Polluted plot sampling point distribution encryption method for determining pollution boundary

Abstract

The invention relates to the technical field of soil analysis, and provides a polluted plot sampling point distribution encryption method for determining a polluted boundary, which comprises the following steps: S1, acquiring an initial sample point set; s2, generating a grid chart of the industrial site to be investigated; s3, generating a prediction result graph; s4, obtaining a pixel value standard deviation according to the prediction result map; s5, determining an undetermined area according to the mean value prediction result graph and a threshold value; s6, determining a first weight of each grid in the to-be-determined area; s7, adding sample points to the to-be-determined area; s8, judging whether the number of times of increasing the sample points reaches specified H times or not, if not, returning to S3, and otherwise, ending point distribution encryption; and S9, according to the initial sample point set after point distribution encryption is completed, obtaining a boundary between the polluted area and the clean area of the industrial site to be investigated. According to the method, the pollutant distribution prediction result is more accurate, and the positions of the added sample points are determined in a quantitative mode, so that the determination of the pollution boundary is 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 method, or a combination of expert knowledge and experience, is 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 layout additional sample points in some high-pollution areas. However, how to reasonably layout the additional sample points is still a challenging problem. Currently, there are mainly two methods for laying out additional sample points. The first method is to manually layout sample points in certain 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 sample points in areas with high estimated error or high uncertainty based on the kriging interpolation method of geostatistics. However, this method has the problem that 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 result is low.

[0003] In summary, there is an urgent need for a new method to scientifically layout additional sample 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 layout, the position distribution of the additional 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, generating 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 one pixel point per grid;

[0010] S3, generating a prediction result map;

[0011] The initial sample point set containing initial sample points is subjected to times of sampling with replacement, and the sample size of each sampling is , wherein , , all are positive integers; the IDW spatial interpolation method is used to obtain prediction result maps, and then a mean prediction result map is obtained according to the prediction result maps;

[0012] S4, obtaining a pixel value standard deviation according to the prediction result map;

[0013] The pixel value standard deviation of each pixel point is calculated according to the prediction result maps;

[0014] S5, determining a pending area according to the mean prediction result map, the pixel value standard deviation and a threshold value;

[0015] S6, determining a first weight of each grid in the pending area;

[0016] For each grid in the pending area, the first weight of each grid is calculated;

[0017] S7, adding sample points to the pending area;

[0018] According to the size of the area of the pending area, the total number of sample points to be added is not more than , and the sample points are added in times, that is, , the number of sample points added in the time is , and the time of point encryption selection is performed;

[0019] S8, judging whether the number of times of adding sample points reaches times, if not, returning to S3 to perform the time of point encryption, otherwise ending the point encryption;

[0020] S9, obtaining a pollution area and a clean area boundary of the industrial site to be investigated according to the sample point set after the point encryption is completed, taking the pollutant concentration value as the pixel value of the grid map of the industrial site to be investigated.

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

[0022] 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 industrial site to be investigated; and the pollutant concentration value is obtained by testing after sampling according to the spatial position of the initial sample point.

[0023] Preferably, in S3, , .

[0024] Preferably, in S3, the IDW spatial interpolation method is used to obtain a prediction result map, and then the mean prediction result map is obtained according to a prediction result map, specifically:

[0025] For each sampling, according to the spatial position of the extracted initial sample, the IDW spatial interpolation method is used to interpolate the pollutant concentration of the grid map of the industrial site to be investigated to generate a prediction result map, and the pixel value of each pixel point in the prediction result map is the pollutant concentration value; then the mean value of the pollutant concentration of each pixel point is obtained as the pixel value of the pixel point, and a mean prediction result map is obtained.

[0026] Preferably, in S4, according to a prediction result map, the pixel value standard deviation of each pixel point is calculated, specifically:

[0027] (1);

[0028] Wherein, is the i-th pixel point in the grid map of the industrial site to be investigated, indicates the pixel value standard deviation of the i-th pixel point, indicates the pixel value of the i-th pixel point in the j-th prediction result map, and is the pixel value of the i-th pixel point in the mean prediction result map.

[0029] Preferably, in S5, the mean prediction result map, the pixel value standard deviation and the threshold value are used to determine the pending area, specifically:

[0030] According to the mean prediction result map, the pixel value standard deviation and the threshold value, the entire industrial site to be investigated is divided into three regions, and the division standard is as follows:

[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; The 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 weight and the second weight of each candidate point are multiplied to obtain the final weight of each candidate sample point, and the candidate sample point corresponding to the maximum value of the final weight is found out in the set ; the candidate sample point is added to the set and the set , and is removed from the set ;

[0041] S73, judging whether the number of sample points added for the first time reaches , if not, returning to S72, otherwise executing S74;

[0042] S74, obtaining the spatial position of the sample point and the pollutant concentration value;

[0043] S75, adding the initial sample point;

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

[0045] Preferably, the method for obtaining the spatial position of the sample point and the pollutant concentration value in S74 is as follows:

[0046] The spatial position of the sample point is determined according to the position of the sample point in the set in the grid map of the industrial site to be investigated, and the pollutant concentration value is obtained after field sampling and testing of the industrial site to be investigated.

[0047] Compared with the prior art, the present application has the following advantages:

[0048] 1. The Bootstrap method is used for sampling with replacement, and a mean prediction result map and a mean prediction standard deviation map are obtained according to multiple prediction result maps, which solves the problem that the traditional deterministic interpolation method such as IDW cannot measure the uncertainty of the prediction result when predicting the pollutant distribution of a contaminated land.

[0049] 2. The entire region is divided into a contaminated area, a clean area and a pending area by using the preset pollutant concentration threshold combined with the pixel value standard deviation determination method, which can more accurately find the area that needs to be encrypted, and overcomes the defects of the traditional method of determining the encryption sample position relying on experience.

[0050] 3. The first weight and the second weight are combined to select the added sample points, which realizes the balance between the uncertainty of the pollution distribution and the spatial distribution position of the encryption points in the pending area, and improves the representativeness of the encryption 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, is a positive integer, the initial sample point quantity is usually related to the size of the industrial site to be investigated. The spatial position of the initial sample point is usually laid out according to the detailed investigation of the historical data of the production process, pollution source, functional partition and the like of the industrial site to be investigated, combined with the experience of experts, the existing standards or norms and the like to lay out the background area and the hot spot area. The spatial position can be recorded by directly saving the longitude and latitude of the initial sample point or the relative position of the initial sample with respect to the industrial site to be investigated, and the pollutant concentration value is the accurate pollutant concentration value obtained after the field sampling and laboratory testing of the industrial site to be investigated according to the spatial position of the initial sample point. Of course, the corresponding spatial position can also be modified and saved according to the field sampling position of the industrial site to be investigated.

[0063] In this embodiment, it is assumed that there is an industrial plant area which may have soil pollution. According to the detailed investigation of the historical data of the production process, pollution source, functional partition and the like of the industrial site, combined with the experience of experts, the existing standards or norms and the like, 64 initial sample points are laid out at an interval of 60 m in the background area and the hot spot area, which has a certain guarantee of spatial representativeness, and the spatial distribution of the initial sample points and the concentration value of a certain pollutant are obtained after field sampling and laboratory testing, as shown in Figure 2 .

[0064] S2, generating 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, and the resolution of the grid map of the industrial site to be investigated is at least one pixel point per grid, and the grid map is generated.

[0066] Each grid is taken as one pixel point for the convenience of subsequent description and operation, and if the resolution of the grid map of the industrial site to be investigated is higher than this resolution, each grid can be taken as one pixel point by reducing the image resolution.

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

[0068] S3, generating a prediction result map;

[0069] The initial sample point set containing initial sample points is subjected to times non-replacement sampling, and the sample quantity extracted each time is slightly less than and is variable in the sampling process, and is preferably , , wherein , , are positive integers. For each sampling, according to the spatial position of the extracted initial sample, a predicted result map is generated by using the IDW spatial interpolation method to interpolate the pollutant concentration of the grid map of the industrial site to be investigated. The pixel value of each pixel point in the predicted result map is the pollutant concentration value. Therefore, the following steps are performed times of sampling with replacement, a predicted result map is generated.

[0070] According to the predicted result map, the mean value of the pollutant concentration of each pixel point is calculated as the pixel value of the pixel point, and a mean predicted result map is obtained. In this embodiment, 5000 times of sampling with replacement are performed on the 64 sample point set as the population, i.e.

[0071] , . The sample size extracted each time is a value between 58 and 63. For each sampling, the IDW method is used to interpolate the pollutant concentration of the fine grid to generate a predicted result map, and finally 5000 predicted result maps are obtained. According to the pollutant concentration values of the pixel points of the 5000 predicted result maps, a mean predicted result map is obtained, as shown in . Figure 3 S4, the pixel value standard deviation is obtained according to the predicted result map;

[0072] According to the predicted result map, the pixel value standard deviation of each pixel point is calculated:

[0073] (1);

[0074] wherein, x represents the i-th pixel point in the grid map of the industrial site to be investigated, σi represents the pixel value standard deviation of the i-th pixel point, xi,j represents the pixel value of the i-th pixel point in the j-th predicted result map, and μi represents the pixel value of the i-th pixel point in the mean predicted result map.

[0075] The standard deviation of all pixel points can also be used as the pixel value to form an error distribution map. In this embodiment, the error distribution map of the industrial site to be investigated is shown in

[0076] . Figure 4 S5, determining the pending area according to the mean predicted result map, the pixel value standard deviation and a threshold value;

[0077]

[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. The 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; The 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 6shown;

[0089] S7, adding sample points to the pending area;

[0090] According to the size of the pending area, the total number of sample points to be added is not more than , and the sample points are added times, that is , the number of sample points added in the first time is , and the number of sample points added in the last time is

[0091] , and the number of sample points added in the first time is , and the number of sample points added in the last time is .

[0092] The second selection of the encryption point distribution process is as follows:

[0093] S71, record the set composed of all initial sample points as , as the determined sample point set; record the set composed of all grid points in the pending area that do not contain sample points as , as the candidate sample point set, each element in the candidate sample point set is called a candidate sample point, record the set composed of selected grid points as , as the encryption point distribution set, the initial value of the encryption point distribution set is empty. Because the grid points in the present application correspond one-to-one to the pixel points, the elements in the sample point set, the candidate sample point set, and the encryption point distribution set are the serial numbers of the pixel points in the grid map of the industrial site to be investigated.

[0094] S72, calculate the nearest distance of each candidate sample point in the set to the sample points in the set , and take the distance as the second weight of each candidate sample point, respectively. Multiply the first weight and the second weight of each candidate point to obtain the maximum weight of each candidate sample point, and find the candidate sample point in corresponding to the maximum value according to the maximum weight , add to the determined sample point set and the encryption point distribution set , and remove it from the candidate sample point set .

[0095] S73, judge whether the number of sample points added in the first time reaches , if not, return to S72, otherwise execute S74.

[0096] S74, obtaining the spatial position of the sample point and the pollutant concentration value;

[0097] According to the sample points in the set , the th sampling point encryption is carried out, the spatial position of the sample point is determined according to the position of the sample point in the grid map of the industrial site to be investigated, and the accurate pollutant concentration value is obtained after the industrial site to be investigated is sampled and tested in the laboratory according to the spatial position of the sample point.

[0098] S75, adding initial sample points;

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

[0100] S8, judging whether the number of added sample points reaches , if not, returning to S3 to carry out the th point encryption, otherwise ending the point encryption.

[0101] The total number of sample points to be added in this embodiment does not exceed 20, and the sample points are added twice, and the number of added sample points is allocated according to 13 and 7, that is, , , , , after the point encryption is ended, the distribution diagram of the added sample points in this embodiment is as shown in Figure 7 .

[0102] S9, according to the sample point set after the point encryption is completed, taking the pollutant concentration value as the pixel value of the grid map of the industrial site to be investigated, to obtain the relatively accurate boundary between the contaminated area and the clean area of the industrial site to be investigated.

[0103] The sample point set here is the sample point set after th sample points are added on the basis of the initial sample point set at the beginning .

[0104] The present application can also display the initial sample points in S1 and the sample points in each set in S74 in different colors in the grid map of the industrial site to be investigated, to clearly show the specific process of the sampling point encryption of the contaminated land.

[0105] ​The above described embodiments are only to illustrate the preferred embodiments of the present application, and are not intended to limit the scope of the present application. Any modification and improvement of the technical solutions of the present application made by those skilled in the art without departing from the design spirit of the present application shall fall within the protection scope of the present application.

Claims

1. A method for densifying sampling points in contaminated sites to determine contamination boundaries, characterized in that, The steps involved are as follows: S1, Obtain the initial set of sample points; The initial sample point set is obtained from the initial sample database, which includes at least three fields: sample number, spatial location, and pollutant concentration value. S2, Generate a grid map of the industrial site to be investigated; 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. S3, Generate the prediction result map; For containing The initial sample point set of the initial sample points is used for 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 amplitude prediction result map; S4, obtain the standard deviation of pixel values ​​based on the prediction result map; according to For each predicted image, calculate the standard deviation of the pixel value for each pixel. S5. Determine the undetermined area based on the mean prediction result map, pixel value standard deviation and threshold; S6, determine the first weight of each grid in the undetermined region; For each grid cell within the undetermined region, calculate the first weight for each grid cell; S7, Add sample points to the area to be determined; Based on the size of the area to be determined, the total number of sample points to be added shall 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; 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. 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.

2. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: The initial sample database in S1 is specifically as follows: 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.

3. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: In S3, , .

4. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: In 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: 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.

5. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: In S4, according to The standard deviation of the pixel value for each pixel in the predicted image is calculated as follows: （1）； 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.

6. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 5, characterized in that: S5 determines the undetermined region based on the mean prediction result map, pixel value standard deviation, and threshold, specifically as follows: 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: （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 The standard deviation of pixel values ​​for each pixel.

7. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 6, characterized in that: S6 determines the first weight of each grid within the region to be determined, specifically as follows: For each grid cell within the region to be determined, calculate the first weight for each grid cell: （3）； 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.

8. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: In S7 The allocation ratio is , ,in .

9. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 1, characterized in that: In S7, the first... The specific process for selecting secondary encryption deployment points is as follows: S71, The set of sample points has been determined. Candidate sample point set Encrypted deployment set Encrypted deployment set The initial value is empty; 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; 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. S74, obtain the spatial location of the sample point and the pollutant concentration value; S75, add initial sample points; set The sample points in the set are added to the initial sample point set and also used as initial sample points.

10. The method for densifying sampling points in contaminated sites for determining contamination boundaries according to claim 9, characterized in that: The spatial location of the sample point and the pollutant concentration value are obtained in step S74 as follows: 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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