Towards More Accurate Risk Assessment of Sediment Trace Metals: Integrating Sedimentary Background Determination and Probabilistic Evaluation in Chaohu Lake, China

Abstract

Accurate ecological risk assessment of trace metals in lake sediments remains a significant challenge due to the widespread use of generalized regional background values, which often fail to capture the spatial and historical heterogeneity of sedimentary environments. This study addresses this gap by establishing sediment-specific background values of heavy metals through high-resolution core sampling from three representative zones (western, central, and eastern) of Chaohu Lake, China. The determined variation depths (36.60 cm, 21.35 cm, and 47.58 cm) allowed for the reconstruction of pre-contamination baselines for key trace metals. These refined background values were then incorporated into enhanced ecological risk assessment frameworks, including the geo-accumulation index (I_geo) and a modified potential ecological risk index (RI), which integrates chemical accumulation with toxicity units (∑TU). A probabilistic risk assessment based on the refined RI was further conducted using a large sediment dataset. The results revealed that 67.0% of the samples posed low ecological risk, while 33.0% fell into the moderate-risk category, with mercury (Hg), arsenic (As), and nickel (Ni) identified as the primary contributors. This study demonstrates that sediment-specific background values, when combined with probabilistic risk modeling, offer a more accurate, site-relevant, and scientifically grounded approach for assessing and managing trace metal contamination in lake systems.

1. Introduction

In recent years, heavy metal pollution in lakes has emerged as a critical environmental concern, driven by rapid industrialization and urban development [1,2,3]. This issue is particularly pronounced in China, where many lakes have experienced significant degradation due to anthropogenic activities, leading to severe environmental consequences [4,5]. While efforts to mitigate eutrophication in Chinese lakes have achieved some success over the past few decades, heavy metal contamination remains a serious challenge. A nationwide survey conducted in 2017 indicated that out of 138 monitored lakes, 38 exhibited varying degrees of heavy metal pollution, posing substantial risks to aquatic ecosystems [6].

Trace metals originate from both natural sources, such as rock weathering and soil erosion, and human activities, including domestic sewage discharge and industrial wastewater emissions. These metals tend to bind with suspended particulate matter and eventually settle in the sediment layer [7]. However, in shallow lakes, disturbances caused by wind and wave action can resuspend sediment-bound trace metals, releasing them into the overlying water. This process not only deteriorates water quality but also facilitates bioaccumulation through the food chain, ultimately threatening both aquatic ecosystems and human health [8,9,10]. Given that sediments act as both a major sink and a secondary source of trace metals in lake ecosystems, their role in ecological risk assessment has become a focal point of research.

Currently, classical assessment methods such as the Nemerow pollution index [11], the geo-accumulation index [12], and the potential ecological risk index [13] are commonly used to evaluate heavy metal contamination in lake sediments. For instance, an ecological risk assessment of sediments in several Chinese lakes found that trace metals such as Pb, Ni, Zn, Cu, Cd, and Hg generally posed moderate ecological risks [14]. These methods typically assess ecological risk by comparing measured metal concentrations with established background levels. However, most of these background values were derived from data collected in the previous century. Given the accelerated industrialization and evolving sedimentation dynamics in shallow lakes, these reference values may no longer accurately reflect current conditions. Consequently, risk assessments that do not account for historical variations in metal deposition may yield unreliable results. To address this limitation, investigating the historical deposition trends of trace metals in sediments is essential for refining ecological risk assessments [15].

Moreover, traditional ecological risk assessments of trace metals in lake sediments predominantly rely on deterministic risk quotient methods, which may either underestimate or overestimate potential risks [16]. To improve assessment accuracy, it is crucial to incorporate uncertainties into risk evaluations. Probabilistic risk assessment (PRA) methods have been proposed to address this issue by employing probability density distributions to characterize uncertainty and variability in ecological risk estimates [17]. This approach enhances the reliability of risk assessments and supports more effective management strategies for heavy metal contamination in sediments.

Chaohu Lake, the fifth-largest freshwater lake in China, is representative of lakes in the middle and lower reaches of the Yangtze River. Over the past few decades, particularly since the 1970s, rapid industrial and urban expansion has resulted in the discharge of significant amounts of metal-laden wastewater into the lake [18]. Consequently, numerous studies have investigated the concentrations and ecological risks of trace metals such as Cu, Pb, Zn, Cr, Cd, As, Hg, and Ni in Chaohu Lake sediments [19]. While existing studies have primarily relied on total metal concentrations and bioavailability assessments using chemical extraction techniques, the application of probabilistic risk assessment to trace metals in lake sediments remains limited [20,21].

The objectives of this study are threefold: (1) to investigate the spatial distribution of trace metals in Chaohu Lake sediments using sequential extraction techniques; (2) to establish background concentration levels based on historical sediment deposition records; (3) to conduct a quantitative probabilistic ecological risk assessment of trace metals in sediments, integrating diagnostic insights from their depositional history. The findings of this study will provide valuable guidance for lake managers and policymakers in assessing and mitigating heavy metal contamination risks in lake sediments.

2. Materials and Methods

2.1. Study Area

Chaohu Lake, situated in Hefei City, Anhui Province, China (31.60° N, 117.87° E), is a representative shallow lake within the Yangtze River Basin. It covers a vast area of approximately 769.55 km² and has a shallow depth of around 2.89 m. The lake is commonly categorized into three sections: western, central, and eastern, based on hydrodynamic conditions and geographic features. The western section, influenced by inflowing rivers, generally experiences a relatively higher flow rate. The central section, where the main island is located, exhibits the most dynamic hydrodynamic conditions, leading to complex sediment transport and deposition patterns. In contrast, the eastern section, being more enclosed, tends to have lower flow velocities, which facilitates finer sediment accumulation [22].

For the analysis of trace metals in lake sediments, 32 sediment core sampling points were systematically selected and evenly distributed across the three lake sections: the western region (W), the central region (C), and the eastern region (E) (Figure 1). Sediment cores were collected using a gravity corer to preserve the original stratification of the samples. The cores were then carefully extruded, sectioned at consistent intervals, and stored in sealed polyethylene containers. Subsequently, the samples were transported to the laboratory under controlled conditions to prevent contamination.

In the laboratory, the sediments were air-dried, homogenized, and sieved prior to chemical analysis to determine trace metal concentrations. To ascertain the metal fractionation factors (f), a sequential extraction procedure based on the modified Community Bureau of Reference (BCR) method was employed, which separates metals into exchangeable, reducible, oxidizable, and residual fractions. The fractionation factor (f) for each metal was calculated as the ratio of the concentration in a given fraction to the total concentration of the metal. Furthermore, a sensitivity analysis was conducted by varying key extraction parameters (such as reagent concentration and extraction time) within controlled ranges to assess the robustness and reproducibility of the fractionation results.

2.2. Sample Collection

Equipment provided by UWITEC of Austria, including the CORE-60 sampler and associated tools, was utilized in this research. In the spring of 2021, columnar sediment core samples were collected. To ensure representativeness, nearby locations were also sampled to mitigate channel variations. The sediment samples were categorized into five vertical layers, with careful attention to the specific height of each columnar sample. Two layers, 0–5 cm and 5–10 cm, were designated for testing. To maintain consistency, three additional layers were selected between the 5–10 cm layer and the base using an equidistant division method. It is recommended that the uppermost layers constitute more than half of the total sample height as a general guideline [23].

Sediment samples were first freeze-dried and homogenized using an agate mortar and pestle. The dried samples were then sieved through a 63 μm mesh to obtain the fine-grained fraction (<63 μm), which is considered more representative for trace metal analysis. Approximately 0.1 g of each sample was accurately weighed and subjected to acid digestion using a mixture of HNO₃-HCl-HF (3:2:1, v/v/v) in Teflon vessels at 180 °C for 8 h in a microwave digestion system. Trace metal concentrations (Cu, Pb, Zn, Cd, Cr, Ni, As, Hg) were subsequently determined using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7900; Agilent Technologies, Santa Clara, CA, USA) and atomic absorption spectrometry (AAS, PerkinElmer AAnalyst 800; PerkinElmer, Waltham, MA, USA) according to standardized protocols. Instrument calibration was performed using multi-element standard solutions, and measurement accuracy was verified through the use of certified reference materials (GBW07314; Institute of Geophysical and Geochemical Exploration, Langfang, China). Method blanks and sample duplicates were analyzed at a rate of 10% to ensure quality assurance and control (QA/QC). These included the use of certified reference materials, method blanks, and duplicate samples. Additionally, instrument calibration was performed using multi-element standard solutions with concentration ranges from 0.1 μg/L to 100 μg/L before each batch of analysis. The detection method adhered to relevant national and international testing standards. Eight trace metals were detected in the lake sediments during this study: Cd, Hg, As, Pb, Cu, Cr, Zn, and Ni.

2.3. Determination of Sediment Accumulation Rates Based on ²¹⁰Pb_ex Dating

In this study, the natural isotope ²¹⁰Pb activity (Bq·kg⁻¹) was utilized as the primary tracer for sedimentation analysis. The rationale for this choice lies in the chemical stability of ²¹⁰Pb, as well as its origin as the sole long-lived product of the radioactive decay of ²²⁶Ra, with a half-life of 22.3 years. Furthermore, the presence of ²¹⁰Pb in modern lake sediments can be attributed to atmospheric deposition and fluvial input (²¹⁰Pb_ex), along with the in situ decay of ²²⁶Ra within sediments (²¹⁰Pb_sup representing the supported fraction). The total ²¹⁰Pb activity at each sediment depth was determined using alpha spectrometry following standard radiochemical procedures. Sediment samples were first dried, ground, and homogenized, and then subjected to radiochemical separation to isolate ²¹⁰Pb. The activity of ²¹⁰Pb_sup was estimated by measuring the activity of ²²⁶Ra via its daughter isotopes (²¹⁴Pb and ²¹⁴Bi) under secular equilibrium conditions using gamma spectrometry. The ²¹⁰Pb_ex was subsequently obtained by subtracting the ²¹⁰Pb_sup from the total ²¹⁰Pb activity at each depth. The ²¹⁰Pb_ex profile was then used for sediment age modeling over multiple decades [24].

The three regions were represented by sediment cores W9, C9, and E5 in Chaohu Lake, selected based on their proximity to the central zones of the respective regions. The vertical distribution of ²¹⁰Pb_ex was analyzed under controlled laboratory conditions. In settings where the effects of natural and anthropogenic disturbances are minimal, the Constant Initial Concentration (CIC) model is preferred for sediment dating. The least squares method was employed to establish the relationship between ²¹⁰Pb_ex activity (A) and depth (h), which was then used to calculate the average sedimentation rate (cm/a), where “cm/a” represents the thickness of sediment accumulated per year. According to the CIC model, the average sedimentation rate r (cm/year) was calculated using the following formula:

$$r=-\lambda /slope$$

(1)

where λ is the decay constant of ²¹⁰Pb (0.03114 year⁻¹), and slope is obtained from the linear regression of InA(h) versus depth (h).

It is acknowledged that sediment accumulation rates based on ²¹⁰Pb_ex dating often exhibit curvilinear trends due to progressive loss over time, especially in catchments affected by anthropogenic influences since the 1940s. However, in this study, the linear trend was comprehensively evaluated based on rigorous experimental analysis. High-precision radionuclide dating techniques, strict quality control measures, and a detailed examination of sediment geochemical properties were employed to ensure the reliability of the calculated sedimentation rates. The selection of the CIC model and subsequent analytical procedures were grounded in a thorough assessment of sedimentation conditions, hydrodynamic stability, and potential external disturbances. These methodological frameworks provided a robust basis for interpreting sediment accumulation trends.

2.4. Methods of Data Processing

2.4.1. Geo-Accumulation Index (I_geo)

The geo-accumulation index (I_geo) is an effective tool for assessing heavy metal pollution in lake sediments. It quantifies the relationship between total heavy metal concentrations in sediments and their geochemical background values [25] as follows:

$$I_{geo}={\mathit{log}}_2[C_n/(K\ast C_B)]$$

(2)

where C_n represents the analyzed heavy metal concentration, while C_B denotes the corresponding background levels in lake sediment. The factor K accounts for potential variations in background levels due to diverse geological characteristics of rocks in different regions. Typically, K is set at 1.5, as suggested by Fang et al. [26]. The pollution levels of trace metals were then classified using I_geo, ranging from 0 (I_geo < 0, uncontaminated) to 6 (I_geo > 5, extremely contaminated).

2.4.2. The Potential Ecological Risk Index

The potential ecological risk index (RI), introduced by Hakanson [27], provides a straightforward method for categorizing sediment heavy metal contamination and the resulting ecological risk in aquatic environments. The method reflects the environmental impact of various trace metals in sediments and the cumulative influence within a specific environmental context. Additionally, it allows for the estimation of the potential ecological risk posed by trace metals, making it a commonly used approach in evaluating ecological risks associated with sedimentary trace metals [17]. The specific procedures are outlined as follows:

$$RI={\sum }_{i=1}^n(T_r^i\ast C_f^i)={\sum }_{i=1}^n(T_r^i\ast f\ast {\displaystyle \raisebox{1ex}{$C_n^i$}\!\left/ \!\raisebox{-1ex}{$C_B^i$}\right.})$$

(3)

where toxicity factors ($T_r^i$) were assigned to different trace metals: 30 for Cd, 40 for Hg, 10 for As, 5 for Cu and Pb, 2 for Cr, 1 for Zn, and 20 for Ni. The overall fraction percentage (f), representing the proportion of bioavailable or potentially mobile fractions relative to the total content, was determined through fractionation analysis following the method described in reference [17]. The obtained f values were 0.95 for Cd, 0.295 for Hg, 0.407 for As, 0.628 for Pb, 0.263 for Cr, 0.66 for Cu, 0.573 for Zn, and 0.433 for Ni. The potential risk index for each heavy metal was designated as $C_f^i$, with $C_n^i$ representing the concentration of heavy metal i. The corresponding background value in the lake sediments was designated as $C_B^i$.

RI can be categorized as follows: RI < 150 indicates low ecological risk, 150 ≤ RI < 300 indicates moderate ecological risk, and RI ≥ 300 indicates high ecological risk. These thresholds were used in the present study to evaluate the ecological risks of sedimentary trace metals. The higher RI values observed in the upper layers of core sediments across all three regions were taken as the representative indicators of the potential ecological risk of trace metals. To map the ecological risk distribution for trace metals in the sediments, spatial equilibrium calculations were performed using the Inverse Distance Weighting (IDW) method. Specifically, the RI values were interpolated using IDW in Origin 2023, along with the boundary vector map of Chaohu Lake to guide the spatial distribution. These calculations facilitated the determination of the ecological risk distribution across the entire study area.

2.4.3. Ecological Risk Assessment Conducted Based on the Total Volume

The toxicity unit (TU) is typically used to assess the impact of trace metals in sediments on the aquatic environment. However, it is important to acknowledge that while TU and other potential ecological risk indices provide useful indicators for evaluating contamination levels, they do not account for metal speciation or transformations over time, which may influence their actual toxicity and bioavailability. As such, these indices should be interpreted with caution, recognizing their inherent limitations.

In this study, TU was employed as one of the assessment methods to comparatively evaluate potential risks across different sampling sites, rather than as an absolute determinant of ecological impact. While this approach provides a standardized framework for risk assessment, we emphasize that it does not fully account for the complexity of metal behavior in sediments. TU is defined by the ratio of concentration ($C_n^i$) to the probable effect level (PEL, probable effect level, derived from Canada’s Assessment and Remediation of Contaminated Sediments (ARCS)) value (PELⁱ), while the definition and specific values of the threshold effect level (TEL, threshold effect level, from the Canadian Council of Ministers of the Environment (CCME 1999)) are provided [28]. The total toxic unit (∑TU) is calculated by summing the TUⁱ for each indexed heavy metal i [29]. Toxicity levels are classified based on the ∑TU value: ∑TU < 4 indicates low toxicity, 4 ≤ ∑TU ≤ 6 indicates moderate toxicity, and ∑TU > 6 indicates heavy toxicity. ∑TU is calculated as follows:

$$\sum TU={\sum }_{i=1}^n(C_n^i/{PEL}^i)$$

(4)

3. Results

3.1. Sedimentary History and Depth Prediction

The sedimentation rate and its variation reflect the complex process of the dynamic balance between sediment inflow and discharge being destroyed and the new balance being established under the influence of regional natural factors and human activities. The vertical monitoring results of ²¹⁰Pb_ex activity in the sediment profiles of the three lake regions yielded the sediment rates of the three lake regions (Figure 2).

Figure 2 illustrates that the deposition rate is higher in the eastern region (0.78 cm/a) than in the western region (0.60 cm/a) and the central region (0.35 cm/a). This is consistent with the flow velocity, with the flow velocity in the central lake area being the largest, and the flow velocity in the western part being larger than the eastern. Furthermore, research data indicated that Chaohu Lake has been a semi-enclosed lake under artificial control since the 1960s. Following the 1970s, there has been a gradual deterioration in the water environment, which subsequently increased in trend. The water quality in the lake has exhibited clear spatial characteristics [30]. The selection of 1960 as the reference year for sediment background values is based on historical hydrological and environmental records. Prior to the 1960s, Chaohu Lake maintained relatively stable natural conditions with minimal human intervention. However, since the 1960s, intensified human activities such as dam construction, land reclamation, and industrial development markedly altered the lake’s hydrodynamic regime and sedimentary environment, leading to observable changes in pollutant accumulation. Therefore, sediments deposited before 1960 are considered representative of baseline environmental conditions unaffected by major anthropogenic disturbances. Therefore, the inflection point for the change of heavy metal content in sediments should be in 1960. The sedimentation rate allows the calculation of the corresponding sediment depth in the three regions. The sediment depth (H) in the west is around 36.60 cm, the central is about 21.35 cm, and the east is around 47.58 cm.

3.2. Vertical Distribution of Trace Metals in Sediments

The concentrations of trace metals in the core sediments at each sampling point were quantified. The vertical distribution of these metals in the sediments across the three regions was then determined by mapping the sampling points (Figure 3).

Figure 3 illustrates the sediment depth profiles for the three regions (W, C, and E) of Chaohu Lake as of 1960. It is evident that each trace metal index exhibits an inflection point at this depth. In other words, below this layer, the concentrations of trace metals remain relatively stable at low levels, whereas above this depth, substantial fluctuations are observed. This pattern is consistent with historical survey findings.

Consequently, this study proposes that the heavy metal content in sediments prior to 1960 represents a safe value and can be employed as a background reference value in the sediments of this lake. By counting the heavy metal content in sediments from the three regions prior to 1960, it is possible to derive a statistical rule for the heavy metal content in sediments from each region (see

Table 1. Statistical results of heavy metal content in sediments before 1960 (mg/kg).

Region	Element	Cd	Hg	As	Pb	Cr	Cu	Zn	Ni
W	Min	0.04	0.013	1.75	14.1	39.3	11	39	10.3
	Max	0.32	0.093	6.08	31.9	57.8	23	116	25.5
	Mean ± sd	0.17 ± 0.08	0.041 ± 0.023	3.79 ± 1.34	23.8 ± 5.3	49.2 ± 4.9	18 ± 3	71 ± 22	18.2 ± 3.1
C	Min	0.07	0.024	0.75	8.7	35	13	34	10.2
	Max	0.24	0.080	10.40	34.0	81.9	28	96	38.6
	Mean ± sd	0.15 ± 0.05	0.049 ± 0.014	3.55 ± 2.19	21.4 ± 6.1	52.6 ± 9.4	19 ± 4	66 ± 17	21.4 ± 6.0
E	Min	0.07	0.017	2.35	8.9	37	10	29	16.4
	Max	0.23	0.173	12.00	26.1	59	23	70	30.0
	Mean ± sd	0.15 ± 0.05	0.056 ± 0.029	4.43 ± 1.96	18.0 ± 4.5	49.3 ± 5.9	16 ± 3	51 ± 9	20.8 ± 3.6

).

Since the three regions form an undivided whole, the dataset exhibits enhanced integrity without boundary-induced biases. Thus, the background value for trace metals in sediments should be calculated by averaging the concentrations from various regions and selecting the lower value. Table 1 provides the background value of trace metals in sediments. The background values for Cd, Hg, As, Pb, Cr, Cu, Zn and Ni were determined to be 0.15 mg/kg, 0.041 mg/kg, 3.55 mg/kg, 18.5 mg/kg, 49.2 mg/kg, 16 mg/kg, 51 mg/kg and 18.2 mg/kg, respectively (See

Table 2. Statistical characteristics of the concentrations of trace metals.

Concentration (mg/kg)	Cd	Hg	As	Pb	Cr	Cu	Zn	Ni
Background (in this study)	0.150	0.041	3.55	18.5	49.2	16.0	51.0	18.2
Background of trace metal in national lakes [31]	0.162	0.048	8.88	21.3	52.9	11.4	41.2	21.3
TEL	0.596	0.174	5.9	35.0	37.3	35.7	123.0	18.0
PEL	3.530	0.486	17.0	91.3	90.0	197.0	315.0	36.0

Note: TEL: Threshold Effect Level; PEL: Probable Effect Level.

).

A comparison between the descriptive statistics of trace metals in surface sediments and the corresponding background values for lake sediments in the Yangtze–Huaihe watershed reveals that the background values obtained in this study are generally lower, although of comparable magnitude, for all metals except Cu and Zn [31]. It is well known that Cu and Zn are among the trace metals most likely to exceed environmental standards in sediments, a phenomenon closely associated with the development of factories around Chaohu Lake since the 1960s. The statistically derived background values in this study are more representative of the true conditions of Chaohu Lake sediments and provide a more reliable basis for evaluating the degree of trace metal pollution. Furthermore, a comparison between the maximum average concentrations of trace metals (Table 1) and the recommended Threshold Effect Level (TEL) values (Table 2) shows that, except for Cr and Ni, the average concentrations of other trace metals are lower than their respective TELs. This finding further corroborates the validity of the sedimentary historical reconstruction and supports the appropriateness of the TEL and Probable Effect Level (PEL) values recommended in Table 2.

3.3. Ecological Risk of Trace Metals in Sediments

The findings indicate that the accumulation of trace metals in sediments has posed a greater ecological risk since 1960. The geo-accumulation index (I_geo) was calculated based on the heavy metal content in the sediments, and the distribution characteristics of I_geo across the three regions at different accumulation levels were analyzed (Figure 4).

The I_geo results revealed that Hg was the predominant pollutant in the surface sediments of Chaohu Lake, with mercury pollution closely linked to industrial and agricultural activities in the watershed [32]. Furthermore, 4.6% of Hg in the upper sediments of the western region is in the heavy grade (3–4). Cd (36.4%), Hg (36.4%), As (9.1%), Pb (4.6%), Cr (4.6%), and Zn (54.6% + 9.1%) I_geo represent a mild or moderate level (1 < I_geo < 3). In the central region, the I_geo values for Hg (4.2%–20.8%), As (8.3%), and Zn (8.3%) in the upper sediment layer indicate moderate pollution (1 < I_geo ≤ 2) for portions of Hg and all As/Zn samples, while the remaining Hg samples show moderately-to-heavily polluted conditions (2 < Igeo ≤ 3) categories. In the eastern region, the I_geo of Cd (29.0%), Hg (9.7%), As (25.8%) and Zn (3.2%) in sediments are within the moderate grade (1–2). All other elements fall within the low contamination category or exhibit negligible ecological risk (I_geo ≤ 1). It can be seen that the ecological risk of heavy metal pollution, as indicated by the index Hg > Zn > As > Cd > Pb = Cr > Cu = Ni.

In this study, the background values and probable effect levels (PEL) of trace metals (Table 2) were used to calculate the risk index (RI) and total toxicity units (∑TU), facilitating a comprehensive assessment of trace metals in these sediment sections. Subsequently, the ecological risk indicators for trace metals in the three regions were determined (Figure 5).

The RI analysis results indicated that the trace metal contents in sediments across the three regions (13 out of 22 in the western, 20 out of 24 in the central, and 25 out of 31 in the eastern region) pose a low risk of pollution (RI ≤ 150). The remaining sediments (8/22 in the western, 4/22 in the central, and 6/31 in the eastern region) are at a moderate risk level (150 < RI < 300). The only instance of a high risk (RI > 300) trace metal content is observed in the western surface layer, with the risk exceeding that of the present sediment surface layer (within 10 cm depth). According to the ∑TU result, the three regions are essentially at low toxicity (∑TU ≤ 4), and the remaining parts (5/22 in the western, 2/24 in the central, 2/31 in the eastern region) are at moderate toxicity (4 < ∑TU < 6), all within 30 cm of the surface sediments.

4. Discussion

4.1. Main Factor Analysis Based on Risks of Trace Metals

The I_geo evaluation results of various trace metals indicators in the upper sediment layers were analyzed to identify principal components (PCA) and correlations among trace metals factors across the three regions. The influence patterns of different trace metals in each region were subsequently determined (Figure 6). In Figure 6, Pearson correlation coefficients were calculated based on pooled Igeo data from the three regions. Correlations with p-values < 0.05 were considered statistically significant and are represented in the correlation matrix. PC1 and PC2 refer to the two main principal components extracted from the principal component analysis (PCA) of trace metal concentrations. The arrows represent the loading vectors of each trace metal on the principal components, indicating both the direction and strength of the correlations. The green, black, and red circle lines are used to visually group metals with similar distribution patterns and potential sources, helping to highlight clustering relationships in the PCA space.

Moderate positive correlations were observed between Cd and Ni, As and Zn, and Pb and Hg, while strong positive correlations were found among As, Cr, and Ni; Pb, Cu, and Zn; and Cr, Cu, and Ni. Overall, Hg demonstrated less stability against external influences compared to other metals, with stronger correlations observed with Cr and Ni, suggesting a common source. The remaining trace metals (Cu, Pb, Zn, and Cd) also exhibited significant correlations, indicating shared anthropogenic sources.

The principal components, PC1 and PC2, collectively explain 70.7% of the total variance, capturing the majority of the dataset’s information. PC1, accounting for 58% of the total variance, is predominantly influenced by Cu, Pb, Zn, and Cd, suggesting contributions mainly from industrial and agricultural activities [33]. PC2, explaining 12.7% of the variance, is primarily associated with Hg, As, Cr, and Ni, reflecting natural sources and moderate influences from soil parent material [34].

Although the current potential ecological risk of trace metals in Chaohu Lake sediments is low (Figure 4), the persistent bioavailability of these metals — especially in river inflow zones (notably the western region) — may lead to future ecological mobilization. This risk exhibits a tendency to spread towards the central lake basin, suggesting that remediation efforts should prioritize region-specific control of key metals, with targeted interventions in the western region.

4.2. Risk Assessment of Trace Metals Mixtures

By integrating the distribution characteristics of environmental pollutants, probabilistic risk assessment can intuitively estimate the likelihood of pollutants causing toxic effects [17]. Given the complexities of directly reflecting the results from traditional ecological risk assessments of trace metals in surface sediments, this method was adopted for a more thorough and comprehensive evaluation.

Probabilistic risk assessment of trace metals in Chaohu Lake surface sediments was performed using statistical methods based on the total and bioavailable concentrations [35]. After calculating the probabilistic risk (RI and ∑TU) for each sampling point (as shown in Figure 5), each point was categorized into low-, moderate-, or high-risk levels according to the corresponding risk classification thresholds. Subsequently, the proportions of sampling points falling into each risk category were statistically analyzed. The cumulative probability distributions of the ecological risk for trace metals in the upper sediments across the three regions were then obtained and are presented in Figure 7.

Based on cumulative probability results of RI, 67.0% of trace metals in sediments pose a lower risk, with the remaining 33.0% indicating moderate risk (with high-risk events being statistically improbable and thus negligible). According to the cumulative probability of ∑TU, 87.4% of trace metals are at low risk, and 12.6% are at moderate risk. It is evident that the ∑TU assessment results suggest a significantly safer status, mainly because the recommended PEL values for Canadian sediments may be higher than the actual conditions in Chaohu Lake. However, this method neglects metal bioavailability, leading to an overestimation of potential risks and rendering the outcomes less convincing. In contrast, the background values of trace metals used in the RI calculation are derived from sedimentary history, accounting for the partitioning of metals between liquid and solid phases [36]. Thus, the RI results from this study are more realistic and closely aligned with the actual environmental conditions.

4.3. Comparative Analysis of Risk Assessment

In this study, the higher RI value observed in the upper layers of all core sediments across the three regions serves as the representative potential ecological risk of trace metals. Spatial equilibrium calculations were subsequently conducted across the entire area, facilitating the determination of ecological risk distribution for trace metals in the sediments (Figure 8).

Based on the spatial distribution results, it is evident that trace metals in the sediments in most areas of Chaohu Lake are in a low-risk state. However, the western region predominantly falls within a medium-risk state, mainly due to the presence of numerous tributary rivers that introduce a significant amount of trace metals into the lake. These metals accumulate in the sediments, leading to ecological risks. In contrast, the medium-risk areas in the other two regions are limited and located near the rivers, which can be attributed to sediment migration and movement patterns. Therefore, the RI calculation results are deemed reliable.

Furthermore, compared with other methods for calculating the RI in assessing trace metals in lake sediments, this study introduces several improvements: (1) the background values of trace metals were determined using sedimentary history data specific to each sampling site; (2) RI cumulative probability calculations were based on a comprehensive set of measured data. These improvements increase the reliability of the assessment outcomes. For example, the 33.0% probability of medium to high ecological risk due to trace metals in this study surpasses the 15.9% reported by Fang et al. [31] and is more closely aligned with the 24.8% probability of toxic effects, as indicated by previous studies on trace metal toxicity. However, this method does not directly assess the ecological impacts of trace metals on aquatic ecosystems, and variations in metal bioavailability and distribution in benthic organisms can lead to significant biological differences [37]. Therefore, RI results serve as a reference for potential sediment metal risks. Future studies will further evaluate the biological toxicity of trace metals in sediments, such as adopting a DGT–SSD coupled Probabilistic Risk Assessment (PRA) approach [38]. In summary, trace metals in Chaohu Lake sediments present a 33.0% probability of ecological risk to aquatic organisms, with Hg, As, and Ni as the primary contributors. The cumulative probability calculation method offers a more objective and scientific basis for managing metal pollution in lakes.

4.4. Broader Contextualization of Ecological Risk Assessment Results

In the context of global and regional studies on lake sediment contamination, the findings from Chaohu Lake present both commonalities and distinctive features. Trace metal pollution in lacustrine sediments has been widely reported, with Hg, As, and Cd frequently identified as dominant ecological risk contributors in other large lake systems [39,40,41]. Consistent with these observations, this study highlights Hg, As, and Ni as primary pollutants contributing to the ecological risk in Chaohu Lake. Notably, the probabilistic risk assessment revealed that 33.0% of the samples posed a medium to high ecological risk, a proportion comparable to that reported for Taihu Lake [42], but somewhat lower than in highly industrialized catchments, such as Lake Geneva [43], where historical industrial discharges have led to more widespread contamination.

Compared to other studies in Chaohu Lake, such as Fang et al. [26], the current research introduces a significant methodological innovation by deriving sediment-specific background values through high-resolution core sampling. While previous works generally employed regional geochemical backgrounds, our approach enabled the reconstruction of localized, pre-industrial baselines, improving the accuracy and relevance of ecological risk assessments. This refinement resulted in a more nuanced understanding of contamination gradients across different lake regions, particularly revealing the western region’s heightened pollution levels due to anthropogenic influences.

Additionally, the application of probabilistic risk analysis, as opposed to traditional deterministic assessments, provides a more robust evaluation of ecological risks, incorporating spatial variability and uncertainty. Similar probabilistic frameworks have been increasingly advocated in recent international studies for their ability to capture real-world risk distributions [44,45]. The integration of sediment-specific background values with probabilistic modeling thus represents a methodological advancement aligning with best practices in environmental risk assessment.

In summary, the ecological risks associated with trace metal contamination in Chaohu Lake are generally at low to moderate levels, comparable to those observed in other major lake systems undergoing anthropogenic pressures. The methodological advances presented in this study—namely, the use of localized background values and probabilistic risk modeling—provide a more accurate and site-relevant basis for ecological management strategies and offer a valuable reference for similar environmental assessments globally.

5. Conclusions

This study assessed the pollution status and ecological risks of eight trace metals (Cu, Pb, Zn, Cd, Cr, Ni, As, and Hg) in the surface sediments of Chaohu Lake through historical sediment self-sedimentation analysis. An enhanced ecological risk assessment method was introduced, along with the calculation of background values and probabilistic risk analysis. The results showed that Hg was the predominant pollutant, particularly in the western region, where 4.6% of surface sediments reached heavy pollution levels (I_geo 3–4). Cd, Hg, As, Pb, Cr, and Zn mainly exhibited mild to moderate pollution (1 < I_geo < 3), while other metals showed low contamination (I_geo ≤ 1). Ecological risk index (RI) analysis indicated that 68.5% of the sampling points posed low risk (RI ≤ 150), 28.1% posed moderate risk (150 < RI < 300), and only one point in the western region showed high risk levels (RI > 300). Toxicity unit (∑TU) analysis revealed that most sediments were at low toxicity (∑TU ≤ 4), with moderate toxicity (4 < ∑TU < 6) observed mainly within the upper 30 cm.

The probabilistic risk assessment showed a 33.0% probability of medium to high ecological risk, primarily attributed to Hg, As, and Ni. Overall, trace metal contamination in Chaohu Lake sediments is at a low to moderate ecological risk level, and the probabilistic statistical method proved effective for characterizing sediment-associated ecological risks. The study suggests that a probabilistic statistical method for potential ecological risk assessment effectively characterizes trace metals ecological risks in sediments.