Distribution Characteristics, Mobility, and Influencing Factors of Heavy Metals at the Sediment–Water Interface in South Dongting Lake

Abstract

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs.

1. Introduction

Lakes play a crucial role in the terrestrial hydrological cycle, serving various important functions. They help regulate local microclimates, improve regional environmental quality, maintain runoff balance, support domestic and agricultural water use, and preserve biodiversity [1,2]. However, the expansion of agriculture, industry, and other socioeconomic activities has intensified anthropogenic disturbances [3]. Heavy metals (HMs) enter lakes through various pathways and re-settle in sediments, leading to significant HM contamination in lake sediments. The sediment–water interface is one of the most critical boundaries within aquatic ecosystems. It is a “hotspot” for HM involvement in biogeochemical cycling and biotic interactions [4]. Under changing environmental conditions, HMs near the sediment–water interface undergo a range of physical and chemical reactions—such as migration, transformation, adsorption/desorption, and diffusion—making this interface a key zone for regulating the exchange and transport of substances between sediments and the overlying water [4,5]. HMs in surface sediments, due to their mobility or incomplete degradation, are highly likely to participate in migration and transformation processes at the sediment–water interface, thereby posing potential threats to the aquatic ecosystem [4,5]. Therefore, investigating the migration and diffusion processes of HMs at the sediment–water interface is of great theoretical and practical significance for understanding their environmental behavior.

As a large river-connected lake in the middle reaches of the Yangtze River, Dongting Lake is known as the “Kidney of the Yangtze.” It serves not only as a crucial wetland resource but also as a natural ecological unit with vital ecological functions [6]. It is regarded as a key ecological security barrier supporting China’s “Yangtze River Economic Belt” [7]. Dongting Lake receives inflows from the Xiangjiang, Zishui, Yuanjiang, and Lishui, along with distributaries from the Jingjiang section of the Yangtze River, forming a centripetal dendritic hydrological system. With the intensification of mineral resource exploitation and anthropogenic activities in the upstream regions of the four tributaries, the issue of HM contamination in sediments has become increasingly prominent, drawing significant research attention. Studies have shown that Dongting Lake sediments are enriched in HMs such as As, Cd, Cr, Cu, Hg, Pb, and Zn [8]. The degree of composite HM contamination in sediments displays a spatial pattern of East Dongting Lake > Datong Lake > Hengling Lake > Wanzi Lake > West Dongting Lake > Caisang Lake [9]. Cd and Pb, which are heavily polluting, predominantly exist in reducible and weak acid-extractable forms, respectively, posing high ecological risks [10]. In recent years, the concentrations of Cu, Pb, Cd, and As in sediments have decreased significantly, while those of Cd, Hg, and Cr have shown a distinct increasing trend [11]. Previous research has primarily focused on characterizing HM contamination and its spatial distribution in sediments, with special emphasis on ecological risk prediction and environmental impact assessments [12,13], investigating the evolutionary patterns of HM contamination [14], and systematically analyzing sediment contamination in lake delta zones, yielding significant findings. However, insufficient attention has been paid to the migration and transformation characteristics, sources, and influencing factors of HMs—issues that are fundamental to the development of effective HM control technologies. Based on this, the present study focuses on the sediments of South Dongting Lake. Through a combination of conventional sampling and the Diffusive Gradients in Thin Films (DGT) technique, high-resolution in situ data on the distribution of HMs at the sediment–water interface were obtained. The study explores the spatial distribution and mobility of HMs at the interface and analyzes their activity. Additionally, variations in key physicochemical indicators (e.g., grain size, pH, ionic strength) between sediment and overlying water profiles were examined to reveal the factors influencing the endogenous release of HMs. The findings aim to provide technical support and scientific reference for the control of HM contamination and the sustainable ecological development of the lake region.

2. Materials and Methods

2.1. Overview of the Study Area

South Dongting Lake is located in the northeastern part of Hunan Province, China, and enjoys a geographically advantageous position. It is bordered by East Dongting Lake to the north and West Dongting Lake to the west, with inflows from both the Xiangjiang and Zishui rivers. The lake covers an area of approximately 1680 km², and its water depth varies significantly due to seasonal flooding and fluctuations in water level. The region has a subtropical monsoon humid climate, with an average annual temperature of 16–18 °C and annual precipitation ranging from 1200 to 1600 mm, most of which occurs during the summer months. The lake area is dominated by wetland vegetation, including widely distributed species such as reeds (Phragmites), sweet flags (Acorus calamus), and lotuses (Nelumbo nucifera). The lake’s hydrological characteristics are significantly influenced by the Yangtze River system. Seasonal fluctuations in water levels are crucial for ecosystem formation and biological reproduction. In addition, anthropogenic activities have adversely affected the ecological environment of the lake, with HM contamination becoming increasingly severe. Agricultural development has led to the excessive use of fertilizers and pesticides, exacerbating ecological crises through increased HM infiltration. Traditional and modern fishing practices threaten biodiversity and deplete resources. The operation of the Three Gorges Project has altered sediment deposition patterns, further aggravating the accumulation of HMs in lake sediments. As a result, South Dongting Lake has become a key research area for understanding the evolution of “natural–social” coupled ecosystems in midstream Yangtze River lakes. Therefore, investigating the mobility of HMs at the sediment–water interface in South Dongting Lake and identifying their influencing factors is of great significance for understanding watershed-scale responses to human activities.

2.2. Sample Collection and Analysis

2.2.1. Sample Collection

In April 2024, sediment samples were collected from 14 sites across South Dongting Lake, including the Zishui estuary (ZE) (Saitoukou D3, Choutang Lake D4, Huangtuzhan D5, and Yanglinzhai Village D6), the Xiangjiang estuary (XE) (Zhangshugang D1, Linzikou Bridge D7, Chengxiyuan D8, and Xiangyin D14), and the northeast of South Dongting Lake (NE-SDL) (Yangjiashan Village D2, Chengxiyuan D8, Qingshan Island D9, Tuishanji D10, Quyuan Farm D11, Fenghuang Township D12, and Qipanshan D13). Surface sediment samples were collected using a grab sampler. Field observations, GPS positioning, data recording, and photographic documentation were conducted on site. The collected surface sediment samples were sealed in clean polyethylene bags and transported to the laboratory for further analysis. The detailed distribution of sampling sites is shown in Figure 1.

Meanwhile, Diffusive Gradients in Thin Films (DGT) devices were deployed at sites D1 and D2. The DGT devices were vertically inserted into the sediment, leaving 3–4 cm of the device above the sediment–water interface, and the interface position was marked using a permanent marker. The devices were left in place to equilibrate within the sediment for 24 h. During this period, the water temperature near the devices was measured and recorded at 0, 12, and 24 h. After the designated deployment period, the DGT devices were carefully and slowly removed. Residual sediment was gently rinsed off the surface using deionized water, and the devices were sealed and transported back to the laboratory. In the laboratory, the plastic frame and diffusion layer were removed. The binding gels were then sectioned at 2 mm intervals using a custom-made ceramic blade cutter. The sliced gel samples were eluted in 1.8 mL of 1 M HNO₃ for 16 h to extract the accumulated cations, which were subsequently analyzed for HMs using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) [15]. All DGT devices and operational protocols were provided by EasySensor Ltd. (Nanjing, China, www.easysensor.net).

2.2.2. Sample Analysis

The analysis of heavy metal concentrations in sediment samples was conducted at Nanjing Zhigan Environmental Technology Co., Ltd. (Nanjing, China) HM concentrations were measured using a Perkin-Elmer Elan 6000 Inductively Coupled Plasma Mass Spectrometer (ICP-MS, made in Germany) in the same laboratory. A 40.0 mg powdered sample was placed in a Teflon vessel, followed by the addition of 0.8 mL of HNO₃ (1:1) and 0.8 mL of HF. Then, 0.8 mL of HClO₄ (1:3) was added. The mixture was sealed, sonicated for 60 s, and subsequently heated at a constant temperature of 100 °C for 2 days, followed by evaporation to dryness. Next, 0.8 mL of HNO₃ (1:1) was added and heated at 100 °C for 24 h, followed by evaporation. Then, 0.8 mL of a HF and HClO₄ mixture was added, sealed in a high-pressure digestion vessel, and heated at 170 °C for 48 h, followed by evaporation. Subsequently, 4 mL of 4N HNO₃ was added and heated again at 170 °C for 4 h. The digest was diluted with 3% HNO₃ and transferred into a 50 mL volumetric flask. Rh-Re internal standard solution was added, and the solution was diluted with 1% HNO₃ to 40.0 g for ICP-MS analysis. The ICP-MS operating conditions were as follows: RF power of 1000–1100 W, nebulizer flow rate of 1.14 L/min, lens voltage with automatic focus, peak-hopping scan mode, and an integration time of 100 ms. Sample accuracy was validated using the national standard reference material GSR-3, with five parallel measurements. The detection limit was 10 × 10⁻⁹, and the analytical precision was better than 5%.

The pretreatment of samples for BCR sequential extraction was conducted at the Key Laboratory of Mechanism and Ecological Remediation of Environmental Heavy Metal Contamination, Hunan Normal University (Changsha, China). A three-step sequential extraction procedure from the modified BCR [16] was applied to extract the four fractions of five HMs investigated: the acid-soluble/exchangeable fraction (F1), reducible fraction (F2), oxidizable fraction (F3), and residual fraction (F4). The detailed extraction procedures referred to Table S1. After pretreatment, the extracted solutions were sent to Nanjing Zhigan Environmental Technology Co., Ltd. (Nanjing, China) for ICP-MS analysis using a Perkin-Elmer Elan 6000 instrument (made in Germany).

Basic statistical analysis and figure plotting in this study were performed using Excel 2019, OriginPro 2024, and ArcGIS 10.2.8. Mathematical statistical analyses, including correlation analysis, discriminant analysis, and principal component analysis (PCA), were conducted using SPSS 26.0 and other commonly used geostatistical software 2016. The drafting and editing of figures were completed using CorelDRAW X7, Photoshop 7.0, and Adobe Illustrator 2021.

2.3. Evaluation Methods

The geo-accumulation index (I_geo), proposed by [17], is a commonly used indicator to assess the enrichment level of HMs in sediments, taking into account anthropogenic contamination and geochemical background levels. The formula is as follows:

$$I_{geo}={\log }_2[C_i/(K{B}_i\left)\right]$$

(1)

where C_i is the measured concentration of HM i in the sediment (mg/kg), B_i is the background value of the HM in the Dongting Lake Basin (mg/kg), and K is a correction factor to account for lithological variations, usually taken as 1.5.

The Pollution Load Index (PLI), developed by [18], provides an intuitive reflection of the contamination contribution of each HM and the spatial-temporal variation trends of metal contamination. The contamination factor (CF) is calculated as follows:

$${CF}_i=C_i/B_i$$

(2)

$$PLI=\sqrt[n]{C{F}_1\times C{F}_2\times \dots \times C{F}_n}$$

(3)

$${PLI}_{zone}=\sqrt[m]{{PLI}_1\times {PLI}_2\times \dots \times {PLI}_n}$$

(4)

where C_i is the measured concentration of HM i in the sediment (mg/kg); B_i is the environmental background value of the evaluated HM (mg/kg), with values adopted from the Dongting Lake Basin; CF_i represents the contamination factor; PLI is the overall contamination load index; n is the total number of HMs evaluated; and PLI_zone refers to the contamination load index for a specific area (e.g., watershed), with m representing the number of sampling sites.

The Risk Assessment Code (RAC), proposed by [19], evaluates the mobility and bioavailability of HMs in sediments based on the percentage of exchangeable fractions extracted by the BCR sequential extraction method. The RAC is calculated as follows:

$$RAC=\left({\displaystyle \frac{C_{F1}}{C_{tot}}}\right)\times 100\%$$

(5)

where CF₁ is the concentration of the exchangeable fraction and C_tot is the total concentration of the HM.

The Potential Ecological Risk Index (PERI), developed by [20], assesses the ecological risk of HMs in sediments by integrating concentration levels, ecological and environmental effects, and toxicological characteristics. It is one of the most widely used approaches for evaluating sediment contamination from a holistic ecological perspective. The formula is as follows:

$$E_r^i=T_r^i·C_i/B_i$$

(6)

$$RI={\sum }_{i=1}^n{E}_r^i$$

(7)

where $T_r^i$ is the toxic-response factor for HM i (Mn, Zn = 1; V, Cr = 2; Cu, Pb, Ni, Co = 5; As = 10; Cd = 30), C_i is the measured concentration (mg/kg), B_i is the background concentration (mg/kg), $E_r^i$ is the potential ecological risk factor for metal i, and RI is the cumulative risk index for all metals considered. The classification criteria for ecological risk levels are presented in

Table 1. Evaluation criteria for contamination and ecological risk.

Igeo	Pollution Level	PLI	Pollution Level	RAC	Risk Level
Igeo < 0	Unpolluted	PLI ≤ 1	Unpolluted	RAC ≤ 1%	No risk
0 < Igeo < 1	Slightly polluted	1 < PLI ≤ 2	Moderately polluted	1% < RAC ≤ 10%	Low risk
1 < Igeo < 2	Slightly to moderately polluted	2 < PLI ≤ 3	Heavily polluted	10% < RAC ≤ 30%	Moderate risk
2 < Igeo < 3	Moderately polluted	PLI > 3	Extremely polluted	30% < RAC ≤ 50%	High risk
3 < Igeo < 4	Moderately to heavily polluted			RAC > 50%	Very high risk
4 < Igeo < 5	Heavily polluted
Igeo > 5	Extremely polluted
$E_r^i$	Risk Level	RI	Ecological Risk
$E_r^i$ < 40	Low risk	RI < 150	Low ecological risk
40 ≤ $E_r^i$ < 80	Moderate risk	150 ≤ RI < 300	Moderate ecological risk
80 ≤ $E_r^i$ < 160	Considerable risk	300 ≤ RI < 600	High ecological risk
160 ≤ $E_r^i$ < 320	High risk	RI ≥ 600	Very high ecological risk
$E_r^i$ ≥ 320	Very high risk

.

3. Results

3.1. Sedimentary Environmental Characteristics of South Dongting Lake

Environmental factors such as sediment grain size, electrical conductivity, and pH can significantly influence the mobility of HMs in sediments. Thus, characterizing the sedimentary environment of the study area is essential. The statistical results of the sedimentary environmental indicators for southern Dongting Lake are presented in Supplementary Table S1. The sediments in South Dongting Lake are mostly silt, making up more than 50%, followed by sand and clay. The proportion of silt is significantly higher at sites in NE-SDL compared to those near the XE and ZE, where the lowest silt content is observed. Conversely, sand content is markedly higher in XE sites. These patterns suggest stronger hydrodynamic forces in the northeastern lake area, likely due to water influx from the Yangtze River, whereas the XE and ZE regions exhibit relatively weaker hydrodynamics. The average pH values across the three regions of southern Dongting Lake are relatively stable, ranging from 6.02 to 6.40, indicating weakly acidic conditions. The average electrical conductivity values in the northeastern lake area, ZE, and XE are 11.18, 9.60, and 9.50 ms/m, respectively.

3.2. Spatial Distribution Characteristics of Heavy Metals in Sediments of South Dongting Lake

The concentrations of HMs in surface sediments at 14 sampling sites passed the Kolmogorov–Smirnov normality test (Z > 0.05). Concentration data for 15 HMs (broadly defined), including V and Cr, are summarized in

Table 2. Heavy metal concentrations in sediments of South Dongting Lake (mg/kg).

Region	Sample	V	Cr	Cu	Pb	Tl	Bi	Co	Ni	As	Se	Cd	Sb	Mn	Zn	Fe
Xiangjiang Estuary	D1	81.53	81.68	44.30	107.2	1.34	3.95	19.73	73.93	84.59	3.29	12.00	6.54	1033	184.7	50,238
	D7	65.18	50.46	23.33	36.34	0.92	3.07	13.54	84.60	19.89	15.76	1.32	5.42	581.8	83.43	30,502
	D8	58.05	63.67	25.88	55.51	1.03	5.57	13.25	65.39	31.30	27.98	2.86	4.28	470.9	89.60	31,386
	D14	53.18	50.74	27.38	80.95	1.32	1.13	16.06	59.33	48.28	11.44	5.92	3.37	419.8	103.8	33,048
	Mean	64.49	61.64	30.22	70.00	1.15	3.43	15.65	70.81	46.01	14.62	5.53	4.90	626.5	115.4	36,294
	Cv	0.19	0.24	0.32	0.44	0.18	0.54	0.19	0.15	0.61	0.70	0.85	0.28	0.45	0.41	0.26
Zishui Estuary	D3	84.50	60.96	25.89	29.20	0.86	2.35	15.87	65.12	21.20	5.71	1.65	18.65	329.2	60.94	40,071
	D4	82.80	61.68	24.00	38.12	0.73	2.94	14.38	38.81	23.41	1.26	0.39	9.25	455.0	66.06	48,817
	D5	89.33	67.65	26.92	34.97	0.88	0.86	15.31	51.12	21.48	10.91	1.19	11.97	365.0	66.32	37,143
	D6	80.27	82.96	25.63	37.48	0.84	3.71	17.27	108.7	21.42	12.54	0.74	6.88	379.4	85.18	48,269
	Mean	84.23	68.31	25.61	34.94	0.83	2.46	15.71	65.93	21.88	7.60	0.99	11.69	382.2	69.63	43,575
	Cv	0.05	0.15	0.05	0.12	0.08	0.49	0.08	0.46	0.05	0.68	0.55	0.44	0.14	0.15	0.13
Northeast of South Dongting Lake	D2	84.70	72.89	34.69	77.49	1.28	3.48	17.32	62.47	55.31	5.25	4.09	4.86	818.8	111.5	44,582
	D9	53.31	60.15	27.14	65.91	1.21	5.40	16.08	131.6	46.88	17.00	5.25	4.74	762.0	113.0	33,395
	D10	84.47	82.68	34.18	72.82	0.54	9.93	18.02	71.69	40.37	11.53	1.89	4.48	391.0	95.04	41,563
	D11	71.42	70.12	34.35	79.78	1.40	1.90	17.31	66.36	56.52	4.90	5.45	5.26	977.3	131.5	43,369
	D12	74.51	75.14	26.82	89.39	0.87	3.64	15.64	88.74	22.27	11.60	0.88	3.40	470.8	73.28	43,219
	D13	79.29	65.81	26.20	59.14	0.83	4.98	13.94	66.74	23.74	4.56	0.36	1.85	415.8	59.46	34,002
	Mean	74.62	71.13	30.56	74.09	1.02	4.89	16.38	81.27	40.85	9.14	2.98	4.10	639.3	97.31	40,022
	Cv	0.16	0.11	0.14	0.14	0.32	0.57	0.09	0.32	0.37	0.55	0.75	0.31	0.38	0.28	0.12

Note: Heavy metal concentrations are expressed in mg/kg.

. Among the metals studied, V, Tl, and Co demonstrated relatively stable concentrations (Cv < 0.20), indicating a more uniform spatial distribution. In contrast, metals such as Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe showed greater variation (Cv > 0.20). Notably, elements like Pb, Bi, As, Cd, Zn, and Mn were significantly enriched in XE sites (D1, D7, D8, D14) and the northeastern part of the lake. In comparison, their concentrations were lower and more stable in ZE sites (D3, D4, D5, D6). For instance, the average concentrations of As in ZE, XE, and NE-SDL sites were 21.88, 46.01, and 40.85 mg/kg, respectively. This indicates that there is a varying degree of anthropogenic influence across the different lake regions. However, the concentrations of Sb and Ni were notably higher in ZE sites (D3, D5, D6), with average Sb concentrations of 11.69, 4.90, and 4.10 mg/kg in ZE, XE, and NE-SDL sites, respectively. These results highlight the complex distribution and enrichment of heavy metals (HMs) in the sediments of South Dongting Lake, suggesting a need for further investigation into their geochemical behavior and mobility.

Compared with the abundance values of the upper continental crust in eastern China and Yangtze River sediments [21,22], as well as the background values of Dongting Lake sediments [23], HMs such as Cd, Bi, Se, Sb, As, Ni, and Pb are significantly elevated in South Dongting Lake. For example, the mean concentrations of Cd and Bi are 39.77 and 16.43 times higher than those of the upper crust, indicating substantial enrichment. The spatial distribution patterns of HMs can be broadly classified into three categories (Figure 2): (1) Cu, Pb, Tl, Bi, Co, As, Se, Cd, Mn, and Zn are primarily enriched in the XE and NE-SDL; (2) V, Cr, Ni, and Fe show enrichment throughout the lake; and (3) Sb is mainly enriched in ZE. These findings suggest that the XE and the NE-SDL are the principal sources of HMs in South Dongting Lake. The Xiangjiang River basin, in particular, is subject to intensive mining, smelting, and agricultural irrigation, making it a major contributor to HM input. Compared with previous studies [24], the concentrations of V, Cr, Cu, Pb, Tl, Bi, Co, Ni, Cd, Sb, Mn, and Zn in sediments at the Quyuan Farm site (D11) in December 2007 were significantly higher than the values recorded in April 2024 in this study, suggesting that HM contamination in South Dongting Lake sediments has markedly improved over the past 16 years. This trend reflects the effectiveness of contamination control and ecological restoration efforts implemented in Hunan Province.

3.3. Spatial Distribution Characteristics of Nutrients in South Dongting Lake Sediments

The sediments of South Dongting Lake exhibit relatively high loads of nitrogen, phosphorus, and organic carbon. As shown in Supplementary Table S1, total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC) contents varied considerably across the three regions (Cv > 0.20, except TN in the northeast), indicating different levels of anthropogenic disturbance. The average TN concentrations in XE, ZE, and NE-SDL were 749.5 mg/kg, 1060 mg/kg, and 1021 mg/kg, respectively. Corresponding TP averages were 666.5 mg/kg, 779.8 mg/kg, and 646.4 mg/kg, while TOC values were 7623 mg/kg, 13,228 mg/kg, and 14,150 mg/kg, respectively. These data indicate that TN and TOC are primarily enriched in ZE and NE-SDL. According to sediment contamination criteria established by the US Environmental Protection Agency (EPA) [25], TN levels in ZE and NE-SDL correspond to moderate contamination, while those in XE indicate mild contamination. All three regions exhibit severe TP contamination. TOC levels in ZE and NE-SDL reflect moderate contamination, whereas the XE shows mild contamination. These findings suggest that nutrient contamination levels in ZE and NE-SDL are comparable and relatively high, which may be attributed to intensive agricultural activities, particularly the application of fertilizers and pesticides. Compared to previous studies [26], TN concentrations have declined, while TP concentrations have increased, indicating the need for targeted nutrient contamination management, especially for phosphorus.

3.4. Geochemical Fractionation of Heavy Metals in Sediments

The HMs in the sediments of South Dongting Lake exhibit different geochemical fractionation. The BCR method was used to analyze the HM fractionation in sediments from the XE (D1), ZE (D3), and NE-SDL (D2 and D11). BCR-extracted fractions (F1, F2, F3, and F4) from HMs were shown in Table S1 and Figure 3; the metals V, Cr, Cu, Tl, Bi, Ni, As, Se, and Sb (Figure 3a–c,e,f,h–j,l) in South Dongting Lake are predominantly in the F4 (residual) form. For example, in the four sampling sites, the proportion of Cu in the F4 form exceeds 70%. The F4 form is relatively stable, unaffected by human activities and environmental changes, with weak mobility and transformation capacity, and has low bioavailability, meaning it will not cause further environmental contamination [27]. Pb, Co, Cd, Mn, and Zn (Figure 3d,g,k,m,n) are mainly in secondary forms (weak acid-soluble F1 + reducible F2 + oxidizable F3). For instance, in the D1 site, the proportions of Pb in F1, F2, and F3 are 2%, 5%, and 81%, respectively, indicating varying degrees of bioactivity. Pb and Co predominantly exist in F3 (oxidizable) form, where F3 is bound to sulfides and organic matter. These forms are generally only released under strong oxidative conditions and are relatively stable [28]. It is worth noting that previous studies have shown that Pb predominantly exists in F2 (reducible) form [29,30]. This may be because Pb is primarily bound to iron and manganese oxides. When the redox potential in the water decreases or the water becomes severely anoxic [31], the reducible form of Pb (e.g., Pb²⁺) combines with oxygen, sulfur, and other elements to form stable compounds (e.g., PbO, PbSO₄). These compounds have low solubility in water and soil, reducing the mobility and bioavailability of Pb. F1 and F2 forms of Cd, Mn, and Zn are predominant. For example, in the D1 site, Mn in F1 and F2 forms accounts for 51% and 13%, respectively. The F1 form of metals is readily released under neutral or weakly acidic conditions, making them easily absorbed and utilized by organisms, with the highest toxicity [32]. Cd and Zn predominantly occur in the F1 fraction, indicating anthropogenic origins [33]. In particular, the proportions of F1 in Cd at D2 and D3, Zn at D1, and Mn at D1 are significantly higher than at other sites, indicating more pronounced anthropogenic input. Therefore, these three HMs should be the primary focus of concern.

Although the sequential extraction method can effectively analyze the geochemical fractionation of HMs in sediments, to elucidate the vertical distribution and activity status of HMs, explore the geochemical characteristics and sources of HMs, and analyze the mechanisms of secondary release of HMs in sediments, it is necessary to combine sampling techniques that can microscopically characterize the migration and transformation of HMs at the sediment–water interface (SWI). The DGT (Diffusive Gradients in Thin Films) technique can be used to study the in situ re-transfer process of HMs between sediments and the overlying water [34]. Therefore, DGT devices were deployed at the D1 and D2 sites at the river mouths flowing into South Dongting Lake, and the vertical spatial distribution is shown in Figure 4. The concentrations of DGT-V, DGT-Cr, DGT-Cu, DGT-Pb, DGT-Ni, DGT-As, and DGT-Sb generally increase from sediment to overlying water (Figure 4a–d,g,i,k), indicating that these HMs exhibit some activity and will be released from sediments into overlying water as environmental conditions (such as redox potential and pH) change. The concentration of DGT-Co (0.02–0.28 μg/L) (Figure 4e) increases from sediment to overlying water at the D1 site, whereas the concentration at the D2 site behaves oppositely and intersects at −30 mm. The concentration of DGT-Cd (3.79–6.41 μg/L) is highly variable and unstable in both overlying water and sediments, suggesting that the behavior and activity of Cd in the sediments are complex and warrant further investigation. The concentrations of DGT-Mn, DGT-Zn, and DGT-Fe (Figure 4j,k,l) decrease from sediment to overlying water, which is due to the transition of sediment conditions from anoxic to low-oxygen and then to aerobic environments. The reduction of Mn, Zn, and Fe (hydr)oxides weakens, inhibiting the release of ions, thus leading to decreased concentrations of DGT-Mn, DGT-Zn, and DGT-Fe [35].

Combining BCR and DGT analyses, although V, Cr, Cu, Ni, and As are predominantly in the F4 form, their concentrations increase from interstitial water to overlying water, suggesting that these HMs are prone to be released from sediments into overlying water and exhibit some activity. This trend is related to their speciation distribution. For example, Cr has a secondary phase (F1 + F2 + F3) proportion exceeding 15% at almost all sites, with the highest proportion reaching 34%, indicating a certain release risk. Pb and Co predominantly occur in the F3 form, showing strong activity. The concentrations of Pb and Co at the D1 site increase with decreasing depth. Mn and Zn are primarily in the F1 and F2 forms, with strong activity, and their concentrations decrease from interstitial water to overlying water. The former verifies that F3, as the depth decreases and exposure to oxygen increases, experiences stronger oxidation reactions, leading to more HMs being released into the overlying water. In contrast, the latter indicates that F2 shows a weak-strong-weak reduction reaction pattern as the depth decreases, transitioning from anoxic to low-oxygen and then to aerobic conditions, consistent with changes in the effective-state content. The proportion of Cd in the F1 form is high, and its concentrations in overlying water and interstitial water are unstable. This may be because the pH of the sediment is weakly acidic, and F1 form HMs are easily released under neutral or weakly acidic conditions, causing Cd to be released into interstitial waters Moreover, as South Dongting Lake primarily consists of fine particles, Cd ions are easily adsorbed onto the surface of small particles [36], which subsequently increases the concentration of HMs in sediments, leading to instability. In summary, V, Cr, Cu, Ni, and As are predominantly in the F4 form, with weak mobility and low bioavailability. Pb and Co are primarily in the F3 form, Mn and Zn are mainly in F2 and F1 forms, and Cd is mainly in the F1 form. These metals exhibit high mobility and bioavailability, but their activity is unstable, posing a release risk, which necessitates risk assessment.

4. Discussion

4.1. Contamination Assessment of Heavy Metals in Sediments

4.1.1. Assessment Results Based on Geo-Accumulation Index (I_geo)

The geo-accumulation index (I_geo) of HMs in sediments from South Dongting Lake is shown in

Table 3. Evaluation results of geo-accumulation index (I_geo) in South Dongting Lake.

	Xiangjiang Estuary				Zishui Estuary				Northeast of South Dongting Lake
	Max	Min	Mean	Level	Max	Min	Mean	Level	Max	Min	Mean	Level
V	0.37	−0.24	0.01	Sp	0.50	0.35	0.42	Sp	0.43	−0.24	0.23	Sp
Cr	0.31	−0.39	−0.13	Up	0.33	−0.11	0.04	Sp	0.33	−0.13	0.1	Sp
Cu	0.56	−0.36	−0.04	Up	−0.16	−0.32	−0.23	Up	0.21	−0.2	0.02	Sp
Pb	1.7	0.14	0.97	Sp	0.21	−0.18	0.07	Sp	1.44	0.84	1.15	Smp
Tl	0.57	0.03	0.34	Sp	−0.03	−0.31	−0.12	Up	0.64	−0.73	0.11	Sp
Bi	3.63	1.32	2.71	Mp	3.04	0.93	2.27	Mp	4.46	2.08	3.26	Mhp
Co	0.35	−0.22	0	Up	0.16	−0.10	0.02	Sp	0.22	−0.15	0.08	Sp
Ni	1.41	0.9	1.14	Smp	1.77	0.29	0.94	Sp	2.05	0.97	1.3	Smp
As	2.23	0.14	1.15	Smp	0.38	0.24	0.28	Sp	1.65	0.31	1.09	Smp
Se	3.3	0.21	1.99	Smp	2.14	−1.18	0.97	Sp	2.58	0.68	1.49	Smp
Cd	4.6	1.41	3.03	Mhp	1.74	−0.33	0.81	Sp	3.46	−0.46	2.04	Mp
Sb	1.99	1.03	1.53	Smp	3.50	2.06	2.73	Mp	1.67	0.17	1.23	Smp
Mn	0.61	−0.69	−0.2	Up	−0.57	−1.04	−0.83	Up	0.53	−0.79	−0.17	Up
Zn	0.7	−0.45	−0.06	Up	−0.42	−0.90	−0.72	Up	0.21	−0.94	−0.28	Up
Fe	0.26	−0.46	−0.24	Up	0.22	−0.18	0.04	Sp	0.09	−0.33	−0.08	Up

Note: Unpolluted is denoted as Up, Slightly polluted as Sp, Slightly to moderately polluted as Smp, Moderately polluted as Mp, Moderately to heavily polluted as Mhp, Heavily polluted as Hp, and Extremely polluted as Ep.

. The contamination levels of various HMs across the three regions tend to be similar. Overall, the I_geo values of V, Cr, Cu, Tl, Co, Mn, Zn, and Fe at all 14 sampling sites are less than 1, indicating an unpolluted to slightly polluted status with minor or negligible environmental impact. For Pb, 42.86% of the sediment samples fall under the slightly to moderately polluted category, while the remaining samples are relatively low. Bi is mainly moderately to heavily polluted (42.86%) and moderately polluted (35.71%), with one sample reaching a heavily polluted and another a slightly to moderately polluted status. Ni and As are primarily slightly to moderately polluted, accounting for 64.29% and 35.71% of the samples, respectively. Each also has one sample classified as moderately polluted. Se is predominantly moderately polluted (42.86%), with two samples slightly to moderately polluted and one moderately to heavily polluted. Cd shows both slightly to moderately polluted and moderately to heavily polluted conditions, each in 28.57% of the samples. Additionally, one sample is heavily polluted and another moderately polluted. Sb is primarily slightly to moderately polluted (64.29%), followed by moderately polluted (21.43%), with one sample classified as moderately to heavily polluted. Therefore, Pb, Bi, Ni, As, Se, Cd, and Sb present higher levels of environmental concern, particularly Bi and Cd, which show notably elevated contamination levels, including several sites classified as heavily polluted. These findings are broadly consistent with those of previous studies [37]. Compared with the I_geo evaluation for Quyuan Farm (D11) by [23,24], the Cd level has decreased from extremely polluted to moderately to heavily polluted. Bi, Pb, and Sb levels have declined from heavily polluted to slightly to moderately polluted. Cu, Mn, and Zn have improved from slightly to moderately polluted to slightly polluted, suggesting an overall improvement in contamination status.

4.1.2. Pollution Load Index Evaluation Results

The Pollution Load Index (PLI) of sediment HMs at 14 sites in South Dongting Lake is shown in Figure 5. Except for the PLI values at D4 and D13, which are below two, indicating moderately polluted, the PLI values at other sites are greater than 2, indicating heavily polluted or worse. Notably, the PLI values at D1 and D9 exceed 3, indicating extremely polluted. Regarding primary spatial distribution, the PLI zones in XE, ZS, and NE-SDL are 2.64, 2.04, and 2.56, respectively. All three regions are heavily polluted, with the contamination levels in XE and the NE-SDL being significantly higher than those in ZE.

4.2. Ecological Risk Assessment of Sediment Heavy Metals

4.2.1. Risk Assessment Coding Evaluation Results

The RAC risk assessment coding method was used to analyze the morphological results of HMs at four sites in South Dongting Lake. D1 represents the XE, D3 represents the ZE, and D2 and D11 represent the NE-SDL. The evaluation results are shown in

Table 4. Risk Assessment Code (RAC) evaluation results for South Dongting Lake.

	D1			D2			D3			D11
	F1	Total	RAC	F1	Total	RAC	F1	Total	RAC	F1	Total	RAC
V	0.03	77.44	0%	0.03	74.84	0%	0.02	73.15	0%	0.06	80.31	0%
Cr	0.20	53.12	0%	0.24	54.49	0%	0.27	37.30	1%	0.20	54.46	0%
Cu	1.90	46.20	4%	2.62	56.70	5%	2.26	38.98	6%	1.93	50.37	4%
Pb	1.08	68.96	2%	0.48	54.59	1%	0.63	34.37	2%	0.51	36.13	1%
Tl	0.01	0.69	1%	0.01	1.25	1%	0.01	0.83	1%	0.01	1.12	0%
Bi	0.00	3.23	0%	0.00	2.16	0%	0.00	2.03	0%	0.00	2.18	0%
Co	0.70	23.97	3%	0.37	16.63	2%	0.79	9.39	8%	1.34	25.05	5%
Ni	1.40	43.87	3%	2.04	48.55	4%	2.48	39.07	6%	1.20	48.47	2%
As	0.20	36.14	1%	0.14	46.86	0%	0.15	46.50	0%	0.30	49.25	1%
Se	0.11	3.80	3%	0.13	6.50	2%	0.21	4.64	5%	0.11	3.34	3%
Cd	2.08	7.35	28%	2.95	5.66	52%	1.66	2.88	58%	0.89	3.17	28%
Sb	0.09	6.36	1%	0.09	3.55	3%	0.15	4.70	3%	0.13	4.39	3%
Mn	505.18	992.48	51%	134.95	766.19	18%	13.41	317.23	4%	112.88	913.18	12%
Zn	82.57	148.38	56%	27.73	120.41	23%	22.19	77.26	29%	10.82	88.56	12%

. At the four sites, the RAC values of V, Cr, Cu, Pb, Tl, Bi, Co, Ni, As, Se, and Sb are all less than 10%, indicating a low-risk level. The RAC values of Cd at sites D2 and D3 are 52% and 58%, respectively, indicating a very high risk, while at D1 and D11, the RAC is 28%, corresponding to a moderate risk level. The RAC of Mn at D1 is 51%, indicating a very high risk, which is significantly higher than at other sites. The RAC values at D2 and D11 are 18% and 12%, respectively, indicating moderate risk. The RAC of Zn at D1 is 56%, indicating a very high risk, while at other sites, the RAC ranges from 10% to 30%, indicating a moderate risk. Overall, the RAC risk levels of the same HM are consistent across the four sites, indicating no significant spatial differences in the mobility and bioavailability of the same HM across the three regions (XE, ZE, and NE-SDL). Therefore, ecological protection strategies and contamination control measures should consider the entire South Dongting Lake system.

4.2.2. Potential Ecological Risk Index

Among the HMs investigated, only V, Cr, Cu, Pb, Co, Ni, As, Cd, Mn, and Zn have corresponding toxicity coefficients, so the risk evaluation was conducted only for these 10 metals. The single-factor ecological risk evaluation results for HMs in South Dongting Lake sediments are shown in

Table 5. Evaluation results of the Potential Ecological Risk Index in South Dongting Lake.

	Xiangjiang Estuary				Zishui Estuary				Northeast of South Dongting Lake
	Max	Min	Mean	Risk Level	Max	Min	Mean	Risk Level	Max	Min	Mean	Risk Level
V	3.88	2.53	3.07	Low	4.25	3.82	4.01	Low	4.03	2.54	3.55	Low
Cr	3.71	2.29	2.80	Low	3.77	2.77	3.11	Low	3.76	2.73	3.23	Low
Cu	11.07	5.83	7.56	Low	6.73	6.00	6.40	Low	8.67	6.55	7.64	Low
Pb	24.37	8.26	15.91	Low	8.66	6.64	7.94	Low	20.32	13.44	16.84	Low
Co	9.58	6.43	7.59	Low	8.38	6.98	7.62	Low	8.75	6.77	7.95	Low
Ni	19.95	13.99	16.70	Low	25.63	9.15	15.55	Low	31.05	14.73	19.17	Low
As	70.49	16.57	38.35	Low	19.51	17.67	18.23	Low	47.10	18.56	34.04	Low
Cd	1091.07	119.75	502.44	Vh	149.82	35.70	90.28	Cons	495.06	32.63	271.30	High
Mn	2.30	0.93	1.39	Low	1.01	0.73	0.85	Low	2.17	0.87	1.42	Low
Zn	2.43	1.10	1.52	Low	1.12	0.80	0.92	Low	1.73	0.78	1.28	Low
RI	597.32			High	154.91			Mod	366.43			High

. Except for Cd, the potential ecological risk coefficients of the other HMs are all below 40, indicating low risk with minimal environmental contamination. In the XE (D1), the $E_r^i$ value of Cd reaches 1091, which far exceeds the threshold for the “very high ecological risk” category. The average $E_r^i$ value at this site is 502.4, indicating a very high risk. In the NE-SDL, the average $E_r^i$ value of Cd remains elevated at 271.3, indicating a high risk. The $E_r^i$ value of Cd in ZE shows a tendency of improvement, with an average of 90.28, yet still indicates a considerable risk. Therefore, the potential ecological risk coefficients of Cd across the three regions are all concerning, posing a significant threat to environmental safety and warranting close attention. The comprehensive potential ecological risk indices (RI) for the three regions are 597.32, 154.91, and 366.43, corresponding to high, moderate, and high ecological risk levels, respectively. Particularly in XE, the RI approaches 600, nearing the threshold of very high ecological risk, with Cd being the dominant contributing factor. Studies have indicated that Cd mainly originates from industrial emissions. Numerous non-ferrous metal smelters are located upstream of the Xiangjiang River, where extensive flue gases are produced during calcination and smelting processes. These pollutants enter the watershed via atmospheric deposition and wastewater discharge [38]. Additionally, the area surrounding the NE-SDL is also home to many metal mining enterprises, highlighting the urgent need for strengthened industrial regulation. By comparing the potential ecological risks of Honghu and Chi Lake in the middle reaches of the Yangtze River [7], it was found that the ecological risk of Nan Dongting Lake was higher than that of Honghu but lower than that of Chi Lake. This might be related to the types of human activities around the three lakes.

By integrating four assessment methods, the distribution characteristics and risk status of HMs in the study area can be evaluated and reflected from multiple dimensions in a more comprehensive manner. Based on the results of the four risk assessment methods, this study found that the RI values were consistent with the PLI_zone results across the three regions. The XE exhibited high ecological risk and the most severe contamination (RI = 597.32, PLI_zone = 2.64), followed by the NE-SDL (RI = 366.43, PLI_zone = 2.56), while the ZE showed the lowest values but still indicated a moderate ecological risk and heavy contamination level (RI = 154.91, PLI_zone = 2.04). The potential ecological risk assessment revealed that Cd is the primary contributing factor to the high ecological risk in all three regions. According to the geo-accumulation index evaluation, Pb, Bi, Ni, As, Se, Cd, and Sb posed considerable threats to the environment, with Bi and Cd being the most serious pollutants in different regions. The results of the Risk Assessment Code (RAC) indicated that Cd, Mn, and Zn had relatively high risk levels. This is inconsistent with the results of the geo-accumulation index, possibly because I_geo is based solely on the total concentration of HMs. In contrast, the RAC method evaluates risk based on the bioavailable fraction of HMs. If bioavailability is overlooked [39], risk assessments based solely on total concentrations may fail to reflect the actual environmental risks, leading to overestimation. Therefore, a comprehensive risk assessment of a region requires the integration of multiple evaluation methods. It is also necessary to incorporate indicators that reflect the bioavailability of HMs in sediments. This ensures a multidimensional and comprehensive understanding of the dynamic behavior and biological effectiveness of HMs in the environment.

4.3. Analysis of Factors Influencing the Activity of Heavy Metals in Sediments

The above analysis revealed varying degrees of composite contamination by HMs such as Cd, Pb, and Zn in the sediments of South Dongting Lake, with differences in their respective activities. The occurrence of composite contamination involving HMs with differing activities is the result of multiple interacting factors. Therefore, this study investigates the influencing factors of HM activity from the perspectives of nutrients (TN, TP, TOC), physicochemical parameters (pH, EC), and sedimentary characteristics (sand, silt, clay). Correlation analysis is a commonly used method to infer relationships among factors; more significant correlations often indicate similar sources or analogous environmental behavior [40,41]. After standardizing the data, Pearson correlation analysis was conducted. The results (Figure 6) revealed the following: (1) Tl, Co, Cd, Sb, and Zn were significantly correlated with clay particles (r > 0.22, p < 0.05), suggesting that these metals are primarily associated with clay minerals. Meanwhile, V, Cr, Cu, Bi, Co, and Ni were positively correlated with silt particles (r > 0.22, p < 0.01), indicating that they are predominantly hosted in primary minerals such as quartz sandstones. (2) All HMs, except Pb, Tl, and Co, showed significant negative correlations with pH, indicating that lower pH (higher acidity) corresponds to increased metal activity. Thus, pH is a key factor influencing HM activity in the sediments of South Dongting Lake. (3) Bi, As, Se, Mn, and Ni exhibited relatively strong correlations with electrical conductivity (r > 0.20, p < 0.01), suggesting that their activity is primarily influenced by redox conditions. (4) Mn, As, Zn, Cd, and Tl showed significant positive correlations with TP (r > 0.11, p < 0.01), V was strongly correlated with TN (r = 0.37, p < 0.01), and Bi, Cr, and V were significantly positively correlated with TOC (r > 0.24, p < 0.01). These results suggest that nutrient levels influence the activity of these HMs in sediments. Notably, although Pb showed no strong correlation with individual environmental factors, it was highly correlated with Cd, Cu, Zn, and As (r > 0.72, p < 0.01), indicating similar environmental behavior. Hence, the factors influencing these metals are likely to also affect Pb. Thus, the factors influencing Pb activity in the sediments of South Dongting Lake are complex and result from the combined effects of multiple environmental parameters.

In summary, the composite contamination of HMs in the sediments of South Dongting Lake is primarily influenced by sediment particle size, pH, electrical conductivity, and nutrient concentrations. Specifically, Cd, Zn, and Tl are mainly affected by a combination of grain size, pH, and nutrients. V, Cr, Bi, Mn, As, and Se are largely influenced by redox conditions and nutrient levels. Co, Sb, Cu, and Ni are primarily governed by sediment particle size. Pb is influenced by a combination of factors, including sediment particle size, pH, electrical conductivity, and nutrient concentrations.

4.4. Source Analysis of Heavy Metals in Sediments

To further investigate the sources of HMs with varying activity in the sediments of South Dongting Lake, the Positive Matrix Factorization (PMF) model was applied to 12 HMs—V, Cr, Cu, Pb, Tl, Co, Ni, As, Cd, Sb, Mn, and Zn (excluding Bi, Se, and Fe due to detection limit constraints). Experimental data were input into the EPA PMF 5.0 software, with the number of factors set between 3 and 6. Twenty iterations were performed for each run to compare results across different factor numbers. When the number of factors was set to four, both Q_robust and Q_true stabilized at 168.0, all HMs were classified as “Strong” (S/N > 8.9), residuals mostly fell within the range of −3 to 3, and R² values exceeded 0.62 for all metals, indicating a high goodness-of-fit between observed and predicted values. The results were stable and reliable [42,43,44], confirming that the model effectively explains the variance in the original data. The source contributions and profiles derived from PMF analysis are shown in Figure 7.

Factor 1 explained 25.4% of the total HM load, with Cd accounting for 75% of this factor, indicating a strong association between Factor 1 and Cd contamination. Additionally, As (45%), Pb (31%), and Zn (31%) also contributed significantly to this factor. As shown in Figure 2, peak concentrations of these metals are mainly located in the Xiangjiang River Basin. Previous studies [45] have revealed that the upper reaches of the Xiangjiang River are rich in non-ferrous metal resources, and industrial activities such as mining and smelting discharge wastewater, waste gas, and slag, which contribute to elevated levels of HMs such as Cd, Pb, and Zn in downstream sediments. Arsenic is mainly governed by the geological background, where weathered bedrock releases trace elements that are subsequently transported and deposited in lakes and rivers [43]. Therefore, Factor 1 was identified as a mixed source of metal smelting and natural geogenic input.

Factor 2 accounted for 29.1% of the total HM contribution, with notable loadings from Pb (44%), Cr (42%), V (40%), and Cu (38%). Studies have indicated that up to 80% of Pb in South Dongting Lake sediments originates from anthropogenic sources [23,24]. The Zishui Basin is characterized by intensive agricultural activities, where HMs such as Pb, Cr, Cu, As, and Zn are commonly used as feed additives in livestock farming [46,47,48]. These metals are excreted in animal waste and subsequently applied to farmland as organic fertilizer [49]. Figure 2 shows that V is primarily enriched near the ZE, further supporting this interpretation. Thus, Factor 2 was identified as an agricultural source.

Factor 3 explained 25.1% of the total contribution, with Ni (56%) and Tl (38%) as the representative elements. Ni is predominantly influenced by natural processes such as rock weathering, erosion, and fluvial transport [45,50,51]. Tl also originates mainly from geogenic sources [23,24]. Therefore, Factor 3 was identified as a natural source.

Factor 4 accounted for 20.4% of the total contribution, with Sb (65%) being the dominant element. Previous studies have reported severe Sb contamination in the sediments of the Zishui River [52]. The upper reaches of the Zishui Basin are rich in antimony mining activities, including the world’s largest Sb mine at Xikuangshan. Mining and smelting processes release Sb into the environment through atmospheric deposition and surface runoff, resulting in elevated Sb concentrations in sediments [53,54]. In this study, Sb was found to be highly enriched near the ZE, which is consistent with previous findings. Therefore, Factor 4 was identified as an Sb-related metal smelting source.

5. Conclusions

(1)

In the surface sediments of South Dongting Lake, HMs such as Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibit considerable variability (Cv > 0.20), while metals including V, Tl, and Co show relatively stable concentrations (Cv < 0.20) and more uniform spatial distribution. The concentrations of all 15 HMs exceed background values for the upper continental crust of eastern China, the Yangtze River, and Dongting Lake. Spatially, HM concentrations display significant heterogeneity, with the highest levels observed in XE, followed by the northeast region. Pb, Bi, As, Cd, Mn, and Zn are markedly enriched in both regions, while the ZE shows the lowest levels, with Sb being the only metal showing notable enrichment.

(2)

Integrated BCR and DGT analyses revealed that V, Cr, Cu, Ni, and As predominantly exist in the residual fraction (F4). Pb and Co are mainly present in the oxidizable fraction (F3), exhibiting relatively high mobility, with their concentrations increasing from pore water to overlying water. Mn and Zn are primarily associated with exchangeable and reducible fractions (F1 and F2), showing strong mobility, with concentrations decreasing from pore water to overlying water. These patterns indicate that the release of metals into pore water varies with depth and oxygen availability. Cd is mainly found in the F1 fraction, and its unstable concentration profile is influenced by both its chemical speciation and the sedimentary environment.

(3)

The geo-accumulation index (I_geo) indicated that Pb, Bi, Ni, As, Se, Cd, and Sb—particularly Bi and Cd—pose a relatively serious threat to the environment. Pollution load index (PLI) assessments showed that all three zones are heavily polluted, in the order of XE (PLI_zone = 2.64) > northeast region (PLI_zone = 2.56) > ZE (PLI_zone = 2.04). Risk Assessment code (RAC) analysis revealed higher risk levels for Cd, Mn, and Zn. According to the Potential Ecological Risk Index (RI), Cd was identified as the primary contributor to ecological risk in all three regions. The comprehensive RI values indicated predominantly high ecological risk, consistent with the PLI ranking: XE (RI = 597) > northeast region (RI = 366) > ZE (RI = 154.91), with Cd being the dominant contributor to RI.

(4)

The composite contamination of HMs in South Dongting Lake sediments is influenced by multiple environmental factors. The contamination levels of Cd, Zn, and Tl are mainly affected by sediment grain size, pH, and nutrient content. V, Cr, Bi, Mn, As, and Se are more susceptible to the combined effects of electrical conductivity and nutrient concentrations. The distribution of Co, Sb, Cu, and Ni is closely linked to sediment texture. Pb contamination is driven by a combination of grain size, pH, electrical conductivity, and nutrient levels.

(5)

Based on the PMF model, the primary sources of HMs in the sediments were identified as metal smelting–natural mixed sources (25.4%), agricultural sources (29.1%), natural sources (25.1%), and mining and smelting sources (20.4%).