Ecological risk assessment method for heavy metals in sediment based on benthic organism heavy metal tissue residues

Abstract

The invention belongs to the field of ecological risk assessment, and particularly relates to an ecological risk assessment method for heavy metal in sediment based on benthic organism heavy metal tissue residues. The method comprises the following steps: (1) extracting and testing the total amount of heavy metals and the occurrence form of the heavy metals in a surface sediment sample of a target drainage basin, and measuring the content Ci of the ith heavy metal in an effective indication organism; (2) constructing a biological residue safety threshold Pi of the ith heavy metal in the sediment of the target drainage basin for the effective indicator organism; and (3) based on the improved single-factor tissue residual index TI and the comprehensive tissue residual index NTI, dividing the ecological risk of a single heavy metal element to the target drainage basin and the comprehensive ecological risk level of multiple heavy metal elements to the target drainage basin in the sediment. Important reference and basis are provided for evaluating the implementation effect of developed treatment measures and formulating scientific sediment heavy metal pollution treatment plans in the later period.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of ecological risk assessment, and particularly relates to a method for assessing ecological risk of heavy metals in sediments based on heavy metal residue in benthic organisms. BACKGROUND

[0002] With the rapid development of industrialization and urbanization, the problem of heavy metal pollution in river sediments is increasingly prominent. Heavy metals have the characteristics of strong toxicity, difficult degradation, and easy enrichment. Heavy metals discharged into rivers are transferred in water, sediment and sediments, and enter the biological body through the food chain, and through biological enrichment and biological amplification, seriously threaten the safety of water ecological system and also pose potential harm to human health. Therefore, accurately assessing the ecological risk of heavy metals in sediments is of great importance to water environment governance and ecological protection.

[0003] At present, there are many methods for assessing the ecological risk of heavy metals in sediments, but all have certain defects: Chinese patent CN114323846A discloses a method for evaluating the risk of heavy metals in sediments in water ecological system. This method detects the content of heavy metals in sediments in water ecological system, which can to some extent judge the degree of environmental ecological toxicity risk of heavy metals, but the operation is complex, time-consuming and high-cost. More importantly, this method cannot accurately evaluate the biological toxicity and bioavailability of heavy metals; Chinese patent CN112950044A provides a method for evaluating the ecological risk of heavy metals in sediments. It divides the detection area, collects plant samples for detection and analysis, and establishes a relationship model between plant and heavy metal pollutant concentration in sediments to evaluate the ecological risk of river. Due to the biological test link, the plant samples used in this method only obtain heavy metals through ion exchange and adsorption of rhizosphere soil, and the contact range and depth of heavy metals are limited, without considering the dynamic changes and bioavailability of heavy metals, resulting in deviation of the evaluation results. Chinese patent CN105608324A discloses a method for evaluating the ecological risk of heavy metals in river sediments based on toxicity effect. Although this method considers the toxicity effect of heavy metals on aquatic organisms, it mainly calculates the risk index based on the concentration of heavy metals in sediments, release coefficient and toxicity data, without directly measuring the content of heavy metals in organisms. This makes the evaluation unable to accurately reflect the actual harm degree of heavy metals to organisms (high concentration of heavy metals in sediments does not mean high actual absorption and accumulation of organisms), ignoring the key factor of bioavailability.

[0004] Therefore, it is of great significance to develop an analysis method that can reflect the toxicity and bioavailability of heavy metals in water sediments, and comprehensively reveal the migration and transformation path of heavy metals in sediment-biological system and potential ecological health risk. SUMMARY

[0005] The purpose of this invention is to provide a method for ecological risk assessment of heavy metals in sediments based on heavy metal tissue residues in benthic organisms, so as to reveal the potential pollution and ecological effects of heavy metals in a more scientific, accurate and comprehensive manner, 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 solutions: A method for ecological risk assessment of heavy metals in sediments based on heavy metal tissue residues in benthic organisms comprises the following steps: (1) Grid-based sampling was used to collect surface sediment samples and macrobenthic animal samples from the target basin, and the total amount of heavy metals and their occurrence forms in the surface sediment samples were extracted and tested. At the same time, the macrobenthic animal species were identified and effective indicator organisms that characterize heavy metal pollution in the sediments of the target basin were screened. i Heavy metal content C i Conducting measurements; (2) Construct the first i The biological residual safety threshold of the heavy metals for the effective indicator organisms mentioned in step (1) P i : (2-1) Collect surface sediment samples from the target basin. i The total amount of heavy metals in the target basin sediments was used as the i An initial database of baseline values ​​for various heavy metals; (2-2) The iterative double standard deviation method and the relative cumulative frequency method were used to filter out abnormal data in the initial database described in step (2-1), and the first i The average value of the baseline values ​​of the three heavy metals was used as the first i Final baseline values ​​of the heavy metals; (2-3) Combined with the sediments in the target basin i The final baseline values ​​of the heavy metals and i The non-residue content of 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 risk of a single heavy metal element in sediment to the target watershed and the comprehensive ecological risk of multiple heavy metal elements to the target watershed are divided into different levels; the TI and N TI The calculation formula is as follows: ; ; wherein, C i The unit of the total residual index is mg / kg. P i The unit of the total residual index is mg / kg. max The maximum value of the single-factor tissue residue index. mean The average value of the single-factor tissue residue index.

[0007] Further, the surface layer sediments in step (1) refer to the uppermost part of the sediments, usually a few centimeters to tens of centimeters (such as 0-10 cm or 0-20 cm) below the sediment-water interface.

[0008] Further, the effective indicator organisms for characterizing heavy metal pollution in sediments in the target river basin in step (1) are screened according to the dominance index Y of benthic animals, the relative importance index IRI, and their enrichment ability for heavy metals. The enrichment ability of benthic organisms for heavy metals is evaluated using the bio-sediment accumulation factor BSAF. The greater the BSAF, the stronger the enrichment ability of benthic organisms for heavy metals. The calculation formula of BSAF is as follows: ; wherein, C org The heavy metal content in the benthic organism is, C sed The heavy metal content in the sediment inhabited by the benthic organism is.

[0009] Further, the non-residual state content of heavy metals in step (2-3) refers to the content of heavy metals in the non-residual form in the surface layer sediments, which is the average value of the non-residual form heavy metals in each surface layer sediment sample obtained in step (1).

[0010] Further, the optimal fitting mode in step (2-3) refers to fitting the data such as "sediment heavy metal baseline value" and "non-residual state content" through a specific mathematical algorithm, so that the fitting curve (or model) can maximize the internal law of the data, thereby ensuring the scientificity and accuracy of the biological residual safety threshold. P i The core goal is to establish a migration and transformation model of heavy metals in the sediment-biological system through quantitative analysis. The specific implementation methods include least squares method, nonlinear regression, and machine learning algorithms (such as random forest, neural network), etc. P i The core goal is to establish a migration and transformation model of heavy metals in the sediment-biological system through quantitative analysis. The specific implementation methods include least squares method, nonlinear regression, and machine learning algorithms (such as random forest, neural network), etc.

[0011] Further, the ecological risk of a single heavy metal element in the deposit of step (3) to the target river basin and the comprehensive ecological risk grade division standard of multiple heavy metals to the target river basin are shown in the following table: .

[0012] Further, the heavy metals in the deposit of the target river basin include Cr, Ni, Cu, Zn, As, Cd, Hg and Pb.

[0013] The beneficial effects of the present application are: The present application takes benthic animals directly inhabiting in the deposit as the research object, and screens effective indicator organisms that can represent the heavy metal pollution level of the deposit of the target river basin. On this basis, the real existing form and potential activity of heavy metals in the deposit are fully considered, and the calculation method of the deposit heavy metal baseline value and the potential ecological risk assessment is optimized. Through the assessment of the heavy metal organization residue risk of the effective indicator organisms, the potential ecological risk of the heavy metals in the deposit of the target river basin under the influence of accumulation, release and biological toxicity effect is revealed.

[0014] On the one hand, the selection of the research object of the present application avoids the evaluation deviation caused by the insufficient contact between the organisms and the deposit in the prior art method which only relies on plankton or plant samples, so that the risk transmission at the deposit-organism interface can be more accurately captured. On the other hand, the threshold value determined by the present application is more scientific and reasonable, and can accurately reflect the actual threat degree of the heavy metals in the deposit to the organisms. Compared with the prior art evaluation method which only relies on a single index or simple weighted summation, the method of the present application can more comprehensively and meticulously divide the ecological risk grade, and significantly improves the accuracy of the ecological risk assessment. The method of the present application can provide an important reference and basis for evaluating the implementation effect of the treatment measures carried out and formulating a scientific sediment heavy metal pollution treatment plan in the later period. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 Figure 1 is a data distribution graph of the sediment heavy metal baseline value of M lake screened based on the iterative method and the cumulative frequency method.

[0016] Figure 2 Figure 3 is a spatial distribution graph of the single factor organization residue index TI of each heavy metal element in the effective indicator organism Corbicula fluminea in M lake: (a) Cr, (b) Ni, (c) Cu, (d) Zn, (e) As, (f) Hg, (g) Pb.

[0017] Figure 3 Figure 4 is a spatial distribution characteristic graph of the heavy metal N TI value in the effective indicator organism Corbicula fluminea in M lake. DETAILED DESCRIPTION

[0018] The following takes a large shallow lake with a high degree of human disturbance, M Lake, as the research object to further illustrate the present application.

[0019] 1. Natural geographical profile The average elevation of the bottom of M Lake is 1.1 m (Wusong elevation), the bottom of the lake is flat, the average water depth is 1.95 m, and the maximum water depth is about 2.66 m, which is a typical shallow butterfly-shaped lake.

[0020] 2. Field sampling survey 2.1 Sampling point layout 36 sampling points were laid out in M Lake, and surface sediments and macrobenthic animal samples were collected in October 2022. The detailed information of the sampling points is shown in Table 1.

[0021] Table 1. Distribution of sampling points in M Lake 2.2 Sample collection and pretreatment (1) Surface sediment sample The surface sediment sample about 0~10 cm was collected using a grab bucket, stored at low temperature and brought back to the laboratory for pretreatment, and then the total amount of heavy metals and its occurrence form in the surface sediment were extracted and tested.

[0022] (2) Macrobenthic animal sample Two samples of macrobenthic animals were collected at each point in the lake, respectively, for species identification and heavy metal content analysis in macrobenthic animals. The specific collection method is to use a triangular trawl to drag a certain distance on the bottom to obtain the bottom mud, then wash the bottom mud obtained in the net with a 40-mesh sample sieve, pick out the macrobenthic animals and fix them with 75% alcohol solution.

[0023] 2.3 Determination of heavy metals in sediments and macrobenthic animals The total amount of heavy metals in the sediment was extracted using an acid digestion system of HNO3+H2O2, the digestion liquid was chased acid at 160 ℃, then 2% HNO3 was used to constant volume to the colorimetric tube, filtered through a 0.45 μm filter membrane, and then measured. The total amount of Cr, Ni, Cu, Zn, As, Cd, Pb and other 8 elements was determined by inductively coupled plasma mass spectrometry (ICP-MS).

[0024] The total amount of heavy metal elements in the macrobenthic animals was determined by using nitric acid-microwave digestion pretreatment method to digest the macrobenthic animal samples, the digestion liquid was chased acid at 100 ℃, then 2% HNO3 was used to constant volume to the colorimetric tube, filtered through a 0.22 μm filter membrane, and then measured, and ICP-MS was used for analysis and determination.

[0025] The heavy metal forms are obtained by using a multi-step continuous extraction method. The BCR continuous extraction method (Table 2) is used for Cr, Ni, Cu, Zn, Cd, Hg and Pb, and four different chemical forms of samples are obtained, i.e. exchangeable and carbonate-bound state (B1), iron and manganese oxide-bound state (B2), organic matter and sulfide-bound state (B3) and residual state (B4). Meanwhile, the As form is extracted by using the method (Table 3) based on Wenzel et al. (Wenzel W W, 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 organic-bound As, and six forms are obtained, i.e. non-specific adsorption state (F1), specific adsorption state (F2), amorphous iron oxide-bound state (F3), crystalline iron oxide-bound state (F4), organic-bound state (F5) and residual state (F6), and the content of As in each form is determined by using ICP-MS analysis.

[0026] In order to ensure the extraction quality and recovery rate of heavy metals, the sediment standard substance (GBW07366) is used in the experiment, the scallop standard substance (GBW0024) is used for benthic animals, and the recovery rates of the total amount of heavy metals in the sediment, the form and the total amount of heavy metals in the benthic animals are all in the range of 94% to 108%.

[0027] Table 2. BCR continuous extraction steps Table 3. Sediment As form continuous extraction method 3. The ecological risk of heavy metals in the sediment in the M lake basin is evaluated by using the method, and the following steps are included: (1) According to the dominance index Y of benthic organisms in the target basin, the relative importance index IRI and the enrichment ability of heavy metals, the effective indicator organisms for representing the heavy metal pollution in the sediment in the M lake basin are screened, and the content of the first heavy metal in the effective indicator organisms is determined; i C i (2) The content of the first heavy metal in the effective indicator organisms is determined; According to the investigation and analysis of the species structure of benthic animals in the M lake, the occurrence frequency, density and biomass of the river clam belonging to the mollusca in the M lake basin are at the highest level in the whole benthic animal community structure, wherein the occurrence frequency of the river clam in the M lake basin is 90.9%, the density is 142.68 ind. / m 2 ​Biomass 119.90 g / m 2 The dominance index (DI) was 0.35 and the relative importance index (IRI) was 9957.96, indicating that the species was absolutely dominant in the M Lake benthos. Y

[0028] In addition, in order to evaluate the enrichment ability of the river clam to heavy metals, the bio-sediment accumulation factor (BSAF) of the heavy metals in the river clam in the M Lake basin was calculated in the embodiment: ; 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 the BSAF ≥ 2, it indicates that the heavy metal accumulation in the organism is relatively large; 1<BSAF<2, it indicates that there is mild accumulation; and BSAF≤1, it indicates that there is no accumulation.

[0029] The calculation shows that the BSAF index of the heavy metals in the river clam in the M Lake basin is in the order of 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, indicating that there is no biological accumulation; the BSAF indexes of Cu, Zn and Cd are 2.53, 2.85 and 7.51 respectively, indicating that the accumulation level is relatively high, and the biological accumulation of Cd is the most serious. The result shows that the river clam has obvious enrichment effect on several important heavy metals such as Cu, Zn and Cd in the M Lake basin.

[0030] In addition, the inventors further established the coupling relationship between the content of the eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg and Pb) in the sediment in the M Lake basin and the corresponding heavy metal content in the river clam, and the analysis result shows that the content of the eight heavy metals in the sediment and the heavy metal content in the river clam all present significant positive correlation (P<0.01), and the correlation coefficients of Cr, Ni, Cu, Zn, As, Cd, Hg and Pb are 0.861, 0.848, 0.890, 0.875, 0.930, 0.893, 0.666 and 0.756 respectively. The result also shows that the river clam has strong enrichment ability on several important heavy metals in the target basin, can effectively reflect the pollution status of the heavy metals, and can be used as an effective indicator organism of the heavy metal pollution in the M Lake basin.

[0031] (2) Constructing the first heavy metal element in the sediment in the target basin i for the biological residual safety threshold of the effective indicator organism P i : ​(2-1) Collect the total amount of heavy metals in the surface sediment samples of the target basin, which is used as the initial database for constructing the baseline value of the heavy metals in the sediment of the target basin; i i Specifically, the heavy metal content data in the surface sediment of 0-10 cm in the M lake basin is used to carry out the construction of the baseline value of the sediment. The change ranges of Cr, Ni, Cu, Zn, As, Cd, Hg and Pb in the sediment in this interval are 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, and the average values are 76.08, 35.84, 26.85, 91.29, 10.29, 0.56, 0.09 and 24.66 mg / kg, respectively.

[0032] (2-2) The abnormal data in the initial database is screened out by using the 2 times standard deviation method and the relative cumulative frequency method, respectively, and the average value of the sediment heavy metal baseline value obtained based on the two methods is used as the final baseline value of the heavy metal in the sediment of the M lake basin; i Specifically, the iterative 2 times standard deviation method is to define the range of the baseline value from the mathematical point of view, and the specific steps are to calculate the average value and the standard deviation of the initial data column (X σ ), discard all values outside the average value ±2 σ interval, repeat the step until all remaining values are within the value range, and calculate the average value as the baseline value of the data column.

[0033] The relative cumulative frequency method is a method for calculating the background value of pollutants based on the difference in the slope of the fitting curve of the cumulative frequency-element concentration between the uncontaminated sample points and the contaminated sample points. There are three possible situations for the cumulative frequency curve: ① The distribution curve is approximately a straight line, and the inflection point is not obvious, so the sample concentration itself represents the background concentration; ② There is an inflection point, and the inflection point is the boundary between natural values and abnormal values, and the concentration values below the inflection point concentration are used to calculate the baseline value; ③ There are two inflection points, and the lower inflection point represents the upper limit of the element concentration, and the average value of the element concentration below the upper limit can be used as the baseline value, and the high value point may represent the lower limit of the abnormal value.

[0034] ​​​The baseline values ​​of heavy metals Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb in the sediments of Lake M obtained by the iterative 2-times standard deviation method in this study were 74.72, 35.1323.91, 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 by 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 in the figure below. Figure 1 shown.

[0035] There was no significant difference in the baseline values ​​calculated by the two methods, and both could represent 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 value of heavy metals in the sediments of Lake M, that is, 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.

[0036] (2-3) The average content of non-residual heavy metals in the surface sediment samples of the target basin is obtained. i The non-residue content of the heavy metals corresponding to the first step (2-2) and the final baseline values ​​of the eight heavy metals in the M Lake Basin were obtained. i The corresponding non-residue contents of the eight heavy metals were calculated using the optimal fitting method to obtain the bioresidue safety thresholds of the eight heavy metals in Clams. The results are shown in Table 4. The bioresidue safety thresholds of the eight heavy metals, 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 were used as the criteria for assessing the residue risk in benthic organisms and were used to conduct the heavy metal residue risk assessment.

[0037] Table 4. Safety thresholds for heavy metal bioresidues in benthic organisms in the M Lake basin (3) Based on single factor tissue residue index TI and comprehensive tissue residue index N TI To comprehensively evaluate the residual risk of heavy metals in river clams in the M Lake Basin, and thus to classify the ecological risk of a single heavy metal in the M Lake Basin to the target basin and the comprehensive ecological risk level of multiple heavy metals to the target basin; wherein, the TI and N TI The calculation formula is as follows: ; ; Where, C i The first i Content of heavy metals, mg / kg; P i It is the first sediment in the M Lake Basin. i Bioresidue safety threshold of heavy metal elements, mg / kg; TI max is the maximum value of the single factor tissue residue index; TI mean is the average value of the single factor tissue residue index.

[0038] Table 5. Based on TI and N TI Risk classification standards for heavy metal residues in benthic animals The calculation results show that the TI values ​​of eight heavy metals, including Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb, in river clams in the M Lake basin range from 0.11 to 3.28, 0.10 to 1.30, 0.27 to 2.80, 0.48 to 2.13, 0.52 to 1.81, 0.27 to 3.05, 0.02 to 0.65, and 0.18 to 1.85, respectively, with average values ​​of 0.64, 0.37, 0.91, 0.81, 0.98, 1.06, 0.31, and 0.67, respectively. Only the TI value of Cd is greater than 1, indicating a low risk, while the other elements show no residual risk. From the spatial distribution of Cd TI values ​​( Figure 2 ). Clams distributed in the western, southern, and northern parts of Lake M all show low-level Cd residue risks, potentially causing biotoxicity. Studies have shown that exposure to heavy metals can inhibit some physiological behaviors (such as diving and respiration) and growth and development in clams. Furthermore, heavy metals can be transferred to organisms at higher trophic levels through the food chain.

[0039] Heavy metals in M ​​Lake clams N TI The values ​​range from 0.53 to 2.68, with an average of 1.09. Among the sampling points in the lake, the percentages of points with low and medium risks of heavy metal bioresidues were 90.91% and 9.09%, respectively, indicating that the overall risk of heavy metal bioresidues in river clams was low ( Figure 3 It is worth noting that the risk of heavy metal residues in river clams in parts of the western and northern regions of Lake M reached a medium level. The total amount and bioavailability of heavy metals in sediments in these areas are relatively high, and the impact on heavy metal residues in river clams is also greater.

Claims

1. A method for ecological risk assessment of heavy metals in sediments based on heavy metal tissue residues in benthic organisms, characterized in that: The following steps are involved: (1) Grid-based sampling was used to collect surface sediment samples and macrobenthic animal samples from the target basin, and the total amount of heavy metals and their occurrence forms in the surface sediment samples were extracted and tested. At the same time, the macrobenthic animal species were identified and effective indicator organisms that characterize heavy metal pollution in the sediments of the target basin were screened. i Heavy metal content C i Conducting measurements; (2) Construct the first i The biological residual safety threshold of the heavy metals for the effective indicator organisms mentioned in step (1) P i : (2-1) Collect surface sediment samples from the target basin. i The total amount of heavy metals in the target basin sediments was used as the i An initial database of baseline values ​​for various heavy metals; (2-2) The iterative 2-times standard deviation method and the relative cumulative frequency method were used to filter out abnormal data in the initial database described in step (2-1), and the first i The average value of the baseline values ​​of the three heavy metals was used as the first i Final baseline values ​​of the heavy metals; (2-3) Combined with the sediments in the target basin i The final baseline values ​​of the heavy metals and i The non-residue content of 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 risk of a single heavy metal element in sediment to the target watershed and the comprehensive ecological risk of multiple heavy metal elements to the target watershed are divided into different levels; the TI and N TI The calculation formula is as follows: ； ； Where, C i The unit is mg / kg; P i The unit is mg / kg; TI max is the maximum value of the single factor tissue residue index; TI mean is the average value of the single factor tissue residue index.

2. The method for ecological risk assessment of heavy metals in sediments according to claim 1, characterized in that: In step (1), the effective indicator organisms for characterizing heavy metal pollution in the target watershed sediments are screened based on the dominance index Y, relative importance index IRI and the enrichment capacity of benthic animals for heavy metals.

3. The method for ecological risk assessment of heavy metals in sediments according to claim 2, characterized in that: The heavy metal accumulation capacity of the benthic organisms is assessed using the bio-sediment accumulation factor (BSAF). The larger the BSAF, the stronger the heavy metal accumulation capacity of the benthic organisms. The calculation formula of BSAF is as follows: ； Where, C org is the content of heavy metals in benthic organisms, C sed The heavy metal content in sediments inhabited by benthic organisms.

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

5. The method for ecological risk assessment of heavy metals in sediments according to claim 1, characterized in that: The optimal fitting methods described in steps (2-3) include least squares method, nonlinear regression and machine learning algorithm.

6. The method for ecological risk assessment of heavy metals in sediments according to claim 1, characterized in that: The ecological risk classification standards for a single heavy metal element in the sediment to the target watershed and the comprehensive ecological risk classification standards for multiple heavy metals to the target watershed in step (3) are shown in the following table: 。 7. The method for ecological risk assessment of heavy metals in sediments according to claim 1, characterized in that: The heavy metals in the target watershed sediments include Cr, Ni, Cu, Zn, As, Cd, Hg and Pb.

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