A method for evaluating ecological risk of heavy metals in sediments based on heavy metal tissue residues of benthos

Abstract

This invention belongs to the field of ecological risk assessment, specifically relating to a method for assessing the ecological risk of heavy metals in sediments based on the heavy metal tissue residues of benthic organisms. The method includes the following steps: (1) extracting and testing the total amount and occurrence forms of heavy metals in surface sediment samples from the target watershed, and effectively indicative of their presence in organisms. i Heavy metal content C i (2) Construct the first sediment in the target watershed. i Biosafety thresholds for heavy metals as effective indicator organisms P i (3) Based on the improved single-factor tissue residue index (TI) and the comprehensive tissue residue index N TI This invention classifies the ecological risks of a single heavy metal element in sediments to a target watershed, as well as the combined ecological risks of multiple heavy metal elements to the target watershed. This invention will provide important reference and basis for assessing the effectiveness of existing remediation measures and for developing scientific plans for the remediation of heavy metal pollution in sediments.

Description

Technical Field

[0001] This invention belongs to the field of ecological risk assessment, specifically relating to a method for assessing the ecological risk of heavy metals in sediments based on the heavy metal tissue residues of benthic organisms. Background Technology

[0002] With rapid industrialization and urbanization, heavy metal pollution in watershed sediments has become an increasingly prominent problem. Heavy metals are highly toxic, difficult to degrade, and prone to bioaccumulation. Heavy metals discharged into rivers are transported through water, sediment, and silt, and enter organisms via the food chain. Through bioaccumulation and biomagnification, they seriously threaten the safety of aquatic ecosystems and pose potential hazards to human health. Therefore, accurately assessing the ecological risks of heavy metals in sediments is crucial for water environment management and ecological protection.

[0003] Currently, numerous methods exist for assessing the ecological risk of heavy metals in sediments, but all have certain shortcomings. Chinese patent CN114323846A discloses a method for assessing the risk of heavy metals in sediments within aquatic ecosystems. This method detects the heavy metal content in sediments of aquatic ecosystems and can, to some extent, determine the degree of environmental and ecotoxicological risk of heavy metals. However, it is complex, time-consuming, and costly. More importantly, this method cannot accurately evaluate the biotoxicity and bioavailability of heavy metals. Chinese patent CN112950044A provides an ecological risk assessment method for heavy metals in sediments. This method assesses river ecological risk by dividing the detection area, collecting plant samples for analysis, and establishing a relationship model between plant samples and the concentration of heavy metal pollutants in sediments. However, this method, in the biotesting stage, relies solely on plant samples to acquire heavy metals through ion exchange and adsorption in the rhizosphere soil. This limits the contact range and depth of heavy metals and fails to consider dynamic changes and bioavailability of heavy metals, leading to biased assessment results. Chinese patent CN105608324A discloses a method for assessing the ecological risk of heavy metals in watershed sediments based on toxicity effects. While this method considers the toxic effects of heavy metals on aquatic organisms, it primarily calculates a risk index based on heavy metal concentrations, release coefficients, and toxicity data in sediments, without directly measuring the heavy metal content within organisms. This makes the assessment unable to accurately reflect the actual degree of harm to organisms (a high concentration of heavy metals in sediments does not necessarily mean a high amount absorbed and accumulated by organisms), and it neglects the crucial factor of bioavailability.

[0004] Therefore, it is of great significance to develop an analytical method that can reflect the toxicity and bioavailability of heavy metals in aquatic sediments and comprehensively reveal the migration and transformation pathways and potential ecological health risks of heavy metals in sediment-biological systems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for assessing the ecological risk of heavy metals in sediments based on heavy metal residues in benthic organism tissues, so as to reveal the potential pollution and ecological effects of heavy metals more scientifically, accurately and comprehensively, and provide important scientific and technological support for the accurate identification and risk assessment of heavy metal pollution in lake sediments.

[0006] The present invention adopts the following technical solution:

[0007] A method for assessing the ecological risk of heavy metals in sediments based on heavy metal residues in benthic organism tissues includes the following steps:

[0008] (1) A grid-based sampling method was used to collect surface sediment samples and macrobenthic animal samples from the target watershed. The total amount and occurrence forms of heavy metals in the surface sediment samples were extracted and tested. At the same time, macrobenthic animal species were identified and effective indicator organisms for heavy metal pollution in the sediments of the target watershed were screened. The first effective indicator organism was then analyzed. i Heavy metal content C i Perform the measurement;

[0009] (2) Construct the first sediment in the target watershed i The biosafety threshold for heavy metals relative to the effective indicator organisms described in step (1) P i :

[0010] (2-1) Collect the first layer of surface sediment samples from the target watershed. i Total heavy metal data were used as the basis for constructing the first type of heavy metal data in sediments of the target watershed. i An initial database of baseline values ​​for heavy metals;

[0011] (2-2) Abnormal data in the initial database described in step (2-1) are screened out using the iterative 2x standard deviation method and the relative cumulative frequency method, respectively. The sediment data obtained based on these two methods are then processed. i The average value of the baseline values ​​of the heavy metals was used as the first heavy metal in the sediments of the target watershed. i Final baseline values ​​for each heavy metal;

[0012] (2-3) Combining the sediments of the target watershed with the first i Final baseline values ​​for each heavy metal and the first i The non-residual content of several heavy metals was calculated using the optimal fitting method. P i ;

[0013] (3) Based on the improved single-factor tissue residue index (TI) and comprehensive tissue residue index N TIThe ecological risks of a single heavy metal element in sediments to the target watershed and the comprehensive ecological risk levels of multiple heavy metal elements to the target watershed are classified separately; the TI and N TI The calculation formula is as follows:

[0014] ;

[0015] ;

[0016] In the formula, C i The unit is mg / kg; P i The unit is mg / kg; TI max The maximum value of the single-factor tissue residual index; TI mean This represents the average value of the single-factor tissue residual index.

[0017] Furthermore, the surface sediments mentioned in step (1) refer to the uppermost part of the sediments, which are usually a few centimeters to tens of centimeters below the sediment-water interface (e.g., 0~10 cm or 0~20 cm).

[0018] Furthermore, in step (1), effective indicator organisms for characterizing heavy metal pollution in sediments of the target watershed are screened based on the dominance index Y, relative importance index IRI, and their ability to enrich heavy metals.

[0019] The bioaccumulation capacity of benthic organisms for heavy metals was assessed using the bioaccumulation factor-sediment (BSAF). A higher BSAF indicates a stronger bioaccumulation capacity for heavy metals. The formula for calculating BSAF is as follows:

[0020] ;

[0021] In the formula, C org The content of heavy metals in benthic organisms. C sed The heavy metal content of sediments inhabited by benthic organisms.

[0022] Furthermore, the non-residual content of heavy metals mentioned in step (2-3) refers to the content of heavy metals in the surface sediments in a non-residual form, which is the average value of non-residual heavy metals in each surface sediment sample obtained in step (1).

[0023] Furthermore, the optimal fitting method described in steps (2-3) refers to the method used in constructing the biosafety threshold for biological residues. P iAt that time, specific mathematical algorithms are used to fit data such as "baseline values ​​of heavy metals in sediments" and "content of non-residue states" to ensure that the fitted curve (or model) can reflect the inherent laws of the data to the greatest extent possible, thereby ensuring... P i The scientific rigor and accuracy of this research are paramount. Its core objective is to establish a migration and transformation model of heavy metals in sediment-biological systems through quantitative analysis. Specific implementation methods include least squares, nonlinear regression, and machine learning algorithms (such as random forests and neural networks).

[0024] Furthermore, the classification criteria for the ecological risk of a single heavy metal element in the sediment to the target watershed and the comprehensive ecological risk of multiple heavy metals to the target watershed in step (3) are shown in the table below:

[0025] .

[0026] Furthermore, the heavy metals in the sediments of the target watershed include Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb.

[0027] The beneficial effects of this invention are as follows:

[0028] This invention focuses on benthic animals that directly inhabit sediments, screening for effective indicator organisms that can characterize heavy metal pollution levels in sediments of a target watershed. Based on this, it fully considers the actual forms and potential activities of heavy metals in sediments, optimizing the calculation methods for sediment heavy metal baseline values ​​and potential ecological risk assessments. By evaluating the risk of heavy metal tissue residues in effective indicator organisms, it reveals the potential ecological risks under the influence of heavy metal accumulation, release, and biotoxic effects in sediments of the target watershed.

[0029] On the one hand, the selection of research objects in this invention avoids the assessment bias caused by insufficient contact between organisms and sediments due to relying solely on plankton or plant samples in existing methods, thus more accurately capturing the risk transmission at the sediment-biota interface. On the other hand, the thresholds determined by this invention are more scientifically sound and can accurately reflect the actual threat level of sediment heavy metals to organisms. Compared with existing assessment methods that rely on a single indicator or simple weighted summation, the method of this invention can more comprehensively and meticulously classify ecological risk levels, significantly improving the accuracy of ecological risk assessment. This method will provide important reference and basis for evaluating the implementation effects of existing remediation measures and for formulating scientific sediment heavy metal pollution remediation plans in the future. Attached Figure Description

[0030] Figure 1 This is a distribution map of heavy metal baseline values ​​in sediments of Lake M after screening using the iterative method and the cumulative frequency method.

[0031] Figure 2Spatial distribution of single-factor tissue residue index (TI) of various heavy metal elements in the body of the river clam, an effective indicator organism of Lake M: (a) Cr, (b) Ni, (c) Cu, (d) Zn, (e) As, (f) Hg, (g) Pb.

[0032] Figure 3 To serve as an effective indicator of heavy metal N in the river clam, an organism in Lake M. TI Spatial distribution characteristics of the values. Detailed Implementation

[0033] The following section uses Lake M, a large, shallow lake that is subject to significant human disturbance, as a case study to further illustrate the invention.

[0034] 1. General Overview of Natural Geography

[0035] Lake M has an average elevation of 1.1 m (Wusong elevation), a flat bottom, an average water depth of 1.95 m, and a maximum water depth of about 2.66 m. It is a typical shallow, butterfly-shaped lake.

[0036] 2. Field sampling survey

[0037] 2.1 Sampling point layout

[0038] Thirty-six sampling sites were set up in Lake M, and surface sediments and macrobenthic animal samples were collected simultaneously in October 2022. Detailed information on the sampling sites is shown in Table 1.

[0039] Table 1. Distribution of sampling points at Lake M

[0040]

[0041] 2.2 Sample Collection and Pretreatment

[0042] (1) Surface sediment samples

[0043] Surface sediment samples of approximately 0–10 cm were collected using a mud trap, preserved at low temperature, and brought back to the laboratory. After pretreatment, the total amount of heavy metals and their occurrence forms in the surface sediments were extracted and tested.

[0044] (2) Benthic animal samples

[0045] Two benthic animal samples were collected from various locations in the lake, one for species identification and the other for heavy metal content analysis. The specific collection method involved using a triangular trawl to drag a distance along the bottom to obtain sediment. The sediment was then washed through a 40-mesh sieve to remove sand, and the benthic animals were selected and fixed with a 75% alcohol solution.

[0046] 2.3 Determination of heavy metals in sediments and benthic animals

[0047] The total heavy metals in the sediment were extracted using an acid digestion system of HNO3 + H2O2. After the digestion solution was deacidified at 160 °C, it was diluted to a volume of 2% HNO3 in a colorimetric tube and filtered through a 0.45 μm filter before analysis. The total amounts of eight elements, including Cr, Ni, Cu, Zn, As, Cd, and Pb, were determined by inductively coupled plasma mass spectrometry (ICP-MS).

[0048] The total amount of heavy metals in benthic animals was determined by a pretreatment method of nitric acid-microwave digestion. The benthic animal samples were digested, and after the digestion solution was deacidified at 100℃, 2% HNO3 was added to the volume of the solution and diluted to a colorimetric tube. The solution was then filtered through a 0.22 μm filter membrane and analyzed by ICP-MS.

[0049] The heavy metal speciation was obtained using a multi-step continuous extraction method. Among them, Cr, Ni, Cu, Zn, Cd, Hg, and Pb were extracted using the BCR continuous extraction method (Table 2), yielding four different chemical speciations: exchangeable and carbonate-bound (B1), iron-manganese oxide-bound (B2), organic matter and sulfide-bound (B3), and residue (B4). Meanwhile, the As speciation was continuously extracted using a method based on Wenzel et al. (Wenzel WW, Kirchbaumer N, Prohaska T, et al. Arsenic fractionation in soils using an improved sequential extraction procedure. Analytica Chimica Acta, 2001, 436 (2): 309~323) with the addition of organically bound As (Table 3). Six speciations of As were extracted: non-specifically adsorbed (F1), specifically adsorbed (F2), amorphous iron oxide bound (F3), crystalline iron oxide bound (F4), organically bound (F5), and residual (F6). The As content of each speciation was determined by ICP-MS analysis.

[0050] To ensure the quality and recovery rate of heavy metal extraction, sediment standard material (GBW07366) was used in the experiment, and scallop standard material (GBW0024) was used for benthic animals. The recovery rates of total heavy metals, speciation of heavy metals in sediments and total heavy metals in benthic animals were all between 94% and 108%.

[0051] Table 2. BCR Serial Extraction Steps

[0052]

[0053] Table 3. Continuous extraction method of As speciation in sediments

[0054]

[0055] 3. The ecological risk of heavy metals in the sediments of the M Lake Basin is evaluated by using the method of the present invention, including the following steps:

[0056] (1) Screening effective indicator organisms for heavy metal pollution in the sediments of the M Lake Basin according to the dominance index Y, relative importance index IRI of benthic organisms in the target basin and their enrichment ability for heavy metals, and determining the content of the i th heavy metal in the effective indicator organisms C i ;

[0057] According to the investigation and analysis of the species structure of benthic animals in Lake M, the occurrence frequency, density and biomass of Corbicula fluminea belonging to Mollusca are at the highest level in the whole benthic animal community structure. Among them, the occurrence frequency of Corbicula fluminea in the M Lake Basin is 90.9%, the density is 142.68 ind. / m 2 , and the biomass is 119.90 g / m 2 . From this, the dominance index ( Y ) is 0.35, and the relative importance index (IRI) is 9957.96, which belongs to the absolute dominant species among the benthic animals in Lake M.

[0058] At the same time, in order to evaluate the enrichment ability of Corbicula fluminea for heavy metals, the heavy metal biota-sediment accumulation factor (BSAF) in Corbicula fluminea in the M Lake Basin was calculated in this example:

[0059] ;

[0060] In the formula, C org is the heavy metal content in the organism, C sed is the heavy metal content in the sediment where the organism inhabits. When BSAF ≥ 2, it indicates that the heavy metal accumulation degree in the organism is relatively large; 1 < BSAF < 2 indicates mild accumulation; BSAF ≤ 1 indicates no accumulation.

[0061] It can be calculated that the order of the BSAF index of heavy metals in Corbicula fluminea in the M Lake Basin is Cd > Zn > Cu > Hg > As > Ni > Cr > Pb; the average values of the BSAF indexes of Cr, Ni, As, Hg, and Pb are all less than 1, showing no biological accumulation degree. The BSAF indexes of Cu, Zn, and Cd are 2.53, 2.85, and 7.51 respectively, and the accumulation levels are relatively high. Among them, the biological accumulation degree of Cd is the most serious. This result indicates that Corbicula fluminea has an obvious enrichment effect on several relatively important heavy metals such as Cu, Zn, and Cd in the M Lake Basin.

[0062] Furthermore, the inventors established a coupling relationship between the contents of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, Pb) in the sediments of Lake M and the corresponding heavy metal contents in freshwater clams. The analysis showed a significant positive correlation between the contents of the eight heavy metals in the sediments and the heavy metal contents in the freshwater clams (P<0.01). The correlation coefficients for Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb were 0.861, 0.848, 0.890, 0.875, 0.930, 0.893, 0.666, and 0.756, respectively. This result also indicates that freshwater clams have a strong ability to accumulate several important heavy metals in the target watershed, effectively reflecting the heavy metal pollution status, and can serve as an effective indicator organism for heavy metal pollution in the Lake M watershed.

[0063] (2) Constructing the first sediment of the target watershed i The biosafety threshold for certain heavy metal elements as effective indicators of biological residues in organisms P i :

[0064] (2-1) Collect the first layer of surface sediment samples from the target watershed. i Total heavy metal data were used as the basis for constructing the first type of heavy metal data in sediments of the target watershed. i An initial database of baseline values ​​for heavy metals;

[0065] Specifically, this study used heavy metal content data from the top 0–10 cm sediments in the M Lake basin to construct sediment baseline values. The ranges of heavy metal content in sediments within this range were 49.57–116.28, 21.39–56.74, 16.48–49.73, 51.35–160.09, 6.17–20.27, 0.18–1.82, 0.03–0.30, and 12.91–33.02 mg / kg, respectively, with average values ​​of 76.08, 35.84, 26.85, 91.29, 10.29, 0.56, 0.09, and 24.66 mg / kg, respectively.

[0066] (2-2) Outlier data in the initial database were screened out using the 2x standard deviation method and the relative cumulative frequency method, respectively. The average value of the sediment heavy metal baseline values ​​obtained based on the two methods was taken as the first standard deviation value of heavy metals in the sediments of Lake M. i Final baseline values ​​for each heavy metal;

[0067] Specifically, the iterative 2x standard deviation method, from a mathematical perspective, defines the range of baseline values ​​through the range of normal distribution values. The specific steps involve calculating the mean and standard deviation of the initial data series. σ Discard all values ​​exceeding ±2 of the average. σRepeat this step for the values ​​in the interval until all remaining values ​​fall within this range. Calculate the average value as the baseline value for the data column.

[0068] The relative cumulative frequency method is a method for calculating background pollutant values ​​based on the difference in the slope of the fitted curves of cumulative frequency versus elemental concentration between uncontaminated and contaminated sample points. The cumulative frequency curve may exhibit three scenarios: ① The distribution curve approximates a straight line with no obvious inflection point; in this case, the sample concentration itself is considered to represent the background concentration. ② There is one inflection point; this inflection point represents the boundary between natural and outlier values, and concentrations below this inflection point are used to calculate the baseline value. ③ There are two inflection points; the lower inflection point represents the upper limit of elemental concentration, and the average value of elemental concentrations below this upper limit can be used as the baseline value. The higher inflection point may represent the lower limit of outliers.

[0069] The baseline values ​​of heavy metals Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb in Lake M sediments obtained using the iterative two-standard-deviation method were 74.72, 35.13, 23.91, 84.26, 9.37, 0.38, 0.06, and 24.88 mg / kg, respectively. The baseline values ​​of Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb in the sediments calculated using the relative cumulative frequency method were 74.58, 33.81, 23.44, 84.02, 9.17, 0.38, 0.07, and 23.85 mg / kg, respectively. The data distribution is shown below. Figure 1 As shown.

[0070] The baseline values ​​calculated by the two methods showed no significant difference and could both characterize the baseline values ​​of heavy metals in the sediments of Lake M. Therefore, the average value of the two methods was selected as the final baseline values ​​of heavy metals in the sediments of Lake M. The baseline values ​​of Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb were 74.65, 34.47, 23.68, 84.14, 9.27, 0.36, 0.06, and 24.37 mg / kg, respectively.

[0071] (2-3) The content of non-residual heavy metals in each surface sediment sample of the target watershed was averaged to obtain the first... i The non-residual content of each heavy metal; combined with the final baseline values ​​of the eight heavy metals in Lake M obtained in step (2-2) and the first i The non-residue content of each heavy metal was determined, and the optimal fitting method was used to calculate the biosafety thresholds for eight heavy metals in *Clams maculatus*. The results are shown in Table 4. The biosafety thresholds for the eight heavy metals, namely Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb, were 8, 30, 75, 330, 5, 4.7, 0.2, and 5 mg / kg, respectively. These thresholds were used as the standard for assessing the residue risk in benthic organism tissues, and a heavy metal residue risk assessment was conducted accordingly.

[0072] Table 4. Safety thresholds for heavy metal bioresidues in benthic organisms in Lake M basin

[0073]

[0074] (3) Based on the single-factor tissue residue index (TI) and the comprehensive tissue residue index N TI To comprehensively assess the risk of heavy metal residues in river clams within the M Lake watershed, thereby classifying the ecological risk of a single heavy metal to the target watershed and the comprehensive ecological risk level of multiple heavy metals to the target watershed; wherein, the TI and N TI The calculation formula is as follows:

[0075] ;

[0076] ;

[0077] In the formula, C i The first in the body of the river clam i The content of each heavy metal, mg / kg; P i The first in the sediments of Lake M basin i Bioresidue safety thresholds for several heavy metal elements, mg / kg; TI max The maximum value of the single-factor tissue residual index; TI mean This represents the average value of the single-factor tissue residual index.

[0078] Table 5. Based on TI and N TI Classification criteria for heavy metal residue risk levels in benthic animals

[0079]

[0080] The calculation results show that the TI ranges of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb) in the river clams of Lake M are 0.11–3.28, 0.10–1.30, 0.27–2.80, 0.48–2.13, 0.52–1.81, 0.27–3.05, 0.02–0.65, and 0.18–1.85, respectively, with average values ​​of 0.64, 0.37, 0.91, 0.81, 0.98, 1.06, 0.31, and 0.67. Only Cd has an average TI value greater than 1, indicating a low risk; the other elements do not show any residual risk. From the spatial distribution of Cd's TI values ​​(…), Figure 2Low levels of Cd residue were found in some locations in the western, southern, and northern parts of Lake M, potentially causing biotoxicity. Previous studies have shown that heavy metal exposure can inhibit some physiological behaviors of clams (such as diving and respiration) as well as their growth and development. Furthermore, heavy metals can be transferred to organisms at higher trophic levels through the food chain.

[0081] M Lake River Clams Heavy Metals N TI The values ​​ranged from 0.53 to 2.68, with an average of 1.09. Among all sampling points in the lake, the proportions of points at low and medium risk for heavy metal biological residues were 90.91% and 9.09%, respectively, indicating that the overall risk level of heavy metal biological residues in river clams was low. Figure 3 It is worth noting that the risk of heavy metal residues in river clams in the western and northern parts of Lake M is at a moderate level. The total amount and bioavailability of heavy metals in the sediments in this area are relatively high, which has a greater impact on the heavy metal residues in river clams.

Claims

1. A method for assessing the ecological risk of heavy metals in sediments based on heavy metal tissue residues in benthic organisms, characterized in that, Includes the following steps: (1) A grid-based sampling method was used to collect surface sediment samples and macrobenthic animal samples from the target watershed. The total amount and occurrence forms of heavy metals in the surface sediment samples were extracted and tested. At the same time, macrobenthic animal species were identified and effective indicator organisms for heavy metal pollution in the sediments of the target watershed were screened. The first effective indicator organism was then analyzed. i Heavy metal content C i Perform the measurement; Among them, effective indicator organisms for characterizing heavy metal pollution in sediments of the target watershed are screened based on the dominance index Y, relative importance index IRI of benthic animals and their ability to enrich heavy metals. The bioaccumulation capacity of benthic organisms for heavy metals was assessed using the bioaccumulation factor-sediment accumulation factor (BSAF). A higher BSAF indicates a stronger bioaccumulation capacity for heavy metals in benthic organisms. The formula for calculating BSAF is as follows: ； In the formula, C org The content of heavy metals in benthic organisms. C sed Heavy metal content in sediments inhabited by benthic organisms; (2) Construct the first sediment in the target watershed i The biosafety threshold for heavy metals relative to the effective indicator organisms described in step (1) P i : (2-1) Collect the first layer of surface sediment samples from the target watershed. i Total heavy metal data were used as the basis for constructing the first type of heavy metal data in sediments of the target watershed. i An initial database of baseline values ​​for heavy metals; (2-2) Abnormal data in the initial database described in step (2-1) are screened out using the iterative 2x standard deviation method and the relative cumulative frequency method, respectively. The sediment data obtained based on these two methods are then compared to the first... i The average value of the baseline values ​​of the heavy metals was used as the first heavy metal in the sediments of the target watershed. i Final baseline values ​​for each heavy metal; (2-3) Combining the sediments of the target watershed with the first i Final baseline values ​​for each heavy metal and the first i The non-residual content of several heavy metals was calculated using the optimal fitting method. P i ; (3) Based on the improved single-factor tissue residue index (TI) and comprehensive tissue residue index N TI The ecological risks of a single heavy metal element in sediments to the target watershed and the comprehensive ecological risk levels of multiple heavy metal elements to the target watershed are classified separately; the TI and N TI The calculation formula is as follows: ； ； In the formula, C i The unit is mg / kg; P i The unit is mg / kg; TI max The maximum value of the single-factor tissue residual index; TI mean This represents the average value of the single-factor tissue residual index.

2. The method for assessing the ecological risk of heavy metals in sediments according to claim 1, characterized in that, The non-residual content of heavy metals mentioned in step (2-3) refers to the content of heavy metals in the surface sediment in a non-residual form.

3. The method for assessing the ecological risk of heavy metals in sediments according to claim 1, characterized in that, The optimal fitting methods mentioned in steps (2-3) include least squares method, nonlinear regression and machine learning algorithms.

4. The method for assessing the ecological risk of heavy metals in sediments according to claim 1, characterized in that, The following table shows the classification criteria for the ecological risks of a single heavy metal element in the sediment to the target watershed and the comprehensive ecological risks of multiple heavy metals to the target watershed, as described in step (3): 。 5. The method for assessing the ecological risk of heavy metals in sediments according to claim 1, characterized in that, The heavy metals in the sediments of the target watershed include Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb.

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