i}} =\\sum\\limits_{{i=1}}^{n} {T_{r}^{i}} \\times C_{f}^{i}=\\sum\\limits_{{i=1}}^{n} {T_{r}^{i}} \\times \\frac{{C_{d}^{i}}}{{C_{r}^{i}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003erepresents the potential ecological risk index corresponding to heavy metal \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the toxicity response factor associated with heavy metal \u003cem\u003ei\u003c/em\u003e, with typical assigned values for Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr designated as 40, 10, 30, 5, 1, 5, 5, and 2, respectively (Dang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hakanson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The parameter\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C_{f}^{i}\\)\u003c/span\u003e\u003c/span\u003erefers to the contamination factor. For heavy metal \u003cem\u003ei\u003c/em\u003e, its measured concentration in sediments (mg/kg) is denoted by\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C_{d}^{i}\\)\u003c/span\u003e\u003c/span\u003e, and its geochemical baseline level (mg/kg) (CMEMC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) is represented by\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e​, which is equivalent to that applied in the I\u003csub\u003egeo\u003c/sub\u003e calculation\u0026zwnj;. The criteria used to classify Er and RI are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCriteria for classifying heavy metal pollution grades in sediments via I\u003csub\u003egeo\u003c/sub\u003e and RI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrades\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrades\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrades\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003euncontaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e \u0026lt;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003euncontaminated to moderately contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40⩽ \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e \u0026lt;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150⩽ \u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emoderately contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80⩽ \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e \u0026lt;160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConsiderable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300⩽ \u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConsiderable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately to heavily contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160⩽ \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e \u0026lt;320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e600 ⩽\u003cem\u003eRI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavily contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320⩽ \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e ⩽5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavily to extremely contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtremely contaminated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e\u0026zwnj;Data analysis of sediment heavy metal contamination was conducted using Microsoft Excel 2016, including calculations of \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eRI\u003c/em\u003e to characterize basic properties and assess pollution status. \u0026zwnj;Origin 2021 was used for data validation and graphical visualization\u0026zwnj;. IBM SPSS Statistics 26 was employed to perform principal component analysis (PCA) and Pearson correlation analysis, with the aim of elucidating the interrelationships among heavy metals and identifying their potential sources. \u0026zwnj;Source identification was further executed using EPA PMF 5.0 software\u0026zwnj; (Norris et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eConcentration analysis of heavy metals\u003c/p\u003e\u003cp\u003eThe mean concentrations of heavy metals in the sediments were 0.289\u0026thinsp;\u0026plusmn;\u0026thinsp;0.260, 9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47, 0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019, 29.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.48, 70.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99, 36.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61, 32.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.85, and 53.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08 mg/kg for Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Relative to Jiangsu Province\u0026rsquo;s corresponding soil background values, As, Cd, and Cr exhibited mean concentrations below the background values, whereas those of Cu, Zn, Pb, and Ni exceeded the background values by factors of 1.33, 1.12, 1.38, and 1.22, respectively. All measured heavy metal concentrations remained below the threshold limits specified in China's Soil Environmental Quality Standards (GB 15618\u0026thinsp;\u0026minus;\u0026thinsp;2018), implying that no significant pollution risk exists. Meanwhile, the coefficient of variation (CV) reflects the influence of external inputs and natural evolutionary processes on heavy metal concentrations (Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study found relatively low CVs for As, Cd, Cu, Zn, Pb, Ni, and Cr, with values of 15.0%, 37.3%, 15.1%, 17.0%, 29.3%, 18.0%, and 15.1%, respectively. In general, a small CV indicates limited influence from human activities, though it could also result from sediment mixing during dredging. The concentration of Hg present in the sediments varied between 0.068 and 0.958 mg/kg, showing significant fluctuations with a CV reaching 90%. The elevated mean concentration, extreme maximum value, and high variability of Hg collectively indicate that it is likely dominated by anthropogenic pollution sources (Anaman et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical properties of sediment heavy metal data (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElements\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e111.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e71.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e53.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e54.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBV\u003csup\u003ea\u003c/sup\u003e (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSV\u003csup\u003eb\u003c/sup\u003e (mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e BV: Background value in soil of Jiangsu Province\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e SV: Risk control standard in GB 15618\u0026ndash;2018\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eEcological risk assessment of heavy metals\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea presents the I\u003csub\u003egeo\u003c/sub\u003e results, which show that the mean I\u003csub\u003egeo\u003c/sub\u003e values of all heavy metals exhibited the following ranking: Pb (-0.18)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (-0.19)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (-0.32)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (-0.44)\u0026thinsp;\u0026gt;\u0026thinsp;As (-0.63)\u0026thinsp;\u0026gt;\u0026thinsp;Hg (-1.04)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (-1.14)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (-1.98). According to I\u003csub\u003egeo\u003c/sub\u003e classification criteria, all sampling points for As, Cd, and Cr exhibited negative I\u003csub\u003egeo\u003c/sub\u003e values, indicating the absence of significant contamination. Most samples of the remaining metal elements also fell within the uncontaminated level. However, Pb (-0.13\u0026ndash;0.61) and Cu (-0.6\u0026ndash;0.29) exhibited a certain level of contamination, spanning from uncontaminated to moderately contaminated. The proportion of samples corresponding to this contamination range was 36.96% for Pb and 23.91% for Cu, respectively. Hg (-2.67\u0026ndash;1.14), consistent with its significant spatial heterogeneity, had 4.35% of samples reaching the moderately contaminated level.\u003c/p\u003e\u003cp\u003eFor each individual heavy metal, the mean potential ecological risk index (Er) had the following ranking: Hg (39.95)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (12.23)\u0026thinsp;\u0026gt;\u0026thinsp;As (9.82)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (6.90)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (6.64)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (6.09)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (1.38)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (1.12), as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. The mean \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values of all analyzed metals fell within the low ecological risk level, and this consistency matches the contamination levels assessed using the I\u003csub\u003egeo\u003c/sub\u003e method. For Hg, the \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values ranged from 9.41 to 132.60, with 21.74% of samples reaching a considerable ecological risk level. Furthermore, the RI values varied between 49.09 and 189.27 (mean\u0026thinsp;=\u0026thinsp;84.14), with most sampling points in the low ecological risk category. Hg was determined to be the primary contributor to ecological risk, accounting for 47.49% of the total RI, while Cd and As contributed 14.53% and 11.67% to the total RI, respectively. This is because Hg poses a high ecological risk, as \u0026zwnj;it can affect human and ecosystem health even at extremely low concentrations (Hu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, attention to Hg pollution issues is crucial.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource identification of heavy metals\u003c/p\u003e\u003cp\u003ePearson correlation analysis\u003c/p\u003e\u003cp\u003eElemental correlations play a vital role in distinguishing specific categories of emission sources. Elements with high correlations may share a common source, exhibit similar transformation behaviors, or follow analogous transport pathways (Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Correlation analysis was conducted to examine heavy metals in the study area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Pearson correlation coefficients revealed that Cu exhibited significant positive correlations with all other measured metals except Hg. A correlation coefficient of 0.829 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was found between Cu and Ni, representing the strongest correlation and indicating that these two metals are affected by similar factors or share a common source. Ni showed significant moderate or weak positive correlations with Hg, As, and Zn (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, a weak negative correlation was observed between Hg and Cd (r = -0.308, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Further investigations are required to elucidate the associations among these heavy metals and their potential natural or anthropogenic sources.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMatrix of correlations among heavy metals in the sediments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.308*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.519**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.485**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.366*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.495**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.425**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.422**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.496**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.311*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.347*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.457**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.360*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.348*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e⁎⁎ Significant correlation coefficient at the 0.01 level\u003c/p\u003e\u003cp\u003e⁎ Significant correlation coefficient at the 0.05 level\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA)\u003c/p\u003e\u003cp\u003ePCA reduces a dataset to a few key components while preserving the most significant variation in the data (Sheng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the test results for heavy metal concentration data (Kaiser-Meyer-Olkin (KMO) measure\u0026thinsp;=\u0026thinsp;0.633\u0026thinsp;\u0026gt;\u0026thinsp;0.5; Bartlett's test of sphericity, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the first three principal components were selected based on eigenvalues exceeding 1, and their cumulative contribution rate to the total variance amounted to 71.97%, which confirms the effective performance of the PCA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, PC1 exhibited relatively high positive loadings for Cu (0.94), Ni (0.86), As (0.63), Zn (0.62), Cr (0.59), and Cd (0.43), accounting for 39.51% of the total variance. Among these elements, Cu and Ni showed higher loading values compared to others and a significant correlation. However, when compared with their respective background values, only the mean concentrations of Cu, Ni, and Zn were found to be higher. Therefore, PC1 represents the combined influence of anthropogenic activities and natural sources. Characterized by Hg (0.83), PC2 accounted for 18.56% of the total variance. Prior discussions noted that Hg exhibits high concentration and variability. Additionally, the weak or non-significant correlations observed between Hg and other metal elements indicate that PC2 primarily originates from anthropogenic sources. PC3, accounting for 13.90% of the total variance, was primarily characterized by the dominance of Pb (0.67). Pb showed slight enrichment, indicating that PC3 likely reflects anthropogenic influences rather than geogenic or natural background sources.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrincipal component loadings for the heavy metals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHeavy metal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePrincipal component\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage of variance (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage of cumulative variance (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePositive Matrix Factorization (PMF) model\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePMF, a multivariate receptor modeling technique, quantifies the contribution of pollution sources by decomposing sample data into factor distributions and contributions (Paatero \u0026amp; Tapper, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This method is particularly effective for identifying and apportioning heavy metal pollution sources, with demonstrated applications in sediment and particulate matter studies (Mousavi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Proshad et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, according to the number of components in PCA, PMF was performed with factor numbers set at 3, 4, and 5, with each configuration running 20 times. An optimal solution was determined when 3 factors were used, as evidenced by the smallest Q value and residuals predominantly falling within the \u0026minus;\u0026thinsp;3 to 3 range. Q (DISP% dQ) showed a change below 1%, accompanied by a matching rate of over 90% for Bootstrap (BS) factors. These results indicate that the model outputs demonstrate statistically valid decomposition and source apportionment capabilities for the heavy metal concentration dataset (Shi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Results obtained from the PMF analysis are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFactor 1 contributed 34.60% of the total source contribution and was primarily driven by Cd (77.14%), significantly greater than that observed for other heavy metals. As an indicator element of agricultural activities, Cd is introduced in measurable amounts via the application of pesticides, phosphate fertilizers, and other related agricultural inputs (Hu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A fraction of this Cd is absorbed by crops, while the remainder is transported into rivers via rainfall and subsequently accumulates in riverbed sediments. However, Cd concentrations across the study area are consistently below background levels, \u0026zwnj;indicating minimal influence from anthropogenic activities. Consequently,\u0026zwnj; Cd concentrations in sediments remain relatively stable and \u0026zwnj;are\u0026zwnj; primarily derived from soil parent materials and rocks. (Islam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Factor 1 is classified as deriving from natural sources.\u003c/p\u003e\u003cp\u003eFactor 2 accounted for 24.20% of the total variance and displayed a significant loading of Hg (76.16%). Hg contamination in the study area exhibited varying intensities. Studies have pinpointed the combustion of coal and other fossil fuels as the main source of Hg emissions, and this process facilitates long-range atmospheric transport of Hg (Lv \u0026amp; Wang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Prior to 2018, most industrial enterprises in Gaoyou city relied on scattered coal-fired boilers for steam supply, with the Hg-containing waste gas and coal smoke generated in this process accumulating in sediments through dry and wet deposition. Therefore, Factor 2 may originate from industrial sources.\u003c/p\u003e\u003cp\u003eFactor 3 explained 47.45% of the total variance, and its characteristic feature was a descending order of Cr, As, Pb, Zn, Cu, and Ni, with their respective contribution rates being 54.79%, 53.64%, 46.03%, 45.48%, 43.87%, and 42.98%. This result is consistent with the significant correlations observed among these metal elements in the preceding analysis. Pb, Zn and Cu primarily originate from industrial processes and transportation-related emissions (Shi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Despite the ban on leaded gasoline in China since 2000, Pb enrichment continues due to the legacy of decades-long use (Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several studies have demonstrated that the accumulation of Pb, Zn, and Cu can also be caused by tire wear, lubricating oil usage, bearing wear, and galvanized component corrosion (Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, Ni and Cu are extensively utilized as Ni-Cu alloys in the manufacturing of specific structural components for ship hulls, such as cooling systems and pipelines. When the protective coatings are compromised, this may result in the release of metal pollutants (Ma et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The traffic along the Gaoyou section is well-developed, with a multitude of cross-river roads and riverside roads. In addition, according to the Statistical Yearbook of Gaoyou City (2024), approximately 29,000 ships of various types passed through this section throughout the year. Therefore, transportation activities are likely to act as a key contributor to the presence of Pb, Zn, Cu, and Ni in sediments. As primarily originates from the utilization of fertilizers, pesticides, aquaculture feeds, and fossil fuel combustion (Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sheng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while Cr primarily originates from soil parent materials and accumulated weathering products (Lv et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Panda et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that the concentrations of As and Cr are consistently below or close to the background values, indicating that natural processes serve as the main sources of As and Cr. Thus, Factor 3 is understood to be a mixed source combining transportation emissions and natural contributions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe concentrations of eight heavy metals (Hg, As, Cu, Ni, Zn, Cr, Pb, and Cd) in the dredged sediments from the Gaoyou section of the Beijing-Hangzhou Grand Canal were determined and systematically analyzed in this study. The findings showed that although the overall concentrations of Cu, Zn, Pb, and Ni in the sediments remained low, their mean values were generally higher than the respective background levels. Multi-method pollution and risk assessments revealed slight enrichment of Hg, Pb, and Cu, among which Hg poses a relatively higher potential ecological risk due to its strong toxicity. In general, the sediments were characterized by a low comprehensive ecological risk. Source identification findings demonstrated that As, Cd, and Cr mainly originated from natural sources; Hg was primarily ascribed to industrial sources such as coal combustion; Pb, Cu, Zn, and Ni were mainly associated with anthropogenic activities, particularly shipping and transportation emissions. This study offers valuable perspectives on the heavy metal pollution status in the Gaoyou section and lays a scientific basis for formulating targeted strategies for pollution control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cp\u003eThe authors have no financial or proprietary interests in any material discussed in this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the following funds: the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institute (No. GYZX240201), the Financial Budget Project of the Ministry of Ecology and Environment (Special Investigation and Remediation of Historically Accumulated Bulk Solid Waste), and the Financial Budget Project of the Ministry of Ecology and Environment (Technical Support for Environmental Risk Prevention and Control of Hazardous Waste in Key Regions).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJH: Writing-original draft, Software, Formal analysis, Data curation. LL: Validation, Project administration, Investigation, Data curation. WL: Validation, Project administration, Investigation, Data curation. XY: Writing-review \u0026amp; editing, Supervision, Methodology, Investigation, Formal analysis. ZH: Writing-review \u0026amp; editing, Supervision, Project administration, Methodology, Investigation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnaman, R., Peng, C., Jiang, Z., Liu, X., Zhou, Z., Guo, Z., \u0026amp; Xiao, X. (2022). Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e823\u003c/em\u003e, 153759. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.153759\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.153759\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao, J., Guo, Z., Ran, H., Xu, R., Anaman, R., \u0026amp; Liang, H. (2023). Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e329\u003c/em\u003e, 121713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2023.121713\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2023.121713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChettri, U., Chakrabarty, T. K., \u0026amp; Joshi, S. R. (2022). Pollution index assessment of surface water and sediment quality with reference to heavy metals in Teesta River in Eastern Himalayan range, India. \u003cem\u003eEnvironmental Nanotechnology, Monitoring \u0026amp; Management\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 100742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enmm.2022.100742\u003c/span\u003e\u003cspan address=\"10.1016/j.enmm.2022.100742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCMEMC. (1990). \u003cem\u003eThe Backgrounds of Soil Environment in China\u003c/em\u003e. China Environmental Science Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai, L., Wang, L., Li, L., Liang, T., Zhang, Y., Ma, C., \u0026amp; Xing, B. (2018). Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e621\u003c/em\u003e, 1433\u0026ndash;1444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2017.10.085\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2017.10.085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDang, P., Gu, X., Lin, C., Xin, M., Zhang, H., Ouyang, W.,\u0026hellip; Wang, B. (2021). Distribution, sources, and ecological risks of potentially toxic elements in the Laizhou Bay, Bohai Sea: Under the long-term impact of the Yellow River input. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e413\u003c/em\u003e, 125429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2021.125429\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2021.125429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan, L., Song, J., Liang, X., Yin, M., Yuan, H., Li, X.,\u0026hellip; Yin, X. (2019). Dynamics and diagenesis of trace metals in sediments of the Changjiang Estuary. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e675\u003c/em\u003e, 247\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2019.04.190\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2019.04.190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan, W., Xia, W., Lin, L. S., Liu, H., Qu, X., Xin, W.,\u0026hellip; Chen, Y. (2025). Heavy metal contamination in fish and human health risks from lakes of a mega inter-basin water diversion. \u003cem\u003eChemosphere\u003c/em\u003e, \u003cem\u003e379\u003c/em\u003e, 144407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2025.144407\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2025.144407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang, K., Xu, G., Wang, Y., Cheng, Y., Li, J., Chen, X., \u0026amp; Wang, B. (2025). Spatial Distribution, Source Identification, and Risk Assessment of Heavy Metals in Sediments of the Yellow River Basin, China. \u003cem\u003eApplied Sciences\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app15095188\u003c/span\u003e\u003cspan address=\"10.3390/app15095188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHakanson, L. (1980). An ecological risk index for aquatic pollution control.a sedimentological approach. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(8), 975\u0026ndash;1001.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, L., Gao, B., Lu, J., Zhou, Y., Xu, D., Gao, L., \u0026amp; Sun, K. (2017). Pollution characteristics and source identification of trace metals in riparian soils of Miyun Reservoir, China. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e, \u003cem\u003e144\u003c/em\u003e, 321\u0026ndash;329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2017.06.021\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2017.06.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou, D., He, J., Lu, C., Ren, L., Fan, Q., Wang, J., \u0026amp; Xie, Z. (2013). Distribution characteristics and potential ecological risk assessment of heavy metals (Cu, Pb, Zn, Cd) in water and sediments from Lake Dalinouer, China. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e, 135\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2013.03.012\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2013.03.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, C., Ma, Y., Liu, Y., Wang, J., Li, B., Sun, Y., \u0026amp; Shui, B. (2024). Trophodynamics and potential health risk assessment of heavy metals in the mangrove food web in Yanpu Bay, China. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e920\u003c/em\u003e, 171028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.171028\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.171028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, W., Wang, H., Dong, L., Huang, B., Borggaard, O. K., Bruun Hansen, H. C.,\u0026hellip; Holm, P. E. (2018). Source identification of heavy metals in peri-urban agricultural soils of southeast China: An integrated approach. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e237\u003c/em\u003e, 650\u0026ndash;661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2018.02.070\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2018.02.070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, L., Jin, Q., Tandon, P., Li, A., Shan, A., \u0026amp; Du, J. (2018). High-resolution insight into the competitive adsorption of heavy metals on natural sediment by site energy distribution. \u003cem\u003eChemosphere\u003c/em\u003e, \u003cem\u003e197\u003c/em\u003e, 411\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2018.01.056\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2018.01.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, W., Liu, Y., Bi, X., Wang, Y., Li, H., Qin, J.,\u0026hellip; Qiu, R. (2025). Source-specific soil heavy metal risk assessment in arsenic waste mine site of Yunnan: Integrating environmental and biological factors. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e486\u003c/em\u003e, 136902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2024.136902\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2024.136902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam, A. R. M. T., Rabbi, A. H. M. F., Anik, A. H., Khan, R., Masud, M. A. A., Nedjoud, G.,\u0026hellip; Senapathi, V. (2025). Source distribution, ecological risks, and controlling factors of heavy metals in river sediments: Receptor model-based study in a transboundary river basin. \u003cem\u003eInternational Journal of Sediment Research\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 45\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijsrc.2024.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ijsrc.2024.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang, F., Ren, B., Hursthouse, A., Deng, R., \u0026amp; Wang, Z. (2019). Distribution, source identification, and ecological-health risks of potentially toxic elements (PTEs) in soil of thallium mine area (southwestern Guizhou, China). \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(16), 16556\u0026ndash;16567. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-019-04997-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-019-04997-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang, X., Xiong, Z., Liu, H., Liu, G., \u0026amp; Liu, W. (2017). Distribution, source identification, and ecological risk assessment of heavy metals in wetland soils of a river-reservoir system. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 436\u0026ndash;444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-016-7775-x\u003c/span\u003e\u003cspan address=\"10.1007/s11356-016-7775-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, W., Liu, F., Zhang, W., \u0026amp; Wang, X. (2025). Spatial distribution, sources, and ecological risk assessment of heavy metals in Lianyungang coastal sediments of China. \u003cem\u003eEnviron Monit Assess\u003c/em\u003e, \u003cem\u003e197\u003c/em\u003e(3), 280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-025-13736-w\u003c/span\u003e\u003cspan address=\"10.1007/s10661-025-13736-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang, H., Wang, G., Guo, H., Niu, L., \u0026amp; Yang, Q. (2025). Evaluation of heavy metal accumulation and sources in surface sediments of the Pearl River Estuary (China). \u003cem\u003eMar Environ Res\u003c/em\u003e, \u003cem\u003e204\u003c/em\u003e, 106948. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.marenvres.2025.106948\u003c/span\u003e\u003cspan address=\"10.1016/j.marenvres.2025.106948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao, J., Chen, J., Ru, X., Chen, J., Wu, H., \u0026amp; Wei, C. (2017). Heavy metals in river surface sediments affected with multiple pollution sources, South China: Distribution, enrichment and source apportionment. \u003cem\u003eJournal of Geochemical Exploration\u003c/em\u003e, \u003cem\u003e176\u003c/em\u003e, 9\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gexplo.2016.08.013\u003c/span\u003e\u003cspan address=\"10.1016/j.gexplo.2016.08.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLv, J., Liu, Y., Zhang, Z., \u0026amp; Dai, J. (2013). Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e261\u003c/em\u003e, 387\u0026ndash;397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2013.07.065\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2013.07.065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLv, J., Liu, Y., Zhang, Z., Dai, J., Dai, B., \u0026amp; Zhu, Y. (2014). Identifying the origins and spatial distributions of heavy metals in soils of Ju country (Eastern China) using multivariate and geostatistical approach. \u003cem\u003eJournal of Soils and Sediments\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 163\u0026ndash;178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11368-014-0937-x\u003c/span\u003e\u003cspan address=\"10.1007/s11368-014-0937-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLv, J., \u0026amp; Wang, Y. (2019). PMF receptor models and sequential Gaussian simulation to determine the quantitative sources and hazardous areas of potentially toxic elements in soils. \u003cem\u003eGeoderma\u003c/em\u003e, \u003cem\u003e353\u003c/em\u003e, 347\u0026ndash;358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2019.07.020\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2019.07.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa, Y., Sun, Y., Wang, J., Liu, Y., Guo, M., Hu, C., \u0026amp; Shui, B. (2025). Analysis of heavy metal sources and potential ecological risk assessment of mangroves in Aojiang Estuary. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e173\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2025.113343\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2025.113343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiranda, L. S., Ayoko, G. A., Egodawatta, P., \u0026amp; Goonetilleke, A. (2022). Adsorption-desorption behavior of heavy metals in aquatic environments: Influence of sediment, water and metal ionic properties. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e421\u003c/em\u003e, 126743. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2021.126743\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2021.126743\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiranda, L. S., Wijesiri, B., Ayoko, G. A., Egodawatta, P., \u0026amp; Goonetilleke, A. (2021). Water-sediment interactions and mobility of heavy metals in aquatic environments. \u003cem\u003eWater Res\u003c/em\u003e, \u003cem\u003e202\u003c/em\u003e, 117386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.watres.2021.117386\u003c/span\u003e\u003cspan address=\"10.1016/j.watres.2021.117386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMousavi, A., Sowlat, M. H., \u0026amp; Sioutas, C. (2018). Diurnal and seasonal trends and source apportionment of redox-active metals in Los Angeles using a novel online metal monitor and Positive Matrix Factorization (PMF). \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e174\u003c/em\u003e, 15\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2017.11.034\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2017.11.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuller, G. (1969). Index of geoaccumulation in sediments of the Rhine river. \u003cem\u003eGeoJournal\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 109\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorris, G., Duvall, R., Brown, S., \u0026amp; Bai, S. (2014). \u003cem\u003eEPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaatero, P., \u0026amp; Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. \u003cem\u003eEnvironmetrics\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanda, D., Ghosh, A. K., Ballesta, R. J., Espinosa, M. T. R., Pradhan, S. N., \u0026amp; Patra, A. (2024). Heavy Metals in the Sediments of the Chilika Lake, India: Contamination, Source and Toxicity Analysis. \u003cem\u003eWater, Air, \u0026amp; Soil Pollution\u003c/em\u003e, \u003cem\u003e235\u003c/em\u003e(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-024-07226-x\u003c/span\u003e\u003cspan address=\"10.1007/s11270-024-07226-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, J. F., Song, Y. H., Yuan, P., Cui, X. Y., \u0026amp; Qiu, G. L. (2009). The remediation of heavy metals contaminated sediment. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e(2\u0026ndash;3), 633\u0026ndash;640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2008.04.061\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2008.04.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, J. Y., Zhang, S., Han, Y., Bate, B., Ke, H., \u0026amp; Chen, Y. (2022). Soil heavy metal pollution of industrial legacies in China and health risk assessment. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e816\u003c/em\u003e, 151632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.151632\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.151632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eProshad, R., Dey, H. C., Khan, M. S. U., Baroi, A., Kumar, S., \u0026amp; Idris, A. M. (2023). Source-oriented risks apportionment of toxic metals in river sediments of Bangladesh: a national wide application of PMF model and pollution indices. \u003cem\u003eEnviron Geochem Health\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(9), 6769\u0026ndash;6792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10653-022-01455-x\u003c/span\u003e\u003cspan address=\"10.1007/s10653-022-01455-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQian, Y., Cheng, C., Feng, H., Hong, Z., Zhu, Q., Kolencik, M., \u0026amp; Chang, X. (2020). Assessment of metal mobility in sediment, commercial fish accumulation and impact on human health risk in a large shallow plateau lake in southwest of China. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e, \u003cem\u003e194\u003c/em\u003e, 110346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2020.110346\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2020.110346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen, L., Gan, Y., Li, C., \u0026amp; Wang, C. (2020). Pollution Level and Ecological Risk Rssessment of Heavy Metals in Riverside Sediments of the Grand Canal (Beijing, Tianjin and Hebei section). \u003cem\u003eBull Environ Contam Toxicol\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(3), 440\u0026ndash;445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00128-020-02957-z\u003c/span\u003e\u003cspan address=\"10.1007/s00128-020-02957-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheng, D., Meng, X., Wen, X., Wu, J., Yu, H., \u0026amp; Wu, M. (2022). Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e841\u003c/em\u003e, 156733. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.156733\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.156733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi, G., Chen, Z., Xu, S., Zhang, J., Wang, L., Bi, C., \u0026amp; Teng, J. (2008). Potentially toxic metal contamination of urban soils and roadside dust in Shanghai, China. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e156\u003c/em\u003e(2), 251\u0026ndash;260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2008.02.027\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2008.02.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi, J., Yang, Y., Shen, Z., Lin, Y., Mei, N., Luo, C.,\u0026hellip; Wang, D. (2024). Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e478\u003c/em\u003e, 135481. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2024.135481\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2024.135481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoares, H. M., Boaventura, R. A., Machado, A. A., \u0026amp; Esteves da Silva, J. C. (1999). Sediments as monitors of heavy metal contamination in the Ave river basin (Portugal): multivariate analysis of data. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(3), 311\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0269-7491(99)00048-2\u003c/span\u003e\u003cspan address=\"10.1016/s0269-7491(99)00048-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, H., Ren, J., Wang, J., Yu, J., Zuo, L., Wu, X.,\u0026hellip; Cheng, X. (2025). Spatial distributions, potential sources, and ecological risks of heavy metals in stream sediments in Zambia. \u003cem\u003eJournal of Geochemical Exploration\u003c/em\u003e, \u003cem\u003e270\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gexplo.2024.107659\u003c/span\u003e\u003cspan address=\"10.1016/j.gexplo.2024.107659\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, W., \u0026amp; Wang, W. X. (2017). Trace metal behavior in sediments of Jiulong River Estuary and implication for benthic exchange fluxes. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e225\u003c/em\u003e, 598\u0026ndash;609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2017.03.028\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2017.03.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, X., Han, J., Xu, L., Gao, J., \u0026amp; Zhang, Q. (2011). Effects of anthropogenic activities on chemical contamination within the Grand Canal, China. \u003cem\u003eEnviron Monit Assess\u003c/em\u003e, \u003cem\u003e177\u003c/em\u003e(1\u0026ndash;4), 127\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-010-1623-0\u003c/span\u003e\u003cspan address=\"10.1007/s10661-010-1623-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Y., Zhang, J., Yan, Q., Guo, J., Liu, G., Hu, H., \u0026amp; Zhao, Y. (2025). Spatial distribution, sediment\u0026ndash;water partitioning, risk assessment and source apportionment of heavy metals in the Golmud River-Dabson Salt Lake ecosystem. \u003cem\u003eEnviron Res\u003c/em\u003e, \u003cem\u003e268\u003c/em\u003e, 120792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2025.120792\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2025.120792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia, F., Zhao, Z., Niu, X., \u0026amp; Wang, Z. (2024). Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil. \u003cem\u003eJ Hazard Mater\u003c/em\u003e, \u003cem\u003e465\u003c/em\u003e, 133215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhazmat.2023.133215\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2023.133215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao, R., Guo, D., Ali, A., Mi, S., Liu, T., Ren, C.,\u0026hellip; Zhang, Z. (2019). Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e248\u003c/em\u003e, 349\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2019.02.045\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2019.02.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie, Q., Ren, B., Deng, X., Yin, W., \u0026amp; Lu, Y. (2023). Quantitative source identification, risk assessment and pollution of heavy metals in soils around a typical Sb smelter in central and southern China. \u003cem\u003eStochastic environmental research and risk assessment\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(7), 2495\u0026ndash;2511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00477-023-02402-7\u003c/span\u003e\u003cspan address=\"10.1007/s00477-023-02402-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, X., Chen, X., Zheng, W., Zhang, G., \u0026amp; Dong, A. (2024). Heavy Metals in the Sediments of Dongshan Bay, China: Sources, Distribution, and Controlling Factors. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(6), 905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w16060905\u003c/span\u003e\u003cspan address=\"10.3390/w16060905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYi, Y., Yang, Z., \u0026amp; Zhang, S. (2011). Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze River basin. \u003cem\u003eEnviron Pollut\u003c/em\u003e, \u003cem\u003e159\u003c/em\u003e(10), 2575\u0026ndash;2585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2011.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2011.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, G., Liu, Y., Yu, S., \u0026amp; Li, G. (2011). Enrichment and potential ecological risk assessment of heavy metals in surface sediment from urban sections along the Grand Canal of China. \u003cem\u003eEnvironmental Chemistry\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(11), 1906\u0026ndash;1911.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, C., Yu, Z. G., Zeng, G. M., Jiang, M., Yang, Z. Z., Cui, F.,\u0026hellip; Hu, L. (2014). Effects of sediment geochemical properties on heavy metal bioavailability. \u003cem\u003eEnviron Int\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 270\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2014.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2014.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Z., Huang, S., Chen, H., \u0026amp; Wang, J. (2024). Deciphering the pollution risks, sources and their links of heavy metals in soils. \u003cem\u003eSci Total Environ\u003c/em\u003e, \u003cem\u003e950\u003c/em\u003e, 175331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.175331\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.175331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Z., Juying, L., Mamat, Z., \u0026amp; QingFu, Y. (2016). Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River, Northwest China. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e, 94\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2015.12.025\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2015.12.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhuang, Q., Li, G., \u0026amp; Liu, Z. (2018). Distribution, source and pollution level of heavy metals in river sediments from South China. \u003cem\u003eCatena\u003c/em\u003e, \u003cem\u003e170\u003c/em\u003e, 386\u0026ndash;396. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2018.06.037\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2018.06.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhuang, W., Liu, Y., Chen, Q., Wang, Q., \u0026amp; Zhou, F. (2016). A new index for assessing heavy metal contamination in sediments of the Beijing-Hangzhou Grand Canal (Zaozhuang Segment): A case study. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e, 252\u0026ndash;260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2016.04.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2016.04.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sediment, Heavy metal, Risk assessment, Source identification, Beijing-Hangzhou Grand Canal","lastPublishedDoi":"10.21203/rs.3.rs-7701802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7701802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeavy metals demonstrate a marked tendency to accumulate in riverine sediments, primarily attributed to their chemical persistence and intrinsic toxicity, thereby posing latent long-term threats to both ecosystem integrity and human health. This study aimed to examine the occurrence of heavy metals (Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr) in dredged sediments obtained from the Gaoyou section of the Beijing-Hangzhou Grand Canal. The geoaccumulation index (Igeo) and potential ecological risk index (RI) were employed to evaluate the heavy metal pollution levels in the sediments and their associated ecological risks. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) models were used as analytical tools to identify and quantify the sources of the heavy metals. The findings revealed that Hg, As, Cd, Cu, Zn, Pb, Ni, and Cr had mean concentrations of 0.289, 9.82, 0.051, 29.63, 70.33, 36.17, 32.54, and 53.54 mg/kg, respectively. Notably, the mean concentrations of Cu, Zn, Pb, and Ni exceeded the soil background values of Jiangsu Province. Results from I\u003csub\u003egeo\u003c/sub\u003e and RI analyses indicated slight enrichment of Hg, Pb, and Cu. The comprehensive ecological risk remained low, with Hg being the primary contributor. Based on PCA and PMF analyses, three primary sources of heavy metals were identified. Hg was primarily derived from industrial sources such as coal combustion; Pb, Ni, Zn, and Cu were mainly attributed to emissions from shipping and transportation; while As, Cr, and Cd occurred at low concentrations, indicating their predominantly natural origins.\u003c/p\u003e","manuscriptTitle":"Ecological risk assessment and source identification of heavy metal in dredged sediments from the Gaoyou section of the Beijing- Hangzhou Grand Canal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 12:53:59","doi":"10.21203/rs.3.rs-7701802/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ab632f0-58d2-4350-a672-4109a4870461","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T10:26:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 12:53:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7701802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7701802","identity":"rs-7701802","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}