Method, system, device and medium for detecting organic pollutants in mining wastewater

Abstract

The application provides a kind of exploitation wastewater organic pollutant detection method, system, equipment and medium, by identifying the signal intensity of wastewater sample is first rapidly rising and then rapidly falling when being oxidized by free radical, and then the accurate mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal is extracted as the mass identification of the candidate intermediate product;Extract a plurality of complete molecular ions from the candidate intermediate product as a list to be identified;Generate the ion chromatogram of the mass identification corresponding to each complete molecular ion in the oxidation process, and then determine the matching degree between each ion chromatogram and the standard transient peak shape template;Based on all matching degrees, identify the potential toxic transient intermediate product from the list to be identified, and generate a pollution identification report of the wastewater sample. Using the scheme of the application, the actual conversion path of the pollutant can be reflected to reduce the identification omission of the potential toxic transient intermediate product in wastewater.

Description

Technical Field

[0001] This application relates to the field of pollutant detection technology, and more specifically, to a method, system, equipment, and medium for detecting organic pollutants in mining wastewater. Background Technology

[0002] Pollutant detection refers to a monitoring technology that uses physical sensing, chemical analysis, or biological indicators to transform potentially hazardous substances in the environment from an invisible, dispersed state into quantifiable characteristic data through a systematic process of sampling enrichment, signal conversion, feature recognition, and quantitative analysis. This enables the accurate identification of target pollutants from potential risks, ultimately providing a scientific basis for environmental assessment, health protection, and governance decisions.

[0003] The detection of organic pollutants in mining wastewater refers to a systematic technical process involving water sample collection and pretreatment, chromatography-mass spectrometry (GC-MS) analysis, characteristic spectral interpretation, and quantitative comparison with standard substances. This process separates, identifies, and determines the concentration of toxic organic substances in wastewater generated from oil and gas fields and mines, transforming the complex industrial wastewater system into a concrete list of organic pollutants and their distribution patterns. Traditional methods for detecting organic pollutants in mining wastewater, which generally rely on offline sampling and laboratory analysis strategies with minute-level time resolution, inherently suffer from time lag, making it difficult to effectively capture transient toxic intermediates generated during advanced oxidation processes. Traditional methods are prone to misclassifying hydroxylation byproducts with extremely short half-lives generated during the oxidation of wastewater by free radicals as background interference due to their rapidly rising and then falling signals. The root cause is that these transient intermediates decompose within milliseconds, making them impossible to capture again at subsequent discrete sampling points for verification. The direct consequence of this missed detection is that the final toxicity assessment report fails to accurately reflect the actual transformation pathway of the pollutant, resulting in a lack of information on key potentially toxic transient intermediates. Therefore, how to reflect the actual transformation pathway of pollutants to reduce the omission of identification of potentially toxic transient intermediates in wastewater has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method, system, equipment, and medium for detecting organic pollutants in mining wastewater, which can reflect the actual transformation path of pollutants, thereby reducing the omission of identification of potentially toxic transient intermediate products in wastewater.

[0005] In a first aspect, this application provides a method for detecting organic pollutants in mining wastewater, comprising the following steps: Collect mass spectrometry data from wastewater samples; Based on the temporal characteristics of the mass spectrometry data, the mass-to-charge ratio signal of the wastewater sample when it is oxidized by free radicals is identified, which first rises rapidly and then falls rapidly. Then, the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal is extracted as the mass identifier of the candidate intermediate product. The quality identifiers of the candidate intermediate products are matched with a pre-set pollutant database, and multiple complete molecular ions are extracted from the candidate intermediate products based on the matching results as a list to be identified. Generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process, and then determine the matching degree between each ion chromatogram and the standard transient peak template. Based on all matching degrees, potentially toxic transient intermediates are identified from the list of products to be identified, and a pollution identification report for the wastewater sample is generated.

[0006] In some embodiments, identifying the mass-to-charge ratio signal whose signal intensity first rises rapidly and then falls rapidly when a wastewater sample is oxidized by free radicals based on the time-domain characteristics of the mass spectrometry data specifically includes: Extract the signal intensity sequence of each mass-to-charge ratio point in the time dimension from the mass spectrometry data; Abrupt change feature detection is performed on the signal intensity sequence at each mass-to-charge ratio point; Based on the mutation feature detection results, the mass-to-charge ratio signal is identified when the wastewater sample is oxidized by free radicals, and the signal intensity first rises rapidly and then falls rapidly.

[0007] In some embodiments, extracting the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as a mass identifier for the candidate intermediate specifically includes: The mass-to-charge ratio signal was analyzed by high-resolution mass spectrometry to obtain the accurate mass-to-charge ratio value; The precise mass-to-charge ratio value was matched and verified with the molecular ion mass rule; The verified accurate mass-to-charge ratio is marked as the quality identifier of the candidate intermediate.

[0008] In some embodiments, matching the quality identifier of the candidate intermediate product with a pre-set pollutant database, and extracting multiple complete molecular ions from the candidate intermediate product based on the matching results as a list to be identified, specifically includes: Mass spectrometry reference information of known organic pollutants and fragment ions is obtained from a pre-set pollutant database; The quality identifier of the candidate intermediate product is matched with the mass spectrometry reference information. Based on the matching results, complete molecular ions of candidate intermediates that do not match any fragment ion reference information are extracted from the candidate intermediates and used as a list to be identified.

[0009] In some embodiments, generating ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process specifically includes: Extract time-series data from the mass marker of the complete molecular ion; Based on the time series data, time-intensity relationship curves for each complete molecular ion were constructed. The time-intensity relationship curve is optimized for peak shape to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process.

[0010] In some embodiments, determining the matching degree between each ion chromatogram and the standard transient peak shape template specifically includes: Obtain the characteristic parameter template of the standard transient peak shape template; Determine the characteristic parameters of each ion chromatogram; The matching degree between each ion chromatogram and the standard transient peak shape template is determined based on the characteristic parameters of each ion chromatogram and the characteristic parameter template.

[0011] In some embodiments, identifying potentially toxic transient intermediates from the list of products to be identified based on all matching degrees and generating a pollution identification report for the wastewater sample specifically includes: Preset the matching threshold for pollution detection; Potentially toxic transient intermediates were detected from each intact molecular ion based on the matching degree threshold and all matching degrees. Pollution identification reports for wastewater samples generated from all transient intermediate products.

[0012] Secondly, this application provides a system for detecting organic pollutants in mining wastewater, comprising: The acquisition module is used to acquire mass spectrometry data from wastewater samples; The processing module is used to identify the mass-to-charge ratio signal when the wastewater sample is oxidized by free radicals based on the time-domain characteristics of the mass spectrometry data. The signal intensity rises rapidly and then falls rapidly. The module then extracts the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as the mass identifier of the candidate intermediate product. The processing module is also used to match the quality identifier of the candidate intermediate product with a pre-set pollutant database, and extract multiple complete molecular ions from the candidate intermediate product as a list to be identified based on the matching results. The processing module is also used to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process, thereby determining the matching degree between each ion chromatogram and the standard transient peak template. The execution module is used to identify potentially toxic transient intermediates from the list of products to be identified based on all matching degrees, and to generate a pollution identification report for the wastewater sample.

[0013] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for detecting organic pollutants in mining wastewater.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for detecting organic pollutants in mining wastewater.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The method, system, equipment, and medium for detecting organic pollutants in mining wastewater provided in this application firstly identify the mass-to-charge ratio (MTR) signal, which shows a rapid increase followed by a rapid decrease, when the wastewater sample is oxidized by free radicals, based on the temporal characteristics of the MTR data. Then, the precise MTR value of the molecular ion corresponding to the MTR signal is extracted as a mass identifier for candidate intermediate products. This establishes a preliminary basis for distinguishing between potentially toxic transient intermediate products and stable pollutants. By focusing on the pulse-like temporal characteristics unique to free radical oxidation, the rapidly rising and falling MTR signal intensity is effectively distinguished from the stable ion signals in the background matrix. This provides a key screening basis for capturing short-lived, low-abundance intermediates from complex mass spectrometry data streams. Subsequently, ion chromatograms of the mass identifiers corresponding to each complete molecular ion are generated during the oxidation process, and the matching degree between each ion chromatogram and the standard transient peak template is determined. That is, a secondary verification of the authenticity and structural relevance of candidate intermediate products is performed. This method quantifies the consistency of the chromatographic behavior of each candidate ion over time with known standards, accurately distinguishing false-positive pulse signals caused by random noise or matrix interference from real, repeatable chemical substances. Furthermore, during the generation of the pollution identification report, potentially toxic transient intermediates in the wastewater sample are detected from each intact molecular ion based on all matching degrees. These intermediates are then integrated into the final report in a quantitative or semi-quantitative form. By dynamically quantifying the identification confidence level of each candidate ion through matching degrees, a multi-dimensional cross-validated list of byproducts is provided for the final risk assessment. This ensures that the pollution identification report is no longer a speculative result based solely on mass-to-charge ratio, but a genuine list of byproducts with dual authentication of pulse occurrence time and standard chromatographic retention time, effectively suppressing toxicity misjudgments and omissions caused by signal interference or missed detection. In summary, this method reflects the actual transformation pathway of pollutants, reducing the omission of identification of potentially toxic transient intermediates in wastewater. Attached Figure Description

[0016] Figure 1This is a schematic flowchart illustrating a method for detecting organic pollutants in mining wastewater according to some embodiments of this application; Figure 2 This is a schematic flowchart illustrating the determination of ion chromatograms according to some embodiments of this application; Figure 3 This is a flowchart illustrating the construction time-intensity relationship curve according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an organic pollutant detection system for mining wastewater according to some embodiments of this application; Figure 5 This is an internal structural diagram of a computer device for implementing a method for detecting organic pollutants in mining wastewater, according to some embodiments of this application. Detailed Implementation

[0017] To better understand the technical solutions in this embodiment, the technical solutions in this embodiment will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0018] refer to Figure 1 The figure is a flowchart illustrating a method for detecting organic pollutants in mining wastewater according to some embodiments of this application. This environmental quality monitoring method mainly includes the following steps: In step 101, mass spectrometry data of the wastewater sample are collected.

[0019] In specific implementation, the mass spectrometry data can be acquired through an online mass spectrometry analysis system integrated into the bypass of the advanced oxidation reactor flow channel. Preferably, a micro-flow puncture probe is used to continuously extract trace amounts of wastewater samples from the main circulation channel of the reactor at millisecond intervals. These samples are then rapidly desolvated and vaporized through a high-temperature transmission pipeline to form gaseous sample molecules. Subsequently, an atmospheric pressure chemical ionization source is used to convert the gaseous sample molecules into charged ions, and a quadrupole mass analyzer or time-of-flight mass analyzer is used for rapid mass scanning within a preset mass range. Preferably, during the acquisition process, a dynamic... Background subtraction techniques are used to eliminate matrix background interference, for example, by monitoring the mass spectrometry characteristics of the reactor inlet as background for real-time differential processing; the instrument response drift is quantitatively calibrated using the internal standard method, with the internal standard continuously added to the sampling flow path via synchronous injection; finally, a mass spectrum sequence containing two-dimensional information on mass-charge ratio and intensity and the corresponding acquisition timestamp is output as the mass spectrometry data of the wastewater sample. In order to achieve real-time monitoring of transient processes in the reactor, this method uses a proprietary online sampling and ionization interface system to convert complex liquid samples into gaseous ion streams suitable for mass spectrometry analysis.

[0020] It should be noted that the mass spectrometry data mentioned in this application specifically refers to a two-dimensional spectral sequence containing mass-charge ratio and signal intensity information acquired in real time by an online mass spectrometer; the sampling frequency with millisecond intervals needs to be optimized according to the kinetic characteristics of advanced oxidation reactions, and is generally required to be no less than 10 Hz to ensure the complete life cycle of transient intermediates can be captured; the design of the micro-flow puncture probe needs to meet the requirements of low dead volume and corrosion resistance, and its sampling position should be set in a well-mixed area within the reactor to ensure sample representativeness; in addition, as a preferred embodiment, the mass resolution of the mass spectrometer should be no less than 10000 FWHM, and the mass accuracy should be better than 5 ppm to ensure accurate differentiation of intermediates with similar molecular weights (such as homologues differing by one oxygen atom), and the mass scanning range needs to cover the possible molecular weight distribution range of the target pollutant, which is not limited in this application.

[0021] In step 102, based on the temporal characteristics of the mass spectrometry data, the mass-to-charge ratio signal of the wastewater sample when it is oxidized by free radicals is identified, where the signal intensity first rises rapidly and then falls rapidly. Then, the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal is extracted as the mass identifier of the candidate intermediate product.

[0022] In some embodiments, identifying the mass-to-charge ratio signal, in which the signal intensity of a wastewater sample first rises rapidly and then falls rapidly when oxidized by free radicals, based on the time-domain characteristics of the mass spectrometry data, can be achieved using the following steps: Extract the signal intensity sequence of each mass-to-charge ratio point in the time dimension from the mass spectrometry data; Abrupt change feature detection is performed on the signal intensity sequence at each mass-to-charge ratio point; Based on the mutation feature detection results, the mass-to-charge ratio signal is identified when the wastewater sample is oxidized by free radicals, and the signal intensity first rises rapidly and then falls rapidly.

[0023] In specific implementation, the signal intensity sequence of each mass-to-charge ratio point in the time dimension can be extracted from the mass spectrometry data in the following manner, for example: First, the mass-to-charge ratio signals are arranged in chronological order. For each mass-to-charge ratio point, all mass spectra are traversed within a preset mass tolerance window to extract the signal intensity value corresponding to that mass-to-charge ratio point. Then, the intensity values ​​of the same mass-to-charge ratio point at different time points are connected in chronological order of acquisition time to form a continuous signal intensity sequence of that mass-to-charge ratio point. In a preferred embodiment, missing data points caused by instrument fluctuations during the extraction process can be filled using a time-series-based interpolation algorithm, and abnormal fluctuation data points caused by random noise can be smoothed using a sliding window averaging filter algorithm. Finally, the signal intensity sequences of all mass-to-charge ratio points are organized into a two-dimensional time-series data matrix as the signal intensity sequence of each mass-to-charge ratio point in the time dimension, where the row dimension corresponds to different mass-to-charge ratio points, the column dimension corresponds to different acquisition time points, and the matrix elements are the relative intensity values ​​after background signal correction.

[0024] It should be noted that the signal intensity sequence mentioned in this application refers to a continuous data sequence used to characterize the signal intensity change characteristics of a specific mass-to-charge ratio point in the time dimension, and used to reflect the dynamic behavior characteristics of the concentration change of the corresponding chemical component during the oxidation reaction.

[0025] In specific implementation, the mutation feature detection of the signal intensity sequence at each mass-to-charge ratio point can be achieved in the following way: First, perform first-order difference calculation on the signal intensity sequence to obtain the signal intensity change rate sequence over time; then, set a dynamic change rate threshold based on the change characteristics of transient signals in the advanced oxidation reaction process. When the change rate at multiple consecutive time points in the sequence exceeds the rising threshold and the change rate at multiple subsequent consecutive time points exceeds the falling threshold, the time segment is marked as a candidate mutation segment; next, extract multidimensional feature parameters for each candidate mutation segment, including peak intensity, rising phase duration, falling phase duration, and total mutation width; finally, verify the compliance of the multidimensional feature parameters of the candidate mutation segment based on the typical life cycle characteristics of transient intermediates in free radical oxidation reactions.

[0026] In specific implementation, the mass-to-charge ratio signal with a rapid increase followed by a rapid decrease in signal intensity when a wastewater sample is oxidized by free radicals, based on the mutation feature detection results, can be achieved in the following ways: First, the candidate mutation segments obtained from the mutation feature detection are associated with the time nodes of key events in the oxidation reaction, and signals with a rapid increase followed by a rapid decrease in signal intensity that appear during the free radical active period are screened out; then, the signal-to-noise ratio of the screened signals is evaluated, and signals with a signal-to-noise ratio that does not meet the requirements are eliminated; next, a pattern recognition method is used to perform cluster analysis on the retained signals to identify signal clusters with typical transient characteristics; finally, the mass-to-charge ratio signal that conforms to the transient intermediate change law in the clustering results is determined as the mass-to-charge ratio signal with a rapid increase followed by a rapid decrease in signal intensity when oxidized by free radicals. In other embodiments, other methods can also be used, which are not limited here.

[0027] It should be noted that the mass-to-charge ratio signal, which shows a rapid increase followed by a rapid decrease in signal intensity when oxidized by free radicals, as described in this application, refers to a characteristic mass spectrometry signal used to indicate the generation and decay process of transient intermediates in wastewater. Its intensity exhibits a rapid increase followed by a rapid decrease over time, and is used to identify reaction intermediates that exist only briefly during the free radical oxidation process.

[0028] In some embodiments, extracting the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as a mass identifier for the candidate intermediate can be achieved by the following steps: The mass-to-charge ratio signal was analyzed by high-resolution mass spectrometry to obtain the accurate mass-to-charge ratio value; The precise mass-to-charge ratio value was matched and verified with the molecular ion mass rule; The verified accurate mass-to-charge ratio is marked as the quality identifier of the candidate intermediate.

[0029] In specific implementation, the high-resolution mass-to-charge ratio signal is analyzed by mass spectrometry to obtain an accurate mass-to-charge ratio value. This can be achieved in the following ways: First, the rapidly rising and then rapidly falling mass-to-charge ratio signal is input into a high-resolution mass spectrometer, and a real-time mass calibration method based on a reference material is used to correct the systematic error of the mass-to-charge ratio signal. Then, the high-resolution capability of the mass spectrometer is used to perform a fine scan of the mass spectrometry region where the target mass-to-charge ratio signal is located, and the mass measurement accuracy is improved by increasing the number of sampling points and smoothing. Finally, the mass-to-charge ratio measurement value after systematic error correction and fine scanning is used as the accurate mass-to-charge ratio value. In a preferred embodiment, the high-resolution mass spectrometry analysis can use time-of-flight mass spectrometry or orbital trap mass spectrometry. In other embodiments, multi-stage mass spectrometry analysis or ion mobility spectrometry-assisted measurement can also be used to further improve the accuracy of mass analysis, which is not limited in this application.

[0030] It should be noted that the precise mass-to-charge ratio value mentioned in this application refers to a high-precision mass-to-charge ratio measurement value used to uniquely identify the identity of a molecular ion.

[0031] In specific implementation, the matching and verification of the precise mass-to-charge ratio with the molecular ion mass rules can be achieved in the following ways, for example: First, a molecular ion mass rule library is constructed based on the principles of organic mass spectrometry analysis. This includes: applying nitrogen rule verification, calculating the possible molecular formulas corresponding to the precise mass-to-charge ratio, and verifying whether the parity relationship between the mass number and the number of nitrogen atoms conforms to the nitrogen number rule for organic compounds; verifying the rationality of the number of hydrogen atoms by calculating the unsaturation of possible molecular formulas to determine whether their values ​​are within the reasonable chemical bonding range of organic compounds; and performing isotope distribution pattern verification by comparing the similarity between the measured isotope peak intensity distribution and the theoretical isotope distribution based on the elemental composition. The process involves verifying whether the mass-to-charge ratio conforms to the statistical law of natural abundance. Then, the precise mass-to-charge ratio obtained from high-resolution mass spectrometry analysis is sequentially subjected to the above three verifications. The precise mass-to-charge ratio is deemed to have passed verification only if it simultaneously meets the requirements of nitrogen rule, reasonable hydrogen atom number, and isotope distribution matching degree reaches the preset standard. Finally, the precise mass-to-charge ratio that has passed the above comprehensive verification is taken as the matching verification result, i.e., as the precise mass-to-charge ratio that has passed verification. In a preferred embodiment, the mass characteristics of known standard substances can be introduced as a reference benchmark during the verification process. In other embodiments, the strictness of the verification rules can be adjusted according to the specific type of the target pollutant, which is not limited in this application.

[0032] In specific implementation, marking verified accurate mass-to-charge ratio values ​​as quality identifiers for candidate intermediates can be achieved in the following ways: First, a structured data record is created for each verified accurate mass-to-charge ratio value. The structured data record includes the following fields: accurate mass-to-charge ratio value, corresponding pulse characteristic parameters (including peak intensity, rise time, fall time, and pulse width), and its position in the time series. Then, all records are sorted and classified based on the magnitude of the accurate mass-to-charge ratio value to establish a hierarchical quality identifier index system. Finally, the sorted and classified set of structured records is used as the quality identifier for candidate intermediates. In a preferred embodiment, the marking process also includes calculating a confidence score for each quality identifier, which is determined based on the degree of matching during the verification process and the pulse signal quality. In other embodiments, the quality identifier can also be preliminarily compared with a known pollutant mass spectrum database to mark possible compound type information. This application does not limit this.

[0033] It should be noted that the quality identification of candidate intermediates described in this application refers to a structured data set used to systematically record the key characteristics of potential reaction intermediates, including multi-dimensional information such as precise quality and time-domain characteristics, providing a complete characteristic description basis for subsequent pollutant identification.

[0034] In step 103, the quality identifier of the candidate intermediate product is matched with a pre-set pollutant database, and multiple complete molecular ions are extracted from the candidate intermediate product based on the matching results as a list to be identified.

[0035] In some embodiments, matching the quality identifier of the candidate intermediate product with a pre-set pollutant database, and extracting multiple complete molecular ions from the candidate intermediate product as a list to be identified based on the matching results, can be achieved through the following steps: Mass spectrometry reference information of known organic pollutants and fragment ions is obtained from a pre-set pollutant database; The quality identifier of the candidate intermediate product is matched with the mass spectrometry reference information. Based on the matching results, complete molecular ions of candidate intermediates that do not match any fragment ion reference information are extracted from the candidate intermediates and used as a list to be identified.

[0036] In specific implementation, obtaining mass spectrometry reference information of known organic pollutants and fragment ions from a pre-set pollutant database can be achieved in the following ways: First, the pre-set pollutant database is a database containing standard mass spectra of known organic pollutants, mass-to-charge ratios of characteristic fragment ions, and corresponding mass spectrometry fragmentation patterns; the mass spectrometry reference information includes the precise molecular mass of known organic pollutants, mass-to-charge ratios of characteristic fragment ions, isotope distribution patterns, and characteristic ion ratios; then, a subset of mass spectrometry reference information related to the current detection is obtained through a database query interface; finally, the obtained subset of mass spectrometry reference information is organized into a dataset containing a complete mass characteristic description; wherein, as a preferred embodiment, the database may contain standardized mass spectrometry data obtained under different instrument conditions; in other embodiments, it may also contain mass spectrometry information of potential pollutants predicted based on theoretical calculations, which is not limited in this application.

[0037] It should be noted that the mass spectrometry reference information mentioned in this application refers to a data reference set used to provide standard characteristics of known organic pollutants and fragment ions, serving as a benchmark for comparison of actual detection results.

[0038] In specific implementation, feature matching of the mass identifier of the candidate intermediate product with the mass spectrometry reference information can be achieved in the following ways: First, the precise mass-to-charge ratio of the candidate intermediate product is compared with the precise molecular mass of the known organic pollutant in the mass spectrometry reference information, and matching items are found within a preset mass tolerance range; then, the isotope distribution pattern of the candidate intermediate product is compared with the standard isotope distribution in the mass spectrometry reference information for similarity; next, the pulse characteristics of the candidate intermediate product are correlated with the typical time-domain characteristics of the known pollutant in the mass spectrometry reference information for correlation analysis; finally, the matching results of the above multiple dimensions are comprehensively evaluated to generate feature matching results including matching degree and matching type. In a preferred embodiment, the feature matching can adopt a multi-level matching strategy, prioritizing precise mass matching, followed by isotope distribution verification. In addition, when performing preliminary screening or when the signal-to-noise ratio is low, a relatively large mass tolerance range is set to retain more potential matching items; when performing precise identification or when the signal-to-noise ratio is high, a relatively small mass tolerance range is set to improve matching accuracy. This application does not limit this.

[0039] In specific implementation, the complete molecular ions of candidate intermediates that do not match any fragment ion reference information are extracted from the candidate intermediates based on the matching results and used as a list to be identified. This can be achieved in the following way: First, the matching situation of each candidate intermediate with the fragment ion reference information in the matching results is analyzed to identify candidate intermediates that do not significantly match any fragment ion; then, the identified candidate intermediates are verified a second time to ensure that they do not conform to the mass characteristics of known fragment ions, nor to the fragmentation rules of typical fragment ions; finally, the candidate intermediates that pass the second verification are classified as complete molecular ions, and all complete molecular ions are used as a list to be identified. In a preferred embodiment, the classification process can be combined with the stability of the mutation characteristics of candidate intermediates for auxiliary judgment; in other embodiments, the completeness can be further verified by analyzing the mass difference relationship between multiple related mass-to-charge ratio signals, which is not limited in this application.

[0040] It should be noted that the intact molecular ion mentioned in this application refers to a molecular ion that has not undergone covalent bond breakage, and is a pure detection target after excluding interference from fragment ions, in order to ensure the molecular integrity of the object to be analyzed subsequently.

[0041] In step 104, ion chromatograms of the mass markers corresponding to each complete molecular ion are generated during the oxidation process, thereby determining the degree of matching between each ion chromatogram and the standard transient peak template.

[0042] In some embodiments, reference Figure 2As shown in the figure, this is a schematic flowchart of the determination of ion chromatograms in some embodiments of this application. The generation of ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process can be achieved by the following steps: First, in step 1041, time-series data is extracted from the mass identifier of the complete molecular ion; Then, in step 1042, time-intensity relationship curves for each complete molecular ion are constructed based on the time series data; Finally, in step 1043, the time-intensity relationship curve is optimized to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process.

[0043] In specific implementation, the extraction of time series data from the mass identifier of the complete molecular ion can be achieved in the following manner, for example: First, traverse the mass identifier of each complete molecular ion and extract its occurrence position information and corresponding signal intensity value in the time series; then, according to the timestamp in the occurrence position information, arrange the signal intensity values ​​of the same complete molecular ion at different time points in chronological order; finally, organize the arranged time-signal intensity data into continuous time series data; wherein, as a preferred embodiment, the extraction process also includes a consistency check on the timestamps to ensure that the time reference of all data points is unified; in other embodiments, the time series data can also be resampled according to the analysis requirements to unify the time resolution of different complete molecular ions, which is not limited in this application.

[0044] For specific implementation, refer to Figure 3 As shown in the figure, this is a schematic flowchart illustrating the construction of time-intensity relationship curves in some embodiments of this application. The construction of time-intensity relationship curves for each complete molecular ion based on the time series data can be achieved in the following manner: First, using the timestamps in the time series data as the abscissa and the corresponding signal intensity values ​​as the ordinate, each data point is marked in a two-dimensional coordinate system; then, a curve fitting algorithm is used to sequentially connect adjacent data points to form a continuous time-intensity relationship curve; finally, the continuity of the constructed curve is checked, and any abnormal discontinuities are repaired using an interpolation algorithm; wherein, as a preferred embodiment, the curve fitting can select linear interpolation or cubic spline interpolation methods according to the distribution density of the data points; in other embodiments, the curve can also be smoothed based on a sliding window to eliminate local fluctuations caused by random instrument noise, which is not limited in this application.

[0045] It should be noted that the time-intensity relationship curve mentioned in this application refers to a two-dimensional curve used to visually demonstrate the change in concentration of intact molecular ions over time during oxidation.

[0046] In specific implementation, peak shape optimization processing of the time-intensity relationship curve to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process can be achieved in the following ways: First, dynamic baseline correction is performed on the time-intensity relationship curve to remove background interference signals caused by instrument background and sample matrix; then, a digital filtering algorithm is used to smooth and denoise the baseline-corrected curve, retaining the true peak shape characteristics; finally, peak shape features are extracted from the processed curve, including determining the positions of the peak start point, peak apex, and peak end point, to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process; wherein, as a preferred embodiment, the dynamic baseline correction can use the asymmetric least squares method to fit the baseline trajectory; in other embodiments, the area normalization processing of the finally generated ion chromatogram can also be performed to eliminate the comparison bias caused by the difference in the absolute value of signal intensity, which is not limited in this application.

[0047] It should be noted that the ion chromatograms in the oxidation process described in this application refer to the characteristic peak patterns used to describe the survival process of intact molecular ions in advanced oxidation reaction systems, reflecting the complete kinetic behavior characteristics of wastewater sample generation, reaching peak value, and decay.

[0048] In some embodiments, determining the degree of matching between each ion chromatogram and a standard transient peak shape template can be achieved using the following steps: Obtain the characteristic parameter template of the standard transient peak shape template; Determine the characteristic parameters of each ion chromatogram; The matching degree between each ion chromatogram and the standard transient peak shape template is determined based on the characteristic parameters of each ion chromatogram and the characteristic parameter template.

[0049] It should be noted that the characteristic parameter template of the standard transient peak shape template mentioned in this application refers to a reference model pre-established based on the typical chromatographic behavior characteristics of known transient intermediates in advanced oxidation reactions. The establishment of the characteristic parameter template is achieved by systematically analyzing the chromatographic data of known transient intermediates under standard oxidation conditions. Specifically, it involves collecting ion chromatogram data of various typical transient intermediates in advanced oxidation reactions and extracting key characteristic parameters for each ion chromatogram, including peak width, peak symmetry, rising slope, falling slope, and peak area ratio. Among these parameters, the peak width parameter reflects the duration of the transient intermediate in the reaction system, the peak symmetry parameter characterizes the kinetic equilibrium state of intermediate formation and degradation, the rising slope reflects the formation rate of the intermediate, the falling slope reflects the degradation rate of the intermediate, and the peak area ratio parameter describes the integrity of the peak shape profile. For different types of transient intermediates, corresponding characteristic parameter benchmark ranges are established according to their differences in chemical structure and reaction characteristics. The characteristic parameter template is established through statistical analysis of a large amount of experimental data to ensure that it can accurately reflect the characteristic peak shape patterns of different types of transient intermediates. This application does not limit this aspect.

[0050] In practice, the characteristic parameters of each ion chromatogram can be determined in the following ways: First, feature points are located for each ion chromatogram, including identifying key positions such as peak start points, peak apex, peak end points, and inflection points. Then, based on these feature points, geometric characteristic parameters of each ion chromatogram are calculated, including peak width, peak height, peak area, rising slope, falling slope, peak symmetry, and tailing factor. The peak width is calculated using the time difference between the peak end point and the peak start point; the peak symmetry is determined by comparing the slope ratio of the rising and falling edges; and the tailing factor is calculated using the peak width ratio at a specific peak height percentage. Finally, the calculated geometric characteristic parameters are combined with corresponding peak shape statistical parameters (such as signal-to-noise ratio and peak shape variation coefficient) to form the characteristic parameters of each ion chromatogram.

[0051] It should be noted that the characteristic parameters mentioned in this application refer to a set of parameters used to quantitatively describe the geometric features and statistical characteristics of ion chromatograms, including key indicators such as peak width and symmetry, which are used to achieve objective characterization and comparison of ion chromatograms.

[0052] In specific implementation, the matching degree between each ion chromatogram and the standard transient peak shape template can be determined based on the feature parameters of each ion chromatogram and the feature parameter template in the following ways: First, the feature parameters of each ion chromatogram are compared one by one with the benchmark range of the corresponding parameters in the feature parameter template, and the deviation of each parameter from the benchmark value is calculated; preferably, a multi-parameter weighted similarity evaluation method can be used to comprehensively consider the matching of key parameters such as peak width, peak symmetry, rising slope, and falling slope, wherein the weight of different parameters is dynamically adjusted according to their importance for the identification of transient intermediates; finally, the deviation of each parameter is comprehensively calculated according to the weight to obtain a quantified matching degree value; wherein, as a preferred embodiment, the matching degree calculation can be carried out using a multi-dimensional feature space distance evaluation method based on Euclidean distance or cosine similarity; in other embodiments, when the ion chromatogram has time axis stretching deformation, a dynamic time warping algorithm can also be introduced to align and match the peak shape sequence, which is not limited in this application.

[0053] It should be noted that the matching degree between the ion chromatogram and the standard transient peak template mentioned in this application refers to a quantitative indicator used to evaluate the degree of agreement between the chromatogram of the analyte ion and the standard template.

[0054] In step 105, potentially toxic transient intermediates are identified from the list of products to be identified based on all matching degrees, and a pollution identification report for the wastewater sample is generated.

[0055] In some embodiments, identifying potentially toxic transient intermediates from the list of products to be identified based on all matching degrees and generating a pollution identification report for the wastewater sample can be achieved through the following steps: Preset the matching threshold for pollution detection; Potentially toxic transient intermediates were detected from each intact molecular ion based on the matching degree threshold and all matching degrees. Pollution identification reports for wastewater samples generated from all transient intermediate products.

[0056] It should be noted that the matching threshold for pollution detection mentioned in this application refers to a critical value used to determine whether the similarity between the ion chromatogram and the standard transient peak template reaches the requirement for identifying potentially toxic transient intermediates. The matching threshold is set based on the statistical analysis results of the matching degree of a large number of known potentially toxic transient intermediates. Specifically, it involves collecting ion chromatogram data of potentially toxic transient intermediates at different concentration levels in advanced oxidation processes, analyzing the distribution characteristics of their matching degree values, and determining the optimal range of the matching threshold by combining the requirements for detection sensitivity and false alarm rate control. When the detection sensitivity requirement is high, the matching threshold can be appropriately lowered to retain more signals of potentially toxic transient intermediates. When the accuracy requirement is high, the matching threshold can be appropriately increased to reduce false alarms. The setting of the matching threshold also needs to consider the complexity of the specific wastewater matrix and the different requirements of the analysis purpose. For industrial wastewater with complex composition, the threshold can be appropriately increased to ensure the reliability of the results. For conventional monitoring scenarios, a relatively lenient threshold can be used to maintain detection sensitivity. This application does not impose any limitations on this.

[0057] In specific implementation, the detection of potentially toxic transient intermediates from each intact molecular ion based on the matching degree threshold and all matching degrees can be achieved in the following manner, for example: First, the matching degree of the ion chromatogram of each intact molecular ion is compared with a preset matching degree threshold, and intact molecular ions with matching degrees exceeding the matching degree threshold are screened as candidate potentially toxic transient intermediates; then, the screened candidate potentially toxic transient intermediates are further verified, preferably by comparing their precise mass-to-charge ratio with a database of known toxic compounds to confirm whether their molecular structure characteristics conform to the typical characteristics of potentially toxic transient intermediates; then, combined with the candidate potential The pulse characteristic parameters and ion chromatographic characteristics of toxic transient intermediates are used to evaluate whether their formation patterns during oxidation are consistent with the behavior patterns of known potentially toxic transient intermediates. Finally, candidate potentially toxic transient intermediates that pass all validation conditions are identified as the final detection results for potentially toxic transient intermediates. In a preferred embodiment, the validation process can employ a multi-dimensional evidence chain validation method, comprehensively utilizing mass spectrometry characteristics, chromatographic behavior, and time-domain characteristics for cross-validation. In other embodiments, a toxicity prediction model can be introduced to assess the toxicity of candidate potentially toxic transient intermediates that cannot be matched in the existing database; this application does not limit this approach.

[0058] It should be noted that the potentially toxic transient intermediates mentioned in this application refer to chemical substances that have potential ecological risks and health hazards generated during advanced oxidation processes. The potential toxicity is a conclusion drawn based on the accurate quality identification of the corresponding transient intermediates, through querying a database of known toxic compounds or conducting a risk association assessment using a quantitative structure-activity relationship model.

[0059] In specific implementation, the pollution identification report for wastewater samples generated from all transient intermediate products can be achieved in the following ways: First, integrate the complete information of the transient intermediate products, including their precise molecular weight, molecular formula prediction results, the time interval of their appearance during the oxidation process, the estimated peak concentration, and the toxicity level assessment; then, sort and classify the transient intermediate products according to their toxicity level and concentration level, highlighting high-risk pollutants; next, analyze the transformation pathways and key control nodes of the pollutants by combining the oxidation process parameters and the generation patterns of the transient intermediate products; finally, generate a structured pollution identification report that includes a summary description, detailed test results, risk assessment, and recommended measures. In a preferred embodiment, the report generation can employ template-based automatic generation technology to ensure the standardization of the report format and the completeness of the content. In other embodiments, raw data spectral data such as mass spectra and ion chromatograms of potentially toxic transient intermediate products can also be attached to the report for verification; this application does not limit this.

[0060] In another aspect, in some embodiments, this application provides a system for detecting organic pollutants in mining wastewater, with reference to... Figure 4 The figure is a schematic diagram of the structure of a mining wastewater organic pollutant detection system 200 according to some embodiments of this application. The mining wastewater organic pollutant detection system 200 includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: Acquisition module 201, in this application, is mainly used to acquire mass spectrometry data of wastewater samples; Processing module 202, in this application, is mainly used to identify the mass-to-charge ratio signal when the signal intensity of the wastewater sample is oxidized by free radicals based on the time-domain characteristics of the mass spectrometry data, and then extract the accurate mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as the mass identifier of the candidate intermediate product. In addition, the processing module 202 in this application is also used to match the quality identifier of the candidate intermediate product with a preset pollutant database, and extract multiple complete molecular ions from the candidate intermediate product as a list to be identified based on the matching results. In addition, the processing module 202 in this application is also used to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process, thereby determining the matching degree between each ion chromatogram and the standard transient peak template. The execution module 203 in this application is mainly used to identify potentially toxic transient intermediates from the list of products to be identified based on all matching degrees, and to generate a pollution identification report for the wastewater sample.

[0061] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for detecting organic pollutants in mining wastewater.

[0062] In some embodiments, reference Figure 5 This figure is an internal structural diagram of a computer device for implementing a method for detecting organic pollutants in mining wastewater, according to some embodiments of this application. The method for detecting organic pollutants in mining wastewater in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0063] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the method for detecting organic pollutants in mining wastewater in this application.

[0064] The communication bus 302 is used to transmit information between the aforementioned components.

[0065] Memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 303 may exist independently and be connected to processor 301 via communication bus 302. Memory 303 may also be integrated with processor 301.

[0066] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the method for detecting organic pollutants in mining wastewater can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0067] Communication interface 304 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0068] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core processor or a multi-core processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0069] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device may be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0070] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting organic pollutants in mining wastewater.

[0071] In summary, the method, system, equipment, and medium for detecting organic pollutants in mining wastewater disclosed in this application firstly collects mass spectrometry data of the wastewater sample; based on the temporal characteristics of the mass spectrometry data, the mass-to-charge ratio signal of the wastewater sample during free radical oxidation is identified, showing a rapid increase followed by a rapid decrease in signal intensity; then, the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal is extracted as a mass identifier for candidate intermediate products; the mass identifier of the candidate intermediate products is matched with a pre-set pollutant database, and multiple complete molecular ions are extracted from the candidate intermediate products as a list to be identified based on the matching results; ion chromatograms of the mass identifiers corresponding to each complete molecular ion are generated during the oxidation process, and the matching degree between each ion chromatogram and a standard transient peak template is determined; based on all matching degrees, potentially toxic transient intermediate products are identified from the list to be identified, and a pollution identification report for the wastewater sample is generated; this reflects the actual transformation path of pollutants, thereby reducing the omission of identification of potentially toxic transient intermediate products in wastewater.

[0072] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0073] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting organic pollutants in mining wastewater, characterized in that, Includes the following steps: Collect mass spectrometry data from wastewater samples; Based on the temporal characteristics of the mass spectrometry data, the mass-to-charge ratio signal of the wastewater sample when it is oxidized by free radicals is identified, which first rises rapidly and then falls rapidly. Then, the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal is extracted as the mass identifier of the candidate intermediate product. The quality identifiers of the candidate intermediate products are matched with a pre-set pollutant database, and multiple complete molecular ions are extracted from the candidate intermediate products based on the matching results as a list to be identified. Generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process, and then determine the matching degree between each ion chromatogram and the standard transient peak template. Based on all matching degrees, potentially toxic transient intermediates are identified from the list of products to be identified, and a pollution identification report for the wastewater sample is generated.

2. The method as described in claim 1, characterized in that, Based on the time-domain characteristics of the mass spectrometry data, the specific mass-to-charge ratio signal in which the signal intensity first rises rapidly and then falls rapidly when the wastewater sample is oxidized by free radicals includes: Extract the signal intensity sequence of each mass-to-charge ratio point in the time dimension from the mass spectrometry data; Abrupt change feature detection is performed on the signal intensity sequence at each mass-to-charge ratio point; Based on the mutation feature detection results, the mass-to-charge ratio signal is identified when the wastewater sample is oxidized by free radicals, and the signal intensity first rises rapidly and then falls rapidly.

3. The method as described in claim 1, characterized in that, Extracting the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as a mass identifier for candidate intermediates specifically includes: The mass-to-charge ratio signal was analyzed by high-resolution mass spectrometry to obtain the accurate mass-to-charge ratio value; The precise mass-to-charge ratio value was matched and verified with the molecular ion mass rule; The verified accurate mass-to-charge ratio is marked as the quality identifier of the candidate intermediate.

4. The method as described in claim 1, characterized in that, The quality identifiers of the candidate intermediate products are matched with a pre-set pollutant database. Based on the matching results, multiple complete molecular ions are extracted from the candidate intermediate products as a list to be identified. Specifically, this includes: Mass spectrometry reference information of known organic pollutants and fragment ions is obtained from a pre-set pollutant database; The quality identifier of the candidate intermediate product is matched with the mass spectrometry reference information. Based on the matching results, complete molecular ions of candidate intermediates that do not match any fragment ion reference information are extracted from the candidate intermediates and used as a list to be identified.

5. The method as described in claim 1, characterized in that, The ion chromatograms of the mass markers corresponding to each complete molecular ion generated during the oxidation process specifically include: Extract time-series data from the mass marker of the complete molecular ion; Based on the time series data, time-intensity relationship curves for each complete molecular ion were constructed. The time-intensity relationship curve is optimized for peak shape to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process.

6. The method as described in claim 1, characterized in that, Determining the matching degree between each ion chromatogram and the standard transient peak shape template specifically includes: Obtain the characteristic parameter template of the standard transient peak shape template; Determine the characteristic parameters of each ion chromatogram; The matching degree between each ion chromatogram and the standard transient peak shape template is determined based on the characteristic parameters of each ion chromatogram and the characteristic parameter template.

7. The method as described in claim 1, characterized in that, Based on all matching degrees, a pollution identification report is generated for the wastewater samples, specifically including the identification of potentially toxic transient intermediates from the list of products to be identified, and the identification of all such intermediates. Preset the matching threshold for pollution detection; Potentially toxic transient intermediates were detected from each intact molecular ion based on the matching degree threshold and all matching degrees. Pollution identification reports for wastewater samples generated from all transient intermediate products.

8. A system for detecting organic pollutants in mining wastewater, characterized in that, include: The acquisition module is used to acquire mass spectrometry data from wastewater samples; The processing module is used to identify the mass-to-charge ratio signal when the wastewater sample is oxidized by free radicals based on the time-domain characteristics of the mass spectrometry data. The signal intensity rises rapidly and then falls rapidly. The module then extracts the precise mass-to-charge ratio value of the molecular ion corresponding to the mass-to-charge ratio signal as the mass identifier of the candidate intermediate product. The processing module is also used to match the quality identifier of the candidate intermediate product with a pre-set pollutant database, and extract multiple complete molecular ions from the candidate intermediate product as a list to be identified based on the matching results. The processing module is also used to generate ion chromatograms of the mass markers corresponding to each complete molecular ion during the oxidation process, thereby determining the matching degree between each ion chromatogram and the standard transient peak template. The execution module is used to identify potentially toxic transient intermediates from the list of products to be identified based on all matching degrees, and to generate a pollution identification report for the wastewater sample.

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 detecting organic pollutants in mining wastewater as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting organic pollutants in mining wastewater as described in any one of claims 1 to 7.

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