A method and device for screening sample points of multi-source pollution tracing based on carbon-nitrogen ratio concentration

Abstract

This invention discloses a method and apparatus for screening multi-source pollution tracing samples based on carbon-nitrogen ratio concentration, relating to the fields of environmental monitoring and pollutant source tracing technology. The method includes: on-site sampling in potential pollution source areas, extracting data such as the target pollutant concentration C and the carbon-nitrogen ratio (C / N); constructing equidistant concentration reciprocal sampling intervals to screen statistically representative samples; constructing a linear regression model, eliminating outliers using Cook distance, and delineating the pollution source characteristic envelope; obtaining representative samples through isotope screening and chemical composition discrimination; and finally, measuring the stable isotope abundance of the representative samples. This invention can significantly utilize a small number of highly representative samples to achieve high-precision fitting of the regression model, thereby reducing costs while achieving accurate identification and quantification of the contribution rate of multi-source superimposed pollution.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and pollutant source tracing technology, and in particular to a method and apparatus for screening multi-source pollution source tracing samples based on carbon-nitrogen ratio concentration. Background Technology

[0002] In isotope source tracing studies of multi-source, complex pollution sites, the high cost of detection and the complex pretreatment process make the representativeness of sample selection a core bottleneck determining the accuracy of the source tracing model. Existing gridded or random sampling methods often lack specific consideration for site heterogeneity, resulting in uneven distribution of samples along concentration gradients. Especially when performing endmember analysis, the lack or clustering of key high-concentration samples can easily lead to regression fitting failure. At the same time, when faced with superimposed pollution sources such as coal combustion, oil spills, and sewage discharge, which have significantly different carbon-to-nitrogen ratios (C / N), blind sampling makes it difficult to capture key signals characterizing the source features, resulting not only in a serious waste of detection resources but also in systematic biases in the source tracing conclusions. Summary of the Invention

[0003] To address the technical problems of low accuracy in source tracing models, poor multi-source resolution, and high detection costs caused by blind sampling in existing technologies, this invention provides a method and apparatus for screening multi-source pollution source tracing samples based on carbon-nitrogen ratio concentration. The technical solution is as follows:

[0004] On the one hand, a method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration is provided. This method is implemented by a multi-source pollution tracing sampling point screening device based on carbon-nitrogen ratio concentration, and includes: S1: Collect historical site data, extract information and input it into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area; S2: Based on the potential pollution source area, conduct on-site sampling and collection, measure total organic carbon and total nitrogen, extract total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C), and carbon-nitrogen ratio (C / N) data to obtain a pollution characteristic dataset; S3: Sort the pollution feature dataset and identify extreme points. Combined with the concentration range of the target pollutant in the survey area, automatically construct equidistant inverse concentration sampling intervals. After sample point matching and screening, obtain a statistically representative sample point set. S4: Construct a linear regression model expression. Using the statistically representative sample set, calculate the Cook distance, and remove outliers that cause bias interference to the regression slope to obtain an optimized sample dataset. S5: Construct a two-dimensional coordinate system, input the two-dimensional coordinate system, draw a two-dimensional scatter plot, delineate the feature chemical space based on the known source fingerprint data to obtain the envelope, and based on the relative positional relationship between the optimized sample dataset and the envelope, first perform isotope value overlap screening, and then obtain representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the horizontal axis and the carbon-nitrogen ratio C / N as the vertical axis. S6: By measuring the stable isotope abundance of the representative samples and calculating the structured probability distribution, the original isotope abundance data and source determination assessment results are obtained.

[0005] Preferably, the historical data of the collection site in S1 is extracted and input into a geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area, including: S11: Collect historical site data to obtain a set of original site information. The historical site data includes, but is not limited to, historical site production drawings, process flow, raw and auxiliary material lists and / or underground storage tank distribution maps. S12: Extract information from the original site information set to obtain structured site feature data, which includes spatial information and associated attribute information; S13: Based on the pollution source identification rules, attribute information is superimposed and filtered to obtain the source strength area determination criteria. The determination criteria include the spatial proximity of the pollution source or the material correlation of the process flow. S14: Input the structured site feature data and the source strength area determination criteria into the geographic information analysis system to obtain potential pollution source areas, and the potential pollution source areas are identified as source strength areas; S15: Based on the potential pollution source area, the potential background area is spatially verified and its scope is corrected to obtain the final background area delineation result.

[0006] Preferably, in step S2, based on the potential pollution source area, on-site sampling and collection are carried out, total organic carbon and total nitrogen are measured, and data on total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C), and carbon-nitrogen ratio (C / N) are extracted to obtain a pollution characteristic dataset, including: S21: In the potential pollution source area, on-site sampling points are set up to obtain the sampling point locations; S22: Based on the sampling point settings, samples are collected to obtain soil samples; S23: The soil sample is pretreated to obtain the sample to be tested; S24: The total organic carbon (TOC) of the sample to be tested is determined to obtain the TOC data. S25: Perform total nitrogen determination on the sample to be tested to obtain total nitrogen TN data; S26: The concentration of the target pollutant in the sample to be tested is determined to obtain the target pollutant concentration C data, wherein the target pollutant includes TPH and PAHs; S27: Calculate the ratio between the total organic carbon (TOC) data and the total nitrogen (TN) data to obtain the carbon-nitrogen ratio (C / N); S28: The carbon-nitrogen ratio Y is correlated with the target pollutant concentration C data to obtain a pollution feature dataset.

[0007] Preferably, step S3 sorts the pollution feature dataset and identifies extreme points, and automatically constructs equidistant inverse concentration sampling intervals based on the concentration range of the target pollutant within the survey area. After sample point matching and filtering, a statistically representative sample point set is obtained, including: S31: Sort the pollution feature dataset to obtain the concentration reciprocal sorting result, which is a sequence formed by arranging each sampling point in ascending order according to the reciprocal of its pollutant concentration value; S32: Based on the reciprocal sorting results of the concentration, after extreme point identification, source strength points and control points are obtained. The source strength point is the sampling point with the highest concentration value, and the control point is the sampling point with the lowest concentration value. S33: Using the Sturgess criterion, the optimal number of sample groups k for the sample distribution is calculated based on the number of sampling points n. The formula for calculating the Sturgess criterion includes k = 1 + 3.322 log 10 n; S34: Based on the pollutant concentration values ​​at the source strength point and the control point, the reciprocal span of the concentration gradient is obtained by reciprocal calculation. S35: Using the inverse concentration span and the optimal number of groups k, the interval is divided to obtain multiple equally spaced inverse concentration sampling intervals, which together cover the inverse concentration span. S36: Based on the equidistant concentration reciprocal sampling intervals, a statistically representative sample point set is obtained through sample point matching and screening.

[0008] Preferably, the linear regression model expression constructed in S4 is obtained by using the statistically representative sample set, calculating the Cook distance, and removing outliers that cause bias interference to the regression slope, to obtain an optimized sample dataset, including: S41: Using the statistically representative sample point set, extract the pollutant concentration C and the C / N ratio to obtain a sample dataset, which includes N sample points; S42: Construct a linear regression model expression, which is Y=a / C+b, where a is the regression coefficient and b is the intercept; S43: Input the sample dataset into the linear regression model expression to obtain the initial regression analysis results, which include the initial fitting parameters for each sample point; S44: Based on the initial regression analysis results, the Cook distance Di value for each sample point is obtained after Cook distance calculation; S45: Using statistical criteria, threshold calculation is performed to obtain outlier determination rules. The outlier determination rules include Di>4 / (Nk-1) as the determination condition, where N is the total number of samples and k is the number of independent variables. S46: Based on the Cook distance Di value of each sample point, and by comparing it with the outlier determination rule, the set of outliers is obtained; S47: A data filtering operation is performed to remove sample points and obtain an optimized sample dataset. The data filtering operation includes removing sample points from the outlier set that cause bias interference to the regression slope. S48: Input the optimized sample dataset into the linear regression model expression to obtain the final regression model. The final regression model is determined with the goal of maximizing the coefficient of determination R² and includes the final sample size n.

[0009] Preferably, in step S5, a two-dimensional coordinate system is constructed. This system is input, a two-dimensional scatter plot is drawn, and an envelope is obtained by delineating the characteristic chemical space based on known source fingerprint data. Based on the relative positional relationship between the optimized sample dataset and the envelope, isotope value overlap screening is performed first, followed by chemical composition difference discrimination to obtain representative sample points. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate, including: S51: Construct a two-dimensional coordinate system to obtain a coordinate system with 1 / C as the abscissa and the C / N ratio as the ordinate; S52: Input the optimized sample dataset into the two-dimensional coordinate system to obtain a two-dimensional scatter plot; S53: Based on the known source fingerprint data, the characteristic chemical space is delineated by calibration on the two-dimensional scatter plot to obtain the envelope. The known source fingerprint data includes coal source fingerprint data and oil source fingerprint data. S54: Determine the distribution area of ​​the sample points to obtain the relative positional relationship between the sample points and the envelope; S55: Based on the relative positional relationship between the sample points and the envelope, a preliminary source classification sample point set is obtained through screening. The screening process includes retaining sample points distributed within different envelopes when the sample points are distributed within different envelopes. S56: Isotope value comparison analysis is used to screen for overlapping isotope values ​​and obtain a candidate sample point set, which includes multiple candidate sample points with similar isotope values. S57: Using chemical composition difference discrimination, a secondary judgment is made on each candidate sample in the candidate sample set to obtain the chemical envelope belonging information of each candidate sample; S58: Based on the chemical envelope attribution information, the candidate sample point set is processed by a priority retention strategy to obtain representative sample points. The priority retention strategy includes prioritizing the retention of sample points that fall within different chemical envelopes to solve the source resolution problem caused by overlapping isotope values.

[0010] Preferably, step S6 involves determining the stable isotope abundance of the representative samples and calculating a structured probability distribution to obtain the original isotope abundance data and source determination assessment results, including: S61: By measuring the stable isotope abundance of the representative sample points, isotope information is collected and the structured probability distribution of each pollution source is calculated to obtain the original isotope abundance data, which includes the carbon stable isotope abundance δ13C and the nitrogen stable isotope abundance δ15N. S62: Based on the original isotope abundance data, the pre-acquired pollutant concentration gradient data, and the end-member characteristic values ​​of the pollution source, the original isotope abundance data is obtained through data integration processing. The end-member characteristic values ​​include traffic, oil, coal, and natural soil characteristics. S63: Input the original isotope abundance data into a preset Bayesian mixture model to perform Markov chain Monte Carlo simulation calculations to obtain structured probability distribution data of the original isotope abundance. Calculate the contribution rate confidence interval parameters using a preset confidence interval statistical algorithm. The contribution rate confidence interval parameters include the width or range of the confidence interval. The Bayesian mixture model includes the MixSIAR model. S64: Using the contribution rate confidence interval parameter, compare it with the confidence interval calculated based on the prior distribution to obtain the confidence interval narrowing ratio value. The confidence interval narrowing ratio value is the ratio of the narrowing amount obtained by subtracting the posterior confidence interval width from the prior confidence interval width to the prior confidence interval width. S65: Based on the confidence interval narrowing ratio, the source tracing deterministic assessment result is obtained by comparing it with a preset threshold.

[0011] On the other hand, a multi-source pollution tracing sample point screening device based on carbon-nitrogen ratio concentration is provided. This device is applied to a multi-source pollution tracing sample point screening method based on carbon-nitrogen ratio concentration. The device includes: Pollution source area module: used to collect historical site data, extract information and input it into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area; Sample collection module: used to conduct on-site sampling and collection based on the potential pollution source area, measure total organic carbon and total nitrogen, extract total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C) data and carbon-nitrogen ratio (C / N) to obtain pollution characteristic dataset; Sampling interval module: used to sort the pollution feature dataset and identify extreme points, and automatically construct equidistant inverse concentration sampling intervals by combining the concentration range of the target pollutant in the survey area. After sample point matching and screening, a statistically representative sample point set is obtained. Linear Regression Module: Used to construct the linear regression model expression. Through the statistically representative sample set, Cook distance is calculated to remove outliers that cause bias interference to the regression slope, thus obtaining an optimized sample dataset. Representative sample point module: used to construct a two-dimensional coordinate system. Input the two-dimensional coordinate system, draw a two-dimensional scatter plot, delineate the feature chemical space based on known source fingerprint data to obtain the envelope, and first perform isotope value overlap screening based on the relative positional relationship between the optimized sample dataset and the envelope, and then obtain representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate. Source determination module: used to obtain original isotope abundance data and source determination assessment results by measuring the stable isotope abundance of the representative samples and calculating the structured probability distribution.

[0012] On the other hand, a multi-source pollution tracing sample screening device based on carbon-nitrogen ratio concentration is provided. The multi-source pollution tracing sample screening device based on carbon-nitrogen ratio concentration includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the above-described multi-source pollution tracing sample screening methods based on carbon-nitrogen ratio concentration is implemented.

[0013] On the other hand, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores program code, which can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By pre-identifying high and low concentration gradients and key evolution zones of pollutants, sampling points are preferentially deployed at concentration inflection points and characteristic ratio sensitive areas that reflect source characteristics. A small, high-precision sample is used to achieve a high-accuracy fit to the regression model, thereby reducing costs while achieving precise identification and contribution quantification of multi-source superimposed pollution. With approximately 35% of the original sample size, source tracing accuracy equal to or even better than that of the full sample is achieved. While maintaining a high signal-to-noise ratio in isotope analysis, sampling and experimental testing costs are reduced by 64.9%. This "less is more" strategy demonstrates the scientific validity of gradient-optimized sampling and provides a feasible engineering solution for achieving accurate source tracing in large industrial sites within a limited budget. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a multi-source pollution tracing and sampling method based on carbon-nitrogen ratio concentration provided in an embodiment of the present invention; Figure 2 This is a sample screening diagram and a probability distribution function diagram of coal source contribution rate provided in an embodiment of the present invention; Figure 3 This is a block diagram of a multi-source pollution tracing and sampling device based on carbon-nitrogen ratio concentration provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a multi-source pollution tracing sample screening device based on carbon-nitrogen ratio concentration provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a method for screening multi-source pollution tracing samples based on carbon-to-nitrogen ratio concentration. This method can be implemented using a multi-source pollution tracing sample screening device based on carbon-to-nitrogen ratio concentration, which can be a terminal or a server. Figure 1 The flowchart shown is for a multi-source pollution tracing and sampling method based on carbon-nitrogen ratio concentration. The processing flow of this method may include the following steps:

[0023] Preferably, historical site data is collected, information is extracted and input into a geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area, including: Collect historical site data to obtain a set of original site information. The historical site data includes, but is not limited to, historical site production drawings, process flow, raw and auxiliary material lists and / or underground storage tank distribution maps. Information is extracted from the original site information set to obtain structured site feature data, which includes spatial information and associated attribute information. Based on pollution source identification rules, attribute information is superimposed and filtered to obtain source strength area determination criteria. The determination criteria include the spatial proximity of pollution sources or the material correlation of process flow. The structured site feature data and the source strength zone determination criteria are input into the geographic information analysis system to obtain potential pollution source areas, which are then identified as source strength zones. Based on the potential pollution source area, the potential background area is spatially verified and its scope is corrected to obtain the final background area delineation result.

[0024] In some embodiments, historical data surveys are conducted around the target steel plant to identify steelmaking workshops and storage tank areas as high-risk source areas, and non-production areas within the plant area as potential background areas.

[0025] Preferably, based on the potential pollution source area, on-site sampling and collection are carried out, total organic carbon (TOC) and total nitrogen (TN) are measured, and the total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C), and carbon-to-nitrogen ratio (C / N) data are extracted to obtain a pollution characteristic dataset, including: In the potential pollution source area, on-site sampling points were set up to determine the sampling location; Based on the sampling point settings, samples were collected to obtain soil samples; The soil sample was pretreated to obtain the sample to be tested; The total organic carbon (TOC) of the sample to be tested was determined to obtain TOC data. The total nitrogen (TN) data of the sample to be tested were obtained by measuring the total nitrogen content. The concentration of the target pollutant in the sample to be tested was determined to obtain the target pollutant concentration C data, wherein the target pollutants include TPH and PAHs; The total organic carbon (TOC) data and the total nitrogen (TN) data are compared to obtain the carbon-nitrogen ratio (C / N). The carbon-nitrogen ratio Y is correlated with the target pollutant concentration C to obtain a pollution feature dataset.

[0026] In some embodiments, a total of 37 sampling points were set up in the survey area. Laboratory measurements showed that the concentration of the target pollutant BaP (benzo[a]pyrene) ranged from 0.05 to 138 mg / kg. Total organic carbon (TOC) and total nitrogen (TN) were also measured at the sampling points. The carbon-to-nitrogen ratio Y (i.e., TOC / TN) for each sampling point was calculated, and this value ranged from 44.83 to 64.56, preliminarily indicating the diversity of organic matter sources.

[0027] Preferably, the pollution feature dataset is sorted and extreme points are identified. Combined with the concentration range of the target pollutant within the survey area, equidistant inverse concentration sampling intervals are automatically constructed. After sample point matching and filtering, a statistically representative sample point set is obtained, including: The pollution feature dataset is sorted to obtain a concentration reciprocal sorting result, which is a sequence formed by arranging each sampling point in ascending order according to the reciprocal of its pollutant concentration value. Based on the reciprocal sorting results of the concentration, after extreme point identification, source strength points and control points are obtained. The source strength point is the sampling point with the highest concentration value, and the control point is the sampling point with the lowest concentration value. Using the Sturgess criterion, the optimal number of sample groups k for the sample distribution is calculated based on the number of sampling points n. The formula for calculating the Sturgess criterion includes k = 1 + 3.322 log 10 n; Based on the pollutant concentration values ​​at the source intensity point and the control point, the reciprocal span of the concentration gradient is obtained by reciprocal calculation. The concentration inverse span and the optimal number of groups k are used to divide the interval into multiple equally spaced concentration inverse sampling intervals, which together cover the concentration inverse span. Based on the equidistant concentration reciprocal sampling intervals, a statistically representative set of sample points is obtained through sample point matching and screening.

[0028] In some embodiments, the BaP concentrations of 37 samples are taken as the reciprocal of 1 / C and arranged in ascending order. The sample with the smallest 1 / C (highest concentration) is identified as the representative source intensity. The 1 / C axis (unit: (mg / kg)) is used for grading. -1 Ten equidistant intervals were defined according to the Sturgess criterion.

[0029] Preferably, a linear regression model expression is constructed. Using the statistically representative sample set, outliers that bias the regression slope are removed through Cook distance calculation to obtain an optimized sample dataset, including: By using the statistically representative sample point set, the pollutant concentration C and the C / N ratio are extracted to obtain a sample dataset, which includes N sample points; Construct a linear regression model expression, which is Y=a / C+b, where a is the regression coefficient and b is the intercept; The sample dataset is input into the linear regression model expression to obtain the initial regression analysis results, which include the initial fitting parameters for each sample point. Based on the initial regression analysis results, the Cook distance Di value for each sample point was obtained after Cook distance calculation. Statistical criteria are used to calculate thresholds and obtain outlier determination rules. The outlier determination rules include Di>4 / (Nk-1) as the determination condition, where N is the total number of samples and k is the number of independent variables. Based on the Cook distance Di value of each sample point, and by comparing it with the outlier determination rule, the set of outliers is obtained. A data filtering operation is performed to remove sample points and obtain an optimized sample dataset. The data filtering operation includes removing sample points from the sample dataset that cause bias interference to the regression slope from the set of outliers. The optimized sample dataset is input into the linear regression model expression to obtain the final regression model. The final regression model is determined with the goal of maximizing the coefficient of determination R² and includes the final sample size n.

[0030] In some embodiments, the regression equation is established as Y = -2.6019(1 / C) + 55.0400.

[0031] Preferably, a two-dimensional coordinate system is constructed. The two-dimensional coordinate system is input, and a two-dimensional scatter plot is drawn. Based on known source fingerprint data, the envelope of the characteristic chemical space is delineated. Based on the relative positional relationship between the optimized sample dataset and the envelope, isotope value overlap screening is performed first, and then representative sample points are obtained through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate, including: A two-dimensional coordinate system is constructed to obtain a coordinate system with 1 / C as the abscissa and the C / N ratio as the ordinate; Input the optimized sample dataset into the two-dimensional coordinate system to obtain a two-dimensional scatter plot; Based on the known source fingerprint data, the characteristic chemical space is delineated by calibration on the two-dimensional scatter plot to obtain the envelope. The known source fingerprint data includes coal source fingerprint data and oil source fingerprint data. The distribution area of ​​the sample points is determined to obtain the relative positional relationship between the sample points and the envelope. Based on the relative positional relationship between the sample points and the envelope, a preliminary source classification sample point set is obtained after screening. The screening process includes retaining sample points distributed within different envelopes when the sample points are distributed within different envelopes. Isotope value comparison analysis is used to screen for overlapping isotope values ​​and obtain a candidate sample point set, which includes multiple candidate sample points with similar isotope values. Chemical composition difference discrimination is used to make a secondary judgment on each candidate sample in the candidate sample set to obtain the chemical envelope belonging information of each candidate sample; Based on the chemical envelope attribution information, the candidate sample point set is processed by a priority retention strategy to obtain representative sample points. The priority retention strategy includes prioritizing the retention of sample points that fall within different chemical envelopes to solve the source resolution problem caused by overlapping isotope values.

[0032] In some embodiments, the total sample size n=37, and the threshold for the judgment criterion is 4 / n = 0.1081.

[0033] Elimination process: During the diagnostic process, the system identified the Cook distance D of sample point number 24. iThe value reached 4.9547, far exceeding the judgment threshold of 0.1081. Although this point was in a low-concentration background area, statistical diagnosis confirmed that its chemical fingerprint seriously deviated from the pollution dilution and evolution trend. By eliminating this biased point, the logical consistency of the front-end data of the source tracing system was effectively ensured. According to the regression residuals, the characteristic envelope bandwidth of the dominant source (coal source) was calculated to be 45.50 ~ 64.58, the characteristic envelope bandwidth of the petroleum source was 79.92 ~ 89.46, the characteristic envelope bandwidth of the transportation source was 66.49 ~ 72.21, and the bandwidth of the natural soil source was -6.54 ~ 12.54.

[0034] Preferably, by determining the stable isotope abundance of the representative samples and calculating the structured probability distribution, the original isotope abundance data and source determination assessment results are obtained, including: By measuring the stable isotope abundance of the representative sample points, collecting isotope information and calculating the structured probability distribution of each pollution source, the original isotope abundance data is obtained. The original isotope abundance data includes the carbon stable isotope abundance δ13C and the nitrogen stable isotope abundance δ15N. Based on the original isotope abundance data, the pre-acquired pollutant concentration gradient data, and the end-member characteristic values ​​of the pollution sources, the original isotope abundance data is obtained through data integration processing. The end-member characteristic values ​​include characteristics of transportation, oil, coal, and natural soil. The original isotope abundance data is input into a preset Bayesian mixture model to perform Markov chain Monte Carlo simulation calculations to obtain structured probability distribution data of the original isotope abundance. The contribution rate confidence interval parameters are calculated using a preset confidence interval statistical algorithm. The contribution rate confidence interval parameters include the width or range of the confidence interval. The Bayesian mixture model includes the MixSIAR model. The contribution rate confidence interval parameter is compared with the confidence interval calculated based on the prior distribution to obtain the confidence interval narrowing ratio. The confidence interval narrowing ratio is the ratio of the narrowing amount obtained by subtracting the posterior confidence interval width from the prior confidence interval width to the prior confidence interval width. Based on the narrowing ratio of the confidence interval, the result of the source tracing deterministic assessment is obtained by comparing it with a preset threshold.

[0035] In some embodiments, to ensure the capture of multi-source mixed features, the system employs a forced retention strategy, locking in sample points 34 and 35 located outside the shadow envelope. Although these two points deviate from the dominant coal source evolution path, they carry key transportation and oil source characteristic signals.

[0036] Final screening results: Through dual verification of gradient spacing sampling and fingerprint envelope, 13 representative samples were finally selected from 37 original samples to participate in subsequent isotope analysis.

[0037] It should be noted that stable isotope abundance was determined for the 13 selected representative samples. The concentration reciprocal weights, isotope abundances, and four preset end-member (transportation, oil, coal, and natural soil) characteristic values ​​of the refined samples were input into the coupled analytical model.

[0038] By performing a dual screening process of "gradient spacing sampling + fingerprint envelope verification" on 37 original sampling points, the 13 final representative points showed significant advantages in analytical performance: 0) Accuracy has steadily improved, and the confidence interval (CI) has been significantly optimized. Comparing the Bayesian analysis results of the full sample (37 points) and the refined sample (13 points), the certainty of the core source analysis is significantly improved.

[0039] The 95% confidence interval (CI) width for traffic sources was narrowed from 0.0650 to 0.0602, resulting in an accuracy optimization of 7.5%.

[0040] The coal pulverized coal combustion source, which has the highest contribution rate, still maintains an extremely low CI width of 0.1073 after reduced sampling (optimized ratio 0.7%), proving that the 13 representative sites have fully covered the dominant pollution characteristics of the study area.

[0041] The moderate increase in the 95% CI width for oil sources reflects that by forcibly retaining key tracer points 34 and 35, the model successfully captured the oil source-specific signals that were masked by the full-sample averaging effect.

[0042] The accuracy optimization rate of the natural soil source was close to zero (-0.7%), which proves that the 13 refined representative sites have a high degree of consistency with the whole sample in terms of background signal coverage.

[0043] Table 1. Comparison of Contribution Rates of Various Sources 1) The probability distribution function (PDF) is closer to the logic of physical reality. The PDF comparison chart of coal source contribution rate shows that: The refined PDF curve (solid line) exhibits a sharper single-peak distribution compared to the full sample (dashed line), with a higher peak, indicating a significant reduction in the uncertainty of model analysis. Logical correction: After removing biased outliers such as point 24, the peak position of the refined curve shifted logically to the left, correcting from approximately 84% to 80.58%, which better reflects actual working conditions. This indicates that the refining scheme not only preserves the main signal but also makes the results more consistent with the actual contamination patterns of the site through noise reduction.

[0044] This method achieves the same or even better traceability accuracy as the full sample size using only about 35% (13 / 37) of the original sample. While maintaining a high signal-to-noise ratio in isotope analysis, sampling and experimental testing costs are reduced by 64.9%. This "less is more" strategy demonstrates the scientific validity of gradient-optimized sampling and provides a feasible engineering solution for achieving accurate traceability in large industrial sites with limited budgets.

[0045] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0046] Figure 3 This is a block diagram illustrating a multi-source pollution tracing sample point screening device based on carbon-nitrogen ratio concentration, according to an exemplary embodiment. The device is used in a multi-source pollution tracing sample point screening method based on carbon-nitrogen ratio concentration. (Refer to...) Figure 3 The device includes a pollution source area module, a sample collection module, a sampling interval module, a linear regression module, a representative sample point module, and a source tracing determinism module.

[0047] Pollution source area module: used to collect historical site data, extract information and input it into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area; Sample collection module: used to conduct on-site sampling and collection based on the potential pollution source area, measure total organic carbon and total nitrogen, extract total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C) data and carbon-nitrogen ratio (C / N) to obtain pollution characteristic dataset; Sampling interval module: used to sort the pollution feature dataset and identify extreme points, and automatically construct equidistant inverse concentration sampling intervals by combining the concentration range of the target pollutant in the survey area. After sample point matching and screening, a statistically representative sample point set is obtained. Linear Regression Module: Used to construct the linear regression model expression. Through the statistically representative sample set, Cook distance is calculated to remove outliers that cause bias interference to the regression slope, thus obtaining an optimized sample dataset. Representative sample point module: used to construct a two-dimensional coordinate system. Input the two-dimensional coordinate system, draw a two-dimensional scatter plot, delineate the feature chemical space based on known source fingerprint data to obtain the envelope, and first perform isotope value overlap screening based on the relative positional relationship between the optimized sample dataset and the envelope, and then obtain representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate. Source determination module: used to obtain original isotope abundance data and source determination assessment results by measuring the stable isotope abundance of the representative samples and calculating the structured probability distribution.

[0048] A multi-source pollution tracing sample screening device based on carbon-nitrogen ratio concentration, the multi-source pollution tracing sample screening device based on carbon-nitrogen ratio concentration includes: a processor; a memory, the memory storing computer-readable instructions, when the computer-readable instructions are executed by the processor, to implement the method described in any of the above-described multi-source pollution tracing sample screening methods based on carbon-nitrogen ratio concentration.

[0049] A computer-readable storage medium, characterized in that the computer-readable storage medium stores program code, the program code being invoked by a processor to execute the method as described in any one of claims 1 to 7.

[0050] Figure 4 This is a schematic diagram of the structure of a multi-source pollution tracing and sampling point screening device based on carbon-nitrogen ratio concentration provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the multi-source pollution tracing sampling point screening equipment based on carbon-nitrogen ratio concentration can include the above-mentioned... Figure 3 The illustrated multi-source pollution tracing sample screening device is based on carbon-nitrogen ratio concentration. Optionally, the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration may include a first processor 2001.

[0051] Optionally, the multi-source pollution tracing sampling device 410 based on carbon-nitrogen ratio concentration may also include a memory 2002 and a transceiver 2003.

[0052] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0053] The following is combined Figure 4 A detailed description of each component of the multi-source pollution tracing and sampling device 410 based on carbon-nitrogen ratio concentration is provided below: The first processor 2001 is the control center of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0054] Optionally, the first processor 2001 can perform various functions of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0055] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0056] In a specific implementation, as one example, the multi-source pollution tracing sampling device 410 based on carbon-nitrogen ratio concentration may also include multiple processors, for example... Figure 4 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0057] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0058] Optionally, the memory 2002 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 (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media 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. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0059] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0060] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0061] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be connected to the interface circuit of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0062] It should be noted that, Figure 4 The structure of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0063] Furthermore, the technical effect of the multi-source pollution tracing sample screening device 410 based on carbon-nitrogen ratio concentration can be referred to the technical effect of the multi-source pollution tracing sample screening method based on carbon-nitrogen ratio concentration described in the above method embodiments, and will not be repeated here.

[0064] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0065] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0066] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0067] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0068] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0069] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0075] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration, characterized in that, The method includes: S1: Collect historical site data, extract information and input it into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area; S2: Based on the potential pollution source area, conduct on-site sampling and collection, measure total organic carbon and total nitrogen, extract total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C), and carbon-nitrogen ratio (C / N) data to obtain a pollution characteristic dataset; S3: Sort the pollution feature dataset and identify extreme points. Combined with the concentration range of the target pollutant in the survey area, automatically construct equidistant inverse concentration sampling intervals. After sample point matching and screening, obtain a statistically representative sample point set. S4: Construct a linear regression model expression. Using the statistically representative sample set, calculate the Cook distance, and remove outliers that cause bias interference to the regression slope to obtain an optimized sample dataset. S5: Construct a two-dimensional coordinate system, input the two-dimensional coordinate system, draw a two-dimensional scatter plot, delineate the feature chemical space based on the known source fingerprint data to obtain the envelope, and based on the relative positional relationship between the optimized sample dataset and the envelope, first perform isotope value overlap screening, and then obtain representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the horizontal axis and the carbon-nitrogen ratio C / N as the vertical axis. S6: By measuring the stable isotope abundance of the representative samples and calculating the structured probability distribution, the original isotope abundance data and source determination assessment results are obtained.

2. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, The historical data of the collection site in S1 is extracted and input into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area, including: S11: Collect historical site data to obtain a set of original site information. The historical site data includes, but is not limited to, historical site production drawings, process flow, raw and auxiliary material lists and / or underground storage tank distribution maps. S12: Extract information from the original site information set to obtain structured site feature data, which includes spatial information and associated attribute information; S13: Based on the pollution source identification rules, attribute information is superimposed and filtered to obtain the source strength area determination criteria. The determination criteria include the spatial proximity of the pollution source or the material correlation of the process flow. S14: Input the structured site feature data and the source strength area determination criteria into the geographic information analysis system to obtain potential pollution source areas, and the potential pollution source areas are identified as source strength areas; S15: Based on the potential pollution source area, the potential background area is spatially verified and its scope is corrected to obtain the final background area delineation result.

3. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, S2 involves on-site sampling and collection based on the potential pollution source area, determination of total organic carbon (TOC) and total nitrogen (TN), extraction of TOC, TN, target pollutant concentration (C), and carbon-to-nitrogen ratio (C / N) data to obtain a pollution characteristic dataset, including: S21: In the potential pollution source area, on-site sampling points are set up to obtain the sampling point locations; S22: Based on the sampling point settings, samples are collected to obtain soil samples; S23: The soil sample is pretreated to obtain the sample to be tested; S24: The total organic carbon (TOC) of the sample to be tested is determined to obtain the TOC data. S25: Perform total nitrogen determination on the sample to be tested to obtain total nitrogen TN data; S26: The concentration of the target pollutant in the sample to be tested is determined to obtain the target pollutant concentration C data, wherein the target pollutant includes TPH and PAHs; S27: Calculate the ratio between the total organic carbon (TOC) data and the total nitrogen (TN) data to obtain the carbon-nitrogen ratio (C / N); S28: The carbon-nitrogen ratio Y is correlated with the target pollutant concentration C data to obtain a pollution feature dataset.

4. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, S3 sorts the pollution feature dataset and identifies extreme points. Combined with the concentration range of the target pollutant within the survey area, it automatically constructs equidistant reciprocal sampling intervals for concentration. After sample point matching and filtering, a statistically representative sample point set is obtained, including: S31: Sort the pollution feature dataset to obtain the concentration reciprocal sorting result, which is a sequence formed by arranging each sampling point in ascending order according to the reciprocal of its pollutant concentration value; S32: Based on the reciprocal sorting results of the concentration, after extreme point identification, source strength points and control points are obtained. The source strength point is the sampling point with the highest concentration value, and the control point is the sampling point with the lowest concentration value. S33: Using the Sturgess criterion, the optimal number of sample groups k for the sample distribution is calculated based on the number of sampling points n. The formula for calculating the Sturgess criterion includes k = 1 + 3.322 log 10 n; S34: Based on the pollutant concentration values ​​at the source strength point and the control point, the reciprocal span of the concentration gradient is obtained by reciprocal calculation. S35: Using the inverse concentration span and the optimal number of groups k, the interval is divided to obtain multiple equally spaced inverse concentration sampling intervals, which together cover the inverse concentration span. S36: Based on the equidistant concentration reciprocal sampling intervals, a statistically representative sample point set is obtained through sample point matching and screening.

5. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, The linear regression model expression in S4 is constructed by using the statistically representative sample set, calculating the Cook distance, and removing outliers that bias the regression slope to obtain an optimized sample dataset, including: S41: Using the statistically representative sample point set, extract the pollutant concentration C and the C / N ratio to obtain a sample dataset, which includes N sample points; S42: Construct a linear regression model expression, which is Y=a / C+b, where a is the regression coefficient and b is the intercept; S43: Input the sample dataset into the linear regression model expression to obtain the initial regression analysis results, which include the initial fitting parameters for each sample point; S44: Based on the initial regression analysis results, the Cook distance Di value for each sample point is obtained after Cook distance calculation; S45: Using statistical criteria, threshold calculation is performed to obtain outlier determination rules. The outlier determination rules include Di>4 / (Nk-1) as the determination condition, where N is the total number of samples and k is the number of independent variables. S46: Based on the Cook distance Di value of each sample point, and by comparing it with the outlier determination rule, the set of outliers is obtained; S47: A data filtering operation is performed to remove sample points and obtain an optimized sample dataset. The data filtering operation includes removing sample points from the outlier set that cause bias interference to the regression slope. S48: Input the optimized sample dataset into the linear regression model expression to obtain the final regression model. The final regression model is determined with the goal of maximizing the coefficient of determination R² and includes the final sample size n.

6. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, The S5 step involves constructing a two-dimensional coordinate system, inputting the two-dimensional coordinate system, plotting a two-dimensional scatter plot, delineating the feature chemical space based on known source fingerprint data to obtain the envelope, and first performing isotope value overlap screening based on the relative positional relationship between the optimized sample dataset and the envelope, then obtaining representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate, including: S51: Construct a two-dimensional coordinate system to obtain a coordinate system with 1 / C as the abscissa and the C / N ratio as the ordinate; S52: Input the optimized sample dataset into the two-dimensional coordinate system to obtain a two-dimensional scatter plot; S53: Based on the known source fingerprint data, the characteristic chemical space is delineated by calibration on the two-dimensional scatter plot to obtain the envelope. The known source fingerprint data includes coal source fingerprint data and oil source fingerprint data. S54: Determine the distribution area of ​​the sample points to obtain the relative positional relationship between the sample points and the envelope; S55: Based on the relative positional relationship between the sample points and the envelope, a preliminary source classification sample point set is obtained through screening. The screening process includes retaining sample points distributed within different envelopes when the sample points are distributed within different envelopes. S56: Isotope value comparison analysis is used to screen for overlapping isotope values ​​and obtain a candidate sample point set, which includes multiple candidate sample points with similar isotope values. S57: Using chemical composition difference discrimination, a secondary judgment is made on each candidate sample in the candidate sample set to obtain the chemical envelope belonging information of each candidate sample; S58: Based on the chemical envelope attribution information, the candidate sample point set is processed by a priority retention strategy to obtain representative sample points. The priority retention strategy includes prioritizing the retention of sample points that fall within different chemical envelopes to solve the source resolution problem caused by overlapping isotope values.

7. The method for screening multi-source pollution tracing sampling points based on carbon-nitrogen ratio concentration according to claim 1, characterized in that, The process S6 involves determining the stable isotope abundance of the representative samples and calculating the structured probability distribution to obtain the original isotope abundance data and the source determination assessment results, including: S61: By measuring the stable isotope abundance of the representative sample points, isotope information is collected and the structured probability distribution of each pollution source is calculated to obtain the original isotope abundance data, which includes the carbon stable isotope abundance δ13C and the nitrogen stable isotope abundance δ15N. S62: Based on the original isotope abundance data, the pre-acquired pollutant concentration gradient data, and the end-member characteristic values ​​of the pollution source, the original isotope abundance data is obtained through data integration processing. The end-member characteristic values ​​include traffic, oil, coal, and natural soil characteristics. S63: Input the original isotope abundance data into a preset Bayesian mixture model to perform Markov chain Monte Carlo simulation calculations to obtain structured probability distribution data of the original isotope abundance. Calculate the contribution rate confidence interval parameters using a preset confidence interval statistical algorithm. The contribution rate confidence interval parameters include the width or range of the confidence interval. The Bayesian mixture model includes the MixSIAR model. S64: Using the contribution rate confidence interval parameter, compare it with the confidence interval calculated based on the prior distribution to obtain the confidence interval narrowing ratio value. The confidence interval narrowing ratio value is the ratio of the narrowing amount obtained by subtracting the posterior confidence interval width from the prior confidence interval width to the prior confidence interval width. S65: Based on the confidence interval narrowing ratio, the source tracing deterministic assessment result is obtained by comparing it with a preset threshold.

8. A multi-source pollution source tracing sample screening device based on carbon-nitrogen ratio concentration, wherein the multi-source pollution source tracing sample screening device based on carbon-nitrogen ratio concentration is used to implement the multi-source pollution source tracing sample screening method based on carbon-nitrogen ratio concentration as described in any one of claims 1-7, characterized in that, The device includes: Pollution source area module: used to collect historical site data, extract information and input it into the geographic information analysis system to obtain the delineation results of potential pollution source areas and the final background area; Sample collection module: used to conduct on-site sampling and collection based on the potential pollution source area, measure total organic carbon and total nitrogen, extract total organic carbon (TOC), total nitrogen (TN), target pollutant concentration (C) data and carbon-nitrogen ratio (C / N) to obtain pollution characteristic dataset; Sampling interval module: used to sort the pollution feature dataset and identify extreme points, and automatically construct equidistant inverse concentration sampling intervals by combining the concentration range of the target pollutant in the survey area. After sample point matching and screening, a statistically representative sample point set is obtained. Linear Regression Module: Used to construct the linear regression model expression. Through the statistically representative sample set, Cook distance is calculated to remove outliers that cause bias interference to the regression slope, thus obtaining an optimized sample dataset. Representative sample point module: used to construct a two-dimensional coordinate system. Input the two-dimensional coordinate system, draw a two-dimensional scatter plot, delineate the feature chemical space based on known source fingerprint data to obtain the envelope, and first perform isotope value overlap screening based on the relative positional relationship between the optimized sample dataset and the envelope, and then obtain representative sample points through chemical composition difference discrimination. The two-dimensional coordinate system is a coordinate system with 1 / C as the abscissa and the carbon-nitrogen ratio C / N as the ordinate. Source determination module: used to obtain original isotope abundance data and source determination assessment results by measuring the stable isotope abundance of the representative samples and calculating the structured probability distribution.

9. A multi-source pollution tracing and sampling point screening device based on carbon-nitrogen ratio concentration, characterized in that, The multi-source pollution tracing sample screening processor based on carbon-nitrogen ratio concentration; a memory, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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