Source Apportionment and Ecological Risk Assessment of Heavy Metals in Sediments of Dongping Lake Based on PCA-PMF Model

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This study selected Dongping Lake, a typical inland shallow lake in eastern China, as the research area. A comprehensive methodology was applied, integrating mathematical statistical analysis, cluster analysis, principal component analysis (PCA), and the Positive Matrix Factorization (PMF) model, combined with the enrichment factor method, geoaccumulation index method, and potential ecological risk assessment. This approach systematically investigated the distribution characteristics, source apportionment, and ecological risks of eight heavy metals (As, Zn, Cu, Ni, Cd, Hg, Pb, Cr) in the lake sediments. The results revealed an overall improving trend in heavy metal concentrations, with significant spatial heterogeneity—higher concentrations were observed in the central and southern regions. Multivariate statistical analysis identified three primary sources of heavy metal enrichment: industrial and agricultural activities (As, Cu, Hg, Cd), traffic emissions (Zn, Ni), and natural geological background (Cr, Pb). Enrichment characteristics indicated moderate to severe accumulation of Cd, As, and Hg. The potential ecological risk index (RI) highlighted Hg and Cd as the dominant risk contributors, accounting for 41.83% and 37.77% of the total risk, respectively. This study underscores the dominant role of anthropogenic activities in driving heavy metal accumulation in Dongping Lake sediments, providing a scientific basis for targeted pollution control and ecological restoration in the watershed. Earth and environmental sciences/Environmental sciences/Environmental chemistry Earth and environmental sciences/Hydrology Dongping Lake PCA Model PMF Model Source Apportionment Ecological Risk Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 0 Introduction Heavy metals, characterized by their ubiquitous sources in ecological systems, persistent migration across biological communities, and intrinsic properties such as non-degradability, bioaccumulation, and carcinogenicity, pose significant challenges to environmental protection authorities due to their recalcitrance to remediation. Contamination by multiple heavy metals, particularly arsenic (As), zinc (Zn), copper (Cu), nickel (Ni), cadmium (Cd), mercury (Hg), lead (Pb), and chromium (Cr), imposes severe threats to both ecological systems and human health [ 1 ] . Lakes, particularly urban lakes, serve as unique natural and cultural landscapes in cities while also fulfilling ecological regulation functions [ 2 ] . With the rapid development of the economy, society, and agriculture, lakes, serving as discharge zones for rivers and groundwater, receive industrial, agricultural, and domestic wastewater from within the watershed. Large quantities of pollutants are discharged into lake basins in various forms and subsequently accumulate in lake water bodies. Sediments, as an essential component of lake water bodies [ 3 ] ,act as a critical medium for the accumulation and enrichment of various pollutants, with heavy metal concentrations significantly exceeding local background levels [ 4 ] . Under the combined influence of hydrodynamic processes and biotic uptake, heavy metal pollutants accumulated in sediments are released into water bodies, leading to secondary pollution of lake water resources [ 5 ] ,Concurrently, through mechanisms such as food chain amplification and bioaccumulation, these pollutants threaten human health and severely compromise the healthy functioning of lake ecosystems [ 6 ] . As accumulation zones for heavy metal contamination, lake sediments have become a focal research area for domestic and international scholars in water resource investigations. Commonly employed methodologies include the Single Pollution Index, Integrated Pollution Index, Nemerow Index Method, Enrichment Factor Method, and Geoaccumulation Index Method [ 7 ] . Foreign scholars, such as Proshad et al. [ 8 ] applied the Enrichment Factor Method to analyze the sources of surface sediment distribution in the Rupsa River of Bangladesh, while Wijesiri et al. [ 9 ] investigated the migration and accumulation processes of heavy metals between sediments and water bodies in urban rivers. Domestic scholars, such as Sun Bowen [ 10 ] 、Zhang Chuang [ 11 ] 、and Wang Henian [ 12 ] evaluated the distribution characteristics of heavy metal concentrations in sediments from Baiyangdian Lake, Hengshui Lake, an urban lake in Jiangxi Province, and Erhai Lake using the Single Pollution Index Method, Nemerow Pollution Index Method, and Pollution Load Index, respectively. Meanwhile, researchers in China have applied the Geoaccumulation Index Method, Enrichment Factor Method, and Potential Ecological Risk Index Method to analyze heavy metal contamination risks and sources in surface water sediments from urban lakes in Ezhou City, Hubei Province [ 13 ] , Dachaidan Salt Lake [ 14 ] , and urban rivers in Jiangsu Province [ 15 ] . However, most studies remain largely confined to statistical characterization and risk assessment of heavy metals or can only qualitatively identify primary pollutants and their sources, failing to quantitatively assess the specific contribution ratios of individual contaminants [ 16 ] . Dongping Lake, situated at the border of Henan and Shandong provinces, is a typical shallow inland lake in the eastern plains of China. Serving as a crucial flood detention zone in the Yellow River Basin and a regulating reservoir for the Eastern Route of the South-to-North Water Diversion Project, the heavy metal characteristics of its sediments have garnered significant attention from researchers. Scholars such as Ge Huimin [ 17 ] ,Zhang Ju [ 18 ] ,and Ai Liuhuan [ 19 ] have evaluated the distribution patterns and ecological risks of heavy metals in Dongping Lake sediments. However, most studies have focused on analyzing the spatial distribution characteristics and ecological health risks of heavy metals, with relatively limited sampling points. This study comprehensively analyzed the spatial distribution characteristics of eight typical heavy metals in Dongping Lake sediments using the enrichment factor method, geoaccumulation index, and potential ecological risk assessment. Furthermore, cluster analysis, correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) were employed to conduct qualitative analysis of the enrichment mechanisms of these heavy metals, while quantitatively assessing their respective contamination contributions. The findings provide critical data support for the prevention and control of heavy metal pollution, the formulation of monitoring strategies, and ecological risk management in the Dongping Lake watershed. 1 Materials and Methods 1.1 Study Area Dongping Lake is located in the western part of Dongping County, Tai’an City, Shandong Province, at the lower reaches of the Dawen River near its confluence with the Yellow River, spanning 116°00′-116°30′E and 35°30′-36°20′N (see Fig. 1 for specific location). Bordered by the Grand Canal to the west, connected to the Daqing River in the east, and linked to the Yellow River in the north, Dongping Lake has a perennial water surface area of approximately 209 km², making it the second-largest freshwater lake in Shandong Province and a typical shallow lake in eastern China [ 20 ] .As a critical component of the Eastern Route of the South-to-North Water Diversion Project, Dongping Lake serves as both a key water conveyance channel and a regulating reservoir. It is the highest-altitude regulating reservoir within Shandong Province for this project. Situated among Dongping, Liangshan, and Wensheng counties, the lake is a perennially regulated, gourd-shaped shallow lake. The northern section comprises the narrow Xiaoqing River, while the southern section forms a broad lake basin with a well-developed hydrological network. With a documented human activity history spanning 4,500 years, Dongping Lake integrates flood detention, drought resistance, water storage, flow regulation, navigation, and tourism. Its water quality directly impacts the implementation of the South-to-North Water Diversion Project and the ecological and economic development of adjacent regions, positioning it as a vital node for ecological security in Shandong Province. 1.2 Sample Collection and Analysis 1.2.1Sample Collection Following the Lake Sediment Survey Specifications and Technical Guidelines for Heavy Metal Pollution Assessment in Sediments (DB37/T4471-2021), sampling sites were strategically designed to avoid areas with intense hydrodynamic fluctuations or significant anthropogenic disturbances. A grid-based comprehensive layout was adopted based on the Dongping Lake watershed morphology and protected area boundaries.Prior to sampling, precise latitude and longitude coordinates were recorded using an SR6 Pro RTK GPS system. Surface sediment samples (0–20 cm depth) were collected using a grab sampler. To minimize sampling errors, three parallel samples were collected at each site, homogenized, and sieved to remove gravel, plant/animal debris, and sediment in direct contact with the sampler’s inner walls. Each sample (0.5 kg) was sealed in polyethylene plastic bags and stored at 4°C.A total of 20 sediment sampling sites were established across the study area (Fig. 1 ), with sample collection completed on June 14, 2024. 1.2.2 Analytical Testing The concentrations of Zn, Cu, Ni, Cd, and Pb in laboratory samples were determined using Closed-Acid Digestion-Inductively Coupled Plasma Mass Spectrometry (ICP-MS) with detection limits of 0.5 mg/kg, 1.0 mg/kg, 2.0 mg/kg, 0.01 mg/kg, and 1.0 mg/kg, respectively, following the standards GB/T 14506.30–2010 and DZ/T 0279.1–2016.Cr was analyzed via Powder Pelletization-X-ray Fluorescence Spectrometry (XRF) with a detection limit of 0.01 mg/kg, in accordance with DZ/T 0279.13–2016.As and Hg were measured using Hydride Generation-Atomic Fluorescence Spectrometry (HG-AFS) and Vapor Generation-Cold Vapor Atomic Fluorescence Spectrometry (CV-AFS), respectively, with detection limits of 0.2 mg/kg and 0.05 mg/kg, adhering to DZ/T 0279.17–2016. 1.3Source Apportionment Methods for Sediment Heavy Metals For source identification, a combination of cluster analysis, correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) was applied. Cluster analysis and correlation analysis were employed to elucidate interrelationships among heavy metal elements. PCA facilitated data dimensionality reduction and extraction of dominant pollution sources, while PMF resolved source contributions through non-negative constrained matrix decomposition, thereby addressing the interpretive limitations of PCA [ 21 – 23 ] 。 1.4 Heavy Metal Source Apportionment and Ecological Risk Assessment Three methods were applied: the Enrichment Factor ( EF ) method, Geoaccumulation index ( I geo ), and Potential ecological risk assessment. The EF method quantifies the enrichment degree of heavy metals by calculating the ratio of their concentrations to environmental background values, typically using aluminum (Al) as the normalization element. [ 24 ] . The I geo evaluates contamination levels by comparing measured values to background values, with adjustments introduced via a correction factor [ 25 , 26 ] . The potential ecological risk index ( RI ) integrates heavy metal concentrations, ecological sensitivity, and toxicological effects to classify sediment contamination levels and their associated ecological risks [ 12 ] . The classification criteria for EF , I geo , and RI are summarized in Table 1 . Table 1 The classification criteria for EF , I geo , and RI EF Enrichment Degree \(\:{I}_{geo}\) Evaluation Levels \(\:{E}_{r}^{i}\) \(\:RI\) Potential Ecological Risk ≤ 1 No Enrichment (No Contamination) ≤ 0 No Contamination ≤ 40 ≤ 150 Low 1 ~ 2 Slight Enrichment (Slight Contamination) 0 ~ 1 No to Moderate Contamination 40 ~ 80 150 ~ 300 Moderate 2 ~ 5 Moderate Enrichment (Moderate Contamination) 1 ~ 2 Moderate Contamination 80 ~ 160 300 ~ 600 Considerable 5 ~ 20 High Enrichment (Heavy Contamination) 2 ~ 3 Moderate to Heavy Contamination 160 ~ 320 ≥ 600 High 20 ~ 40 Extremely High Enrichment (Severe Contamination) 3 ~ 4 Heavy Contamination ≥ 320 / Very High / / 4 ~ 5 Heavy to Extreme Contamination / / / / / >5 Extreme Contamination / / / 1.5Data Processing and Graphical Representation Data processing and graphical representation were performed using ArcGIS 10.7 (Geostatistical Analyst module) to generate the regional location map of the study area and spatial distribution maps of sediment heavy metals through Kriging spatial interpolation. IBM SPSS Statistics 26 was utilized for mathematical statistical analysis, cluster analysis, principal component analysis (PCA), and statistical evaluation of potential ecological risks, with Pearson correlation coefficients applied to explore relationships among heavy metal indicators. Origin 2022 was employed to plot box diagrams for the geoaccumulation index ( I geo ) and enrichment factor (EF), as well as to visualize the source contribution profiles of heavy metals in sediments. 2 Results and Discussion 2.1 Statistical Characteristics of Heavy Metals in Sediments 2.1.1 Concentration Characteristics of Heavy Metals Statistical analysis of eight heavy metals in Dongping Lake sediments using IBM SPSS Statistics 26 revealed that the measured concentrations (or transformed values) of these elements generally conformed to a normal distribution. Consequently, the mean concentrations are representative for statistical characterization. Based on the 1990 Shandong Province Soil Element Background Values (published by the China Geological Environmental Monitoring Center), the analysis results (Fig. 2 ) demonstrate that the average concentrations of eight heavy metals in Dongping Lake sediments are 19.21, 107.32, 32.87, 29.83, 0.20, 0.04, 27.47, and 70.32 mg/kg for As, Zn, Cu, Ni, Cd, Hg, Pb, and Cr, respectively, significantly exceeding the provincial soil background levels. For example, concentrations of As (19.21 vs. 8.7 mg/kg), Zn (107.32 vs. 40.0 mg/kg), and Cd (0.20 vs. 0.078 mg/kg) markedly surpass regional background values. Furthermore, the mean concentrations of As, Zn, Cu, Ni, Cd, Hg, Pb, and Cr are 2.21, 2.68, 1.51, 1.49, 2.56, 2.50, 1.13, and 1.08 times their respective background values, with maximum concentrations of Cd, Hg, and As exceeding 3 times the background levels. Additionally, the exceedance rates (relative to background values) reach 100% for As, Zn, Ni, Cd, and Hg, while Cu, Pb, and Cr exhibit exceedance rates of 95%, 80%, and 50%, respectively. These results not only confirm the long-term accumulation of heavy metals in lake sediments as a typical consequence of soil erosion and deposition but also underscore the persistent ecological pressure and potential risks faced by Dongping Lake. By comparing with historical data from 2009 (surface sediment averages, Reference 2) and 2015 (heavy metal distribution characteristics and ecological risk assessments, Reference 3), this study reconstructed the temporal trends of heavy metal concentrations in Dongping Lake sediments from 1990 to 2024 (Fig. 3 , Table 2 ). Analysis reveals that arsenic (As) and zinc (Zn) concentrations exhibit a steady upward trend, strongly correlated with intensified industrial activities and rapid urbanization. Chromium (Cr) and lead (Pb) concentrations declined between 1990 and 2009 but showed a significant rebound in 2014 and 2024, indicating potential new pollution sources or enhanced natural enrichment processes in the Dongping Lake and associated river systems. Nickel (Ni) and mercury (Hg) concentrations followed a rise-then-decline pattern, reflecting the effectiveness of recent environmental remediation efforts. Copper (Cu) displayed complex fluctuations, with an initial increase followed by a decrease and subsequent minor rebound, likely linked to dynamic lake processes and variable anthropogenic pressures. These findings suggest that while heavy metal concentrations in Dongping Lake sediments have stabilized overall, the lake—as a depositional sink—continues to experience long-term accumulation effects, posing persistent ecological challenges. Therefore, urgent analysis of pollution sources and potential ecological risks is essential to guide comprehensive water management and ecological conservation strategies. 2.1.2 Spatial Distribution Characteristics of Heavy Metals in Sediments The Coefficient of Variation (CV) serves as a statistical measure to quantify the dispersion of heavy metal concentrations in Dongping Lake sediments, indirectly reflecting their spatial heterogeneity. Scholars have suggested that when CV exceeds 20%, anthropogenic activities become the primary driving factor behind the spatial variability of heavy metals in sediments [ 27 – 30 ] . As shown in Table 2 , among the eight heavy metals in Dongping Lake sediments, only Ni (15.2%) and Pb (18.7%) exhibited coefficients of variation (CV) below 20%, while the CV values of other metals exceeded 20%. Notably, Zn (32.5%) and Cr (34.1%) displayed moderate variability (CV > 30%) [ 2 ] . These results indicate that the spatial distributions of As, Zn, Cu, Cd, Hg, and Cr are strongly influenced by anthropogenic activities or hydrodynamic disturbances, serving as primary drivers of their heterogeneous spatial patterns. The spatial distribution maps of heavy metals (Fig. 4 ) reveal significant heterogeneity. Arsenic (As), mercury (Hg), copper (Cu), and lead (Pb) exhibit highly consistent spatial patterns, with elevated concentrations predominantly clustered in the central, central-western, and southern regions of Dongping Lake. This distribution correlates with multiple factors: (1) The inflow of the Liuchang River contributes to heavy metal accumulation, leading to deposition in the central-western and southern lake areas; (2) Frequent anthropogenic activities (e.g., agriculture, livestock farming, and industrial/mining operations) in southern villages and towns serve as major pollution sources; (3) High concentrations in the central region may stem from historical residential areas on the lake’s islands and slow hydrodynamic conditions, facilitating long-term pollutant accumulation. In contrast, lower concentrations of As, Hg, Cu, and Pb are observed near the Dawen River inlet and western lake areas, indicating minimal heavy metal inputs from the Dawen River. Zinc (Zn), nickel (Ni), and Pb show elevated levels primarily in the central and southwestern regions, likely linked to transportation corridors and agricultural non-point source pollution in the south [31, 32]. Notably, the South-to-North Water Diversion inlet exhibits lower concentrations, suggesting dilution effects from water transfer and associated hydrodynamic disturbances. Additionally, the northern and eastern regions display low heavy metal concentrations, attributed to thinner Quaternary sediment layers and stronger hydrodynamic scouring, which accelerate sediment turnover; The spatial distribution patterns of Cd and Cr exhibit distinct heterogeneity. Cd concentrations are predominantly elevated in the northern and western regions, while Cr hotspots cluster near the Dawen River inlet, reflecting divergent pollution sources for these two elements. The high Cd levels likely originate from agricultural pollution (e.g., fertilizer and pesticide use), whereas Cr enrichment is attributed to soil leaching in the Dawen River catchment and inputs from industrial wastewater and domestic sewage along its course. Overall, heavy metal concentrations are generally higher in the central and southern regions of Dongping Lake, driven by intensive human activities (e.g., industrial discharges) and inputs from the Liuchang River. Lower concentrations in the northern and eastern regions are attributed to minimal anthropogenic interference and dilution effects from the Dawen River. Elevated levels in the central area correlate with historical residential zones on the lake’s islands, sluggish hydrodynamic conditions, and severe algal aggregation, which collectively hinder pollutant dispersion and promote accumulation. Table 2 Statistical Summary of Heavy Metals in Dongping Lake Sediments Statistic parameters(n = 20) As(mg/kg) Zn(mg/kg) Cu(mg/kg) Ni(mg/kg) Cd(mg/kg) Hg(mg/kg) Pb(mg/kg) Cr(mg/kg) MIN 9.17 72.5 18.9 23.3 0.081 0.018 19 40 MAX 27.8 204 53.9 38 0.3 0.055 34.8 135 AVG 19.21 107.32 32.87 29.83 0.20 0.04 27.47 70.32 SD 4.21 38.83 8.55 3.54 0.06 0.01 4.03 22.15 CV 21.92 36.19 26.03 11.86 29.18 26.40 14.68 31.50 Reference value 1 8.7 40.0 21.7 20.0 0.078 0.016 24.3 65.2 Reference value 2 10.06 74.9 37.9 30.8 0.83 0.03 13.8 51.30 Reference value 3 11.4 100.5 23 42.0 0.32 0.07 25 55 * Reference 1 : Background values of heavy metals in Shandong Province soils ( China Geological Environmental Monitoring Center, 1990 ); Reference 2 : Average heavy metal concentrations in Dongping Lake surface sediments ( 2009 ) [ 18 ] and comparative data on elemental composition in sediments from the Yangtze River and Yellow River estuaries ( 2008 ) [ 31 ] ; Reference 3 : Studies on heavy metal distribution characteristics and ecological risk assessment in Dongping Lake sediments ( 2014 ) [ 32 ] and research on typical heavy metal pollution in Dongping Lake sediments ( 2015 ) [ 33 ] . 2.2Source Apportionment of Heavy Metals in Sediments 2.2.1 Cluster Analysis Cluster analysis groups heavy metals with similar origins into clusters and separates elements with distinct pollution sources. A shorter clustering distance indicates closer source relationships between elements [ 34 ] , To explore the interrelationships among heavy metals in Dongping Lake sediments, cluster analysis was performed on raw concentration data. Results (Fig. 5 ) reveal that the heavy metals in sediments can be categorized into three clusters: Subcluster 1: comprises two subclusters. The first subcluster includes As, Cu, and Hg, which exhibit close clustering distances and align with their spatial distribution patterns, confirming a common origin. Hg enrichment is primarily attributed to increased coal combustion for heating, where Hg released from coal burning is directly emitted into the environment and retained in surrounding soils via atmospheric deposition [ 35 ] , Subsequently, Hg accumulates in Dongping Lake through rainwater runoff and agricultural irrigation. Arsenic (As) is commonly associated with pollution sources such as metallurgical activities, pesticides/fertilizers, and livestock farming wastewater [ 28 ] ,indicating that these contaminants primarily originate from industrial discharges, domestic sewage, and agricultural non-point sources in surrounding villages; Subcluster 2: Zn, Ni, and Pb form another subcluster with close clustering distances, consistent with earlier assessments, indicating a common origin. Zn and Pb are recognized as traffic-related pollutants [ 34 , 36 ] ,: the combustion of leaded gasoline makes vehicle emissions a major source of Pb [ 37 ] , with accumulation strongly correlated to traffic density. Vehicle exhaust, brake wear, and tire abrasion also contribute to Zn enrichment in the environment [ 38 ] ,. Ni contamination is primarily influenced by metal processing and chemical industries (e.g., coking plant emissions) [ 39 ] . This cluster reflects combined impacts from traffic pollution, industrial activities, and agricultural practices. Cluster II: Cd. Cadmium (Cd) is recognized as a pollutant predominantly derived from agricultural activities, including the use of phosphate fertilizers, manure, and pesticides, representing a classic anthropogenic element introduced into ecosystems through human activities. Spatial distribution maps of Cd reveal that its high-concentration zones consistently overlap with areas of intensive agricultural and livestock farming [ 40 , 41 ] , confirming that this cluster is primarily driven by agricultural practices. Cluster III: Cr . Chromium (Cr) exhibits weak correlations with the other seven heavy metals, aligns with earlier spatial distribution analyses, and shows a low exceedance rate relative to background values, indicating that Cr enrichment is primarily linked to the natural soil background of the Dawen River catchment area. 2.2.2 Correlation Analysis Correlation analysis of heavy metals in Dongping Lake sediments is shown in Fig. 6 . As exhibits a highly significant positive correlation (P ≤ 0.001) with Cu and Hg, and Cu-Hg also shows a highly significant positive correlation, indicating that As, Cu, and Hg in sediments share common sources. Zn demonstrates a highly significant positive correlation with Ni and Pb, as does Ni-Pb, suggesting that Zn, Ni, and Pb originate from the same sources. Hg is significantly positively correlated with Pb, implying a shared origin for these two elements. Cr shows a highly significant negative correlation only with Cd, indicating an antagonistic interaction between Cr and Cd. Cr exhibits weak correlations with other heavy metals, and its mean enrichment factor (EF) of 1.01 further supports that Cr enrichment is primarily derived from natural soil parent material in the Dawen River basin. 2.2.3 Principal Component Analysis (PCA) Principal Component Analysis (PCA) reduces dimensionality by identifying composite variables that represent the majority of information from original variables, serving as an effective method to trace heavy metal sources in sediments [ 42 ] . PCA results for heavy metals in Dongping Lake sediments (Fig. 7 ) were analyzed to explore potential pollution sources. Prior to PCA, Kaiser-Meyer-Olkin (KMO) and Bartlett’s sphericity tests were conducted to ensure data reliability and suitability [ 43 ] .The KMO statistic (0.688) and Bartlett’s test significance probability (0.000) confirmed the dataset’s appropriateness for factor analysis [ 16 ] .Using varimax rotation on the component matrix, three principal components with eigenvalues > 1 were extracted (Table 3 ), accounting for 89.22% cumulative variance contribution, thereby sufficiently explaining the original variable information. Table 3 Principal Component Analysis (PCA) of Heavy Metals in Dongping Lake Sediments Element PC1 PC2 PC3 As 0.88 0.04 0.19 Zn 0.08 0.95 -0.08 Cu 0.85 0.42 -0.13 Ni 0.41 0.83 0.12 Cd 0.23 0.22 0.88 Hg 0.87 0.28 0.22 Pb 0.67 0.71 0.09 Cr 0.00 0.17 -0.94 Variance Percentage (%) 54.62 22.14 12.46 Cumulative Contribution Rate (%) 54.62 76.76 89.22 PC1 : The first principal component (PC1) accounts for 54.62% of the total variance, with As, Cu, and Hg as the primary loading factors (loadings: 0.88, 0.85, and 0.87, respectively). These three heavy metals exhibit similar spatial distributions in high-concentration zones and strong inter-correlations, consistent with prior correlation analyses, confirming their common origin. Previous statistical analyses revealed high coefficients of variation (CV) for these metals, collectively indicating that PC1 is predominantly influenced by anthropogenic activities. Spatial distribution patterns of As, Cu, and Hg show enrichment in the central-southern regions of Dongping Lake. These elements are commonly associated with metallurgical activities, agricultural practices, and livestock farming. Investigations indicate abundant iron ore resources in the central-eastern Dongping County and extensive agricultural areas and livestock farming zones in the western and southern regions. Under hydrodynamic influences within the lake, these anthropogenic inputs have shaped the current spatial distribution of As, Cu, and Hg. Thus, PC1 is interpreted as representing industrial and agricultural sources. PC2: The second principal component (PC2) explains 22.14% of the total variance, with Zn, Ni, and Pb as dominant loading factors (loadings: 0.95, 0.83, and 0.71, respectively). These metals share similar spatial distribution patterns, characterized by elevated concentrations in the southwestern region. Zn is typically linked to vehicle exhaust, tire wear, and certain industrial processes [ 44 ] ; Ni enrichment arises from metal processing, chemical industries, and traffic-related pollution; while Pb shows a relatively high natural background level based on Shandong Province soil reference values, suggesting a natural origin. Field surveys further indicate that the western region, adjacent to National Highway 220 and Provincial Highway S326, lacks significant industrial or mining activities. Thus, PC2 is interpreted as representing traffic-related sources and natural background contributions. PC3: The third principal component (PC3) contributes 12.46% of the total variance and is primarily associated with Cd and Cr, aligning with correlation analysis results. Cr exhibits high background levels in Shandong Province soils, indicating a predominant natural origin. In contrast, Cd is a hallmark element of agricultural pollution [ 45 ] , with sources including high-cadmium phosphate fertilizers, improper application of livestock manure, and aquaculture feed additives [ 46 – 47 ] . Thus, PC3 reflects combined contributions from agricultural activities and natural sources. Synthesis of Findings, Heavy metal pollution in Dongping Lake sediments primarily originates from local industrial activities (e.g., iron ore mining, coal combustion, and cement production) and agricultural practices. Traffic-related emissions also contribute to specific elements such as lead (Pb), underscoring the importance of addressing these combined anthropogenic and natural sources in environmental management strategies. 2.2.4 PMF Source Apportionment Quantitative source apportionment of heavy metals in the study area was performed using Positive Matrix Factorization (PMF). Following the EPA PMF 5.0 User Guide, concentration data and uncertainty values were input, with the signal-to-noise ratio (S/N) classifying all eight heavy metals as "Strong". The model was iterated 20 times with factor numbers set between 3 and 5. By comparing Q Robust /Q true values across different factor counts, the optimal fit was achieved with 3 factors Q Robust /Q true =17.4 for all), where residuals for all elements fell within [-3, 3] and followed a normal distribution, confirming model stability. Source apportionment results yielded R² values of 0.75 (As), 0.69 (Zn), 0.92 (Cu), 0.94 (Ni), 0.92 (Cd), 0.86 (Hg), 0.96 (Pb), and 0.63 (Cr), demonstrating strong explanatory power and effective interpretation of the original dataset. As shown in Fig. 8 , Factor 1 accounts for 38.4% of the total contribution, with primary loadings from Hg (46.11%), As (45.78%), and Cu (45.44%). Factor 1 aligns with the characteristic elements of PC1, confirming its identification as industrial and agricultural sources, with a total contribution of 38.4%. Factor 2 contributes 32.1%, dominated by Cr (62.81%). Given Cr’s high proportion and its previously established linkage to natural background levels, Factor 2 is classified as a natural source, contributing 32.10% overall. Factor 3 explains 29.6% of the variance, primarily driven by Cd (60.24%). This aligns with PC3, which associates Cd with agricultural pollution, thus identifying Factor 3 as an agricultural source with a total contribution of 29.6%. Comprehensive analyses—including cluster analysis, correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF)—reveal that heavy metal pollution in Dongping Lake sediments originates from three primary sources: industrial activities, agricultural practices, and natural background. Both PCA and PMF results consistently demonstrate the spatial distribution patterns and source contributions of heavy metals. Industrial and agricultural sources (PC1/Factor 1), dominated by As, Cu, and Hg, account for the largest contribution (38.4%), with elevated concentrations clustered in the central-southern regions of Dongping Lake. These hotspots correlate with local iron ore mining, coal combustion, and intensive agricultural/livestock activities, confirming that industrial processes (e.g., metallurgy, cement production) and agricultural non-point sources (e.g., fertilizer use, livestock farming) are key drivers. Traffic and natural sources (PC2), characterized by Zn, Ni, and Pb, contribute 32.1%, with higher concentrations in the southwestern region near National Highway 220 and Provincial Highway S326. This reflects impacts from vehicle emissions (e.g., exhaust, tire/brake wear) and natural background inputs, particularly for Pb, which has a high inherent soil background in Shandong Province. Agricultural and natural sources (PC3/Factor 2/Factor 3), linked to Cd (agricultural pollution from fertilizers, livestock manure) and Cr (natural soil parent material), explain 29.6% of the variance. Cd enrichment arises from phosphate fertilizers and aquaculture feed additives, while Cr levels align with geological background from the Dawen River basin. In summary, heavy metal accumulation in Dongping Lake sediments results from synergistic effects of industrial operations, agricultural practices, and traffic emissions, with natural background also playing a role. These findings provide a scientific basis for targeted pollution control and ecological restoration strategies in the region. 2.3 Enrichment Characteristics of Heavy Metals in Sediments 2.3.1 Enrichment Characteristics of Heavy Metals Analysis of enrichment factor (EF) boxplots for eight heavy metals in Dongping Lake sediments (Fig. 9 ) reveals the following mean EF values: Cd (2.35) > As (1.97) > Hg(1.92) > Zn(1.61) > Cu(1.30) > Ni(1.11) > Cr (1.01) > Pb (0.41). Pb shows no significant enrichment, while other elements exhibit varying degrees of enrichment influenced by anthropogenic activities. Cd displays moderate enrichment (EF > 2), with 65% of sampling points classified as moderately enriched, 30% as slightly enriched, and only 5% showing no enrichment. As, Hg, Zn, Cu, Ni, and Cr are slightly enriched (1 < EF < 2), though As (45% moderately enriched), Hg (40%), and Zn (20%) also show localized moderate enrichment. Cr has an EF close to 1, with only 30% of sampling points slightly enriched, suggesting its enrichment primarily stems from natural soil parent material rather than anthropogenic inputs. Overall, EF analysis indicates that anthropogenic activities significantly influence all heavy metals except Pb, with Cd exhibiting the most pronounced enrichment. In contrast, Cr accumulation is predominantly governed by geogenic processes. The interplay between human activities and natural background shapes the spatial distribution of heavy metals in Dongping Lake sediments. 2.3.2Geoaccumulation Index I geo of Heavy Metals in Sediments The geoaccumulation index ( I geo ) analysis of heavy metals in sediments (Fig. 10 ) reveals the following mean I geo values in descending order: As (1.11) > Zn (1.02) > Cu (0.98) > Ni = Hg (0.95) > Cr (0.89) > Cd (0.79) > Pb (0.72). As and Zn exhibit the highest I geo values, averaging 1.11 and 1.02, respectively, indicating moderate pollution levels. Among all sampling sites, 95% of As and 50% of Zn samples fall into the moderately polluted category. Cu, Ni, Hg, and Cr show mean I geo values near 1, suggesting a transition between unpolluted and moderately polluted states. However, 75% (Cu), 95% (Ni), 90% (Hg), and 85% (Cr) of sampling points remain unpolluted, implying these elements are primarily influenced by geological background, with minor anthropogenic contributions. Cd and Pb display lower I geo values (0.79 and 0.72, respectively), also within the unpolluted to moderately polluted transition range. Notably, 10% of Cd samples show moderate pollution, while Pb shows no significant pollution across the lake, likely due to natural geological influences from sediment parent rocks. In summary, As is the predominant pollutant in Dongping Lake sediments, followed by Zn. Cu, Ni, Hg, and Cr exhibit localized moderate pollution, further corroborating anthropogenic contributions. The unpolluted status of Pb aligns with its natural geological background. 2.4 Potential Ecological Risk Assessment The analysis of potential ecological risk indices \(\:{E}_{r}^{i}\) and integrated ecological risk index (RI) for heavy metals in Dongping Lake sediments (Table 4 ) reveals the following: the mean \(\:{E}_{r}^{i}\) values follow the order Hg (80.53) > Cd (72.70) > As (20.65) > Cu (6.85) > Ni (5.78) > Pb (2.16) > Cr (2.13) > Zn (1.69), which aligns completely with results from Ai [ 19 ] et al. (2019 study on heavy metal distribution and risk assessment in Dongping Lake water and sediments). Notably, all \(\:{E}_{r}^{i}\) values have decreased compared to the 2019 data, indicating overall ecological improvement in lake sediments under policy-driven environmental protections. According to ecological risk criteria (Table 2 ), As, Zn, Cu, Ni, Pb, and Cr fall into the low-risk category \(\:{E}_{r}^{i}\) 20%, suggesting localized anthropogenic influences. In contrast, Hg (mean \(\:{E}_{r}^{i}\) = 80.53) and Cd (72.70) pose moderate-to-high risks, with all sampling points classified as moderate or higher. Maximum \(\:{E}_{r}^{i}\) values reach 107.14 (Hg) and 115.79 (Cd), with 50% of Hg and 30% of Cd samples in the high-risk range. Both metals show high spatial variability (CV: 26.40% for Hg, 29.17% for Cd), reflecting significant concentration fluctuations and elevated localized risks. Hg and Cd dominate total ecological risk, contributing 41.83% and 37.77% to RI, respectively, far exceeding other elements, thus emerging as the primary risk drivers requiring prioritized mitigation efforts. Analysis of the integrated potential ecological risk index (RI) for Dongping Lake sediments (Table 5 ) reveals that only sampling site DN06 falls within the low-risk category, while all other sites exhibit moderate-risk levels, with DN07 and DN17 showing the highest RI values, highlighting severe heavy metal pollution in these areas. Spatial distribution analysis of Hg, Cr, and overall RI (Fig. 11 ) further details the risk patterns: Hg-related risks are concentrated in the central and southeastern regions of the lake, classified as high-risk zones, with only a small low-risk area near the Dawen River estuary. Cr risks display a westward-decreasing gradient, with high-risk zones primarily in the central and northern regions of the lake. Combined RI distribution indicates elevated ecological risks in the central and southern regions, where heavy metal pollution poses significant ecological threats, necessitating prioritized pollution control and ecological remediation. Although the Dawen River estuary in the central-eastern region shows relatively lower pollution, sustained monitoring of moderate-risk areas is critical to prevent further environmental degradation. Table 4 Statistical Summary of \(\:{E}_{r}^{i}\) and \(\:RI\) for Heavy Metals in Dongping Lake Sediments Element \(\:{E}_{r}^{i}\) \(\:RI\) As Zn Cu Ni Cd Hg Pb Cr Maximum 29.89 3.21 11.23 7.36 107.14 115.79 2.74 4.09 269.70 Minimum 9.86 1.14 3.94 4.52 28.93 37.89 1.50 1.21 91.96 Mean 20.65 1.69 6.85 5.78 72.70 80.53 2.16 2.13 192.49 SD 4.53 0.61 1.78 0.69 21.21 21.26 0.32 0.67 41.18 CV (%) 21.92 36.14 26.01 11.85 29.17 26.40 14.64 31.48 21.39 Contribution to RI (%) 10.73 0.88 3.56 3.00 37.77 41.83 1.12 1.11 100 Table 5 Statistical Summary of RI for Sampling Sites in Dongping Lake Sediments Sampling Site ID RI Sampling Site ID RI DN01 197.76 DN11 167.66 DN02 211.76 DN12 240.17 DN03 185.51 DN13 201.56 DN04 200.11 DN14 120.80 DN05 216.05 DN15 219.51 DN06 91.96 DN16 221.21 DN07 269.70 DN17 247.07 DN08 151.32 DN18 174.16 DN09 184.11 DN19 182.13 DN10 189.62 DN20 177.61 3. Conclusions (1)The study reveals that the average concentrations of most heavy metals in Dongping Lake sediments are significantly higher than the soil background values of Shandong Province, indicating widespread enrichment of these metals. Coefficients of variation (CV) suggest that spatial distributions of all metals, except Ni and Pb, are strongly influenced by anthropogenic activities. Temporal analysis shows an overall stabilization or improvement trend in sediment heavy metal levels, though As and Zn concentrations exhibit a yearly increase, correlated with intensified industrialization and urbanization. In contrast, Cr and Pb concentrations rebounded after initial declines, primarily due to natural background enrichment processes, while Cd, Ni, and Hg followed a "rise-then-fall" pattern, reflecting phased achievements in environmental remediation. Spatially, high-concentration zones are clustered in the central, central-western, and southern regions of the lake, closely linked to inflows from the Liuchang River, agricultural activities, and industrial operations. Lower concentrations in the northern and eastern regions are attributed to reduced human disturbance and natural purification processes. (2)The analysis reveals that heavy metals in Dongping Lake sediments originate predominantly from industrial activities, agricultural practices, traffic emissions, and natural background sources. Cluster analysis and Principal Component Analysis (PCA) demonstrate that PC1 (As, Cu, Hg) is primarily associated with industrial operations (e.g., metal smelting, coal combustion) and agricultural inputs (e.g., pesticide/fertilizer use), while PC2 (Zn, Ni, Pb) reflects contributions from traffic-related pollution (vehicle exhaust, tire wear) and natural geological processes (e.g., Pb enrichment from soil parent material). PC3 (Cd, Cr) is linked to agricultural activities (phosphate fertilizers, livestock manure) and natural soil formation (Cr derived from parent rock weathering). Positive Matrix Factorization (PMF) quantifies these sources, with industrial sources contributing 38.4%, agricultural sources 29.6%, and natural background 32.1%, confirming the synergistic effects of anthropogenic and geogenic factors on sediment contamination. (3)The analysis of heavy metal enrichment characteristics and geo-accumulation index in Dongping Lake sediments reveals that Cd exhibits the most significant enrichment level, showing moderate enrichment primarily influenced by anthropogenic activities. As, Hg, Zn, Cu, Ni, and Cr display slight enrichment, with Cr demonstrating the weakest enrichment, while Pb shows no enrichment. Geo-accumulation index results indicate that As and Zn are in a moderately polluted state, with As accounting for 95% of moderate pollution cases and Zn for 50%. Cu, Ni, Hg, Cr, Cd, and Pb fall within the transition range from unpolluted to moderately polluted, mainly controlled by geological background, though anthropogenic activities also contribute to their enrichment to some extent. (4)The potential ecological risks of heavy metals in Dongping Lake sediments exhibit significant spatial variations. The potential ecological risk assessment reveals that Hg and Cd are the primary contributors to ecological risks, with their mean values significantly higher than those of other heavy metals. In some areas, they reach strong pollution levels, posing a substantial threat to the ecological environment. Spatially, the central and southeastern regions show higher ecological risks, particularly at sampling sites DN07 and DN17, where the highest potential ecological risk indices (RI) were recorded. This indicates particularly severe heavy metal pollution in these areas, necessitating prioritized implementation of pollution control and ecological restoration measures. The study indicates a gradual improvement trend in the ecological environment quality of heavy metals in Dongping Lake sediments. Through comprehensive analysis including statistical evaluation, source apportionment, enrichment characteristics, and ecological risk assessment, heavy metals were found to originate primarily from industrial activities (38.4%), agricultural practices (29.6%), and natural background sources (32.1%). Specifically, As, Cu, and Hg show strong correlations with industrial and agricultural activities, Zn and Ni are influenced by transportation and industrial emissions, Cd mainly stems from agricultural sources, while Cr and Pb predominantly derive from natural geological backgrounds. Notably, Hg and Cd remain the primary contributors to ecological risks, with elevated risks observed in central and southeastern regions. Sampling sites DN07 and DN17 exhibit the most prominent potential ecological risk indices (RI), demanding prioritized implementation of pollution control and ecological restoration measures. Overall, heavy metal pollution in Dongping Lake sediments results from the combined effects of anthropogenic activities and natural background, underscoring the urgent need for scientific prevention strategies and integrated management to achieve sustainable development. Declarations Author Contribution: †These authors contributed equally to this work and share first authorship. K.M. and X.W. wrote the main manuscript text, while H.A. and B.Y. prepared Figures 1-11. All authors reviewed the manuscript. Author's Profile: Kuanzhen Mao（1989-）,Male, Senior engineer, Graduated from Shijiazhuang Economic College in 2015, majoring in geological engineering. Mainly engaged in water source survey, hydrogeological survey, environmental geological survey, groundwater pollution survey, geological exploration, geothermal survey. E-mail: [email protected] ; Xinfeng Wang (1982–), Male, Senior Engineer, currently pursuing a Ph.D. in Hydrogeology. His main research focuses on hydrogeological surveys in bedrock mountainous areas and the integration of research results. E-mail: [email protected] Corresponding author: Hongyan An（1994-）,Male, Engineer, Graduated from China University of Geosciences (Wuhan) in 2024. majoring in Environmental Science and Engineering. Mainly engaged in water source survey. E-mail: [email protected] ; Baizhong Yan（1988-）, Professor, Ph.D., specializing in hydrogeology and geothermal resources research, with a focus on groundwater system modeling and sustainable utilization of geothermal energy. Email: [email protected] . Funded projects: Supported by the Open Project Program of Hebei Province Collaborative innovation center for sustainable utilization of water resources and optimization of industrial structure(No. SXTCX202407);Supported by Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes (No. SK202305KF10);Open Research Fund Program of the Hebei Provincial Research Center for Applied Technology of Ecological Environment Geology in Universities (No. JSYF-202306);China Geological Survey(No. DD20230505);Ministry of Natural Resources Provincial Cooperation Project(No. 2024ZRBSHZ028). Availability of Data and Materials The original project data involved in this study constitutes proprietary foundational information obtained by the research team during project implementation. Subject to national scientific research confidentiality regulations and project collaboration agreements, these raw datasets are not currently included in public sharing protocols. 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3","display":"","copyAsset":false,"role":"figure","size":195282,"visible":true,"origin":"","legend":"\u003cp\u003eHeavy Metal Concentrations and Temporal Trends in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote:Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/e3de3e11c44092165885ef73.png"},{"id":79600758,"identity":"9a70e844-02d4-4d15-b44d-38cc2683310e","added_by":"auto","created_at":"2025-03-31 15:06:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90471,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of Eight Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote: Generated by ArcGIS 10.8 (Software available at https://www.arcgis.net.cn/)\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/44361ca3929f81893386b694.jpeg"},{"id":79599288,"identity":"05c845b0-86bb-4ab8-963f-e2463041e653","added_by":"auto","created_at":"2025-03-31 14:50:15","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65156,"visible":true,"origin":"","legend":"\u003cp\u003eCluster Analysis of Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote:Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/2361a6de2eb2abbf925b1ec6.jpeg"},{"id":79600755,"identity":"134b1058-95f4-4008-a960-985dd74946f8","added_by":"auto","created_at":"2025-03-31 15:06:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":140457,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis among heavy metals of sediment\u003c/p\u003e\n\u003cp\u003eNote:Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/fbca0823823a7981ee03bc23.png"},{"id":79598811,"identity":"98e0ca61-1a53-4f55-b203-81fea17ff3ef","added_by":"auto","created_at":"2025-03-31 14:42:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":212465,"visible":true,"origin":"","legend":"\u003cp\u003ePMF-Based Source Contributions of Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote: Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/563c3c4dda3fb2a4197b3eee.png"},{"id":79598797,"identity":"2af301a3-75e2-4607-bce0-edfd2a07c46e","added_by":"auto","created_at":"2025-03-31 14:42:15","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":101794,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of Enrichment Factors (EF) for Eight Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote:Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/5e50e9e059ebe52c714610e5.jpeg"},{"id":79599295,"identity":"d9e03204-b817-4746-b593-63ea8037ec44","added_by":"auto","created_at":"2025-03-31 14:50:15","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":111663,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of Geoaccumulation Index for Eight Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote:Created by OriginPro 2025 (Software accessible at https://www.originlab.com/)\u003c/p\u003e","description":"","filename":"image13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/fb4eddee3c151b58d557e15e.jpeg"},{"id":79599299,"identity":"03febcc2-51cb-4a03-b431-e37955892af7","added_by":"auto","created_at":"2025-03-31 14:50:15","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":172986,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of the RI in Dongping Lake Sediments\u003c/p\u003e\n\u003cp\u003eNote: Generated by ArcGIS 10.8 (Software available at https://www.arcgis.net.cn/)\u003c/p\u003e","description":"","filename":"image14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/50eab76bf7f9e6a8b4dde724.jpeg"},{"id":90344778,"identity":"50d92f5b-4ce5-4a8a-a661-daae11b5f9d9","added_by":"auto","created_at":"2025-09-01 15:59:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2682598,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6195487/v1/6eb78f4a-f089-4018-8f0a-a1bd460639d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSource Apportionment and Ecological Risk Assessment of Heavy Metals in Sediments of Dongping Lake Based on PCA-PMF Model\u003c/p\u003e","fulltext":[{"header":"0 Introduction","content":"\u003cp\u003eHeavy metals, characterized by their ubiquitous sources in ecological systems, persistent migration across biological communities, and intrinsic properties such as non-degradability, bioaccumulation, and carcinogenicity, pose significant challenges to environmental protection authorities due to their recalcitrance to remediation. Contamination by multiple heavy metals, particularly arsenic (As), zinc (Zn), copper (Cu), nickel (Ni), cadmium (Cd), mercury (Hg), lead (Pb), and chromium (Cr), imposes severe threats to both ecological systems and human health\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Lakes, particularly urban lakes, serve as unique natural and cultural landscapes in cities while also fulfilling ecological regulation functions\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the rapid development of the economy, society, and agriculture, lakes, serving as discharge zones for rivers and groundwater, receive industrial, agricultural, and domestic wastewater from within the watershed. Large quantities of pollutants are discharged into lake basins in various forms and subsequently accumulate in lake water bodies.\u003c/p\u003e \u003cp\u003eSediments, as an essential component of lake water bodies\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e,act as a critical medium for the accumulation and enrichment of various pollutants, with heavy metal concentrations significantly exceeding local background levels\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Under the combined influence of hydrodynamic processes and biotic uptake, heavy metal pollutants accumulated in sediments are released into water bodies, leading to secondary pollution of lake water resources\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e,Concurrently, through mechanisms such as food chain amplification and bioaccumulation, these pollutants threaten human health and severely compromise the healthy functioning of lake ecosystems\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. As accumulation zones for heavy metal contamination, lake sediments have become a focal research area for domestic and international scholars in water resource investigations. Commonly employed methodologies include the Single Pollution Index, Integrated Pollution Index, Nemerow Index Method, Enrichment Factor Method, and Geoaccumulation Index Method\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Foreign scholars, such as Proshad et al.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e applied the Enrichment Factor Method to analyze the sources of surface sediment distribution in the Rupsa River of Bangladesh, while Wijesiri et al. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003einvestigated the migration and accumulation processes of heavy metals between sediments and water bodies in urban rivers. Domestic scholars, such as Sun Bowen\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e、Zhang Chuang\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e、and Wang Henian\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e evaluated the distribution characteristics of heavy metal concentrations in sediments from Baiyangdian Lake, Hengshui Lake, an urban lake in Jiangxi Province, and Erhai Lake using the Single Pollution Index Method, Nemerow Pollution Index Method, and Pollution Load Index, respectively. Meanwhile, researchers in China have applied the Geoaccumulation Index Method, Enrichment Factor Method, and Potential Ecological Risk Index Method to analyze heavy metal contamination risks and sources in surface water sediments from urban lakes in Ezhou City, Hubei Province\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, Dachaidan Salt Lake\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, and urban rivers in Jiangsu Province\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, most studies remain largely confined to statistical characterization and risk assessment of heavy metals or can only qualitatively identify primary pollutants and their sources, failing to quantitatively assess the specific contribution ratios of individual contaminants\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDongping Lake, situated at the border of Henan and Shandong provinces, is a typical shallow inland lake in the eastern plains of China. Serving as a crucial flood detention zone in the Yellow River Basin and a regulating reservoir for the Eastern Route of the South-to-North Water Diversion Project, the heavy metal characteristics of its sediments have garnered significant attention from researchers. Scholars such as Ge Huimin\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e,Zhang Ju\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e,and Ai Liuhuan\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e have evaluated the distribution patterns and ecological risks of heavy metals in Dongping Lake sediments. However, most studies have focused on analyzing the spatial distribution characteristics and ecological health risks of heavy metals, with relatively limited sampling points. This study comprehensively analyzed the spatial distribution characteristics of eight typical heavy metals in Dongping Lake sediments using the enrichment factor method, geoaccumulation index, and potential ecological risk assessment. Furthermore, cluster analysis, correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) were employed to conduct qualitative analysis of the enrichment mechanisms of these heavy metals, while quantitatively assessing their respective contamination contributions. The findings provide critical data support for the prevention and control of heavy metal pollution, the formulation of monitoring strategies, and ecological risk management in the Dongping Lake watershed.\u003c/p\u003e"},{"header":"1 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Study Area\u003c/h2\u003e\n \u003cp\u003eDongping Lake is located in the western part of Dongping County, Tai\u0026rsquo;an City, Shandong Province, at the lower reaches of the Dawen River near its confluence with the Yellow River, spanning 116\u0026deg;00\u0026prime;-116\u0026deg;30\u0026prime;E and 35\u0026deg;30\u0026prime;-36\u0026deg;20\u0026prime;N (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for specific location). Bordered by the Grand Canal to the west, connected to the Daqing River in the east, and linked to the Yellow River in the north, Dongping Lake has a perennial water surface area of approximately 209 km\u0026sup2;, making it the second-largest freshwater lake in Shandong Province and a typical shallow lake in eastern China\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.As a critical component of the Eastern Route of the South-to-North Water Diversion Project, Dongping Lake serves as both a key water conveyance channel and a regulating reservoir. It is the highest-altitude regulating reservoir within Shandong Province for this project. Situated among Dongping, Liangshan, and Wensheng counties, the lake is a perennially regulated, gourd-shaped shallow lake. The northern section comprises the narrow Xiaoqing River, while the southern section forms a broad lake basin with a well-developed hydrological network. With a documented human activity history spanning 4,500 years, Dongping Lake integrates flood detention, drought resistance, water storage, flow regulation, navigation, and tourism. Its water quality directly impacts the implementation of the South-to-North Water Diversion Project and the ecological and economic development of adjacent regions, positioning it as a vital node for ecological security in Shandong Province.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.2 Sample Collection and Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2.1Sample Collection\u003c/h2\u003e\n \u003cp\u003eFollowing the Lake Sediment Survey Specifications and Technical Guidelines for Heavy Metal Pollution Assessment in Sediments (DB37/T4471-2021), sampling sites were strategically designed to avoid areas with intense hydrodynamic fluctuations or significant anthropogenic disturbances. A grid-based comprehensive layout was adopted based on the Dongping Lake watershed morphology and protected area boundaries.Prior to sampling, precise latitude and longitude coordinates were recorded using an SR6 Pro RTK GPS system. Surface sediment samples (0\u0026ndash;20 cm depth) were collected using a grab sampler. To minimize sampling errors, three parallel samples were collected at each site, homogenized, and sieved to remove gravel, plant/animal debris, and sediment in direct contact with the sampler\u0026rsquo;s inner walls. Each sample (0.5 kg) was sealed in polyethylene plastic bags and stored at 4\u0026deg;C.A total of 20 sediment sampling sites were established across the study area (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with sample collection completed on June 14, 2024.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.2.2 Analytical Testing\u003c/h3\u003e\n\u003cp\u003eThe concentrations of Zn, Cu, Ni, Cd, and Pb in laboratory samples were determined using Closed-Acid Digestion-Inductively Coupled Plasma Mass Spectrometry (ICP-MS) with detection limits of 0.5 mg/kg, 1.0 mg/kg, 2.0 mg/kg, 0.01 mg/kg, and 1.0 mg/kg, respectively, following the standards GB/T 14506.30\u0026ndash;2010 and DZ/T 0279.1\u0026ndash;2016.Cr was analyzed via Powder Pelletization-X-ray Fluorescence Spectrometry (XRF) with a detection limit of 0.01 mg/kg, in accordance with DZ/T 0279.13\u0026ndash;2016.As and Hg were measured using Hydride Generation-Atomic Fluorescence Spectrometry (HG-AFS) and Vapor Generation-Cold Vapor Atomic Fluorescence Spectrometry (CV-AFS), respectively, with detection limits of 0.2 mg/kg and 0.05 mg/kg, adhering to DZ/T 0279.17\u0026ndash;2016.\u003c/p\u003e\n\u003ch3\u003e1.3Source Apportionment Methods for Sediment Heavy Metals\u003c/h3\u003e\n\u003cp\u003eFor source identification, a combination of cluster analysis, correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) was applied. Cluster analysis and correlation analysis were employed to elucidate interrelationships among heavy metal elements. PCA facilitated data dimensionality reduction and extraction of dominant pollution sources, while PMF resolved source contributions through non-negative constrained matrix decomposition, thereby addressing the interpretive limitations of PCA \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e。\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e1.4 Heavy Metal Source Apportionment and Ecological Risk Assessment\u003c/h2\u003e\n \u003cp\u003eThree methods were applied: the Enrichment Factor (\u003cem\u003eEF\u003c/em\u003e) method, Geoaccumulation index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e), and Potential ecological risk assessment. The EF method quantifies the enrichment degree of heavy metals by calculating the ratio of their concentrations to environmental background values, typically using aluminum (Al) as the normalization element.\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The I\u003csub\u003egeo\u003c/sub\u003e evaluates contamination levels by comparing measured values to background values, with adjustments introduced via a correction factor\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The potential ecological risk index (\u003cem\u003eRI\u003c/em\u003e) integrates heavy metal concentrations, ecological sensitivity, and toxicological effects to classify sediment contamination levels and their associated ecological risks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The classification criteria for \u003cem\u003eEF\u003c/em\u003e, \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eRI\u003c/em\u003e are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe classification criteria for \u003cem\u003eEF\u003c/em\u003e, \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eRI\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnrichment Degree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{geo}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvaluation Levels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RI\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePotential Ecological Risk\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Enrichment\u003c/p\u003e\n \u003cp\u003e(No Contamination)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlight Enrichment (Slight Contamination)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo to Moderate Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026thinsp;~\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u0026thinsp;~\u0026thinsp;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;~\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Enrichment (Moderate Contamination)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026thinsp;~\u0026thinsp;160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u0026thinsp;~\u0026thinsp;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsiderable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Enrichment (Heavy Contamination)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate to Heavy Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u0026thinsp;~\u0026thinsp;320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtremely High Enrichment (Severe Contamination)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026thinsp;~\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy to Extreme Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtreme Contamination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.5Data Processing and Graphical Representation\u003c/h3\u003e\n\u003cp\u003eData processing and graphical representation were performed using ArcGIS 10.7 (Geostatistical Analyst module) to generate the regional location map of the study area and spatial distribution maps of sediment heavy metals through Kriging spatial interpolation. IBM SPSS Statistics 26 was utilized for mathematical statistical analysis, cluster analysis, principal component analysis (PCA), and statistical evaluation of potential ecological risks, with Pearson correlation coefficients applied to explore relationships among heavy metal indicators. Origin 2022 was employed to plot box diagrams for the geoaccumulation index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e) and enrichment factor (EF), as well as to visualize the source contribution profiles of heavy metals in sediments.\u003c/p\u003e"},{"header":"2 Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Statistical Characteristics of Heavy Metals in Sediments\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.1 Concentration Characteristics of Heavy Metals\u003c/h2\u003e\n \u003cp\u003eStatistical analysis of eight heavy metals in Dongping Lake sediments using IBM SPSS Statistics 26 revealed that the measured concentrations (or transformed values) of these elements generally conformed to a normal distribution. Consequently, the mean concentrations are representative for statistical characterization.\u003c/p\u003e\n \u003cp\u003eBased on the 1990 Shandong Province Soil Element Background Values (published by the China Geological Environmental Monitoring Center), the analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrate that the average concentrations of eight heavy metals in Dongping Lake sediments are 19.21, 107.32, 32.87, 29.83, 0.20, 0.04, 27.47, and 70.32 mg/kg for As, Zn, Cu, Ni, Cd, Hg, Pb, and Cr, respectively, significantly exceeding the provincial soil background levels. For example, concentrations of As (19.21 vs. 8.7 mg/kg), Zn (107.32 vs. 40.0 mg/kg), and Cd (0.20 vs. 0.078 mg/kg) markedly surpass regional background values. Furthermore, the mean concentrations of As, Zn, Cu, Ni, Cd, Hg, Pb, and Cr are 2.21, 2.68, 1.51, 1.49, 2.56, 2.50, 1.13, and 1.08 times their respective background values, with maximum concentrations of Cd, Hg, and As exceeding 3 times the background levels. Additionally, the exceedance rates (relative to background values) reach 100% for As, Zn, Ni, Cd, and Hg, while Cu, Pb, and Cr exhibit exceedance rates of 95%, 80%, and 50%, respectively. These results not only confirm the long-term accumulation of heavy metals in lake sediments as a typical consequence of soil erosion and deposition but also underscore the persistent ecological pressure and potential risks faced by Dongping Lake.\u003c/p\u003e\n \u003cp\u003eBy comparing with historical data from 2009 (surface sediment averages, Reference 2) and 2015 (heavy metal distribution characteristics and ecological risk assessments, Reference 3), this study reconstructed the temporal trends of heavy metal concentrations in Dongping Lake sediments from 1990 to 2024 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Analysis reveals that arsenic (As) and zinc (Zn) concentrations exhibit a steady upward trend, strongly correlated with intensified industrial activities and rapid urbanization. Chromium (Cr) and lead (Pb) concentrations declined between 1990 and 2009 but showed a significant rebound in 2014 and 2024, indicating potential new pollution sources or enhanced natural enrichment processes in the Dongping Lake and associated river systems. Nickel (Ni) and mercury (Hg) concentrations followed a rise-then-decline pattern, reflecting the effectiveness of recent environmental remediation efforts. Copper (Cu) displayed complex fluctuations, with an initial increase followed by a decrease and subsequent minor rebound, likely linked to dynamic lake processes and variable anthropogenic pressures. These findings suggest that while heavy metal concentrations in Dongping Lake sediments have stabilized overall, the lake\u0026mdash;as a depositional sink\u0026mdash;continues to experience long-term accumulation effects, posing persistent ecological challenges. Therefore, urgent analysis of pollution sources and potential ecological risks is essential to guide comprehensive water management and ecological conservation strategies.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1.2 Spatial Distribution Characteristics of Heavy Metals in Sediments\u003c/h2\u003e\n \u003cp\u003eThe Coefficient of Variation (CV) serves as a statistical measure to quantify the dispersion of heavy metal concentrations in Dongping Lake sediments, indirectly reflecting their spatial heterogeneity. Scholars have suggested that when CV exceeds 20%, anthropogenic activities become the primary driving factor behind the spatial variability of heavy metals in sediments\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, among the eight heavy metals in Dongping Lake sediments, only Ni (15.2%) and Pb (18.7%) exhibited coefficients of variation (CV) below 20%, while the CV values of other metals exceeded 20%. Notably, Zn (32.5%) and Cr (34.1%) displayed moderate variability (CV\u0026thinsp;\u0026gt;\u0026thinsp;30%) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. These results indicate that the spatial distributions of As, Zn, Cu, Cd, Hg, and Cr are strongly influenced by anthropogenic activities or hydrodynamic disturbances, serving as primary drivers of their heterogeneous spatial patterns.\u003c/p\u003e\n \u003cp\u003eThe spatial distribution maps of heavy metals (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) reveal significant heterogeneity. Arsenic (As), mercury (Hg), copper (Cu), and lead (Pb) exhibit highly consistent spatial patterns, with elevated concentrations predominantly clustered in the central, central-western, and southern regions of Dongping Lake. This distribution correlates with multiple factors: (1) The inflow of the Liuchang River contributes to heavy metal accumulation, leading to deposition in the central-western and southern lake areas; (2) Frequent anthropogenic activities (e.g., agriculture, livestock farming, and industrial/mining operations) in southern villages and towns serve as major pollution sources; (3) High concentrations in the central region may stem from historical residential areas on the lake\u0026rsquo;s islands and slow hydrodynamic conditions, facilitating long-term pollutant accumulation. In contrast, lower concentrations of As, Hg, Cu, and Pb are observed near the Dawen River inlet and western lake areas, indicating minimal heavy metal inputs from the Dawen River. Zinc (Zn), nickel (Ni), and Pb show elevated levels primarily in the central and southwestern regions, likely linked to transportation corridors and agricultural non-point source pollution in the south [31, 32]. Notably, the South-to-North Water Diversion inlet exhibits lower concentrations, suggesting dilution effects from water transfer and associated hydrodynamic disturbances. Additionally, the northern and eastern regions display low heavy metal concentrations, attributed to thinner Quaternary sediment layers and stronger hydrodynamic scouring, which accelerate sediment turnover; The spatial distribution patterns of Cd and Cr exhibit distinct heterogeneity. Cd concentrations are predominantly elevated in the northern and western regions, while Cr hotspots cluster near the Dawen River inlet, reflecting divergent pollution sources for these two elements. The high Cd levels likely originate from agricultural pollution (e.g., fertilizer and pesticide use), whereas Cr enrichment is attributed to soil leaching in the Dawen River catchment and inputs from industrial wastewater and domestic sewage along its course. Overall, heavy metal concentrations are generally higher in the central and southern regions of Dongping Lake, driven by intensive human activities (e.g., industrial discharges) and inputs from the Liuchang River. Lower concentrations in the northern and eastern regions are attributed to minimal anthropogenic interference and dilution effects from the Dawen River. Elevated levels in the central area correlate with historical residential zones on the lake\u0026rsquo;s islands, sluggish hydrodynamic conditions, and severe algal aggregation, which collectively hinder pollutant dispersion and promote accumulation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical Summary of Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic parameters(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAs(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZn(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCu(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNi(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCd(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHg(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePb(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCr(mg/kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference value 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference value 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference value 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*\u003cstrong\u003eReference 1\u003c/strong\u003e: Background values of heavy metals in Shandong Province soils (\u003cstrong\u003eChina Geological Environmental Monitoring Center, 1990\u003c/strong\u003e); \u003cstrong\u003eReference 2\u003c/strong\u003e: Average heavy metal concentrations in Dongping Lake surface sediments (\u003cstrong\u003e2009\u003c/strong\u003e) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e and comparative data on elemental composition in sediments from the Yangtze River and Yellow River estuaries (\u003cstrong\u003e2008\u003c/strong\u003e) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e; \u003cstrong\u003eReference 3\u003c/strong\u003e: Studies on heavy metal distribution characteristics and ecological risk assessment in Dongping Lake sediments (\u003cstrong\u003e2014\u003c/strong\u003e) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e and research on typical heavy metal pollution in Dongping Lake sediments (\u003cstrong\u003e2015\u003c/strong\u003e) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2Source Apportionment of Heavy Metals in Sediments\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Cluster Analysis\u003c/h2\u003e\n \u003cp\u003eCluster analysis groups heavy metals with similar origins into clusters and separates elements with distinct pollution sources. A shorter clustering distance indicates closer source relationships between elements\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, To explore the interrelationships among heavy metals in Dongping Lake sediments, cluster analysis was performed on raw concentration data. Results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) reveal that the heavy metals in sediments can be categorized into three clusters:\u003c/p\u003e\n \u003cp\u003eSubcluster 1: comprises two subclusters. The first subcluster includes As, Cu, and Hg, which exhibit close clustering distances and align with their spatial distribution patterns, confirming a common origin. Hg enrichment is primarily attributed to increased coal combustion for heating, where Hg released from coal burning is directly emitted into the environment and retained in surrounding soils via atmospheric deposition\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, Subsequently, Hg accumulates in Dongping Lake through rainwater runoff and agricultural irrigation. Arsenic (As) is commonly associated with pollution sources such as metallurgical activities, pesticides/fertilizers, and livestock farming wastewater\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e,indicating that these contaminants primarily originate from industrial discharges, domestic sewage, and agricultural non-point sources in surrounding villages; Subcluster 2: Zn, Ni, and Pb form another subcluster with close clustering distances, consistent with earlier assessments, indicating a common origin. Zn and Pb are recognized as traffic-related pollutants\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e,: the combustion of leaded gasoline makes vehicle emissions a major source of Pb\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, with accumulation strongly correlated to traffic density. Vehicle exhaust, brake wear, and tire abrasion also contribute to Zn enrichment in the environment\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e,. Ni contamination is primarily influenced by metal processing and chemical industries (e.g., coking plant emissions) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. This cluster reflects combined impacts from traffic pollution, industrial activities, and agricultural practices.\u003c/p\u003e\n \u003cp\u003eCluster II: Cd. Cadmium (Cd) is recognized as a pollutant predominantly derived from agricultural activities, including the use of phosphate fertilizers, manure, and pesticides, representing a classic anthropogenic element introduced into ecosystems through human activities. Spatial distribution maps of Cd reveal that its high-concentration zones consistently overlap with areas of intensive agricultural and livestock farming\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, confirming that this cluster is primarily driven by agricultural practices.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCluster III: Cr\u003c/strong\u003e. \u003cstrong\u003eChromium (Cr)\u003c/strong\u003e exhibits \u003cstrong\u003eweak correlations\u003c/strong\u003e with the other seven heavy metals, aligns with earlier spatial distribution analyses, and shows a \u003cstrong\u003elow exceedance rate\u003c/strong\u003e relative to background values, indicating that Cr enrichment is \u003cstrong\u003eprimarily linked to the natural soil background\u003c/strong\u003e of the Dawen River catchment area.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2.2 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eCorrelation analysis of heavy metals in Dongping Lake sediments is shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. As exhibits a highly significant positive correlation (P\u0026thinsp;\u0026le;\u0026thinsp;0.001) with Cu and Hg, and Cu-Hg also shows a highly significant positive correlation, indicating that As, Cu, and Hg in sediments share common sources. Zn demonstrates a highly significant positive correlation with Ni and Pb, as does Ni-Pb, suggesting that Zn, Ni, and Pb originate from the same sources. Hg is significantly positively correlated with Pb, implying a shared origin for these two elements. Cr shows a highly significant negative correlation only with Cd, indicating an antagonistic interaction between Cr and Cd. Cr exhibits weak correlations with other heavy metals, and its mean enrichment factor (EF) of 1.01 further supports that Cr enrichment is primarily derived from natural soil parent material in the Dawen River basin.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2.3 Principal Component Analysis (PCA)\u003c/h2\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) reduces dimensionality by identifying composite variables that represent the majority of information from original variables, serving as an effective method to trace heavy metal sources in sediments\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. PCA results for heavy metals in Dongping Lake sediments (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) were analyzed to explore potential pollution sources. Prior to PCA, Kaiser-Meyer-Olkin (KMO) and Bartlett\u0026rsquo;s sphericity tests were conducted to ensure data reliability and suitability\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.The KMO statistic (0.688) and Bartlett\u0026rsquo;s test significance probability (0.000) confirmed the dataset\u0026rsquo;s appropriateness for factor analysis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.Using varimax rotation on the component matrix, three principal components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 were extracted (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), accounting for 89.22% cumulative variance contribution, thereby sufficiently explaining the original variable information.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) of Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.95\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.87\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan style=\"color: rgb(41, 105, 176);\"\u003e0.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance Percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative Contribution Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003ePC1\u003c/strong\u003e: The first principal component (PC1) accounts for 54.62% of the total variance, with As, Cu, and Hg as the primary loading factors (loadings: 0.88, 0.85, and 0.87, respectively). These three heavy metals exhibit similar spatial distributions in high-concentration zones and strong inter-correlations, consistent with prior correlation analyses, confirming their common origin. Previous statistical analyses revealed high coefficients of variation (CV) for these metals, collectively indicating that PC1 is predominantly influenced by anthropogenic activities. Spatial distribution patterns of As, Cu, and Hg show enrichment in the central-southern regions of Dongping Lake. These elements are commonly associated with metallurgical activities, agricultural practices, and livestock farming. Investigations indicate abundant iron ore resources in the central-eastern Dongping County and extensive agricultural areas and livestock farming zones in the western and southern regions. Under hydrodynamic influences within the lake, these anthropogenic inputs have shaped the current spatial distribution of As, Cu, and Hg. Thus, PC1 is interpreted as representing industrial and agricultural sources.\u003c/p\u003e\n \u003cp\u003ePC2: The second principal component (PC2) explains 22.14% of the total variance, with Zn, Ni, and Pb as dominant loading factors (loadings: 0.95, 0.83, and 0.71, respectively). These metals share similar spatial distribution patterns, characterized by elevated concentrations in the southwestern region. Zn is typically linked to vehicle exhaust, tire wear, and certain industrial processes\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e; Ni enrichment arises from metal processing, chemical industries, and traffic-related pollution; while Pb shows a relatively high natural background level based on Shandong Province soil reference values, suggesting a natural origin. Field surveys further indicate that the western region, adjacent to National Highway 220 and Provincial Highway S326, lacks significant industrial or mining activities. Thus, PC2 is interpreted as representing traffic-related sources and natural background contributions.\u003c/p\u003e\n \u003cp\u003ePC3: The third principal component (PC3) contributes 12.46% of the total variance and is primarily associated with Cd and Cr, aligning with correlation analysis results. Cr exhibits high background levels in Shandong Province soils, indicating a predominant natural origin. In contrast, Cd is a hallmark element of agricultural pollution\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, with sources including high-cadmium phosphate fertilizers, improper application of livestock manure, and aquaculture feed additives\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Thus, PC3 reflects combined contributions from agricultural activities and natural sources.\u003c/p\u003e\n \u003cp\u003eSynthesis of Findings, Heavy metal pollution in Dongping Lake sediments primarily originates from local industrial activities (e.g., iron ore mining, coal combustion, and cement production) and agricultural practices. Traffic-related emissions also contribute to specific elements such as lead (Pb), underscoring the importance of addressing these combined anthropogenic and natural sources in environmental management strategies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2.4 PMF Source Apportionment\u003c/h2\u003e\n \u003cp\u003eQuantitative source apportionment of heavy metals in the study area was performed using Positive Matrix Factorization (PMF). Following the EPA PMF 5.0 User Guide, concentration data and uncertainty values were input, with the signal-to-noise ratio (S/N) classifying all eight heavy metals as \u0026quot;Strong\u0026quot;. The model was iterated 20 times with factor numbers set between 3 and 5. By comparing Q\u003csub\u003eRobust\u003c/sub\u003e/Q\u003csub\u003etrue\u003c/sub\u003e values across different factor counts, the optimal fit was achieved with 3 factors Q\u003csub\u003eRobust\u003c/sub\u003e/Q\u003csub\u003etrue\u003c/sub\u003e=17.4 for all), where residuals for all elements fell within [-3, 3] and followed a normal distribution, confirming model stability. Source apportionment results yielded R\u0026sup2; values of 0.75 (As), 0.69 (Zn), 0.92 (Cu), 0.94 (Ni), 0.92 (Cd), 0.86 (Hg), 0.96 (Pb), and 0.63 (Cr), demonstrating strong explanatory power and effective interpretation of the original dataset.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, Factor 1 accounts for 38.4% of the total contribution, with primary loadings from Hg (46.11%), As (45.78%), and Cu (45.44%). Factor 1 aligns with the characteristic elements of PC1, confirming its identification as industrial and agricultural sources, with a total contribution of 38.4%.\u003c/p\u003e\n \u003cp\u003eFactor 2 contributes 32.1%, dominated by Cr (62.81%). Given Cr\u0026rsquo;s high proportion and its previously established linkage to natural background levels, Factor 2 is classified as a natural source, contributing 32.10% overall.\u003c/p\u003e\n \u003cp\u003eFactor 3 explains 29.6% of the variance, primarily driven by Cd (60.24%). This aligns with PC3, which associates Cd with agricultural pollution, thus identifying Factor 3 as an agricultural source with a total contribution of 29.6%.\u003c/p\u003e\n \u003cp\u003eComprehensive analyses\u0026mdash;including cluster analysis, correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF)\u0026mdash;reveal that heavy metal pollution in Dongping Lake sediments originates from three primary sources: industrial activities, agricultural practices, and natural background. Both PCA and PMF results consistently demonstrate the spatial distribution patterns and source contributions of heavy metals. Industrial and agricultural sources (PC1/Factor 1), dominated by As, Cu, and Hg, account for the largest contribution (38.4%), with elevated concentrations clustered in the central-southern regions of Dongping Lake. These hotspots correlate with local iron ore mining, coal combustion, and intensive agricultural/livestock activities, confirming that industrial processes (e.g., metallurgy, cement production) and agricultural non-point sources (e.g., fertilizer use, livestock farming) are key drivers. Traffic and natural sources (PC2), characterized by Zn, Ni, and Pb, contribute 32.1%, with higher concentrations in the southwestern region near National Highway 220 and Provincial Highway S326. This reflects impacts from vehicle emissions (e.g., exhaust, tire/brake wear) and natural background inputs, particularly for Pb, which has a high inherent soil background in Shandong Province. Agricultural and natural sources (PC3/Factor 2/Factor 3), linked to Cd (agricultural pollution from fertilizers, livestock manure) and Cr (natural soil parent material), explain 29.6% of the variance. Cd enrichment arises from phosphate fertilizers and aquaculture feed additives, while Cr levels align with geological background from the Dawen River basin. In summary, heavy metal accumulation in Dongping Lake sediments results from synergistic effects of industrial operations, agricultural practices, and traffic emissions, with natural background also playing a role. These findings provide a scientific basis for targeted pollution control and ecological restoration strategies in the region.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Enrichment Characteristics of Heavy Metals in Sediments\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 Enrichment Characteristics of Heavy Metals\u003c/h2\u003e\n \u003cp\u003eAnalysis of enrichment factor (EF) boxplots for eight heavy metals in Dongping Lake sediments (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) reveals the following mean EF values: Cd (2.35)\u0026thinsp;\u0026gt;\u0026thinsp;As (1.97)\u0026thinsp;\u0026gt;\u0026thinsp;Hg(1.92)\u0026thinsp;\u0026gt;\u0026thinsp;Zn(1.61)\u0026thinsp;\u0026gt;\u0026thinsp;Cu(1.30)\u0026thinsp;\u0026gt;\u0026thinsp;Ni(1.11)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (1.01)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (0.41). Pb shows no significant enrichment, while other elements exhibit varying degrees of enrichment influenced by anthropogenic activities. Cd displays moderate enrichment (EF\u0026thinsp;\u0026gt;\u0026thinsp;2), with 65% of sampling points classified as moderately enriched, 30% as slightly enriched, and only 5% showing no enrichment. As, Hg, Zn, Cu, Ni, and Cr are slightly enriched (1\u0026thinsp;\u0026lt;\u0026thinsp;EF\u0026thinsp;\u0026lt;\u0026thinsp;2), though As (45% moderately enriched), Hg (40%), and Zn (20%) also show localized moderate enrichment. Cr has an EF close to 1, with only 30% of sampling points slightly enriched, suggesting its enrichment primarily stems from natural soil parent material rather than anthropogenic inputs. Overall, EF analysis indicates that anthropogenic activities significantly influence all heavy metals except Pb, with Cd exhibiting the most pronounced enrichment. In contrast, Cr accumulation is predominantly governed by geogenic processes. The interplay between human activities and natural background shapes the spatial distribution of heavy metals in Dongping Lake sediments.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.3.2Geoaccumulation Index\u003c/strong\u003e \u003cstrong\u003eI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003egeo\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003eof Heavy Metals in Sediments\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe geoaccumulation index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e) analysis of heavy metals in sediments (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) reveals the following mean \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values in descending order: As (1.11)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (1.02)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (0.98)\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;=\u0026thinsp;Hg (0.95)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (0.89)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (0.79)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (0.72). As and Zn exhibit the highest \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values, averaging 1.11 and 1.02, respectively, indicating moderate pollution levels. Among all sampling sites, 95% of As and 50% of Zn samples fall into the moderately polluted category. Cu, Ni, Hg, and Cr show mean \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values near 1, suggesting a transition between unpolluted and moderately polluted states. However, 75% (Cu), 95% (Ni), 90% (Hg), and 85% (Cr) of sampling points remain unpolluted, implying these elements are primarily influenced by geological background, with minor anthropogenic contributions. Cd and Pb display lower \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values (0.79 and 0.72, respectively), also within the unpolluted to moderately polluted transition range. Notably, 10% of Cd samples show moderate pollution, while Pb shows no significant pollution across the lake, likely due to natural geological influences from sediment parent rocks. In summary, As is the predominant pollutant in Dongping Lake sediments, followed by Zn. Cu, Ni, Hg, and Cr exhibit localized moderate pollution, further corroborating anthropogenic contributions. The unpolluted status of Pb aligns with its natural geological background.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Potential Ecological Risk Assessment\u003c/h2\u003e\n \u003cp\u003eThe analysis of potential ecological risk indices \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e and integrated ecological risk index (RI) for heavy metals in Dongping Lake sediments (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) reveals the following: the mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e values follow the order Hg (80.53)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (72.70)\u0026thinsp;\u0026gt;\u0026thinsp;As (20.65)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (6.85)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (5.78)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (2.16)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (2.13)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (1.69), which aligns completely with results from Ai\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e et al. (2019 study on heavy metal distribution and risk assessment in Dongping Lake water and sediments). Notably, all \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e values have decreased compared to the 2019 data, indicating overall ecological improvement in lake sediments under policy-driven environmental protections. According to ecological risk criteria (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), As, Zn, Cu, Ni, Pb, and Cr fall into the low-risk category \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 40), though As, Zn, Cu, and Cr exhibit coefficients of variation (CV) \u0026gt; 20%, suggesting localized anthropogenic influences. In contrast, Hg (mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e= 80.53) and Cd (72.70) pose moderate-to-high risks, with all sampling points classified as moderate or higher. Maximum \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e values reach 107.14 (Hg) and 115.79 (Cd), with 50% of Hg and 30% of Cd samples in the high-risk range. Both metals show high spatial variability (CV: 26.40% for Hg, 29.17% for Cd), reflecting significant concentration fluctuations and elevated localized risks. Hg and Cd dominate total ecological risk, contributing 41.83% and 37.77% to RI, respectively, far exceeding other elements, thus emerging as the primary risk drivers requiring prioritized mitigation efforts.\u003c/p\u003e\n \u003cp\u003eAnalysis of the integrated potential ecological risk index (RI) for Dongping Lake sediments (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) reveals that only sampling site DN06 falls within the low-risk category, while all other sites exhibit moderate-risk levels, with DN07 and DN17 showing the highest RI values, highlighting severe heavy metal pollution in these areas. Spatial distribution analysis of Hg, Cr, and overall RI (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) further details the risk patterns: Hg-related risks are concentrated in the central and southeastern regions of the lake, classified as high-risk zones, with only a small low-risk area near the Dawen River estuary. Cr risks display a westward-decreasing gradient, with high-risk zones primarily in the central and northern regions of the lake. Combined RI distribution indicates elevated ecological risks in the central and southern regions, where heavy metal pollution poses significant ecological threats, necessitating prioritized pollution control and ecological remediation. Although the Dawen River estuary in the central-eastern region shows relatively lower pollution, sustained monitoring of moderate-risk areas is critical to prevent further environmental degradation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical Summary of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RI\\)\u003c/span\u003e\u003c/span\u003e for Heavy Metals in Dongping Lake Sediments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eElement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}^{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RI\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHg\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContribution to RI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical Summary of RI for Sampling Sites in Dongping Lake Sediments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSampling Site ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSampling Site ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e269.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDN20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Conclusions","content":"\u003cp\u003e(1)The study reveals that the average concentrations of most heavy metals in Dongping Lake sediments are significantly higher than the soil background values of Shandong Province, indicating widespread enrichment of these metals. Coefficients of variation (CV) suggest that spatial distributions of all metals, except Ni and Pb, are strongly influenced by anthropogenic activities. Temporal analysis shows an overall stabilization or improvement trend in sediment heavy metal levels, though As and Zn concentrations exhibit a yearly increase, correlated with intensified industrialization and urbanization. In contrast, Cr and Pb concentrations rebounded after initial declines, primarily due to natural background enrichment processes, while Cd, Ni, and Hg followed a \"rise-then-fall\" pattern, reflecting phased achievements in environmental remediation. Spatially, high-concentration zones are clustered in the central, central-western, and southern regions of the lake, closely linked to inflows from the Liuchang River, agricultural activities, and industrial operations. Lower concentrations in the northern and eastern regions are attributed to reduced human disturbance and natural purification processes.\u003c/p\u003e\u003cp\u003e(2)The analysis reveals that heavy metals in Dongping Lake sediments originate predominantly from industrial activities, agricultural practices, traffic emissions, and natural background sources. Cluster analysis and Principal Component Analysis (PCA) demonstrate that PC1 (As, Cu, Hg) is primarily associated with industrial operations (e.g., metal smelting, coal combustion) and agricultural inputs (e.g., pesticide/fertilizer use), while PC2 (Zn, Ni, Pb) reflects contributions from traffic-related pollution (vehicle exhaust, tire wear) and natural geological processes (e.g., Pb enrichment from soil parent material). PC3 (Cd, Cr) is linked to agricultural activities (phosphate fertilizers, livestock manure) and natural soil formation (Cr derived from parent rock weathering). Positive Matrix Factorization (PMF) quantifies these sources, with industrial sources contributing 38.4%, agricultural sources 29.6%, and natural background 32.1%, confirming the synergistic effects of anthropogenic and geogenic factors on sediment contamination.\u003c/p\u003e\u003cp\u003e(3)The analysis of heavy metal enrichment characteristics and geo-accumulation index in Dongping Lake sediments reveals that Cd exhibits the most significant enrichment level, showing moderate enrichment primarily influenced by anthropogenic activities. As, Hg, Zn, Cu, Ni, and Cr display slight enrichment, with Cr demonstrating the weakest enrichment, while Pb shows no enrichment. Geo-accumulation index results indicate that As and Zn are in a moderately polluted state, with As accounting for 95% of moderate pollution cases and Zn for 50%. Cu, Ni, Hg, Cr, Cd, and Pb fall within the transition range from unpolluted to moderately polluted, mainly controlled by geological background, though anthropogenic activities also contribute to their enrichment to some extent.\u003c/p\u003e\u003cp\u003e(4)The potential ecological risks of heavy metals in Dongping Lake sediments exhibit significant spatial variations. The potential ecological risk assessment reveals that Hg and Cd are the primary contributors to ecological risks, with their mean values significantly higher than those of other heavy metals. In some areas, they reach strong pollution levels, posing a substantial threat to the ecological environment. Spatially, the central and southeastern regions show higher ecological risks, particularly at sampling sites DN07 and DN17, where the highest potential ecological risk indices (RI) were recorded. This indicates particularly severe heavy metal pollution in these areas, necessitating prioritized implementation of pollution control and ecological restoration measures.\u003c/p\u003e\u003cp\u003eThe study indicates a gradual improvement trend in the ecological environment quality of heavy metals in Dongping Lake sediments. Through comprehensive analysis including statistical evaluation, source apportionment, enrichment characteristics, and ecological risk assessment, heavy metals were found to originate primarily from industrial activities (38.4%), agricultural practices (29.6%), and natural background sources (32.1%). Specifically, As, Cu, and Hg show strong correlations with industrial and agricultural activities, Zn and Ni are influenced by transportation and industrial emissions, Cd mainly stems from agricultural sources, while Cr and Pb predominantly derive from natural geological backgrounds. Notably, Hg and Cd remain the primary contributors to ecological risks, with elevated risks observed in central and southeastern regions. Sampling sites DN07 and DN17 exhibit the most prominent potential ecological risk indices (RI), demanding prioritized implementation of pollution control and ecological restoration measures. Overall, heavy metal pollution in Dongping Lake sediments results from the combined effects of anthropogenic activities and natural background, underscoring the urgent need for scientific prevention strategies and integrated management to achieve sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contribution:\u0026nbsp;\u0026dagger;These authors contributed equally to this work and share first authorship.\u003c/p\u003e\u003cp\u003eK.M. and X.W. wrote the main manuscript text, while H.A. and B.Y. prepared Figures 1-11. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026apos;s Profile: Kuanzhen Mao（1989-）,Male, Senior engineer, Graduated from Shijiazhuang Economic College in 2015, majoring in geological engineering. Mainly engaged in water source survey, hydrogeological survey, environmental geological survey, groundwater pollution survey, geological exploration, geothermal survey. E-mail: [email protected]; Xinfeng Wang (1982\u0026ndash;), Male, Senior Engineer, currently pursuing a Ph.D. in Hydrogeology. His main research focuses on hydrogeological surveys in bedrock mountainous areas and the integration of research results. E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eCorresponding author: Hongyan An（1994-）,Male, Engineer, Graduated from China University of Geosciences (Wuhan) in 2024. majoring in Environmental Science and Engineering. Mainly engaged in water source survey. E-mail: [email protected]; Baizhong Yan（1988-）, Professor, Ph.D., specializing in hydrogeology and geothermal resources research, with a focus on groundwater system modeling and sustainable utilization of geothermal energy. Email: [email protected].\u003c/p\u003e\n\u003cp\u003eFunded projects: Supported by the Open Project Program of Hebei Province Collaborative innovation center for sustainable utilization of water resources and optimization of industrial structure(No. SXTCX202407);Supported by Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes (No. SK202305KF10);Open Research Fund Program of the Hebei Provincial Research Center for Applied Technology of Ecological Environment Geology in Universities (No. JSYF-202306);China Geological Survey(No. DD20230505);Ministry of Natural Resources Provincial Cooperation Project(No. 2024ZRBSHZ028).\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\u003cp\u003eThe original project data involved in this study constitutes proprietary foundational information obtained by the research team during project implementation. Subject to national scientific research confidentiality regulations and project collaboration agreements, these raw datasets are not currently included in public sharing protocols. However, it is important to emphasize that all analytical results and research conclusions presented in this paper remain free from confidential data specifics, thereby being fully available for academic verification analyses and scholarly discussions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang, R. X. Research progress on pollution and control technologies of fluoroquinolones in China's environment[J]. \u003cem\u003eChina Resour. Compr. Utilization\u003c/em\u003e. \u003cb\u003e41\u003c/b\u003e (03), 98\u0026ndash;104 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichmond, A. et al. 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Mater.\u003c/em\u003e \u003cb\u003e266\u003c/b\u003e, 141\u0026ndash;166 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dongping Lake, PCA Model, PMF Model, Source Apportionment, Ecological Risk Assessment","lastPublishedDoi":"10.21203/rs.3.rs-6195487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6195487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurately characterizing the spatial distribution patterns of heavy metals in lake surface sediments, identifying their sources, and assessing potential ecological risks are critical scientific foundations for lake ecosystem management. This study selected Dongping Lake, a typical inland shallow lake in eastern China, as the research area. A comprehensive methodology was applied, integrating mathematical statistical analysis, cluster analysis, principal component analysis (PCA), and the Positive Matrix Factorization (PMF) model, combined with the enrichment factor method, geoaccumulation index method, and potential ecological risk assessment. This approach systematically investigated the distribution characteristics, source apportionment, and ecological risks of eight heavy metals (As, Zn, Cu, Ni, Cd, Hg, Pb, Cr) in the lake sediments. The results revealed an overall improving trend in heavy metal concentrations, with significant spatial heterogeneity\u0026mdash;higher concentrations were observed in the central and southern regions. Multivariate statistical analysis identified three primary sources of heavy metal enrichment: industrial and agricultural activities (As, Cu, Hg, Cd), traffic emissions (Zn, Ni), and natural geological background (Cr, Pb). Enrichment characteristics indicated moderate to severe accumulation of Cd, As, and Hg. The potential ecological risk index (RI) highlighted Hg and Cd as the dominant risk contributors, accounting for 41.83% and 37.77% of the total risk, respectively. This study underscores the dominant role of anthropogenic activities in driving heavy metal accumulation in Dongping Lake sediments, providing a scientific basis for targeted pollution control and ecological restoration in the watershed.\u003c/p\u003e","manuscriptTitle":"Source Apportionment and Ecological Risk Assessment of Heavy Metals in Sediments of Dongping Lake Based on PCA-PMF Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 14:42:10","doi":"10.21203/rs.3.rs-6195487/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T20:03:23+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"152618546026044471264809240354027733917","date":"2025-05-07T02:58:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T06:29:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335946116337659899268351791969878136544","date":"2025-03-28T04:13:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-28T01:27:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T00:56:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-27T10:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T05:16:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-27T05:15:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53fb1e53-4e47-4200-a121-aaf62ba38fdc","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46464901,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry"},{"id":46464902,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-09-01T15:58:31+00:00","versionOfRecord":{"articleIdentity":"rs-6195487","link":"https://doi.org/10.1038/s41598-025-16706-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-31 15:56:51","publishedOnDateReadable":"August 31st, 2025"},"versionCreatedAt":"2025-03-31 14:42:10","video":"","vorDoi":"10.1038/s41598-025-16706-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-16706-x","workflowStages":[]},"version":"v1","identity":"rs-6195487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6195487","identity":"rs-6195487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}