Research on the Characteristics of Heavy Metal Pollution in Lake and Reservoir Sediments in China Based on Meta-Analysis

Abstract

To clarify the current state of heavy metal contamination in the sediments of lakes in China, the data on six heavy metals derived from the sediment samples of 71 lakes across China from 2003 to 2022 are collected in this study through meta-analysis. Uncertainty analysis is conducted using the Monte Carlo method to evaluate the heavy metals against cumulative characteristics, potential ecological risk, and toxicity indicators. The following conclusions are reached. (1) There is severe pollution in lake sediments in China. The concentrations of Cu, Pb, Zn, Ni, and Cd in lakes exceed their corresponding soil background values. Cr heavy metal contamination exceeded the soil background values in 54.5% of lakes. (2) Cd is the major pollutant in lake sediments across China, followed by Cu, Zn, Pb, Ni, and Cr in descending order. Lakes with higher ecological risk are predominantly concentrated in quadrants 2 and 3, indicating an overall high ecological risk status for Chinese lakes and significant potential ecological hazards. Pb and Cr are identified as the most toxic elements in lake sediments, with the lakes of higher toxicity mainly concentrated in quadrants 3 and 4. (3) Heavy metal pollution shows a significant trend of variation by region. The sources of heavy metals in lake sediments differ between the southern, central, and northern regions of China. In the lakes located in northern China, pollution is largely attributed to mining and industrial emissions, with agriculture as a less significant factor. In the central region, surface runoff and domestic sewage are the main contributors, while industrial and agricultural emissions play a minor role. In the south, industrial emission is the major source of pollution, with agricultural emission and natural factors being less significant.

1. Introduction

At present, China is faced with an increasingly severe shortage of water resources, which is due to population growth, socio-economic development, and rapid urbanization [1]. Statistically, the per capita water resources available in China are only one-fourth of the global average. Approximately half of the cities across China are troubled by the ongoing problem of water shortages. To compound it, water scarcity is exacerbated by the pollution of lakes and reservoirs. Characterized by their non-degradability and significant toxicity, heavy metal elements are among the most important environmental pollutants in lake ecosystems. These pollutants can accumulate in the human body through the food chain, posing a serious safety risk to both aquatic organisms and humans [2,3,4,5]. Currently, copper, zinc, lead, chromium, and other heavy metals are the focus of scientific research on environmental preservation.

As the main solid medium of the benthic environment, sediment plays an important role in lake ecosystem function. After being introduced into lakes, heavy metals are deposited into sediments through a series of flocculation and precipitation mechanisms. However, these accumulated heavy metals can be re-released into the water body under specific environmental fluctuations, thus becoming potential pollutants that persistently impact the lake water. Therefore, the study of heavy metal content in sediments is considered crucial to the evaluation and regulation of water pollution [6,7,8,9,10,11]. Up to now, there have been plenty of studies conducted to investigate the heavy metal content within the sediments of various lakes and reservoirs nationwide, as prompted by the increased significance attached to water resources, water environment, and aquatic ecosystem management. It has now been demonstrated in studies that Cd pollution is most severe in the sediment of Chinese lakes and reservoirs, of which many have reached moderate to severe levels of pollution. It is followed by Hg, As, Mn, and Pb [12]. Over the past three decades, there has been a significant change in the status of heavy metal pollution within the sediment across China’s lake systems, with pronounced regional characteristics exhibited. In terms of contamination severity, there are higher levels in the eastern regions than in the central areas, which have higher levels than the Western regions. Sedimentary heavy metal pollution is more severe in the regions significantly affected by human activities than in less populated regions. Additionally, heavy metal and metalloid concentrations are higher in urban lake sediment in comparison to rural lake sediment [13]. In terms of spatial distribution, the Pearl River system has the highest concentration of sedimentary heavy metals among the seven primary water systems across China, followed by the Hai River and Yellow River systems. In contrast, there are comparatively lower levels of heavy metals shown by the sediment within the Yangtze River, Liao River, Songhua River, and Huai River systems [14].

There are numerous studies revealing the high levels of heavy metal contamination in lakes and reservoirs across China. However, the variations in heavy metal levels have been discovered in the studies conducted by different scientists focusing on the same body of lakes and reservoirs. In this regard, Chaohu Lake, located in Anhui province, serves as a representative example, where the concentration of Cr in the sediment was found to be four times higher in 2020 compared to a study conducted in 2018 [15,16]. According to some studies, the sediment in Chaohu Lake is in an unpolluted. However, it is indicated in other studies that Chaohu Lake remains at a moderate level of mercury (Hg) pollution and a moderate level of Cd pollution [17]. These discrepancies are considered to result mainly from the uneven distribution of heavy metal concentrations within the sediment, spatial variations, and the impact of various factors, such as pH and temperature [18]. The lack of consistent conclusion impedes the managers from understanding the status of heavy metal pollution in lake sediments, while increasing the difficulty in developing pollution prevention and control strategies. Concurrent with domestic research on heavy metal pollution in lakes and reservoirs, international scholars have established a multi-dimensional research system for heavy metal pollution management. In the fields of water pollution control [19], phytoremediation technology [20] and mine pollution evaluation [21,22], the migration and transformation laws of heavy metals in environmental media have been systematically revealed. This provides important theoretical support and technical pathways for the prevention and control of heavy metal pollution in lake basins. Consequently, there is a necessity for a more objective evaluation method that can accurately describe the pollution status of lake sediments. This would provide a more scientific foundation for decision-making processes related to pollution management.

In recent years, meta-analysis has been increasingly performed to analyze the status of environmental pollution. It is purposed mainly to fill the gap in data from the long-term investigation conducted within specific research areas. For example, the meta-analysis method has been used to assess the status of contamination, distribution characteristics, and industry-specific features of Cd, Pb, Zn, Cu, and As in industrial and mining areas and their surrounding soils across China. Furthermore, the data collected from 427 relevant papers has been analyzed by researchers to evaluate the status of arsenic pollution in Chinese farmland [23,24]. In this paper, meta-analysis is conducted using published data to explore the status of pollution in various environmental media in the absence of monitoring data.

Since the 21st century, significant progress has been made in sediment research across China due to a surge in research investment and academic exchanges both domestically and internationally. In this study, a meta-analysis is conducted using the pollutant data collected from the relevant publications to systematically analyze the levels of heavy metal pollution by heavy metals in sediments. Additionally, the Monte Carlo method is used to analyze the uncertainty in characteristics of heavy metal accumulation and the potential ecological risks they pose. This is aimed at the clear and objective representation of heavy metal pollution levels in Chinese lake sediments, risk classifications, and the variations in pollution among lakes. The research is expected to provide insights into the prevention and control of heavy metal pollution in Chinese lakes. It also provides the fundamental data required for enhancing lake water quality and managing ecological environments. The research method used in this study is expected to serve as a reference for evaluating the status of pollution in other lakes across China and even around the world.

2. Materials and Methods

2.1. Methodology of the Study

2.1.1. Meta-Analysis

Meta-analysis is the statistical method used to analyze and summarize multiple pieces of research data. It provides a quantitative average effect to address research questions, which enhances the credibility of conclusions by increasing the sample size and addressing the inconsistencies in research results. The main steps in conducting this process include the retrieval of scientific literature, the screening of studies (including critical appraisal of eligible studies based on predefined criteria), statistical analysis, and the transparent documentation of each stage. As part of comprehensive research, meta-analysis involves a quantitative synthesis of diverse research outcomes [25]. Over the past few decades, more popularity has been gained by systematic reviews and meta-analyses in various fields due to their compliance with the rigorous analytical protocols that ensure reproducibility and accuracy [26]. In this study, the fundamental principles and process of meta-analysis are followed to examine the accumulation characteristics of heavy metals in lakes across China.

2.1.2. Monte Carlo Simulation

Known as the stochastic or statistical simulation method, Monte Carlo method is intended to resolve the problems arising from mathematics, physics, engineering, technology, or stochastic service systems. Initially, a model (probabilistic model or simulation system model) is constructed in a way that the solution to the problem exactly matches the estimated value or relevant quantity of the parameters or characteristics of this model. Finally, an approximate solution to the problem is obtained [27]. The Crystal Ball 11.1.2.4 software, in conjunction with Spearman’s correlation coefficient, is utilized for the analysis of the accumulation of heavy metals in sediment across a range of lakes and reservoirs in China. Concurrently, the classical Monte Carlo simulation method is employed to address the uncertainty of assessment results. A computational model is established by assuming that the probability distribution follows a normal distribution. On this basis, 1000 simulation calculations are performed for each of the geological accumulation index (I_geo), potential ecological risk index (RI), and toxicity index (TU), thus reflecting the status of pollution by heavy metals in the three lakes.

2.2. Data Collection

For the purpose of data selection, the literature published between 2003 and 2022 is chosen. This is because of its effectiveness in reflecting the current state of heavy metal concentration in sediments, given the rapid economic development that China has experienced over the past 2 decades.

The data on heavy metal levels in the sediment derived from various lakes and reservoirs in China between 2003 and 2022 are collected through the China National Knowledge Infrastructure (CNKI) using the search terms ts = (lake) and ts = (sediment) and ts = (heavy metal). In total, 105 literature records from 71 lakes (Figure 1) and reservoirs are selected according to the search criteria, with the majority of articles published between 2008 and 2022. Then, the monitoring data on heavy metals in the sediments of the aforementioned 71 reservoirs between 2003 and 2022 are collected again from the Web of Science. For example, with Wanghu Lake as the subject of the case study, the search terms used in the database are as follows: ts = (wanghu lake), ts = sediment and ts = (heavy metal). The articles meeting the selection criteria are concentrated mainly between 2004 and 2022, resulting in 70 data records from 28 lakes and reservoirs (Figure 2).

The articles selected for this study are supposed to comply with three fundamental principles as follows: (1) the sampling points should be representative, and the collected sediment samples should be obtained from the surface layer of the lake (0–10 cm); (2) the study is supposed to include a definitive number of sampling points and heavy metal content; (3) in all the studies collected, the total heavy metal content in sediments is routinely analyzed through single or mixed acid digestion, with rigorous quality control and assurance measures in place.

2.3. Data Processing

2.3.1. Sample Size Weighted Mean (SNWM)

In the monitoring data collected, the number of sampling points for the same lake and reservoir usually varies between different research teams. In practice, the representativeness of the concentration levels obtained can be improved by increasing the number of sampling points. Therefore, the number of samples is weighted based on the number of survey sites organized by the research team when the research data collected from many studies on the same lake are processed [18,200].

$$\mathrm{SNWM}=\sum {\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}\times {\mathrm{N}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{N}}_{\mathrm{i}}}}$$

where N_i represents the number of samples in the data set i; C_i indicates heavy metal concentrations in data logging I; n refers to the number of records. N_i and C_i are obtained from the original study.

2.3.2. Geoaccumulation Index Method (GIM)

Classed as a geochemical approach, the Geoaccumulation Index (I_geo) method is applicable to assess pollution conditions and estimate the impact of human activities on the environment [201]. It is commonly used to quantitatively explore the severity of heavy metal contamination in sediments. Through this method, the degree of accumulation of exogenous heavy metals in sediments can be visualized [202]. In this study, the accumulation levels of heavy metals in major lakes nationwide are assessed (

Table 1. Heavy metal contamination ranking indicators based on soil geoaccumulation index.

Igeo	<0	0~1	1~2	2~3	3~4	4~5	>5
grade	0	1	2	3	4	5	6
degree of contamination	unpolluted	mild pollution	light moderate pollution	moderate pollution	light heavy pollution	heavy pollution	serious pollution

). The calculation process is detailed as follows:

$${\mathrm{I}}_{\mathrm{geo}}={\log }_2({\mathrm{C}}_{\mathrm{i}}/(\mathrm{K}{\mathrm{B}}_{\mathrm{i}}\left)\right)$$

where B_i represents the geochemically averaged soil background values of heavy metal elements in common shales; C_i denotes the content of element i in sediment; K, generally assumed to be 1.5, indicates the factor used to account for local rock variations that may cause soil background variations; classify the degree of heavy metal contamination into seven levels based on the magnitude of the I_geo: I_geo < 0, unpolluted; 0 < I_geo < 1, mild pollution; 1 < I_geo < 2, light moderate pollution; 2 < I_geo < 3, moderate pollution; 3 < I_geo < 4, light heavy pollution; 4 < I_geo < 5, heavy pollution; and I_geo > 5, serious pollution.

2.3.3. Potential Ecological Risk Index (PERI) Methodology

Proposed by Hakanson, the potential ecological risk index method requires four conditions as follows [203]:

(1)

Contents Condition: The concentration of metals in surface sediments. The RI value supposedly increases as the metal pollution in surface sediments intensifies.

(2)

Quantity condition: The number of types of metal pollutants. The RI value for the sediments polluted by many types of metals is supposed to be higher than the RI value for the sediments polluted by only a few types of metals.

(3)

Toxicity Condition: The degree of toxicity of metals. Toxicity conditions are differentiated in line with the “abundance principle”. Due to the deposition and affinity of heavy metals to solids, there is a proportional relationship present between toxicity and abundance. The metals with higher toxicity supposedly contribute more to the RI value than the metals with lower toxicity.

(4)

Sensitivity condition: The sensitivity of the water body to metal contamination. The water bodies that are more sensitive to metal pollution are supposed to have higher RI values than the water bodies that are less sensitive.

The Potential Ecological Risk Index (PERI) method is applicable to classify ecological risks using the following formula:

$$\mathrm{RI}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}{\mathrm{C}}_{\mathrm{r}}^{\mathrm{i}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}{\mathrm{C}}_{\mathrm{ald}}^{\mathrm{i}}/{\mathrm{C}}_{\mathrm{n}}^{\mathrm{i}}$$

where Cⁱ_r represents the parameters of heavy metal contamination, Cⁱ_ald indicates the actual levels detected in mg/kg; Cⁱ_n denotes the background values for the location of each lake; Eⁱ_r refers to the environmental risk parameter (single factor); Tⁱ_r represents the toxicity coefficients; Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb toxicity coefficients are 2, 5, 5, 1, 10, 30, 40, and 5, respectively; RI refers to the comprehensive potential ecological risk parameters (

Table 2. Relationship between potential ecological hazard coefficients and hazard indices and pollution levels.

Eir and Degree of Contamination		RI and Degree of Contamination
Eir < 40	Low environmental risk	RI < 150	Low environmental risk
40 ≤ Eir < 80	Moderate ecological risk	150 ≤ RI < 300	Moderate ecological risk
80 ≤ Eir < 160	High environmental risk	300 ≤ RI < 600	High environmental risk
160 ≤ Eir < 320	Very high environmental risk	RI ≥ 600	Extremely high environmental risk
Eir ≥ 320	Extremely high environmental risk

).

2.3.4. Toxic

Toxicity is a measure used to assess the impact of heavy metals in sediments on the aquatic environment [204]. Intended to normalize the toxicity caused by different heavy metals to facilitate the comparison of their relative effects, it is defined as the ratio of the measured concentration (C_i) to the probable effect level (PEL) (P_i). The PEL values for heavy metals can be obtained in the Supplemental Materials. The overall level of toxicity risk in sediments is assessed by means of summing concentrations, which means the toxicity units of all contaminants detected in the sample are summed [205]. The sum toxicity unit (STU) is referred to as the sum of TUi.

$$\mathrm{T}{\mathrm{U}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{P}}_{\mathrm{i}}}}$$

The classification of different toxicity levels is as follows: STU < 4 indicates low toxicity; 4 ≤ STU ≤ 6 indicates moderate toxicity; STU > 6 indicates high toxicity.

In general, the values of Toxicity Unit (TU), which are based on total concentration, may provide a preliminary indication when the data on heavy metal constituents and biological exposure doses are not available (

Table 3. Relationship between STU and toxicity level.

STU and Toxicity Level
STU < 4	Low toxicity
4 ≤ STU≤ 6	Moderate toxicity
6 < STU	Highly toxic

).

2.4. Study Area Division

In different geographical regions, there are certain differences shown by the accumulation of heavy metals in lake sediments, which is due to the variations in soil parent material, climate, landform, human distribution, and other factors. To some extent, the amount of heavy metals in lake sediments is affected by altitude. As revealed by the research based on geographic factors, the southern basin has a higher concentration of heavy metals than the northern basin [14]. Therefore, the lakes are divided into six regions according to the exact geographical conditions and in line with certain rules. The lakes under survey are geographically divided into six regions in line with the following criteria. With the Yangtze River and Yellow River as boundaries in latitude, the lakes are categorized into upper, middle, and lower regions. In terms of longitude, the Chinese territory is divided into left and right sections based on prominent geographical features, including the Greater Khingan Range, Taihang Mountains, Wushan, and Xuefeng Mountains, known as the boundary of the second and third steps of the Chinese topography. Thus, six quadrants are obtained, as illustrated in Figure 3.

3. Result

3.1. Statistical Analysis of Data

Figure 4 shows the distribution of heavy metal concentrations in major lakes in China and the statistical results of heavy metal concentrations in sediment samples obtained from 175 selected papers, focusing mainly on studies of six heavy metals—copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), chromium (Cr), and cadmium (Cd)—in 71 lakes.

3.1.1. Comprehensive Analysis

While collecting heavy metal data from lake sediments, the environmental background values provided in the referenced publications were also recorded. For lakes lacking such reference values, the regional soil background levels were used as substitutes. Across the dataset, Cd shows consistently elevated mean concentrations, with 57 out of 61 lakes (93.4%) exhibiting Cd levels surpassing their corresponding baseline thresholds. In addition to Cd, Zn, Cu, and Pb contamination also reaches concerning levels. Specifically, Zn concentrations exceed reference values in 59 lakes (83.1%), while elevated Cu and Pb levels are observed in 77.5% of the surveyed sites. In contrast, Cr contamination appears less severe: among the 66 lakes with available Cr data, only 36 (54.5%) show concentrations above natural background levels.

Regarding the coefficient of variation (CV), the proportion of lakes with CV values exceeding 0.5 for the concentrations of the six heavy metals ranges from 18% to 43.3%, in ascending order: Ni, Zn, Cr, Pb, Cu, and Cd. To be specific, Songhua Lake, Xiang Lake, and Baogong Lake exhibit high CV values for Cu, reaching 103%, 103%, and 101%, respectively. As for Zn, CV values are 231%, 189%, and 129% in Xiang Lake, Jinshan Lake, and Qionghai Lake, respectively. Additionally, there are plenty of lakes with notably high CV values for specific heavy metals, including Jinshan Lake, Wulungu Lake, Yelang Lake, and Dongchang Lake (Pb), Qinggeda Lake (Ni), Lugu Lake (Cr), and Wulungu Lake, Shengjin Lake, Cao Hai Lake, Aibi Lake, Luoma Lake, Chaohu Lake, Nansi Lake, Dongting Lake, Hulun Lake, and Hongfeng Lake (Cd). A high CV value indicates a high level of data dispersion, suggesting the significant spatial variations in heavy metal concentration by location within the lakes. Therefore, the statistical results of the coefficients of variation, indicating the substantial differences in spatial distribution of the aforementioned heavy metal elements among these lakes, underscore the significant uncertainty in concentration levels.

3.1.2. Analysis by Region

Within the six geographical quadrants, the average concentration of Cu shows a decreasing trend in the following order: 3 > 4 > 5 > 2 > 1 > 6. Within the third geographical quadrant, Daye Lake, Baiyun Lake, and Qingshan Lake are identified as the top three lakes with the highest Cu concentrations among the surveyed lakes, with their average concentrations reaching 496.139, 275.91, and 201.16 mg/kg, respectively. As for Zn, the average concentrations decline in the following order: 3 > 4 > 2 > 5 > 1 > 6. Among them, Xiang Lake, Baiyun Lake, and Daye Lake are found to be the top three lakes with the highest Zn concentrations, with an average of 1604.829, 594.38, and 584.62 mg/kg, respectively. All of them are positioned in the third geographical quadrant.

Regarding Pb, the average concentrations decrease as follows: 4 > 3 > 2 > 1 > 5 > 6. The top three lakes with the highest Pb concentrations are Xiang Lake, Daye Lake, and Yelang Lake, with their average concentrations reaching 205.32, 177.414, and 164.809 mg/kg, respectively. In terms of Ni, the average concentrations decrease in the following order: 4 > 3 > 2 > 1 > 5 > 6. Except for Hongfeng Lake, situated in the fourth quadrant, the two lakes with the highest average concentrations are Baiyun Lake and Dongshan Lake, both of which are positioned in the third quadrant.

The average concentrations of Cr and Cd across the six quadrants follow the same order: 3 > 4 > 2 > 1 > 5 > 6. Specifically, Yaoquan Lake, Danjiangkou Reservoir, and Jinshan Lake are the top three lakes located in quadrants 1, 2, and 3 with the highest Cr concentrations. The top three lakes located in quadrants 1, 2, and 3 with the highest Cd concentrations are Yaoquan Lake, Chaohu Lake, and Daye Lake. Overall, among the six quadrants, quadrants 3 and 4 exhibit higher concentrations of various metals. In contrast, quadrant 6, located mainly in the inland regions of Xinjiang, shows the lowest metal concentrations. Undergoing late-stage development, this area exhibits underdeveloped economic activities and relatively lower levels of human interference [206].

3.2. Igeo Analysis

By using the collected data, 1000 Monte Carlo simulations are conducted to calculate the Geoaccumulation Index (I_geo) for six heavy metal elements in the sediment of 71 lakes. For the calculations, those samples with undetected or non-detectable values are excluded. Figure 5 shows the Igeo index for each heavy metal element within each quadrant.

3.2.1. General Analysis

Cd is identified by statistical analysis as the most polluting element among the six heavy metals. For the majority of lakes, Cd I_geo values are relatively high. Among the Cd I_geo data obtained for 61 lakes, only 6 lakes, namely Wulungu Lake, Xiaohai Lake, Xiaoxingkai Lake, Qinghai Lake, Hulun Lake, and Huaxi Reservoir, have the I_geo grade of 0, accounting for only 9.83% of the total. It is indicated that only a handful of lakes have sediment with a lightly polluted level of Cd, with the sediment in the remaining lakes showing variations in the severity of pollution. In total, there are 9 lakes with Cd I_geo grades reaching 5 (heavy pollution) or 6 (severe pollution), including East Mountain Lake, Caohai Lake, Fuxian Lake, Yaoquan Lake, Gaotang Lake, and Luoma Lake with heavy Cd pollution, as well as Baiyun Lake, Chaohu Lake, and Daye Lake with severe Cd pollution, accounting for a total of 14.8%. In addition, there are 14 lakes with Cd levels indicating mild pollution, accounting for 19.7%, 27 lakes with Cd levels indicating moderate pollution, accounting for 37.9%, and 5 lakes with Cd levels indicating moderately heavy pollution, accounting for 7%.

Except Cd, Cu, and Zn are the two metals with relatively severe pollution. Among them, 30 lakes for Cu and 35 lakes for Zn are in various degrees of pollution, accounting for 42.9% and 50.7% of their respective total numbers. Specifically, Baiyun Lake and Daye Lake are the two lakes with relatively severe Cu pollution, reaching a moderately heavy level of pollution. The levels of Cu pollution in other lakes are moderate or below. The pollution by Zn differs from that by Cu. The level of Zn pollution in Xianghu Lake reaches a severe level, while that in other lakes is moderate or below.

According to the research results, Cr causes the least pollution among the six heavy metals. Among the 66 lakes with statistical data, a total of 56 are unpolluted, accounting for 84.8%. Compared to other heavy metal elements, the level of Cr pollution in Chinese lakes is relatively lower, which is consistent with the findings of other studies [207].

When the I_geo of lakes is compared horizontally, some lakes are found to exhibit clean conditions for all six heavy metals, such as Ulan Lake, Xiaoxingkai Lake, and Qinghai Lake. In contrast, there are some lakes where only a few heavy metals cause slight pollution, including Gucheng Lake, Hengshui Lake, and Manas Lake, which are only mildly polluted by Cd. Meanwhile, there are certain lakes showing significant pollution for all six heavy metals, such as Baiyun Lake, Dongshan Lake, and Daye Lake. This confirms the unevenness in the extent of heavy metal pollution in Chinese lake sediments [208].

3.2.2. Analysis by Region

In the context of the six geographical quadrants, the third quadrant shows the highest pollution levels. Out of the 21 lakes with statistical data on Cd, 20 lakes show variation in the level of pollution. The percentage of lakes with pollution by Cu, Zn, and Pb ranges from 56.5% to 69.6%, all of which exceed the average level of the overall assessment. The pollution patterns of Zn and Pb are found to be highly similar to that of Cu, which is likely due to their similar sources. The discharge of industrial wastewater in such sectors as textiles and dyeing is identified as the major contributor to Pb pollution, while Cu and Zn pollution is attributed mainly to surface runoff and industrial wastewater [209,210]. Certainly, there are variations in the levels of pollution caused by these three metals, with Zn and Pb showing higher levels of pollution than Cu in the lakes downstream of the Yangtze River. This discrepancy may be accounted for by the differences in heavy metal properties and sediment adsorption characteristics. For instance, its propensity to precipitate and easily adsorb leads to the rapid accumulation of Pb near the water inlet. Consequently, Pb concentration varies significantly between different areas of the lake. In the first and sixth quadrants, pollution is relatively light, with 21% of the lakes being mildly contaminated with Cu and Zn. Meanwhile, the remaining lakes are in an unpolluted state. However, Cd remains a metal with a relatively high enrichment level, as only 26.7% of lakes are not contaminated, and 40% of lakes face moderate to severe contamination (Figure 5).

3.3. RI Analysis

According to the statistical data, Monte Carlo simulation is performed 1000 times to calculate the potential ecological risk of heavy metals in the sediment of 71 lakes. Involving six heavy metal elements present in lake sediment, the calculation leads to the Risk Index (RI) as shown in extended date

Table 4. The RI indices for each quadrant.

Quadrant	Eir						RI	Pollution Level
	Cu	Zn	Pb	Ni	Cr	Cd
1	5.89	1.44	6.13	4.79	2.96	253.72	274.93	Moderate
2	9.79	2.31	8.70	5.95	2.77	511.20	540.72	highly
3	19.22	4.62	10.42	11.29	2.52	639.12	687.19	Extremely High
4	6.23	1.95	8.85	8.42	2.1	241.08	268.63	Moderate
5	7.44	2.05	6.39	/	1.35	136.56	153.79	Moderate
6	5.15	1.11	6.72	3.85	1.55	48.54	66.92	Low

. Then, the obtained RI values are used to thoroughly assess the ecological risk posed by heavy metals in the sediment of these lakes.

3.3.1. General Analysis

According to Table 4, it is evident that Cd has the highest individual potential ecological risk index. The Cd ecological risk indices are below 10 for only Wulungu Lake, Xiaohai Lake, Dawangtan Reservoir, and Huaxi Reservoir. Meanwhile, the remaining 61 lakes have higher individual potential ecological risk indices than 15 for Cd. Daye Lake has an exceptionally high Cd ecological risk index of 6423.520, far exceeding that for other heavy metals. To a large extent, this is attributed to the basically low soil background values of Cd and the highest toxic response coefficient [211]. In line with the classification criteria set for single-factor ecological risk pollution degree, only 7 lakes are categorized as low ecological risk for Cd, 13 lakes fall under the medium ecological risk category, while a staggering 41 lakes, or 67.2%, are classified as high ecological risk and above. Among them, the lakes considered to have extremely high ecological risk are Dishui Lake, Baiyun Lake, Liuhua Lake, Dongshan Lake, Huadu Lake, Cao Sea Lake, Fuxian Lake, Dongchang Lake, Yaoquan Lake, Gaotang Lake, Luoma Lake, Chaohu Lake, and Daye Lake. Additionally, the general contribution rate of the heavy metal Cd to potential ecological harm exceeds 50%, indicating that Cd, as a heavy metal element, exhibits a typical accumulation effect in sediments and contributes significantly to ecological risk. This finding aligns with the results obtained by using the sediment heavy metal accumulation index method. Cu is another heavy metal element with a relatively high individual potential ecological risk index, excluding Cd. Four lakes, namely Baiyun Lake, Dongshan Lake, Baogong Lake, and Daye Lake, exhibit moderate levels of ecological risk posed by Cu, accounting for 5.7% of the total. Other heavy metals show lower levels of individual potential ecological risk, with only Baiyun Lake showing a moderate level of risk posed by Ni. In the remaining lakes, relatively low ecological risk hazards are demonstrated, with low contributions made to the RI values.

According to the calculation results of RI, there are 11 lakes with a comprehensive RI index in excess of 600. Overall, these lakes are categorized as extremely high ecological risk, indicating a significant level of potential ecological hazard.

3.3.2. Analysis by Region

Table 4 lists the RI values for each quadrant. The lakes with high ecological risk and above are mainly concentrated in quadrants 2 and 3. On the one hand, these lakes are located in those densely populated areas across China, where most lakes are affected by various human activities such as the changes in land use types [207]. On the other hand, the Cd contribution to RI exceeds 50% in 94.3% of the lakes within these regions, making Cd a significant heavy metal determining the ecological risk index. It has been indicated in previous studies that the main sources of cadmium in water are the wastewater discharges from mining, smelting, and chemical industries [212]. In the research conducted by Shao et al. [211], extensive soil sampling was performed around paper, chemical, and electronic enterprises in Zhangjiagang City to reveal the significantly higher Cd ratios around paper-making enterprises, followed by chemical and electronic enterprises, with the smallest ratio observed around electroplating enterprises [13]. In the provinces located in the second and third quadrants, there are a large number of industrial enterprises, as mentioned above. Moreover, the intensive zones intended for mining development are located in the southern karst regions of China, with severe soil contamination caused by cadmium. Particularly, this results from the extraction and smelting of lead-zinc ores [213] (Figure 6 and Figure 7).

3.4. Toxicity Unit Analysis

3.4.1. General Analysis

Monte Carlo simulation is performed 1000 times to assess the toxicity characteristics of heavy metals in the sediments across 71 lakes. The statistical results are shown in the extended date

Table 5. Toxicity levels in each quadrant.

Quadrant	TUi						STU	Toxicity Level
	Cu	Zn	Pb	Ni	Cr	Cd
1	0.167	0.280	0.961	0.334	0.859	0.595	3.196	Low
2	0.241	0.478	1.210	0.383	0.917	0.617	3.846	Low
3	0.448	0.873	1.693	0.563	0.960	0.761	5.298	Moderate
4	0.298	0.550	1.825	0.733	0.944	0.695	5.045	Moderate
5	0.273	0.367	0.910	0.000	0.480	0.177	2.207	Low
6	0.126	0.229	0.659	0.194	0.439	0.061	1.708	Low

and Figure 8 and Figure 9.

Table 5 details the toxicity contribution of different metal elements in the lakes and the overall toxicity levels of the lakes. To be specific, 9 lakes exhibit a severe level of toxicity, accounting for 12.7%, while 10 lakes show a moderate level of toxicity, comprising 14%. The remaining lakes are classified as having a low level of toxicity. Lead (Pb) and Chromium (Cr) are found to be the heavy metal elements with higher toxicity in the lakes. Pb contributes most significantly to the overall toxicity in 45 lakes, while Cr is predominant in 20 lakes. When combined, they account for 91.5%. Apart from those natural sources like rock weathering, Pb is also derived from various anthropogenic sources, such as Pb-Zn ore deposits, coal, diesel combustion, and atmospheric deposition. Therefore, it contributes significantly to the presence of active Pb in sediments [214]. Probable effect concentration (PEC) includes probable effect level (PEL), effect range median values (ERM); severe effect level (SEL), and toxic effect thresholds (TET), which predict that harmful effects are likely to be observed when concentrations are above the threshold. Notably, the use of the STU index to evaluate heavy metal pollution may result in the underestimated toxicity of Cadmium (Cd) due to its relatively high PEC [215].

3.4.2. Analysis by Region

Table 5 displays the TUi (Toxicity Unit index) obtained for each heavy metal in the six quadrants, along with the overall toxicity levels. Quadrants one, two, five, and six exhibit low toxicity levels. Quadrants three and four have the highest toxicity levels, reaching 5.298 and 5.045, respectively. This indicates a moderate toxicity level. What is positioned in these two quadrants mainly includes the southern coastal areas of Guangdong Province in the Pearl River Basin and the junction of the Pearl River Basin and Yangtze River Basin in Guizhou Province. Located in a subtropical region with a long and intense rainy season, Guangzhou experiences substantial rainfall that removes pollutants from industrial areas into surface runoff. Known as a significant source of replenishment for Baiyun Lake, this runoff plays a role in increasing the level of heavy metal concentrations in the lake [216]. Similar conclusions have been reached by SHANG et al. [211] in their research on heavy metal pollution in the urban park lakes across Guangzhou, which underscores the significant role of urbanization, industrial structure, and land use patterns in heavy metal levels within urban water bodies.

The soil background values of heavy metals in Guizhou Province are found to be significantly higher compared to the national average soil background values. On average, the contents of Cd, Pb, and Zn in the soil background values across Guizhou Province are 0.659, 35.2, and 99.5 mg/kg, respectively. In contrast, the highest content ranges of Cd, Pb, and Zn in the national soil background values are 0.052–0.103, 21.1–26.2, and 63.8–76.0 mg/kg, respectively. This could account for the higher Cu content in the sediments of several lakes located in the fourth quadrant. Additionally, human activities are contributory to the variability in element concentrations between different regions of lakes [217]. In this regard, Hongfeng Lake is a representative. Its northern water area is adjacent to industrial zones (iron-aluminum alloy plants, Qingzhen thermal power plants, etc.) after the surrounding construction. Therefore, a consistent conclusion has been reached in multiple studies conducted by scholars that the pollution level in the northern area of Hongfeng Lake is slightly higher than in the southern area [51,52,218].

4. Analysis of Sources of Heavy Metal Pollution in Lakes

4.1. Correlation Analysis

Correlation analysis is widely used to identify the source of heavy metals. If there is a significant or extremely significant correlation between the mass fractions of heavy metal elements, it can be inferred that they have similar pollution sources or related pollution phenomena, which is of great significance for the analysis of heavy metal pollution sources. The results of the Pearson correlation analysis can be found in

Table 6. Correlation coefficients of heavy metals in surface sediments of lakes in quadrants 1 and 6 of China.

Index	Cu	Zn	Pb	Ni	Cr	Cd
Cu	1
Zn	0.796 **	1
Pb	0.248	0.385	1
Ni	0.329	0.509 *	0.350	1
Cr	0.568 *	0.674 **	0.599 **	0.517 *	1
Cd	0.258	0.510	0.867 **	0.377	0.819 **	1

* and ** represents significant correlations at the p < 0.05 and p < 0.01 levels, respectively.

,

Table 7. Correlation coefficients of heavy metals in surface sediments of lakes in 2 and 5 quadrants of China.

Index	Cu	Zn	Pb	Ni	Cr	Cd
Cu	1
Zn	0.649 **	1
Pb	0.640 **	0.528 *	1
Ni	0.431	0.613 *	0.568 *	1
Cr	0.144	0.638 **	0.123	0.566 *	1
Cd	0.007	0.507 *	0.465	0.254	0.079	1

* and ** significant correlations at the p < 0.05 and p < 0.01 levels, respectively.

and

Table 8. Correlation coefficients of heavy metals in surface sediments of lakes in 3 and 4 quadrants of China.

Index	Cu	Zn	Pb	Ni	Cr	Cd
Cu	1
Zn	0.363 *	1
Pb	0.558 **	0.777 **	1
Ni	0.450 *	0.106	0.122	1
Cr	0.164	−0.117	−0.081	0.547 **	1
Cd	0.865 **	0.574 **	0.652 **	0.281	0.132	1

* and ** significant correlations at the p < 0.05 and p < 0.01 levels, respectively.

. When the confidence level is less than 0.05, it can be shown that the correlation coefficient has reached significance; otherwise, it is not significant. At the regional scale, the concentration levels of heavy metals in lake sediments vary across different regions of China due to the influence of geological environment, economic development level, human activities, and other factors. The pollution degree of heavy metals in lake sediments is higher in quadrants 3 and 4 compared to quadrants 2 and 5, while the pollution degree in quadrants 1 and 6 is the lightest. The lakes were divided into three regions for Correlation Analysis based on their pollution levels. The results are presented below.

In the 1st and 6th quadrants, significant correlations at the p < 0.01 level were observed between Cu and Zn, Zn and Cr, Pb and Cr, and Cd and Cr elements. In the 2nd and 5th quadrants, significant correlations were found at the p < 0.01 level between Cu and Zn, Cu, and Pb, Zn and Cr elements. In addition, significant correlations at p < 0.05 level were observed between Zn and Pb, Ni and Cd. Significant correlations were found between Cu and Pb, Cd, Zn and Pb, Cd and Pb, Ni and Cr at the p < 0.01 level in the 3.4 quadrants.

4.2. Principal Component Analysis (PCA)

Principal component analysis (PCA) is an effective method for identifying the sources of heavy metals in the environment. In this study, PCA was applied to six heavy metals in lake sediments nationwide. The first two principal components, which explained a cumulative variance of 72.668%, were extracted. The Kaiser–Meyer–Olkin (KMO) test, which was used to examine the partial correlations between variables, yielded a KMO statistic of 0.763. Bartlett’s sphericity test, with a sig < 0.01 (

Table 9. Principal component analysis indicator tests.

KMO and Bartlett’s Test
KMO Sample Suitability Quantity		0.763
Bartlett sphericity test	Approximate chi-square	134.379
	degree of freedom	15
	significance	0.000

), rejected the null hypothesis that the correlation matrix is an identity matrix, indicating significant correlations among the variables. The results of the Principal Component Analysis are shown in

Table 10. Principal component analysis of heavy metal elements in sediments in China.

Item	Eigenvalue	Variance/%	Cumulative Variance/%	Element	Principal Component Load Matrix		Principal Component Load Matrix After Rotation
					Principal Component 1	Principal Component 2	Principal Component 1	Principal Component 2
Extract Square and Load	3.447 0.913	57.450 15.218	57.450 72.668	Cu	0.887	−0.156	0.834	0.338
				Zn	0.927	0.012	0.780	0.503
				Pb	0.728	0.352	0.431	0.685
Rotating Square and Load	2.734 1.626	45.568 27.100	45.568 72.668	Ni	0.712	−0.362	0.796	0.071
				Cr	0.718	−0.325	0.781	0.106
				Cd	0.497	0.727	0.036	0.880

.

The cumulative variance contribution rate of the first principal component is 57.450%, representing the most informative component. Cu and Zn have high positive loadings on Principal Component 1, indicating a higher likelihood of similar sources of contamination for these two heavy metals in the sediment. This suggests that they are likely to be co-precipitated or adsorbed and enriched in the sediment, thus categorizing them together. The cumulative variance contribution rate of the second principal component is 15.218%, with Cd and Pb showing high loadings on principal component 2. The above analysis indicates that nationwide, there may be similarities in the sources of these four heavy metals. Divided into three regions, principal component analysis of the lake (

Table 11. Principal component analysis of heavy metal elements in sediments of each region.

Heavy Metal	Principal Component Load Matrix
	1,6 Quadrants		2,5 Quadrants			3,4 Quadrants
	Principal Component 1	Principal Component 2	Principal Component 1	Principal Component 2	Principal Component 3	Principal Component 1	Principal Component 2	Principal Component 3
Cu	0.909	−0.107	0.896	−0.175	0.061	0.884	0.231	−0.261
Zn	0.834	0.391	0.777	−0.163	−0.025	0.830	0.144	−0.391
Pb	0.759	−0.604	0.770	0.479	−0.084	0.828	−0.376	0.246
Ni	0.738	−0.510	0.735	0.267	−0.518	0.675	−0.475	0.467
Cr	0.700	0.520	0.555	−0.764	0.156	0.046	0.718	0.581
Cd	0.487	0.496	0.388	0.366	0.831	0.435	0.632	0.023
Eigenvalue	3.369	1.306	2.999	1.075	0.995	2.804	1.357	0.837
Contribution rate %	56.146	21.759	49.985	17.919	16.584	46.729	22.611	13.954
Cumulative contribution rate %	56.146	77.905	49.985	67.904	84.487	46.729	69.340	83.294

Principal components 1–3 explain the contribution of different pollution sources to heavy metals in each region.

).

In quadrants 1 and 6, the cumulative contribution rate of the extracted two principal components reaches 77.905%, which reflects most of the information about the heavy metal element contents. The cumulative variance contribution rate of principal component 1 is 56.146%, and Cu, Zn, Pb, Ni, and Cr all have high positive loads, indicating that these heavy metals in sediments are more likely to have the same source of pollution. This quadrant mainly includes regions in Northeast China, Inner Mongolia, Xinjiang, Hebei, Shanxi, and Shandong. These regions are not only rich in mineral resources, but also important industrial areas. For example, historically, Tianjin is an old industrial base dominated by traditional industries such as manufacturing, textile, light industry, and steel. Shanxi is a major coal-producing province in China; and Shandong serves as a logistics and transportation hub [219]. In addition, the Mongolian Plateau is known for its abundant mineral resources [220]. Therefore, it is hypothesized that mining activities and industrial discharges may be the sources of the aforementioned heavy metals in principal component 1. According to the correlation analysis, the four elements Cu, Zn, Pb, and Cr have a significant correlation, which also confirms the above view from the side. Notably, Ni shows only a moderate correlation with Zn, suggesting potential differences in the sources of Ni compared to the other heavy metals. Research indicates that emissions from coal combustion and industrial pollutants are significant contributors to heavy metal pollution in airborne dust [221,222]. Many regions in northern China have a long history of using coal for heating or coal-fired power generation. Heavy metals from coal combustion can enter water bodies through atmospheric deposition, thereby affecting the heavy metal content of sediments. Ni is one of the major pollutants produced by coal combustion [223,224]. Therefore, major component 1 can be attributed to mining and industrial emissions. Cr and Cd show high positive loadings on principal component 2, with a cumulative variance contribution rate of 21.759%, making them the main sources of heavy metals in this region, apart from principal component 1. The high correlation between Cr and Cd indicates that they probably share the same source. The northern regions of China, such as the Northeast Plain and the North China Plain, are the main agricultural production areas for crops such as wheat and corn. The river valleys of the Qinghai-Tibet Plateau, including the Hexi Corridor and the Yarlung Zangbo River Valley, serve as important bases for valley farming and animal husbandry. In the process of feeding livestock and growing crops for livestock, the application of fertilizers becomes an important source of Cd and Cr [225,226]. Thus, Principal Component 2 is attributed to agricultural sources. This conclusion is consistent with the results of previous studies by researchers such as Wang, Wei, Zhang, Liu, and others [176,181,186,188,189,194,197] who have analyzed the sources of heavy metals.

In quadrants 2 and 5, Principal Component 1 contributes 49.985% of the variance, with high loadings for Cu, Zn, Pb, and Ni. These four elements show strong correlations, suggesting a common source of pollution. The mid-latitude zone of China includes several densely populated provinces. According to data from China’s seventh national census, four of the five most populous provinces are located in this region, accounting for 25.8% of the total national population. Within this region, 30% of the coefficients of variation for the four heavy metals exceed 0.5, indicating highly uneven spatial distribution and significant anthropogenic impacts [18]. Jamwal et al. [227] showed that urban domestic wastewater is a major contributor to copper and nickel pollution. Cheng et al. [228] found that Zn is the most abundant metal in sludge from municipal wastewater treatment plants, followed by copper. It is speculated that the source of pollution of major component 1 may be the pollution of rain and sewage mixed with surface runoff and domestic sewage into the river. Cr has a high negative load on major component 2. Ref. [229] used dynamic data from the National Pollution Source Census to show that chromium pollution is mainly from chromium salt production (48.2%), chromium iron production (41.6%), and leather tanning (2.5%). Ref. [230] indicated that the high detection rate of Cr in the East China region was due to the developed metal electroplating and leather industries. Therefore, the second principal component can be attributed to industrial sources. The contribution rate of the third principal component reached 16.584%, and Cd had a high positive load. Henan is an important agricultural province in China, with a stable arable land area of over 110 million hectares. During crop growth, nitrogen and phosphorus fertilizers are applied, phosphate fertilizer application is a major source of soil Cd in China, and studies have shown that excessive phosphate fertilizer is applied in Henan and Hubei [231]. This suggests that Principal Component 3 can be attributed to agricultural sources. Our analysis is supported by the research results of Lei, Chen, Wang, Yu, Li, Cao, Zhang, Fu, and others [59,99,100,133,135,137,138,140,141,157,161,162,166,167].

In quadrants 3 and 4, Cu, Zn, and Pb have high positive loadings on principal component 1, suggesting a common source of pollution. The correlation between Zn and Pb is significant, and industrial activities such as fuel combustion, lead-acid battery production, and mining are major sources of lead and zinc [232]. Taking the lead-acid battery production industry as an example, according to the Ministry of Industry and Information Technology’s “Lead-Acid Battery Industry Standard Conditions (2015)”, in terms of the structure of lead-acid battery production, the provinces of Zhejiang, Hunan, and Guangdong in this region account for approximately 48% of the national total production; in addition, Jiangsu and Anhui, which contribute approximately 16%, have some regions located in quadrants 3 and 4. Therefore, quadrants 3 and 4 are important regions for lead-acid battery production in China. Analyzing the distribution of mineral resources, Chuzhou and Tongling in Anhui Province have long been considered the birthplace of copper mines [233], and Hunan Province is dotted with mining and smelting facilities, such as lead-zinc smelters and antimony smelters [234]. Frequent mining and smelting activities have led to heavy metal pollution in cities near these areas, including Liuyang, Zhuzhou, and Changsha. Similarly, cities in Jiangxi such as Pingxiang, Ganzhou, and Nanchang have long-standing smelters and tailings [235]. The pollution situation from mineral extraction in Fujian Province is also severe [236]. In southern China, Baise City has one of the largest bauxite ore deposits in Guangxi and is one of the top ten non-ferrous metal mining areas in China. In addition, Hechi City is known as the “Hometown of Nonferrous Metals” in China. Based on the above, Principal Component 1 is defined as an industrial pollution source. Principal Component 2 contributes 22.611%, with Cr and Cd having high positive loadings. However, their correlation is not significant, indicating that the metal content is not controlled by a single factor, but is influenced by multiple factors [237]. The high Cd metal load in this area may be related to the karst topography. The peculiar shape and soil-forming environment of karst topography result in less soil material, thin soil layers, and a fragile ecological environment. The apparent enrichment of Cd in soils during the weathering process of carbonate rocks is evident in karst regions. Soils developed from carbonate parent material in karst areas often have higher geochemical background levels of Cd than soils developed from other parent materials [238]. Under natural or anthropogenic influences, the geochemically high background levels of Cd in karst soils and its apparent accumulation may result in Cd concentrations exceeding standards. The main source of Cr is probably the weathering of rocks and is also related to human activities, such as fertilization, defining it as a mixed source. In principal component 3, Cr and Ni have high positive loadings, and their correlation is significant. Cr and Ni are mainly controlled by the parent material of the soil, with some areas influenced by agricultural activities [239]. Chen et al. [237] provided a comprehensive review of the progress made in the last decade in the source apportionment of heavy metal pollution in Chinese soils. They indicated that the national average soil background levels for Cr and Ni are 54 and 23 mg/kg, respectively, and the annual increases due to external inputs are 0.128 and 0.054 mg/kg [240]. Based on these calculations, it would take approximately 420 and 430 years, respectively, for the soil content of Cr and Ni to double, making them relatively insensitive to external pollution compared to other metals (20 years for Cd, 70 years for Cu and Zn). Cr and Ni in soils are typically highly correlated [241], and these elements in soil parent material can enter lakes through surface runoff. Therefore, it is inferred that Principal Component 3 represents a natural source. To reduce the uncertainty in the analysis results, literature data were summarized, and it was found that the research conclusions were consistent with ours [41,54,60,65,66,67,124].

5. Conclusions

In this study, meta-analysis principles are applied to select literature, organize data, and summarize the characteristics of spatial distribution. On this basis, the dominant factors of pollution are explored, so as to investigate the status of pollution by six typical heavy metals. The research results show that the average values of Cu, Pb, Zn, Ni, and Cd in lake sediments across China are mostly higher than the corresponding soil background values (background values) in soils, indicating a more severe level of pollution. For Cr, which is relatively less polluting, 54.5% of the lakes exceed soil background values. The lakes in the 3rd and 4th quadrants show the most severe level of pollution, followed by those in the 2nd and 5th quadrants. Differently, the lakes in the 1st and 6th quadrants show a relatively lower severity of pollution. The proportion of heavy metals with a greater coefficient of variation than 0.5 ranges from 18% to 43.3%, indicating that the concentrations of the six heavy metals were more unevenly distributed spatially in the lakes. Ni and Cr show relatively insignificant variations in concentration on a national scale, while Cu and Cd show more significant differences in concentration, with large uncertainties in the concentration levels. The state of the art of this work is that the innovation of this study lies in its integration of meta-analysis with Monte Carlo simulation methods to screen relevant literature, organize extensive datasets, and systematically assess pollution profiles of six typical heavy metals in sediments from 71 Chinese lakes and reservoirs between 2003 and 2022. By comprehensively applying multidimensional evaluation frameworks including I_geo, RI, and TU, this study addresses the limitations of single-method approaches while offering a more scientifically robust decision-making foundation for pollution control strategies.

Both the PERI and I_geo indices indicate that Cd is a major contaminant in lake sediments across China, followed by Cu, Zn, and Cr in order. According to the PERI results, the lakes with higher ecological risk are mainly concentrated in the 2nd and 3rd quadrants, suggesting that lakes in China as a whole are in a state of high ecological risk, posing a significant potential ecological hazard. The research also shows Pb and Cr are the two elements with the highest toxicity in Chinese lake sediments, and the lakes with higher toxicity are concentrated mainly in the 3rd and 4th quadrants. The lakes with high PERI and toxicity (STU) indices differ to some extent in distribution among the six quadrants, which is attributed mainly to the generally low soil background levels of Cd and its highest toxicity response coefficient. This results in a significant contribution to PERI. However, the relatively high PEC of Cd reduces the contribution to the STU index. In contrast, Pb and Cr contribute most to the STU index, showing higher TUi in the 2nd and 3rd quadrants. The STU index is useful to assess the impact of heavy metals in sediments on the aquatic environment. As this method takes into consideration the PEC of heavy metals in sediments rather than the TEC, it is possible that the toxic risk of heavy metals in sediments is underestimated. A combination of the PERI and STU indices offers a more comprehensive approach to assessing the potential risks of lake sediments.

The results of PCA indicate a link between significant regional pollution characteristics and heavy metals. The sources of heavy metals in lake sediments vary by region in southern, central, and northern China. In northern China, the pollution in lake sediments results mainly from mining and industrial emissions, followed by agricultural sources. In the central latitudinal zone, surface runoff and domestic sewage are the major contributors to lake sediment pollution, with industrial and agricultural pollution as two other significant factors. In the southern region, the sources of pollution are even more complex, with industrial pollution as the predominant influencing factor. Meanwhile, agricultural pollution and natural sources are among the contributors, which makes the pollution of the southern lakes more complex.

Heavy metals are non-degradable and pose various hazards such as teratogenicity, carcinogenicity, and mutagenicity. Given the current scarcity to address heavy metal pollution, whether considering the current scarcity of freshwater resources in China or the risk of toxic accumulation in the human body through aquatic products. With data collected from different spatial and temporal dimensions, a comprehensive analysis is conducted on a large scale, aiming to provide fundamental data needed to carry out lake pollution management and contaminated sediment treatment, not only for other regions in China, but also for the lakes facing similar issues worldwide. Thus, the level of early warning can be improved. However, there are some limitations to this study. For example, although the sampled lakes are distributed widely across the country, the data from different regions, such as Gansu, Tibet, and Taiwan are not collected. Therefore, the ecological risks assessed may not be applicable to these areas or other regions with fewer lakes. In addition, this study lacks complete data on the composition of heavy metals, which results in uncertainties in estimating the potential risks associated with heavy metal contamination.