A method for predicting DBPs produced by chlorine disinfection of algae organic matter using EEM-PARAFAC

Abstract

The invention belongs to the technical field of water quality analysis and detection, and discloses a method for predicting DBPs by using EEM-PARAFAC. The invention adopts 3D-EEM technology combined with PARAFAC to characterize algae organic matter, studies the change of three-dimensional fluorescence spectrum after chlorine disinfection, uses GC-ECD to analyze the generation of volatile DBPs after chlorine disinfection, studies the generation law of 6 kinds of DBPs, uses MATLAB software to separate and analyze the change of each fluorescent component before and after chlorine disinfection by PARAFAC, combines the fluorescence change and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and explores the feasibility of using EEM-PARAFAC to predict DBPs such as N-DBPs.

Description

Technical Field

[0001] The invention belongs to the technical field of water quality analysis and detection, and in particular relates to a method for predicting DBPs generated by chlorine disinfection of algae organic matter through EEM-PARAFAC. Background Art

[0002] Water is an indispensable and important substance in human life and production activities, and it is also an irreplaceable and important natural resource. Since the beginning of the 20th century, disinfection has been widely used to kill pathogens in drinking water, remove the color and odor of drinking water, and prevent the regrowth of organisms in water pipes, effectively preventing the occurrence and spread of waterborne infectious diseases. Due to the limitations of water treatment technology and processes, there will be a part of dissolved organic matter (DOM) in raw water that cannot be treated or remains after treatment. DOM mainly comes from the decay and decomposition of soil and plants and animals, and is an important component of natural water bodies. In terms of composition, DOM is a complex organic mixture, mainly including humus, protein and other aromatic or aliphatic organic compounds. They will react chemically with disinfectants during the disinfection process to generate new compounds, namely disinfection byproducts (DBPs). At present, more than 800 DBPs have been discovered. Toxicological studies have shown that most DBPs are cytotoxic and genotoxic. Therefore, DBPs have carcinogenic, teratogenic and mutagenic properties. The presence of DBPs in water poses a risk to human health. In 1974, Rook et al. first reported the detection of trihalomethanes (THMs) in drinking water, and subsequently discovered other carbon-containing DBPs (C-DBPs) such as haloacetic acids (HAAs). The abundance of DON in water promotes the outbreak of algae such as Microcystis aeruginosa. During the metabolism and death of algae, extracellular and intracellular organic matter are produced, resulting in an increased risk of generating nitrogen-containing DBPs (N-DBPs) after disinfection. Studies have shown that N-DBPs have higher cytotoxicity and genotoxicity than C-DBPs, so the generation of N-DBPs has attracted widespread attention from relevant researchers.

[0003] At present, the technologies used to study DBPs precursors are mainly the separate or integrated application of ultraviolet-visible spectroscopy (UV-vis), Fourier transform infrared spectroscopy (FTIR), size exclusion chromatography (SEC) and three-dimensional fluorescence spectroscopy (3D-EEM). The commonly used technologies for detecting DBPs that have been produced in water bodies mainly include total organic halogen analysis (TOX), gas chromatography (GC) related methods and liquid chromatography (LC) and mass spectrometry (MS) related methods. Among them, TOX can be used to characterize the overall content of organic halogens in samples, but the adsorption efficiency of some polar DBPs (such as HAAs) on activated carbon is very low, resulting in deviations. GC-related methods are suitable for detecting volatile and semi-volatile DBPs, while LC-related methods are ideal tools for analyzing polar DBPs or thermally unstable DBPs, but the chromatographic method has the inherent defects of cumbersome sample preparation and is not suitable for rapid and batch detection. Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM) has the advantages of convenient and rapid analysis, simple sample pretreatment for the characteristics of disinfection byproducts generated by algae organic matter after chlorination disinfection, and low cost. It is increasingly used in water quality analysis and detection. 3D-EEM can obtain fluorescence intensity information when the fluorescence excitation wavelength and emission wavelength change simultaneously. The obtained fluorescence information is relatively comprehensive. For systems with complex composition structures, the positions of various fluorescent groups in the EEMs matrix are different, so they can be effectively distinguished, identified and characterized. However, due to the complexity and diversity of the components in the water source, the fluorescence peaks are prone to fluorescence overlap and interference in spectral scanning, and weak fluorescence peaks are difficult to be identified. In recent years, the parallel factor method (PARAFAC) has been widely used in the analysis of DOM fluorescence characteristics. This method can not only identify and separate the fluorescence peaks that are interfered and overlapped in 3D-EEM, but also semi-quantitatively characterize the characteristics of each fluorescent component of DOM. Related studies have shown that the changes in the intensity of certain fluorescence peaks in the three-dimensional fluorescence spectrum have a certain linear relationship with the generation of THMs and HAAs in water bodies, but related studies have focused more on the relationship between C-DBPs and fluorescent components. The relationship between N-DBPs with higher cytotoxicity and genotoxicity and fluorescent components remains to be studied.

[0004] The current state of the art is to study the byproducts formed after chlorination disinfection of algae organic matter. However, this technology mainly uses mass spectrometry or gas chromatography for analysis, and does not utilize three-dimensional fluorescence spectroscopy and PARAFAC methods.

[0005] The technical problems existing in this prior art mainly include:

[0006] Limitations of analytical methods: Existing technologies mainly use mass spectrometry or gas chromatography for analysis. Although these methods can be used to determine the types of disinfection by-products, they are limited in determining the changes in algae organic matter during the disinfection process and the relationship between these changes and the generation of disinfection by-products. The chromatographic method has the inherent defects of cumbersome sample preparation and is not suitable for rapid and batch testing. Due to the complexity and diversity of the components in the water source, the fluorescence peaks are prone to fluorescence overlap and interference in spectral scanning, and weak fluorescence peaks are difficult to identify. The relationship between N-DBPs with higher cytotoxicity and genotoxicity and fluorescent components remains to be studied;

[0007] Incomplete information acquisition: The above methods mainly focus on disinfection byproducts, but fail to pay enough attention to and analyze the changes of algae organic matter before and after disinfection. This leads to an incomplete understanding of new or unknown byproducts generated during the disinfection process;

[0008] Lack of research on specific algae and organic matter: Existing technologies often fail to conduct in-depth analysis and research on specific algae (such as Microcystis aeruginosa) and specific organic matter (such as algae intracellular organic matter and extracellular organic matter), which limits the understanding of the disinfection process of algae organic matter and the generation of its by-products. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for predicting and analyzing disinfection by-products generated after chlorination disinfection of algae organic matter.

[0010] The present invention is implemented as follows: a method for predicting DBPs generated by chlorine disinfection of algae organic matter by EEM-PARAFAC comprises:

[0011] S1. Determine the three-dimensional fluorescence spectrum of algae organic matter before and after chlorine disinfection and the generation of DBPs after chlorine disinfection of algae organic matter;

[0012] S2. Use PARAFAC to separate the three-dimensional fluorescence spectra of algae organic matter before and after chlorine disinfection, and establish a fitting equation between the fluorescence intensity change value of each component and the DBPs concentration;

[0013] S3. Determine the change in fluorescence intensity of each component separated by fluorescence spectrum before and after chlorine disinfection of the algae solution to be tested, and use the result to predict the generation of DBPs in the algae solution to be tested by fitting equation.

[0014] The present invention uses PARAFAC to separate the fluorescent components of algae organic matter, analyzes the change of fluorescence intensity of each component, combines the change of fluorescence intensity and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and reliably predicts DBPs, especially nitrogen-containing DBPs, through EEM-PARAFAC.

[0015] Furthermore, the algae is Microcystis aeruginosa.

[0016] Furthermore, the algae organic matter is algae intracellular organic matter and algae extracellular organic matter.

[0017] Furthermore, the disinfection by-products include C-DBPs and N-DBPs, wherein C-DBPs include chloroform (TCM), trichloroacetaldehyde (CH) and trichloroacetone (TCP), and N-DBPs include dichloroacetonitrile (DCAN), trichloroacetonitrile (TCAN) and trichloropicrin (TCNM).

[0018] Furthermore, the three-dimensional fluorescence method was used to measure the changes in algae organic matter before and after disinfection, and the differences in the three-dimensional fluorescence spectra of extracellular organic matter and intracellular organic matter and the effect of chlorine addition on the three-dimensional fluorescence spectra of extracellular organic matter and intracellular organic matter were analyzed.

[0019] Furthermore, in step S1, by selecting algae organic matter of different concentrations, the effects of different chlorination conditions on the generation of DBPs after chlorine disinfection of the algae organic matter are analyzed.

[0020] Furthermore, extracellular organic matter and intracellular organic matter were selected as research objects, the initial TOC concentration and the amount of chlorine added were changed, the generation of volatile C-DBPs and N-DBPs by extracellular organic matter and intracellular organic matter after chlorination was analyzed, and the relationship between the type, concentration and amount of chlorine added of algae organic matter and the generation of the six DBPs detected was analyzed.

[0021] Further, in step S2, PARAFAC is used to separate the three-dimensional fluorescence spectra of algae organic matter before and after chlorine disinfection. Specifically, four fluorescence components are separated by parallel factor analysis PARAFAC, which are protein component C1 of the microbial product area, humus component C2, marine humic acid component C3 and amino acid component C4.

[0022] Furthermore, four fluorescent components were separated by PARAFAC, and the correlation between the intensity changes of each fluorescent component before and after disinfection and the concentrations of six DBPs after disinfection was analyzed, and a fitting equation for the fluorescence intensity changes of each component and the concentrations of six DBPs was constructed.

[0023] Furthermore, PARAFAC was used to separate the fluorescent components of algae organic matter, and the changes in fluorescence intensity of each component were analyzed. Combined with the fluorescence changes and the generation of DBPs, the correlation between spectral characteristics and DBPs was analyzed to verify the feasibility of using EEM-PARAFAC to predict DBPs, especially nitrogen-containing DBPs.

[0024] Another object of the present invention is to provide a system for predicting DBPs by EEM-PARAFAC, comprising:

[0025] Spectral measurement unit: used to measure the three-dimensional fluorescence spectrum of algae organic matter before and after chlorine disinfection and the generation of DBPs after chlorine disinfection of algae organic matter;

[0026] Analysis and processing unit: with PARAFAC algorithm, used to separate the three-dimensional fluorescence spectra of algae organic matter before and after chlorine disinfection and analyze the correlation between spectral characteristics and DBPs;

[0027] Prediction unit: Based on the results of the analysis and processing unit, combined with the fluorescence changes and the generation of DBPs, DBPs, especially nitrogen-containing DBPs, are predicted;

[0028] Storage unit: used to store the fluorescence spectrum data of algae organic matter, the generation data of DBPs and the prediction results.

[0029] Furthermore, the system for predicting DBPs through EEM-PARAFAC includes:

[0030] Spectral measurement unit: used to measure the changes of algae organic matter before and after disinfection using the three-dimensional fluorescence method, and to explore the differences in the three-dimensional fluorescence spectra of extracellular organic matter and intracellular organic matter;

[0031] Processing and analysis unit: equipped with the parallel factor analysis method PARAFAC, which can separate at least 4 fluorescent components, including protein component C1 in the microbial product area, humus component C2, marine humic acid component C3 and amino acid component C4;

[0032] Disinfection byproduct database: stores the relevant data of known C-DBPs and N-DBPs;

[0033] Prediction unit: combines the information from the processing and analysis unit with the disinfection byproduct database to predict the generation of DBPs;

[0034] Output interface: Display the prediction results in the form of charts or numbers.

[0035] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0036] First, the present invention uses MATLAB software to separate and analyze the changes of each fluorescent component before and after chlorine disinfection through PARAFAC, combines the fluorescence changes and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and verifies the feasibility of EEM-PARAFAC to predict DBPs, especially N-DBPs.

[0037] The present invention adopts 3D-EEM technology combined with PARAFAC to characterize algae organic matter, and studies the changes of three-dimensional fluorescence spectrum after chlorine disinfection, uses GC-ECD to analyze the generation of volatile DBPs after chlorine disinfection, studies the generation law of 6 DBPs, uses MATLAB software to separate and analyze the changes of each fluorescent component before and after chlorine disinfection through PARAFAC, combines the fluorescence change and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and explores the feasibility of EEM-PARAFAC to predict DBPs.

[0038] Due to the convenience, selectivity and sensitivity of fluorescence spectroscopy technology, the present invention uses PARAFAC to separate the fluorescent components of algae organic matter, analyzes the changes in fluorescence intensity of each component, combines the fluorescence changes and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and reliably predicts DBPs, especially N-DBPs, through EEM-PARAFAC.

[0039] Second, the technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0040] The problem of drinking water source pollution is faced both at home and abroad. Algae blooms are produced by algae outbreaks, which release a large amount of nitrogen-containing algae organic matter such as amino acids and proteins into the water body, resulting in an increase in the DON content in the water body. Due to the convenience, selectivity and sensitivity of fluorescence spectroscopy technology, the EEM-PARAFAC technology combined with parallel factors has not been reported for the more toxic N-DBPs in drinking water. Therefore, the present invention successfully uses the EEM-PARAFAC technology to separate the fluorescent components of algae organic matter and analyze the changes in the fluorescence intensity of each component, providing a fast and reliable prediction technology for DBPs such as N-DBPs.

[0041] Does the technical solution of the present invention solve the technical problems that people have been eager to solve but have never been able to solve successfully?

[0042] Previously, the EEM-PARAFAC technology combined with parallel factors was all about separating the fluorescent components of organic matter such as humic acid, analyzing the changes in the fluorescence intensity of each component, and exploring the generation of some of the most common DBPs (such as trihalomethanes). However, as the problem of serious pollution of nitrogen-containing algae organic matter is faced at home and abroad, and these nitrogen-containing algae organic matter will generate more toxic N-DBPs after disinfection after entering the drinking water source, the present invention is aimed at algae organic matter, using PARAFAC to separate its various fluorescent components, and analyzing the changes in the fluorescence intensity of each component, and reliably predicting DBPs, especially N-DBPs, through EEM-PARAFAC.

[0043] Third, from the experimental data of the present invention, it can be seen that, for example, among the extracellular organic matter, C1 has a strong correlation with DCAN, TCAN and TCNM (C1 is the component with the strongest correlation with DCAN and TCAN, with a correlation coefficient R 2 0.899 and 0.700, respectively), which shows that the intensity of C1 can well predict the generation of DCAN, TCAN and TCNM. In addition, C1 has a strong correlation with TCP in C-DBPs (C1 is the component with the strongest correlation with TCP R 2 0.957); Furthermore, in intracellular organic matter, C1 DCAN and TCAN have a strong correlation (and C1 is the component with the strongest correlation with TCAN R 2 0.755), while C4 has a strong correlation with DCAN and TCAN (and C4 is the component with the strongest correlation with DCAN R 2 is 0.825). Therefore, this technology can separate and analyze the changes of each algae organic matter fluorescent component before and after chlorine disinfection through PARAFAC, and quickly and reliably predict the generation of DBPs such as N-DBPs. This correlation coefficient R 2 It represents the fitting of different fluorescent substance intensities and different DBPs generated amounts. The closer it is to 1, the better the fitting effect and the better the prediction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a three-dimensional fluorescence spectrum diagram based on extracellular organic matter (a) and intracellular organic matter (b) provided by an embodiment of the present invention;

[0046] Figure 2 3D fluorescence spectra of extracellular organic matter after disinfection with different chlorine addition amounts provided by the embodiment of the present invention, (a) is a 3D fluorescence spectra of extracellular organic matter when the chlorine addition amount is 0, (b), (c), (d), (e) and (f) respectively represent Cl 2 : Three-dimensional fluorescence spectra after disinfection under conditions of TOC of 1, 1.5, 2, 2.5 and 3;

[0047] Figure 33D fluorescence spectra of intracellular organic matter after disinfection based on different chlorine addition amounts of intracellular organic matter provided by an embodiment of the present invention, (a) is a 3D fluorescence spectra of intracellular organic matter when the chlorine addition amount is 0, (b), (c), (d), (e) and (f) are 3D fluorescence spectra after disinfection under the conditions of Cl2:TOC of 1, 1.5, 2, 2.5 and 3, respectively;

[0048] Figure 4 It is a three-dimensional fluorescence spectrum diagram before and after the removal of Raman and Rayleigh scattering based on extracellular organic matter provided by an embodiment of the present invention;

[0049] Figure 5 is a gas chromatogram of a standard sample provided by an embodiment of the present invention;

[0050] Figure 6 The fluorescence spectra and Ex / Em loadings of the four fluorescent components (C1, C2, C3, C4) of PARAFAC provided in the embodiments of the present invention;

[0051] Figure 7 is the fluorescence intensity of the four fluorescence components of extracellular organic matter and intracellular organic matter PARAFA provided by the embodiment of the present invention;

[0052] Figure 8 The fluorescence intensity of the four fluorescent components of PARAFA after different chlorination amounts of extracellular organic matter (a) and intracellular organic matter (b) provided in the embodiment of the present invention;

[0053] Fig. 9 It is a diagram showing the generation of disinfection byproducts of extracellular organic matter and intracellular organic matter after chlorination provided by an embodiment of the present invention;

[0054] Fig.10 The embodiment of the present invention provides the generation of disinfection by-products after chlorination based on different chlorine addition amounts of extracellular organic matter (a) and intracellular organic matter (b);

[0055] Fig.11 The embodiment of the present invention provides the correlation between the four fluorescent components of extracellular organic matter PARAFAC and TCM concentration;

[0056] Fig.12 The embodiment of the present invention provides the correlation between the four fluorescence components of extracellular organic matter PARAFAC and CH concentration;

[0057] Fig.13 The embodiment of the present invention provides the correlation between the four fluorescent components of extracellular organic matter PARAFAC and TCP concentration;

[0058] Fig.14 The embodiment of the present invention provides the correlation between the four fluorescent components of extracellular organic matter PARAFAC and the DCAN concentration;

[0059] Fig.15 The embodiment of the present invention provides the correlation between the four fluorescent components of extracellular organic matter PARAFAC and the TCAN concentration;

[0060] Fig.16 The embodiment of the present invention provides the correlation between the four fluorescent components of extracellular organic matter PARAFAC and TCNM concentration;

[0061] Fig.17 The embodiment of the present invention provides the correlation between the four fluorescent components of intracellular organic matter PARAFAC and TCM concentration;

[0062] Fig.18 The embodiment of the present invention provides the correlation between the four fluorescent components of intracellular organic matter PARAFAC and CH concentration;

[0063] Fig.19 The embodiment of the present invention provides the correlation between the four fluorescent components of intracellular organic matter PARAFAC and TCP concentration;

[0064] Fig. 20 The embodiment of the present invention provides the correlation between the four fluorescent components of intracellular organic matter PARAFAC and the DCAN concentration;

[0065] Fig.21 The embodiment of the present invention provides the correlation between the four fluorescent components of intracellular organic matter PARAFAC and the TCAN concentration; DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] In view of the problems existing in the prior art, the present invention provides a method for predicting DBPs by using EEM-PARAFAC.

[0068] The method for predicting DBPs by EEM-PARAFAC includes: measuring the three-dimensional fluorescence spectrum of algae organic matter before and after chlorine disinfection and the generation of DBPs after chlorine disinfection of algae organic matter, using PARAFAC to separate the three-dimensional fluorescence spectrum of algae organic matter before and after chlorine disinfection and analyzing the correlation between the spectral characteristics and DBPs, establishing a fitting equation for the fluorescence intensity change value of each component and the DBPs concentration, and predicting the DBPs generation of the algae solution to be tested by measuring the fluorescence intensity change of each component separated by the fluorescence spectrum before and after chlorine disinfection of the algae solution to be tested, and bringing it into the fitting equation.

[0069] Furthermore, the algae is Microcystis aeruginosa.

[0070] Furthermore, the organic matter is algae intracellular organic matter and extracellular organic matter.

[0071] Furthermore, the disinfection byproducts C-DBPs include chloroform (TCM), trichloroacetaldehyde (CH), dichloroacetone (DCP) and trichloroacetone (TCP), and N-DBPs include dichloroacetonitrile (DCAN), trichloroacetonitrile (TCAN) and trichloropicrin (TCNM).

[0072] Furthermore, the three-dimensional fluorescence method was used to measure the changes in algae organic matter before and after disinfection, to explore the differences in the three-dimensional fluorescence spectra of extracellular and intracellular organic matter and the effect of chlorine addition on the three-dimensional fluorescence spectra of extracellular and intracellular organic matter.

[0073] Furthermore, by selecting algae organic matter with different concentrations, the effects of different chlorination conditions on the generation of DBPs after chlorine disinfection of algae organic matter were explored.

[0074] Furthermore, four fluorescent components were separated by PARAFAC, and the correlation between the intensity changes of each fluorescent component before and after disinfection and the concentrations of six DBPs after disinfection was analyzed.

[0075] Furthermore, four fluorescent components were separated by parallel factor analysis method PARAFAC, namely protein component C1 in the microbial product area, humus component C2, marine humic acid component C3 and amino acid component C4.

[0076] Furthermore, extracellular organic matter and intracellular organic matter were selected as research objects, the initial TOC concentration and the amount of chlorine added were changed, the generation of volatile C-DBPs and N-DBPs by extracellular organic matter and intracellular organic matter after chlorination was analyzed, and the relationship between the type, concentration and amount of chlorine added of algae organic matter and the generation of the 6 DBPs detected was explored.

[0077] Furthermore, PARAFAC was used to separate the fluorescent components of algae organic matter, and the changes in fluorescence intensity of each component were analyzed. Combined with the fluorescence changes and the generation of DBPs, the correlation between spectral characteristics and DBPs was analyzed to verify the feasibility of using EEM-PARAFAC to predict DBPs, especially N-DBPs.

[0078] In order to prove the creativity and technical value of the technical solution of the present invention, this section provides application examples of the claimed technical solution on specific products or related technologies.

[0079] The Microcystis aeruginosa used in the present invention was purchased from the Institute of Hydrobiology, Chinese Academy of Sciences, and cultured using BG11 medium, the components of which are shown in Table 1. The components of the BG11 medium were weighed as needed, configured into five 100 mL mother solutions, and stored at 4°C after high temperature sterilization. For each 1L of algae solution, 10 mL of Stock 1 mother solution and 1 mL of the remaining mother solutions were taken, and 1 mol / L HCl or NaOH solution was added to adjust the pH to 7. The algae solution was cultured in a constant temperature incubator at a culture temperature of (25±1)°C, a light intensity of , a humidity of , a light-dark cycle of 12h, and the position of the algae solution was adjusted daily and the bottle was shaken to prevent algae cell precipitation and aggregation.

[0080] Table 1

[0081]

[0082] Measure OD using a UV-Vis spectrophotometer 680 Calculate the algal density using a microscope and a hemocytometer and establish the OD 680 The algal density was monitored by periodically measuring OD680 to obtain a linear relationship with the number of cells counted by microscopy, and algal solutions in the logarithmic growth phase were selected.

[0083] Extraction of extracellular organic matter from algae: Pour the algae solution in the logarithmic growth phase into a centrifuge tube, separate the algae cells and extracellular organic matter with a centrifuge at a speed of 8000rpm for 10 minutes, then use a syringe to take out the supernatant, and use a polyethersulfone (PES) syringe filter with a pore size of 0.45μm to filter the supernatant. The filtrate is the mother solution of extracellular organic matter, which is a transparent or light green solution. The filtrate is stored at 4°C in the dark, and its TOC concentration is measured before use, and diluted to the target concentration.

[0084] Extraction of intracellular organic matter from algae: rinse the algae cells remaining at the bottom of the centrifuge tube after centrifugation with ultrapure water, then add an equal volume of ultrapure water to remix the algae cells, centrifuge again and pour out the supernatant, rinse the algae cells, add ultrapure water to mix and centrifuge again, repeat the above steps 5 times to ensure that no extracellular organic matter remains. The algae solution obtained was repeatedly frozen and thawed 3 times at -18°C and room temperature, then centrifuged and the supernatant was filtered. The filtrate was the mother liquor of intracellular organic matter, which was a light blue solution. The filtrate was stored at 4°C away from light. Its TOC concentration was determined using a total organic carbon analyzer before disinfection and diluted to the target concentration.

[0085] The present invention adopts 3D-EEM technology combined with PARAFAC to characterize algae organic matter, and studies the changes in three-dimensional fluorescence spectra after chlorine disinfection, uses GC-ECD to analyze the generation of volatile DBPs after chlorine disinfection, studies the generation rules of 6 types of DBPs, uses MATLAB software to separate and analyze the changes of each fluorescent component before and after chlorine disinfection through PARAFAC, combines fluorescence changes and the generation of DBPs, analyzes the correlation between spectral characteristics and DBPs, and explores EEM-PARAFAC for reliable prediction of DBPs such as N-DBPs. Based on the present invention, the generation of DBPs such as N-DBPs in drinking water can be quickly predicted, and the generation of these highly toxic N-DBPs can be predicted in advance, providing a reliable warning effect for the potential threat of DBPs in drinking water to human health.

[0086] Specifically, a three-dimensional fluorescence spectrometer was used to measure the three-dimensional fluorescence spectra of algae extracellular organic matter and algae intracellular organic matter at a TOC concentration of 10 mgC / L under different chlorine addition conditions (see Figure 2 , 3 );

[0087] The chlorine disinfection process is as follows: wrap the blue-capped bottle containing the target concentration of algae organic matter solution with tin foil, adjust the algae organic matter solution to pH 6.8 with 1 mol / L NaOH solution and HCl solution, and add phosphate buffer solution (0.01 mol / L). According to the calculation, quickly add a certain amount of sodium hypochlorite solution, then oscillate in the same direction for a certain number of times, and place it in a biochemical incubator for incubation and disinfection at a temperature of 20°C. After the reaction is completed, take 10 mL of the sample to determine the residual chlorine, add an appropriate amount of sodium sulfite solution according to the calculation, and then oscillate in the same direction for a certain number of times to quench the residual chlorine. The sample part after quenching the residual chlorine is used for DBPs detection, and the rest is used for spectral determination.

[0088] The three-dimensional fluorescence spectrum was measured using a LF-1704005 fluorescence spectrometer (Thermo Fisher Scientific) at a temperature of 25°C. The instrument parameters were set as follows: scanning speed of 1200 nm / min, excitation wavelength range of 220-450 nm, excitation wavelength interval of 5 nm, emission wavelength range of 220-600 nm, emission wavelength interval of 1 nm, slit width of excitation and emission monochromators of 5 nm, photomultiplier tube (PMT) voltage of 700 V, ultrapure water (resistivity >18.2 MΩcm-1) as blank, and a 1 cm quartz cuvette for fluorescence measurement within the wavelength range, and the ultrapure water blank was deducted from the measurement results.

[0089] Ideally, the fluorescence intensity should increase linearly with the increase of the concentration of the fluorescent substance. However, due to the inner-filter effect of fluorescence (IFE), the growth of the fluorescence intensity will gradually deviate from the linearity. The present invention corrects the IFE by measuring the ultraviolet absorption of the sample using the formula.

[0090] The obtained fluorescence spectra require data preprocessing, because the EEM of all water samples contains Raman and Rayleigh scattering, which do not represent the fluorescence peaks of organic matter. Under constant conditions, each fluorophore has only one emission and one excitation wavelength, but scattering is different. For each excitation wavelength, the scattered light will change its emission characteristics. Therefore, for each type of scattering, an additional component needs to be set when determining the number of fluorescent components to model Raman scattering and Rayleigh scattering. Therefore, Raman and Rayleigh scattering need to be removed before PARAFAC. In addition, since the region where the emission wavelength is less than the excitation wavelength cannot produce a fluorescence peak, but is interfered by background noise, the fluorescence intensity of this part is set to 0 to facilitate PARAFAC modeling. The three-dimensional fluorescence spectrum of extracellular organic matter before and after removing Raman and Rayleigh scattering is shown below. Figure 4 .

[0091] DBPs determination: DBPs were determined by gas chromatography, referring to USEPA 551.1 standard method, and the results are shown in Fig.10 In this determination, the gas chromatogram of the standard sample was obtained by using the standard sample of DBPs, see Figure 5 By measuring DBPs standard samples with a certain concentration gradient, the relationship between gas chromatographic peak area and DBPs standard sample concentration is established, and a working curve is drawn.

[0092] Too small a component number in PARAFAC may lead to the loss of key fluorescence information, while too large a component number may make it difficult to separate the components. The EvalModel function is used on MATLAB to implement the 3-component model and the 4-component model and the residuals. In the residuals of the 3-component model, obvious peaks can still be seen, indicating that some fluorescence information is not reflected in the model. In the residuals of the 4-component model, there is only background noise and no obvious fluorescence peak. It can be determined that the minimum number of components that fully reflect the fluorescence information of the sample is 4. The components are protein component C1 in the microbial product area, humus component C2, marine humic acid component C3 and amino acid component C4. The fluorescence component analysis results of the embodiment of the present invention are shown in Figure 6-9 .

[0093] The fitting equation was obtained based on the changes in the fluorescence intensity of each fluorescent component before and after disinfection and the six DBPs concentrations (see Figure 11-21), and then measure the fluorescence intensity changes of each component separated by fluorescence spectrum before and after chlorine disinfection of the algae solution to be tested, and bring them into the fitting equation, which can effectively predict the DBPs generation after chlorine disinfection of the algae solution.

[0094] The embodiments of the present invention have achieved some positive effects during the development or use process, and indeed have great advantages over the prior art. The following content is described in conjunction with data, charts, etc. of the test process.

[0095] Fluorescence characteristics of algae organic matter

[0096] The separated extracellular organic matter and intracellular organic matter were diluted to the same TOC concentration and disinfected under the same conditions. The EEM spectra of the extracellular organic matter and intracellular organic matter before and after disinfection were measured. The results are as follows: Figure 1 shown.

[0097] Fluorescence characteristics of extracellular organic matter

[0098] Figure 1(a) is the EEM spectrum of extracellular organic matter with a TOC concentration of 10 mgC / L. The results in the figure show that the EEM spectrum of extracellular organic matter has two relatively high fluorescence peaks in the range of Em = 300-380 nm. The peak at a smaller excitation wavelength is obtained by overlapping two peaks, which are respectively located in region I (Ex / Em = 220-250 / 280-330 nm, representing simple tyrosine substances) and region II (Ex / Em = 220-250 / 330-380 nm, representing tryptophan substances) reported by Chen et al. Among the common amino acids, tryptophan, tyrosine and phenylalanine have relatively high fluorescence quantum efficiencies and can be directly detected in the solution. The relative fluorescence intensities of the three amino acids are 200:20:1, so the weaker tyrosine fluorescence peak is masked by the tryptophan fluorescence peak. The peak at a larger excitation wavelength is region IV (Ex / Em=250-300 / 280-380nm, representing soluble microbial products or protein substances). Some studies have also divided the fluorescence peak in this region into two, namely fluorescence peak B (Ex / Em=275 / 305nm) and fluorescence peak T (Ex / Em=275 / 340nm), where fluorescence peak B represents tyrosine-like substances and fluorescence peak T represents tryptophan-like substances 6. This fluorescence peak is related to the metabolites secreted by cells. The word "like" is added before the two amino acid fluorescence components because the amino acids present in actual water samples are often not free monomers, but more in the form of bound amino acids, polypeptides and even protein molecules. Other groups in the molecular structure will affect their energy levels through energy interactions, often causing the emission wavelength positions of these fluorophores to be blue-shifted (shortened wavelength) or red-shifted (longer wavelength) compared with their monomers. Fluorescence peaks B and T should be bound amino acids or proteins such as tyrosine and tryptophan. In addition, a fluorescence peak with both excitation and emission wavelengths greater than region IV can be seen, and its intensity is less than the two amino acid peaks and protein peaks. It should be region V (Ex / Em=300-400 / 380-480nm, representing humic acid substances) mentioned in previous studies, or the long-wavelength humic acid or macromolecular humic substance fluorescence peak C. There is also a fluorescence peak at a position with an emission wavelength less than region V, which is considered to be region III

[0099] (Ex / Em=250-300 / 380-480nm), or fluorescence peak A, is related to fulvic acid or small molecular humus. Region III and Region V, or fluorescence peak A and fluorescence peak C, are produced by the decomposition of algae organic matter and dead algae cells by bacteria. Their lower response values ​​indicate that the algae cells grow well and there are fewer bacteria in the algae solution.

[0100] The present invention provides fluorescence characteristic analysis of intracellular organic matter.

[0101] Figure 1 (b) is the EEM spectrum of intracellular organic matter with a TOC concentration of 10 mgC / L. The results in the figure show that the EEM spectrum of intracellular organic matter also has obvious amino acid and protein fluorescence peaks, but the intensity of region II or fluorescence peak T in intracellular organic matter is not as good as that of extracellular organic matter, and region I or fluorescence peak B does not exist or has very low intensity. During the freeze-thaw process, substances inside algal cells are released, and their components include amino acids, algal toxins, and phycocyanin, resulting in rich aromatic amino acids in intracellular organic matter. In addition, the intensity of region III or fluorescence peak A and region V or fluorescence peak C in the EEM spectrum of intracellular organic matter is also not as good as that of extracellular organic matter. The humus substances in intracellular organic matter come from bacterial degradation and organic matter destroyed during the freeze-thaw process. The lower fluorescence intensity indicates that the intracellular organic matter is less affected by bacteria and the degree of destruction during the freeze-thaw process is small.

[0102] The invention provides an analysis of the generation characteristics of algae organic matter disinfection by-products.

[0103] In order to study the generation of DBPs in extracellular and intracellular organic matter after chlorine disinfection, the differences in the types and concentrations of the six DBPs studied were compared. The separated extracellular and intracellular organic matter were diluted to the same TOC concentration and disinfected under the same conditions. The results are shown in Figure 2. Fig.10 shown.

[0104] The main DBPs produced after disinfection of extracellular organic matter and intracellular organic matter are TCM and CH, both of which are C-DBP. Among them, TCM has the highest concentration and far exceeds other DBPs, and its production is higher in disinfected extracellular organic matter, reaching a concentration (μg / L). The concentration of aliphatic disinfection byproducts generated by algae organic matter after chlorination disinfection is 190.17μg / L, and the TCM production of intracellular organic matter after disinfection is slightly lower, but also reaches 176.56μg / L. The phenomenon that the THMFP of extracellular organic matter per unit concentration after chlorination is greater than that of intracellular organic matter after chlorination is similar to the results of previous studies. In terms of chemical structure, intracellular organic matter contains more abundant hydroxyl and amide groups and has higher chlorine substitution activity, while extracellular organic matter contains more abundant carboxyl groups, which is not conducive to substitution reaction with chlorine. However, in more studies, when the amount of chlorine added and the reaction time are large enough, the THMFP of intracellular organic matter per unit concentration is greater than the THMFP of extracellular organic matter. The reason for this difference is different chlorination conditions. In these two studies, bromide ions are involved, which affects the THMFP of algal organic matter. If only TCMFP is considered, the TCMFP of extracellular organic matter is still higher than that of intracellular organic matter. In addition, the unit THMFP of extracellular and intracellular organic matter will decrease when the algal cell culture time is longer, but the degree of decrease of intracellular organic matter is greater. At 25 days of culture, the unit THMFP of the two is equal. In this study, the culture time of the algal solution was about 30 days, and there was no bromide ion involved. It was observed that the THMFP of extracellular organic matter per unit concentration after chlorination was greater than that of intracellular organic matter after chlorination.

[0105] The concentration of CH in the chlorinated algae organic matter is not much different, with a concentration of 49.31 μg / L in the extracellular organic matter and 53.69 μg / L in the intracellular organic matter. It is worth noting that as a C-DBP, the production of CH in the chlorinated algae organic matter is greater than that in NOM. In the present invention, the CH concentration of the chlorinated algae organic matter is higher.

[0106] The distribution of HKs in algae organic matter after chlorination varies greatly, among which DCP has a concentration of 2.94 μg / L in extracellular organic matter after chlorination and 0.47 μg / L in intracellular organic matter after chlorination. TCP cannot be detected in extracellular organic matter after chlorination and has a concentration of 3.38 μg / L in intracellular organic matter after chlorination. Studies have found that TCP is easily degraded under pH 7, and the products include TCM and CH. The degradation of HKs occurs more rapidly in the presence of free chlorine. Considering Fig. 9 The corresponding disinfection conditions are: the TOC concentration of algae organic matter is 10mgC / L, the chlorine addition amount is 30mg / L (in terms of Cl 2=Record), that is, the amount of chlorine added: TOC = 3:1. After 18 hours of disinfection, the residual chlorine of extracellular organic matter is 11.80 mg / L, and the residual chlorine of intracellular organic matter is 7.10 mg / L. Therefore, the lower HKs concentration is due to its hydrolysis under the disinfection condition of pH about 7.

[0107] In HANs, the concentration of DCAN in extracellular organic matter after chlorination was 5.36 μg / L, and the concentration in intracellular organic matter was 28.60 μg / L. The concentration of DCAN in algae organic matter after chlorination was much higher than that in NOM after chlorination. Studies have shown that nitrogen-containing organic matter in water bodies is an important precursor of N-DBP. The rich amino acids in algae organic matter are conducive to the generation of DCAN, a characteristic of aliphatic disinfection by-products generated by algae organic matter after chlorination disinfection. Among them, the ability of tryptophan and tyrosine to generate DCAN by chlorination ranks among the top among many amino acids. The phenomenon that extracellular organic matter and intracellular organic matter after chlorination have different concentrations of DCAN can be seen in many reports, which is due to the differences in the composition of extracellular organic matter and intracellular organic matter. From the perspective of molecular weight, DCAN comes more from the chlorination of large molecular weight algae organic matter. Extracellular organic matter contains more proteins in low molecular weight components and more polysaccharides in high molecular weight components, while intracellular organic matter contains more proteins in high molecular weight components and more polysaccharides in low molecular weight components. Therefore, the ability of extracellular organic matter to generate DCAN is not as good as that of intracellular organic matter. From the perspective of composition, intracellular organic matter contains more free amino acids, while extracellular organic matter contains more fatty amines, so intracellular organic matter contains richer DCAN precursors.

[0108] The content of TCAN in algae organic matter after chlorination is very low, with a concentration of 0.45 μg / L in extracellular organic matter after chlorination and 0.70 μg / L in intracellular organic matter. TCAN is an unstable DBP. Studies have found that the concentrations of TCAN and DCAN have a certain correlation, and the amount of TCAN generated is greatly affected by pH. Under the condition of pH of about 7 in the present invention, the generation of TCAN is inhibited. The concentration of TCNM in extracellular organic matter after chlorination is greater than that in intracellular organic matter after chlorination, which are 1.39 μg / L and 0.44 μg / L respectively. The precursor of TCNM is nitrogen-containing hydrophilic organic matter, and hydrophobic acid has an inhibitory effect on the generation of TCNM. Studies have shown that the main components of extracellular organic matter have better hydrophilicity, while intracellular organic matter has higher hydrophobicity. In addition, studies have shown that fatty amines also generate TCNM under chlorine disinfection conditions. Therefore, extracellular organic matter contains more TCNM precursors, which is consistent with the chlorine disinfection results of the present invention.

[0109] According to the fitting equation, DBPs, especially nitrogen-containing DBPs, can be reliably predicted by EEM-PARAFAC. Figure 11-21) shows that, for example, among the extracellular organic matter, C1 has a strong correlation with DCAN, TCAN, and TCNM (C1 is the component with the strongest correlation with DCAN and TCAN, R 2 0.899 and 0.700, respectively), which shows that the intensity of C1 can well predict the generation of DCAN, TCAN and TCNM. In addition, C1 has a strong correlation with TCP in C-DBPs (C1 is the component with the strongest correlation with TCP R 2 0.957); Furthermore, C1DCAN and TCAN have a strong correlation in intracellular organic matter (and C1 is the component with the strongest correlation with TCAN R 2 0.755), while C4 has a strong correlation with DCAN and TCAN (and C4 is the component with the strongest correlation with DCAN R 2 is 0.825).

[0110] Embodiment 1:

[0111] 1. Collection and treatment of algae organic matter: First, collect Microcystis aeruginosa from the environment, and separate the algae intracellular organic matter and extracellular organic matter by centrifugation and other methods.

[0112] 2. Chlorination of algae organic matter: The algae organic matter is then reacted with chlorine to perform disinfection under controlled conditions.

[0113] 3. Determination of three-dimensional fluorescence spectrum of algae organic matter: Determination of three-dimensional fluorescence spectrum of algae organic matter before and after chlorination disinfection to obtain its spectral data.

[0114] 4. Use the PARAFAC method to process and analyze the spectral data: Use the PARAFAC method to separate and analyze the obtained three-dimensional fluorescence spectra and study the correlation between the spectral characteristics and the generation of DBPs.

[0115] Embodiment 2:

[0116] 1. Collection and treatment of algae organic matter: First, collect Microcystis aeruginosa from different water bodies, and obtain the algae intracellular organic matter and extracellular organic matter through ultrasonic disruption and other methods.

[0117] 2. Chlorination treatment of algae organic matter: Then, the algae organic matter is reacted with chlorine solutions of different concentrations for disinfection treatment.

[0118] 3. Three-dimensional fluorescence spectrum measurement of algae organic matter: The three-dimensional fluorescence spectrum of algae organic matter before and after chlorination disinfection is measured to obtain its spectral characteristics.

[0119] 4. Use the PARAFAC method to process and analyze the spectral data: Use the PARAFAC method to separate and analyze the obtained three-dimensional fluorescence spectra to study the spectral characteristics and the generation of DBPs.

[0120] 5. Determination of generated DBPs: Finally, the generated disinfection by-products were identified and determined by gas chromatography-mass spectrometry to further verify the accuracy of the analysis results.

[0121] The above two embodiments provide specific implementation schemes for exploring the characteristics of aliphatic disinfection by-products generated after chlorination disinfection of algae organic matter.

[0122] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for predicting DBPs produced by chlorine disinfection of algae organic matter by EEM-PARAFAC, characterized in that: The method comprises: S1. Determine the three-dimensional fluorescence spectrum of algae organic matter before and after chlorine disinfection and the generation of DBPs after chlorine disinfection of algae organic matter; the algae is Microcystis aeruginosa, the algae organic matter is algae intracellular organic matter and algae extracellular organic matter; the DBPs include C-DBPs and N-DBPs, wherein C-DBPs include chloroform, trichloroacetaldehyde and trichloroacetone, and N-DBPs include dichloroacetonitrile, trichloroacetonitrile and trichloropicrin; by selecting algae organic matter of different concentrations, analyze the effect of different chlorination conditions on the generation of DBPs after chlorine disinfection of algae organic matter; select extracellular organic matter and intracellular organic matter as research objects, change the initial TOC concentration and chlorine addition amount, analyze the generation of volatile C-DBPs and N-DBPs by extracellular organic matter and intracellular organic matter after chlorination, and explore the relationship between the type, concentration and chlorine addition amount of algae organic matter and the generation of 6 DBPs detected; S2. Separate the three-dimensional fluorescence spectra of algae organic matter before and after chlorine disinfection using PARAFAC; Separate the three-dimensional fluorescence spectra of algae organic matter before and after chlorine disinfection using PARAFAC specifically as follows: 4 fluorescence components are separated using parallel factor analysis PARAFAC, namely protein component C1 in the microbial product area, humus component C2, marine humic acid component C3 and amino acid component C4; establish a fitting equation for the change value of the fluorescence intensity of each component and the DBPs concentration; S3. Determine the change in fluorescence intensity of each component separated from the fluorescence spectrum of the algae solution to be tested before and after chlorine disinfection, and use the fitting equation to predict the generation of DBPs in the algae solution to be tested; use the three-dimensional fluorescence method to determine the changes in algae organic matter before and after disinfection, analyze the differences in the three-dimensional fluorescence spectra of extracellular organic matter and intracellular organic matter, and the effect of chlorine addition on the three-dimensional fluorescence spectra of extracellular organic matter and intracellular organic matter; use PARAFAC to separate the various fluorescent components of algae organic matter, and analyze the changes in the fluorescence intensity of each component. Combined with the changes in fluorescence intensity and the generation of DBPs, analyze the correlation between spectral characteristics and DBPs, and predict DBPs through EEM-PARAFAC; analyze the correlation between the intensity changes of each fluorescent component of algae organic matter before and after disinfection and the concentrations of 6 DBPs after disinfection; use PARAFAC to separate the various fluorescent components of algae organic matter, and analyze the changes in the fluorescence intensity of each component. Combined with the fluorescence changes and the generation of DBPs, analyze the correlation between spectral characteristics and DBPs, and verify the feasibility of using EEM-PARAFAC to predict DBPs.

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