Method for identifying leakage range of sewage pipe network based on fluorescence index

Abstract

The invention discloses a method for identifying the leakage range of a sewage pipe network based on fluorescence indexes. A monitoring well is arranged; carrying out sample sampling on each sampling point; carrying out index determination on the sample at each sampling point; pCA principal component analysis is carried out on the indexes of the samples of all the sampling points; and calculating the relative content ratio of each monitoring well sampling point, performing linear fitting on the distance between the pipeline leakage source and the monitoring well sampling point and the relative content ratio of the monitoring well sampling point to obtain a fitting straight line, and determining the influence range of the pollutants according to the fitting straight line. The invention provides a non-invasive and high-sensitivity detection method, organic matters in sewage and underground water can be detected at lower concentration, the correlation between the relative content ratio and the leakage point distance is comprehensively considered, and the leakage range and diffusion degree of pipe network pollutants are accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the field of water environment management, and in particular to a method for identifying leakage ranges of sewage pipe networks based on fluorescence indicators. Background Art

[0002] In the process of modern urbanization, underground pipeline networks are a crucial component of infrastructure, encompassing multiple functions such as water supply, drainage, and sewage treatment. However, due to aging materials, substandard construction, and environmental factors, sewage pipes in many cities are gradually experiencing leakage. These leaks not only waste resources but also have profound impacts on the ecological environment, particularly contaminating soil and groundwater, which in turn affects the health of lake ecosystems. Research has shown that nutrients such as nitrogen and phosphorus in sewage can cause soil eutrophication, which in turn affects plant growth and soil microbial diversity, ultimately threatening the stability of the entire ecosystem. Furthermore, sewage leaks can have a serious impact on groundwater, where pollutants can quickly spread and deteriorate water quality. This not only affects drinking water safety but can also trigger ecological crises in water sources. Harmful microorganisms and chemicals in sewage can spread with the flow of groundwater, causing widespread contamination. Once contaminated, groundwater restoration is extremely difficult and costly.

[0003] In addition, the impact of sewage pipe leakage on lake ecosystems cannot be ignored. As an important aquatic ecosystem, the water quality of lakes is directly affected by the surrounding environment. Pollutants from sewage leakage will be discharged into the lake along with groundwater, causing eutrophication of the lake water body, and then triggering ecological problems such as blue-green algae blooms. These problems not only affect the self-purification capacity of the water body, but also threaten the biodiversity in the lake, causing damage to the ecological chain, and ultimately affecting human life and economic activities. Therefore, preventing and identifying pipe network leaks has become particularly critical. At present, most of the existing identification methods are limited to identifying the source of pollution, but cannot accurately identify the exact scope of the polluted area, which brings certain difficulties to the protection and governance of the ecological environment. Summary of the Invention

[0004] The present invention aims to address the aforementioned issues with existing technologies by providing a method for identifying the scope of sewage pipe network leaks based on fluorescence indicators. Accurately identifying the scope of sewage pipe network leaks and improving the efficiency of sewage treatment and water system repair are of great significance for achieving the rational allocation of water resources and protecting the urban environment and the health of residents.

[0005] The above-mentioned purpose of the present invention is achieved by the following technical means:

[0006] The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators includes the following steps:

[0007] Step 1: Deploy monitoring wells between the leaking pipe network and the lake;

[0008] Step 2: Samples were collected from sewage sampling points, groundwater background sampling points, lake water background sampling points, and monitoring well sampling points.

[0009] Step 3: Determine the indicators of samples from each of the above sampling points, including pollution source characteristic indicators, groundwater characteristic indicators, and organic matter (DOM) indicators;

[0010] Step 4: Perform principal component analysis (PCA) on the indicators of samples from each sewage sampling point, groundwater background sampling point, lake water background sampling point, and monitoring well sampling point, and merge the organic matter (DOM) indicator into a pollution source characteristic indicator or a groundwater characteristic indicator;

[0011] Step 5: Calculate the relative content ratio of each monitoring well sampling point = the maximum load pollution source characteristic index / maximum load groundwater characteristic index. Use linear fitting to obtain a fitting line by combining the distance between the pipeline leakage source and the monitoring well sampling point and the relative content ratio of the monitoring well sampling point. Confirm the impact range of the pollutant based on the fitting line.

[0012] As mentioned above, the monitoring wells are arranged along the direction of groundwater flow.

[0013] As mentioned above, the monitoring wells are arranged at intervals of 10-30 meters from the leakage pipeline network.

[0014] As mentioned above, the sampling time is selected during the dry season.

[0015] As mentioned above, the pollution source characteristic indicators include dissolved organic carbon (DOC) concentration, Cu concentration, Zn concentration, and Pb concentration; the groundwater characteristic indicators include NO3 - concentration, Cl - concentration, and SO4 2- Concentration. Organic matter (DOM) indicators include the relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3.

[0016] As described above, the relative concentrations of component C1, component C2, and component C3 are obtained based on parallel factor analysis of three-dimensional fluorescence spectra (EEMs).

[0017] Merging organic matter (DOM) indicators into pollution source characteristic indicators or groundwater characteristic indicators includes the following steps: if the pollution source characteristic indicator has the highest correlation coefficient with the relative concentration of component C1, the relative concentration of component C1 is merged into the pollution source characteristic indicator; if the groundwater characteristic indicator has the highest correlation coefficient with the relative concentration of component C1, the relative concentration of component C1 is merged into the groundwater characteristic indicator; if the pollution source characteristic indicator has the highest correlation coefficient with the relative concentration of component C2, the relative concentration of component C2 is merged into the pollution source characteristic indicator; if the groundwater characteristic indicator has the highest correlation coefficient with the relative concentration of component C2, the relative concentration of component C2 is merged into the groundwater characteristic indicator; if the pollution source characteristic indicator has the highest correlation coefficient with the relative concentration of component C3, the relative concentration of component C3 is merged into the pollution source characteristic indicator; if the groundwater characteristic indicator has the highest correlation coefficient with the relative concentration of component C3, the relative concentration of component C3 is merged into the groundwater characteristic indicator.

[0018] The impact range of the pollutant is confirmed as described above. On the fitting straight line, when the distance is greater than the distance threshold, the relative content ratio of the monitoring well sampling point is less than the relative content ratio of the groundwater background sampling point. The impact range of the pollutant is confirmed based on the distance threshold.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. The present invention comprehensively considers the fluorescence characteristics of organic matter DOM and the three-dimensional fluorescence spectrum, and can obtain more accurate leakage range identification. Traditional sewage pipe leakage detection technology has the shortcomings of limited application conditions, single detection content, and incomplete detection. The present invention provides a non-invasive, highly sensitive detection method that can detect organic matter in sewage and groundwater at relatively low concentrations. The present invention can also monitor the content changes of various organic components in the water body and the pollution status of the entire water body in real time, which is of great significance for timely detection and response to sewage leakage incidents. By combining the three-dimensional fluorescence spectrum with the principal component analysis method, the present invention can effectively improve the accuracy of substance identification and provide a new rapid monitoring and analysis method for water quality monitoring, process optimization and treatment effect evaluation.

[0021] 2. This invention comprehensively considers the correlation between relative content ratios and distance from the leakage point, and through linear fitting, accurately assesses the leakage range and diffusion extent of pollutants in the pipeline network. This provides a systematic method for tracking pollutant leakage in sewage pipelines. Based on the fitted line, this invention can predict the diffusion path and diffusion rate of pollutants, providing strong technical support for rapid response to leakage incidents and facilitating the scientific development of pollution control and remediation plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1This is a diagram showing the overview of a lake and the layout of sampling points in Example 2;

[0023] Figure 2 is a schematic diagram of the position of the characteristic peaks identified in Example 2;

[0024] Figure 3 It is the PCA scatter plot of each indicator;

[0025] Figure 4 It is a scatter plot of the relative content ratio of each monitoring well sampling point and the distance from the leakage point;

[0026] Figure 5 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0027] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0028] Example 1:

[0029] Excitation-Emission Matrix (EEM) fluorescence spectroscopy is an advanced spectral analysis technique that generates a three-dimensional fluorescence intensity spectrum by measuring the fluorescence intensity of a sample at different excitation and emission wavelengths. This method can provide detailed information about fluorescent substances in a sample, including fluorescence peak position, fluorescence intensity, and fluorescence integrated volume, and is suitable for the qualitative and quantitative analysis of complex organic matter. Therefore, as a water quality monitoring method, analyzing the fluorescence characteristics of dissolved organic matter (DOM) can be used to identify and quantify pollutants in the environment and further assess the pollution status of water bodies.

[0030] The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators includes the following steps:

[0031] Step 1: Deploy monitoring wells along the direction of groundwater flow between the leaking pipeline near the lake and the lake.

[0032] During the dry season, groundwater typically drains into lakes. If a pipeline leaks, pollutants can spread to the lakes along the groundwater flow. Select a leaking pipeline near a lake and install monitoring wells at the leak point and in the potentially affected groundwater and lake areas. The monitoring wells should cover the area between the leak source and the lake to accurately capture the spread of pollutants.

[0033] To ensure adequate monitoring density between the leak site and the lake, monitoring wells were set up every 20 meters, along the direction of groundwater flow. To ensure representative monitoring data, the depth of the monitoring wells needed to be adjusted according to the groundwater level and hydrogeological conditions.

[0034] Step 2: Samples were collected from sewage sampling points, groundwater background sampling points, lake water background sampling points, and monitoring well sampling points.

[0035] Sewage sample: Set up a sewage sampling point close to the pipeline leakage point or inside the pipeline.

[0036] Groundwater background sampling point: Select a groundwater background sampling point in the upstream area of ​​the pipeline leakage that is far away from the leakage source and not affected by pollution, and set it up to compare the water samples affected by pollution.

[0037] Lake water background sampling point: set up a lake water background sampling point in the lake water area far away from the leakage pipeline network.

[0038] Monitoring well sampling points: Each monitoring well serves as a monitoring well sampling point to track the spread of groundwater pollutants in real time.

[0039] Sampling time: Samples were collected during the dry season (November to January) to ensure that groundwater was discharged into the lake.

[0040] Step 3: The indicators of the samples at each sampling point are measured, including pollution source characteristic indicators, groundwater characteristic indicators, and organic matter (DOM) indicators. The pollution source characteristic indicators include dissolved organic carbon (DOC) concentration, Cu concentration, Zn concentration, and Pb concentration; the groundwater characteristic indicators include NO3 - concentration, Cl - concentration, and SO4 2- Concentration. Organic matter (DOM) indicators include the relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3.

[0041] The determination of organic matter (DOM) indicators includes the following steps:

[0042] Samples collected at each of the aforementioned sampling points were measured using a three-dimensional fluorescence spectrometer (3D EEM). This instrument excites molecules within a water sample within a specific excitation wavelength range and records their emission spectra, forming a three-dimensional fluorescence spectrum. Within the 3D fluorescence spectrum, the corresponding characteristic fluorescence peaks are identified based on the positions of the maximum excitation and emission wavelengths, thereby determining the composition of the organic matter (DOM). Table 1 provides a reference table of characteristic fluorescence peaks. Characteristic fluorescence peaks A, C, D, and M are associated with humic substances, while characteristic fluorescence peaks B, T, and N are associated with protein-like substances.

[0043] Table 1

[0044] Characteristic fluorescence peak Ex. / Em.Max(nm) Description of component properties A 230～260 / 380～460 UV humic-like B 230 / 300～320 Protein-like, tyrosine-like C 320～360 / 420～460 Visible humic-like D 390 / 509 soil fulvic acid M 290～310 / 370～410 marine humic-like N 280 / 370 Protein-like, associated with phytoplankton T 275 / 340 Protein-like, tryptophan-like

[0045] Parallel factor analysis (PARAFAC) was performed on the three-dimensional fluorescence spectra (EEMs). The relative concentrations of the organic matter components were calculated. All collected three-dimensional fluorescence spectra required preprocessing, including denoising, background removal, and normalization, to ensure data quality.

[0046] The parallel factor analysis method (PARAFAC) is shown in formula (1).

[0047]

[0048] Where: F is the fluorescence factor number;

[0049] x ijk is the fluorescence intensity of the i-th sample at emission wavelength (Em) j and excitation wavelength (Ex) k in the fluorescence intensity matrix, which is generated from the three-dimensional fluorescence spectrum. I, J, and K are the total number of samples, the total number of emission wavelengths, and the total number of excitation wavelengths, respectively.

[0050] ε ijk is the residual matrix.

[0051] a if is the excitation spectrum matrix;

[0052] b jf is the emission spectrum matrix;

[0053] c kf is the relative concentration matrix;

[0054] The core of parallel factor analysis is to minimize the residual ε ijk To optimize.

[0055]

[0056] The model was fitted by the alternating least squares method (ALS) to obtain the relative concentrations of components C1, C2, and C3 of the organic matter index. According to the maximum excitation wavelength (Excitation) / maximum emission wavelength (Emission) corresponding to components C1, C2, and C3, the component properties corresponding to components C1, C2, and C3 were obtained according to Table 1.

[0057] In the calculation, in order to reduce possible rotation or scale ambiguity, the parameter matrix a can be if 、b jf 、c kf Perform non-negativity constraints.

[0058] Step 4: Analyze the organic carbon (DOC) concentration, Cu concentration, Zn concentration, Pb concentration, NO3 - concentration, Cl - concentration, SO4 2- The concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3 were subjected to principal component analysis (PCA). Based on the correlation coefficients between the relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3 and the pollution source characteristic indicators and groundwater characteristic indicators, the relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3 were merged into the pollution source characteristic indicators or groundwater characteristic indicators.

[0059] If the component with the highest correlation coefficient with the relative concentration of component C1 is the pollution source characteristic indicator, then the relative concentration of component C1 is merged into the pollution source characteristic indicator; if the component with the highest correlation coefficient with the relative concentration of component C1 is the groundwater characteristic indicator, then the relative concentration of component C1 is merged into the groundwater characteristic indicator; if the component with the highest correlation coefficient with the relative concentration of component C2 is the pollution source characteristic indicator, then the relative concentration of component C2 is merged into the pollution source characteristic indicator; if the component with the highest correlation coefficient with the relative concentration of component C2 is the groundwater characteristic indicator, then the relative concentration of component C2 is merged into the groundwater characteristic indicator; if the component with the highest correlation coefficient with the relative concentration of component C3 is the pollution source characteristic indicator, then the relative concentration of component C3 is merged into the pollution source characteristic indicator; if the component with the highest correlation coefficient with the relative concentration of component C3 is the groundwater characteristic indicator, then the relative concentration of component C3 is merged into the groundwater characteristic indicator;

[0060] For example, if the highest correlation coefficient with the relative concentration of component C1 is the organic carbon (DOC) concentration, Cu concentration, Zn concentration, or Pb concentration, then the relative concentration of component C1 belongs to the characteristic indicator of pollution source; if the highest correlation coefficient with the relative concentration of component C1 is NO3 - concentration or Cl - Concentration or SO4 2- The relative concentration of component C1 is a characteristic indicator of groundwater. The same applies to components C2 and C3.

[0061] Through PCA, we can clearly distinguish the pollution source characteristic indicators and groundwater characteristic indicators at each sampling point, and further analyze the groundwater pollution situation. We can obtain the pollution source characteristic indicators with the highest load and the groundwater characteristic indicators with the highest load at each sampling point.

[0062] Step 5: Calculate the relative content ratio of each monitoring well sampling point = (the pollution source characteristic index of the maximum load) / (the groundwater characteristic index of the maximum load). As the distance between the monitoring well sampling point and the pipeline leakage source increases, the relative content ratio will gradually approach that of the groundwater background sampling point.

[0063] A linear fit is performed between the distance between the pipeline leak source and the monitoring well sampling point and the relative concentration ratio at the monitoring well sampling point. This fitted curve can be used to predict the diffusion trend of the pollutant and determine the impact range of the pollutant. Specifically, on the fitted line, when the distance exceeds a distance threshold, the relative concentration ratio at the monitoring well sampling point is lower than the relative concentration ratio at the groundwater background sampling point. This distance threshold is used to determine the impact range of the pollutant.

[0064] Example 2

[0065] This example selects a lake as an example for analysis. Figure 1 .

[0066] In step 2, the sampling time of each sampling point is: December 2023 (dry season).

[0067] Nine sampling points were set up: one sewage sampling point at the pipe network leak, one groundwater background sampling point upstream of the pipe network leak, and one lake water background sampling point. In addition, six monitoring well sampling points were deployed, with one set every 20 meters from the pipe network leak location starting at 0 meters.

[0068] Indicators collected include pollution source characteristics, groundwater characteristics, and organic matter (DOM). Sampling depths ranged from 5 to 25 meters, depending on the hydrogeological conditions of the monitoring site. Before sample collection, water was pumped for at least 5 minutes until the temperature, pH, electrical conductivity (EC), dissolved oxygen (DO), and redox potential (Eh) stabilized, and fresh water samples were then collected. Before sampling, the sampling bottle was rinsed multiple times with the sample water, and suspended solids in the water were filtered through a 0.45 μm filter membrane. Anions and cations were stored in PET bottles. Cation samples were acidified by adding nitric acid, and DOM samples were acidified by adding phosphoric acid and placed in brown glass bottles. The bottles were then stored in a 4°C refrigerator and returned to the laboratory for testing and analysis.

[0069] In step 3, a three-dimensional fluorescence spectrum of each sample was plotted. It can be clearly seen that it is related to the peaks A, B, and C in Table 1. There is a more obvious difference between sewage and uncontaminated groundwater. The three-dimensional fluorescence spectrum of sewage in the pipe network has the highest overall brightness, indicating that its organic matter content is higher. The corresponding peak position is as follows: Figure 2 As shown, the C peak of sewage is brighter than that of groundwater samples, while the C peak in groundwater is not obvious.

[0070] The relative concentrations of organic matter components C1, C2, and C3 were further calculated. Among them, the maximum excitation wavelength Ex.Max of component C1 was 240nm, and the maximum emission wavelength Em.Max was 422nm, corresponding to peak A, and was determined to be microbial humus, usually indicating endogenous sources; the maximum excitation wavelength Ex.Max of component C2 was 225nm, and the maximum emission wavelength Em.Max was 344nm, which can be corresponding to peak C, and was determined to be tyrosine protein, affected by pollution sources with high microbial content such as domestic sewage; the maximum excitation wavelength Ex.Max of component C3 was 265 (360) nm, and the maximum emission wavelength Em.Max was 492 (482) nm, and was determined to be terrestrial humus. The relative concentrations of components C1, C2, and C3 obtained by parallel factor analysis are expressed as the maximum fluorescence intensity Fmax in Raman units (RU). Component C1 has the highest content, accounting for 43.06% to 54.30%, followed by component C2, accounting for 31.10% to 42.77%, and component C3 has the lowest content, accounting for 12.94% to 16.47%.

[0071] In step 4, the content of fluorescent components in each sample was normalized, and principal component analysis (PCA) was used to extract effective information from the chemical parameters of all samples to identify the main controlling factors of sewage affecting groundwater DOM. Figure 3 As shown in the figure, principal component 1 (PC1) accounts for 39.1% of the total variance, component C2 has high positive loads with Cu, Zn, Pb, and DOC, and the content of component C2 may be related to the input of municipal sewage containing heavy metals and organic matter, which can be defined as a pollution source characteristic indicator (municipal sewage-C2, MS-C2). Principal component 2 (PC2) accounts for 28.5% of the total variance, component C3 has high positive loads with SO4 2- 、Cl - With a high positive load, component C3 may be related to the primary components of groundwater, and component C3 can be considered as a groundwater characteristic indicator (groundwater-C3, GW-C3).

[0072] In step 5, the relative content ratio of each monitoring well sampling point is calculated. The distance between the pipeline leakage source and the monitoring well sampling point and the relative content ratio of the monitoring well sampling point are linearly fitted, as shown in Figure 5. Figure 4 As shown in the figure, the relative content ratio of 0.98 can be used as an evaluation index. It can be seen from the figure that when the distance from the leakage point is greater than 60 meters, the relative content ratio of the pollution source characteristic index to the groundwater characteristic index is smaller than the relative content ratio of the groundwater background sampling point. It can be considered that the leaked sewage has not polluted the groundwater at this time, and the pollution plume edge range should be between 40-60m.

[0073] It should be noted that the embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A method for identifying the leakage range of a sewage pipe network based on fluorescence indicators, characterized in that: The following steps are involved: Step 1: Deploy monitoring wells between the leaking pipe network and the lake; Step 2: Collect samples from sewage sampling points, groundwater background sampling points, lake water background sampling points, and monitoring well sampling points. Step 3: Determine the indicators of samples from each of the above sampling points, including pollution source characteristic indicators, groundwater characteristic indicators, and organic matter (DOM) indicators; Step 4: Perform PCA principal component analysis on the indicators of samples from each sewage sampling point, groundwater background sampling point, lake water background sampling point, and monitoring well sampling point, and merge the organic matter (DOM) indicator into the pollution source characteristic indicator or groundwater characteristic indicator; Step 5: Calculate the relative content ratio of each monitoring well sampling point = maximum load pollution source characteristic index / maximum load groundwater characteristic index, and obtain a fitting straight line by linear fitting the distance between the pipeline leakage source and the monitoring well sampling point and the relative content ratio of the monitoring well sampling point, and confirm the impact range of the pollutant based on the fitting straight line.

2. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 1 is characterized in that: The monitoring wells are arranged along the groundwater flow direction.

3. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 1 is characterized in that: The monitoring wells are arranged at intervals of 10-30 meters from the leakage pipeline network.

4. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 1 is characterized in that: The sample collection time is selected during the dry season.

5. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 1 is characterized in that: The pollution source characteristic indicators include dissolved organic carbon (DOC) concentration, Cu concentration, Zn concentration, and Pb concentration; groundwater characteristic indicators include NO3 - Concentration, Cl - concentration, and SO4 2- Concentration. The organic matter (DOM) index includes the relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3.

6. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 5 is characterized in that: The relative concentration of component C1, the relative concentration of component C2, and the relative concentration of component C3 are obtained based on parallel factor analysis of three-dimensional fluorescence spectra (EEMs).

7. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 5 is characterized in that: Merging the organic matter (DOM) index into the pollution source characteristic index or the groundwater characteristic index includes the following steps: if the pollution source characteristic index has the highest correlation coefficient with the relative concentration of component C1, the relative concentration of component C1 is merged into the pollution source characteristic index; if the groundwater characteristic index has the highest correlation coefficient with the relative concentration of component C1, the relative concentration of component C1 is merged into the groundwater characteristic index; if the pollution source characteristic index has the highest correlation coefficient with the relative concentration of component C2, the relative concentration of component C2 is merged into the pollution source characteristic index; if the groundwater characteristic index has the highest correlation coefficient with the relative concentration of component C2, the relative concentration of component C2 is merged into the groundwater characteristic index; if the pollution source characteristic index has the highest correlation coefficient with the relative concentration of component C3, the relative concentration of component C3 is merged into the pollution source characteristic index; if the groundwater characteristic index has the highest correlation coefficient with the relative concentration of component C3, the relative concentration of component C3 is merged into the groundwater characteristic index.

8. The method for identifying the leakage range of a sewage pipe network based on fluorescence indicators according to claim 1, characterized in that: The confirmed influence range of the pollutant includes: on the fitting straight line, when the distance is greater than the distance threshold, the relative content ratio of the monitoring well sampling point is less than the relative content ratio of the groundwater background sampling point, and the influence range of the pollutant is confirmed according to the distance threshold.

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