Method, device and medium for determining content of organic pollutants in industrial wastewater

Abstract

The application discloses a kind of industrial wastewater organic pollutant content determination method, equipment and medium, it is related to environmental analysis monitoring technical field, comprising: collection industrial wastewater water sample data, time series calibration is carried out to industrial wastewater water sample data, and wastewater time series dataset is generated;Through collaborative extraction enrichment method, the water sample of wastewater time series dataset is enriched to organic pollutant, and organic phase extraction dataset is generated;Calibration chromatogram mass spectrum data is matched with mass spectrum comparison in two dimensions, and each organic pollutant component is identified and peak area data is extracted, and pollutant characteristic dataset is generated;The content of each pollutant in pollutant characteristic dataset is normalized calculation and time change relation is arranged, and pollutant content scheme is generated.The application improves the accuracy and stability of industrial wastewater organic pollutant content determination.

Description

Technical Field

[0001] This invention relates to the field of environmental analysis and monitoring technology, and in particular to a method, equipment and medium for determining the content of organic pollutants in industrial wastewater. Background Technology

[0002] With the continuous expansion of industrial production scale and the increasingly complex emission structures of industries such as chemical, pharmaceutical, and dyeing, the types and composition of organic pollutants in industrial wastewater exhibit characteristics of multi-component, high complexity, and dynamic changes. Accurate determination of organic pollutant content has gradually become an important research direction in the fields of water pollution detection, environmental monitoring, and pollution control. Existing methods typically employ gas chromatography-mass spectrometry (GC-MS) or high-performance liquid chromatography (HPLC) for qualitative and quantitative analysis of organic pollutants in industrial wastewater, combined with single sampling and pretreatment processes to detect target components. Some methodologies also introduce enrichment techniques such as solid-phase extraction and liquid-liquid extraction to improve the detection sensitivity of low-concentration pollutants. Simultaneously, component identification is achieved through retention time matching and mass spectrometry fragment information comparison, thereby completing the calculation and assessment of pollutant content.

[0003] Faced with the dynamic evolution of organic pollutants in industrial wastewater over time, existing methods are mostly based on single-time or discrete sampling data for analysis, lacking a systematic way to characterize the continuous changes in pollutant content over time. Especially in complex multi-component systems, the detection data corresponding to different sampling times often lack a unified time benchmark and correlation expression, making it difficult to achieve effective alignment and continuous integration of multiple time series data when performing content analysis and trend determination. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for determining the content of organic pollutants in industrial wastewater, which solves the problem of difficulty in unifying time alignment and continuous correlation expression of multi-time series detection data.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for determining the content of organic pollutants in industrial wastewater, comprising: collecting industrial wastewater sample data; performing time-series calibration on the industrial wastewater sample data to generate a wastewater time-series dataset; enriching the wastewater samples in the time-series dataset with organic pollutants using a synergistic extraction enrichment method to generate an organic phase extraction dataset; performing full two-dimensional gas chromatography separation and mass spectrometry detection on the organic phase extraction dataset to generate a chromatographic mass spectrometry dataset; dynamically correcting the positions of each component in the chromatographic mass spectrometry dataset and maintaining a stable two-dimensional correspondence using a multidimensional retention calibration method to generate calibration chromatographic mass spectrometry data; performing two-dimensional retention time matching and mass spectrometry comparison on the calibration chromatographic mass spectrometry data to identify each organic pollutant component and extract peak area data to generate a pollutant characteristic dataset; and normalizing the content of each pollutant in the pollutant characteristic dataset and organizing the time-varying relationships to generate a pollutant content scheme.

[0007] As a preferred embodiment of the method for determining the organic pollutant content in industrial wastewater according to the present invention, the specific steps for collecting industrial wastewater sample data and performing time-series calibration on the industrial wastewater sample data to generate a wastewater time-series dataset are as follows: Collect industrial wastewater sample data and record sampling identification information simultaneously. Perform unified time format conversion and time sequence arrangement on the industrial wastewater sample data to generate time-sorted sample data. Time anomalies in time-sorted water sample data are corrected item by item to obtain time-corrected water sample data. The time-corrected water sample data is then combined with sampling identification information and time correction is performed to generate time-standardized water sample data. Time-normalized water sample data are mapped onto a time axis and written to the corresponding time position one by one. The water sample record data at each time position are continuously arranged and combined to generate a wastewater time series dataset.

[0008] As a preferred embodiment of the method for determining the organic pollutant content in industrial wastewater according to the present invention, the specific steps for enriching organic pollutants in water samples from a wastewater time-series dataset using a synergistic extraction enrichment method are as follows: Target water sample data is extracted from the wastewater time series dataset, and the target water sample data is subjected to homogeneity determination and impurity identification and filtering to generate pretreated water sample data. The acidity of the pretreated water sample data was adjusted and salting-out values ​​were assigned using a synergistic extraction enrichment method. The state was updated and homogeneity was checked according to the mixing rules to generate salting-out acidified water sample data. Based on the salting-out acidified water sample data, the extractant ratio parameters are loaded and the mixing state is calculated and the stratification state is determined to generate the initial extraction mixing data. The initial extraction mixture data is jointly identified and the stratified regions are determined to obtain the organic phase. Supplementary extraction determination and merging rules are then applied to the organic phase to generate merged extract data.

[0009] As a preferred embodiment of the method for determining the content of organic pollutants in industrial wastewater according to the present invention, the generation of organic phase extraction dataset refers to the process of dehydrating and concentrating the merged extract data to obtain concentrated extract data, and recording the concentrated extract data according to time location and sampling identification information.

[0010] As a preferred embodiment of the method for determining the content of organic pollutants in industrial wastewater according to the present invention, the specific steps for performing full two-dimensional gas chromatography separation and mass spectrometry detection on the organic phase extraction dataset to generate a chromatographic mass spectrometry dataset are as follows: Read the concentrated extract data from the organic phase extraction dataset item by item according to the time position, and perform quantitative sampling and injection packaging of the concentrated extract data to generate the test injection liquid data. The sample data to be tested is injected splitlessly into a first-dimensional chromatographic column for temperature-programmed separation, generating one-dimensional separation flow data. The one-dimensional separation flow data is thermally modulated, cut, and focused to obtain modulated slice flow data. Based on the modulated slice flow data, it is entered into a second-dimensional chromatographic column for rapid separation, and simultaneously imported into a mass spectrometer for ionization and mass scanning to generate chromatographic mass spectrometry response data. The initial chromatographic mass spectrometry response data are jointly judged, screened and organized to obtain organized response data. The organized response data are then recorded according to time location and sampling identification information to generate a chromatographic mass spectrometry dataset.

[0011] As a preferred embodiment of the method for determining the content of organic pollutants in industrial wastewater according to the present invention, the step of dynamically correcting the positions of each component in the chromatographic mass spectrometry data and maintaining the stability of the two-dimensional correspondence through a multidimensional retention calibration method to generate calibration chromatographic mass spectrometry data is as follows: Using a multidimensional retention calibration method, the one-dimensional retention time, two-dimensional retention time, and ion intensity of each peak are extracted from the chromatographic mass spectrometry dataset, and then aligned and matched with the ion characteristics in chronological order to generate component localization response data. Import the component location response data into the standard reference component and establish a two-dimensional preserved mapping relationship; Based on the two-dimensional preserved mapping relationship, the two-dimensional position offset in the component positioning response data is jointly characterized and position compensation is performed to generate position correction data; The position correction data is subjected to stability constraints and anomaly removal to obtain two-dimensional stable corresponding data. The two-dimensional stable corresponding data is recorded accordingly to generate calibration chromatography-mass spectrometry data.

[0012] As a preferred embodiment of the method for determining the content of organic pollutants in industrial wastewater according to the present invention, the specific steps of performing two-dimensional retention time matching and mass spectrometry comparison on the calibration chromatographic mass spectrometry data, identifying each organic pollutant component and extracting peak area data to generate a pollutant feature dataset are as follows: Read the two-dimensional stable corresponding data in the calibration chromatography-mass spectrometry data item by item according to the time position, and extract the first-dimensional retention time, the second-dimensional retention time and the ion intensity distribution from the two-dimensional stable corresponding data to generate matching candidate data; Two-dimensional retention time matching and mass spectrometry comparison are performed on the candidate matching data to generate pollutant identification data; Based on pollutant identification data, the two-dimensional retention region is located in the matching candidate data, and the chromatographic peaks in the two-dimensional retention region are integrated and recorded to generate a pollutant feature dataset.

[0013] As a preferred embodiment of the method for determining the content of organic pollutants in industrial wastewater according to the present invention, the specific steps for normalizing the content of each pollutant in the pollutant characteristic dataset and organizing the time-varying relationships to generate a pollutant content scheme are as follows: Read the peak area data in the pollutant characteristic dataset item by item according to the time position, and extract the peak area value and internal standard peak area value of each pollutant from the peak area data to generate effective peak area data. The effective peak area data is converted into a ratio to obtain relative content data. The relative content data is then normalized to generate normalized content data. The content changes in the normalized content data are correlated, organized, and continuously arranged to obtain the content time series data. The content time series data are recorded accordingly to generate a pollutant content scheme.

[0014] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the method for determining the content of organic pollutants in industrial wastewater as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for determining the content of organic pollutants in industrial wastewater as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: it achieves effective migration and centralized separation of organic pollutants in complex wastewater through a synergistic extraction and enrichment step, and achieves unified alignment and position correction of chromatographic and mass spectrometry data in two-dimensional space through a multi-dimensional retention calibration step. At the same time, it combines time series calibration, full two-dimensional separation and detection, and normalization processing to complete the continuous expression and correlation of pollutant content, thereby forming a complete analytical link from sampling to content characterization for water pollution detection, which improves the accuracy and stability of the determination of organic pollutant content in industrial wastewater. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the generation of a wastewater time-series dataset.

[0019] Figure 2 This diagram illustrates the generation of datasets for synergistic extraction enrichment and organic phase extraction.

[0020] Figure 3 This is a schematic diagram of chromatography-mass spectrometry detection and calibration identification.

[0021] Figure 4 A schematic diagram for generating pollutant content schemes. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4As one embodiment of the present invention, this embodiment provides a method for determining the content of organic pollutants in industrial wastewater, comprising the following steps: S1. Collect industrial wastewater sample data, perform time series calibration on the industrial wastewater sample data, and generate wastewater time series dataset.

[0026] S1.1 Collect industrial wastewater sample data and record sampling identification information simultaneously. Perform unified time format conversion and time sequence arrangement on the industrial wastewater sample data to generate time-sorted sample data.

[0027] It should be noted that data from each batch of industrial wastewater samples during the industrial wastewater discharge process should be collected, and the sampling time, sampling location, sampling batch, and sample number corresponding to the sampling identification information should be recorded at the same time. Different time representations in the industrial wastewater sample data should be uniformly converted into the same time format, and the industrial wastewater sample data should be arranged item by item according to the converted time sequence, so that each industrial wastewater sample data corresponds to a clear time position, generating time-sorted sample data.

[0028] S1.2. Correct time anomalies in the time-sorted water sample data item by item, obtain time-corrected water sample data, combine the time-corrected water sample data with the sampling identification information and perform time correction to generate time-normalized water sample data.

[0029] It should be noted that the time missing, time duplicate, time jump, and record misalignment in the time-sorted water sample data are checked item by item, and the abnormal time positions are corrected according to the time interval relationship between adjacent records and the sampling order relationship to obtain time-corrected water sample data. The time-corrected water sample data is combined with the corresponding sampling identification information item by item, and time correction is performed on records with inconsistent identification and time to ensure that the time position of each record is consistent with the sampling identification information, thus generating time-normalized water sample data.

[0030] S1.3. Map the time-normalized water sample data to a time axis and write each item to the corresponding time position. Arrange and combine the water sample record data at each time position continuously to generate a wastewater time series dataset.

[0031] It should be noted that a continuous time axis is established based on the time records in the time-normalized water sample data, and the time-normalized water sample data is mapped to the corresponding time position item by item. After the time axis mapping is completed, the water sample record data at each time position is arranged continuously according to the chronological relationship, and the related water sample record data within the same time period is combined and organized to form a continuous organizational relationship in the time dimension of the water sample record data, thereby generating a wastewater time-series dataset.

[0032] It should also be noted that the wastewater time series dataset is used to unify industrial wastewater sample data from different sampling times onto a continuous time axis and establish a correlation between them, so that the sample data can form a traceable continuous expression in the time dimension. This provides a consistent data foundation for the enrichment, separation, detection and content change analysis of organic pollutants, and improves the accuracy and consistency of multi-time series data processing.

[0033] S2. Organic pollutants are enriched in water samples from the wastewater time series dataset using a collaborative extraction enrichment method to generate an organic phase extraction dataset.

[0034] S2.1 Extract target water sample data from the wastewater time series dataset, and perform homogeneity determination and impurity identification and filtering on the target water sample data to generate pretreated water sample data.

[0035] It should be noted that, based on the time location and sampling identification information, the corresponding records in the wastewater time series dataset are located item by item and the target water sample data is extracted. During the extraction process, multiple records at the same time location are aligned and matched to ensure consistency of source. The uniformity of the distribution of each component in the target water sample data is determined by analyzing the differences in concentration distribution, particle density, and phase stability in different regions of the liquid to judge the degree of mixing consistency. Water samples with continuous concentration fluctuations and no obvious stratification trend are judged as uniform. After completing the uniformity determination, suspended impurities, sedimentary impurities, and non-target particulate matter in the target water sample data are identified item by item. Different types of impurities are classified and labeled by particle size distribution, density differences, and sedimentation behavior. Based on the labeling results, corresponding filtration treatment is performed to remove non-liquid phase interference components. After filtration treatment, the phase partitioning capacity of the remaining liquid phase is evaluated. By detecting the dissolution distribution, acidification response characteristics, and salting-out sensitivity of the target organic pollutants in the liquid phase, it is determined whether the liquid phase has the conditions to migrate to the organic phase in the subsequent co-extraction and enrichment process. The liquid phase that meets the migration and stratification requirements is retained to generate pretreated water sample data.

[0036] S2.2. The acidity of the pretreated water sample data is adjusted and salting-out values ​​are assigned by the synergistic extraction and enrichment method, and the state update and homogeneity verification are performed according to the mixing rules to generate salting-out acidified water sample data.

[0037] It should be noted that the processing of pretreated water sample data using the synergistic extraction enrichment method requires the addition of acidity adjustment components to the pretreated water sample data to achieve a predetermined acidification state. Salting-out components are then added to the acidified pretreated water sample data to enhance the driving conditions for the migration of organic pollutants into the organic phase. During the addition process, the liquid mixing state is continuously updated according to the mixing rules, and the uniformity of the mixed distribution is verified to ensure that the acidification and salting-out processes form a stable and consistent treatment state in the pretreated water sample data, generating salting-out acidified water sample data.

[0038] It should also be noted that the mixing rules are operational specifications that uniformly constrain the order of addition, proportion, mixing method, and mixing duration of acidity adjustment components and salting-out components in pretreated water sample data. They are set by combining the physicochemical characteristics of the target pollutants, the relationship of solubility changes, and the phase distribution behavior to ensure that each component reaches a stable dispersion state during the mixing process and meets the subsequent extraction and stratification conditions.

[0039] The synergistic extraction enrichment method is a treatment method that uses acidity adjustment and salting-out to synergistically change the distribution behavior of target organic pollutants between the aqueous and organic phases. It combines various extractant ratios and mixing conditions to enable the target components to migrate efficiently from the aqueous phase to the organic phase and be concentrated in the organic phase, thereby achieving the effective enrichment and separation of low-concentration organic pollutants in complex wastewater.

[0040] The predetermined acidification state is determined based on the dissociation form and phase distribution characteristics of the target organic pollutants under different acidity conditions. By analyzing the changes in the pollutants' transformation from ionic to non-ionic states and their migration ability between the aqueous and organic phases, an acidity range favorable for migration is selected. This acidity range is derived from the acidity response patterns in the pollutant's physicochemical parameters, historical detection data, and standard reference data, and is further constrained by the requirements of subsequent synergistic extraction on stratification stability and migration efficiency.

[0041] S2.3. Based on the salting-out acidified water sample data, load the extractant ratio parameters and perform mixing state calculation and stratification state determination to generate the initial extraction mixing data.

[0042] It should be noted that, based on the salting-out acidified water sample data, the corresponding extractant ratio parameters were retrieved, and each extractant was added to the salting-out acidified water sample data in sequence according to the ratio. During the addition process, the order of addition of each extractant, the color change of the liquid layer after addition, the local turbidity changes, and the response differences between the upper and lower regions were recorded item by item. The dispersion equilibrium of the liquid phase mixture after addition was calculated. By comparing the component response distribution, concentration fluctuation amplitude, and particle or droplet distribution in different locations of the mixture, it was determined whether each component had achieved uniform dispersion in the liquid phase. This was combined with the response of the target organic pollutant between the upper and lower regions. Based on the migration situation, enrichment changes near the interface, and distribution changes in adjacent time periods, the migration activity of the target organic pollutant between the aqueous and organic phases is determined, and the mixing state calculation results are obtained. After obtaining the mixing state calculation results, the interface boundary position, interface clarity, interface thickness changes, and component distribution differences on both sides of the interface are analyzed item by item in conjunction with the changes in the liquid phase interface. When the upper and lower liquid layers are clearly separated and the target organic pollutant is concentrated in one side of the liquid layer, it is determined that a stratified state has been formed, and the initial extraction mixing data containing the preliminary distribution relationship between the aqueous and organic phases is generated accordingly.

[0043] S2.4. Perform joint identification and stratification of the initial extraction mixture data to obtain the organic phase, and perform supplementary extraction determination and merging rules on the organic phase to generate merged extract data.

[0044] It should be noted that when jointly identifying different liquid layer regions in the initial extraction mixture data, the aqueous phase layer, transition interface layer, and organic phase layer are first distinguished based on the spatial position of each liquid layer in the container, the change in liquid layer thickness, the clarity of the interface, and the differences in phase distribution between the upper and lower layers. The liquid layer located in the upper or lower part and corresponding to the distribution characteristics of the organic phase is determined as the layered region where the organic phase is located, and the organic phase is separated and obtained from the initial extraction mixture data. After obtaining the organic phase, the remaining liquid phase is subjected to the determination of the target organic pollutants to be migrated. By detecting the residual distribution state of the target organic pollutants in the remaining liquid phase, the degree of binding stability with the current liquid phase, and the possibility of continued migration to the organic phase after the addition of extractant, a comprehensive analysis is conducted. When there are still target organic pollutants in the remaining liquid phase that are continuously distributed, have not been fully migrated, and have the ability to be re-partitioned, it is determined that the conditions for supplementary extraction are met. After the conditions for supplementary extraction are met, the remaining liquid phase is subjected to extraction treatment again, and the organic phase obtained by supplementation is summarized and organized according to the merging rules of consistent time and location, consistent sampling identification, and consistent liquid phase properties to generate merged extraction liquid data.

[0045] S2.5. Dehydrate and concentrate the merged extract data to obtain concentrated extract data, and record the concentrated extract data according to time location and sampling identification information to generate an organic phase extraction dataset.

[0046] It should be noted that a dehydration medium was added to the combined extract data to remove residual water, and the combined extract data was concentrated after dehydration to concentrate organic pollutants in a smaller volume. After concentration, a volume adjustment operation was performed to obtain concentrated extract data with a clear volume-concentration relationship. The concentrated extract data was recorded item by item according to the time position and sampling identification information to ensure that the concentrated extract data is consistent with the time sequence of the preceding wastewater, thus generating an organic phase extraction dataset.

[0047] It should also be noted that the organic phase extraction dataset is a collection of data consisting of organic phase extracts after co-extraction enrichment, stratification, and dehydration concentration, along with their corresponding time locations and sampling identification information. The organic phase extraction dataset is used to centrally store the enrichment results of organic pollutants in industrial wastewater and to provide a stable and consistent injection basis for subsequent full two-dimensional gas chromatography separation and mass spectrometry detection, thereby improving the sensitivity of pollutant detection and the comparability of data.

[0048] S3. Perform full two-dimensional gas chromatography separation and mass spectrometry detection on the organic phase extraction dataset to generate a chromatographic mass spectrometry dataset.

[0049] S3.1 Read the concentrated extract data in the organic phase extraction dataset item by item according to the time position, and perform quantitative sampling and injection packaging of the concentrated extract data to generate the test injection liquid data.

[0050] It should be noted that the concentrated extract data in the organic phase extraction dataset is read item by item according to the time position, and a quantitative volume of the analyte is extracted from the concentrated extract data according to the detection requirements; the extracted analyte is subjected to injection and packaging treatment so that the analyte is ready to enter the full two-dimensional gas chromatography separation and mass spectrometry detection process, and the analyte injection data is generated.

[0051] S3.2. Perform splitless injection of the sample solution to be tested into the first-dimensional chromatographic column for programmed temperature separation to generate one-dimensional separation flow data.

[0052] It should be noted that after the sample data is imported into the injection channel, the connection between the injection channel and the front end of the first-dimensional chromatographic column is first stabilized to ensure continuous transmission of the sample data before entering the separation process. Splitless injection is then performed to ensure that all sample data enters the first-dimensional chromatographic column without sample bypass dispersion, thus guaranteeing that all organic pollutant components in the sample data participate in subsequent separation in an intact state. After the sample data enters the first-dimensional chromatographic column, the temperature change process within the column is controlled in stages according to the programmed temperature rise conditions. This method allows different components to migrate at different speeds within the chromatographic column based on their differences in volatility, boiling point, and interaction strength with the stationary phase. As the temperature continues to change, the retention times of each component in the first-dimensional chromatographic column gradually increase, and the eluted and subsequent eluted components form a continuous development relationship in time sequence, maintaining the corresponding peak distribution state during elution. By continuously recording and sequentially correlating the component fragments eluted at each time position, one-dimensional separation flow data that can characterize the sequential separation process, elution order, and elution state of each component in the first-dimensional chromatographic column is obtained.

[0053] It should also be noted that the preset temperature program is a combination setting of the initial temperature value, heating rate, stage residence time and termination temperature of the first-dimensional chromatographic column during the separation process. It is determined by combining the volatility difference, boiling point distribution and stationary phase characteristics of the target organic pollutants, so that each component will elute in sequence according to its retention characteristics during the heating process and form an effective separation, thereby ensuring the stability and resolution of subsequent modulation cutting and two-dimensional separation.

[0054] S3.3. Perform thermal modulation cutting and focusing on the one-dimensional separation flow data to obtain modulated slice flow data. Based on the modulated slice flow data, enter the second-dimensional chromatographic column for rapid separation and simultaneously import it into the mass spectrometer for ionization and mass scanning to generate chromatographic mass spectrometry response data.

[0055] It should be noted that the one-dimensional separation flow data is continuously thermally modulated and sliced ​​according to a preset modulation period. The fluid flowing through the modulation region within the same modulation period is divided into multiple fluid slices with clear boundaries in chronological order. Focusing processing is performed on each fluid slice to compress the slice width and increase the concentration of components, making it an input form suitable for rapid separation. The modulated slice flow data is sequentially fed into a second-dimensional chromatographic column. Utilizing different retention relationships, each component is further separated in a short time based on polarity differences and adsorption characteristics. After the second-dimensional separation output, each component is simultaneously introduced into the mass spectrometry detection channel for ionization and mass scanning to obtain ion response information. Finally, the first-dimensional time position, the second-dimensional separation position, and the mass spectrometry response information are correlated to generate chromatographic-mass spectrometry response data.

[0056] It should also be noted that the chromatographic mass spectrometry response data is used to uniformly characterize the positional distribution information of each organic pollutant in the two-dimensional chromatographic separation process and the ion response characteristics obtained by mass spectrometry detection. By correlating and recording the first-dimensional time position, the second-dimensional separation position and the corresponding ion intensity, the spatial positioning and mass characteristics of each component in the separation process are expressed synchronously, thereby providing consistent data basis for subsequent multidimensional retention calibration, component identification and peak area extraction, and improving the accuracy and stability of qualitative and quantitative analysis of pollutants.

[0057] S3.4. Perform joint judgment and screening on the initial chromatographic mass spectrometry response data, obtain the sorted response data, and record the sorted response data according to the time position and sampling identification information to generate a chromatographic mass spectrometry dataset.

[0058] It should be noted that the response peaks, background interference signals, and abnormal scanning fragments in the initial chromatographic mass spectrometry response data are jointly judged, and the initial chromatographic mass spectrometry response data are screened and organized according to the completeness of the response, the validity of the peak shape, and the continuity of the scan. The organized response data that meets the subsequent multidimensional retention calibration conditions are retained. Then, the organized response data are recorded according to the time position and sampling identification information to generate a chromatographic mass spectrometry dataset with time sequence correspondence.

[0059] It should also be noted that the chromatographic mass spectrometry dataset is a structured representation of the two-dimensional retention time and ion response information formed after the organic phase extraction dataset is separated by full two-dimensional gas chromatography and detected by mass spectrometry. The chromatographic mass spectrometry dataset is used to simultaneously characterize the separation position and mass characteristics of each organic pollutant, providing a unified basis for subsequent multidimensional retention calibration, component identification and peak area extraction, thereby improving the accuracy and stability of qualitative and quantitative analysis of pollutants.

[0060] S4. Through a multidimensional retention calibration method, the positions of each component in the chromatographic mass spectrometry dataset are dynamically corrected and the two-dimensional correspondence is kept stable to generate calibration chromatographic mass spectrometry data.

[0061] S4.1. Using a multidimensional retention calibration method, the one-dimensional retention time, two-dimensional retention time, and ion intensity of each peak are extracted from the chromatographic mass spectrometry dataset, and then aligned and matched with the ion characteristics in chronological order to generate component localization response data.

[0062] It should be noted that, through the multidimensional retention calibration method, the first-dimensional retention time, the second-dimensional retention time, and the ion intensity are extracted peak by peak from the chromatographic mass spectrometry dataset, and the responses of each peak are arranged in chronological order. After the chronological order is completed, the responses of similar peaks are aligned and matched item by item according to the ion characteristics, so that the two-dimensional position and ion response relationship of the same component in different detection records are uniformly expressed, and component localization response data are generated.

[0063] It should also be noted that the multidimensional retention calibration method is a processing method for jointly characterizing and dynamically correcting the positional shifts of each component in the first and second dimensions of retention time in chromatographic mass spectrometry data. It establishes a two-dimensional mapping relationship by introducing a standard reference component and performs alignment compensation and stability constraints on the position of each component to ensure consistency and comparability of the two-dimensional separation positions, thereby improving the accuracy of component identification and subsequent quantitative analysis.

[0064] S4.2 Import the component location response data into the standard reference component and establish a two-dimensional retention mapping relationship.

[0065] It should be noted that after importing the component localization response data into the standard reference component, the first-dimensional and second-dimensional retention positions corresponding to each reference peak are read from the standard reference component, and the two-dimensional positions of each reference peak are combined as two-dimensional reference coordinates. The responses of each peak in the component localization response data are read in chronological order, and the first-dimensional retention position, second-dimensional retention position, and ion intensity distribution of each peak are extracted. First, in the first dimension, the peak to be calibrated is compared item by item with the first-dimensional retention position of the standard reference component to screen out candidate reference peaks within adjacent retention intervals. Then, in the second dimension, the second-dimensional retention position of the peak to be calibrated is compared with the second-dimensional retention position of the corresponding candidate reference peak. The process involves comparing each item to determine the proximity of the peak to be calibrated in the second dimension. After completing the comparisons in both dimensions, the positional correspondences of the same peak to be calibrated in the first and second dimensions are jointly correlated to determine the corresponding landing point of the peak to be calibrated relative to the standard reference component in the two-dimensional space. Subsequently, based on the sequential distribution of multiple reference peaks in the two-dimensional space, a two-dimensional positional connection relationship is established between the peak to be calibrated and adjacent reference peaks, so that each peak to be calibrated simultaneously obtains both the first and second dimension positional references, ultimately forming a two-dimensional retention mapping relationship that can jointly characterize the correspondence between the first and second dimension retention times.

[0066] It should also be noted that the two-dimensional retention mapping relationship is used to describe the corresponding positional relationship of each component in the two separation dimensions of the first-dimensional retention time and the second-dimensional retention time. It is achieved by jointly aligning the first-dimensional retention position and the second-dimensional retention position of the same component in different detection records, and establishing a mapping based on a standard reference component. This allows each component to form a unique and stable localization expression in the two-dimensional space, thereby simultaneously constraining the one-dimensional volatility separation feature and the two-dimensional polarity separation feature, ensuring the consistency of the positional association of each component in the two dimensions, and providing a unified reference for subsequent position compensation and component identification.

[0067] S4.3. Based on the two-dimensional preserved mapping relationship, the two-dimensional position offset in the component positioning response data is jointly characterized and position compensation is performed to generate position correction data.

[0068] It should be noted that, based on the two-dimensional preserved mapping relationship, the first-dimensional position offset and the second-dimensional position offset of each component in the component positioning response data are jointly characterized, and position compensation is performed on the offset position based on the joint characterization result; during the position compensation process, the two-dimensional coordinate correspondence is adjusted synchronously so that each component is close to the reference position in both the first and second dimensions after compensation, thereby generating position correction data.

[0069] S4.4. Perform stability constraints and anomaly removal on the position correction data, obtain two-dimensional stable corresponding data, record the two-dimensional stable corresponding data, and generate calibration chromatography-mass spectrometry data.

[0070] It should be noted that stability constraints are applied to the position correction data to filter out peak records with discontinuous shifts, unstable responses, and abnormal two-dimensional correspondences, while retaining valid peak records with stable two-dimensional positional relationships and continuous responses, thus obtaining two-dimensional stable correspondence data. The two-dimensional stable correspondence data are recorded according to time position and sampling identification information to ensure that the calibrated two-dimensional component information remains complete and traceable, generating calibration chromatography-mass spectrometry data.

[0071] S5. Perform two-dimensional retention time matching and mass spectrometry comparison on the calibration chromatographic mass spectrometry data, identify each organic pollutant component and extract peak area data to generate a pollutant feature dataset.

[0072] S5.1 Read the two-dimensional stable corresponding data in the calibration chromatography-mass spectrometry data item by item according to the time position, and extract the first dimension retention time, the second dimension retention time and the ion intensity distribution from the two-dimensional stable corresponding data to generate matching candidate data.

[0073] It should be noted that the two-dimensional stable corresponding data in the calibration chromatography-mass spectrometry data are read item by item according to the time position, and the first-dimensional retention time, second-dimensional retention time and ion intensity distribution corresponding to each component are extracted from the two-dimensional stable corresponding data. After the extraction is completed, the extracted retention information and ion information are organized according to the component candidate relationship to generate matching candidate data for subsequent identification processing.

[0074] S5.2 Perform two-dimensional retention time matching and mass spectrometry comparison on the candidate matching data to generate pollutant identification data.

[0075] It should be noted that the first and second dimension retention times in the candidate data are compared item by item with the corresponding retention time ranges in the target pollutant reference information. The positional deviation of the candidate data in the two-dimensional retention space is calculated, and it is determined whether the deviation falls within the preset allowable range. Candidate components with corresponding relationships in the two-dimensional position are screened out. After completing the two-dimensional retention time matching, the ion intensity distribution corresponding to the screened candidate components is compared peak by peak with the standard ion distribution in the target pollutant reference information. By analyzing the occurrence position, relative intensity ratio, and spectral morphology consistency of characteristic ions, it is determined whether they meet the mass spectrometry matching requirements. Subsequently, the two-dimensional retention time matching results and the mass spectrometry comparison results are jointly judged. Only when the candidate component meets the identification conditions in both two-dimensional position matching and ion distribution consistency are the organic pollutant category and specific component identity of the candidate component determined. The identification results are then associated with their corresponding time position and sampling identification information to generate pollutant identification data.

[0076] S5.3 Based on the pollutant identification data, locate the two-dimensional retention region in the matching candidate data, and perform integral calculation and corresponding recording of the chromatographic peaks in the two-dimensional retention region to generate a pollutant feature dataset.

[0077] It should be noted that, based on pollutant identification data, the corresponding two-dimensional retention region is located in the matching candidate data, and the chromatographic peak corresponding to the target pollutant is extracted within the two-dimensional retention region; integral calculation is performed on the located chromatographic peak to obtain peak area data that can characterize the pollutant response intensity, and the peak area data is recorded in correspondence with pollutant identification information, time location and sampling identification information to generate a pollutant feature dataset.

[0078] S6. Normalize the content of each pollutant in the pollutant feature dataset and organize the time change relationship to generate a pollutant content scheme.

[0079] S6.1 Read the peak area data in the pollutant characteristic dataset item by item according to the time position, and extract the peak area value corresponding to each pollutant and the internal standard peak area value from the peak area data to generate effective peak area data.

[0080] It should be noted that the peak area data in the pollutant characteristic dataset is read item by item according to the time position, and the peak area value and internal standard peak area value corresponding to each pollutant are extracted from the peak area data respectively. During the extraction process, the area records corresponding to invalid peaks, abnormal peaks and missing peaks are screened out or marked, and the effective area information that can be used for subsequent ratio conversion is retained to generate effective peak area data.

[0081] S6.2. Perform ratio conversion on the effective peak area data to obtain relative content data, and perform normalization processing on the relative content data to generate normalized content data.

[0082] It should be noted that the peak area values ​​of each pollutant in the effective peak area data are converted to the peak area values ​​of the internal standard to eliminate the response differences between different injection records, so as to obtain the relative content data reflecting the relative levels of each pollutant; the relative content data are normalized so that the content expression of different pollutants at different time positions has a unified basis for comparison, and normalized content data is generated.

[0083] The expression for converting effective peak area data into ratios is as follows: ; in, For the first Pollutants in time and location The corresponding relative content values; For the first Pollutants in time and location The corresponding peak area value; For the same time location The corresponding internal standard peak area value; For the same time location The corresponding background interference peak area value is obtained by identifying and integrating the baseline signal and non-characteristic ion response of the region corresponding to the target pollutant in the chromatographic mass spectrometry data; For the numbering index of pollutant components; This is the index of the corresponding time location.

[0084] S6.3. Correlate and arrange the content changes in the normalized content data to obtain content time series data, record the content time series data accordingly, and generate a pollutant content scheme.

[0085] It should be noted that the content changes of each pollutant at different time positions in the normalized content data are correlated and organized, and the normalized content data are continuously arranged in chronological order to form content time series data that can characterize the pollutant change process; the content time series data and corresponding time positions and sampling identification information are recorded item by item to fully express the content evolution relationship of each pollutant and generate a pollutant content scheme.

[0086] This embodiment also provides a computer device applicable to the method for determining the content of organic pollutants in industrial wastewater, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for determining the content of organic pollutants in industrial wastewater as proposed in the above embodiment.

[0087] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0088] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for determining the content of organic pollutants in industrial wastewater as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0089] In summary, this invention achieves effective migration and centralized separation of organic pollutants in complex wastewater through a synergistic extraction and enrichment step, and achieves unified alignment and position correction of chromatographic and mass spectrometry data in two-dimensional space through a multi-dimensional retention calibration step. At the same time, it combines time series calibration, full two-dimensional separation and detection, and normalization processing to complete the continuous expression and correlation of pollutant content, thereby forming a complete analytical link from sampling to content characterization for water pollution detection, improving the accuracy and stability of the determination of organic pollutant content in industrial wastewater.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for determining the content of organic pollutants in industrial wastewater, characterized in that, include: Collect industrial wastewater sample data, perform time series calibration on the industrial wastewater sample data, and generate wastewater time series dataset; Organic pollutants were enriched in water samples from a wastewater time-series dataset using a collaborative extraction enrichment method, generating an organic phase extraction dataset. The organic phase extraction dataset was subjected to full two-dimensional gas chromatography separation and mass spectrometry detection to generate a chromatographic mass spectrometry dataset. By using a multidimensional retention calibration method, the positions of each component in the chromatographic mass spectrometry dataset are dynamically corrected while maintaining a stable two-dimensional correspondence, thereby generating calibrated chromatographic mass spectrometry data. By performing two-dimensional retention time matching and mass spectrometry comparison on the calibration chromatographic mass spectrometry data, the components of each organic pollutant are identified and peak area data is extracted to generate a pollutant characteristic dataset. The pollutant content in the pollutant feature dataset is normalized and its time variation is analyzed to generate a pollutant content scheme.

2. The method for determining the content of organic pollutants in industrial wastewater as described in claim 1, characterized in that, The process of collecting industrial wastewater sample data, performing time-series calibration on the industrial wastewater sample data, and generating a wastewater time-series dataset is as follows: Collect industrial wastewater sample data and record sampling identification information simultaneously. Perform unified time format conversion and time sequence arrangement on the industrial wastewater sample data to generate time-sorted sample data. Time anomalies in time-sorted water sample data are corrected item by item to obtain time-corrected water sample data. The time-corrected water sample data is then combined with sampling identification information and time correction is performed to generate time-standardized water sample data. Time-normalized water sample data are mapped onto a time axis and written to the corresponding time position one by one. The water sample record data at each time position are continuously arranged and combined to generate a wastewater time series dataset.

3. The method for determining the content of organic pollutants in industrial wastewater as described in claim 2, characterized in that, The method of synergistic extraction enrichment is used to enrich organic pollutants in water samples from a wastewater time-series dataset. The specific steps are as follows: Target water sample data is extracted from the wastewater time series dataset, and the target water sample data is subjected to homogeneity determination and impurity identification and filtering to generate pretreated water sample data. The acidity of the pretreated water sample data was adjusted and salting-out values ​​were assigned using a synergistic extraction enrichment method. The state was updated and homogeneity was checked according to the mixing rules to generate salting-out acidified water sample data. Based on the salting-out acidified water sample data, the extractant ratio parameters are loaded and the mixing state is calculated and the stratification state is determined to generate the initial extraction mixing data. The initial extraction mixture data is jointly identified and the stratified regions are determined to obtain the organic phase. Supplementary extraction determination and merging rules are then applied to the organic phase to generate merged extract data.

4. The method for determining the content of organic pollutants in industrial wastewater as described in claim 3, characterized in that, The generation of the organic phase extraction dataset refers to the process of dehydrating and concentrating the merged extract data to obtain concentrated extract data, and recording the concentrated extract data according to time location and sampling identification information.

5. The method for determining the content of organic pollutants in industrial wastewater as described in claim 1, characterized in that, The organic phase extraction dataset is subjected to full two-dimensional gas chromatography separation and mass spectrometry detection to generate a chromatographic mass spectrometry dataset. The specific steps are as follows: Read the concentrated extract data from the organic phase extraction dataset item by item according to the time position, and perform quantitative sampling and injection packaging of the concentrated extract data to generate the test injection liquid data. The sample data to be tested is injected splitlessly into a first-dimensional chromatographic column for temperature-programmed separation, generating one-dimensional separation flow data. The one-dimensional separation flow data is thermally modulated, cut, and focused to obtain modulated slice flow data. Based on the modulated slice flow data, it is entered into a second-dimensional chromatographic column for rapid separation, and simultaneously imported into a mass spectrometer for ionization and mass scanning to generate chromatographic mass spectrometry response data. The initial chromatographic mass spectrometry response data are jointly judged, screened and organized to obtain organized response data. The organized response data are then recorded according to time location and sampling identification information to generate a chromatographic mass spectrometry dataset.

6. The method for determining the content of organic pollutants in industrial wastewater as described in claim 5, characterized in that, The method of multidimensional retention calibration dynamically corrects the positions of each component in the chromatographic mass spectrometry dataset and maintains a stable two-dimensional correspondence to generate calibration chromatographic mass spectrometry data. The specific steps are as follows: Using a multidimensional retention calibration method, the one-dimensional retention time, two-dimensional retention time, and ion intensity of each peak are extracted from the chromatographic mass spectrometry dataset, and then aligned and matched with the ion characteristics in chronological order to generate component localization response data. Import the component location response data into the standard reference component and establish a two-dimensional preserved mapping relationship; Based on the two-dimensional preserved mapping relationship, the two-dimensional position offset in the component positioning response data is jointly characterized and position compensation is performed to generate position correction data; The position correction data is subjected to stability constraints and anomaly removal to obtain two-dimensional stable corresponding data. The two-dimensional stable corresponding data is recorded accordingly to generate calibration chromatography-mass spectrometry data.

7. The method for determining the content of organic pollutants in industrial wastewater as described in claim 6, characterized in that, The specific steps for performing two-dimensional retention time matching and mass spectrometry comparison on the calibration chromatographic mass spectrometry data, identifying each organic pollutant component and extracting peak area data to generate a pollutant feature dataset are as follows: Read the two-dimensional stable corresponding data in the calibration chromatography-mass spectrometry data item by item according to the time position, and extract the first-dimensional retention time, the second-dimensional retention time and the ion intensity distribution from the two-dimensional stable corresponding data to generate matching candidate data; Two-dimensional retention time matching and mass spectrometry comparison are performed on the candidate matching data to generate pollutant identification data; Based on pollutant identification data, the two-dimensional retention region is located in the matching candidate data, and the chromatographic peaks in the two-dimensional retention region are integrated and recorded to generate a pollutant feature dataset.

8. The method for determining the content of organic pollutants in industrial wastewater as described in claim 1, characterized in that, The steps for normalizing the content of each pollutant in the pollutant feature dataset and organizing the time-varying relationships to generate a pollutant content scheme are as follows: Read the peak area data in the pollutant characteristic dataset item by item according to the time position, and extract the peak area value and internal standard peak area value of each pollutant from the peak area data to generate effective peak area data. The effective peak area data is converted into a ratio to obtain relative content data. The relative content data is then normalized to generate normalized content data. The content changes in the normalized content data are correlated, organized, and continuously arranged to obtain the content time series data. The content time series data are recorded accordingly to generate a pollutant content scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for determining the content of organic pollutants in industrial wastewater according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for determining the content of organic pollutants in industrial wastewater according to any one of claims 1 to 8.

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