Source analysis method for perfluorinated-polyfluorinated compounds in surface water

Abstract

The invention discloses a source analysis method for perfluorinated-polyfluorinated compounds in surface water, and belongs to the field of source analysis of pollutants in surface water. According to the method, PMF / FCM-SVR and POI are integrated, the problems that an existing source analysis model is sensitive to noise of data, nonlinear and high-dimensional data regression are solved, non-negative constraint can be conducted on the data, the contribution rate of various pollution sources to the perfluoro-polyfluorinated compound is determined, and the method has the advantages of being high in practicability and high in practicability. According to the method, the number of POIs in each manufacturing industry is determined, the correlation between the POIs and the perfluorinated-polyfluorinated compounds is analyzed, the effect of perfluorinated-polyfluorinated compound source analysis is finally achieved, correlation data is expanded by including spatial information and auxiliary data in each form in a model, and the analysis efficiency of the perfluorinated-polyfluorinated compound source analysis is improved. The contribution weights of various perfluorinated-polyfluorinated compound pollution sources are better defined, so that the comprehensiveness and integrity of water body pollution information are realized, and help is provided for later environmental governance.

Description

Technical Field

[0001] The invention relates to a source analysis method for perfluorinated and polyfluorinated compounds in surface water, belonging to the field of source analysis of pollutants in surface water. Background Art

[0002] Perfluoro-polyfluoro compounds are a class of fluorinated chemicals with high stability and durability. Since perfluoro-polyfluoro compounds are difficult to degrade in the environment, they are widely used in industrial production such as food packaging, tableware coatings, and stain-resistant furniture. In recent years, with the large-scale production and use of perfluoro-polyfluoro compounds, their concentration in natural water bodies has continued to increase. These compounds pose a serious threat to the ecological environment and human health due to their biological toxicity and strong stability.

[0003] Since field investigation of pollution sources is difficult and the workload is large. At present, the source analysis methods of perfluoro-polyfluoro compounds in the field of water environment mainly include ratio method, absolute principal component-multiple linear regression model (APCS-MLR), etc. The ratio method is to judge the source of perfluoro-polyfluoro compounds by the ratio of PFHpA / PFOA, PFOA / PFOS, and PFOS / PFOA. This method is not generally applicable and requires prior knowledge of the pollution source category in the target area. The APCS-MLR method does not require exact and clear knowledge of the specific emission information of the pollution source. It performs principal component analysis on the pre-processed data, calculates the absolute principal component score (APCS) of each sample on the selected principal component, and uses APCS as the independent variable to perform multivariate linear regression analysis on the target variable. Through regression analysis, the contribution of each principal component to the target variable is determined, and a regression equation is established. However, the APCS-MLR model requires that the data meet the assumptions of multivariate linear regression, such as linear relationship, normal distribution, etc. If the data does not meet these assumptions, the model may fail. APCS-MLR is sensitive to noise in the data, which may lead to inaccurate results of principal component analysis. In addition, APCS-MLR does not impose non-negativity constraints on each component, the final result may be difficult to interpret, and the physical or chemical meaning of the principal components may be unclear. Summary of the invention

[0004] In order to solve the problems that the existing source analysis methods of perfluorinated and polyfluorinated compounds in surface water lack consideration of the information of the pollution sources in the field, the source analysis results are out of touch with reality, the ability to handle outliers and noise is limited, the data demand is large and meets the linear requirements, and the non-negativity of the data cannot be constrained, making it difficult to analyze the specific pollution sources or pollution source types, the present invention provides a source analysis method for perfluorinated and polyfluorinated compounds in surface water, and the technical scheme is as follows:

[0005] Step 1: Collect water samples from the target area and pre-treat the perfluorinated and polyfluorinated compounds to be tested;

[0006] Step 2: Analyze the pre-treated water sample to obtain the concentration data of the perfluorinated-polyfluorinated compounds to be tested;

[0007] Step 3: Screen the number of POIs of different categories around the target area from the electronic map, perform linear fitting on the concentration data of the perfluoro-polyfluoro compounds and the number of each type of POI, and obtain the correlation between human activities and the concentration of perfluoro-polyfluoro compounds;

[0008] Step 4: further divide the POI into several specific industry categories, and perform Origin descriptive statistics and Spearman correlation analysis on the PFAS concentration data and the specific industry quantity to obtain the correlation between the PFAS in the target area and the specific industry;

[0009] Step 5: Inputting the perfluoro-polyfluoro compound concentration data and uncertainty into the PMF model to obtain contribution information of the perfluoro-polyfluoro compound concentration data;

[0010] Step 6: Based on the correlation between perfluoro-polyfluoro compounds and specific industries obtained in step 4, the contribution information of specific industries to each source is output as the perfluoro-polyfluoro compound source analysis result.

[0011] Optionally, the calculation formula for the uncertainty UNc of perfluoro-polyfluoro compounds in step 5 is:

[0012] When the concentration is less than or equal to MDL:

[0013]

[0014] When the concentration is greater than MDL:

[0015]

[0016] Where MDL is the method detection limit; g is the error fraction, and d represents the concentration of perfluorinated and polyfluorinated compounds.

[0017] Optionally, the basic equation of the PMF model is as follows:

[0018] A=BC+K

[0019] In the formula: the sample concentration matrix A is an n×m matrix, n is the number of samples, and m is the number of perfluorinated-polyfluorinated compound types; the source contribution rate matrix B is an n×p matrix, p is the number of perfluorinated-polyfluorinated compounds; the source component spectrum matrix C is a p×m matrix; and the residual matrix K is an n×m matrix.

[0020] Optionally, the step 2 uses high performance liquid chromatography tandem mass spectrometry to analyze the concentration of perfluoro-polyfluoro compounds in the water sample.

[0021] Optionally, the step 5 includes: decomposing the perfluoro-polyfluoro compound concentration data matrix by running the PMF model 20 times, and then setting the optimal factors to 5 to obtain factor distribution results and factor contribution results, and using the error estimation method in PMF to evaluate the deviation and uncertainty of the PMF results.

[0022] The present invention also provides a method for source analysis of perfluorinated and polyfluorinated compounds in surface water, comprising:

[0023] Step 1: Collect water samples from the target area and pre-treat the perfluorinated and polyfluorinated compounds to be tested;

[0024] Step 2: Analyze the pre-treated water sample to obtain the concentration data of the perfluorinated-polyfluorinated compounds to be tested;

[0025] Step 3: Screen the number of POIs of different categories around the target area from the electronic map, perform linear fitting on the concentration data of the perfluoro-polyfluoro compounds and the number of each type of POI, and obtain the correlation between human activities and the concentration of perfluoro-polyfluoro compounds;

[0026] Step 4: further divide the POI into several specific industry categories, and perform Origin descriptive statistics and Spearman correlation analysis on the PFAS concentration data and the specific industry quantity to obtain the correlation between the PFAS in the target area and the specific industry;

[0027] Step 5: clustering the concentration data of perfluorinated and polyfluorinated compounds using the FCM model to obtain several sources of perfluorinated and polyfluorinated compounds in the target area and the contribution rate of each source;

[0028] Step 6: using the SVR algorithm to compare the measured values ​​and predicted values ​​of perfluoro-polyfluoro compounds to evaluate the performance of the FCM model;

[0029] Step 7: Based on the correlation between the perfluoro-polyfluoro compounds and the specific industry obtained in step 4, the spatial correlation information of the perfluoro-polyfluoro compound sources is output as the perfluoro-polyfluoro compound source analysis result.

[0030] Optionally, the objective function of the FCM model is:

[0031]

[0032] where X = {x1, ..., x n} is the input perfluorinated-polyfluorinated compound concentration dataset; D = {d1, ..., d k} represents the set of each cluster center; the element u of Uzk represents the membership of the kth sample to the zth cluster; w is the number of sources identified, and the optimal value of w is obtained by trial and error.

[0033] Optionally, the regression function of the SVR algorithm is:

[0034] f(x z )=H T β(x z )+E

[0035] The weight vector and bias vector are represented by variable coefficients H and E respectively, β(x z ) is x z Mapping function to a hyperplane.

[0036] Optionally, step 6 uses root mean square error RMSE and R 2 It reflects the deviation between the measured value and the predicted value and the fit of the model.

[0037] Optionally, the method further comprises: standardizing and normalizing the perfluoro-polyfluoro compound concentration data before inputting into the FCM.

[0038] The beneficial effects of the present invention are:

[0039] The present invention solves the problems of existing source analysis models being sensitive to data noise, nonlinearity and high-dimensional data regression by integrating PMF / FCM-SVR and POI, and can also impose non-negativity constraints on the data, determine the contribution rate of various pollution sources to perfluoro-polyfluoro compounds, clarify the number of POIs in each manufacturing industry and analyze their correlation with perfluoro-polyfluoro compounds, ultimately achieving the effect of perfluoro-polyfluoro compound source analysis. In addition, by including spatial information and various forms of auxiliary data in the model to expand the correlation data, the contribution weights of various perfluoro-polyfluoro compound pollution sources are better defined, thereby achieving the comprehensiveness and completeness of water pollution information, and providing help for subsequent environmental governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 It is a flow chart of the perfluoro-polyfluoro compound source analysis method of the present invention.

[0042] Figure 2It is a schematic diagram of the correlation between the perfluoro-polyfluoro compounds and industries provided in the embodiments of the present invention.

[0043] Figure 3 It is a diagram of average source allocation results of various parameters of POI-FCM-SVR provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] Embodiment 1:

[0046] This example provides a method for source analysis of perfluoro-polyfluoro compounds, see Figure 1 , the method comprising:

[0047] Step 1: Sample the surface water in the target area, set up monitoring points, and collect water samples.

[0048] In the process of water sampling, in order to eliminate the impact of the objective environment on the data, the relationship between time and location should be considered during sampling. For example, a total of 180 samples were sampled in this embodiment.

[0049] Step 2: Select the type of pollutant, determine the target substance to be analyzed, and pre-treat the target substance in the collected water sample.

[0050] In this example, the target pollutants are determined to be 11 perfluoro-polyfluoro compounds, including: perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorobutyl sulfonic acid (PFBS), perfluorohexyl sulfonic acid (PFHxS) and perfluorooctane sulfonic acid (PFOS), sodium perfluorononenyloxybenzene sulfonate (OBS), perfluorohexylethyl sulfonic acid (6:2FTSA) and hexafluoropropylene oxide dimer (HFPO-DA) for concentration detection. The blank analysis results are all less than the detection limit, and the spiked recovery rates of blank and sample are controlled at 90% to 120%; the detection limits of various substances in the sample are 0.011 to 2.063 ng / L.

[0051] The pre-processing process of this embodiment mainly includes:

[0052] Step 21: Pass 1 L of sample through a 0.45 μm filter membrane;

[0053] Step 22: Install the solid phase extraction column on the solid phase extraction device, and add 4 mL of 0.1% ammonia / methanol solution, 4 mL of methanol and 4 mL of pure water in sequence, and keep the column head moist during the process of adding solvent;

[0054] Step 23: Elute the solid phase extraction column with 4 mL of a 25 mmol / L ammonium acetate aqueous solution (pH = 4);

[0055] Step 24: After the solid phase extraction column is dried by vacuum filtration, it is sequentially eluted with 4 mL of methanol and 4 mL of 0.1% ammonia / methanol solution, and the eluent is collected in a 15 mL polypropylene centrifuge tube;

[0056] Step 25: Concentrate the collected eluent with nitrogen on a nitrogen blow dryer, set the temperature of the water bath to 50°C, make up to 1 mL with methanol, filter through a 0.22 μm filter membrane, and place in a 1.5 mL brown injection bottle and store at 4°C.

[0057] Step 3: Analyze the test solution using high performance liquid chromatography tandem mass spectrometry to obtain the concentration data of perfluorinated and polyfluorinated compounds in the water sample.

[0058] Step 4: Filter the number of POIs of different categories around the monitoring points from the electronic map, use different POI categories to represent different human activities, and establish a correlation between human activities and PFAS.

[0059] Step 41: Obtain a key from the electronic map open platform and use the POI data capture tool "POIKit" to capture POI information from the electronic map (https: / / www.amap.com / ).

[0060] Step 42: Keyword search uses EasyPoi (version_v10.93), with a search radius of 2500m, and sets keywords such as "company enterprise", "transportation facility service" and "accommodation service", etc., and searches according to their corresponding POI codes.

[0061] Step 43: Filter out the number of POIs in the accommodation service category, the number of POIs in the transportation category, the number of POIs in the agriculture, forestry, fishery and animal husbandry category, and the number of POIs in the company and enterprise category, and perform linear fitting between perfluoro-polyfluoro compounds and the number of the above four types of POIs, respectively, to explore the relationship between PFASs and residential life, transportation, agriculture, forestry, fishery and animal husbandry, and industry.

[0062] In this embodiment, perfluoro-polyfluoro compounds have a significant correlation with corporate POI.

[0063] Step 44: Divide the company-type POIs into 13 manufacturing categories, such as textile industry, chemical fiber manufacturing industry, and metal manufacturing industry; perform Origin descriptive statistics and Spearman correlation analysis on the perfluoropolyfluoro compound concentration data and the 13 types of POIs around the monitoring points to obtain the correlation between perfluoropolyfluoro compounds in the target area and various manufacturing industries.

[0064] In this example, Origin software was used for descriptive statistics and Spearman correlation analysis, and the concentration data of perfluoro-polyfluoro compounds in the sample water was used for summary statistics; Spearman correlation analysis was used to explore the significant relationship between perfluoro-polyfluoro compounds and the number of POIs in each manufacturing industry, and its significance was represented by p value, p < 0.05 indicated significant correlation, p < 0.01 indicated highly significant correlation, Figure 2 A schematic diagram of basic characteristic information of the correlation between perfluoro-polyfluoro compounds and the manufacturing industry provided in this embodiment.

[0065] Step 5: Input the perfluoro-polyfluoro compound concentration data and uncertainty into the PMF model to obtain the contribution information of the perfluoro-polyfluoro compound concentration data.

[0066] Among them, the basic equation of the PMF model is as follows:

[0067] A=BC+K

[0068] In the formula: the sample concentration matrix A is an n×m matrix, n is the number of samples, and m is the number of perfluorinated-polyfluorinated compound types; the source contribution rate matrix B is an n×p matrix, p is the number of perfluorinated-polyfluorinated compounds; the source component spectrum matrix C is a p×m matrix; and the residual matrix K is an n×m matrix.

[0069] The uncertainty (UNc) for perfluorinated and polyfluorinated compounds is calculated as follows:

[0070] When the pollutant concentration is less than or equal to MDL:

[0071]

[0072] When the pollutant concentration is greater than the MDL:

[0073]

[0074] Where MDL is the method detection limit; g is the error fraction, and d represents the concentration of PFASs, ng / L.

[0075] The input data of the PMF model are the concentration of perfluoro-polyfluoro compounds and the results obtained by the uncertainty calculation formula, and the output data are factor / contribution diagrams, factor fingerprint diagrams, residual analysis, etc. The perfluoro-polyfluoro compound concentration data matrix was decomposed by running the PMF model 20 times, and then the optimal factors were set to 5 to obtain the factor distribution results and factor contribution results. At the same time, three error estimation methods in PMF were used, namely BS (Bootstrap), DISP (Displacement) and BS-DISP (Bootstrap-Displacement), to evaluate the deviation and uncertainty of PMF results. DISP determines the rationality of the PMF source analysis results, and BS identifies factors with poor reproducibility. The main sources of uncertainty are identified by the DISP interval and the DISP interval in BS-DISP.

[0076] Step 6: Combined with the correlation between the above POI and the manufacturing industry, the contribution information of the manufacturing industry to each source is output as the source analysis result of perfluorinated and polyfluorinated compounds. The results are shown in Table 1:

[0077] Table 1 PMF-POI source analysis output results of this embodiment

[0078]

[0079] This embodiment solves the problem of ignoring the pollution sources around the study area and the non-negative constraints of data in the source analysis process by integrating PMF and POI, determines the contribution rate of various pollution sources to perfluoro-polyfluoro compounds, clarifies the number of POIs in each manufacturing industry and analyzes their correlation with perfluoro-polyfluoro compounds, and finally achieves the effect of perfluoro-polyfluoro compound source analysis, improves the accuracy of analysis data and results, makes the results more practical, and provides help for subsequent environmental governance.

[0080] Embodiment 2:

[0081] This example provides a method for source analysis of perfluoro-polyfluoro compounds, see Figure 1 , the method comprising:

[0082] Step 1: Sample the surface water in the target area, set up monitoring points, and collect water samples.

[0083] In the process of water sampling, in order to eliminate the impact of the objective environment on the data, the relationship between time and location should be considered during sampling. For example, a total of 180 samples are sampled in this embodiment.

[0084] Step 2: Select the type of pollutant, determine the target substance to be analyzed, and pre-treat the target substance in the collected water sample.

[0085] In this example, the target pollutants are determined to be 11 perfluoro-polyfluoro compounds, including: perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorobutyl sulfonic acid (PFBS), perfluorohexyl sulfonic acid (PFHxS) and perfluorooctane sulfonic acid (PFOS), sodium perfluorononenyloxybenzene sulfonate (OBS), perfluorohexylethyl sulfonic acid (6:2FTSA) and hexafluoropropylene oxide dimer (HFPO-DA) for concentration detection. The blank analysis results are all less than the detection limit, and the spiked recovery rates of blank and sample are controlled at 90% to 120%; the detection limits of various substances in the sample are 0.011 to 2.063 ng / L.

[0086] The pre-processing process of this embodiment mainly includes:

[0087] Step 21: Pass 1 L of sample through a 0.45 μm filter membrane;

[0088] Step 22: Install the solid phase extraction column on the solid phase extraction device, and add 4 mL of 0.1% ammonia / methanol solution, 4 mL of methanol and 4 mL of pure water in sequence, and keep the column head moist during the process of adding solvent;

[0089] Step 23: Elute the solid phase extraction column with 4 mL of a 25 mmol / L ammonium acetate aqueous solution (pH = 4);

[0090] Step 24: After the solid phase extraction column is dried by vacuum filtration, it is sequentially eluted with 4 mL of methanol and 4 mL of 0.1% ammonia / methanol solution, and the eluent is collected in a 15 mL polypropylene centrifuge tube;

[0091] Step 25: Concentrate the collected eluent with nitrogen on a nitrogen blow dryer, set the temperature of the water bath to 50°C, make up to 1 mL with methanol, filter through a 0.22 μm filter membrane, and place in a 1.5 mL brown injection bottle and store at 4°C.

[0092] Step 3: Analyze the test solution using high performance liquid chromatography tandem mass spectrometry to obtain the concentration data of perfluorinated and polyfluorinated compounds in the water sample.

[0093] Step 4: Filter the number of POIs of different categories around the monitoring points from the electronic map, use different POI categories to represent different human activities, and establish a correlation between human activities and PFAS.

[0094] Step 41: Obtain a key from the electronic map open platform and use the POI data capture tool "POIKit" to capture POI information from the electronic map (https: / / www.amap.com / ).

[0095] Step 42: Keyword search uses EasyPoi (version_v10.93), with a search radius of 2500m, and sets keywords such as "company enterprise", "transportation facility service" and "accommodation service", etc., and searches according to their corresponding POI codes.

[0096] Step 43: Filter out the number of POIs in the accommodation service category, the number of POIs in the transportation category, the number of POIs in the agriculture, forestry, fishery and animal husbandry category, and the number of POIs in the company and enterprise category, and perform linear fitting between perfluoro-polyfluoro compounds and the number of the above four types of POIs, respectively, to explore the relationship between PFASs and residential life, transportation, agriculture, forestry, fishery and animal husbandry, and industry.

[0097] In this embodiment, perfluoro-polyfluoro compounds have a significant correlation with corporate POI.

[0098] Step 44: Divide the company-type POIs into 13 manufacturing categories, such as textile industry, chemical fiber manufacturing industry, and metal manufacturing industry; perform Origin descriptive statistics and Spearman correlation analysis on the perfluoropolyfluoro compound concentration data and the 13 types of POIs around the monitoring points to obtain the correlation between perfluoropolyfluoro compounds in the target area and various manufacturing industries.

[0099] In this example, Origin software was used for descriptive statistics and Spearman correlation analysis, and the concentration data of perfluoro-polyfluoro compounds in the sample water was used for summary statistics; Spearman correlation analysis was used to explore the significant relationship between perfluoro-polyfluoro compounds and the number of POIs in each manufacturing industry, and its significance was represented by p value, p < 0.05 indicated significant correlation, p < 0.01 indicated highly significant correlation, Figure 2 A schematic diagram of basic characteristic information of the correlation between perfluoro-polyfluoro compounds and the manufacturing industry provided in this embodiment.

[0100] Step 5: The FCM-SVR model was used to process the PFPC concentration dataset to obtain the source assignment results of PFPCs in the study area. The FCM algorithm was built in Python 3.8, with the initial fuzziness set to 2 and the number of iterations set to 100. SVR was modeled based on the LIBSVM toolkit.

[0101] Step 51: The perfluorinated and polyfluorinated compounds are pre-standardized and normalized to eliminate the influence of the dimension, enhance the stability of the model and reduce the noise response.

[0102] Step 52: Cluster the perfluorinated-polyfluorinated compound data set using the FCM algorithm. The input data is the perfluorinated-polyfluorinated compound data. In this example, the number of clusters is 5, indicating that there are five sources of perfluorinated-polyfluorinated compounds in this area, and their source contribution rates are clearly defined. The results are as follows: Figure 3 shown.

[0103] The objective function of the FCM algorithm is:

[0104]

[0105] where X = {x1, ..., x n} is the input perfluorinated-polyfluorinated compound concentration dataset; D = {d1, ..., d k} represents the set of each cluster center; the element u of U zk represents the membership of the kth sample to the zth cluster; w is the number of sources identified, and the optimal value of w is obtained by trial and error.

[0106] Step 53: Use the SVR algorithm to compare the measured values ​​and predicted values ​​of perfluoro-polyfluoro compounds to evaluate the performance of the FCM model. The measured values ​​and predicted values ​​obtained by FCM are regressed by fitting the kernel function to obtain R 2 and root mean square error (RMSE).

[0107] Before SVR, cross validation and grid search are used to confirm the kernel function and optimize the regression parameters. The goal of SVR is to locate a hyperplane in the feature space so that the mapping of perfluoro-polyfluoro compound data to the hyperplane is as close to the true value as possible and the distance between the hyperplane and the sample points is maximized to enhance robustness and generalization ability. The regression function of SVR is:

[0108] f(x z )=H T β(x z )+E

[0109] The weight vector and bias vector are represented by variable coefficients H and E respectively, and β(x z ) is x z Mapping function to a hyperplane.

[0110] This example uses RMSE and R 2 It reflects the deviation between the measured value and the predicted value and the fit of the model.

[0111]

[0112]

[0113] in, is the measured value of perfluoro-polyfluoro compounds, is the model's prediction for perfluorinated and polyfluorinated compounds, T′ r is the actual observed mean, T′ p is the predicted mean value of PFAS and m is the total number of PFAS samples.

[0114] Step 6: Output the correlation between PFAS and human activities and the spatial correlation information of PFAS sources obtained by FCM-SVR as the PFAS source apportionment results, such as Figure 3 shown.

[0115] This embodiment integrates FCM-SVR and POI to solve the problems of existing source parsing models being sensitive to data noise, nonlinearity, and high-dimensional data regression.

[0116] On the basis of the source analysis methods of Example 1 and Example 2, the two can also be comprehensively considered, that is, integrating PMF, FCM-SVR and POI, which can solve the problems of existing source analysis models being sensitive to data noise, nonlinearity and high-dimensional data regression, and can perform non-negative constraints on the data, determine the contribution rate of various pollution sources to perfluoro-polyfluoro compounds, clarify the number of POIs in each manufacturing industry and analyze their correlation with perfluoro-polyfluoro compounds, and finally achieve the effect of perfluoro-polyfluoro compound source analysis. In addition, by including spatial information and various forms of auxiliary data in the model to expand the correlation data, the contribution weights of various perfluoro-polyfluoro compound pollution sources are better defined, thereby achieving the comprehensiveness and completeness of water pollution information, and providing assistance for subsequent environmental governance.

[0117] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for source analysis of perfluorinated and polyfluorinated compounds in surface water, characterized in that: The method comprises: Step 1: Collect water samples from the target area and pre-treat the perfluorinated and polyfluorinated compounds to be tested; Step 2: Analyze the pre-treated water sample to obtain the concentration data of the perfluorinated-polyfluorinated compounds to be tested; Step 3: Screen the number of POIs of different categories around the target area from the electronic map, perform linear fitting on the concentration data of the perfluoro-polyfluoro compounds and the number of each type of POI, and obtain the correlation between human activities and the concentration of perfluoro-polyfluoro compounds; Step 4: further divide the POI into several specific industry categories, and perform Origin descriptive statistics and Spearman correlation analysis on the PFAS concentration data and the specific industry quantity to obtain the correlation between the PFAS in the target area and the specific industry; Step 5: Inputting the perfluoro-polyfluoro compound concentration data and uncertainty into the PMF model to obtain contribution information of the perfluoro-polyfluoro compound concentration data; Step 6: Based on the correlation between perfluoro-polyfluoro compounds and specific industries obtained in step 4, the contribution information of specific industries to each source is output as the perfluoro-polyfluoro compound source analysis result.

2. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 1, characterized in that: The calculation formula for the uncertainty UNc of perfluoro-polyfluoro compounds in step 5 is: When the concentration is less than or equal to MDL: When the concentration is greater than MDL: Where MDL is the method detection limit; g is the error fraction, and d represents the concentration of perfluorinated and polyfluorinated compounds.

3. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 1, characterized in that: The basic equation of the PMF model is as follows: A=BC+K Where: the sample concentration matrix A is an n×m matrix, n is the number of samples, and m is the number of perfluorinated-polyfluorinated compound types; The source contribution rate matrix B is an n×p matrix, where p is the number of perfluorinated and polyfluorinated compounds; the source component spectrum matrix C is a p×m matrix; and the residual matrix K is an n×m matrix.

4. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 1, characterized in that: The step 2 uses high performance liquid chromatography tandem mass spectrometry to analyze the concentration of perfluoro-polyfluoro compounds in the water sample.

5. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 1, characterized in that: The step 5 comprises: decomposing the perfluoro-polyfluoro compound concentration data matrix by running the PMF model 20 times, and then setting the optimal factors to 5, thereby obtaining factor distribution results and factor contribution results, and using the error estimation method in PMF to evaluate the deviation and uncertainty of the PMF results.

6. A method for source analysis of perfluorinated and polyfluorinated compounds in surface water, characterized in that: The method comprises: Step 1: Collect water samples from the target area and pre-treat the perfluorinated and polyfluorinated compounds to be tested; Step 2: Analyze the pre-treated water sample to obtain the concentration data of the perfluorinated-polyfluorinated compounds to be tested; Step 3: Screen the number of POIs of different categories around the target area from the electronic map, perform linear fitting on the concentration data of the perfluoro-polyfluoro compounds and the number of each type of POI, and obtain the correlation between human activities and the concentration of perfluoro-polyfluoro compounds; Step 4: further divide the POI into several specific industry categories, and perform Origin descriptive statistics and Spearman correlation analysis on the PFAS concentration data and the specific industry quantity to obtain the correlation between the PFAS in the target area and the specific industry; Step 5: clustering the concentration data of perfluorinated and polyfluorinated compounds using the FCM model to obtain several sources of perfluorinated and polyfluorinated compounds in the target area and the contribution rate of each source; Step 6: using the SVR algorithm to compare the measured values ​​and predicted values ​​of perfluoro-polyfluoro compounds to evaluate the performance of the FCM model; Step 7: Based on the correlation between the perfluoro-polyfluoro compounds and the specific industry obtained in step 4, the spatial correlation information of the perfluoro-polyfluoro compound sources is output as the perfluoro-polyfluoro compound source analysis result.

7. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 6, characterized in that: The objective function of the FCM model is: where X = {x1, ..., x n } is the input perfluorinated-polyfluorinated compound concentration dataset; D = {d1, ..., d k } represents the set of each cluster center; the element u of U zk represents the membership of the kth sample to the zth cluster; w is the number of sources identified, and the optimal value of w is obtained by trial and error.

8. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 7, characterized in that: The regression function of the SVR algorithm is: f(x z )=H T β(x z )+E The weight vector and bias vector are represented by variable coefficients H and E respectively, β(x z ) is x z Mapping function to a hyperplane.

9. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 6, characterized in that: Step 6 uses the root mean square error RMSE and R 2 It reflects the deviation between the measured value and the predicted value and the fit of the model.

10. The method for source analysis of perfluorinated and polyfluorinated compounds in surface water according to claim 6, characterized in that: The method further comprises: standardizing and normalizing the perfluoro-polyfluoro compound concentration data before inputting into the FCM.

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