Method for identifying and judging characteristic precursors of disinfection byproducts in drinking water containing new pollutants

Abstract

According to the method for identifying and judging the characteristic precursors of the disinfection byproducts in the drinking water containing the new pollutants, LC-OCD-OND, EEM-PARAFAC and FT-ICR MS are adopted for conducting in-situ multi-dimensional comprehensive characterization on DOM in the drinking water, the method has the advantages of being small in detection error, high in DOM component reduction degree, capable of being quantified / semi-quantified and the like, and the relation between the DOM component content and DBPs generation can be better analyzed. According to the method, a DOM component dimensionality reduction analysis method based on NDMS analysis is further constructed, dimensionality reduction analysis and classification can be carried out on dozens of DOM components including a new pollutant quantitative result, then the dominant dimensionality influencing a target result is extracted from the dozens of DOM components, and then the interaction among the factors is further clarified through the relative distance relation among the factors; therefore, the real structure of the data can be described more accurately, the interaction relationship between the DBPs yield and the DOM component can be constructed in a fit manner, and the high-risk DBPs precursor having significant influence on the generation of the DBPs can be screened out.

Description

Technical Field

[0001] The invention belongs to the technical field of water pollutant analysis and identification, and relates to a method for identifying and determining characteristic precursors of disinfection by-products in drinking water containing new pollutants. Background Art

[0002] Disinfection is an essential and key link in drinking water treatment. At present, drinking water is mainly disinfected by chlorine-containing disinfectants. However, chemical reactions will occur between chlorine-containing disinfectants and organic matter and other matrices contained in drinking water to generate disinfection by-products (DBPs). DBPs represented by halogenated compounds all show potential biological toxicity and have carcinogenic, teratogenic and mutagenic effects. Related studies have also revealed the correlation between long-term exposure to DBPs and reproductive system diseases. Therefore, the control research of DBPs in drinking water has become a research hotspot at home and abroad.

[0003] The identification of precursors and the exploration of their generation mechanisms are the foundation and core of DBPs control. Dissolved organic matter (DOM) is an important matrix component of drinking water sources, and is also the main source of precursors for drinking water DBPs. However, DOM is a complex mixture composed of tens of thousands of different natural organic matter, new pollutants, etc. The various functional groups it contains, such as aldehydes, amino groups, ketones and phenols, can react with chemical disinfectants to generate DBPs, and exhibit different reaction activities and DBPs yields, so their contributions to the generation of high-risk DBPs are not the same. Therefore, clarifying the categories of characteristic DBPs precursors in DOM and effectively controlling them is the core work to achieve risk control of drinking water DBPs.

[0004] Although the traditional screening methods that rely on physical processes such as molecular weight separation, hydrophilicity classification and polarity separation can screen different characteristic components of DOM to a certain extent, and compare the differences in the potential of each component to quantify DBPs through DBPs generation potential experiments, the time-consuming and laborious experimental process, the macroscopic characterization characteristics of rough screening, and the data errors in the experimental process itself cannot accurately reflect the precise occurrence domain of high-risk DBPs, and cannot clearly define the molecular categories of high-risk disinfection by-product precursors at the molecular level. Therefore, it is urgent to construct a method that can accurately identify high-risk DBPs precursors in DOM contained in drinking water. Summary of the invention

[0005] The purpose of the present invention is to provide a method for identifying and determining characteristic precursors of disinfection by-products in drinking water containing new pollutants, so as to solve the problem that existing methods cannot accurately identify high-risk DBPs in DOM contained in drinking water containing new pollutants.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The present application provides a method for identifying and determining characteristic precursors of disinfection byproducts in drinking water containing new pollutants, the method comprising: S01: After filtering multiple drinking water samples, the DOC content of the water samples is measured, and the DOC concentration of all the water samples is adjusted to the same level using ultrapure water.

[0007] Select multiple drinking water samples and filter all of them with a 0.45μm pore size filter to remove suspended particles in the water samples. After filtering, measure the DOC (dissolved organic carbon) content of each water sample. Then, use ultrapure water to adjust the DOC concentration of all water samples to the same level to avoid systematic errors due to different concentrations when performing gel chromatography, fluorescence spectroscopy and other tests.

[0008] In this application, the number of drinking water samples is 10-30, and the DOC concentration of all water samples is adjusted to 2.0-3.0 mg / L using ultrapure water.

[0009] S02: performing quantitative detection of new pollutants, multi-detector gel chromatography detection, fluorescence spectrum detection and Fourier transform ion cyclotron mass spectrometry detection on the water sample to obtain new pollutant concentration information and DOM component information; wherein the DOM component information includes DOC content, DON content, UV absorbance, fluorescence intensity information, molecular structure and response intensity information of DOM components with different molecular weights.

[0010] The water samples were subjected to multi-detector gel chromatography (LC-OCD-OND), fluorescence spectrometer (EEM) and Fourier transform ion cyclotron mass spectrometer (FT-ICR MS) detection, respectively, to obtain DOM component information including DOC content, DON content, UV absorbance, fluorescence intensity information, molecular structure and response intensity information of DOM components with different molecular weights.

[0011] Specifically, the multi-detector gel chromatograph (LC-OCD-OND) is a liquid phase-organic carbon-organic nitrogen detector, which can use a size exclusion chromatography column to realize non-separation in-situ analysis of different components in DOM. At the same time, combined with its equipped OCD detector and OND detector, the detection results are processed based on the Gaussian integral method, and the organic carbon content and organic nitrogen content of organic matter with different molecular weights can be quantitatively identified to obtain the DOC content, DON content and UV absorbance of DOM components with different molecular weights. In this application, the detection conditions of LC-OCD-OND are: injection volume: 1000-3000μL, molecular weight detection range: 0.15kDa-100 kDa, detection time: 50-70min, mobile phase pH: 6.5-7.5.

[0012] The detection of the fluorescence spectrometer (EEM) is based on the parallel factor analysis method to process the test results. The test results are usually characterized as a three-dimensional matrix containing excitation wavelength, emission wavelength and fluorescence intensity. Then, PARAFAC analysis is used to decompose this three-dimensional matrix to extract different fluorescent components and their proportional coefficients. These components represent different fluorescent substances in the water sample, and the proportional coefficients reflect their relative content in the sample. Therefore, through EEM-PARAFAC analysis, component separation and semi-quantitative functions that cannot be completed by traditional EEM fluorescence spectrum partition analysis can be achieved. In this application, the detection conditions of EEM are: voltage: 400-700V, step size: 1-5nm, Ex: 200-450nm, Em: 200-500nm, response time: 0.002-0.1s.

[0013] Fourier transform ion cyclotron mass spectrometer (FT-ICR MS) is a mass spectrometer that measures the mass-to-charge ratio (m / z) of ions based on the ion cyclotron frequency in a given magnetic field. The water sample is first ionized to produce gas-phase ions. The ions are produced outside the ion source, gathered in the ion optical system, and transferred to the ion cyclotron accelerator unit. The unit is embedded in a spatially uniform magnetic field (B) and electric field (E). Under the combined action of the magnetic and electric fields, the ions undergo cyclotron motion. When the cyclotron ion beam approaches a pair of trapping plates, an image current signal is detected on the trapping plates. This signal is called free induction decay (FID). The FID signal is a transient or interference pattern composed of many overlapping sine waves. Through Fourier transform, useful signals can be extracted from these signal data to form a mass spectrum. This detection method has a detection accuracy of more than 1×10 6 The ultra-high mass resolution can accurately identify the mass peaks of organic matter with a mass error of less than <0.005m / z, and then identify thousands of molecules contained in complex matrices such as DOM, and provide the precise elemental composition of specific compounds. According to certain classification rules, DOM components can be classified and characterized at the molecular structure level.

[0014] In the present application, the detection conditions of FT-ICR MS are as follows: chromatographic conditions are: chromatographic column is C18 column, column temperature is 40°C, mobile phase flow rate is 0.4mL / min, injection volume is 10μL, mobile phase is 0.1% formic acid water and methanol solution, solvent flow rate is 0.3mL / min; mass spectrometry conditions: Capillary voltage is 3.5kV, lens radio frequency voltage is 50V, sheath gas volume flow rate is 40arb, auxiliary gas flow rate is 10arb, capillary temperature is 320°C, nebulization temperature is 350°C, scanning mode is full scan / data-dependent secondary scan, scanning range is m / z = 50-800, and collision energy is set to 30ev when performing data-dependent secondary scan.

[0015] S03: The water sample is subjected to chlorination experiment, liquid-liquid extraction enrichment and DBPs quantitative detection in sequence to obtain DBPs yield; wherein the quantitative detection indicators include THMs, HAAs, HANs, HALs, HNMs and HAMs.

[0016] The water samples were subjected to a simulated chlorination experiment, wherein the experimental conditions were: bromide ion concentration of the water sample: 100-200 μg / L, water sample volume: 200-500 mL, reaction room temperature: 25±1°C, chlorine addition amount: , where X0 is the amount of chlorine added, X a is the residual chlorine after 24 hours of reaction, and (0.1-0.5) is the coefficient.

[0017] After the chlorination reaction is completed, ascorbic acid is added to terminate the chlorination reaction, wherein the amount of residual chlorine to ascorbic acid added is 1: (1.1-1.3). Liquid-liquid extraction is used for enrichment, and DBPs are quantitatively detected by gas chromatograph / gas chromatography-mass spectrometry and other instruments to obtain the DBPs yield. Among them, the quantitative detection indicators include THMs (trihalomethanes), HAAs (haloacetic acids), HANs (haloacetonitrile), HALs (haloacetaldehyde), HNMs (halonitromethanes) and HAMs (haloacetamides).

[0018] S04: performing NMDS calculation according to the DOM component information and the DBPs yield to obtain a NDMS model.

[0019] When deconstructing the quantitative-effect relationship between organic matter components and DBPs generation potential, the traditional linear regression model cannot effectively identify key factors from multidimensional space indicators. Non-metric multidimensional scaling (NDMS) analysis is a data analysis method that simplifies the research objects (samples or variables) in multidimensional space to low-dimensional space for positioning, analysis and classification, while retaining the original relationship between objects. This method simplifies the research objects (samples or variables) in multidimensional space to low-dimensional space for positioning, analysis and classification, while retaining the original relationship between objects. It can then construct potential gradients in complex data sets in complex and diverse data sets. Therefore, in this application, based on the quantitative analysis of DBPs generation potential through simulated chlorination experiments, combined with the quantitative / semi-quantitative organic characterization results obtained by DOM multidimensional analysis, and further combined with NDMS dimensionality reduction analysis, the DOM components that have a key impact on DBPs generation can be clearly identified, avoiding the errors introduced in the traditional component separation and analysis process, and has the advantages of convenient operation and accurate judgment results.

[0020] SPSS 2022 software was used for NMDS analysis in this application. Specifically, the DOM component information and DBPs yield of the water sample were input into SPSS 2022 software, and SPSS 2022 software was used to perform data cleaning, missing value processing, and data standardization on the above information data; then, a suitable distance algorithm was selected based on the Bray-Curtis distance, Jaccard distance, and Euclidean distance algorithm to calculate the distance between samples, and the selected distance algorithm was used to calculate the distance between all samples and construct a distance matrix; then, the NMDS calculation was started, and the NDMS model was drawn based on the NMDS calculation results. During the NMDS calculation, the dimensionality reduction axis was 2-3, the number of iterations was 200-500 times, the Stress value of the NDMS model was less than 0.1, and the R of the Shepard diagram was 2 Above 0.7.

[0021] S05: Determine the DOM components significantly correlated with the DBPs yield based on the relative distances between the various indicators in the NDMS model.

[0022] Specifically, DBPs yield and DOM component are two elements of information in the NDMS model. After drawing a visualization diagram according to the NDMS model, DBPs yield and DOM component need to be in the same interval in the visualization diagram, and the angle between the center point and the line connecting any element is less than 45°.

[0023] Further, based on the above determination results, standard chemicals belonging to the above DOM specific components are purchased / prepared to carry out simulated chlorination experiments. The chlorination experimental conditions are the same as those involved in S03 "DBPs generation potential determination" so as to verify the accuracy of the experimental results through yield verification results.

[0024] The present invention has the following beneficial effects: (1) In this application, the DOM characterization method system based on LC-OCD-OND, EEM-PARAFAC and FT-ICR MS can perform in situ multi-dimensional comprehensive characterization of DOM in drinking water. Compared with traditional molecular weight separation, hydrophilicity separation and polarity separation methods, it has the advantages of small detection error, high degree of DOM component reduction and quantitative / semi-quantitative analysis, and can better analyze the relationship between DOM component content and DBPs generation.

[0025] (2) Construct a DOM component dimensionality reduction analysis method based on NDMS analysis, which can perform dimensionality reduction analysis and classification on dozens of DOM components, and then extract the dominant dimensions that affect the target results, and further clarify the interaction between factors through the relative distance relationship between the elements. Compared with traditional principal component (PCA) analysis and linear regression analysis, NDMS analysis is not limited by linear models and is more suitable for processing data with nonlinear relationships. NMDS simplifies data from multidimensional space to low-dimensional space by retaining the original relationship between objects (such as similarity or dissimilarity), so as to more accurately describe the true structure of the data, which is in line with the construction of the interaction between DBPs yield and DOM components, and then screen out high-risk DBPs precursors that have a significant impact on DBPs generation.

[0026] (3) By comparing the difference in DBPs yield between the samples and standard compounds, the precursor categories that have a significant contribution to the generation of specific DBPs can be further confirmed / verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of the method for identifying and determining characteristic precursors of disinfection by-products in drinking water provided in this application; Figure 2 The original record of LC-OCD-OND test results of representative samples; Figure 3 This is the EEM analysis result diagram; Figure 4 Visualization of FT-ICR MS detection results of representative samples - Van-Krevelen plot; Figure 5 This is the NDMS analysis result diagram; Figure 6 A comparison chart of HNM yields of different model compounds. DETAILED DESCRIPTION

[0028] As attached Figure 1 As shown, the present application provides a method for identifying and determining characteristic precursors of disinfection byproducts in drinking water, the method comprising: S01: Select 10-30 drinking water samples, filter all the water samples using a 0.45μm pore size filter membrane, and measure the DOC content of each water sample after filtration. Then, use ultrapure water to adjust the DOC concentration of all water samples to the same level.

[0029] S02: Multi-detector gel chromatography (LC-OCD-OND), fluorescence spectrometer (EEM) and Fourier transform ion cyclotron mass spectrometer (FT-ICR MS) were used to perform multi-detector gel chromatography, fluorescence spectrometry and Fourier transform ion cyclotron mass spectrometry on water samples to obtain DOM component information including DOC content, DON content, UV absorbance, fluorescence intensity information, molecular structure and response intensity information of DOM components with different molecular weights.

[0030] S03: Conduct a simulated chlorination experiment on the water sample, where the experimental conditions are: water sample bromide ion concentration: 100-200μg / L, water sample volume: 200-500mL, reaction room temperature: 25±1℃, chlorine addition amount: , where X0 is the amount of chlorine added, X a is the residual chlorine after 24 hours of reaction, and (0.1-0.5) is the coefficient. After the chlorination experiment reaction is completed, ascorbic acid is added to terminate the chlorination reaction, wherein the amount of residual chlorine and ascorbic acid added is 1:(1.1-1.3). Liquid-liquid extraction is used for enrichment, and DBPs are quantitatively detected by gas chromatograph / gas chromatography-mass spectrometry and other instruments to obtain the DBPs yield. Among them, the quantitative detection indicators include THMs, HAAs, HANs, HALs, HNMs and HAMs.

[0031] S04: Input the DOM component information and DBPs yield of the water sample into SPSS 2022 software, and use SPSS 2022 software to clean the above information data, process missing values, and standardize the data; then, select the appropriate distance algorithm based on the Bray-Curtis distance, Jaccard distance, and Euclidean distance algorithm to calculate the distance between samples, use the selected distance algorithm to calculate the distance between all samples, and construct a distance matrix; then start the NMDS calculation, and draw the NDMS model based on the NMDS calculation results. When calculating NMDS, the dimension reduction axis is 2-3, the number of iterations is 200-500 times, the Stress value of the NDMS model is less than 0.1, and the R value of the Shepard diagram is 2 Above 0.7.

[0032] S05: DBPs yield and DOM component are two elements of information in the NDMS model. After drawing the visualization diagram according to the NDMS model, DBPs yield and DOM component need to be in the same interval in the visualization diagram, and the angle between the center point and the connecting line of any element is less than 45°.

[0033] The technical solution of the present invention is further explained and illustrated by means of specific embodiments below.

[0034] This application example selects different atmospheric precipitation as the main research object to explore the DOM components that have a significant contribution to the generation of nitrogen-containing disinfection by-products during the chlorination process of atmospheric precipitation. The specific process is: 1. Water sample pretreatment 16 atmospheric precipitation samples collected from different locations at similar times were selected and all water samples were filtered using a 0.45 μm pore size filter membrane. After filtration, the DOC content of each water sample was measured, and based on the measurement results, the DOC concentration of all water samples was adjusted to 2.0 mg / L using ultrapure water.

[0035] 2. DOM detection and characterization The 16 water samples were numbered and subjected to multi-detector gel chromatography, fluorescence spectrum and Fourier transform ion cyclotron mass spectrometry detection using solid phase extraction-liquid chromatography-mass spectrometry (LC-MS), multi-detector gel chromatography (LC-OCD-OND), fluorescence spectrometer (EEM) and Fourier transform ion cyclotron mass spectrometer (FT-ICR MS), respectively. The DOM component information including DOC content, DON content, UV absorbance, fluorescence intensity information, molecular structure and response intensity information of DOM components with different molecular weights was obtained.

[0036] (1) Detection of new pollutants Solid phase extraction-liquid chromatography-mass spectrometry was used to detect the specific content of typical new pollutants in the samples. The results are shown in Table 1.

[0037] Table 1: Quantitative detection results of typical new pollutants (2) LC-OCD-OND detection The 16 water samples were tested using the LC-OCD-OND detector to obtain the DOC content, DON content, and UV absorbance of DOM components with different molecular weights. The test results are shown in Table 2 and Figure 2 shown.

[0038] Table 2: LC-OCD-OND test results (3) EEM detection The 16 water samples were tested using an EEM detector to obtain the fluorescence intensity information of DOM components with different molecular weights. The fluorescence intensity information was analyzed using PARAFAC analysis to obtain quantitative analysis results and visualization results. The test results are shown in Table 3 and Figure 3 shown.

[0039] Table 3: Semi-quantitative results of EEM-PARAFAC detection (4) FT-ICR MS detection The 16 water samples were tested using the FT-ICR MS detector to obtain the molecular structure and response intensity information of DOM components with different molecular weights. The test results are shown in Table 4 and Figure 4 shown.

[0040] Table 4: Relative intensities of responses of DOM components with different molecular weights detected by FT-ICR MS 3. DBPs quantitative detection The water sample was subjected to a simulated chlorination experiment, wherein the experimental conditions were: bromide ion concentration of the water sample: 150 μg / L, water sample volume: 200 mL, reaction room temperature: 25 ± 1 °C, chlorine addition amount: , where X0 is the amount of chlorine added, X a is the residual chlorine after 24 hours of reaction, and (0.1-0.5) is the coefficient. After the chlorination experiment reaction is completed, ascorbic acid is added to terminate the chlorination reaction, and liquid-liquid extraction is used for enrichment. DBPs are quantitatively detected by gas chromatograph / gas chromatography-mass spectrometry and other instruments to obtain the DBPs yield. Among them, the quantitative detection indicators include THMs, HAAs, HANs, HALs and HNMs.

[0041] 4. NDMS analysis The DOM component information and DBPs yield of the water sample were input into SPSS 2022 software, and the NMDS calculation was performed on the above information data using SPSS 2022 software. The NDMS model was drawn based on the NMDS calculation results, as shown in the attached figure. Figure 5 As shown. Based on the material composition, properties and distribution characteristics of each DOM component, the dimension 1 represented by the horizontal axis is determined to be the aroma of organic matter. The DOM components in the positive semi-axis have stronger aroma than the DOM components in the negative semi-axis; while the dimension 2 represented by the vertical axis is the molecular weight of organic matter. The DOM components in the positive semi-axis have a larger molecular weight than the DOM components in the negative semi-axis. In addition, the correlation between different indicators can be judged according to the relative distance of each indicator, among which the specific approach represents the strong correlation between the components.

[0042] By the attached Figure 5 It can be seen that DBPs such as THMs and HAAs have a strong correlation with EEM1 (humic acid-like) and EEM2 (Furic acid-like), and are located on the positive semi-axis of dimension 1, indicating that aromatic humic acid-like substances contained in DOM are the main precursors of THMs and HAAs. HALs are located on the negative semi-axis of dimension 1, indicating that their main precursors are low-molecular-weight organic matter in DOM. For HNMs, its yield is significantly correlated with the DON / DOC index, that is, the organic nitrogen content in DOM; in addition, the CHON index and its close distance relationship indicate that the oxidized components in DON have a high correlation with it.

[0043] In order to verify the accuracy of the conclusions obtained by the method provided in the examples of this application, this application also selected DON samples with different oxidation states for verification. Specifically, based on the different oxidation degrees of DON, the reduced DON of N-ethylmethylamine, sarcosine, and 2-aminomalonamide and the oxidized DON of dimethyl nitromalonate and 2-methyl-4-nitrophenol were selected for simulated chlorination experiments, so as to verify the conclusion that the main precursor of HNM in NDMS analysis is oxidized DON by comparing the HNMs yields of different model compounds, and the attached Figure 6 , where the conditions of the chlorination experiment are the same as those of “3. DBPs quantitative detection”.

[0044] By the attached Figure 6 It can be seen that compared with the HNMs yield of reduced DON, oxidized DON represented by dimethyl nitromalonate and 2-methyl-4-nitrophenol has a higher HNMs yield. Therefore, the oxidized DON component in DOM is determined to be the main precursor source of HNM. This proves that the method provided in this application can accurately and effectively identify high-risk DBPs in DOM contained in drinking water.

[0045] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying and determining characteristic precursors of disinfection by-products in drinking water, characterized in that: include: After filtering a plurality of drinking water samples, the DOC content of the water samples was measured, and the DOC concentration of all the water samples was adjusted to the same level using ultrapure water; The water samples are subjected to new pollutant detection, multi-detector gel chromatography detection, fluorescence spectrum detection and Fourier transform ion cyclotron mass spectrometry detection to obtain new pollutant concentration information and DOM component information; wherein the DOM component information includes DOC content, DON content, UV absorbance, fluorescence intensity information, molecular structure and response intensity information of DOM components with different molecular weights; The water sample is subjected to a chlorination experiment, a liquid-liquid extraction enrichment and a DBPs quantitative detection in sequence to obtain a DBPs yield; wherein the quantitative detection index includes at least one of THMs, HAAs, HANs, HALs, HNMs and HAMs; Perform NMDS calculation according to the DOM component information and the DBPs yield to obtain a NDMS model; The DOM components significantly correlated with DBPs yield were determined based on the relative distances between the various indicators in the NDMS model.

2. The method for identifying and determining characteristic precursors of disinfection by-products in drinking water according to claim 1, characterized in that: Determining the DOM components significantly correlated with the DBPs yield according to the relative distances between the various indicators in the NDMS model includes: the DBPs yield and the DOM component are located in the same interval in the visualization diagram drawn according to the NDMS model, the angle between the center point of the visualization diagram and the DBPs yield is less than 45°, and the angle between the center point of the visualization diagram and the DOM component is also less than 45°.

3. The method for identifying and determining characteristic precursors of disinfection byproducts in drinking water according to claim 1, characterized in that: The water samples were tested by multi-detector gel chromatography. The test results were processed based on the Gaussian integration method to obtain the DOC content, DON content and UV absorbance of DOM components with different molecular weights. The detection conditions were as follows: injection volume: 1000-3000 μL, molecular weight detection range: 0.15 kDa-100 kDa, detection time: 50-70 min, mobile phase pH: 6.5-7.

5.

4. The method for identifying and determining characteristic precursors of disinfection by-products in drinking water according to claim 1, characterized in that: The water samples were tested with a fluorescence spectrometer, and the test results were processed based on the parallel factor analysis method to obtain the fluorescence intensity information of DOM components with different molecular weights. The detection conditions were: voltage: 400-700 V, step size: 1-5 nm, Ex: 200-450 nm, Em: 200-500 nm, response time: 0.002-0.1 s.

5. The method for identifying and determining characteristic precursors of disinfection by-products in drinking water according to claim 1, characterized in that: Fourier transform ion cyclotron mass spectrometer was used to detect water samples to obtain the molecular structure and response intensity information of DOM components with different molecular weights; the chromatographic conditions were as follows: the chromatographic column was a C18 column, the column temperature was 40°C, the mobile phase flow rate was 0.4mL / min, the injection volume was 10μL, the mobile phase was 0.1% formic acid in water and methanol solution, and the solvent flow rate was 0.3mL / min; the mass spectrometry conditions were as follows: capillary voltage 3.5kV, lens radio frequency voltage 50V, sheath gas volume flow rate 40arb, auxiliary gas flow rate 10arb, capillary temperature 320°C, nebulization temperature 350°C, scanning mode was full scan / data-dependent secondary scan, the scanning range was m / z = 50-800, and the collision energy was set to 30ev during data-dependent secondary scan.

6. The method for identifying and determining characteristic precursors of disinfection byproducts in drinking water according to claim 1, characterized in that: The experimental conditions of the chlorination experiment are: bromide ion concentration of water sample: 100-200 μg / L, water sample volume: 200-500 mL, reaction room temperature: 25±1°C, chlorine addition amount: , where X0 is the amount of chlorine added, X a is the residual chlorine after 24 hours of reaction, and (0.1-0.5) is the coefficient.

7. The method for identifying and determining characteristic precursors of disinfection by-products in drinking water according to claim 1, characterized in that: When calculating the NMDS, the dimension reduction axis is 2-3, the number of iterations is 200-500, the Stress value of the NDMS model is less than 0.1, and the R value of the Shepard diagram is 2 Above 0.

7.

8. The method for identifying and determining characteristic precursors of disinfection by-products in drinking water according to claim 1, characterized in that: The number of the drinking water samples is 10-30.

9. The method for identifying and determining characteristic precursors of disinfection byproducts in drinking water according to claim 1, characterized in that: The DOC concentration of all the water samples was adjusted to 2.0-3.0 mg / L using ultrapure water.

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