Lake sediment environmental quality comprehensive evaluation and risk factor identification method

Abstract

The application discloses a kind of lake sediment environmental quality comprehensive evaluation and risk factor identification method, comprising: collecting sediment samples of different point of lake, analyzing the pollution occurrence characteristics of traditional pollutants and trace / trace pollutants of sediment, and deducing pollutant sediment facies prediction no-effect concentration data based on existing database;According to the measured pollutant concentration data of pollutant sediment facies prediction no-effect concentration, dimension reduction is three parameters, and a sediment environmental quality comprehensive evaluation equation is established based on the three parameters;According to the equation, the sediment environmental quality comprehensive score is calculated using the measured concentration data, the sediment environmental quality grade is divided, and the main risk factor of sediment is identified.The method of the application can realize fast, accurate and comprehensive evaluation of lake sediment environmental quality and identification of main risk factors.

Description

Technical Field

[0001] This invention belongs to the field of water environment management technology, specifically relating to a method for comprehensive evaluation of the environmental quality of lake sediments and identification of risk factors. Background Technology

[0002] Sediments are an essential component of aquatic ecosystems, serving as necessary habitats for benthic organisms and many other aquatic life. Rapid socio-economic development in lake basins has led to a dramatic increase in anthropogenic pollutant emissions, which continuously enter the surface water environment through wastewater discharge and surface runoff, ultimately accumulating in sediments under the influence of media distribution and sedimentation. As important surface water resources, lakes, compared to rivers and oceans, have longer water exchange cycles, better water stability, and lower pollutant dilution capacity, resulting in higher pollutant accumulation loads in their sediments. However, for most pollutants, changes in sediment physicochemical conditions and hydrodynamic conditions will cause them to be released back into the overlying water bodies. Therefore, sediments play a dual role as both a "sink" and a "source" in the environmental fate of pollutants. Simultaneously, lakes have multiple trophic levels, playing a vital ecosystem service role in maintaining regional biodiversity and ecosystem balance. Large amounts of lipophilic and hydrophobic pollutants accumulate in aquatic organisms, undergoing bioaccumulation and biomagnification through the food chain, posing potential hazards to higher trophic level organisms and human health. In summary, the environmental quality of lake sediment pollutants is related to the water quality of overlying water bodies and the safety and health of aquatic ecosystems. Conducting a comprehensive assessment of the environmental quality of lake sediments is an important basis for scientifically assessing the risks to the lake water environment. It has important guiding significance for formulating accurate and effective strategies for the treatment and remediation of lake sediment pollution and for improving water environment management methods.

[0003] Compared to water quality assessment, sediment environmental quality assessment started later. Currently, the main development in lake sediment environmental quality assessment is the evolution of evaluation indicators from single indicators such as chemical composition and biotoxicity to multiple indicators encompassing "chemical composition-biotoxicity-ecological characteristics." Evaluation methods are also evolving from single-factor to multi-factor comprehensive assessments, but a unified sediment environmental quality assessment method has yet to be established. Sediment environmental quality assessment relies on the establishment of pollutant baseline values, primarily including the Biological Effect Database for Sediment (BEDS) method and the Equilibrium Partitioning Approach (EqPA). The BEDS method establishes a corresponding biological effect database by collecting bioeffect data on pollutants and uses statistical analysis to establish sediment quality guidelines (SQGs) for target pollutants, as exemplified by organizations such as NOAA in the United States and CCME in Canada. EqPA, based on the principle of phase equilibrium partitioning, establishes sedimentary facies baselines based on pollutant aqueous phase baselines; countries such as the Netherlands and the United Kingdom have established numerical pollutant quality guidelines. Currently, the environmental quality assessment of freshwater sediments in my country mainly adopts the SQGs (Sedimentary Quality Groups) established by developed countries such as the United States, Canada, and Europe. However, the significant spatial heterogeneity of biological species composition in different regions leads to considerable uncertainty in the assessment results obtained using SQGs. Furthermore, although some scholars have established numerical sedimentary benchmarks for pollutants including heavy metals using the EqPA method, these primarily focus on evaluating single compounds, lacking sufficient consideration for joint risk assessment of pollutants. Therefore, there is an urgent need to establish a comprehensive assessment method for the environmental quality of freshwater lake sediments in my country, based on the composition of native lake biota, biological effect databases, phase equilibrium allocation methods, and other theoretical foundations. This method should integrate the chemical composition, biotoxicity, and ecological response characteristics of sediment pollutants. Such a method would scientifically and comprehensively assess sediment quality, identify major pollution source areas and risk factors, and provide scientific and technological support for the precise remediation of sediment pollution. Summary of the Invention

[0004] The purpose of this invention is to provide a method for comprehensive evaluation of the environmental quality of lake sediments and identification of risk factors.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0006] A method for comprehensive assessment of lake sediment environmental quality and identification of risk factors, comprising:

[0007] Sediment samples were collected from different locations in the lake to analyze the pollution occurrence characteristics of traditional pollutants and trace / ultra-scale pollutants in the sediments, and the predicted ineffective concentration data of pollutant sedimentary facies were derived based on existing databases.

[0008] Based on the predicted ineffective concentration of pollutants in sedimentary facies, the measured pollutant concentration data are reduced to three parameters: the proportion of pollutant types with measured concentrations higher than the predicted ineffective concentration of sedimentary facies, the proportion of data with measured concentrations higher than the predicted ineffective concentration of sedimentary facies, and the degree to which the measured concentrations are higher than the predicted ineffective concentration of sedimentary facies. Based on these three parameters, a comprehensive evaluation equation for sediment environmental quality is established.

[0009] Based on measured environmental concentration data, the equation is used to calculate the comprehensive environmental quality score of sediments, classify the environmental quality level of sediments, and identify the main risk factors of sediments.

[0010] In this invention, the dimensionality reduction method comprehensively considers the content of multiple pollutants in sediments, their biotoxicity, and their response characteristics to the ecosystem. Using the predicted ineffective concentration of the sedimentary facies as a benchmark, it reduces a large amount of original pollutant concentration data into three parameters: the proportion of pollutant species with measured concentrations higher than the predicted ineffective concentration of the sedimentary facies, the proportion of data with measured concentrations higher than the predicted ineffective concentration of the sedimentary facies, and the degree to which the measured concentrations are higher than the predicted ineffective concentration of the sedimentary facies. This dimensionality reduction analysis solves the problem of inconsistencies in sediment environmental quality assessment results for single pollutants, enabling rapid and accurate evaluation of lake sedimentary environmental quality.

[0011] In one preferred embodiment, the conventional pollutants include total nitrogen, total phosphorus, etc.; the trace / ultra-scale pollutants include heavy metals, polycyclic aromatic hydrocarbons, organochlorine pesticides, polychlorinated biphenyls, antibiotics, and phthalates, etc.

[0012] As a preferred embodiment, the method for analyzing the presence of trace / ultra-scale pollutants is as follows: the sample is pretreated by accelerated solvent extraction combined with silica gel chromatography purification, and then the content of polycyclic aromatic hydrocarbons is determined by high performance liquid chromatography, organochlorine pesticides, polychlorinated biphenyls, and phthalates are determined by gas chromatography, and the content of antibiotics is determined by ultra-high performance liquid chromatography-mass spectrometry; metal elements are determined by inductively coupled plasma mass spectrometry.

[0013] As a preferred embodiment, the deposition phase prediction ineffective concentration (PNEC) of the trace / ultra-contaminant is... s (PNEC) is a method for predicting the ineffective concentration from the aqueous phase using the phase equilibrium partitioning method. w It is derived from this process. The specific calculation process is as follows:

[0014] PNEC s =K oc ×F oc ×PNEC w ×A

[0015] Among them, PNECs Predicted ineffective concentrations for sedimentary phases, ng / g; PNEC w For the predicted ineffective concentration in the aqueous phase, ng / L; K oc F represents the organic carbon-water partition coefficient of pollutants, expressed in L / kg. oc The organic carbon content of the sedimentary phase is %, and A is the conversion coefficient, 10. -3 kg / g.

[0016] The pollutant's organic carbon-water partition coefficient K oc The value can be obtained from the EPI Suite database.

[0017] As a preferred implementation method, the aqueous phase predicts the ineffective concentration of PNEC. w The calculation process is as follows:

[0018]

[0019] Among them, HC 5(慢性) To protect 95% of species from affected chronic toxicity data, ng / L; AF is an evaluation factor with a value between 1 and 5, which is related to the uncertainty in deriving HC5.

[0020] If there is limited data on the chronic toxicity of a pollutant but ample data on its acute toxicity, the acute-chronic toxicity ratio (ACR) can be used for extrapolation, i.e., HC... 5(慢性) =HC 5(急性) / ACR is typically the geometric mean of the ratio of acute to chronic toxicity data for three (or more) species. If the ACR cannot be derived, a value of 10 is used as the default value.

[0021] As a preferred implementation method, the species are native species selected based on the characteristics of the lake area and the composition of the native flora. my country's "Technical Guidelines for the Establishment of Water Quality Standards for Freshwater Aquatic Organisms" requires that "the species should cover at least three trophic levels, and the number of species should include at least five. The three trophic levels are aquatic plants / primary producers, invertebrates / primary consumers, and vertebrates / secondary consumers. The at least five species should include one bony carp family, one bony non-carp family, one zooplankton, one benthic animal, and one aquatic plant."

[0022] As a preferred implementation method, HC5 is derived using the sensitivity distribution curve (SSD) method fitted by the optimal model. The HC5 value refers to the concentration value corresponding to a 5% cumulative probability on the SSD curve. The toxicity data for the SSD curves are obtained from the ECOTOX database on the US EPA (https: / / cfpub.epa.gov / ecotox / ). When pollutant toxicity data is lacking, the ECOSAR model is used for prediction. Typically, toxicological data are screened in the ECOTOX database based on provided screening criteria (compound, species category, test endpoint, exposure time, exposure mode, etc.).

[0023] In a preferred embodiment, the proportion of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies is characterized by the ratio of the number of pollutant types to the number of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies.

[0024] In a preferred embodiment, the proportion of data with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies is characterized by the ratio of the total data volume to the amount of data with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies.

[0025] In a preferred embodiment, the degree to which the measured concentration is higher than the predicted ineffective concentration of the sedimentary phase is characterized by the degree to which the measured concentration of the pollutant deviates from the predicted ineffective concentration of the sedimentary phase, obtained using an asymptotic function, and is calculated using the following set of equations:

[0026]

[0027]

[0028]

[0029] Among them, DV i For pollutant i deviates from PNEC si The amount; MEC i The measured concentration was higher than that of PNEC. si Concentration value; PNEC si Predict the ineffective concentration for the sedimentary phase of pollutant i; n = D o SE represents the normalized offset; C represents the deviation of pollutants from PNEC obtained using the asymptotic function. si The degree setting is mainly used to scale the offset between 0 and 100.

[0030] As a preferred embodiment, the comprehensive evaluation equation for sediment environmental quality is:

[0031]

[0032] Where A represents the percentage of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies, B represents the percentage of data with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies, and C represents the degree to which the measured concentrations are higher than the predicted ineffective concentrations of the sedimentary facies.

[0033] As a preferred implementation method, sediment risk factor identification is achieved by comparing the relative risk magnitudes of different contaminants. The relative risk magnitude is related to the deviation of the measured contaminant concentration from the PNEC (Potentially Negative Emissions Rate). s The quantity is related to the number of points exceeding the standard. The specific calculation process is as follows:

[0034]

[0035] Among them, RF i This represents the relative risk level of pollutant i; For all sites where the concentration of pollutant i exceeds PNEC si The total amount; S io For pollutant i concentration exceeding PNEC si The number of points; S it This represents the total number of points.

[0036] This invention addresses the limitations, uncertainties, and inconsistencies in evaluating sedimentary quality based on phase equilibrium partitioning and biological effect database methods, as well as the inconsistencies in sedimentary environmental quality assessment results among single indicators. It fully considers the combined pollution effects of traditional pollutants such as nitrogen and phosphorus, as well as trace / ultra-level pollutants such as heavy metals and emerging pollutants. By investigating the composition of native lake biota and integrating native biological effect databases with phase equilibrium partitioning principles, and employing techniques such as dimensionality reduction processing of large amounts of raw pollutant data, this invention achieves rapid, accurate, and comprehensive evaluation of lake sedimentary environmental quality and identifies key risk factors. This provides a scientific basis for developing lake pollutant treatment and control strategies. Attached Figure Description

[0037] Figure 1 This is a graph showing the SeQI score results of sediment environmental quality in Gaoyou Lake in the embodiment. Detailed Implementation

[0038] The following is a detailed explanation of the technical solution of the present invention, which is a method for comprehensive evaluation of the environmental quality of lake sediments and identification of risk factors, in order to better understand the technical solution of the present invention.

[0039] The present invention is used for a comprehensive environmental quality assessment and risk factor identification of sediments in Gaoyou Lake, comprising the following steps:

[0040] Step 1: Obtain sediment samples from different locations in Gaoyou Lake through field sampling, and analyze the pollution occurrence characteristics of traditional pollutants and trace / ultra-scale pollutants in the sediments.

[0041] According to the "Standard for Investigation of Eutrophication of Lakes" (Second Edition) and the "Standard for Investigation of Lake Sediments", the total nitrogen and total phosphorus concentrations in the sediments were measured to be 3.75–7.62 mg / g (mean 5.46 mg / g) and 0.41–0.82 mg / g (mean 0.56 mg / g) respectively using an ultraviolet spectrophotometer.

[0042] The mass concentrations of typical heavy metals (Cu, Zn, As, Cr, Cd, Pb, Ni) were determined using an inductively coupled plasma mass spectrometer to be 210.49–322.26 mg / kg (mean 282.87 ± 36.88 mg / kg).

[0043] The polycyclic aromatic hydrocarbon content was determined by high performance liquid chromatography to be 84.94–473.83 ng / gdw (mean value 196.53 ± 123.72 ng / g);

[0044] The contents of organochlorine pesticides, polychlorinated biphenyls, and phthalates were determined by gas chromatography to be 157.43-538.59 ng / g (mean 230.5 ng / g), 0.75-1.87 ng / g (mean 1.13 ng / g), and 322.9-15364.4 ng / g (mean 3287.0 ng / g).

[0045] The antibiotic content was determined to be 2.90–10.0 ng / g (mean 5.20 ng / g) using ultra-high performance liquid chromatography-mass spectrometry.

[0046] Step 2: Using existing databases (such as ECOTOX, ECOSAR, and EPISuite databases), derive the predicted ineffective concentration data of pollutant deposition phases; in this embodiment, ECOTOX and EPISuite databases are used.

[0047] (1) Predicted no-effect concentration of PNEC in aqueous phase w Derivation

[0048] PNEC w The calculation process is as follows:

[0049]

[0050] Among them, HC 5(慢性) To protect 95% of species from affected chronic toxicity data, ng / L; AF is an evaluation factor with a value between 1 and 5, which is related to the uncertainty in deriving HC5.

[0051] If there is limited data on the chronic toxicity of a pollutant but ample data on its acute toxicity, the acute-chronic toxicity ratio (ACR) can be used for extrapolation, i.e., HC... 5(慢性) =HC 5(急性) / ACR is typically taken as the geometric mean of the ratio of acute and chronic toxicity data for three (or more) species. Alternatively, when the ACR cannot be derived, the value of 10, recommended by the US Environmental Protection Agency (USEPA) and the Organisation for Economic Co-operation and Development (OECD), is used as the default value for the ACR.

[0052] In terms of species selection, local species should be chosen based on the characteristics of the lake area and the differences in the composition of the native flora. my country's "Technical Guidelines for the Establishment of Water Quality Standards for Freshwater Aquatic Organisms" requires that "species should cover at least three trophic levels, and the number of species should include at least five. The three trophic levels are aquatic plants / primary producers, invertebrates / primary consumers, and vertebrates / secondary consumers. The minimum number of species included is one bony carp family, one bony non-carp family, one zooplankton, one benthic animal, and one aquatic plant."

[0053] HC5 is derived using the species sensitivity distribution curve (SSD) method, which fits the optimal model. The HC5 value is the concentration value corresponding to a 5% cumulative probability on the SSD curve.

[0054] The toxicity data for SSD curves are obtained from the ECOTOX database established by USEPA (https: / / cfpub.epa.gov / ecotox / ). When pollutant toxicity data are lacking, predictions are made using the ECOSAR model based on the structure-activity correlation principle. Toxicological data are typically screened in the ECOTOX database according to provided screening criteria (compound, species category, test endpoint, exposure time, exposure mode, etc.).

[0055] (2) Derivation of the ineffective concentration of sedimentary facies prediction

[0056] Predicted Negative Concentrations (PNECs) of Sedimentary Phases for Trace / Extra Contaminants are obtained by predicting negative concentrations (PNECs) from the aqueous phase using the phase equilibrium partitioning method. w It is derived from this process. The specific calculation process is as follows:

[0057] PNEC s =K oc ×F oc ×PNEC w ×A

[0058] Among them, PNEC s Predicted ineffective concentrations for sedimentary phases (Table 1), ng / g; PNEC w For the predicted ineffective concentration in the aqueous phase, ng / L; K oc F represents the organic carbon-water partition coefficient of pollutants, in L / kg, derived from the EPI Suite database; oc The organic carbon content of the sedimentary phase; A is the conversion coefficient, 10 -3kg / g.

[0059] The PNECs of traditional pollutants were referenced to the benchmark thresholds for total nitrogen and total phosphorus in shallow lake sediments in China, with total nitrogen at 1100 mg / kg and total phosphorus at 450 mg / kg.

[0060] Table 1. Predicted no-effect concentrations of PNEC in the sedimentary phase of contaminants. s (ng / g)

[0061]

[0062]

[0063] Step 3: Based on the predicted ineffective concentration of pollutants, construct a comprehensive evaluation equation for sediment environmental quality using dimensionality reduction analysis.

[0064] Dimensionality reduction analysis refers to reducing the measured pollutant data to three parameters: A (measured concentration higher than PNEC). s (Percentage of pollutant types), B (measured concentration higher than PNEC) s (data volume percentage) and C (measured concentration higher than PNEC) s (The degree of). The formula for calculating parameter A is as follows:

[0065]

[0066] Where A represents the measured concentration in the dataset that is higher than PNEC. s The percentage of pollutant types; N t N represents the number of pollutant types in the dataset. o The measured concentration in the dataset is higher than that of PNEC. s The number of pollutant types.

[0067] The formula for calculating parameter B is as follows:

[0068]

[0069] Where B represents the measured concentration in the dataset that is higher than PNEC. s Data volume percentage; D t D represents the total amount of data in the dataset. o This represents the amount of data in the dataset where the measured concentration is higher than that of PNEC.

[0070] The formula for calculating parameter C is as follows:

[0071]

[0072]

[0073]

[0074] Among them, DV i For the deviation of pollutant i from PNEC si The amount; PNEC si PNEC for pollutant i s Concentration value; MEC i The measured concentration was higher than that of PNEC. s Concentration value; n = D o SE represents the normalized offset; C represents the deviation of pollutants from PNEC obtained using the asymptotic function. i The degree setting is mainly used to scale the offset between 0 and 100.

[0075] The comprehensive evaluation equation for sediment environmental quality is:

[0076]

[0077] Here, 1.732 is the normalization factor, which normalizes all values ​​to the range of 0 to 100.

[0078] Step 4: Based on the measured concentration data of the environment, calculate the comprehensive environmental quality score of the sediments, classify the environmental quality level of the sediments, and identify the main risk factors of the sediments.

[0079] N is obtained based on the measured concentration and the calculation formula of parameter A. t The value (number of pollutant types in the dataset) is 112; N o Value (measured concentration higher than PNEC) s The number of pollutant types was 112; the A value (measured concentration higher than PNEC) was 112. s The proportion of pollutant types was 27.68%.

[0080] D is obtained based on the measured concentration and the calculation formula of parameter B. t The value (total amount of data in the dataset) is 112 × 10 = 1120; D o The B value (the amount of data with measured concentrations higher than PNEC) is 225; the B value (the amount of data with measured concentrations higher than PNEC) is... s The data volume ratio was 19.64%.

[0081] The DV value (the deviation of pollutant i from PNEC) is obtained based on the measured concentration and the calculation formula of parameter C. s The amount of pollutants (%) ranged from 0.004 to 241; the SE value (normalized deviation) was 0.87; the C value (pollutant deviation from PNEC) was... s The degree is 46.5.

[0082] Sediment environmental quality is classified into 5 levels based on SeQI scores:

[0083] (a) When 0 < SeQI ≤ 30, the sediment environmental quality is grade V, which means that the overall sediment quality is very poor;

[0084] (b) When 30 < SeQI ≤ 50, the sediment environmental quality is grade IV, indicating that the overall sediment quality is poor;

[0085] (c) When 50 < SeQI ≤ 70, the sediment environmental quality is grade III, indicating that the overall sediment quality is good;

[0086] (d) When 70 < SeQI ≤ 90, the sediment environmental quality is grade II, indicating that the overall sediment quality is relatively good;

[0087] (e) When 90 < SeQI ≤ 100, the sediment environmental quality is Grade I, which means that the overall sediment quality is very good.

[0088] Substituting the values ​​of A, B, and C into the comprehensive evaluation equation for sediment environmental quality, the overall comprehensive quality score of Gaoyou Lake sediments was calculated to be 66.15, with the overall environmental quality of sediments classified as Grade III, indicating that the overall sediment quality is at a good level.

[0089] Similarly, the SeQI values ​​of sediment environmental quality at various locations in Gaoyou Lake, calculated using dimensionality reduction analysis, ranged from 56 to 84. Figure 1 The sediment quality in the Baita River estuary (GY2), the ancient canal estuary (GY10), and the eastern part of Gaoyou Lake (GY8 and GY9, near Gaoyou City) was poor, with SeQI values ​​between 56 and 70, indicating good sediment quality. In contrast, the SeQI values ​​in other areas were between 70 and 85, indicating relatively good sediment quality.

[0090] Sediment risk factor identification is achieved by comparing the relative risk magnitudes of different pollutants, where the relative risk magnitude is related to the deviation of the measured pollutant concentration from the PNEC (Potentially Negative Emissions Rate). s The quantity is related to the number of points exceeding the standard. The specific calculation process is as follows:

[0091]

[0092] Among them, RF i This represents the relative risk level of pollutant i; For all sites where the concentration of pollutant i exceeds PNEC si The total amount; S o For pollutant i concentration exceeding PNEC si The number of points; S it This represents the total number of points.

[0093] In this embodiment The value is between 0 and 274.4; S o (Concentration of pollutant i exceeds PNEC)i The number of points (s) is between 0 and 10; S it (Total number of points) is 10; relative risk of pollutants RF i The values ​​ranged from 0 to 66.93. The main risk factors in the sediments of Gaoyou Lake are shown in Table 2.

[0094] Table 2. Major risk factors in sediments from Gaoyou Lake

[0095]

Claims

1. A method for comprehensive evaluation of lake sediment environmental quality and identification of risk factors, characterized in that, include: Sediment samples were collected from different locations in the lake to analyze the pollution occurrence characteristics of traditional pollutants and trace / ultra-scale pollutants in the sediments, and the predicted ineffective concentration data of pollutant sedimentary facies were derived based on existing databases. Based on the predicted ineffective concentration of pollutants in sedimentary facies, the measured pollutant concentration data are reduced to three parameters: the proportion of pollutant types with measured concentrations higher than the predicted ineffective concentration of sedimentary facies, the proportion of data with measured concentrations higher than the predicted ineffective concentration of sedimentary facies, and the degree to which the measured concentrations are higher than the predicted ineffective concentration of sedimentary facies. Based on these three parameters, a comprehensive evaluation equation for sediment environmental quality is established. Based on measured environmental concentration data, the comprehensive environmental quality score of sediments is calculated using the aforementioned equation to classify sediment environmental quality levels. The main risk factors of sediments are identified by comparing the relative risk levels of different pollutants, whereby the relative risk levels are calculated based on the following formula: Among them, RF i This represents the relative risk level of pollutant i; For all locations where the concentration of pollutant i exceeds PNEC si The total amount; S io For pollutant i concentration exceeding PNEC si The number of points; S it Total number of points; PNEC si Predict the ineffective concentration for the sedimentary phase of pollutant i; The comprehensive evaluation equation for the environmental quality of sediments is as follows: Where A represents the percentage of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies, B represents the percentage of data with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies, and C represents the degree to which the measured concentrations are higher than the predicted ineffective concentrations of the sedimentary facies.

2. The method according to claim 1, characterized in that, The traditional pollutants include total nitrogen and total phosphorus; the trace / ultra-level pollutants include heavy metals, polycyclic aromatic hydrocarbons, organochlorine pesticides, polychlorinated biphenyls, antibiotics, and phthalates.

3. The method according to claim 1, characterized in that, The formula for calculating the predicted ineffective concentration of the sedimentary phase of the trace / ultra-contaminant is as follows: Among them, PNEC s Predicted ineffective concentrations for sedimentary phases, ng / g; PNEC w For the aqueous phase, the predicted ineffective concentration is given in ng / L; K oc F represents the organic carbon-water partition coefficient of pollutants, expressed in L / kg. oc The percentage of organic carbon in the sedimentary phase is %; A is the conversion coefficient, 10. -3 kg / g.

4. The method according to claim 3, characterized in that, The PNEC w The calculation process is as follows: Among them, HC 5(慢性) To protect 95% of species from being affected by chronic toxicity data, ng / L; AF is the evaluation factor, with a value between 1 and 5.

5. The method according to claim 4, characterized in that, The species mentioned are native species selected based on the characteristics of the lake region and the composition of the native flora.

6. The method according to claim 1, characterized in that, The proportion of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies is characterized by the ratio of the number of pollutant types to the number of pollutant types with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies.

7. The method according to claim 1, characterized in that, The percentage of data with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies is characterized by the ratio of the total data volume to the data volume with measured concentrations higher than the predicted ineffective concentrations of the sedimentary facies.

8. The method according to claim 1, characterized in that, The degree to which the measured concentration is higher than the predicted ineffective concentration of the sedimentary facies is characterized by the degree to which the measured concentration of the pollutant deviates from the predicted ineffective concentration of the sedimentary facies, obtained using an asymptotic function, and is calculated using the following set of equations: Among them, DV i For pollutant i deviates from PNEC si The amount; MEC i The measured concentration was higher than that of PNEC. si Concentration value; PNEC si Predict the ineffective concentration for the sedimentary phase of pollutant i; The total amount of data in the dataset; n=D o D o The measured concentration in the dataset is higher than that of PNEC. s The amount of data; SE represents the normalized offset; C represents the deviation of pollutants from PNEC obtained using the asymptotic function. si degree.

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