Heavy Metal Contamination in Surface Sediments of Wanfeng Lake, Southwest China: Spatial Distribution Patterns and Ecological Risk Assessment

Abstract

Heavy metal pollution poses a significant threat to aquatic ecosystems and drinking water sources, necessitating comprehensive environmental assessments. This study investigated the spatial distribution, potential ecological risks, and sources of heavy metals in the surface sediments of Wanfeng Lake to inform effective pollution management strategies. Twelve sediment samples were collected and analyzed for eight heavy metals (Hg, As, Cu, Pb, Cd, Cr, Ni, Zn) using inductively coupled plasma mass spectrometry. The geo-accumulation index (I_geo) and potential ecological risk index (RI) were applied to assess contamination levels and ecological risks. Cluster analysis and Kriging interpolation were used to identify potential pollution sources and spatial patterns. Results revealed that heavy metal concentrations decreased in the order Zn > Pb > Cu > Cr > Ni > As > Cd > Hg, with Hg concentrations exceeding the national average for Chinese lake sediments. Ecological risk assessments identified Hg (0.06 μg/g) and Cd (0.10 μg/g) as the priority pollutants. The Hg posed a moderate ecological risk, particularly in upstream areas, while Cd pollution was concentrated downstream. Atmospheric deposition was identified as the primary source of Hg, whereas agricultural activities were determined to be the main driver of Cd contamination. These findings provide a scientific basis for developing targeted pollution control measures in Wanfeng Lake.

1. Introduction

Heavy metals are persistent, highly toxic, and bioaccumulative pollutants widely distributed in the environment [1]. Unlike many organic pollutants, these hazardous substances are hardly degraded by microorganisms in water. Instead, they are enriched and amplified in aquatic organisms through the food web, leading to long-lasting and irreversible pollution. Furthermore, heavy metals can migrate with particulate matter in various aquatic systems and ultimately deposit in sediments [2]. This makes sediment monitoring crucial for assessing heavy metal pollution in both unpolluted and seriously polluted water bodies. Therefore, there is a pressing need to analyze the pollution status and spatial distribution of heavy metals, track their sources, and conduct risk assessments, particularly in the surface sediments of drinking water sources. Such work can provide not only a basis for water quality assessment but also a scientific foundation for environmental risk management decisions [3].

Wanfeng Lake, located in Xingyi City, Qianxinan Buyi and Miao Autonomous Prefecture, Guizhou Province, serves as a junction for the provinces of Guizhou, Yunnan, and Guangxi, with its waters connecting the three regions [4]. It is a large valley reservoir formed by the construction of the TianShengQiao first-level hydropower station, a national key engineering project. Covering an area of 176 square kilometers and having a total storage capacity of 10.26 billion cubic meters, it is one of China’s five largest artificial freshwater lakes [5]. Wanfeng Lake serves multiple critical functions, including power generation (as an important energy base for the “west-to-east power transmission” project), navigation, tourism, and providing water for urban, agricultural, and industrial use in the “Pearl River Delta” economic zone [6]. However, since the rapid development of tourism and irrational exploitation began around 2002, water quality has deteriorated rapidly, inevitably impacting the health of residents living along its shores. In recent years, water quality in this region has continued to decline due to high population density, rapid economic development, and frequent human activities in the reservoir area, posing a major constraint on sustainable development. Current research on Wanfeng Lake primarily focuses on water quality pollution, eutrophication assessment, and aquatic organisms [7,8]. While some researchers have measured the content of certain heavy metals in the sediment, a comprehensive risk assessment has often been lacking [9]. Other studies have been limited to the contamination distribution and assessment of specific individual metals [10]. Consequently, there is a significant gap in the investigation and research on the spatial distribution and comprehensive risk assessment of multiple heavy metals throughout the sediments of Wanfeng Lake.

Assessing heavy metal pollution in sediments serves guiding, and early warning functions, and provides essential background data for remediation efforts [11]. To date, a variety of methods for assessing heavy metal risk have been developed, including the potential ecological risk index, geo-accumulation index, environmental quality benchmarks, pollution load index, and Nemero comprehensive pollution index. Each method has its own characteristics and applicability. For instance, the geo-accumulation index and sediment enrichment factor are based on the total content of heavy metals, providing a general understanding of pollution degree but struggling to distinguish between natural and anthropogenic sources or reflect the chemical activity and bioavailability of metals in sediments [12]. In contrast, the potential ecological risk index is a widely used method that is simple, rapid, and accurate. It integrates chemical analysis, data aggregation, biological toxicity, and index sensitivity, while also considering geographical variations in background values [13].

However, it has limitations, such as subjective determination of toxicity weighting coefficients, lack of consideration for synergistic or antagonistic effects, and insufficient reflection of the influence of water chemistry, hydrology, geomorphology, and other sedimentary environmental conditions on element geochemical distribution. Given these limitations, accurately reflecting the contamination status of heavy metals in sediments often requires a combination of multiple evaluation methods. For example, Li Fei et al. [14] used sediment quality guidelines and the Häkanson potential ecological risk index [15] to assess heavy metal pollution in Dongting Lake. Yu Fei et al. [16] employed the Nemero index and the potential ecological risk index for soil heavy metal risk assessment. Liu Xin et al. [17] conducted a comprehensive evaluation of heavy metal contamination and ecological risk in surface sediments of a freshwater ecosystem in Zhenjiang City using four indicators: pollution factor, enrichment coefficient, geoaccumulation index, and potential ecological risk.

To clarify the pollution status and sources of heavy metals in sediments, in-depth research is needed to understand their potential ecological risks. This is essential for maintaining the stability of water resources in Wanfeng Lake and safeguarding both ecological security and the sustainable, coordinated development of the reservoir—factors that are vital for the region’s economic and social well-being. In this study, twelve sampling sites along Wanfeng Lake were selected for surface sediment sampling and analysis. The concentrations of eight heavy metals (Hg, As, Cu, Pb, Cd, Cr, Ni, and Zn) were measured to reveal their spatial distribution in surface sediments. The study also sought to analyze the degree of pollution and the ecological risk associated with these heavy metals, aiming to provide a scientific foundation for the comprehensive management of heavy metal pollution in Wanfeng Lake.

2. Materials and Methods

2.1. Sample Collection and Laboratory Analysis

Twelve sampling sites were established across Wanfeng Lake, and their precise locations were recorded using GPS (Figure 1). At each site, surface sediment samples (0–3 cm) were carefully collected with a layered box mud collector (WH500-LK, Qingdao Lanke Marine Instrument Equipment Co., Ltd., Qingdao, China), and subsequently transferred into glass bottles. In total, 12 surface sediment samples were obtained. All sediment samples were immediately stored in a vehicle-mounted refrigerator at 4 °C and transported to the laboratory for further analysis.

Upon arrival at the laboratory, sediment samples were processed by removing visible impurities such as gravel, snails, and plant roots, and then stored at –20 °C. These samples were subsequently freeze–dried to constant weight using a vacuum freeze dryer, ground with a ball mill, sieved through a 0.25 mm nylon mesh, and placed in labeled, airtight bags before storage in a desiccator until analysis, following the procedures described by [18]. The concentrations of eight heavy metals (Hg, As, Cu, Pb, Cd, Cr, Ni, and Zn) in sediment samples were determined using an inductively coupled plasma mass spectrometer (ICP-MS, Agilent 7500a, Santa Clara, CA, USA) [19]. Sediment samples were digested with a mixture of HNO₃, HCl, HF, and H₂O₂ using a microwave digestion system. The heavy metal concentrations detected in blanks were below 0.1 μg·L⁻¹. The relative standard deviation among duplicates was less than 5%, confirming that all results met the established quality control criteria [20].

2.2. Index of Geo-Accumulation Method

To quantitatively evaluate heavy metal contamination in the surface sediments, the geo-accumulation index (I_geo) method was employed. This index was calculated for each heavy metal using the following formula [21]:

$${\mathrm{I}}_{\mathrm{geo}}={\log }_2\left[{\mathrm{C}}_{\mathrm{n}}/\left(1.5\times {\mathrm{B}}_{\mathrm{n}}\right)\right]$$

(1)

where C_n is the measured value of heavy metals in sediment, B_n is the background value of heavy metals in sediment, mg·kg⁻¹; elemental background values are from the average value of Chinese lake sediment [22]. In the study and application of the geo-accumulation index, Müler’s grading standard is generally used [21], which divides the contamination level from clean to very heavy pollution into seven pollution levels, and the grading criteria are shown in

Table 1. Geo-accumulation index grading.

Igeo Value	≤0	(0,1]	(1,2]	(2,3]	(3,4]	(4,5]	>5
Igeo class	0	1	2	3	4	5	6
Pollution level	Clean	Mild	Moderate	Moderate to heavy	Heavy	Serious	Extremely heavy

.

In Formula (1), C_n represents the measured concentration of the heavy metal in the sediment sample (mg·kg⁻¹), and B_n is the corresponding elemental background value (mg·kg⁻¹). The background values (B_n) utilized in this study were derived from the average concentrations of heavy metals found in Chinese lake sediments [22]. The calculated I_geo values were then interpreted based on Müler’s classification system [21], which categorizes the contamination levels into seven grades, ranging from “clean” to “extremely heavy”. The specific grading criteria used for this interpretation are detailed in Table 1.

2.3. Potential Ecological Risk Index Method

The potential ecological risk index method, initially developed by Lars Häkanson in 1980 [15], was employed to evaluate the potential ecological risks associated with heavy metal contamination in the surface sediments. This approach is widely applied in the risk assessment of heavy metals within aquatic environments [23]. The calculation procedure followed established methodologies [24], encompassing several steps:

First, the contamination factor ($C_{\mathrm{f}}^{\mathrm{i}}$) for each individual heavy metal was determined using the ratio of the measured concentration to its background value. Here, $C_R^i$ represents the measured concentration (mg·kg⁻¹) of the heavy metal in the sediment, while $C_n^i$ denotes the corresponding background concentration (mg·kg⁻¹). The background values employed were derived from the average levels reported for heavy metals in Chinese lake sediments. Second, specific toxic-response factors $T_f^i$ for each heavy metal were incorporated to reflect their inherent toxicity and the sensitivity of the aquatic ecosystem [15]. These factors were taken as follows: Hg = 40, As = 10, Cu = 5, Pb = 5, Cd = 30, Cr = 2, Ni = 5, and Zn = 1. Subsequently, the potential ecological risk factor (${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$) for a single element was calculated by multiplying its contamination factor by its toxic-response factor:

$$E_f^i=T_f^i\times C_f^i$$

Finally, the comprehensive potential ecological risk index (RI) for a sampling site was computed as the sum of the potential ecological risk factors of all analyzed heavy metals:

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

The calculated values for RI were interpreted according to established classification schemes [15,24], which categorize the risk levels from low to high. The specific classification criteria used for both individual element risk and the combined risk index are presented in detail in

Table 2. The relation between ${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$, RI, and degree.

${\mathbf{E}}_{\mathbf{f}}^{\mathbf{i}}$	Potential Ecological Risk of Individual Elements	RI	Comprehensive Potential Ecological RISK
${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$ < 40	Low	RI < 150	Low
40 ≤ ${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$ < 80	Moderate	150 ≤ RI < 300	Moderate
80 ≤ ${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$ < 160	Considerable	300 ≤ RI < 600	High
160 ≤ ${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$ < 320	High	RI ≥ 600	Serious
${\mathrm{E}}_{\mathrm{f}}^{\mathrm{i}}$ ≥ 320	Very high

.

2.4. Cluster Analysis

To investigate the potential sources of heavy metal pollution in the sediment samples from Wanfeng Lake, cluster analysis was performed [25]. This multivariate statistical method groups samples or variables based on the similarity of their characteristics, typically assessed by calculating distances between data points. In this study, hierarchical clustering was specifically applied to the heavy metal concentration data. This technique was used to examine the relationships and patterns among the different heavy metals, aiding in the preliminary identification of potential pollution source types based on their co-occurrence and concentration profiles.

3. Results

3.1. Contents and Spatial Distribution of Heavy Metals in Surface Sediments

Heavy metal concentrations in the surface sediments of Wanfeng Lake exhibited considerable spatial variability. The average contents (and ranges) for each element were determined as follows: Zn 31.72 μg/g (15.39–75.20), Pb 11.55 μg/g (2.93–38.75), Cu 10.34 μg/g (3.53–23.85), Cr 9.28 μg/g (5.10–12.41), Ni 5.11 μg/g (2.60–8.30), As 1.86 μg/g (0.97–4.03), Cd 0.10 μg/g (0.03–0.16), and Hg 0.06 μg/g (0.02–0.15), as shown in

Table 3. Descriptive statistics of each metal content.

	Hg	As	Cu	Pb	Cd	Cr	Ni	Zn
Ave.	0.06	1.86	10.34	11.55	0.1	9.28	5.11	31.72
Max.	0.15	4.03	23.85	38.75	0.16	12.41	8.3	75.2
Min.	0.02	0.97	3.53	2.93	0.03	5.1	2.6	15.39
S.D.	0.04	0.82	6.97	8.99	0.04	2.22	1.43	15.68
CV	0.69	0.44	0.67	0.78	0.43	0.24	0.28	0.49
BSX [26]	0.1437	58.91	60.52	58.64	0.5459	138.2	67.19	134.2
ASC [22]	0.053	12.1	31.7	31	0.194	85	36.8	88
ASSC [27]	0.075	13.1	25	32.3	0.23	67	29	81

. Based on these average levels, the elements were ranked in descending order of abundance: Zn > Pb > Cu > Cr > Ni > As > Cd > Hg. Significant spatial heterogeneity was evident, particularly for Pb, Hg, and Cu, which displayed high coefficients of variation (77.8%, 68.8%, and 67.4%, respectively). Coefficients exceeding 50% suggested an uneven distribution across the lakebed, likely reflecting anthropogenic influences and indicating the potential presence of localized point sources at certain sampling sites.

Comparisons with established reference values provided further context for the observed concentrations. Relative to the average values reported for Chinese lake sediments, the average Hg content was higher, the average Cd content was comparable, and the average contents of all other elements were lower. When compared to the average sediment values of the southern China water system, both the mean Hg and Cd contents were close to the regional average, while the mean contents of the remaining elements were lower. Notably, the average concentrations of all analyzed heavy metals did not exceed the background values for soil in Xinyi. Furthermore, an examination of the maximum recorded concentrations revealed that the highest values for Hg and Pb surpassed the average Chinese lake sediment values. This finding highlighted specific locations with notably elevated concentrations of these elements, which warrant particular attention in future monitoring and management efforts.

The spatial distribution of heavy metals in the surface sediments of Wanfeng Lake was illustrated in Figure 2. Peak concentrations of Hg, As, Cu, Pb, and Ni were observed at sampling point No. 2, located in the upstream region. In contrast, elevated levels of Cd and Zn were primarily concentrated in the middle and downstream reaches. Hg concentrations were also relatively high across the middle and upper reaches, exhibiting a gradual decrease towards the downstream area. Elevated Cu levels were additionally noted at sampling sites No. 10 and 11 in the downstream. The distribution of Zn also showed high concentrations mainly in the middle and downstream, with comparatively lower levels detected in the upper reaches. Overall, significant spatial heterogeneity in the distribution of these heavy metals was evident across Wanfeng Lake. This variation is likely linked to the different types of human activities prevalent around the lake. The upstream and middle sections are characterized by tourism and recreational activities [28,29], including the Wanfeng Lake Scenic Area, numerous fishing gardens, and farmhouses. Conversely, the downstream region is predominantly agricultural. These distinct land uses and associated human activities may contribute to the observed differences in heavy metal concentrations and spatial patterns.

3.2. Risk Assessment of Heavy Metals in Surface Sediments

Figure 3 presents the results of the geo-accumulation index (I_geo) evaluation for heavy metals in the surface sediments of Wanfeng Lake. For most elements (As, Cu, Pb, Cd, Cr, Ni, and Zn), I_geo values were consistently below 0, indicating negligible to no contamination (eigenstate). In contrast, several sampling sites showed I_geo values greater than 0 for Hg, suggesting mild contamination. Considering these I_geo results alongside the spatial concentration patterns (Figure 2), a potential for Hg contamination was identified in the upstream sediments. Specifically, sampling sites No. 2 and No. 5 exhibited mild Hg pollution, suggesting that localized anthropogenic activities may be contributing to Hg contamination in certain upstream lake sections.

Assessment of potential ecological risk provided further insights. Figure 4 summarizes the potential ecological risk index ($E_f^i$) for each heavy metal. The average $E_f^i$ values (with ranges in parentheses) were calculated as follows: Hg, 46.52 (18.27–109.58); As, 1.54 (0.80–3.33); Cu, 1.63 (0.56–3.76); Pb, 1.86 (0.47–6.25); Cd, 14.82 (4.02–25.24); Cr, 0.22 (0.12–0.29); Ni, 0.69 (0.35–1.13); and Zn, 0.36 (0.17–0.85) (Figure 4). Compared to the other elements, Hg exhibited a significantly higher Eir, placing it in a medium ecological hazard category, and certain sites demonstrated high potential ecological risk specifically from Hg. In contrast, all sampling points for As, Cu, Pb, Cd, Cr, Ni, and Zn fell into the low potential ecological risk level. These findings indicate that Hg is the primary contributor to potential ecological hazards in the surface sediments of Wanfeng Lake, aligning with the conclusions drawn from the geo-accumulation index analysis. Spatial variation in the potential ecological risk of Hg was observed (Figure 4), with elevated risk levels consistently found in the upstream northeast corner (sampling sites No. 1–3) and middle/upstream reaches (sampling sites No. 5–7) compared to other locations.

Figure 5 presents the comprehensive potential ecological risk index (RI) for each sampling site. The sites were ranked by increasing RI values as follows: No. 4 (38) < No. 11 (46) < No. 8 (47) < No. 12 (52) < No. 6 (54) < No. 3 (58) < No. 1 (60) < No. 10 (64) < No. 7 (66) < No. 9 (67) < No. 2 (123) < No. 5 (136). RI values across all sampling sites ranged from 38 to 136, with an average of 68, indicating an overall slight risk level for the lake. Although sampling sites No. 2 and No. 5 showed the highest comprehensive ecological risk levels, their RI values remained within the slight risk category (below 150). This suggests that while the overall heavy metal risk level in Wanfeng Lake is relatively low, certain sampling sites are subject to localized anthropogenic influence. Consistently, Hg was the largest contributor to the potential risk index at each site, with a mean contribution of 65.34% and a maximum of 86.66%. These findings underscore the need to prioritize the management of Hg pollution in the ecological protection efforts for Wanfeng Lake.

3.3. Correlation and Source Analysis of Heavy Metals

Figure 6 presents the results of the dual hierarchical cluster analysis (DHCA). The vertical dendrogram illustrates the clustering of heavy metals in the surface sediments, grouping the eight analyzed heavy metal elements into three distinct clusters. Cluster 1 comprised Hg, Cd, As, and Ni, while Cluster 2 included Cu, Cr, and Pb. Zn formed a separate cluster (Cluster 3). These results suggest that elements within Cluster 1 likely share similar sources or environmental behaviors, distinct from those in Cluster 2 and the unique profile of Zn. Based on the potential ecological risk assessment results (Figure 4), Hg and Cd exhibited the highest levels of contamination and risk among the analyzed elements. While both Hg and Cd can originate from various anthropogenic activities, they are commonly associated with diffuse surface pollution sources such as agricultural fertilizers and pesticides. Specifically, Hg is highly volatile, with major anthropogenic inputs to aquatic systems including atmospheric deposition, urban emissions, agriculture, mining, and combustion/industrial processes [30]. In the upstream Wanfeng Lake scenic area, characterized by high visitor flow, atmospheric deposition, and emissions may contribute to elevated Hg concentrations. Conversely, the middle and downstream reaches, dominated by agricultural land use, likely experience inputs of Cd from the extensive use of fertilizers and pesticides in cultivation.

The horizontal dendrogram represents the clustering of the sampling sites, which were categorized into four groups by DHCA. Cluster 1 consisted solely of sampling site No. 8, and Cluster 2 comprised only sampling site No. 2. Cluster 3 grouped sampling sites No. 9, 10, and 11 located in the downstream region of Wanfeng Lake. The remaining sites were clustered together as the fourth category. With the exception of sampling site No. 12, the sites within the fourth cluster were predominantly distributed in the upstream and midstream areas. This spatial grouping of sites based on their heavy metal profiles is generally consistent with the observed pollution distribution patterns across Wanfeng Lake (Figure 2). The DHCA results, particularly the isolation of high-risk sites like No. 2 and No. 5 into distinct or small clusters (No. 5 being in the fourth cluster, but potentially a driver of its separation), further support the notion that heavy metal pollution, especially from specific elements like Hg, is concentrated in certain middle and upper reaches of Wanfeng Lake, likely influenced by localized anthropogenic activities.

4. Discussion

Comparing the heavy metal concentrations in Wanfeng Lake surface sediments with those reported for other freshwater lakes on the Yunnan–Guizhou Plateau (as shown in Table 4), the heavy metal pollution in Wanfeng Lake sediments appears relatively low. Specifically, the levels of various heavy metals (Hg, As, Cu, Pb, Cd, Cr, Ni, and Zn) in nearby plateau lakes such as Caohai Lake, Hongfeng Lake, and Baihua Lake were significantly higher than those observed in Wanfeng Lake. This discrepancy likely stems from a combination of factors, including the relatively high environmental background values inherent to Guizhou Province and the effective pollution prevention and control measures implemented around Wanfeng Lake, given its critical role as a drinking water source.

Furthermore, when compared to other lakes known to receive substantial sewage inputs (Table 5), Wanfeng Lake exhibited lower Hg content than most, with levels closer to those found in Taihu Lake. Similarly, the concentrations of As, Cu, Pb, Cd, Ni, and Zn were considerably lower than in these lakes. However, the average concentration of Cr in Wanfeng Lake sediments was noted to be higher than that in Taihu Lake. Overall, the current heavy metal content in Wanfeng Lake surface sediments remains low, with only Hg posing a moderate ecological risk based on preliminary assessments. It is important to note that the pollution levels in other lakes within the surrounding region are comparatively higher. This regional pattern may be attributed to Wanfeng Lake’s location within a typical karst area of southwestern China, characterized by abundant mineral resources and consequently elevated natural background levels of heavy metals. Additionally, the legacy of historical human activities, particularly mercury mining and smelting in the region, likely contributes to the observed heavy metal distribution. Therefore, continuous monitoring and focused efforts on pollution prevention are crucial to maintaining the current favorable environmental quality of Wanfeng Lake. What is more, it is acknowledged that water depth, which was not systematically measured in this study, can influence sediment characteristics and pollutant accumulation. Future comprehensive investigations in Wanfeng Lake could benefit from integrating bathymetric data and water depth measurements at sampling sites to further elucidate the role of hydrodynamic conditions in the spatial distribution of heavy metals, complementing the source apportionment findings presented here [31,32].

Table 4. Comparison of the mean value of heavy metal content in surface sediments of Wanfeng Lake with other lakes and reservoirs on the Yunnan–Guizhou Plateau Plateau.

Nearby Lake Reservoirs	Geographic Location	Average Concentrations of Heavy Metal (mg-kg−1)								Data Source
		Hg	As	Cu	Pb	Cd	Cr	Ni	Zn
This study	Southwest China (Guizhou)	0.06	1.86	10.34	11.55	0.10	9.28	5.11	31.72
Weining Caohai	Southwest China (Guizhou)	0.46	21.81	19.75	41.27	9.74	49.81	-	307.00	[33]
Red Maple Lake	Southwest China (Guizhou)	0.59	34.90	90.00	43.50	1.50	-	-	125.00	[34]
Lake Hundred Flowers	Southwest China (Guizhou)	0.45	26.23	43.16	27.84	0.61	76.38	-	-	[35]
Lake Aha	Southwest China (Guizhou)	-	28.74	107.63	40.51	-	103.11	216.59	398.78	[36]
Fuxian Lake	Southwest China (Yunnan)	-	-	74.00	75.00	-	89.00	42.00	175.00	[37]
Jianhu	Southwest China (Yunnan)	-	-	46.10	56.40	0.41	144.00	-	147.00	[38]

Table 4. Comparison of the mean value of heavy metal content in surface sediments of Wanfeng Lake with other lakes and reservoirs on the Yunnan–Guizhou Plateau Plateau.

Nearby Lake Reservoirs	Geographic Location	Average Concentrations of Heavy Metal (mg-kg−1)								Data Source
		Hg	As	Cu	Pb	Cd	Cr	Ni	Zn
This study	Southwest China (Guizhou)	0.06	1.86	10.34	11.55	0.10	9.28	5.11	31.72
Weining Caohai	Southwest China (Guizhou)	0.46	21.81	19.75	41.27	9.74	49.81	-	307.00	[33]
Red Maple Lake	Southwest China (Guizhou)	0.59	34.90	90.00	43.50	1.50	-	-	125.00	[34]
Lake Hundred Flowers	Southwest China (Guizhou)	0.45	26.23	43.16	27.84	0.61	76.38	-	-	[35]
Lake Aha	Southwest China (Guizhou)	-	28.74	107.63	40.51	-	103.11	216.59	398.78	[36]
Fuxian Lake	Southwest China (Yunnan)	-	-	74.00	75.00	-	89.00	42.00	175.00	[37]
Jianhu	Southwest China (Yunnan)	-	-	46.10	56.40	0.41	144.00	-	147.00	[38]

Table 5. Average heavy metal content in surface sediments of lakes with typical exogenous pollution inputs.

Nearby Lake Reservoirs	Geographic Location	Average Concentrations of Heavy Metal (mg-kg−1)								Data Source
		Hg	As	Cu	Pb	Cd	Cr	Ni	Zn
Dongting Lake	Central China (Hunan)	0.157	29.71	47.48	60.99	4.65	88.29	-	185.25	[14]
Erhai	Southwest China (Yunnan)	0.167	26.9	63.1	47.4	1.10	103.8	52.2	109	[37]
Taihu	Eastern China (Jiangsu)	0.08	13.77	34.20	36.36	0.42	1.85	43.75	116.40	[39]
East Campsite River	Eastern China (Zhejiang)	-	48.49	47.87	41.73	1.08	67.88	39.18	200.62	[40]
Hefei drinking water source	Central China (Anhui)	0.098	24.41	21.68	26.40	0.258	45.66	25.26	61.35	[41]
Lake Townsend	Central China (Hubei)	0.17	12.88	51.28	41.60	0.66	85.28	40.49	145.01	[42]
Houguan Lake	Central China (Hubei)	0.393	30.0	38.6	39.3	2.68	76.3	-	90.7	[43]

Table 5. Average heavy metal content in surface sediments of lakes with typical exogenous pollution inputs.

Nearby Lake Reservoirs	Geographic Location	Average Concentrations of Heavy Metal (mg-kg−1)								Data Source
		Hg	As	Cu	Pb	Cd	Cr	Ni	Zn
Dongting Lake	Central China (Hunan)	0.157	29.71	47.48	60.99	4.65	88.29	-	185.25	[14]
Erhai	Southwest China (Yunnan)	0.167	26.9	63.1	47.4	1.10	103.8	52.2	109	[37]
Taihu	Eastern China (Jiangsu)	0.08	13.77	34.20	36.36	0.42	1.85	43.75	116.40	[39]
East Campsite River	Eastern China (Zhejiang)	-	48.49	47.87	41.73	1.08	67.88	39.18	200.62	[40]
Hefei drinking water source	Central China (Anhui)	0.098	24.41	21.68	26.40	0.258	45.66	25.26	61.35	[41]
Lake Townsend	Central China (Hubei)	0.17	12.88	51.28	41.60	0.66	85.28	40.49	145.01	[42]
Houguan Lake	Central China (Hubei)	0.393	30.0	38.6	39.3	2.68	76.3	-	90.7	[43]

Various methods have been developed globally for assessing heavy metal pollution risks in aquatic sediments, including the potential ecological risk index (RI), geo-accumulation index (I_geo), pollution load index (PLI), Nemero comprehensive pollution index (NCPI), sediment quality guidelines (SQGs), enrichment factor (EF), and secondary and primary ratio (SPR) [23]. Among these, Müller’s I_geo [21], Häkanson’s RI [15], and Zoller’s EF [44] are frequently utilized, each offering distinct perspectives [23]. The I_geo and NCPI methods are favored for their computational simplicity and clear interpretability. I_geo, in particular, accounts for both natural geological formations and potential anthropogenic influences alongside background chemical values, although it assesses only single metal elements and does not capture the synergistic effects of multiple pollutants. Conversely, the NCPI highlights the most severe pollution drivers but can disproportionately emphasize the impact of extreme values on overall environmental quality. A notable limitation of both methods is their inability to directly incorporate the toxicological effects of heavy metals on living organisms [45].

In contrast, the RI method is widely adopted due to its consideration of both heavy metal concentrations and their associated toxicity response factors [46]. This index is designed to reflect several key aspects: ① the RI should increase commensurate with the degree of pollution; ② sediments contaminated by multiple metals should yield a higher RI than those affected by only a few; ③ highly toxic metals contribute more significantly to the overall RI; and ④ water bodies with greater sensitivity to metal pollution should exhibit higher RI values. Thus, the RI method provides a comprehensive assessment by integrating both the pollution level and the potential toxicity of heavy metals [47].

Given these considerations, this study employed a combined approach utilizing both the I_geo and RI methods to assess sediment pollution in Wanfeng Lake [15,21]. This strategy was chosen to mitigate potential regional differences and to concurrently evaluate the impacts of anthropogenic activities, variations in background values, and the bio-toxic effects of heavy metals [48]. This dual methodology aimed to provide a more comprehensive understanding of the ecological risks posed by heavy metals in the lake sediments [49].

The sources of heavy metals in lake bottom sediments are broadly categorized into natural and anthropogenic origins. Natural sources encompass processes such as the sedimentary sorting of mineral elements and the natural weathering of rocks and soil parent material. Anthropogenic sources are diverse and include surface runoff, discharge of wastewater, transportation emissions, and the application of agricultural fertilizers and pesticides, among others.

China holds the third largest mercury reserves globally, primarily concentrated in eastern Guizhou Province, making it a major mercury-producing nation. Although significant mercury mining operations in Guizhou, including those at Wanshan, Buchuan, Danzhai, Tongren, Abusive Wood Factory, and Kaiyang, have ceased production and closed their pits, the prolonged history of industrial mining and smelting has resulted in substantial environmental impacts on the surrounding ecosystems, particularly the aquatic environment. Estimates suggest that atmospheric mercury emissions from Guizhou Province alone account for approximately 12% of total global anthropogenic emissions [50], rendering the province one of the most severely mercury-polluted areas in China. Consequently, it is plausible that atmospheric deposition of emissions from historical and potentially ongoing nonferrous metal activities significantly contributes to the Hg contamination observed in Wanfeng Lake surface sediments. Supporting this, previous studies have demonstrated that Hg concentrations in soils within abandoned mercury mining areas in Guizhou remain severely elevated [51]. Similarly, investigations into the impact of a power plant in Bijie, Guizhou, found that Hg concentrations in atmospheric particulate matter exceeded background values [52]. These findings collectively indicate that atmospheric deposition continues to be a principal pathway for metal pollution into lakes and soils across Guizhou Province.

Elevated concentrations of Cd may be linked to the application of phosphate fertilizers in local agriculture, as these fertilizers are generally known to contain relatively high levels of Cd [53]. While phosphate fertilizers are the primary concern, nitrogen and potassium fertilizers also contain trace amounts of Cd. A study in New Zealand, based on 50 years of continuous soil sampling at the same location, reported that soil Cd content more than doubled following the application of phosphate fertilizers [54]. Furthermore, Cd is incorporated as a heat stabilizer in the production of agricultural plastic products, such as greenhouse films and fishing nets [55]. These materials are prone to aging and degradation in the natural environment, and residual plastics, if not properly removed, can become localized sources of soil Cd pollution [45]. Ultimately, these Cd contaminants can be transported and accumulate in lake sediments via surface runoff and erosion processes.

5. Conclusions

This study characterized the spatial distribution and ecological risks associated with heavy metal contamination in the surface sediments of Wanfeng Lake. Our analysis of As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg concentrations revealed a distinct elemental abundance pattern (Zn > Pb > Cu > Cr > Ni > As > Cd > Hg). While most elements exhibited relatively low levels, mercury (Hg) concentrations significantly exceeded their corresponding background values, displaying pronounced spatial heterogeneity with hotspots identified in the upstream and midstream lake areas.

Integrated assessment using the geo-accumulation index (I_geo) and potential ecological risk index (RI) consistently demonstrated that Wanfeng Lake sediments are moderately contaminated by Hg, whereas other analyzed elements (As, Cu, Pb, Cd, Cr, Ni, Zn) pose only slight contamination risks. The comprehensive ecological risk assessment indicated an overall slight risk level for the lake (mean RI = 68, range: 38–136), though specific sites (2# and 5#) exhibited elevated risks. Critically, Hg emerged as the overwhelming determinant of ecological risk, contributing up to 86.66% of the total potential risk in certain areas.

Source apportionment analysis, primarily through clustering, strongly suggests that the elevated levels of Hg and Cd are predominantly linked to anthropogenic activities. Atmospheric deposition of metals, likely originating from regional industrial emissions, is implicated as the main source of Hg contamination. Conversely, elevated Cd levels appear primarily associated with non-point source pollution from agricultural practices within the watershed, such as the application of phosphate fertilizers and pesticides.

In conclusion, our findings underscore that despite the generally low heavy metal burden in Wanfeng Lake sediments compared to other regional lakes, Hg contamination presents a localized moderate ecological risk and is the principal element driving potential adverse effects. Effective management strategies must prioritize targeted interventions focusing on reducing atmospheric Hg emissions impacting the lake’s catchment, particularly upstream sources, and implementing best management practices in agriculture to mitigate Cd inputs from fertilizers and pesticides. These measures are essential for preserving the ecological integrity and water quality of Wanfeng Lake.