Methods for distinguishing different sources of dissolved organic nitrogen in wastewater and their applications

Abstract

To provide a soluble organic nitrogen discrimination method that has high detection throughput, strong versatility, simple and rapid operation, and capable of achieving a qualitative and quantitative discrimination of foreign and endogenous soluble organic nitrogen in sewage.SOLUTION: The method includes the steps of: (1) extracting soluble organic nitrogen in sewage; (2) detecting mass spectral peaks in soluble organic nitrogen extraction liquid; (3) performing preliminary treatment on spectrogram data of a sewage sample; (4) establishing the network relation of material reactions in sewage; (5) screening the material-reaction relation of soluble organic nitrogen; and (6) discriminating soluble organic nitrogen with different sources.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention belongs to the field of sewage treatment, and more particularly to a method and application for distinguishing different sources of dissolved organic nitrogen in sewage. [Background technology]

[0002] Dissolved organic nitrogen (DON) is a significant component of total dissolved nitrogen in urban sewage treatment plants, with concentrations ranging from approximately 0.50 to 6.46 mg / L, accounting for approximately 25 to 80% of total dissolved nitrogen in sewage. DON in sewage can serve as a precursor to disinfection by-products. While carcinogenic disinfection by-products are generated during the disinfection stage, DON in sewage treatment plants stimulates algae growth, leading to eutrophication in the receiving water. Therefore, DON in sewage is a significant issue during sewage treatment. DON in sewage at urban sewage treatment plants can be classified into exogenous DON and endogenous DON depending on its source. Exogenous DON is DON derived from the influent water at the sewage treatment plant and includes DON that is not completely degraded during sewage treatment and DON that cannot be degraded or is difficult to biodegrade in the influent water. Endogenous DON is DON derived from the sewage treatment process and includes microbial DON, microbial transformation products of exogenous DON, and DON by-products from the sewage treatment process. According to existing research, exogenous and endogenous DON differ significantly in terms of their main molecular structure, transformation behavior, environmental impact, and management factors. Therefore, it is necessary to distinguish between exogenous and endogenous DON when evaluating the operational effectiveness of sewage treatment plants and formulating operational management measures for sewage treatment plants.

[0003] In recent years, researchers have increasingly recognized the differences in DON from different sources and the need to distinguish between DON from different sources in wastewater. However, there are still many obstacles to distinguishing between DON from different sources in wastewater and related qualitative and quantitative research. During urban sewage treatment, both exogenous and endogenous DON can coexist in wastewater, making it difficult to separate them using most analytical techniques. Differentiating DON from different sources primarily involves macroconcentration and molecular structure. Regarding macroconcentration, traditional methods address the issue of quantifying endogenous DON in mixed systems by predicting the concentration of endogenous DON in wastewater using mathematical models or by conducting simulated sewage experiments, and then quantifying exogenous DON through subtraction. Regarding molecular structure, traditional methods address the issue of qualitatively differentiating exogenous and endogenous DON by analyzing the representative characteristic components of DON from different sources. While breakthroughs have been made in distinguishing DON from different sources, distinguishing between different sources based on macroconcentration and molecular structure remains a major challenge. Chinese Patent Application No. CN201910861998.7 discloses a prediction model for microbial dissolved organic nitrogen in wastewater and its application. Based on the dynamic principles of microbial DON generation and depletion in activated sludge systems, a prediction model for microbial DON was constructed. The accuracy of the prediction model was calibrated through model sensitivity analysis and parameter estimation results, and the calibrated model was then applied to predict microbial DON in wastewater treatment plants. While this method can quantify microbial DON in mixed systems, it only quantifies a portion of endogenous DON (microbial DON), and there are many areas for improvement in distinguishing between different sources. DON in wastewater has a complex structure and diverse molecular structure. Therefore, it is necessary to distinguish between different sources of DON in wastewater based on its molecular structure and combine this analysis with changes in macroconcentration. DISCLOSURE OF THE INVENTION [Problem to be solved by the invention]

[0004] The present invention aims to solve the problem of the inability of conventional methods for distinguishing DON from different sources in sewage to simultaneously distinguish exogenous DON from endogenous DON based on both macro-concentration and molecular structure, improve the dependency of conventional methods on equipment and parameter conditions, and provide a method and application for distinguishing soluble organic nitrogen from different sources in sewage. [Means for solving the problem]

[0005] The method for distinguishing between different sources of dissolved organic nitrogen in sewage is a method for simultaneously qualitatively and quantitatively determining exogenous and endogenous DON in sewage samples, which was developed using liquid chromatography-high-resolution mass spectrometry and reaction omics analysis techniques. The specific development steps are as follows:

[0006] (a) Extraction of DON in sewage. The sewage sample is pretreated using a solid-phase extraction column, activated by the column, and the sample is loaded, rinsed, and eluted to separate DON from other interfering substances, concentrating the DON in the sewage and obtaining a DON extract that can be detected.

[0007] (b) Mass spectrum peaks in the DON extract are detected. A full-scan analytical detection of the DON extract is performed using a liquid chromatograph and high-resolution mass spectrometer to obtain a detection spectrogram of DON in the wastewater.

[0008] (c) Preprocessing the spectrogram data of the sewage sample: subtracting background signal values, eliminating interference peaks, and removing noise signals from the detected spectrogram of DON in the sewage to obtain preprocessed spectrogram data of the sewage sample.

[0009] (d) Building a network of material reactions in sewage. Reaction omics analysis technology is used to extract peaks from spectrogram data of sewage samples. retention timeHierarchical clustering is performed to remove redundancy from the peaks, simplify the spectrogram data of the sewage samples, establish all the reaction mass difference relationships of the sewage samples, screen the independent mass spectrum peaks therein, construct a high-frequency mass difference matrix of the substance reactions in the sewage samples, and combine the high-frequency mass difference matrix and the substance reaction database to generate a network relationship diagram of the substance reactions in the sewage.

[0010] (e) Screening the substance-reaction relationship of DON: The mass spectrum peaks in the network relationship diagram are matched with the mass analysis database to obtain the substance molecular formula corresponding to the mass spectrum peaks, and the high-frequency mass difference relationships associated with DON in the network relationship diagram are screened to obtain the substance-reaction relationship of DON.

[0011] (f) Identifying DON from different sources: Calculating the reaction-relationship connectivity and reaction distance of each DON molecule in the DON substance-reaction relationship, and distinguishing exogenous DON from endogenous DON according to the reaction-relationship connectivity and reaction distance.

[0012] Furthermore, the sewage samples include sewage samples from a single treatment unit at a municipal sewage treatment plant, a collection of sewage samples from multiple treatment units at a municipal sewage treatment plant, a collection of sewage samples from the full process at a municipal sewage treatment plant, and sewage samples from a municipal sewage treatment plant. Exogenous DON refers to DON that is not completely decomposed during sewage treatment, DON that cannot be decomposed in the influent water, or DON that is difficult to biodegrade, while endogenous DON refers to microbial DON, microbial conversion products of exogenous DON, and DON by-products during the sewage treatment process.

[0013] Furthermore, in step (a), the solid-phase extraction column is a commercially available solid-phase extraction column with a styrene-divinylbenzene copolymer filler, and the pretreatment of the sewage sample involves filtering it through a 0.45 μm acetate fiber filter membrane and then oxidizing it with ACS-grade high-purity hydrochloric acid until the pH reaches 2. The column is then activated by filtering the solid-phase extraction column with 10–15 mL of LC-MS-grade methanol and 20–25 mL of ultrapure water with a pH of 2, followed by rinsing. The solid-phase extraction column was filtered with 20-25 mL of ultrapure water with a pH of 2. The liquid in the solid-phase extraction column was removed until the eluate was dried with nitrogen gas, and then the solid-phase extraction column was filtered with 5-15 mL of LC-MS-grade methanol. The filtration flow rate of the solid-phase extraction column was controlled at 0.5-2.0 mL / min, and the DON extract was filtered through a 0.22 μm organic filtration membrane until the DON concentration was detected by the instrument and was between 50 and 100 mg / L.

[0014] Furthermore, in step (b), the specifications of the liquid chromatography-high-resolution mass spectrometer are a scan mode of positive and negative ion ionization, an electrospray ionization source, a flow rate of 0.2 to 0.5 mL / min, and a spectrogram collection range of 50 to 1200 Da.

[0015] Furthermore, in step (c), the method for obtaining the background signal value is to use ultrapure water as the sample to be treated, and obtain the background signal by the same operations as in step (a) and step (b). The background signal is subtracted by peak intensity difference subtraction, and the mass spectrum peaks whose peak intensity after subtraction is at least 1000 are retained. The spectrogram data of the sewage sample is converted into ".mzrt" data using the "XCMS" package in R language.

[0016] Furthermore, in step (d), the reaction omics analysis technology can obtain the reactant DON, reaction product DON, and material reaction process in which DON participates in a sample from the mass difference relationship under the condition that the entire material reaction process in which DON participates during a biochemical reaction has a corresponding reactant DON and reaction product DON, and a corresponding mass difference relationship between the reactant DON and reaction product DON, and the material reaction process in which DON participates in a sample from the mass difference relationship, under the condition that the material reaction process is unknown.

[0017] Removing redundancy from peaks means removing excess mass spectral peaks in the spectrogram data of the sewage sample.

[0018] retention time The hierarchical clustering was performed by calculating the relationship between the mass differences in the sewage samples using the "getpaired" function in the R language, and then using the recurrence formula (1) retention time Clustering is performed, and a D value of 10 or more is used as the threshold for the cutoff distribution.

[0019] TIFF0007762958000001.tif1267

[0020] During the ceremony, RT m , R.T. n : retention time Joined clusters of data

[0021] RT1: Calculated single hold time data

[0022] D value: RT1 and retention time The combined clusters of data (RT m , R.T. n ) distance

[0023] The high-frequency mass difference relationship can be calculated using the "getstd" function in the R language. 、 neutral loss , isotopes and co-fragments DeductedThe high frequency mass difference matrix is ​​a substance reaction matrix that converts mass spectrum peaks into matrix elements according to equation (2). TIFF0007762958000002.tif2496

[0024] During the ceremony, TIFF0007762958000003.tif725: Mass spectrum peak TIFF0007762958000004.tif625: Potential parent reactant of mass spectrum peak

[0025] The substance reaction database is the KEGG database and the HMDB database. The substance reaction network relationship diagram is a topology relationship in which the minimum mass difference between substance reactions is connected, mass spectrum peaks are used as nodes, and the relationship value is at least 0.6.

[0026] Furthermore, the formula for the minimum mass difference is: TIFF0007762958000005.tif7121

[0027] During the ceremony, PMD BK : Minimum mass difference for material reaction TIFF0007762958000006.tif725: Mass spectrum peak TIFF0007762958000007.tif625: Potential parent reactant of mass spectrum peak

[0028] Furthermore, in step (e), the matching rule for the substance molecular formula corresponding to the mass spectrum peak is: mass spectrum peak error < 5 ppm; retention time The error is <2 min, the relative molecular mass of the DON molecule containing an even number of nitrogen atoms is even, the relative molecular mass of the DON molecule containing an odd number of nitrogen atoms is odd, and the molecular formula matching score is >90.

[0029] Furthermore, in step (f), the connectivity is the number of substance reactions in which each DON molecule participates in the substance-reaction matrix, and the reaction distance is the connection distance between DON molecules in the substance-reaction matrix. TIFF0007762958000008.tif723, DON molecular connectivity is TIFF0007762958000009.tif822, the reaction distance of the DON molecule is TIFF0007762958000010.tif1235, where A is the adjacent matrix, TIFF0007762958000011.tif68 is a descriptor of the relationship between DON molecule i and DON molecule j, N is the number of DON molecules, TIFF0007762958000012.tif76 is the connectivity of DON molecule i, TIFF0007762958000013.tif76 is the number of shortest connection relationships between DON molecule i and DON molecule j, and L is the average connection length of the substance-reaction relationship network of DON.

[0030] The main cutoff value for the reaction-related connectivity and reaction distance is the median connectivity in the sewage samples, and the secondary cutoff value is the median reaction distance. The DON molecules in TIFF0007762958000014.tif767 were classified as foreign DON molecules. TIFF0007762958000015.tif766 shows that the DON molecule is an endogenous DON molecule, TIFF0007762958000016.tif721 is the median connectivity, TIFF0007762958000017.tif722 is the median reaction distance.

[0031] The present invention also provides an application of the above method, which can be applied to the qualitative and quantitative determination of exogenous and endogenous soluble organic nitrogen in wastewater, thereby enabling the quantification of the ratio of exogenous DON to endogenous DON. [Effects of the Invention]

[0032] (1) The present invention provides a simple and synchronous qualitative and quantitative method for distinguishing DON from different sources based on macroscopic concentration and molecular structure, thereby resolving the difficulty of distinguishing exogenous DON from endogenous DON in sewage using conventional analytical methods.

[0033] (2) Based on the reaction principles and rules of DON in sewage systems, this invention combines the rapid detection and highly efficient separation characteristics of liquid chromatography and high-resolution mass spectrometry to effectively screen the substance-reaction relationships using high frequencies in advance, greatly simplifying the calculation steps and reducing the false positive detection rate, while ensuring high throughput and accuracy of detection.

[0034] (3) The present invention uses reaction omics analysis technology to study the specific reaction relationships of DON molecules in sewage, and distinguishes DON from different sources based on the differences in the topological structure of DON's substance-reaction relationships. This reduces the dependency of traditional methods on instrument conditions, enhances the versatility of the method for distinguishing DON from different sources, and improves the applicability of the method. [Brief explanation of the drawings]

[0035] [Figure 1] 1 is a flowchart of the present invention for distinguishing DON from different sources in sewage. [Figure 2] 1 shows the material reaction topology structure diagram of DON from different sources in Example 1 of the present invention. [Figure 3] 1 shows the material reaction topology structure diagram of DON from different sources in Example 2 of the present invention. [Figure 4] 1 shows the material reaction topology structure diagram of DON from different sources in Example 3 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] The present invention will now be described in more detail in conjunction with specific examples, but the present invention is not limited thereto. [Example]

[0037] The application target of this embodiment is the sewage from the biological stage of a certain sewage treatment plant A. The sewage treatment plant A has a daily treatment capacity of 45,000 m 3 The main process is the oxidation ditch process, and the biological stage wastewater contained COD 25.59 mg / L, total nitrogen 7.31 mg / L, total phosphorus 0.40 mg / L, ammonia nitrogen 0.06 mg / L, nitrate nitrogen 2.31 mg / L, nitrite nitrogen 0.09 mg / L, and DON 4.85 mg / L. The steps for distinguishing exogenous and endogenous DON in the biological stage wastewater DON are shown in Figure 1. A method for distinguishing between exogenous and endogenous DON in wastewater samples was developed using liquid chromatography-high-resolution mass spectrometry and reaction omics analysis techniques to simultaneously qualitatively and quantitatively analyze exogenous and endogenous DON. The specific steps are as follows:

[0038] Step 1: Extraction of DON from wastewater. DON was concentrated using a commercially available solid-phase extraction column filled with styrene-divinylbenzene copolymer. The sewage sample was filtered through a 0.45 μm acetate fiber filter membrane. The filtered sewage sample was acidified with ACS-grade high-purity hydrochloric acid to a pH of 2, then activated with 10 mL of LC-MS-grade methanol and 20 mL of ultrapure water at pH 2. The column was then loaded with the sample at a flow rate of 0.5 mL / min, rinsed with 20 mL of ultrapure water at pH 2, and finally dried with nitrogen gas. The column was then extracted again with 10 mL of LC-MS-grade methanol. The extract was then filtered through a 0.22 μm organic filter membrane until the DON concentration reached 100 mg / L.

[0039] Step 2: Detect mass spectral peaks of the DON extract. Perform full-scan analytical detection of the DON extract using a liquid chromatography-high-resolution mass spectrometer. Set the liquid chromatography-high-resolution mass spectrometer to positive and negative ionization modes, use an electrospray ionization source, and use a flow rate of 0.2 mL / min. The spectrogram collection range is 50–1200 Da. Install the instrument and perform detection to obtain a spectrogram of DON in the wastewater.

[0040] Step 3: Preprocess the spectrogram data of the sewage sample. Use ultrapure water as the sample to be treated. Obtain the background signal using the same procedure as for the sewage sample. Subtract the background signal using peak intensity difference subtraction. Retain mass spectrum peaks with a peak intensity of at least 1000 after subtraction. Subtract the background signal value from the detected spectrogram of the sewage sample, eliminate interfering peaks, and remove noise signals. Then use the "XCMS" package in R to convert the spectrogram data of the sewage sample into ".mzrt" format data.

[0041] Step 4: Build a network of material reactions in sewage. Using reaction omics analysis technology, peaks are extracted from the spectrogram data of sewage samples. retention timeHierarchical clustering is performed to eliminate peak redundancy and simplify the sewage sample spectrogram data, establishing all reaction mass difference relationships for the sewage sample, screening independent mass spectral peaks within it, constructing a high-frequency mass difference matrix of the substance reactions in the sewage sample, and combining the high-frequency mass difference matrix with the substance reaction database to generate a network relationship diagram of the substance reactions in the sewage sample. Reaction omics analysis technology determines that the entire process of substance reactions involving DON during biochemical reactions has a corresponding mass difference relationship between the reactant DON and the reaction product DON, and between the reactant DON and the reaction product DON. Even if the substance reaction process is unknown, the reactant DON, reaction product DON, and the substance reaction process involving DON in the sample can be obtained from the mass difference relationship. Eliminating peak redundancy means removing excess mass spectral peaks from the sewage sample spectrogram data. retention time To perform hierarchical clustering, the relationship between the mass differences in the sewage samples was calculated using the "getpaired" function in the R language, and the recurrence formula The D value of 10 or more according to TIFF0007762958000018.tif1261 was used as the threshold for the cutoff distribution. retention time Among them, RT m and RT n teeth retention time The combined clusters of data, RT1 is calculated as a single retention time Data, D value is RT1 retention time The combined clusters of data (RT m , R.T. n ) The high frequency mass difference relationship is calculated using the "getstd" function in the R language, and is the mass difference relationship after deducting adducts, neutral losses, isotopes, and co-fragments. The high frequency mass difference matrix is ​​a substance reaction matrix with mass spectrum peaks as matrix elements. That is, TIFF0007762958000019.tif2570

[0042] Among them, TIFF0007762958000020.tif725 is the mass spectrum peak, TIFF0007762958000021.tif625 is the potential parent reactant of the mass spectrum peak. The substance-reaction database is the KEGG database and the HMDB database. The substance-reaction network relationship diagram is a topology relationship that links the minimum mass difference between the substance reactions, with the mass spectrum peaks as nodes and a relationship value of at least 0.6. The formula for the minimum mass difference is as follows:

[0043] TIFF0007762958000022.tif7110

[0044] Step 5: Screening the substance-reaction relationship of DON. According to the matching rule, the mass spectrum peak error is <5 ppm. retention time The error is <2 min, the relative molecular mass of DON molecules containing an even number of nitrogen atoms is even, and the relative molecular mass of DON molecules containing an odd number of nitrogen atoms is odd, the molecular formula matching score is >90, the mass spectrum peaks in the network relationship diagram are matched with the mass analysis database to obtain the substance molecular formula corresponding to the mass spectrum peak, and the high frequency mass difference relationships related to DON in the network relationship diagram are screened to obtain the substance reaction relationship of DON.

[0045] Step 6: Identify DON from different sources. Calculate the reaction relationship connectivity and reaction distance of each DON molecule in the substance-reaction relationship of DON, and distinguish exogenous DON from endogenous DON according to the reaction relationship connectivity and reaction distance. Connectivity is the number of substance reactions in which each DON molecule participates in the substance-reaction relationship matrix, and reaction distance is the connection distance between DON molecules in the substance-reaction relationship matrix. That is, adjacent matrices TIFF0007762958000023.tif723, DON molecular connectivity is TIFF0007762958000024.tif822, the reaction distance of the DON molecule is TIFF0007762958000025.tif1235, where A is the adjacent matrix, TIFF0007762958000026.tif68 is a descriptor of the relationship between DON molecule i and DON molecule j, N is the number of DON molecules, TIFF0007762958000027.tif76 is the connectivity of DON molecule i, TIFF0007762958000028.tif76 is the number of shortest connections between DON molecule i and DON molecule j, and L is the average connection length of the DON substance-reaction relationship network. The reaction relationship connectivity and reaction distance are determined by the median connectivity in the sewage sample as the main cutoff value, the median reaction distance as the secondary cutoff value, and The DON molecules in TIFF0007762958000029.tif767 were classified as foreign DON molecules. TIFF0007762958000030.tif766 shows that the DON molecule is an endogenous DON molecule, TIFF0007762958000031.tif721 is the median connectivity, TIFF0007762958000032.tif722 is the median reaction distance.

[0046] As shown in Figure 2, this method was used to separate the DON molecules in the sewage from the biological stage of sewage treatment plant A into exogenous DON molecules and endogenous DON molecules, and the mass spectral peak intensity values ​​of the exogenous DON molecules and endogenous DON molecules were calculated. This confirmed that the proportion of exogenous DON in the sewage from the biological stage of sewage treatment plant A, where the DON concentration was 4.85 mg / L, was 58.3% and the proportion of endogenous DON was 41.7%. [Example]

[0047] Unlike Example 1, this example is applied to the entire wastewater of a certain sewage treatment plant B. The sewage treatment plant B has a daily treatment volume of 150,000 m 3The main water treatment process was an A2 / O process + secondary sedimentation tank + high-efficiency sedimentation tank + quartz sand filter. The total wastewater contained 17.32 mg / L of COD, 10.95 mg / L of total nitrogen, 0.10 mg / L of total phosphorus, 0.33 mg / L of ammonia nitrogen, 7.47 mg / L of nitrate nitrogen, 0.04 mg / L of nitrite nitrogen, and 3.11 mg / L of DON. The steps for distinguishing exogenous and endogenous DON in the total wastewater DON are shown in Figure 1. A method for distinguishing between different sources of dissolved organic nitrogen in wastewater was developed using liquid chromatography-high-resolution mass spectrometry and reaction omics analysis techniques to simultaneously qualitatively and quantitatively analyze exogenous and endogenous DON in wastewater samples. The specific steps are as follows:

[0048] Step 1: Extraction of DON from wastewater. DON was concentrated using a commercially available solid-phase extraction column filled with styrene-divinylbenzene copolymer. The sewage sample was filtered through a 0.45 μm acetate fiber filter membrane. The filtered sewage sample was acidified with ACS-grade high-purity hydrochloric acid to a pH of 2, then activated with 15 mL of LC-MS-grade methanol and 25 mL of ultrapure water at pH 2. The column was then loaded with the sample at a flow rate of 2.0 mL / min, rinsed with 25 mL of ultrapure water at pH 2, and finally dried with nitrogen gas. The column was then extracted again with 15 mL of LC-MS-grade methanol. The extract was then filtered through a 0.22 μm organic filter membrane until the DON concentration reached 70 mg / L.

[0049] Step 2: Detect mass spectral peaks in the DON extract. Perform full-scan analytical detection of the DON extract using a liquid chromatography-high-resolution mass spectrometer. Set the liquid chromatography-high-resolution mass spectrometer to positive and negative ionization modes, use an electrospray ionization source, and use a flow rate of 0.5 mL / min with a spectrogram collection range of 100–1000 Da. Install the instrument and perform detection to obtain a spectrogram of DON in the wastewater.

[0050] Step 3: Preprocess the spectrogram data of the sewage sample. Ultrapure water is used as the sample to be treated, and the background signal is obtained using the same procedure as for the sewage sample. The background signal is subtracted by peak intensity difference subtraction, and the mass spectrum peaks with a peak intensity of at least 3000 after subtraction are selected. The background signal values ​​are subtracted from the detected spectrogram of the sewage sample, and interference peaks and noise signals are removed. The spectrogram data of the sewage sample is then converted into ".mzrt" format data using the "XCMS" package in R language.

[0051] Step 4: Build a network of material reactions in sewage. Using reaction omics analysis technology, peaks are extracted from the spectrogram data of sewage samples. retention time Hierarchical clustering is performed to eliminate peak redundancy and simplify the sewage sample spectrogram data, establishing all reaction mass difference relationships for the sewage sample, screening independent mass spectral peaks within it, constructing a high-frequency mass difference matrix of the substance reactions in the sewage sample, and combining the high-frequency mass difference matrix with the substance reaction database to generate a network relationship diagram of the substance reactions in the sewage sample. Reaction omics analysis technology determines that the entire process of substance reactions involving DON during biochemical reactions has a corresponding mass difference relationship between the reactant DON and the reaction product DON, and between the reactant DON and the reaction product DON. Even if the substance reaction process is unknown, the reactant DON, reaction product DON, and the substance reaction process involving DON in the sample can be obtained from the mass difference relationship. Eliminating peak redundancy means removing excess mass spectral peaks from the sewage sample spectrogram data. retention time To perform hierarchical clustering, the relationship between the mass differences in the sewage samples was calculated using the "getpaired" function in the R language, and the recurrence formula The D value of 20 or more according to TIFF0007762958000033.tif1261 was used as the threshold for the cutoff distribution. retention timeAmong them, RT m and RT n teeth retention time The combined clusters of data, RT1 is calculated as a single retention time Data, D value is RT1 retention time The combined clusters of data (RT m , R.T. n ) The high frequency mass difference relationship is calculated using the "getstd" function in the R language, and is the mass difference relationship after deducting adducts, neutral losses, isotopes, and co-fragments. The high frequency mass difference matrix is ​​a substance reaction matrix with mass spectrum peaks as matrix elements. That is, TIFF0007762958000034.tif2570

[0052] Among them, TIFF0007762958000035.tif725 is the mass spectrum peak, TIFF0007762958000036.tif625 is the potential parent reactant of the mass spectrum peak. The substance / reaction database is the KEGG database and HMDB database. The substance / reaction network relationship diagram is connected by the minimum mass difference between the substance / reaction, with the mass spectrum peak as the node, and the relationship is a topological relationship with a correlation value of at least 0.8. The formula for the minimum mass difference is as follows:

[0053] TIFF0007762958000037.tif7106

[0054] Step 5: Screening the substance-reaction relationship of DON. According to the matching rule, the mass spectrum peak error is <3 ppm. retention timeThe error is <1 min, the relative molecular mass of DON molecules containing an even number of nitrogen atoms is even, and the relative molecular mass of DON molecules containing an odd number of nitrogen atoms is odd, the molecular formula matching score is >90, the mass spectrum peaks in the network relationship diagram are matched with the mass analysis database to obtain the substance molecular formula corresponding to the mass spectrum peak, and the high frequency mass difference relationships related to DON in the network relationship diagram are screened to obtain the substance reaction relationship of DON.

[0055] Step 6: Identify DON from different sources. Calculate the reaction relationship connectivity and reaction distance of each DON molecule in the substance-reaction relationship of DON, and distinguish exogenous DON from endogenous DON according to the reaction relationship connectivity and reaction distance. Connectivity is the number of substance reactions in which each DON molecule participates in the substance-reaction relationship matrix, and reaction distance is the connection distance between DON molecules in the substance-reaction relationship matrix. That is, adjacent matrices In TIFF0007762958000038.tif723, the DON molecular connectivity is TIFF0007762958000039.tif822, the reaction distance of the DON molecule is TIFF0007762958000040.tif1235. A is the adjacent matrix, TIFF0007762958000041.tif68 is a descriptor of the relationship between DON molecule i and DON molecule j, N is the number of DON molecules, TIFF0007762958000042.tif76 is the connectivity of DON molecule i, TIFF0007762958000043.tif76 is the number of shortest connections between DON molecule i and DON molecule j, and L is the average connection length of the DON substance-reaction network. The reaction-relationship connectivity and reaction distance are determined by the median connectivity in the sewage sample as the main cutoff value, and the median reaction distance as the secondary cutoff value. The DON molecules in TIFF0007762958000044.tif767 were classified as foreign DON molecules. TIFF0007762958000045.tif766 shows that the DON molecule is an endogenous DON molecule, TIFF0007762958000046.tif721 is the median connectivity, TIFF0007762958000047.tif722 is the median reaction distance.

[0056] As shown in Figure 3, this method was used to separate the DON molecules in the total sewage from Sewage Treatment Plant B into exogenous DON molecules and endogenous DON molecules, and the mass spectral peak intensity values ​​of the exogenous DON molecules and endogenous DON molecules were calculated. This confirmed that the proportion of exogenous DON in the total sewage from Sewage Treatment Plant B, where the DON concentration was 3.11 mg / L, was 62.5% and the proportion of endogenous DON was 37.5%. [Example]

[0057] Unlike Example 1, this example is applied to the improved sewage of a certain sewage treatment plant C. The sewage treatment plant C has a daily treatment volume of 60,000 m 3 The primary water treatment process is an oxidation ditch process, with a coagulation tank and denitrification filter added after improvement. The improved wastewater had a COD of 13.13 mg / L, total nitrogen of 7.34 mg / L, total phosphorus of 0.03 mg / L, ammonia nitrogen of 0.16 mg / L, nitrate nitrogen of 2.94 mg / L, nitrite nitrogen of 0.06 mg / L, and DON of 4.18 mg / L. The steps for distinguishing exogenous and endogenous DON in improved wastewater at WWTP C are shown in Figure 1. The method and application of soluble organic nitrogen from different sources in wastewater was developed using liquid chromatography-high-resolution mass spectrometry and reaction omics analysis to simultaneously qualitatively and quantitatively analyze exogenous and endogenous DON in wastewater samples. The specific steps are as follows:

[0058] Step 1: Extraction of DON from wastewater. DON was concentrated using a commercially available solid-phase extraction column filled with styrene-divinylbenzene copolymer. The sewage sample was filtered through a 0.45 μm acetate fiber filter membrane. The filtered sewage sample was acidified with ACS-grade high-purity hydrochloric acid to a pH of 2, then activated with 12 mL of LC-MS-grade methanol and 20 mL of ultrapure water at pH 2. The column was then loaded with the sample at a flow rate of 1.0 mL / min, rinsed with 25 mL of ultrapure water at pH 2, and finally dried with nitrogen gas. The column was then extracted again with 5 mL of LC-MS-grade methanol. The extract was then filtered through a 0.22 μm organic filter membrane until the DON concentration reached 50 mg / L.

[0059] Step 2: Detect mass spectral peaks in the DON extract. Perform full-scan analytical detection of the DON extract using a liquid chromatography-high-resolution mass spectrometer. Set the liquid chromatography-high-resolution mass spectrometer to positive and negative ionization modes, use an electrospray ionization source, a flow rate of 0.4 mL / min, and a spectrogram collection range of 150–1200 Da. Install the instrument and perform detection to obtain a spectrogram of DON in the wastewater.

[0060] Step 3: Preprocess the spectrogram data of the sewage sample. Ultrapure water is used as the sample to be treated, and the background signal is obtained using the same procedure as for the sewage sample. The background signal is subtracted using peak intensity difference subtraction, and mass spectrum peaks with a peak intensity of at least 2000 after subtraction are retained. The background signal values ​​are subtracted from the detected spectrogram of the sewage sample, interfering peaks are eliminated, and noise signals are removed. The spectrogram data of the sewage sample is then converted into ".mzrt" format data using the "XCMS" package in R language.

[0061] Step 4: Build a network of material reactions in sewage. Reaction omics analysis technology is used to extract peaks from the spectrogram data of sewage samples. retention time Hierarchical clustering is performed to eliminate peak redundancy and simplify the sewage sample spectrogram data, establishing all reaction mass difference relationships for the sewage sample, screening independent mass spectral peaks within it, constructing a high-frequency mass difference matrix of the substance reactions in the sewage sample, and combining the high-frequency mass difference matrix with the substance reaction database to generate a network relationship diagram of the substance reactions in the sewage sample. Reaction omics analysis technology determines that the entire process of substance reactions involving DON during biochemical reactions has a corresponding mass difference relationship between the reactant DON and the reaction product DON, and between the reactant DON and the reaction product DON. Even if the substance reaction process is unknown, the reactant DON, reaction product DON, and the substance reaction process involving DON in the sample can be obtained from the mass difference relationship. Eliminating peak redundancy means removing excess mass spectral peaks from the sewage sample spectrogram data. retention time To perform hierarchical clustering, the relationship between the mass differences in the sewage samples was calculated using the "getpaired" function in the R language, and the recurrence formula The D value of 15 or more according to TIFF0007762958000048.tif1261 was used as the threshold for the cutoff distribution. retention time Among them, RT m and RT n teeth retention time The combined clusters of data, RT1 is calculated as a single retention time Data, D value is RT1 retention time The combined clusters of data (RT m , R.T. n ) The high frequency mass difference relationship is calculated using the "getstd" function in the R language, and is the mass difference relationship after deducting adducts, neutral losses, isotopes, and co-fragments. The high frequency mass difference matrix is ​​a substance reaction matrix with mass spectrum peaks as matrix elements. That is, TIFF0007762958000049.tif2570

[0062] Among them, TIFF0007762958000050.tif725 is the mass spectrum peak, TIFF0007762958000051.tif625 is the potential parent reactant of the mass spectrum peak. The substance / reaction database is the KEGG database and the HMDB database. The substance / reaction network relationship diagram is a topology relationship that links the minimum mass difference between the substance / reaction, with the mass spectrum peak as the node, and the relationship value is at least 0.7. The formula for the minimum mass difference is as follows: TIFF0007762958000052.tif7106

[0063] Step 5: Screening the substance-reaction relationship of DON. According to the matching rule, the mass spectrum peak error is <4 ppm. retention time The error is <1.5 min, the relative molecular mass of DON molecules containing an even number of nitrogen atoms is even, and the relative molecular mass of DON molecules containing an odd number of nitrogen atoms is odd, the molecular formula matching score is >90, the mass spectrum peaks in the network relationship diagram are matched with the mass analysis database to obtain the substance molecular formula corresponding to the mass spectrum peak, and the high-frequency mass difference relationships related to DON in the network relationship diagram are screened to obtain the substance reaction relationship of DON.

[0064] Step 6: Identify DON from different sources. Calculate the reaction relationship connectivity and reaction distance of each DON molecule in the substance-reaction relationship of DON, and distinguish exogenous DON from endogenous DON according to the reaction relationship connectivity and reaction distance. Connectivity is the number of substance reactions in which each DON molecule participates in the substance-reaction relationship matrix, and reaction distance is the connection distance between DON molecules in the substance-reaction relationship matrix. That is, adjacent matrices TIFF0007762958000053.tif723, DON molecular connectivity is TIFF0007762958000054.tif822, the reaction distance of the DON molecule is TIFF0007762958000055.tif1235. A is the adjacent matrix, TIFF0007762958000056.tif68 is a descriptor of the relationship between DON molecule i and DON molecule j, N is the number of DON molecules, TIFF0007762958000057.tif76 is the connectivity of DON molecule i, TIFF0007762958000058.tif76 is the number of shortest connections between DON molecule i and DON molecule j, and L is the average connection length of the DON substance-reaction relationship network. The reaction relationship connectivity and reaction distance are determined by the median connectivity in the sewage sample as the main cutoff value, and the median reaction distance as the secondary cutoff value. The DON molecules in TIFF0007762958000059.tif767 were classified as foreign DON molecules. TIFF0007762958000060.tif766 shows that the DON molecule is an endogenous DON molecule, TIFF0007762958000061.tif721 is the median connectivity, TIFF0007762958000062.tif722 is the median reaction distance.

[0065] As shown in Figure 3, this method was used to separate the DON molecules in the improved stage sewage from Sewage Treatment Plant C into exogenous DON molecules and endogenous DON molecules, and the mass spectral peak intensity values ​​of the exogenous DON molecules and endogenous DON molecules were calculated. This confirmed that the proportion of exogenous DON in the improved stage sewage from Sewage Treatment Plant C, where the DON concentration was 4.18 mg / L, was 28.6% and the proportion of endogenous DON was 71.4%.

Claims

1. This is a qualitative and quantitative method for exogenous and endogenous DON in sewage samples, developed using liquid chromatography, high-resolution mass spectrometry, and reaction omics analysis techniques. The specific development steps are as follows: (a) extracting DON from sewage, which comprises pretreating a sewage sample on a solid-phase extraction column, activating the column, loading the sample, rinsing, and eluting the sample to separate DON from other interfering substances, thereby concentrating the DON in the sewage and obtaining a DON extract; (b) detecting mass spectral peaks in the DON extract, which is a step of performing full scan analytical detection on the DON extract using a liquid chromatography / high-resolution mass spectrometer to obtain a detection spectrogram of DON in the sewage; (c) performing preprocessing on the spectrogram data of the sewage sample, which is a step of subtracting background signal values, eliminating interference peaks, and removing noise signals from the detection spectrogram of DON in the sewage to obtain spectrogram data of the preprocessed sewage sample; (d) constructing a network relationship of substance reactions in sewage, which is a step of using reaction omics analysis technology to extract peaks from the spectrogram data of the sewage samples, performing hierarchical clustering of retention times, eliminating redundancy on the peaks, simplifying the spectrogram data of the sewage samples, establishing all the reaction mass difference relationships of the sewage samples, screening independent mass spectrum peaks in all the reaction mass difference relationships, constructing a high frequency mass difference matrix of substance reactions in the sewage samples, and combining the high frequency mass difference matrix and the substance reaction database to generate a network relationship diagram of substance reactions in sewage; (e) a step of screening the substance-reaction relationship of DON, which is a step of matching the mass spectrum peak in the network relationship diagram with the DON database to obtain the substance molecular formula corresponding to the mass spectrum peak, and screening the high-frequency mass difference relationship related to DON in the network relationship diagram to obtain the substance-reaction relationship of DON; (f) calculating the reaction relationship connectivity and reaction distance of each DON molecule in the substance-reaction relationship of DON, and distinguishing exogenous DON from endogenous DON according to the reaction relationship connectivity and reaction distance, thereby determining DON from different sources. A method for distinguishing between different sources of dissolved organic nitrogen in sewage, comprising:

2. The method for distinguishing soluble organic nitrogen from different sources in sewage, as described in claim 1, characterized in that the sewage samples include sewage samples from a single treatment unit in a municipal sewage treatment plant, a collection of sewage samples from multiple treatment units in a municipal sewage treatment plant, a collection of sewage samples from the full process of a municipal sewage treatment plant, and sewage samples from a municipal sewage treatment plant, the exogenous DON is DON that is not completely decomposed during sewage treatment, DON that cannot be decomposed in influent water, or DON that is difficult to biodegrade, and the endogenous DON is microbial DON, products of microbial conversion of exogenous DON, and DON by-products in the sewage treatment process.

3. In step (a), the solid-phase extraction column is a commercially available solid-phase extraction column with a styrene-divinylbenzene copolymer filler, and the pretreatment of the sewage sample is to filter it through a 0.45 μm acetate fiber filter membrane and then oxidize it with ACS-grade high-purity hydrochloric acid until the pH reaches 2. The activation of the column is then performed by filtering the solid-phase extraction column with 10-15 mL of LC-MS-grade methanol and 20-25 mL of ultrapure water with a pH of 2. The rinsing is performed with 20-25 mL of ultrapure water with a pH of 2. The method for distinguishing different sources of soluble organic nitrogen in sewage as described in claim 1, characterized in that the eluate is filtered through a solid-phase extraction column with 5 to 15 mL of LC-MS grade methanol, the liquid remaining in the solid-phase extraction column is removed until the eluate is dried with nitrogen gas, and then the solid-phase extraction column is filtered with 5 to 15 mL of LC-MS grade methanol, the filtration flow rate of the solid-phase extraction column is controlled at 0.5 to 2.0 mL / min, and the DON extract is filtered through a 0.22 μm organic filtration membrane until the DON extract is detected by the device and the concentration of soluble organic carbon therein is 50 to 100 mg / L.

4. The method for distinguishing dissolved organic nitrogen from different sources in sewage, as described in claim 1, characterized in that in step (b), the specifications of the liquid chromatography-high-resolution mass spectrometer are: a scan mode of positive ion and negative ion ionization mode, an ionization source of electrospray ionization, a flow rate of 0.2 to 0.5 mL / min, and a spectrogram collection range of 50 to 1200 Da.

5. The method for distinguishing different sources of dissolved organic nitrogen in sewage as described in claim 1, characterized in that in step (c), the method for obtaining background signal values ​​is to use ultrapure water as the sample to be treated, obtain the background signal by the same operations as in step (a) and step (b), subtract the background signal by peak intensity difference subtraction, and retain the mass spectrum peaks whose peak intensity after subtraction is at least 1000, and the spectrogram data of the sewage sample is ".mzrt" data converted by the "XCMS" package in R language.

6. In the step (d), the reaction omics analysis technology determines that the reactant DON and the reaction product DON corresponding to the substance reaction, and the mass difference between the reactant DON and the reaction product DON are determined in the entire process of the substance reaction in which DON participates during the biochemical reaction, and the process of the substance reaction is unknown, and the reactant DON, the reaction product DON, and the process of the substance reaction in which DON participates in the sample can be obtained according to the mass difference relationship; Removing redundancy from the peaks means removing excess mass spectral peaks in the spectrogram data of the sewage sample; The hierarchical clustering of the retention times was performed by calculating the relationship between the mass differences in the sewage samples using the "getpaired" function in the R language, and clustering the retention times using the recurrence formula (1). A D value of 10 or more was used as the cutoff distribution threshold. During the ceremony, RT ｍ , R.T. n： : Merged clusters of retention time data RT １： Calculated single retention time data D value: RT １ and the combined cluster of retention time data (RT ｍ , R.T. n ) distance The high frequency mass difference relationship is the mass difference relationship after subtracting the adducts, neutral losses, isotopes, and co-fragments in the mass difference relationship using the 'getstd' function in the R language. The high frequency mass difference matrix is ​​a substance reaction matrix in which mass spectrum peaks are used as matrix elements according to formula (2). During the ceremony, : Mass spectrum peak : Potential parent reactant of mass spectrum peak The substance reaction databases are the KEGG database and the HMDB database, The network diagram of the above-mentioned substance and reaction is a topology diagram in which the minimum mass difference between the substance and reaction is connected, the mass spectrum peaks are used as nodes, and the correlation value is at least 0.

6.

2. The method for distinguishing dissolved organic nitrogen from different sources in sewage according to claim 1.

7. The formula for the minimum mass difference is: During the ceremony, PMD BK is the minimum mass difference for material reactions : Mass spectrum peak : Potential parent reactant of mass spectrum peak 7. The method for distinguishing between different sources of dissolved organic nitrogen in sewage according to claim 6, wherein:

8. 2. The method for distinguishing dissolved organic nitrogen of different sources in sewage according to claim 1, wherein in step (e), the matching rules for the molecular formulas of substances corresponding to the mass spectrum peaks are: mass spectrum peak error < 5 ppm, retention time error < 2 min, relative molecular mass of DON molecules containing an even number of nitrogen atoms is even, and relative molecular mass of DON molecules containing an odd number of nitrogen atoms is odd, and the molecular formula matching score is > 90.

9. In step (f), the connectivity is the number of substance reactions in which each DON molecule participates in the substance-reaction relationship matrix; The reaction distance is the connection distance between DON molecules in the matrix of substance-reaction relationships, that is, the connection distance between adjacent matrices And the molecular connectivity is , the reaction distance of the DON molecule is where A is the adjacent matrix, is a descriptor of the relationship between DON molecule i and DON molecule j, N is the number of DON molecules, is the connectivity of DON molecule i, is the number of shortest connections between DON molecule i and DON molecule j, L is the average connection length of the substance-reaction relationship network of DON, The main cutoff value for the reaction-related connectivity and reaction distance is the median of the connectivity in the sewage samples, and the secondary cutoff value is the median of the reaction distance. DON molecules are classified as foreign DON molecules, The DON molecule is an endogenous DON molecule, among which is the median connectivity, is the median reaction distance 2. The method for distinguishing dissolved organic nitrogen from different sources in sewage according to claim 1.

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