Qualitative and semi-quantitative integrated analysis method for environmental trace micropollutants based on mass spectrum full-scanning mode

Abstract

The invention provides a mass spectrum full-scanning mode-based qualitative and semi-quantitative integrated analysis method for environmental trace micro-pollutants, which comprises the following steps of: analyzing precursor ions without MS / MS secondary mass spectrum in a full-scanning MS mass spectrum, and forming a characteristic table by using data of the precursor ions, reserving candidate compounds corresponding to the precursor ions with high matching degree to generate a first candidate library; searching data of fragment ions with collision energy less than or equal to 20 eV corresponding to the candidate compound in an open source library, integrating the data of the fragment ions and the data of precursor ions in the first candidate library to construct a targeted screening library, performing targeted retrieval in a full-scanning MS mass spectrum, and then performing confidence evaluation and semi-quantitative analysis. According to the method, a large number of lost low-abundance fragment ions can be retrieved, trace micropollutants can be effectively identified, the lowest detection concentration can reach 0.5 ppt level or below, and the method has high confidence coefficient.

Description

Technical Field

[0001] This invention relates to the field of environmental trace pollutant analysis technology, and in particular to an integrated qualitative and semi-quantitative analysis method for environmental trace pollutants based on full-scan mass spectrometry. Background Technology

[0002] Organic trace pollutants (OMPs) are widely present in aquatic environments and have long been recognized as key anthropogenic stressors with multifaceted ecological impacts. These OMPs often coexist in the environment and may trigger adverse biochemical responses in aquatic organisms through nutrient translocation mechanisms, posing potential risks to human health even at ultra-trace concentrations (ppt levels).

[0003] Comprehensive measurement of the diverse range of micro-pollutants present in the environment is crucial, and one of the main challenges is filling detection gaps to bring undetectable trace levels down to detectable concentrations. The rapid development of chemical mass spectrometry libraries has revolutionized the field of micro-pollutant monitoring with a suspect screening workflow enabled by high-resolution mass spectrometry (HRMS). This analytical approach employs a data-dependent acquisition (DDA) strategy to generate experimental secondary spectra (MS / MS), which are then compared with a reference library for compound identification. This technology has proven highly effective in detecting hundreds of organic pollutants in complex environmental matrices, even those originating from thousands of non-target chromatographic features.

[0004] However, DDA technology can only generate fragment spectra of high-abundance chemicals. Since most micropollutants typically exist in the environmental matrix at ultra-trace levels, traditional screening methods may lead to significant identification gaps, causing a large number of low-abundance characteristic substances to remain unidentified as "chemical dark matter." For example, patent CN119936286 A discloses an autonomous non-targeted qualitative and quantitative analysis method for identifying halogenated organic compounds, used to detect halogenated organic compounds in raw water samples. Using a data-dependent acquisition mode, it identified halogenated organic compound concentrations of only 10~30 μg / L (ppb level), far below trace levels. Patent CN115469036 A discloses a screening and non-targeted analysis method for suspected veterinary drugs in food using UPLC-HRMS. It is also based on a data-dependent acquisition mode for data analysis, used for automated MS / MS mass spectrometry acquisition to detect low concentrations of unknown exogenous organisms in complex biological samples. In biological samples (pork samples), a total of 48 veterinary drugs considered unknown were detected and identified. However, it did not consider that some chemical substances may undergo intrasource cleavage, and their relatively low-abundance ion signals cannot be detected, resulting in identification loss. Therefore, relying solely on DDA technology to identify substances in samples has significant limitations. Although data-independent acquisition (DIA) technology can generate fragments for all precursor ions in a full-scan spectrum, studies have shown that approximately half of the characteristic signals are missing in DIA spectra compared to full-scan data. Furthermore, full-scan mass spectrometry reveals that most precursor ions generate fragments within the source, in quantities comparable to secondary fragments generated at low energies in a collision cell. Research confirms that identification methods based on in-source fragmentation can be more than 10 times more sensitive than traditional targeted analysis, providing a new approach for detecting trace chemicals in the environment.

[0005] Current research is based on In-Source Fragmentation (ISFrag) methods. However, traditional ISFrag methods rely on feature tables generated by MS / MS secondary mass spectrometry to identify precursor ions and their source fragment ions. However, MS / MS secondary mass spectrometry feature tables often fail to cover a large number of low-abundance fragment ions, resulting in many true fragment-precursor ion pairs going unrecognized, significantly increasing the false negative rate. Furthermore, in complex biological samples, low-abundance or poorly chromatographically behaving ion signals are often lost during this preprocessing stage, leading to a large number of true fragment-precursor ion pairs going unrecognized, further increasing the false negative rate. To address this fundamental limitation, new methods such as EISA-EXPOSOME attempt to bypass feature extraction and directly match the theoretical spectra of the entire compound database with the raw mass spectrometry data. Unfortunately, this indiscriminate search strategy inevitably introduces extremely high false positive matches due to its vast chemical space, accompanied by staggering computational costs and severely insufficient ability to distinguish trace signals in high-noise environments, thus limiting its applicability in large-scale practical studies.

[0006] Therefore, there is an urgent need in this field to develop a full-scan identification method for environmental trace pollutants based on in-source fragment identification. This method should overcome the limitations of the reliability of identification of low-concentration pollutants and propose a rapid screening method for trace pollutants in environmental samples using full scanning, so as to achieve effective identification of trace compounds and their in-source fragments in complex exposure group samples. Summary of the Invention

[0007] The technical problem this invention aims to solve is that existing methods for identifying environmental micropollutants based on in-source fragment identification cannot cover a large number of low-abundance fragment ions, resulting in the inability to effectively identify trace concentrations of micropollutants and thus a high rate of false negative matches. This invention provides an integrated qualitative and semi-quantitative analysis method for environmental trace micropollutants based on a full-scan mass spectrometry model. Using this method, a large number of lost low-abundance fragment ions can be recovered, effectively identifying trace micropollutants and reducing false negative matches. The lowest detection concentration in complex environmental samples can reach below 0.5 ppt (ng / L or ng / g), with high confidence.

[0008] The above-mentioned objective of the present invention is achieved through the following technical solution: S1. Data preparation: Data were acquired from the extracts of environmental samples using the UPLC-HRMS system to obtain full-scan MS mass spectrometry and MS / MS secondary mass spectrometry. S2. Preliminary screening of candidate substances: Analyze the full-scan MS mass spectrometry and MS / MS secondary mass spectrometry, screen the precursor ions that do not have MS / MS secondary mass spectrometry, form a feature table from the data of the precursor ions, sort the compounds corresponding to each precursor ion in the feature table by matching degree, retain the top 2 to 5 candidate compounds for each precursor ion with the highest matching degree, and generate the first candidate library. S3. Construct a targeted screening library: Search for fragment ions with collision energies ≤20eV that correspond to candidate compounds in the first candidate library in an open-source library, and integrate the fragment ion data with the precursor ion data in the first candidate library to construct a targeted screening library. S4. Verification and retrieval: In the full-scan MS mass spectrometer obtained in step S1, the mass-to-charge ratio, retention time and mass-to-charge ratio of the precursor ions corresponding to the candidate compounds in the targeted screening library are retrieved, and the mass-to-charge ratio, intensity and chromatographic peak of the fragment ions that match the candidate compounds in the full-scan MS mass spectrometer are extracted. S5. Confidence assessment: Based on the number of matched fragment ions and the reverse dot product similarity, the candidate compound with the highest matching degree is selected, which is the micro-pollutant corresponding to the corresponding precursor ion. S6. Perform semi-quantitative analysis on the micro-pollutants.

[0009] Most existing technologies use full-scan MS mass spectrometry to analyze precursor ions with MS / MS secondary mass spectrometry, resulting in the loss of a large number of low-abundance fragments. However, this application makes a breakthrough by using full-scan mass spectrometry to analyze precursor ions without MS / MS secondary mass spectrometry, which can recover a large number of lost low-abundance fragments and effectively identify trace micro-pollutants.

[0010] To analyze precursor ions that lack MS / MS secondary mass spectrometry, this invention first searches for low-collision-energy fragment ion data of candidate compounds with high matching degrees corresponding to the precursor ions in existing open-source libraries. Then, the characteristic information corresponding to the retrieved candidate compounds is used for targeted searching in full-scan mass spectrometry. The mass-to-charge ratio, intensity, and chromatographic peak of the fragment ions corresponding to each compound are extracted in the full-scan MS mass spectrometry. Based on the number of matching fragment ions and the reverse dot product similarity, the candidate compounds with the highest matching degree corresponding to the respective precursor ions are screened, which are the micro-pollutants.

[0011] Trace contaminants can be broken down into fragment ions in low-voltage ion sources. Therefore, this invention retrieves data on fragment ions of candidate compounds with collision energies ≤20 eV from open-source libraries to simulate fragmentation generated by in-source fragmentation, thereby improving the reliability of the analysis.

[0012] Specifically, the environmental samples are selected from any one of surface water, river sediments, sewage treatment plant wastewater, or medical wastewater, specifically surface water and river sediments.

[0013] When the environmental sample is surface water, the extract is obtained by pretreating the surface water. The pretreatment specifically includes the following steps: loading the water sample into a pre-activated polystyrene-divinylbenzene copolymer stationary phase extraction column; after the polystyrene-divinylbenzene copolymer stationary phase extraction column adsorbs the target analyte, vacuum drying is performed, elution is carried out with acetonitrile or methanol, and the eluent is collected. The eluent is then passed through a nitrogen stream to a dry state to obtain the extract.

[0014] When the environmental sample is river sediment, the extract is obtained by pretreatment of the river sediment. The pretreatment specifically includes the following steps: loading the extract of the river sediment into a pre-activated polystyrene-divinylbenzene copolymer stationary phase extraction column; after the polystyrene-divinylbenzene copolymer stationary phase extraction column adsorbs the target analyte, vacuum drying is performed, elution is carried out with methanol, and the eluent is collected. The eluent is then passed through a nitrogen stream to a dry state to obtain the extract.

[0015] Specifically, the polystyrene-divinylbenzene copolymer stationary phase extraction column is an HLB solid phase extraction column (Oasis MCX® SPE solid phase extraction column, 500mg, 6mL).

[0016] Specifically, the surface water extraction column is pre-activated using methanol and ultrapure water, while the river sediment extraction column is pre-activated using methanol and a 0.2% (v / v) formic acid aqueous solution.

[0017] Specifically, the parameters for data acquisition of the sample extract using the UPLC-HRMS system include: The chromatographic column is a C18 reversed-phase column. By volume concentration, the mobile phase in positive ion mode is 0.1% formic acid aqueous solution-acetonitrile solution, and the mobile phase in negative ion mode is 0.5mM ammonium formate aqueous solution-acetonitrile solution. The acetonitrile is phase B, and the mobile phase gradient is as follows: 1~3.0 min, 1~15% B; 3.0~6 min, 15~50% B; 6.0~7.5 min, 50~98% B; 7.5~11.5 min, 98% B; 11.5~11.6 min, 98~2% B; 11.6~15 min, 2% B. The column is kept at a constant temperature of 40℃.

[0018] More specifically, the C18 reversed-phase chromatographic column is selected from any one of the following: Agilent ZORBAX RRHD Eclipse Plus C18, 1.8µm, 95Å, 2.1×100mm; Thermo Scientific Accucore C18, 1.8µm, 80Å, 2.1×100mm; or ACQUITY UPLC® HSS, T3, 1.8µm, 100Å, 2.1x100mm, Waters, specifically ACQUITY UPLC® HSS, T3, 1.8µm, 100Å, 2.1x100mm, Waters.

[0019] Specifically, the data acquisition was conducted within a molecular weight range of 70 to 1050 Da.

[0020] Specifically, the full-scan MS mass spectrometer includes precise mass-to-charge ratio (m / z), ion intensity, isotope distribution information, retention time rt, and fragment ion information generated within the source.

[0021] Specifically, the collision energies used to obtain the MS / MS secondary mass spectrometry in step S1 are 15 eV, 30 eV, and 45 eV.

[0022] Specifically, the analysis in step S2 is performed using Compound Discoverer software, and the specific settings are shown in Table 1. After peak annotation using Compound Discoverer software, the annotated substances, i.e., precursor ions with MS / MS secondary mass spectra, can be obtained. After comparing with the full-scan MS mass spectra, precursor ions without MS / MS secondary mass spectra are screened out, and the data of the precursor ions are compiled into a feature table.

[0023] Specifically, the matching degree ranking in step S2 is based on the precise mass-to-charge ratio and isotopic pattern. The matching degree ranking of the compounds corresponding to each precursor ion in the feature table is sorted, and 2 to 5 candidate compounds with the highest matching degree ranking are retained for each precursor ion to generate the first candidate library, thereby achieving a sharp dimensionality reduction of the search space.

[0024] More specifically, the isotopic pattern is formed by the natural isotopes of elements in the high-resolution mass spectrometry of compound molecules, and is an important basis for determining the molecular formula and characterizing the compound.

[0025] Preferably, the compounds corresponding to each precursor ion in the feature table are sorted by matching degree, and the top 3 candidate compounds with the highest matching degree are retained for each precursor ion.

[0026] Specifically, the data of the precursor ion in step S2 includes Name, mass-to-charge ratio m / z, retention time rt, molecular formula, and peak area.

[0027] Specifically, the targeted screening library mentioned in step S3 is based on the theoretical fragmentation rules of the candidate compounds in the first candidate library in step S2, and a highly focused list of targeted ions is constructed. The core advantage of this design is that it directly traces back to the original full-scan MS mass spectrometry data for targeted extraction and verification.

[0028] Specifically, the data of the fragment ions in step S3 includes Name, mass-to-charge ratio (m / z), intensity, and chromatographic peak.

[0029] Specifically, the collision energies mentioned in step S3 are 0 eV, 10 eV, and 20 eV.

[0030] Specifically, the open-source library is the NORMAN Database.

[0031] Considering that some chemical substances can undergo intrasource fragmentation, the fragments generated from intrasource fragmentation are fragment ions produced at low energy levels and do not have MS / MS secondary mass spectrometry, but are present in the full-scan MS mass spectrometry data. Therefore, when performing the verification search in step S4, in addition to verifying and searching the mass-to-charge ratio and retention time of the precursor ions in the target screening library, it is also necessary to match the mass-to-charge ratio of the fragment ions with the full-scan MS mass spectrometry data in step S1, and extract the mass-to-charge ratio, intensity, and chromatographic peak of the matched fragment ions.

[0032] Specifically, the matching tolerance for the mass-to-charge ratio of the precursor ions or the mass-to-charge ratio of the fragment ions is ±5 ppm; the matching tolerance for the retention time is ±0.2 min.

[0033] Specifically, the confidence assessment method described in step S5 is as follows: (1) Count the number of matching fragment ions. The number of matching fragment ions needs to be ≥2 ions. The specific evaluation criteria are: peak shape and symmetry, that is, the chromatographic peak has good peak shape and symmetry, is symmetrical left and right, and has no obvious tailing, leading edge, bifurcation or shoulder peak, which conforms to the characteristics of Gaussian peak; the chromatographic width of the annotation peak does not exceed 1 min, matches at least 2 characteristic fragment ions, and the mass-to-charge ratio m / z of the fragment ions needs to match the standard spectrum in the target screening library. Figure 1(1) The accuracy and precision deviation are ≤5ppm; (2) The reverse dot product similarity is calculated using an intentionally reduced threshold, and the threshold is set to 0.4; The mass spectrometry signal of trace micro-pollutants is usually weak, and the conventional RDP threshold may filter out the effective spectral information. Reducing the threshold can retain such weak similarity signals and avoid omission. Essentially, it is to capture weak signals and verify them in conjunction with fragmentation technology by reducing the RDP similarity threshold, so as to maximize the sensitivity to trace micro-pollutants in the environment.

[0034] Specifically, the environmental trace pollutants include aromatic compounds, perfluorinated and polyfluoroalkyl substances, and phthalate compounds.

[0035] Compared with the prior art, the beneficial effects of the present invention are: Using the detection and analysis method of this invention, a total of 122 organic micropollutants were detected in the surface water of the Dongjiang River Basin through non-targeted analysis, with median concentrations ranging from 0.066 ng to 197.2 ng / L. A total of 124 organic micropollutants were detected in the river sediments of the Dongjiang River Basin, with median concentrations ranging from 0.22 ng / g to 187.2 ng / g. This demonstrates that the method improves the comprehensiveness of the identification of trace micropollutants in samples from complex environments. The detection and analysis method of this invention screens all organic micropollutants with more than two matching fragment ions. The main peak of the chromatogram is symmetrical and conforms to the characteristics of a Gaussian peak. The chromatographic width of the annotation peak does not exceed 1 min. The precise mass deviation between the mass-to-charge ratio m / z of the fragment ions and the mass-to-charge ratio m / z of the fragment ions in the standard spectrum in the open source library is ≤5 ppm, indicating that the analytical method of this invention has a very high confidence level. Furthermore, perfluorinated compounds, which are difficult to detect by trace signals in existing technologies, were detected in river sediments in the Dongjiang River Basin. The concentration of perfluorononanoic acid (PFNA) among the perfluorinated compounds was 0.63 ng / g. The analytical method of this invention can accurately detect trace concentrations of perfluorinated compounds, which can not only provide key data support for the early identification, risk warning and environmental management of emerging trace pollutants in regional water environments, but also provide a more sensitive and reliable technical solution for the systematic monitoring of low-abundance and difficult-to-detect perfluorinated compounds. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the process for an integrated qualitative and semi-quantitative analysis method for environmental trace pollutants based on full-scan mass spectrometry full-scan mode.

[0037] Figure 2 (a) is a chromatogram of the precursor ions extracted from the fluorocarbon nitrile compound, and (b) is a chromatogram of the extracted ions of the precursor ions and fragment ions of the fluorocarbon nitrile compound.

[0038] Figure 3 This is a comparison of the dd-MS / MS spectra of the experimental group (top) and the database group (bottom) of fluoroformonitrile compounds.

[0039] Figure 4 This is an isotopic characteristic spectrum of fluorocarbon nitrile compounds. Detailed Implementation

[0040] The present invention will be further described below with reference to specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise stated, the raw materials and reagents used in the embodiments of the present invention are conventionally purchased raw materials and reagents.

[0041] Example 1 A method for qualitative and semi-quantitative analysis of trace environmental pollutants based on full-scan mass spectrometry in full-scan mode includes the following steps: S1. Data preparation: Obtain environmental sample extracts that can be used for instrumental analysis through specific sample and extraction methods; Samples were analyzed using an UPLC-HRMS system (Orbitrap Exploris 120, Thermo Fisher Scientific). 5 μL of purified extract was chemically separated using an ACQUITYUPLC® HSS, T3, 1.8 µm, 100 Å, 2.1 x 100 mm, Waters column at 40 °C. The mobile phase consisted of 0.1% formic acid and acetonitrile solution (positive ion mode) and 0.5 mM ammonium formate and acetonitrile solution (negative ion mode). The mobile phase gradient was as follows: 1–3.0 min, 1–15% B; 3.0–6 min, 15–50% B; 6.0–7.5 min, 50–98% B; 7.5–11.5 min, 98% B; 11.5–11.6 min, 98–2% B; 11.6–15 min, 2% B. During data acquisition, full-scan MS mass spectrometry was obtained in the range of 70-1050 Da; and MS / MS secondary mass spectrometry was obtained using collision energies of 15 eV, 30 eV and 45 eV.

[0042] S2. Preliminary screening of candidate substances: After peak enhancement and annotation of the MS / MS secondary mass spectra using Compound Discoverer software, the parameters involved are shown in Table 1. This will yield the annotated substances (i.e., specific precursor ions with MS / MS secondary mass spectra). Similarly, after analyzing the original full-scan MS mass spectra, precursor ions without MS / MS secondary mass spectra are screened, and the data of these precursor ions are compiled into a feature table. Subsequently, based on the precise mass-to-charge ratio and isotopic mode, the compounds corresponding to each precursor ion in the feature table are ranked by matching degree. The top 3 candidate compounds with the highest matching degree for each precursor ion are retained to generate the first candidate library of candidate compounds. S3. Construct a targeted screening library: Search the open-source NORMAN Database for fragment ion data (including fragment ion mass-to-charge ratio, intensity, and chromatographic peaks) corresponding to candidate compounds in the first candidate library with collision energies of 0eV, 10eV, and 20eV. Integrate the fragment ion data with the precursor ion data in the first candidate library to construct a targeted screening library.

[0043] S4. Verification and retrieval: In the full-scan MS mass spectrometer obtained in step S1, target the candidate compound in the targeted screening library for the mass-to-charge ratio, retention time and mass-to-charge ratio of the precursor ion, and extract the mass-to-charge ratio, intensity and chromatographic peak of the fragment ion that matches the candidate compound in the full-scan MS mass spectrometer. S5. Confidence assessment: (1) When performing the verification search, count the number of matching fragment ions and screen the candidate compound with the highest matching degree based on the reverse dot product similarity. This is the micro-pollutant corresponding to the corresponding precursor ion. Verify the existence of fragment ions and the number of matching fragment ions. The specific evaluation criteria are: peak shape and symmetry, that is, the chromatographic peak has good peak shape and symmetry, is symmetrical, has no obvious tailing, leading edge, bifurcation or shoulder peak, conforms to the characteristics of Gaussian peak, and the chromatographic width of the annotation peak does not exceed 1 min. It matches at least 2 characteristic fragment ions, and the mass-to-charge ratio m / z of the fragment ions needs to be consistent with the spectrum in the library. Figure 1 (1) The accuracy of the mass deviation is ≤5ppm (parts per million); (2) Calculate the reverse dot product similarity (RDP), set the threshold of the reverse dot product similarity to 0.4. When the calculated reverse dot product similarity is ≥0.4, it indicates that the similarity between the database spectrum and the experimental spectrum is high, proving that the target micro-pollutant exists; when the calculated reaction dot product is <0.4, it indicates that the similarity between the database spectrum and the experimental spectrum is not high, and the target compound does not exist.

[0044] S6. Semi-quantitative Analysis: Preparation of Standards: To analyze micro-contaminants in the samples, we established a set of standard curves using existing laboratory reference standards. The concentration gradients were 0.5 ng / L, 1 ng / L, 2 ng / L, 5 ng / L, 10 ng / L, 20 ng / L, 50 ng / L, 100 ng / L, and 200 ng / L. The standards were analyzed simultaneously using UPLC-HRMS. The response values ​​of these substances were extracted using CD software to establish response-concentration curves. The coefficients of determination (R²) of all standard curves exceeded 0.99. Their response values ​​were compared with the corresponding standard curves to calculate the concentration values ​​as the custom compounds for this quantification.

[0045] Log in to the Quantum platform and configure the parameters. Key parameter configurations (ESI mode, concentration units, solvent configuration, and gradient program) are shown in Table 2. Upload a .CSV file, which includes information on the target compound and the customized compound. The target compound is the substance to be semi-quantitatively analyzed. The customized compound is measured under the exact same analytical conditions as the target substance (ESI mode, solvent system, gradient program, etc.) and has a known concentration. The platform relies on the "known concentration-signal intensity" correlation of the customized compound, combined with the signal intensity of the target substance, to generate concentration prediction results for specific analytical conditions, ensuring the accuracy and applicability of the predictions. Information on the customized compounds we used is shown in Table 3.

[0046] Table 1. Specific parameter settings for peak boosting nodes

[0047] Table 2 Key Parameter Settings for Semi-Quantitative Platform

[0048] Table 3 Customized Compound Information

[0049] Example 2 The only difference compared to Example 1 is: S1. Data Preparation: Surface Water Sampling: In the Dongjiang River Basin in southern China, 51 instantaneous surface water samples were collected along the gradient of human activity intensity, including the main stream of the Dongjiang River (DJ01-DJ22) and three tributaries, namely the Xizhi River (XZJ01-XZJ16), the Lijiang River (LJ01-LJ06), and the Shima River (SM01-SM06). Surface water samples were obtained by directly immersing stainless steel buckets into designated sampling points, and then directly placing the samples into 5L brown glass bottles that had been washed three times with sample water.

[0050] Sample Pretreatment and Micropollutant Analysis: Surface water samples were extracted using an HLB solid-phase extraction column (Oasis MCX®SPE solid-phase extraction column, 500 mg, 6 mL). The specific procedure was as follows: 1 liter of water sample was taken, filtered through a glass fiber membrane to remove particulate matter, and then transferred to a glass bottle. First, the HLB solid-phase extraction column (Oasis MCX®SPE solid-phase extraction column; 500 mg, 6 mL) was activated with 6.00 mL of methanol, followed by rinsing with 6.00 mL of ultrapure water. After activation, the water sample was added to the extraction column at a flow rate below 5.00 mL / min. After the water sample was dried, the solid-phase extraction column was rinsed with 6.00 mL of ultrapure water to remove interfering impurities. The solid-phase extraction column was then dried under vacuum for 30 min. Finally, the solid-phase extraction column was eluted with 6.00 mL of acetonitrile and 3.00 mL of methanol, and the eluent was collected in a glass test tube. The elution solution was dried to near dryness under a gentle nitrogen stream, reconstituted with 0.5 mL of methanol, filtered through a 0.22 μm organic filter membrane, and stored in a liquid chromatography vial for later analysis. The other preparation and analysis steps are the same as in Example 1.

[0051] Example 3 The only difference compared to Example 1 is: S1. Data Preparation: River Sediment Sample Collection: In the Dongjiang River basin in southern China, 32 river sediment samples were collected along the gradient of human activity intensity, including the main stream of the Dongjiang River (DJ01-DJ22) and three tributaries, namely the Xizhi River (XZJ01-XZJ16), the Lijiang River (LJ01-LJ06), and the Shima River (SM01-SM06).

[0052] Sample pretreatment and micropollutant analysis: River sediment samples were freeze-dried and then ground into particles. To extract chemical substances, 80 mg of sample was weighed and mixed with 10 ng of internal standard mixture (bisphenol A-d6), and allowed to stand at room temperature for 30 minutes. 5 mL of a methanol-water (5:3, v / v) mixture was used to extract the sample, followed by shaking for 60 minutes and then sonication for 30 minutes. The supernatant was removed by centrifugation at 5000 rpm for 5 minutes, and the extraction steps were repeated once. The combined supernatants were concentrated to approximately 3 mL by nitrogen purging and then mixed with 0.2% formic acid in 10 mL of Milli-Q (ultrapure water). First, activate the HLB solid-phase extraction column (Oasis MCX® SPE solid-phase extraction column; 500 mg, 6 mL) by adding 10 mL of methanol and 10 mL of 0.2% formic acid solution. Then, add the diluted extract to the solid-phase extraction column at a flow rate below 5.00 mL / min. After the sample is dried, rinse the solid-phase extraction column with 0.2% formic acid solution to remove interfering impurities. Finally, elute with 9 mL of methanol, and collect the eluent in a glass test tube. The eluent is then purged with nitrogen until nearly dry, reconstituted with 500 μL of methanol, filtered through a 0.22 μm organic filter membrane, and stored in a liquid chromatography vial for later analysis.

[0053] The other preparation and analysis steps are the same as in Example 1.

[0054] Comparative Example 1 The only difference from Example 2 is that: S1. Data preparation: The surface water sample was pretreated to obtain sample extract. The sample extract was collected using a UPLC-HRMS system to obtain full scan MS mass spectrometry and MS / MS secondary mass spectrometry. S2. Peak highlighting and annotation: Analyze the MS / MS secondary mass spectrometry and annotate the peaks after peak highlighting; S3. Confidence assessment: Based on the number of matched fragment ions and the reverse dot product similarity, the candidate compound with the highest matching degree is selected, which is the micro-pollutant corresponding to the corresponding precursor ion. S4. Perform semi-quantitative analysis on the micro-pollutants.

[0055] The other preparation and analysis steps are the same as in Example 2.

[0056] Comparative Example 2 Compared with Example 2, the only difference is that the threshold for the reverse dot product similarity in step S5 is 0.7, while the other analysis steps are the same as in Example 2.

[0057] Table 4 shows the information on micropollutants detected in Example 2. As can be seen from the results in Table 4, the integrated qualitative and semi-quantitative analysis method for environmental trace micropollutants based on full-scan mass spectrometry in full-scan mode detected a total of 122 organic micropollutants in the non-targeted analysis of surface water in the Dongjiang River Basin in Example 2. Calculations show that 31.58% of these substances were detected in more than two river basins. These results indicate that the detection and analysis method of this invention can improve the comprehensiveness of micropollutant screening. One insecticide, fluorobenzonitrile, was detected only in the Shima River basin in the lower reaches of the Dongjiang River. Figure 2 (a) shows the chromatographic peaks of the precursor ions corresponding to fluorocarbonyl, displaying relevant information such as the mass-to-charge ratio, retention time, and peak intensity of fluorocarbonyl; Figure 2 (b) is the extracted ion chromatogram of the precursor ions and fragment ions corresponding to fluorobenzonitrile, from... Figure 2 (a) and Figure 2 (b) shows that the retention times of the precursor ion and the fragment ion are the same. The experimental spectrum of fluorobenzonitrile was compared with the standard spectrum in the database (targeted screening library) (e.g., Figure 3 As shown), the perfect matching of fragment ions provides a high degree of confidence in the identification results. The matching fragmentation rate is 4 / 4, and the reverse dot product similarity (spectral similarity) is 0.64, which is greater than the reverse dot product similarity threshold of 0.4. Figure 4 The isotope distribution diagram shows that fluorocarbonyl nitrile typically contains isotopes such as Cl, Br, or F. Figure 4 The characteristic peaks of isotopes such as Cl and Br can be clearly seen (with a significant signal at 388.96210), and their intensity basically matches the theoretical isotopic abundance, indicating that the elemental composition conforms to the molecular formula of fluorocarbonitrile. This further confirms that the candidate compound is fluorocarbonitrile, and the same screening and identification steps were repeated for all other possible candidate compounds.

[0058] Table 4. Information on micro-pollutants detected in Example 2

[0059] Note: In the table, W-DJ represents surface water - the main stream of the Dongjiang River Basin, W-XZJ represents surface water - the Xizhi River Basin, W-LJ represents surface water - the Lijiang River Basin, and W-SM represents surface water - the Shima River Basin.

[0060] Table 5 Information on micro-contaminants detected in Example 3

[0061] Note: In the table, S-DJ represents river sediments - Dongjiang main stream, S-XZJ represents river sediments - Xizhi River basin, S-LJ represents river sediments - Lijiang basin, and S-SM represents river sediments - Shima River basin.

[0062] As shown in Table 5, Example 3 used the detection and analysis method of the present invention to screen and identify micropollutants in river sediments of the Dongjiang River Basin. The identified compounds are summarized in Table 5, totaling 124 micropollutants. Among them, the detected perfluorinated compounds, such as perfluorononanoic acid (PFNA), are present. In conventional river sediments, PFNA is mostly 0.02~0.5 ng / g, while in polluted area sediments, it is mostly 0.5~6 ng / g. The concentration detected in the present invention is 0.63 ng / g, which is typical for the region and relatively low. Existing detection methods are difficult to detect due to the easy suppression of trace signals. However, the detection and analysis method of the present invention achieves accurate qualitative and semi-quantitative analysis, and has a greater advantage in the detection ability and qualitative reliability of low abundance in sediments. Studies have shown that perfluorinated and polyfluoroalkyl substances have reproductive toxicity to aquatic organisms, affecting their reproductive capacity and leading to a decline in population size. Toxicological and epidemiological studies have shown that perfluorinated and polyfluoroalkyl substances have direct or indirect toxic effects on multiple organs. Therefore, the detection and analysis method of this invention can accurately detect low concentrations of PFNA, which can not only provide key data support for the early identification, risk warning and environmental management of emerging pollutants in regional water environments, but also provide a more sensitive and reliable technical solution for the systematic monitoring of low-abundance and difficult-to-detect perfluorinated compounds.

[0063] Comparative Example 1, by not considering the influence of fragment ions within the source and only analyzing the precursor ions obtained from the MS / MS secondary mass spectrometry, lost the characteristic fragment ion signals of low-abundance trace micropollutants, resulting in an increase of over 30% in the number of unidentified environmental micropollutants. This significantly reduced the accuracy of micropollutant identification and increased the likelihood of false positives. The reliability and coverage of the identification results were far lower than the detection and analysis method of this invention. Comparative Example 2, by using a conventional reverse dot product similarity threshold (0.7), while improving the accuracy of effective matching to some extent, easily lost potential effective signals of weak similarity. Compared to the results with a threshold of 0.4, the number of matched micropollutants decreased by over 40%, and in river sediments (extremely complex matrices), the reduction was even greater, with the number of matched micropollutants decreasing by over 50%.

[0064] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for qualitative and semi-quantitative analysis of trace environmental pollutants based on full-scan mass spectrometry, characterized in that, Includes the following steps: S1. Data preparation: Data were acquired from the extracts of environmental samples using the UPLC-HRMS system to obtain full-scan MS mass spectrometry and MS / MS secondary mass spectrometry. S2. Preliminary screening of candidate substances: Analyze the full-scan MS mass spectrometry and MS / MS secondary mass spectrometry, screen the precursor ions that do not have MS / MS secondary mass spectrometry, form a feature table from the data of the precursor ions, sort the compounds corresponding to each precursor ion in the feature table by matching degree, retain the top 2 to 5 candidate compounds for each precursor ion with the highest matching degree, and generate the first candidate library. S3. Construct a targeted screening library: Search for fragment ions with collision energies ≤20eV that correspond to candidate compounds in the first candidate library in an open-source library, and integrate the fragment ion data with the precursor ion data in the first candidate library to construct a targeted screening library. S4. Verification and retrieval: In the full-scan MS mass spectrometer obtained in step S1, the mass-to-charge ratio, retention time and mass-to-charge ratio of the precursor ions corresponding to the candidate compounds in the targeted screening library are retrieved, and the mass-to-charge ratio, intensity and chromatographic peak of the fragment ions that match the candidate compounds in the full-scan MS mass spectrometer are extracted. S5. Confidence assessment: Based on the number of matched fragment ions and the reverse dot product similarity, the candidate compound with the highest matching degree is selected, which is the micro-pollutant corresponding to the corresponding precursor ion. S6. Perform semi-quantitative analysis on the micro-pollutants.

2. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The environmental samples were selected from any one of surface water, river sediments, sewage treatment plant wastewater, or medical wastewater.

3. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to any one of claims 1 to 2, characterized in that, When the environmental sample is surface water, the extract is obtained by pretreating the surface water. The pretreatment specifically includes the following steps: loading the water sample into a pre-activated polystyrene-divinylbenzene copolymer stationary phase extraction column; after the polystyrene-divinylbenzene copolymer stationary phase extraction column adsorbs the target analyte, vacuum drying is performed, elution is carried out with acetonitrile or methanol, and the eluent is collected. The eluent is then passed through a nitrogen stream to a dry state to obtain the extract.

4. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to any one of claims 1 to 2, characterized in that, When the environmental sample is river sediment, the extract is obtained by pretreatment of the river sediment. The pretreatment specifically includes the following steps: loading the extract of the river sediment into a pre-activated polystyrene-divinylbenzene copolymer stationary phase extraction column; after the polystyrene-divinylbenzene copolymer stationary phase extraction column adsorbs the target analyte, vacuum drying is performed, elution is carried out with methanol, and the eluent is collected. The eluent is then passed through a nitrogen stream to a dry state to obtain the extract.

5. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The parameters for data acquisition include: The chromatographic column is a C18 reversed-phase column. By volume concentration, the mobile phase in positive ion mode is 0.1% formic acid aqueous solution-acetonitrile solution, and the mobile phase in negative ion mode is 0.5mM ammonium formate aqueous solution-acetonitrile solution. The acetonitrile is phase B, and the mobile phase gradient is as follows: 1~3.0 min, 1~15% B; 3.0~6 min, 15~50% B; 6.0~7.5 min, 50~98% B; 7.5~11.5 min, 98% B; 11.5~11.6 min, 98~2% B; 11.6~15 min, 2% B. The column is kept at a constant temperature of 40℃.

6. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The collision energies mentioned in step S3 are 0 eV, 10 eV, and 20 eV.

7. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that... The matching tolerance for the mass-to-charge ratio of the precursor ions or the mass-to-charge ratio of the fragment ions is ±5 ppm, and the matching tolerance for the retention time is ±0.2 min.

8. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The number of matched fragment ions in step S5 is ≥2.

9. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The threshold for the reverse dot product similarity in step S5 is 0.

4.

10. The integrated qualitative and semi-quantitative analysis method for trace environmental pollutants according to claim 1, characterized in that, The environmental trace pollutants include aromatic compounds, perfluorinated and polyfluoroalkyl substances, and phthalate compounds.

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