Method for densifying sampling points in contaminated sites to determine contamination boundaries

Abstract

This invention relates to the field of soil analysis technology and provides a method for densifying sampling points in contaminated sites to determine pollution boundaries. The method includes the following steps: S1, obtaining an initial set of sample points; S2, generating a grid map of the industrial site to be investigated; S3, generating a prediction result map; S4, obtaining the standard deviation of pixel values ​​based on the prediction result map; S5, determining the undetermined area based on the mean prediction result map and a threshold; S6, determining the first weight of each grid within the undetermined area; S7, adding sample points to the undetermined area; S8, determining whether the number of times sample points have been added has reached a specified H times; if not, returning to S3, otherwise ending the sampling point densification; S9, obtaining the boundary between the contaminated and clean areas of the industrial site to be investigated based on the initial set of sample points after the sampling point densification is completed. This invention makes the pollutant distribution prediction results more accurate and uses a quantitative method to determine the location of added sample points, making the determination of pollution boundaries more accurate and objective.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of soil analysis, in particular to a method for determining the pollution boundary of a contaminated land. BACKGROUND

[0002] Soil pollution in industrial sites or industrial parks often has the characteristics of strong spatial heterogeneity and difficulty in determining the pollution boundary. In actual investigation, a phased investigation strategy is often adopted. In the first phase, systematic sampling or random sampling methods, or a combination of expert knowledge and experience, are used to layout a certain number of sample points for preliminary investigation to analyze the general pattern of pollution distribution. Then, in order to further determine the range of the pollution area exceeding the threshold value, it is necessary to increase the number of points in some high-pollution areas. However, how to reasonably layout the encryption points is still a challenging problem. Currently, there are mainly two methods for laying out encryption points. The first method is to manually layout some areas according to the experience and knowledge of field investigators or experts. This method is highly subjective and not easy to quantify. The second method is to increase the number of points in areas with high estimated error or high uncertainty based on the kriging interpolation method of geostatistics. However, the weak spatial autocorrelation and strong spatial heterogeneity of soil pollution make the actual object not consistent with the theoretical assumption, and the reliability of the interpolation results is low.

[0003] In summary, there is an urgent need for a new method to scientifically layout the encryption points for soil pollution in industrial sites and obtain a relatively accurate pollution boundary in order to solve the above problems. SUMMARY

[0004] In order to solve the above problems of the prior art, the purpose of the present application is to provide a method for determining the pollution boundary of a contaminated land, which provides a solution for accurate identification of the pollution boundary. The main feature of this technology is to use the IDW interpolation method and the Bootstrap self-sampling method strategy to perform multiple replacement sampling on the preliminary investigation points based on the preliminary investigation sample points, to form a series of interpolation results, and to obtain the uncertainty distribution of the interpolation results by statistical analysis of the series of interpolation results. Then, by comprehensively considering the interpolation uncertainty distribution map and the uniformity of spatial point layout, the position distribution of the encryption sampling points is recommended.

[0005] Specifically, the present application provides a method for determining the pollution boundary of a contaminated land, which comprises the following steps:

[0006] S1, obtaining an initial sample point set;

[0007] The initial sample point set is obtained from an initial sample database, and the initial sample database includes at least three fields of sample number, spatial position and pollutant concentration value;

[0008] S2, Generate a grid map of the industrial site to be investigated;

[0009] The industrial site to be investigated is divided into regular grids, and the resolution of the grid map of the industrial site to be investigated is that each grid is a pixel.

[0010] S3, Generate the prediction result map;

[0011] For containing The initial sample point set of 1 initial sample point Sampling with replacement, the sample size drawn each time is... ,in , , All values ​​are positive integers; obtained using the IDW spatial interpolation method. A prediction result map, and then based on The mean prediction result is obtained from the image prediction result map;

[0012] S4, obtain the standard deviation of pixel values ​​based on the prediction result map;

[0013] according to For each predicted image, calculate the standard deviation of the pixel value for each pixel.

[0014] S5. Determine the undetermined area based on the mean prediction result map, pixel value standard deviation and threshold;

[0015] S6, determine the first weight of each grid in the undetermined region;

[0016] For each grid cell within the undetermined region, calculate the first weight for each grid cell;

[0017] S7, Add sample points to the area to be determined;

[0018] Based on the size of the area to be determined, the total number of sample points to be added is assumed to not exceed [a certain value]. ,point The number of sample points is increased by one time, that is , No. The number of sample points added this time is , to proceed with the first Secondary encryption point selection;

[0019] S8, determine whether the number of times to add sample points has been reached. If the target is not reached, return to S3 and proceed to the next step. The encryption process continues until the next point is encrypted; otherwise, the encryption process ends.

[0020] S9. Based on the sample point set after the densification of the sampling points, the pollutant concentration value is used as the pixel value of the grid map of the industrial site to be investigated, and the boundary between the contaminated area and the clean area of ​​the industrial site to be investigated is obtained.

[0021] Preferably, the initial sample database in S1 is specifically:

[0022] The sample number is the unique ID of each initial sample point; the spatial location of the initial sample point is its latitude and longitude or its relative location to the industrial site to be investigated; the pollutant concentration value is obtained by on-site sampling based on the spatial location of the initial sample point.

[0023] Preferably, in S3, , .

[0024] Preferably, in step S3, the IDW spatial interpolation method is used to obtain... A prediction result map, and then based on The mean prediction result is obtained from the amplitude prediction result map, as follows:

[0025] For each sampling, based on the spatial location of the initial sample, the IDW spatial interpolation method is used to interpolate the pollutant concentration in the grid map of the industrial site to be investigated, generating a prediction result map. The pixel value of each pixel in the prediction result map is the pollutant concentration value; then, the mean pollutant concentration of each pixel is obtained. The pixel value is used as the pixel value to obtain a mean prediction result image.

[0026] Preferably, in S4, according to The standard deviation of the pixel value for each pixel in the predicted image is calculated as follows:

[0027] (1);

[0028] in, The first one in the grid map of the industrial site to be investigated 1 pixel Indicates the first Standard deviation of pixel values ​​per pixel Indicates the first The pixel at the th point Pixel values ​​in the amplitude prediction result image. The first one in the mean prediction results graph The pixel value of each pixel.

[0029] Preferably, step S5 determines the region to be determined based on the mean prediction result image, the standard deviation of pixel values, and a threshold, specifically as follows:

[0030] Based on the mean prediction results, pixel value standard deviation, and threshold, the entire industrial site under investigation was divided into three areas, with the following criteria:

[0031] (2);

[0032] in, The first one in the grid map of the industrial site to be investigated 1 pixel; For the first The classification of individual pixels is as follows: 1 represents the contaminated area, 2 represents the clean area, and 3 represents the undetermined area. The preset pollutant concentration threshold; To be at the significance level Standard normal distribution table below value, Indicates the first The standard deviation of pixel values ​​for each pixel.

[0033] Preferably, step S6 determines the first weight of each grid within the region to be determined, specifically as follows:

[0034] For each grid cell within the region to be determined, calculate the first weight for each grid cell:

[0035] (3);

[0036] in, No. The first weight of each pixel; The first one in the mean prediction results graph The pixel value of each pixel; It is an integral variable; This is a preset pollutant concentration threshold; It is the first The standard deviation of pixel values ​​for each pixel.

[0037] Preferably, in S7 The allocation ratio is , ,in .

[0038] Preferably, in S7, the first step is performed. The specific process for selecting secondary encryption deployment points is as follows:

[0039] S71, The set of sample points has been determined. Candidate sample point set Encrypted deployment set Encrypted deployment set The initial value is empty;

[0040] S72, Calculation Each candidate sample point in The closest distance of the middle sample points As the second weight, the first and second weights of each candidate point are multiplied to obtain the final weight of each candidate sample point, and the maximum value corresponding to the final weight is found based on the final weight. Candidate sample points ;Will Add as selected point to the collection and set In, and from the set Remove from;

[0041] S73, determine the first Has the number of sample points increased reached the required level? If the condition is not met, return to S72; otherwise, execute S74.

[0042] S74, obtain the spatial location of the sample point and the pollutant concentration value;

[0043] S75, add initial sample points;

[0044] set The sample points in the set are added to the initial sample point set and also used as initial sample points.

[0045] Preferably, the spatial location of the sample point and the pollutant concentration value are obtained in step S74 in the following specific way:

[0046] According to the set The location of the sample points in the grid map of the industrial site to be investigated is determined, and the spatial location of the sample points is determined. After on-site sampling and testing of the industrial site to be investigated, the pollutant concentration values ​​are obtained.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] 1. By using the Bootstrap method for sampling with replacement, a mean prediction result map and a mean prediction standard deviation map are obtained from multiple prediction result maps. This solves the problem that traditional deterministic interpolation methods such as IDW cannot measure the uncertainty of prediction results when predicting the distribution of pollutants in contaminated sites.

[0049] 2. By using a combination of preset pollutant concentration thresholds and pixel value standard deviations to divide the entire area into contaminated, clean, and undetermined areas, the area that needs to be encrypted can be found more accurately, overcoming the shortcomings of traditional methods that rely on experience to determine the location of encrypted samples.

[0050] 3. By combining the first and second weights to select additional sample points, the uncertainty of pollution distribution and the spatial distribution balance of the densified points are simultaneously taken into account within the undetermined area, thereby improving the representativeness of the densified points.

[0051] 4. By adding sample points in stages, the redundancy and waste of point information caused by adding too many sample points at once can be avoided. This can effectively reduce the number of sample points and further increase the spatial representativeness of each encrypted point. Attached Figure Description

[0052] Figure 1 This is a flowchart of the sampling point densification method for determining pollution boundaries of contaminated sites according to the present invention;

[0053] Figure 2 This is a schematic diagram of the initial sample points of the industrial site to be investigated in an embodiment of the present invention;

[0054] Figure 3 This is a graph showing the mean prediction results of the industrial site to be investigated in an embodiment of the present invention;

[0055] Figure 4 This is an error distribution map of the industrial site to be investigated in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the undetermined area of ​​the industrial site to be investigated in an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of the first weight of the grid within the undetermined region in an embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of the sample point distribution added in an embodiment of the present invention. Detailed Implementation

[0059] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0060] This invention discloses a method for densifying sampling points in contaminated sites to determine contamination boundaries, such as... Figure 1 The specific steps shown include:

[0061] S1, Obtain the initial set of sample points;

[0062] In this embodiment, the initial sample point set is directly imported from the initial sample database. The initial sample database includes at least three fields: sample number, spatial location, and pollutant concentration value. The sample number is the unique ID of each initial sample point. The initial sample point set contains... One initial sample point, The initial sample point number is a positive integer, typically related to the size of the industrial site under investigation. The spatial location of the initial sample points is usually determined based on detailed historical data regarding the industrial site's production processes, pollution sources, and functional zoning, combined with expert experience and existing standards or specifications, to establish background and hotspot areas. The spatial location can be recorded by directly saving the latitude and longitude of the initial sample points or their relative position to the industrial site. The pollutant concentration values ​​are obtained accurately based on the spatial location of the initial sample points after on-site sampling and laboratory testing at the industrial site. Of course, the corresponding spatial location can also be modified and saved based on the on-site sampling locations at the industrial site.

[0063] In this embodiment, it is assumed that there is a piece The industrial plant area may have soil contamination. Based on a detailed preliminary investigation of historical data regarding the industrial site's production processes, pollution sources, and functional zoning, and combined with expert experience and existing standards or specifications, 64 initial sample points were established at 60m intervals in the background and hotspot areas. This ensured a certain degree of spatial representativeness. After on-site sampling and laboratory testing, the spatial distribution of the initial sample points and the concentration values ​​of a certain pollutant were obtained, such as... Figure 2 As shown.

[0064] S2, Generate a grid map of the industrial site to be investigated;

[0065] The industrial site to be investigated is divided into regular grids to obtain a grid map of the industrial site to be investigated. The resolution of the grid map of the industrial site to be investigated is at least one pixel per grid.

[0066] Using one pixel per grid is for ease of subsequent description and operation. If the resolution of the grid map of the industrial site to be investigated is higher than this resolution, the image resolution can be reduced to make each grid a pixel.

[0067] In this embodiment, each grid is set as a square with a side length of 1 meter.

[0068] S3, Generate the prediction result map;

[0069] For containing The initial sample point set of 1 initial sample point Sampling with replacement, the sample size drawn each time. Slightly smaller Furthermore, it can vary during the sampling process, and is preferably [optional]. , ,in , , All values ​​are positive integers. For each sampling, based on the spatial location of the initial sample, the IDW spatial interpolation method is used to interpolate the pollutant concentration on the grid map of the industrial site to be investigated, generating a predicted result map. The pixel value of each pixel in the predicted result map is the pollutant concentration value. Therefore, the following steps are performed: The second sampling with replacement generates A graph showing the predicted results.

[0070] according to The average pollutant concentration of each pixel in the prediction result map is calculated and used as the pixel value to obtain an average prediction result map.

[0071] In this embodiment, 5000 samplings with replacement are performed using this set of 64 sample points as the population. , The sample size drawn each time. The value is between 58 and 63. For each sampling, the IDW method is used to interpolate the pollutant concentration on the fine grid to generate a prediction result image, resulting in 5000 prediction result images. Based on the pollutant concentration values ​​of the pixels in the 5000 prediction result images, a mean prediction result image is obtained, such as... Figure 3 As shown.

[0072] S4, obtain the standard deviation of pixel values ​​based on the prediction result map;

[0073] according to For each predicted image, calculate the standard deviation of the pixel value for each pixel:

[0074] (1);

[0075] in, The first one in the grid map of the industrial site to be investigated 1 pixel Indicates the first Standard deviation of pixel values ​​per pixel Indicates the first The pixel at the th point Pixel values ​​in the amplitude prediction result image. The first one in the mean prediction results graph The pixel value of each pixel.

[0076] The standard deviation of all pixels can also be used as pixel values ​​to form an error distribution map. In this embodiment, the error distribution map of the industrial site to be investigated is as follows: Figure 4 As shown.

[0077] S5. Determine the undetermined area based on the mean prediction result map, pixel value standard deviation and threshold;

[0078] Based on the mean prediction results, pixel value standard deviation, and threshold, the entire industrial site under investigation is divided into three areas: a contaminated area, a clean area, and a pending area. The division criteria are as follows:

[0079] (2);

[0080] in, The first one in the grid map of the industrial site to be investigated 1 pixel; For the first The classification of individual pixels is as follows: 1 represents the contaminated area, 2 represents the clean area, and 3 represents the undetermined area. This is a preset pollutant concentration threshold; To be at the significance level Standard normal distribution table below The value is usually taken as 1. ; Indicates the first The standard deviation of pixel values ​​for each pixel.

[0081] The undetermined area will be used as the sampling point deployment area to increase the number of sampling points.

[0082] In this embodiment, the preset pollutant concentration threshold ,Pick The range of the undetermined region is obtained through formula (2) as follows: Figure 5 As shown.

[0083] S6, determine the first weight of each grid in the undetermined region;

[0084] For each grid cell within the region to be determined, calculate the first weight for each grid cell:

[0085] (3);

[0086] in, No. The first weight of each pixel; The first one in the mean prediction results graph The pixel value of each pixel; It is an integral variable; This is a preset pollutant concentration threshold; It is the first The standard deviation of pixel values ​​for each pixel.

[0087] Because each grid cell represents one pixel, therefore the... The first weight of the nth pixel is the... The first weight of each grid.

[0088] In this embodiment, the first weight of the grid within the undetermined region is as follows: Figure 6As shown;

[0089] S7, Add sample points to the area to be determined;

[0090] Based on the size of the area to be determined, the total number of sample points to be added is assumed to not exceed [a certain value]. ,point The number of sample points is increased by one time, that is , No. The number of sample points added this time is Typically, the number of sample points added at the beginning is greater than that added later.

[0091] Preferably, the sample points are increased in two stages, with the following allocation ratio: , ,in .

[0092] No. The next step in selecting and encrypting the deployment points is as follows:

[0093] S71, let the set of all initial sample points be S71. Let be the set of known sample points; let be the set of all grid points in the region to be determined that do not contain sample points. As a set of candidate sample points, each element in the set of candidate sample points is called a candidate sample point, and the set of selected grid points is denoted as . As a set of encrypted data points, the initial value of the encrypted data point set is empty. Because there is a one-to-one correspondence between grid points and pixels in this invention, the elements in the sample point set, the candidate sample point set, and the encrypted data point set are the serial numbers of the pixels in the grid map of the industrial site to be investigated.

[0094] S72, Calculate Sets Each candidate sample point is added to the set. The closest distance of the middle sample points that distance These are respectively used as the second weight for each candidate sample point. The first and second weights of each candidate point are multiplied to obtain the final weight for each candidate sample point. Then, the maximum value corresponding to the highest weight is found in the... Candidate sample points ,Will Add as a selected point to the set of determined sample points. and encrypted deployment set In, and from the set of candidate sample points Removed from the middle.

[0095] S73, determine the first Has the number of sample points increased reached the required level? If the condition is not met, return to S72; otherwise, execute S74.

[0096] S74, obtain the spatial location of the sample point and the pollutant concentration value;

[0097] According to the set The sample points in the middle are used for the first time The sampling point density is increased based on the set. The location of the sample points in the grid map of the industrial site to be investigated is determined, and the spatial location of the sample points is determined. Based on the spatial location of the sample points, the industrial site to be investigated is sampled on-site and tested in the laboratory to obtain accurate pollutant concentration values.

[0098] S75, add initial sample points;

[0099] set The sample points in the set are added to the initial sample point set and also used as initial sample points.

[0100] S8, determine whether the number of times to add sample points has been reached. If the target is not reached, return to S3 and proceed to the next step. The encryption process continues until all points are encrypted; otherwise, the encryption process ends.

[0101] In this embodiment, the total number of sample points to be added does not exceed 20. The sample points will be added in two batches, with the number of added sample points allocated as 13 and 7 respectively. , , , After the sampling points are deployed, the additional sample point distribution map in this embodiment is as follows: Figure 7 .

[0102] S9. Based on the sample point set after the densification of the sampling points, the pollutant concentration value is used as the pixel value of the grid map of the industrial site to be investigated, so as to obtain a relatively accurate boundary between the contaminated area and the clean area of ​​the industrial site to be investigated.

[0103] The set of sample points here is based on the initial set of sample points obtained through... This has been added. The set of sample points after sampling points .

[0104] This invention can also display the initial sample points in S1 and each set in S74 in different colors on the grid map of the industrial site to be investigated. The sample points in the data are used to clearly show the specific process of densifying the sampling points in contaminated sites.

[0105] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for densifying sampling points in contaminated sites to determine contamination boundaries, characterized in that, It comprises the following steps: S1, obtaining an initial sample point set; The initial sample point set is obtained from an initial sample database, and the initial sample database at least includes three fields of sample number, spatial position and pollutant concentration value; S2, generating a to-be-investigated industrial site grid map for the to-be-investigated industrial site; The to-be-investigated industrial site is divided into regular grids, and the resolution of the to-be-investigated industrial site grid map is one pixel point for each grid; S3, generating a prediction result map; An initial sample point set comprising initial sample points is obtained times with replacement, and the sample size of each time is , wherein , , are all positive integers; an IDW spatial interpolation method is used to obtain prediction result maps, and a mean prediction result map is obtained according to prediction result maps. S4, obtaining a pixel value standard deviation according to the prediction result map; According to a standard deviation of pixel values of each pixel point is calculated from the prediction result image; S5, a pending area is determined according to the mean prediction result image, the standard deviation of pixel values and a threshold value; According to the mean prediction result map, the pixel value standard deviation and a threshold, the entire to-be-investigated industrial site is divided into three regions, and the division standard is as follows: （2）； in, The first one in the grid map of the industrial site to be investigated 1 pixel; For the first The classification of individual pixels is as follows: 1 represents the contaminated area, 2 represents the clean area, and 3 represents the undetermined area. This is a preset pollutant concentration threshold; To be at the significance level Standard normal distribution table below value, Indicates the first Standard deviation of pixel values ​​for each pixel; The first one in the mean prediction results graph The pixel value of each pixel; S6, determining a first weight of each grid in the to-be-determined region; For each grid in the to-be-determined region, the first weight of each grid is calculated; S7, adding sample points to the to-be-determined region; According to the size of the area of the pending region, the total number of sample points to be increased is not more than , the sample points are increased times, that is , the number of sample points to be increased in the th time is , and the th encryption point selection is performed. S8, judge whether the number of times of adding sample points reaches second, if not, return to S3, and perform the first point encryption, otherwise, end the point encryption; S9, obtaining a to-be-investigated industrial site pollution region and clean region boundary according to the sample point set after the point distribution encryption, taking the pollutant concentration value as the pixel value of the to-be-investigated industrial site grid map.

2. The method for determining the sampling site encryption of contaminated plots according to claim 1, characterized in that: The initial sample database in S1 is specifically: The sample number is the ID of each initial sample point, which is unique; the spatial position of the initial sample point is the latitude and longitude of the initial sample point or the relative position to the to-be-investigated industrial site; and the pollutant concentration value is obtained by testing after field sampling according to the spatial position of the initial sample point.

3. The method for determining the sampling sites of a pollution plot according to claim 1, wherein: S3, , .

4. The method for determining the sampling sites of a pollution plot according to claim 1, wherein: In the S3, the IDW spatial interpolation method is used to obtain the amplitude prediction result map, and then the mean value prediction result map is obtained according to the amplitude prediction result map, specifically as follows: For each sampling, according to the spatial position of the extracted initial sample, a grid map of the industrial site to be investigated is interpolated for the pollutant concentration by using the IDW spatial interpolation method to generate a prediction result map, and the pixel value of each pixel point in the prediction result map is the pollutant concentration value; the average value of the pollutant concentration of each pixel point is obtained , and a mean prediction result map is obtained as the pixel value of the pixel point.

5. The method for determining the sampling sites of a pollution plot according to claim 1, wherein: In the S4, according to The standard deviation of the pixel value of each pixel point in the amplitude prediction result map is calculated as follows: （1）； wherein is the pixel value of the i-th pixel in the j-th prediction result map, is the pixel value of the i-th pixel in the j-th prediction result map, is the pixel value of the i-th pixel in the j-th prediction result map, is the pixel value of the i-th pixel in the j-th prediction result map, is the pixel value of the i-th pixel in the j-th prediction result map,​​​​ 6. The method for determining pollution boundary of claim 1, wherein: The S6 determines the first weight of each grid in the to-be-determined region, and specifically: For each grid in the to-be-determined region, the first weight of each grid is calculated: （3）； wherein, the first weight of the i-th pixel point; the i-th pixel point in the mean prediction result map; is the pixel value of the i-th pixel point in the mean prediction result map; is the integral variable; is the integral variable; is a preset pollutant concentration threshold value; is the pixel value of the i-th pixel point in the mean prediction result map; is the pixel value of the i-th pixel point in the mean prediction result map.

7. The method for determining pollution boundary of claim 1, wherein: In the S7 , the distribution ratio is , wherein .

8. The method for determining pollution boundary of claim 1, wherein: The S7 carries out the first encryption point selection, and the specific process is as follows: S71, a set of sample points has been determined , a set of alternative sample points , a set of encryption points , a set of encryption points initial value is empty; S72, calculate the nearest distance between each candidate sample point to the sample point As the second weight, multiply the first weight and the second weight of each candidate point to obtain the final weight of each candidate sample point, and find the candidate sample point corresponding to the maximum value in according to the final weight ; add to the set and the set , and remove from the set ; S73, determine whether the number of sample points of the second increment has reached the predetermined number, if not, return to S72, otherwise execute S74; S74, obtaining sample point spatial position and pollutant concentration value; S75, adding initial sample points; adding the sample points in the set to the initial sample point set as initial sample points.

9. The method for determining the sampling sites of a pollution plot according to claim 8, wherein: The S74 obtains the sample point spatial position and the pollutant concentration value, and the specific method is: According to the set The location of the sample points in the grid map of the industrial site to be investigated is determined, and the spatial location of the sample points is determined. After on-site sampling and testing of the industrial site to be investigated, the pollutant concentration values ​​are obtained.

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