Sedimentary regularity and ecological risk of heavy metals in Chaohu Lake sediments

doi:10.21203/rs.3.rs-4941594/v1

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Sedimentary regularity and ecological risk of heavy metals in Chaohu Lake sediments

https://doi.org/10.21203/rs.3.rs-4941594/v1

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One of the most commonly used methods to assess the ecological risk of heavy metals in lake sediments is referring to the background values of soil. However, the background values currently used are the statistical average of the elemental contents in a given region with a large range of areas, which are not relevant for lake sediments with spatially differentiated characteristics. So far, there are few studies focusing on the diagnosis and analysis of background values of heavy metals considering lake sedimentary history. To fill the gap, this study investigated the variation in heavy metals in undisturbed core sediments across the western, central and eastern regions of Chaohu Lake. The background values of heavy metals were then deduced based on the distribution of heavy metals in sediments. The background values were used to inform the ecological evaluation methods based on weight, including the geological accumulation index (I_geo), and the improved potential ecological risk index (RI) based on chemical accumulation and toxicity unit (∑TU). A probabilistic risk assessment was conducted based on the improved RI using a large number of monitoring data. The results indicated that the improved RI based on chemical sorting identified a lower risk of 67.0% and a moderate risk of 33.0%, with Hg, As and Ni being the major contributors. The comparative analysis indicated that the probabilistic statistical method based on the improved RI can provide a more objective and scientific basis for the management of lake heavy metals pollution.

Heavy metals

Vertical distribution

Sedimentary history

Background value

Probabilistic risk assessment

In recent years, heavy metals pollution in lake sediments has become one of the most significant environmental issues due to unprecedented development (Lin et al., 2016; Luo et al., 2020; Sojka et al., 2022). This issue is equally evident in China, where lakes have also suffered from different degrees of destruction and human interference, causing many environmental and ecological problems (Liang et al., 2022; Wang et al., 2022). Although the eutrophication of lakes in China has been improved by the comprehensive treatment of water environment in the past decades, heavy metals pollution of lakes is still serious. According to the monitoring data of 138 lakes in China in 2017, 38 of them have shown heavy metals pollution, posing a great impact on the ecological functioning of lakes (Huang et al., 2019). As for the sources, previous studies showed that most of the heavy metals are from natural sources, e.g., rock weathering and soil erosion, and human activities also play an important role, e.g., domestic sewage and industrial wastewater. These heavy metals can adsorb onto suspended particles and finally enter the sediment layer (Liu et al., 2016). However, heavy metals in sediments can be resuspended by wind and waves, especially in shallow lakes like Chaohu Lake in this study, which will not only lead to water quality degradation, but also continue to amplify through the food chain, posing a serious threat to aquatic ecosystems and human health (Zhu et al., 2018; Sun et al., 2019; Chen et al., 2021). Therefore, as a major sink and secondary source of heavy metals in lacustrine ecosystems, the ecological risk and assessment of heavy metals in sediments have been the focus of research.

Currently, researchers are still accustomed to using classical assessment methods, including the Nemerow pollution index method (Nazarpour et al., 2019), the soil accumulation index method (Jia et al., 2017), and the potential ecological accumulation index method (da Silva Montes et al., 2020), to assess the ecological risks of heavy metals in lakes. For example, the risk assessment of heavy metals in some lake sediments in China showed that the overall potential ecological risk of Pb, Ni, Zn, Cu, Cd, Hg and other typical heavy metals is moderate (Qin and Tao, 2022). These methods calculate the ecological risk value of heavy metals using the ratio of the actual measured heavy metals values to the corresponding soil background values developed by the country. However, the soil background values are mostly from the last century. With the development of the industrial age and the natural evolution of lakes, the dynamic changes of sediments in shallow lakes are more frequent. These soil background values may no longer applicable to current conditions. If the self-deposition law of heavy metals is ignored, the results of risk assessment will often become unreliable. Therefore, the historical deposition process of heavy metals in sediments should be further investigated in risk assessment (Valero et al., 2020).

Furthermore, the ecological risk assessment of heavy metals in sediments of lakes has focused on the use of deterministic risk quotient methods, which may underestimate or overestimate the risk of metal exposure (Razak et al., 2021). In fact, uncertainties in the ecological risk of heavy metals must be included in the overall ecological assessment of heavy metals in sediments. Therefore, probabilistic risk assessment is proposed to address the uncertainty problem by establishing the probability density distribution to describe the uncertainty and variability of the assessment results (Guan et al., 2022), which will contribute to effective risk management of heavy metals in sediments.

Chaohu Lake is the fifth largest freshwater lake in China, and it is a typical shallow lake in the Yangtze River basin. In recent decades, due to the rapid development of the basin, especially since the 1970s, a large amount of domestic and industrial wastewater containing metal pollutants has been discharged into the lake (Zhang et al., 2019). The concentration status and ecological risk assessment of Cu, Pb, Zn, Cr, Cd, As, Hg, Ni and other trace metals in lake sediments have received much attention (Li et al., 2021). At present, many scholars have conducted quantitative studies on the risk assessment of trace metals in lake sediments based on the total concentrations measured by chemical extraction and deterministic risk quotient (Liu and Shen, 2014; Wu et al., 2018). However, the ecological probabilistic risk assessment of heavy metals in lake sediments is limited due to the regularity of heavy metals in sediments. Therefore, the aims of this study are to: (1) investigate the sedimentary distribution of trace metals in Chaohu Lake sediments using sequence extraction; (2) construct the background values based on the history of heavy metal deposition in Chaohu Lake sediments; (3) perform quantitative probabilistic ecological risk assessment of heavy metals in sediments based on the diagnostic results of heavy metal deposition in sediments. The results can provide reliable information for lake managers and decision makers on risk characterization of trace metals in sediments.

Study area

Chaohu Lake is a typical shallow lake in the Yangtze River Basin, which locates in Hefei City, Anhui Province, China (31.60° N, 117.87° E). It has a large water area (about 769.55 km²) and a shallow water depth (about 2.89 m). It is divided into three separate parts: the western, central, and eastern lakes. In general, the western region exhibits a higher flow rate than the eastern part. The central lake area typically experiences the highest flow rate, which results in an uneven distribution of lake sediments (Luo et al., 2022).

To conduct an analysis of the heavy metals content in lake sediments, a total of 32 sampling points were chosen for sediment cores, distributed evenly across the three lake areas: the western region denoted by “W”, the central region denoted by “C”, and the eastern region denoted by “E” (see Fig. 1).

Sample collection

The equipment provided by UWITEC of Austria, including the CORE-60 sampler and its associated tools, was employed in the research conducted. In the spring of 2021, samples of sediment cores in a columnar shape were obtained. To ensure the representativeness of the sediment samples in the region, nearby locations were also sampled in order to mitigate channel variations. When categorizing the sediment samples into five vertical layers, particular attention was paid to the specific height of the columnar sediment samples, with each layer undergoing individual testing. Two specific layers of sediment samples from the columnar core, distinguished by their heights of 0–5 cm and 5–10 cm, were designated for testing purposes. To ensure consistency, an approach of equidistant equal division was employed to select three layers of sediment samples positioned between the 5–10 cm layer and the base for analysis. It is recommended that the uppermost layers of the sediment sample being analyzed should constitute more than half of the total sample height, as a general guideline (Luo et al., 2024).

Subsequently, the samples were analyzed and tested in a laboratory setting using a dedicated testing instrument. The detection method was carried out in accordance with the relevant testing specifications. In this study, eight heavy metals in lake sediments were detected, namely Cd, Hg, As, Pb, Cu, Cr, Zn and Ni.

²¹⁰ Pb _ex model measures the sedimentation rate of sediments

In this study, it was of paramount importance to utilize the activity of the natural isotope ²¹⁰Pb activity (Bq·kg^− 1) as the key detection material. The rationale behind this choice can be attributed to the chemical stability of ²¹⁰Pb, in addition to its status as a sole enduring outcome resulting from the decay process of uranium ²²⁶Ra, which has a half-life of 22.3 years. Furthermore, the origin of ²¹⁰Pb found in modern lake sediments can be attributed to a combination of factors, including atmospheric deposition, fluvial input (²¹⁰Pb_ex), resulting from the decay of ²²⁶Ra in the sediment, and the presence of ²¹⁰Pb_sup in the background sediment. The amount of ²¹⁰Pb presented at a specific depth in the sediment core is determined through laboratory analysis. Following the elimination of ²¹⁰Pb_sup, ²¹⁰Pb_ex is conducted through a model that spans multiple years (Appleby and Oldfield, 1978).

The three regions are represented by the core sediments of W9, C9, and E5 in Chaohu Lake. This is primarily due to the close proximity of the selected representative points to the central areas of the three regions. The assessment of ²¹⁰Pb_ex activity distribution in various depths was conducted within a controlled laboratory environment. In situations where minimal impact from natural or anthropogenic factors is considered, the application of the Constant Initial Concentration (CIC) model is favored. The least squares method was employed to derive the relationship between ²¹⁰Pb_ex activity (A) and depth (h), which was then used to calculate the average sedimentation rate (cm/a).

Methods of data processing

Geo-accumulation index (I_geo)

The geo-accumulation index (I_geo) is a useful tool for evaluating heavy metals pollution in lake sediments. It establishes a quantitative link between the total heavy metals concentrations in lake sediments and their geochemical background values (Müller, 1969):

$$\:{I}_{geo}={{log}}_{2}[{C}_{n}/\left(K*{C}_{B}\right)]$$

where, C_n represents the analyzed heavy metal concentration, while C_B denotes the corresponding background levels in lake sediment, K is used to consider the potential variations in background levels resulting from the diverse geological characteristics of rocks found in different regions. Typically, K is established at a value of 1.5 as suggested by Fang et al. (2019). Heavy metals pollution levels were then classified based on I_geo ranging from 0 (I_geo < 0, uncontaminated) to 6 (I_geo > 5, extremely contaminated).

The potential ecological risk index

The potential ecological risk index (RI), proposed by Hakanson (1980), presents a relatively straightforward approach to categorize the level of sediment heavy metals contamination and the resultant ecological risk in aquatic environments. This approach reflects the environmental impact of a variety of heavy metals found in sediments and the cumulative influence of different heavy metals within a specific environmental setting. Furthermore, it enables the estimation of the potential ecological risk posed by heavy metals, rendering it a frequently employed approach in the evaluation of ecological risks associated with heavy metals in sedimentary environments (Guan et al., 2022). The specific procedures employed are outlined below:

$$\:RI=\sum\:_{i=1}^{n}\left({T}_{r}^{i}\text{*}{C}_{f}^{i}\:\right)=\sum\:_{i=1}^{n}\left({T}_{r}^{i}\text{*}f*\raisebox{1ex}{${C}_{n}^{i}$}\!\left/\:\!\raisebox{-1ex}{${C}_{B}^{i}$}\right.\:\right)$$

With regard to the toxicity of different heavy metals, $\:{T}_{r}^{i}$ was assigned (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 calculated value of f represented the overall fraction percentage determined through fractionation analysis (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 individual heavy metal was designated as $\:{C}_{f}^{i}$. $\:{C}_{n}^{i}$ was the heavy metal concentration for an indexed heavy metal i. The corresponding background value of heavy metal in the lake sediments was designated as $\:{C}_{B}^{i}$.

Ecological risk assessment conducted based on the total volume

Toxicity unit (TU) is usually used to evaluate the impact of heavy metals in sediments on water environment. It can be defined as determining the ratio of concentration (C_nⁱ) to PEL value (PELⁱ) (Pedersen et al., 1998). The total toxic unit (∑TU) was the sum of TUⁱ for each indexed heavy metal i (Niu et al., 2015), and different levels of toxicity could be classified based on the value of ∑TU: ∑TU < 4, low toxicity; 4 ≤ ∑TU ≤ 6, moderate toxicity; and ∑TU > 6, heavy toxicity.

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

Sedimentary history and depth prediction

The sedimentation rate and its change 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 (see Fig. 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 was a gradual deterioration in the water environment, which subsequently increased in trend. The water quality in the lake exhibited clear spatial characteristics (Yin & Zhang, 2003). 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 in the west is around 36.60 cm, the central is about 21.35 cm, and the west is around 47.58 cm.

Vertical distribution of heavy metals in sediments

The content of heavy metals in the core sediments of each sampling point was quantified. Subsequently, the vertical distribution of heavy metals in sediments in the three regions can be obtained by mapping the distribution of sampling points across the three regions (see Fig. 3).

Figure 3 presents the sedimentation depth lines for sediments in the three regions (W, C & E) of Chaohu Lake in 1960. It is evident that the content of each heavy metal index in the sediment exhibits an inflection point at this settlement depth. In other words, below this depth, the heavy metal content is essentially stable within a low content range. Conversely, above this depth, there is a considerable fluctuation in the heavy metal content. This is consistent with the historical investigation results.

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).

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

Given that there is no division between the three regions, the integrity is more complete. Therefore, the background value of heavy metals in sediments should be calculated by taking the average value of heavy metals in various regions and then taking the smaller value. Table 1 provides the background value of heavy 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).

Table 2

Statistical characteristics of the concentrations of heavy 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 heavy metal in national lakes (Fang et al., 2022)	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
TEL, threshold effect level, from CCME, Canadian Council of Ministers of the Environment (CCME 1999);
PEL: probable effect level, Canada Assessment and Remediation of Contaminated Sediments (ARCS).

A comparison of the descriptive statistics for trace metals in the surface sediment and the associated background values for lake sediment in the Yangtze-Huaihe watershed (Fang et al., 2022) with the background values of all the heavy metals (except Cu and Zn) reveals that the background values of the latter are smaller, although they are similar in order of magnitude in this study. It is well established that Cu and Zn are the most likely to exceed the standard for heavy metals in sediments. This is also related to the production of factories around Chaohu Lake after the 1960s. The background value for heavy metals obtained by statistics is more representative of lake sediments and more convincing for evaluating the reliability of the degree of heavy metal pollution in sediments. Furthermore, a comparison of the maximum value of the average heavy metals in Table 1 with the recommended TEL (Table 2) revealed that the average content of other heavy metals, with the exception of Cr and Ni, was found to be lower than the TEL. This further corroborates the veracity of the sediment history diagnosis and the soundness of the TEL and PEL values recommended in Table 2.

Ecological risk of heavy metals in sediments

The results of the above detection demonstrate that the accumulation of heavy metals in the sediment poses a greater ecological risk after 1960. I_geo can be calculated based on the content of heavy metals in the sediments. The distribution characteristics of I_geo in the three areas under different accumulation levels were analyzed (Fig. 4).

Results for I_geo demonstrated that Hg was the most typical pollutant in the surface sediment of Chaohu Lake. Mercury pollution in sediment was closely related to industrial and agricultural activities within the watershed (Tang et al., 2010). 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 are in the 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 layer of sediments are in the moderate or moderately moderate grade (1<I_geo<3). 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 are in the light or pollution-free category (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, is greatest in the western region, followed by the central region and then the eastern region.

In this study, the background value and PEL of heavy metals in Table 2 were used as a basis for calculating the RI and ∑TU, which enabled a comprehensive assessment of heavy metals in this part of the sediment to be conducted. The ecological risk indicators of heavy metals in the three regions were then obtained (Fig. 5).

The results of the RI analysis indicated that the heavy metal content of sediments in the three regions (13/22 in the western, 20/24 in the central and 25/31 in the eastern) is at lower risk of pollution (RI ≤ 150). The remaining sediments (8/22 in the western, 4/22 in the central and 6/31 in the eastern) are at a moderate risk (150<RI<300). The only instance of a high risk (RI > 300) 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) are at moderate toxicity (4<∑TU<6), and these are all within 30 cm of the surface sediments.

Main factor analysis based on risks of heavy metals

The I_geo evaluation results of various indicators of heavy metals in the upper layers of sediments were analyzed in order to identify the principal component (PCA) and the correlation between the factors of heavy metals in sediments in the three regions. The influence regularity of various indicators of heavy metals in sediments in each region was obtained (Fig. 6).

Moderate positive correlations existed between Cd and Ni, As and Zn, Pb and Hg; strong positive correlations existed between As, Cr and Ni, Pb, Cu and Zn, Cr, Cu and Ni. In general, Hg is not sufficiently stable to be strongly influenced by other factors. Its correlation with Cr and Ni is stronger than with other metals, suggesting a similar source. The remaining heavy metals (Cu, Pb, Zn and Cd) also exhibit similar correlations with each other, indicating a shared origin.

The principal components are PC1 and PC2, and the cumulative variance contribution is 70.7%, which can already contain the majority of the information present in all the data. The variance contribution of these two factors is also considerably greater than that of other factors that play a decisive role in the distribution of heavy metal concentrations in lake surface sediments. PC1 accounts for 58% of the total variance and is primarily associated with Cu, Pb, Zn and Cd, which are indicative of industrial and agricultural sources (Cui et al., 2018). PC2 explains 12.7% of the total variance and is mainly related to Hg, As Cr and Ni, representing natural sources. Both were moderately correlated and mainly controlled by soil parent material (Fei et al., 2019).

Although the potential ecological risk of heavy metals in Chaohu Lake sediments is currently low (Fig. 4), the risk of their release in sediments persists, particularly in areas where rivers enter the lake (e.g., the western area is most evident). This ecological risk has the characteristic of spreading towards the center of the lake. Consequently, in the subsequent stages of remediation, it is imperative to identify and control the principal risk metals in each region, with particular attention to the western region.

Risk assessment of heavy metal mixtures

Probabilistic risk assessment which combined the distribution of environmental pollutants, could intuitively provide a probability that pollutants would have toxic effects (Guan et al., 2022). Following the ecological risk assessment of heavy metals in surface sediments, it is challenging to directly reflect the results. Therefore, this method was selected for an in-depth and comprehensive assessment.

Probabilistic risk assessment of trace metals in surface sediments from Chaohu Lake was conducted based on total and bioavailable concentrations using statistical method. After calculating the probabilistic risk of a single sampling point, the combined risk of the investigated metal mixtures was estimated by addition of the probabilities (Solomon et al., 2013). The results of the assessment of the heavy metals RI and ∑TU in the upper sediments of each sampling point in the three regions were calculated, and the probability distribution results of the ecological risk of heavy metals were obtained (Fig. 7).

According to the cumulative probability results of RI, 67.0% of heavy metals in sediments are at a lower risk and the remaining 33.0% are at a moderate risk (the high-risk event is of statistically low probability that can be ignored). According to the cumulative probability of ∑TU, 87.4% of heavy metals in sediments are at a lower risk and the remaining 12.6% are at a moderate risk. It is not difficult to see that the result of the ∑TU assessment is significantly safer, mainly because the recommended PEL in Canadian sediments may be higher than the actual situation in Chaohu Lake. Therefore, this method did not consider metal bioavailability and thus overestimated the potential risk, so that the results seemed unconvincing. However, the background value of heavy metals in the sediments used in the calculation of RI is inferred based on the deposition history of the sediments themselves, and the distribution of heavy metals in the sediments between the liquid and solid phases is also considered in the revised calculation (Yu et al., 2021). Therefore, the results of the RI calculation in this study are more reasonable and closer to the actual situation.

Comparative analysis of risk assessment

In this study, the larger value of RI in the upper layer of all core sediments in the three regions is used as the representative potential ecological risk of heavy metals, and the spatial equilibrium calculation is further performed on the whole space, and then the ecological risk distribution of heavy metals in sediments can be obtained (Fig. 8).

Based on the spatial distribution results, it is not difficult to see that heavy metals in sediments in most areas of Chaohu Lake are at a lower risk state. However, most of the western region is in a medium risk state, mainly because there are many tributary rivers in the western region, and these rivers bring a large number of heavy metals into the lake, which accumulate in the sediment, thus creating ecological risks. In the other two regions, the medium risk area is small and close to the river, which is also related to the sediment migration and movement rule. Therefore, this RI calculation result is basically reliable.

In addition, compared with other RI calculation methods for the assessment of heavy metals in lake sediments, the following improvements were made in this study: 1) to avoid the residual effect of heavy metal state change, an improved RI assessment method based on metal fractionation obtained from chemical extraction was adopted; 2) the background value of heavy metals used is based on its own sediment history; 3) the cumulative probability calculation of RI is based on mathematical statistics of a large number of measured data. These improvements make the results of the assessment calculations more reliable. For example, the 33.0% probability of heavy metals in sediments in this study being of medium and high risk is much higher than 15.9% calculated by Fang et al. (2022) and closer to the 24.8% probability of toxic effects of heavy metals in sediments on aquatic organisms. Of course, this approach does not actually assess the direct effects of heavy metals on aquatic ecosystems, and differences in the bioavailability and distribution of metals in benthic organisms may lead to significant differences in biological effects (Amato et al., 2018). Therefore, the RI results can only be used as a reference for the potential risk of heavy metals in sediments. In the next phase, we will continue to assess the biological toxicity of heavy metals in sediments. Such as, a DGT-SSD coupled PRA will be adopted (Gu et al., 2022). In summary, the potential ecological risk probability of heavy metals in sediments to aquatic organisms is 33.0% with Hg, As and Ni being the main contributors. The cumulative probability calculation method can provide a more objective and scientific basis for lake management of metal pollution.

This study employed a historical sediment self-sedimentation diagnosis to evaluate the pollution status of eight heavy metals in the surface sediments of Chaohu Lake. Variation of heavy metals (HMs) in sediments was investigated. This study evaluates the distribution of heavy metals in sediments across the entire region by analyzing the vertical concentration of column core samples of undisturbed sediments. This distribution also provides strong support for the diagnosis of the history of lake deposition and the change in heavy metal concentration. It involved the use of an improved assessment method for the potential ecological risk of heavy metals in sediments, the calculation of the background value for heavy metals in sediments, and the discussion of the probability distribution of potential ecological risks based on the content of heavy metals. The results indicated that although there is a certain risk of heavy metals, the majority of these risks originated from the release of sediments. An ecological risk assessment of total fractionation and chemical fractionation was conducted by combining statistical methods. The findings revealed that the ecological risk of heavy metals in surface sediments was at a low to medium level, with a probability of 33.0% for these metals. The main contributors to this risk were Hg, As and Ni. The results suggested that the probabilistic statistical method based on potential ecological risk can be employed to characterize the ecological risk of heavy metals in sediments.

Acknowledgment:

This study is supported by the National Natural Science Foundation of China (Grant No. 52109099, 52079094, 52109030), the Financial Project of Hubei Province (ZB01XZ-202306-ZCFW0685).

Conflicts of Interest: the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

Author Contribution

Conceptualization, L.W. and Z.S.; methodology, L.W. and P.Y.; validation, F.Y. and L.J.; investigation, L.W. and P.Y.; resources, L.J.; data curation, L.W. and L.J.; writing—original draft preparation, L.W. and P.Y.; writing—review and editing, L.W., Z.S., and L.J.; supervision, L.W. and L.J.; project administration, L.W.; funding acquisition, L.W., F.Y. and L.J. All authors have read and agreed to the published version of the manuscript.

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Resultsofheavymetaldetectioninsediments.docx

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Sedimentary regularity and ecological risk of heavy metals in Chaohu Lake sediments

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Study area

Sample collection

Methods of data processing

Geo-accumulation index (I_geo)

The potential ecological risk index

Ecological risk assessment conducted based on the total volume

Results

Sedimentary history and depth prediction

Vertical distribution of heavy metals in sediments

Ecological risk of heavy metals in sediments

Discussion

Main factor analysis based on risks of heavy metals

Risk assessment of heavy metal mixtures

Comparative analysis of risk assessment

Conclusions

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Version 1

Sedimentary regularity and ecological risk of heavy metals in Chaohu Lake sediments

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Study area

Sample collection

Methods of data processing

Geo-accumulation index (Igeo)

The potential ecological risk index

Ecological risk assessment conducted based on the total volume

Results

Sedimentary history and depth prediction

Vertical distribution of heavy metals in sediments

Ecological risk of heavy metals in sediments

Discussion

Main factor analysis based on risks of heavy metals

Risk assessment of heavy metal mixtures

Comparative analysis of risk assessment

Conclusions

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Version 1

Geo-accumulation index (I_geo)