Lake water environment optimal control pollutant identification method

Abstract

The invention discloses a lake water environment optimal control pollutant identification method, and belongs to the field of environmental analysis, ecotoxicology and water environment risk assess.The method comprises the steps that lake deposition column samples are collected for high-resolution mass spectrum non-targeted analysis to establish a pollutant time sequence database; the substances with the occurrence abundance in the continuous increasing trend are screened through time sequence trend statistical analysis; integrating environment durability, mobility, biological accumulation potential and biological toxicity multi-dimensional risk indexes, performing risk weighting calculation through a toxicity priority index model, and outputting risk index sorting in combination with relative abundance; chemical structure identification is performed on the high-risk substances to determine a priority control pollutant list. According to the method, the risk-driven identification of unknown, historically accumulated and composite high-risk pollutants in the lake water environment is realized, and a key technical means is provided for the construction of a regional new pollutant list.

Description

Technical Field

[0001] This invention belongs to the technical fields of environmental analysis, ecotoxicology and water environment risk assessment, and particularly relates to a method for identifying key pollutants in lake water environments. Background Technology

[0002] Identifying priority pollutants for lake water environment control is crucial for maintaining water ecological security and formulating pollution control strategies. Currently, this field mainly relies on a technical approach combining traditional laboratory toxicology simulations with targeted quantitative analysis. Specifically, existing technologies involve targeted analysis of dozens to hundreds of known pollutants, and determining their priority control level based on whether their sampled concentrations exceed established water quality standards or ecological benchmarks.

[0003] However, this existing technical solution has several limitations in practical environmental management applications. First, its laboratory simulation conditions cannot fully replicate the complex physicochemical conditions and biogeochemical processes of the real aquatic environment, leading to discrepancies between the assessment results and actual environmental risks. Second, the targeted analysis technology itself has inherent limitations; it can only detect compounds within a pre-set list and cannot identify the much larger number of unknown or emerging pollutants in the environment, resulting in serious blind spots and incompleteness in the risk assessment system. Furthermore, traditional methods typically rely on instantaneous or short-cycle water sampling, reflecting only the pollution level at the moment of sampling and failing to reveal long-term historical emission patterns, accumulation trends, and future potential risk changes, thus hindering forward-looking management decisions. In addition, existing technical solutions rely excessively on concentration as a single indicator, generally neglecting key ecotoxicological characteristics of pollutants, such as acute and chronic toxicity, environmental persistence, and bioaccumulation, which may lead to an underestimation of the risks of some low-concentration but highly toxic, persistent, and easily accumulated substances.

[0004] Solving these problems has presented numerous challenges, such as how to systematically acquire historical pollutant emission data, how to establish a non-targeted screening method capable of covering unknown compounds, and how to construct a comprehensive risk assessment model that integrates exposure levels and multi-dimensional toxicity attributes. These challenges have constrained the scientific rigor of the prioritized pollutant inventory and the effectiveness of management measures. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention proposes a method for identifying pollutants that require priority control in lake water environments. This method enables risk-driven identification of unknown, historically accumulated, and compound high-risk pollutants in lake water environments, providing a key technical means for the construction of regional new pollutant inventories.

[0006] To achieve the above objectives, the present invention provides a method for identifying key pollutants in lake water environment, comprising: collecting lake sediment column samples, performing high-resolution mass spectrometry non-targeted analysis on samples from different layers, and establishing a time-series database of pollutants; A time-series trend analysis was performed on the pollutants in the time-series database to screen out substances whose abundance showed a continuous increasing trend. For the screened substances, their environmental persistence, mobility, bioaccumulation potential and biotoxicity multidimensional risk indicators are integrated, and risk weighting is calculated through the toxicity priority index model. The risk index is then combined with the relative abundance to make priority ranking. Chemical structure identification is performed on substances with high risk indices to determine a priority list of pollutants for control.

[0007] Optionally, lake sediment column samples may be collected including: The sediment column was collected using an acrylic glass tube gravity sampler, and the upper residual water was drained. The deposition column was stored vertically in a dark environment at 4°C and cut into 2cm thick spaced samples within 24 hours. The sample was wrapped in aluminum foil, freeze-dried, and then stored at -80°C. use 210 Pb and 137 The Cs dating method uses an ultra-low background well-type high-purity germanium gamma spectroscopy testing system to date sedimentary columns and determine the depositional history of samples from different layers.

[0008] Optionally, high-resolution mass spectrometry non-targeted analysis includes: The layer samples were extracted and purified by ultrasonic extraction with ammonia-methanol solution, centrifugation to collect the supernatant, nitrogen blowing concentration, and solid-phase extraction purification. All samples were scanned non-targeted using ultra-high performance liquid chromatography tandem with Orbitrap high-resolution mass spectrometry to obtain the precise mass number and fragmentation information of chemical substances; Liquid chromatography used a C18 column with gradient elution of ammonium acetate-water solution and acetonitrile solution as the mobile phase. Mass spectrometry used ESI ionization and scanning in positive and negative ion modes.

[0009] Optional time-series trend analysis includes: MS-DIAL software was used to extract and align response peaks in HRMS scan data to achieve data standardization. Environmental samples and blank samples were classified and processed, and blank values ​​were subtracted to eliminate background noise; Spearman correlation analysis was used to quantify the temporal correlation between compound peak area and deposition depth, with the correlation coefficient as the screening index. Set a correlation coefficient threshold to screen out substances that show a significant increasing trend over time; Hierarchical clustering analysis was further employed to classify and analyze the vertical variation trends of the screened substances.

[0010] Optional, integrated multidimensional risk indicators include: For the screened substances, the median acute lethal concentration (LD50) for fish was predicted using MS2Tox via secondary mass spectrometry fragmentation. Biological half-life, Log Koc, and bioaccumulation factor indices were obtained through literature and database queries and predictive models. The biological half-life, Log Koc, bioaccumulation factor, and median acute lethal concentration are input into the toxicity priority index model for multi-dimensional weighted calculation, and a comprehensive risk score is output.

[0011] Optionally, the risk index can be calculated by including: The peak areas of compounds obtained from HRMS were normalized using the Min-Max method to obtain normalized relative abundance. The risk index for each substance is calculated by multiplying the normalized relative abundance by the comprehensive risk score calculated by the toxicity priority index model. Substances are classified and prioritized based on their risk index values.

[0012] Optional chemical structure identification includes: Mass spectrometry fragment analysis and isotope pattern matching methods were used, along with the use of Compound Discoverer software to match reference mass spectrometry information from the mzCloud, ChemSpider, and NIST mass spectrometry databases. Feature substances whose matching scores reach the threshold are listed as candidates, and structures whose matching scores do not reach the threshold are manually checked. For substances that cannot be matched with the database, the SIRIUS module is used to calculate the molecular formula, and the CSI:FingerID module is used to predict the substance's structure.

[0013] Optionally, determining the priority control pollutant inventory includes: From the continuously growing substances identified through time-series trend analysis, select substances with high risk indices; The chemical structure of the high-risk substance was successfully identified. Substances that simultaneously meet the criteria of continuous growth, high risk index, and successful structural identification, and are combined with the substance's intended use or source, will be identified as priority pollutants for control.

[0014] Technical Advantages of this Invention: This invention discloses a method for identifying priority pollutants in lake water environments. Through high-resolution mass spectrometry non-targeted analysis, it achieves broad screening of unknown and emerging pollutants in the lake environment, overcoming the limitation of traditional targeted methods that can only detect known substances. By constructing a pollutant time-series database using sediment column samples, it can reveal the historical accumulation and growth trends of pollutants, thereby achieving proactive early warning of potential risks. By integrating multi-dimensional indicators such as environmental persistence, mobility, bioaccumulation potential, and biotoxicity, and employing a toxicity priority index model for risk-weighted calculation, a more scientific and comprehensive risk assessment system is established, overcoming the one-sidedness of relying solely on concentration data. Ultimately, this invention can output a priority control pollutant list that combines time-series growth trends, multi-dimensional high-risk attributes, and clearly defined chemical structures, providing crucial technical support for precise regional water environment management and the control of new pollutants. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a method for identifying key pollutants in lake water environment according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0018] like Figure 1 As shown, this embodiment provides a method for identifying key pollutants in lake water environment, including: We collected lake sediment column samples and performed high-resolution mass spectrometry non-targeted analysis on samples from different strata to establish a time-series database of pollutants. A time-series trend analysis was performed on the pollutants in the time-series database to screen out substances whose abundance showed a continuous increasing trend. For the screened substances, their environmental persistence, mobility, bioaccumulation potential and biotoxicity multidimensional risk indicators are integrated, and risk weighting is calculated through the toxicity priority index model. The risk index is then combined with the relative abundance to make priority ranking. Chemical structure identification is performed on substances with high risk indices to determine a priority list of pollutants for control.

[0019] Furthermore, the collection of lake sediment column samples included: The sediment column was collected using an acrylic glass tube gravity sampler, and the upper residual water was drained. The deposition column was stored vertically in a dark environment at 4°C and cut into 2cm thick spaced samples within 24 hours. The sample was wrapped in aluminum foil, freeze-dried, and then stored at -80°C. use 210 Pb and 137 The Cs dating method uses an ultra-low background well-type high-purity germanium gamma spectroscopy testing system to date sedimentary columns and determine the depositional history of samples from different layers.

[0020] Specifically, the implementation process of this embodiment includes: Sample collection and stratification: Sediment columns were collected in the target lake area using a free-fall gravity sampler (10 cm id × 100 cm) made of polymethyl methacrylate (PMMA). After collection, residual water in the upper sample column was drained. The samples were vertically stored in the dark at 4°C and cut into approximately 2 cm thick spacers within 24 hours. The samples were wrapped in aluminum foil, freeze-dried, and then stored at -80°C.

[0021] Sample dating: Deposition column dating is performed using... 210 Pb (lead) and 137 The Cs (cesium) dating method was used with an ultra-low background well-type high-purity germanium gamma spectroscopy system (GWL-120-15-LB-AWT, AMETEK, US) to determine the depositional period of samples from different layers of the sedimentary column.

[0022] Furthermore, high-resolution mass spectrometry non-targeted analysis includes: The layer samples were extracted and purified by ultrasonic extraction with ammonia-methanol solution, centrifugation to collect the supernatant, nitrogen blowing concentration, and solid-phase extraction purification. All samples were scanned non-targeted using ultra-high performance liquid chromatography tandem with Orbitrap high-resolution mass spectrometry to obtain the precise mass number and fragmentation information of chemical substances; Liquid chromatography used a C18 column with gradient elution of ammonium acetate-water solution and acetonitrile solution as the mobile phase. Mass spectrometry used ESI ionization and scanning in positive and negative ion modes.

[0023] Specifically, the implementation process of this embodiment includes: Sample pretreatment: Sediment samples from different layers were extracted and purified. Specific steps: Weigh 3 g of the ground and sieved (100 mesh) sediment sample into a 50 mL PP centrifuge tube. Add 30 mL of 0.2% ammonia-methanol solution and ultrasonically extract for 30 min. Then centrifuge at 4000 r min⁻¹ for 5 min, and collect the supernatant in a large-volume nitrogen blow-off tube. Repeat the above process once. The two extracts (~60 mL) are concentrated to approximately 1 mL under gentle nitrogen atmosphere and water bath heating at 30°C. The concentrate is purified using a solid-phase extraction device by passing it through an ENVI-Carb column pre-activated with 10 mL of methanol. The nitrogen blow-off tube is washed three times with methanol and mixed with the concentrate for purification. Then elute with 10 mL of 0.2% ammonia-methanol solution at a rate of 1–2 drops per second. The eluent was dried again by gentle nitrogen gas, and then diluted to 200 µL with methanol in a sample vial for analysis.

[0024] HRMS analysis: High-resolution mass spectrometry (HRMS) was used to perform non-targeted scanning of all samples to obtain accurate mass numbers and fragmentation information of a large number of chemical substances.

[0025] HRMS analysis was performed using ultra-high performance liquid chromatography (UPLC) coupled to an Orbitrap Exploris 240 (Thermo Fisher, US) system (hereinafter referred to as HRMS), equipped with an ESI ion source HPLC column of AcquityUPLC BEH C18 (1.7 µm, 2.1 × 100 mm, Waters, US). The analysis time for each sample was 15 min. The injection volume for each sample was 1 µL. The mobile phase used a gradient elution of 2 mM ammonium acetate-water solution (A) and acetonitrile solution (B), as shown in Table 1. The mobile phase flow rate was 0.25 mL / min. -1 Mass spectrometry was performed using ESI ionization, with scanning under both positive and negative ion conditions at ESI voltages of 3.5 kV and 3.2 kV, respectively. The Orbitrap resolution was 120,000, and the scan range (m / z) was 70–1050.

[0026] Table 1

[0027] Further time-series trend analysis includes: MS-DIAL software was used to extract and align response peaks in HRMS scan data to achieve data standardization. Environmental samples and blank samples were classified and processed, and blank values ​​were subtracted to eliminate background noise; Spearman correlation analysis was used to quantify the temporal correlation between compound peak area and deposition depth, with the correlation coefficient as the screening index. Set a correlation coefficient threshold to screen out substances that show a significant increasing trend over time; Hierarchical clustering analysis was further employed to classify and analyze the vertical variation trends of the screened substances.

[0028] Specifically, the implementation process of this embodiment includes: Data Alignment and Initial Screening: Peak extraction and alignment of HRMS scan data are standardized using MS-DIAL software. A uniform operating procedure is followed for all environmental and blank samples. Specifically, based on deconvolution, MS2 mass spectrometry data are first matched and analyzed with the mass-to-charge ratio (m / z) of the primary precursor ion. By accurately calculating the correlation between ion fragmentation information and the precursor ion, the retention time (RT) and corresponding peak area of ​​all components are precisely extracted. MS-DIAL effectively eliminates interference from complex matrices, ensuring data accuracy.

[0029] To construct a data matrix suitable for non-targeted time-series analysis, peak alignment of the same component in different samples was subsequently performed. This step comprehensively considers three dimensions: RT, m / z, and MS2 mass spectrometry fragmentation information, achieving accurate peak matching across samples through multi-dimensional data cross-validation. Simultaneously, to eliminate the influence of instrument background noise and systematic errors, environmental samples and instrument blank samples were classified, and the corresponding blank value was subtracted from the raw data of each environmental sample to ensure that the data reflects the true environmental signal. Relevant parameter settings were optimized based on experimental conditions and data characteristics to ensure the reliability and validity of subsequent analytical results.

[0030] Screening for continuously increasing substances: Peak screening is a core step in accurately identifying target compounds from tens of thousands of features. This embodiment employs Spearman correlation analysis to effectively distinguish between anthropogenic and non-anthropogenic substances by quantifying the temporal correlation between compound peak area and deposition depth. Specifically, the Spearman correlation coefficient (r) between compound peak area and deposition depth is used as the screening index. A key threshold of r = 0.5 is set to filter out anthropogenic compounds whose concentration exhibits a significant temporal increasing trend.

[0031] After screening using Spearman correlation (r>0.5), hierarchical clustering analysis (HCA) was further employed to classify and analyze the vertical variation trends of SCs. This analysis process achieves pattern recognition through standardized data preprocessing, similarity measurement, and clustering algorithms, specifically including the following key steps: First, data standardization was performed. The peak area matrix of the screened substances was normalized using the Min-Max standardization method, scaling the variable range to the [0,1] interval to eliminate the influence of differences in the concentration dimensions of different compounds on the clustering results. Second, the similarity between samples was calculated based on the standardized data, using Euclidean distance as the metric to effectively characterize the differences in concentration distribution of different compounds in the sediment column samples. Finally, Ward's minimum variance method was used for clustering. This algorithm achieves hierarchical merging by minimizing the sum of squares within each cluster, and ensures the statistical significance of the clustering results based on the principle of analysis of variance.

[0032] Furthermore, standardized peak area data for all substances within each cluster were extracted, vertical variation curves were plotted, and trend models were fitted. This step, combined with nonparametric fitting methods, clearly presents the concentration evolution patterns of pollutants with deposition depth (time series) in different cluster categories.

[0033] Furthermore, the integration of multidimensional risk indicators includes: For the screened substances, the median acute lethal concentration (LD50) for fish was predicted using MS2Tox via secondary mass spectrometry fragmentation. Biological half-life, Log Koc, and bioaccumulation factor indices were obtained through literature and database queries and predictive models. The biological half-life, Log Koc, bioaccumulation factor, and median acute lethal concentration are input into the toxicity priority index model for multi-dimensional weighted calculation, and a comprehensive risk score is output.

[0034] Specifically, the implementation process of this embodiment includes: Multidimensional data integration: For the screened substances, MS2Tox was first used to predict their median acute lethal concentration (LC50) for fish using secondary mass spectrometry fragmentation. 50 Then, through literature and database queries (US EPA's CompTox Chemicals Dashboard (https: / / comptox.epa.gov / dashboard / ) and predictive models (US EPA Estimation Programs Interface (EPI) Suite (version 4.1)), indicators such as biological half-life (t1 / 2, reflecting environmental persistence), Log Koc (reflecting mobility), and bioaccumulation factor (Log BCF, assessing bioaccumulation potential) were obtained.

[0035] ToxPi Risk Score: To identify potentially high-risk substances in lake environments, this study employed a multi-criteria priority screening strategy, introducing the ToxPi model as a decision support tool. The ToxPi model integrates multi-dimensional toxicity endpoint data, transforming it into a dimensionless comprehensive index to prioritize chemicals, offering advantages in both quantitative assessment and visualization. This model overcomes the limitations of single-indicator assessment, systematically coupling environmental fate and biotoxicity characteristics. The aforementioned multi-dimensional risk data were input into the ToxPi model, with equal weight assigned to each of the four dimensions, and a multi-dimensional weighted calculation was performed to output a comprehensive risk score for each substance.

[0036] Furthermore, the calculation of the risk index includes: The peak areas of compounds obtained from HRMS were normalized using the Min-Max method to obtain normalized relative abundance. The risk index for each substance is calculated by multiplying the normalized relative abundance by the comprehensive risk score calculated by the toxicity priority index model. Substances are classified and prioritized based on their risk index values.

[0037] Specifically, the implementation process of this embodiment includes: Risk Index (RI) Ranking: Due to factors such as ionization efficiency, matrix effects, and instrument response bias, the peak area obtained by HRMS only reflects the relative abundance of chemical substances, rather than a direct quantification of absolute concentration. Despite these limitations, considering the relative abundance of detected compounds remains essential for assessing the chemical characterization of samples, especially when a few highly responsive compounds dominate the overall chemical characterization. Therefore, the aquatic ecological risk index (RI) is calculated by multiplying the peak area after Min-Max normalization by ToxPi. Based on the risk index, SCs can be divided into four categories: RI ≥ 0.1 for high risk, 0.01 ≤ RI < 0.1 for medium risk, 0.001 ≤ RI < 0.01 for low risk, and RI < 0.001 for no potential risk according to ToxPi.

[0038] Further chemical structure identification includes: Mass spectrometry fragment analysis and isotope pattern matching methods were used, along with the use of Compound Discoverer software to match reference mass spectrometry information from the mzCloud, ChemSpider, and NIST mass spectrometry databases. Feature substances whose matching scores reach the threshold are listed as candidates, and structures whose matching scores do not reach the threshold are manually checked. For substances that cannot be matched with the database, the SIRIUS module is used to calculate the molecular formula, and the CSI:FingerID module is used to predict the substance's structure.

[0039] Specifically, the implementation process of this embodiment includes: Structural Identification: Based on practical management needs, the structures of substances with high Risk Index (RI) scores are identified. Precise chemical structure identification is achieved using methods such as fragment analysis, isotope pattern matching, and comparison with secondary spectral databases. First, substances are matched against standard reference materials in a mass spectrometry database (primarily mzCloud (ThermoFisher, US), ChemSpider, the NIST 2023 mass spectrometry database (National Institute of Standards and Technology, US), and a self-built database) using Compound Discoverer. Then, the MS and MS spectra of the samples are analyzed. 2 Mass spectrometry was matched with reference mass spectrometry information in the database. Characteristic substances with a match score ≥ 70 were listed as candidates for further screening. Structures with match scores between 30 and 70 in CompoundDiscoverer were further analyzed using isotopic characteristics, MS and MS-MS. 2 The consistency between the matching results and the prediction results is manually checked to further confirm the possible material structure.

[0040] Furthermore, for substances that cannot be matched with the database, the SIRIUS molecular network structure model is used to analyze the structure of unknown peaks and predict their molecular formulas and structures. The results, after deconvolution and alignment by MS-DIAL, are imported into SIRIUS. First, using the SIRIUS module with the instrument set to Orbitrap and MS2MassDev set to 5 ppm, the molecular formula of the peak is calculated based on the molecular weight of the parent ion. Then, using the CSI:FingerID module, all spectra are selected from databases such as PubChem and NORMAN to further predict the structure of the substance based on the molecular formula results. The ion addition modes are [M + H]. + and [M - H] - [M + Cl] - .

[0041] Furthermore, the list of priority pollutants to be controlled includes: From the continuously growing substances identified through time-series trend analysis, select substances with high risk indices; The chemical structure of the high-risk substance was successfully identified. Substances that simultaneously meet the criteria of continuous growth, high risk index, and successful structural identification, and are combined with the substance's intended use or source, will be identified as priority pollutants for control.

[0042] Specifically, the implementation process of this embodiment includes: Priority control list: Substances that are continuously increasing in number, have a high risk index (RI), have successfully identified structures, and have a clear intended use or source are identified as priority pollutants for lake water environment control.

[0043] This invention discloses a method for identifying priority pollutants in lake water environments. Through high-resolution mass spectrometry non-targeted analysis, it achieves broad screening of unknown and emerging pollutants in the lake environment, overcoming the limitation of traditional targeted methods that can only detect known substances. By constructing a pollutant time-series database using sediment column samples, it can reveal the historical accumulation and growth trends of pollutants, thus enabling proactive early warning of potential risks. By integrating multi-dimensional indicators such as environmental persistence, mobility, bioaccumulation potential, and biotoxicity, and employing a toxicity priority index model for risk-weighted calculation, a more scientific and comprehensive risk assessment system is established, overcoming the limitations of relying solely on concentration data. Ultimately, this invention can output a priority control pollutant list that combines time-series growth trends, multi-dimensional high-risk attributes, and clearly defined chemical structures, providing crucial technical support for precise regional water environment management and the control of new pollutants.

[0044] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying pollutants for optimal control of a lake water environment, characterized by, The method comprises the following steps: Collecting lake sediment column samples and performing high-resolution mass spectrometry non-target analysis on different layer samples to establish a time series database of pollutants; Performing time trend analysis on the pollutants in the time series database to screen out substances with a continuously increasing abundance; Integrating the multi-dimensional risk indicators of the screened substances, such as environmental persistence, mobility, biological accumulation potential and biological toxicity, and performing risk weighted calculation through a toxicity priority index model, and calculating a risk index by combining the relative abundance to prioritize; Chemical structure identification of substances with high risk index to determine the list of priority control pollutants.

2. The method for identifying priority control pollutants in a lake water environment according to claim 1, wherein the collection of lake sediment column samples comprises: Using an organic glass tube gravity sampler to collect the sediment column and draining the residual water in the upper layer; Storing the sediment column vertically in a 4℃ dark environment and cutting it into 2cm-thick interval samples within 24 hours; Wrapping the samples with aluminum foil and storing them in a freezer at -80℃ after freeze-drying; 3. The method for identifying priority control pollutants in a lake water environment according to claim 1, wherein the high-resolution mass spectrometry non-target analysis comprises: Adopt 210 Pb and 137 The Cs dating method is used to determine the deposition history period of different stratified samples by using the ultra-low background well-type high-purity germanium gamma spectrum test system. Extracting and purifying the layer samples, ultrasonic extraction with ammonia-methanol solution, centrifugation to obtain supernatant, nitrogen blowing concentration, and solid-phase extraction purification; Using ultra-high performance liquid chromatography coupled with Orbitrap high-resolution mass spectrometry to perform non-target scanning on all samples to obtain the accurate mass and fragment information of the chemical substances; Using a C18 column for liquid chromatography, gradient elution with ammonium acetate-water solution and acetonitrile solution as the mobile phase, ESI ionization for mass spectrometry, and positive and negative ion mode scanning.

4. The method for identifying priority control pollutants in a lake water environment according to claim 1, wherein the time trend analysis comprises: Using MS-DIAL software to extract response peaks and align peaks from HRMS scanning data to realize data standardization; Classifying the environmental samples and blank samples, and deducting the blank value to eliminate background noise; Using Spearman correlation analysis to quantify the time correlation between compound peak area and sediment depth, and using the correlation coefficient as a screening indicator; Setting a correlation coefficient threshold to screen out substances with a significant time increasing trend; Further using hierarchical cluster analysis to classify and analyze the vertical variation trend of the screened substances.

5. The method for identifying priority control pollutants in a lake water environment according to claim 1, wherein the integration of multi-dimensional risk indicators comprises: For the screened substances, predicting their 50% acute lethal concentration to fish through MS2Tox using secondary mass spectrometry fragments; Obtaining biological half-life, Log Koc, and biological accumulation factor indicators through literature, database query, and prediction models; Inputting the biological half-life, Log Koc, biological accumulation factor, and 50% acute lethal concentration indicators into a toxicity priority index model for multi-dimensional weighted calculation and outputting a comprehensive risk score.

6. The method for identifying priority control pollutants in a lake water environment according to claim 1, wherein the calculation of risk index comprises: ​ ​ ​ ​ ​ The peak area of the compound obtained by HRMS is subjected to Min-Max standardization to obtain normalized relative abundance; The normalized relative abundance is multiplied by the comprehensive risk score calculated by the toxicity priority index model to calculate the risk index of each substance; According to the risk index value, the substances are classified and prioritized.

7. The method for identifying priority pollutants in a lake water environment according to claim 1, wherein the chemical structure identification comprises: Using mass spectrum fragment analysis, isotope pattern matching method, and matching reference mass spectrum information in mzCloud, ChemSpider, and NIST mass spectrum database through Compound Discoverer software; Substances with matching scores reaching the threshold value are listed as candidates, and structural formulas with matching scores not reaching the threshold value are manually checked; For substances that cannot be matched with the database, the SIRIUS module is used to calculate the molecular formula, and the CSI:FingerID module is used to predict the substance structure.

8. The method for identifying priority pollutants in a lake water environment according to claim 1, wherein the determination of the priority control pollutant list comprises: From the substances screened out by the time trend analysis, substances with high risk indexes are selected; Chemical structure identification is performed on the substances with high risk indexes, and the chemical structures are successfully identified; Substances that meet the conditions of continuous growth, high risk index, and successful structure identification are determined as the priority control pollutant list in combination with the use or source of the substances. ​ ​

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