Method for synergistic identification of high and low altitude pollution emission sources by stationary satellites and ground monitoring

Abstract

The application discloses a method for identifying high and low air pollution emission sources by cooperation of stationary satellites and ground monitoring, wherein based on space-based stationary satellite data and ground-based atmospheric monitoring data, additional auxiliary information such as high-resolution images, land use and enterprise license is added, space-based and ground-based emission products are identified, and then space-based and ground-based spatio-temporal matching cooperation technology is carried out, so that high and low air pollution sources are distinguished, a data set is established, and accurate data sources are provided for online supervision and support of ozone precursor high-value area identification and evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for identifying local atmospheric pollution sources, and in particular to a method for identifying high and low altitude pollution emission sources in cooperation with stationary satellites and ground monitoring. BACKGROUND

[0002] With the deepening of the work of atmospheric pollution control, reducing pollution source emissions from the source has become a more and more direct and effective means, and in order to implement this scheme, accurately knowing where the pollution source is, i.e. accurately identifying local atmospheric pollution sources, has become an increasingly important prerequisite for pollution control.

[0003] Local pollution sources also include various sources such as factory emissions and automobile exhaust emissions. Traditional pollution source detection methods include ground monitoring station detection and satellite remote sensing identification, but ground monitoring stations are difficult to monitor high-altitude emissions of pollutants, which include pollutants emitted by factories through high chimneys, airplane exhaust, and pollutants naturally produced under special weather conditions. Correspondingly, the pollutant concentration column information obtained by satellite remote sensing is also easily disturbed by weather and transported pollutants, and is not suitable for direct use to find local pollution sources; the transported pollutants are pollutants emitted into the air from other areas and transported to the local area under the action of air flow.

[0004] Based on the above problems, the present inventors have made an in-depth analysis of the method for identifying high and low altitude pollution emission sources in order to design a method for identifying high and low altitude pollution emission sources in cooperation with stationary satellites and ground monitoring. SUMMARY

[0005] In order to overcome the above problems, the present inventors have made an in-depth analysis of the method for identifying high and low altitude pollution emission sources in order to design a method for identifying high and low altitude pollution emission sources in cooperation with stationary satellites and ground monitoring.

[0006] Specifically, the purpose of the present application is to provide a method for identifying high and low altitude pollution emission sources in cooperation with stationary satellites and ground monitoring, which comprises:

[0007] S1: finely processing the remote sensing image obtained by the stationary satellite to obtain a trusted remote sensing image;

[0008] S2: calling time point T of remote sensing image obtained by stationary satellite, reading trusted area in trusted remote sensing image, calling atmospheric pollutant concentration data obtained by ground monitoring station in the trusted area at time point T;

[0009] S3: calling continuous multi-scene remote sensing image obtained by stationary satellite in a period of time, repeating S1 and S2;

[0010] S4: recording atmospheric pollutant concentration value obtained by each ground monitoring station, and respectively counting number of atmospheric pollutant concentration values obtained by ground monitoring station exceeding warning value, when the number exceeds a set value, marking position of the ground monitoring station as low-altitude pollution emission position;

[0011] S5: recording pollutant concentration value corresponding to each pixel point in all trusted remote sensing images, and respectively counting number of pollutant concentration values corresponding to each pixel point exceeding early warning value, when the number exceeds a critical value, marking position corresponding to the pixel point as high-altitude pollution emission position;

[0012] S6: statistically summarizing low-altitude pollution emission position and high-altitude pollution emission position into the same scene high spatial resolution remote sensing image.

[0013] The fine processing includes the following steps:

[0014] Step 1, calling high spatial resolution remote sensing image containing the region to be monitored, drawing a grid in the remote sensing image, the grid divides the region to be monitored into a plurality of fixed-size sub-regions;

[0015] Step 2, establishing a label box in the high spatial resolution remote sensing image, the label box includes a high-value label box, a medium-value label box and a low-value label box;

[0016] Step 3, calling atmospheric pollutant concentration remote sensing image of the region to be monitored obtained by stationary satellite, adding the label box and the grid to the remote sensing image, dividing the remote sensing image into a plurality of grid units with the grid, and assigning a value to each label box;

[0017] Step 4, analyzing each grid unit one by one to determine whether the information in the grid unit is normal, if normal, saving atmospheric pollutant concentration information recorded in the remote sensing image in the grid unit, the grid unit is an information grid; if not normal, emptying atmospheric pollutant concentration information recorded in the remote sensing image in the grid unit, the grid unit is an empty grid; thereby obtaining new trusted remote sensing image.

[0018] In the step 2, the process of establishing a label includes the following sub-steps:

[0019] Sub-step 2-1, contouring the high-concentration atmospheric pollutant emission area in the high-spatial-resolution remote sensing image, wherein the high-concentration atmospheric pollutant emission area includes cities, industrial parks, traffic emission areas, and areas where pollutant emission enterprises are located;

[0020] Taking the contours of the multiple high-concentration atmospheric pollutant emission areas obtained in the above process in set, a plurality of preliminary high-value label contours are obtained.

[0021] Sub-step 2-2, contouring the low-concentration atmospheric pollutant emission area in the high-spatial-resolution remote sensing image, wherein the low-concentration atmospheric pollutant emission area includes farmland, desert, and forest areas.

[0022] Taking the contours of the multiple low-concentration atmospheric pollutant emission areas obtained in the above process in set, a plurality of preliminary medium-value label contours are obtained.

[0023] Sub-step 2-3, contouring the no atmospheric pollutant emission area in the high-spatial-resolution remote sensing image, wherein the no atmospheric pollutant emission area includes lakes, reservoirs, and seas.

[0024] Taking the contours of the multiple no atmospheric pollutant emission areas obtained in the above process in set, a plurality of preliminary low-value label contours are obtained.

[0025] Sub-step 2-4, selecting a rectangular area in the label contour, wherein the side length of the rectangular area is an integer multiple of the side length of the smallest pixel point of the atmospheric pollutant concentration remote sensing image, and the label contour includes the preliminary high-value label contour, the preliminary medium-value label contour, and the preliminary low-value label contour.

[0026] In the above process, each sub-area corresponds to at least one preliminary high-value label contour and at least one preliminary medium-value label contour.

[0027] In the sub-step 2-4, the rectangular area is completely located in the label contour, and when the label contour is too small to completely cover the smallest pixel point, the label contour information is abandoned and deleted.

[0028] In the step 3, the process of assigning the label frame is as follows: reading the pollutant concentration values represented by all pixel points in the remote sensing image falling in the label frame, and taking the average value as the pollutant concentration value of the label frame, so as to obtain a complete label.

[0029] In the step 4, in the grid unit, when the value of any high-value label is greater than the value of any medium-value label and the value of any low-value label, the information in the grid unit is normal, otherwise it is considered that the information in the grid unit is abnormal.

[0030] In S6, the overlapping area of the low-altitude pollution emission position and the high-altitude pollution emission position is taken as a key monitoring area.

[0031] The present application has the following beneficial effects:

[0032] (1) The method for identifying high and low altitude pollution emission sources by static satellite and ground monitoring provided by the present application eliminates the interference caused by pollution transmission in the remote sensing image obtained by the static satellite, improves the accuracy of the remote sensing image in identifying and judging the local pollution source, and provides reliable technical support for environmental governance.

[0033] (2) The method for identifying high and low altitude pollution emission sources by static satellite and ground monitoring provided by the present application screens the ground monitoring data by using the more accurate remote sensing information after processing, thereby discarding the ground monitoring data interfered by the migration of pollutants, and statistically analyzing the high pollution area, so as to realize the accurate identification of high and low altitude pollution emission sources. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 Fig. 1 shows the overall logic diagram of the method for identifying high and low altitude pollution emission sources by static satellite and ground monitoring according to a preferred embodiment of the present application;

[0035] Figure 2 Fig. 5 shows a schematic diagram of a grid after the monitoring area is drawn in the embodiment of the present application;

[0036] Figure 3 Fig. 6 shows the atmospheric pollutant concentration remote sensing image of a scene of static satellite in the embodiment of the present application;

[0037] Figure 4 Fig. 7 shows a schematic diagram of a grid unit and the label therein in the embodiment of the present application. DETAILED DESCRIPTION

[0038] The present application will be further described in detail by the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present application will become more apparent.

[0039] The special word "exemplary" here means "as an example, embodiment or illustration". Any embodiment described as "exemplary" here does not necessarily mean that it is superior or better than other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0040] The method for identifying high and low altitude pollution emission sources by static satellite and ground monitoring provided by the present application, as shown in Figure 1 the method comprises:

[0041] S1: Refine the remote sensing images obtained from geostationary satellites to obtain reliable remote sensing images;

[0042] Preferably, the refinement process includes the following steps:

[0043] Step 1: Retrieve a high spatial resolution remote sensing image containing the area to be monitored, and draw a grid in the remote sensing image, which divides the area to be monitored into multiple sub-regions of fixed size;

[0044] The selection of the area to be monitored is generally based on administrative divisions, which can be national, provincial, or municipal administrative regions. For example, Shandong Province can be selected as the area to be monitored. In other words, the area to be monitored can be selected and set according to specific task requirements.

[0045] The high spatial resolution remote sensing images described in this application are satellite photographs obtained from the GF1, GF2, GF6 and other series of satellites.

[0046] The grid refers to a dense network pattern composed of intersecting horizontal and vertical lines, containing multiple adjacent cells, which are referred to as grid units in this application. The grid units are located on a remote sensing image or imagery. The sub-region refers to a specific real-world area on the ground. Each grid unit corresponds to a sub-region.

[0047] By drawing a sufficiently large grid with enough grid cells, the remote sensing image of the entire area to be monitored is located within the grid, that is, the grid completely covers the remote sensing image of the area to be monitored.

[0048] The size of the grid should not be too large, and the number of grid cells in the grid should not be too many. Each grid cell should contain at least a portion of the remote sensing image of the area to be monitored, that is, each sub-region should contain at least a portion of the area to be monitored.

[0049] Preferably, the sub-region is a square region; more preferably, the side length of the square region is 50 km. In this application, by setting this grid and obtaining the sub-regions, subsequent filtering is performed on a sub-region / grid unit basis. By setting the most suitable sub-region size, the subsequent filtering is most accurate and reasonable, ensuring the removal of abnormal data while minimizing workload. Simultaneously, a reasonable sub-region size facilitates subsequent label selection and comparison. The sub-region size provided in this application ensures that each sub-region contains a suitable number of various labels, facilitating quick label selection, assignment, comparison, and judgment.

[0050] Step 2: Create label boxes in the high spatial resolution remote sensing image. Preferably, the label boxes include high-value label boxes, medium-value label boxes, and low-value label boxes.

[0051] In step 2, the process of creating the label box includes the following sub-steps:

[0052] Sub-step 2-1: Draw the outline of high-concentration emission areas of air pollutants in high spatial resolution remote sensing images. The high-concentration emission areas of air pollutants include cities, industrial parks, traffic emission areas and areas where polluting enterprises are located.

[0053] Preferably, by reading high spatial resolution remote sensing images, cities, industrial parks, and traffic emission zones in the area to be monitored are identified and their outlines are marked. This process is automatically completed by the system recognizing high spatial resolution remote sensing images. The outlines can be directly marked when the high spatial resolution remote sensing images are retrieved. However, the outlines may not be accurate enough, which will affect the accuracy of subsequent processing and lead to errors in the final refined remote sensing data.

[0054] Based on this, this application further filters and corrects areas with high concentrations of air pollutants by performing the following operations: retrieving the land cover type information of the area to be monitored, and drawing the outlines of the city and industrial park in a high spatial resolution remote sensing image; the land cover type information mentioned in this application is public information, including a map that records the type of each area and the boundaries between each area.

[0055] The enterprise's pollution discharge permit registration information is retrieved, and the outline of the location of the pollution discharge enterprise is drawn in a high spatial resolution remote sensing image based on this information; the enterprise's pollution discharge permit registration information is also public information, including the specific location and area of ​​the pollution discharge enterprise.

[0056] By combining the outlines of multiple high-concentration emission areas of air pollutants, several preliminary high-value label outlines are obtained.

[0057] In this application, when the position of the contour line cannot be accurately determined during the process of drawing the contour, the uncertain area can be discarded and the contour can be drawn based on the determinable part. Since this application adopts a scheme of simultaneously drawing contours using multiple methods and taking the union of the results, it can ensure the accuracy of the final result while improving efficiency.

[0058] Furthermore, in this application, based on multiple contour information obtained by different means, considering that contours may contain errors, and taking into account the role of contour information, this application performs union processing on overlapping or intersecting contours of the same type, which can ensure the smooth progress of subsequent label box processing and also ensure the accuracy of the final refined remote sensing data.

[0059] Sub-step 2-2: Draw the outline of the low-concentration emission area of ​​air pollutants in the high spatial resolution remote sensing image. The low-concentration emission area of ​​air pollutants includes farmland, desert and forest areas, which may be interspersed with scattered rural residences.

[0060] Preferably, by reading high spatial resolution remote sensing images, farmland, desert, and forest areas in the area to be monitored are identified and their outlines are marked. This process is automatically completed by the system recognizing high spatial resolution remote sensing images. When the high spatial resolution remote sensing images are retrieved, the outlines can be directly marked. However, the outlines may not be accurate enough, which will affect the accuracy of subsequent processing and lead to errors in the final refined remote sensing data.

[0061] Based on this, this application further filters and corrects the low-concentration emission areas of air pollutants by performing the following operations: retrieving the land cover type information of the area to be monitored, and drawing the outlines of farmland, desert and forest in the high spatial resolution remote sensing image accordingly;

[0062] By combining the outlines of multiple low-concentration emission areas of air pollutants, several preliminary median label outlines are obtained.

[0063] Sub-steps 2-3 involve drawing the outline of areas free from air pollutant emissions in high spatial resolution remote sensing images. These areas include water bodies such as lakes, reservoirs, and oceans.

[0064] Preferably, by reading high spatial resolution remote sensing images, lakes, reservoirs, or sea areas in the region to be monitored are identified and their outlines are marked. This process is automatically completed by the system recognizing high spatial resolution remote sensing images. When the high spatial resolution remote sensing images are retrieved, the outlines can be directly marked. However, the outlines may not be accurate enough, which will affect the accuracy of subsequent processing and lead to errors in the final refined remote sensing data.

[0065] Based on this, this application further filters and corrects areas with no air pollutant emissions by performing the following operations: retrieving the land cover type information of the area to be monitored, and drawing the outline of the location of the lake, reservoir or sea in the high spatial resolution remote sensing image accordingly.

[0066] The outlines of multiple areas with no air pollutant emissions are combined to obtain multiple preliminary low-value label outlines.

[0067] In a preferred embodiment, during the union process, overlapping regions are first merged into a single contour region. Alternatively, two closely spaced contours without overlap can be merged into a single contour region. Here, "closely spaced" is a relative concept and needs to be determined based on actual needs. Through the above union process, each sub-region corresponds to at least one preliminary high-value label contour and at least one preliminary median label contour. Furthermore, each sub-region ultimately corresponds to at least one high-value label box and at least one median label box. Preferably, the number of preliminary high-value label contours in each sub-region is generally less than 3, and the number of preliminary median label contours in each sub-region is generally less than 5.

[0068] When other types of contour regions exist between two contour regions of the same type, these two contour regions of the same type cannot intersect. Furthermore, they cannot intersect when the minimum distance between two contour regions of the same type is greater than 7 km. For example, the contours of a high-concentration air pollutant emission area and a low-concentration air pollutant emission area are contour regions of different types and cannot intersect.

[0069] Preferably, when a portion of the high-value label box is located in one sub-region and another portion is located in another sub-region, it is also considered that there is a high-value label box in both sub-regions.

[0070] Sub-steps 2-4 involve selecting a rectangular region within the label outline. The side length of this rectangular region is an integer multiple of the side length of the smallest pixel in the remote sensing image of atmospheric pollutant concentration. The rectangular region located within the high-value label outline is the high-value label box, the rectangular region located within the medium-value label outline is the medium-value label box, and the rectangular region located within the low-value label outline is the low-value label box. In this application, setting the label box as a rectangle and limiting its side length ensures that the smallest pixel falls precisely within this rectangular region, facilitating subsequent value assignment.

[0071] Preferably, the rectangular area is completely within the label outline. When the label outline is too small to completely cover the smallest pixel, the label outline information is discarded and deleted.

[0072] The label profiles include high-value label profiles, medium-value label profiles, and low-value label profiles.

[0073] Preferably, when taking the union of the contours and setting the rectangular regions, the size of the rectangular regions should be controlled as much as possible so that the rectangular region size of each label box can satisfy 3-4 pixels falling within it. The applicant found that by setting the label boxes and each label box corresponding to 3-4 pixels, the number and size of the label boxes obtained are just right. The accuracy of subsequent judgment on whether the grid cells are normal or not is the highest, and the obtained refined remote sensing data is also more accurate.

[0074] Step 3: Retrieve remote sensing images of atmospheric pollutant concentrations in the area to be monitored obtained from geostationary satellites, add label boxes and grids to the remote sensing images, divide the remote sensing images into multiple grid units using the grid, and assign values ​​to each label box;

[0075] The remote sensing images obtained by geostationary satellites were chosen in this application primarily because geostationary satellites can continuously and dynamically acquire the concentrations of various gases such as O3, NO2, and HCHO over a large spatial area, and their frequency is extremely high, reaching a level of one remote sensing image per hour. This high-frequency remote sensing imagery can be matched with high-frequency ground monitoring stations, thereby determining the accuracy of the concentration information from each group of ground monitoring stations and eliminating ground monitoring information interfered with by migrating pollution.

[0076] The addition in this application involves drawing the same label boxes and grids at the same locations in the remote sensing image. Since the ground contours corresponding to the remote sensing image and the high spatial resolution remote sensing image are consistent, the addition process is simple and easy to operate.

[0077] The remote sensing imagery of atmospheric pollutant concentrations can be any type of remote sensing imagery characterizing atmospheric pollution, such as tropospheric NO2 column concentration, tropospheric HCHO column concentration, or near-surface O3 concentration. Preferably, in practical work, one type of pollution source information is selected for analysis and processing, and the resulting refined remote sensing data only reflects the actual local emissions of that pollution source.

[0078] When the remote sensing image of atmospheric pollutant concentration is the tropospheric NO2 column concentration, the pollutant concentration value specifically refers to the tropospheric NO2 column concentration value, and correspondingly, the atmospheric pollutant concentration data retrieved from the ground monitoring station in S2 is also NO2 concentration information; similarly, when the remote sensing image of atmospheric pollutant concentration is the tropospheric HCHO column concentration, the pollutant concentration value specifically refers to the tropospheric HCHO column concentration value, and correspondingly, the atmospheric pollutant concentration data retrieved from the ground monitoring station in S2 is also HCHO concentration information; when the remote sensing image of atmospheric pollutant concentration is the near-surface O3 concentration, the pollutant concentration value specifically refers to the near-surface O3 concentration value, and correspondingly, the atmospheric pollutant concentration data retrieved from the ground monitoring station in S2 is also O3 concentration information.

[0079] The remote sensing images of atmospheric pollutant concentrations, such as Figure 3As shown in the image, each visible pixel is the smallest pixel in the remote sensing image, and different color depths of the pixels represent different pollutant concentration values; that is, each pixel corresponds to a specific pollutant concentration value, which represents the pollutant concentration in the area corresponding to that pixel. When retrieving this remote sensing image of atmospheric pollutant concentration, the retrieved data includes both the location and corresponding color of each pixel, as well as the specific pollutant concentration value corresponding to each pixel.

[0080] For example, when the remote sensing image of atmospheric pollutant concentration is the concentration of NO2 column in the troposphere, the actual ground size corresponding to the smallest pixel is 3.5*7KM.

[0081] Preferably, the process of assigning values ​​to the label boxes is as follows: read the pollutant concentration values ​​represented by all pixels falling within the label boxes in the remote sensing image, and take their average value as the pollutant concentration value of the label box, thus obtaining a complete label; the label obtained after assigning values ​​to the high-value label boxes is called a high-value label, the label obtained after assigning values ​​to the median label boxes is called a median label, and the label obtained after assigning values ​​to the low-value label boxes is called a low-value label.

[0082] More preferably, when the geometric center of the pixel falls within the label frame / rectangular area, the pixel is considered to fall within the rectangular area. Since the acquisition time of each remote sensing image of atmospheric pollutant concentration is different, there are small deviations between them. By calculating the geometric center position of the pixel to determine whether the pixel falls within the rectangular area and thus determining the assignment of the obtained label frame, the influence of the deviation between the remote sensing images of atmospheric pollutant concentration can be eliminated, ensuring the accuracy of the label and the accuracy of the final reliable remote sensing image.

[0083] Step 4: Analyze each grid cell one by one to determine whether the information in the grid cell is normal. If it is normal, save the atmospheric pollutant concentration information recorded in the remote sensing image of the grid cell, and the grid cell is an information grid. If it is abnormal, clear the atmospheric pollutant concentration information recorded in the remote sensing image of the grid cell, and the grid cell is an empty grid. In this way, a new trustworthy remote sensing image is obtained.

[0084] In step 4, within a grid cell, if the value of any high-value label is greater than the value of any median label and also greater than the value of any low-value label, the information in that grid cell is considered normal; otherwise, the information in that grid cell is considered abnormal.

[0085] This application sets up grid cells and three types of labels within them, and further judges based on the size of the label values. This allows for the filtering out of grid cells with abnormal information, indicating the presence of pollutant migration within those grid cells, and discarding those grid cells. This process filters out grid cells without pollutant migration interference, resulting in new, reliable remote sensing images and improving the accuracy of identifying high-altitude pollution emission sources.

[0086] The trusted remote sensing imagery contains only pollutant concentration information for grid cells with normal information, and excludes pollutant concentration information for grid cells with abnormal information. The ground areas corresponding to grid cells with normal information in the trusted remote sensing imagery are considered trusted areas.

[0087] S2: Retrieve the time point T of the remote sensing image obtained by the geostationary satellite, read the trustworthy area in the trustworthy remote sensing image, and retrieve the atmospheric pollutant concentration data obtained by the ground monitoring station in the trustworthy area at the time point T.

[0088] The specific time at which the ground monitoring station obtains monitoring data can be adjusted according to actual needs, thereby adjusting based on the frequency and time nodes of the remote sensing images output by the geostationary satellite, so that each remote sensing image corresponds to a set of ground monitoring data.

[0089] When determining whether a ground monitoring station is located in a trusted area, it is necessary to make a judgment based on the actual installation location of the ground monitoring station and the specific ground area corresponding to the trusted area.

[0090] In this application, the geostationary satellite may be a space-based Korean geostationary satellite, and the ground monitoring station may be a ground monitoring station of the China Monitoring Center.

[0091] In this application, reliable remote sensing imagery is used to select accurate atmospheric pollutant concentration data from ground monitoring stations that exclude interference from pollutant migration. Based on this atmospheric pollutant concentration data from ground monitoring stations, low-altitude pollution emission sources are identified, thereby improving the accuracy of low-altitude pollution emission source identification.

[0092] S3: Retrieve multiple consecutive remote sensing images from geostationary satellites over a period of time, repeating S1 and S2; provide corresponding reliable remote sensing images for each image.

[0093] The time period can be one day, and correspondingly, a geostationary satellite can provide 24 remote sensing images in one day. The time period can also be one week or one month. The specific time can be selected and set according to mission requirements and specific working conditions. Among them, it is necessary to consider the amount of data and time span required when selecting high and low altitude pollution emission locations.

[0094] S4: Record the atmospheric pollutant concentration values ​​obtained by each ground monitoring station, and count the number of times the ground monitoring station obtains atmospheric pollutant concentration values ​​exceeding the warning value. When the number exceeds the set value, mark the location of the ground monitoring station as a low-altitude pollution emission location.

[0095] S5: Record the pollutant concentration value corresponding to each pixel in all trustworthy remote sensing images, and count the number of pollutant concentration values ​​exceeding the warning value corresponding to each pixel. When the number exceeds the critical value, mark the location corresponding to the pixel as the high-altitude pollution emission location.

[0096] In this application, S4 and S5 are executed simultaneously. The number of trustworthy remote sensing images given in step 3 corresponds to the number of times S4 and S5 are executed.

[0097] The warning value is an atmospheric pollutant concentration value obtained from ground monitoring. It can be set according to specific circumstances. When the atmospheric pollutant belongs to different categories, such as NO2 and HCHO, different values ​​can be given.

[0098] The specific value of the setting can be selected based on the length of the time period and the weather conditions during that time period. If the weather conditions are good and there is relatively more data in the reliable remote sensing images, the value of the setting should be increased accordingly, and vice versa.

[0099] The warning value is a pollutant concentration value in the remote sensing image. It can be set according to specific circumstances. When the pollutant belongs to different types, such as O3 and HCHO, different values ​​can be given.

[0100] The specific value of the threshold can be selected and set according to the length of the time period and the weather conditions during that time period. If the weather conditions are good and there is relatively more data in the reliable remote sensing images, the value of the threshold should be increased accordingly, and vice versa.

[0101] S6: The locations of low-altitude and high-altitude pollution emissions are statistically summarized into the same spatial resolution remote sensing image.

[0102] In this application, there are low-altitude pollution emission sources near the low-altitude pollution emission locations, requiring a systematic investigation of the vicinity of these locations; correspondingly, there are high-altitude pollution emission sources near the high-altitude pollution emission locations, requiring a systematic investigation of the vicinity of these locations as well.

[0103] Preferably, the overlapping areas of low-altitude and high-altitude pollution emission locations are designated as key monitoring areas, requiring focused investigation to accurately identify high- and low-altitude pollution emission sources.

[0104] Example

[0105] Step 1: Select Shandong Province as the monitoring area, retrieve a high spatial resolution remote sensing image of the monitoring area, and draw a square grid with a side length of 50 km in the remote sensing image. This grid divides the monitoring area into 75 sub-regions, as shown below. Figure 2 As shown;

[0106] Step 2: In the high spatial resolution remote sensing image, select high-value labels in areas where cities, industrial parks, traffic emission areas and polluting enterprises are located, select medium-value labels in areas where farmland, desert and forest are located, and select low-value labels in areas where lakes, reservoirs and seas are located.

[0107] Step 3: Retrieve the tropospheric NO2 column concentration containing the area to be monitored as a remote sensing image of atmospheric pollutant concentration; add labels and grids to the remote sensing image, and use the grid to divide the remote sensing image into 75 grid cells. Figure 3 This diagram illustrates the tropospheric NO2 column concentration distribution map of Shandong Province on May 1, 2022, divided into 75 grid cells. Further, each label is assigned a value; the label assignment process involves reading the pollutant concentration values ​​of all pixels falling within the area corresponding to the label, and taking their average value as the pollutant concentration value for that label, thus obtaining a complete label. The 20th grid cell and its labels are shown below. Figure 4 As shown in the figure; where hig represents the high value label and mid represents the median value label.

[0108] Step 4: Analyze each grid cell one by one to determine if the information in the grid cell is normal. Figure 4 In the grid cell, there are 3 high-value labels, 7 median labels, and 0 low-value labels. Further reading of the label values ​​shows that the high-value labels are all greater than any median label value and also greater than any low-value label value. The atmospheric pollutant concentration information recorded in the remote sensing image of this grid cell is saved, and this grid cell is an information grid. Thus, a new trustworthy remote sensing image is obtained.

[0109] Step 6: Retrieve the time point of the remote sensing image obtained by the geostationary satellite, specifically 8:00 AM on May 1, 2022. Read the trusted area in the trusted remote sensing image and retrieve the atmospheric pollutant concentration data obtained by the ground monitoring station in the trusted area at 8:00 AM on May 1, 2022.

[0110] Step 7: Retrieve 168 consecutive remote sensing images from geostationary satellites between 8:00 on May 1, 2022 and 8:00 on May 8, 2022. Repeat steps 1 to 7 a total of 167 times to obtain a total of 168 trustworthy remote sensing images.

[0111] Step 8: Record the atmospheric pollutant concentration values ​​obtained by each ground monitoring station, and count the number of times the ground monitoring station obtains atmospheric pollutant concentration values ​​exceeding the warning value. When the number exceeds the set value of 5 times, mark the location of the ground monitoring station as the low-altitude pollution emission location; execute steps 8 and 9 simultaneously.

[0112] Step 9: Record the pollutant concentration value corresponding to each pixel in all trustworthy remote sensing images, and count the number of pollutant concentration values ​​exceeding the warning value for each pixel. When the number exceeds the critical value, mark the location corresponding to the pixel as the location of high-altitude pollution emission.

[0113] Step 10: The locations of low-altitude and high-altitude pollution emissions are statistically summarized into the same high spatial resolution remote sensing image. The resulting high spatial resolution remote sensing image further reveals the sources of high and low-altitude pollution emissions in the area to be monitored.

[0114] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A method for collaboratively identifying high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring, characterized in that, The method includes: S1: Refine the remote sensing images obtained from geostationary satellites to obtain reliable remote sensing images; S2: Retrieve the time point T of the remote sensing image obtained by the geostationary satellite, read the trustworthy area in the trustworthy remote sensing image, and retrieve the atmospheric pollutant concentration data obtained by the ground monitoring station in the trustworthy area at the time point T. S3: Retrieve multiple consecutive remote sensing images obtained from geostationary satellites over a period of time, repeating S1 and S2; S4: Record the atmospheric pollutant concentration values ​​obtained by each ground monitoring station, and count the number of times the ground monitoring station obtains atmospheric pollutant concentration values ​​exceeding the warning value. When the number exceeds the set value, mark the location of the ground monitoring station as a low-altitude pollution emission location. S5: Record the pollutant concentration value corresponding to each pixel in all trustworthy remote sensing images, and count the number of pollutant concentration values ​​exceeding the warning value corresponding to each pixel. When the number exceeds the critical value, mark the location corresponding to the pixel as the high-altitude pollution emission location. S6: Summarize the locations of low-altitude and high-altitude pollution emissions into a single spatial resolution remote sensing image. In step S1, the refinement process includes the following steps: Step 1: Retrieve a high spatial resolution remote sensing image containing the area to be monitored, and draw a grid in the remote sensing image, which divides the area to be monitored into multiple sub-regions of fixed size; Step 2: Create label boxes in the high spatial resolution remote sensing image, including high-value label boxes, medium-value label boxes, and low-value label boxes; Step 3: Retrieve remote sensing images of atmospheric pollutant concentrations in the area to be monitored obtained from geostationary satellites, add label boxes and grids to the remote sensing images, divide the remote sensing images into multiple grid units using the grid, and assign values ​​to each label box; Step 4: Analyze each grid cell one by one to determine if the information in the grid cell is normal. If normal, save the atmospheric pollutant concentration information recorded in the remote sensing image of that grid cell; this grid cell is an information grid. If abnormal, clear the atmospheric pollutant concentration information recorded in the remote sensing image of that grid cell; this grid cell is an empty grid. This process yields a new, reliable remote sensing image. In step 2, the process of creating tags includes the following sub-steps: Sub-step 2-1 involves drawing the outline of high-concentration air pollutant emission areas in a high spatial resolution remote sensing image. These high-concentration emission areas include urban areas, industrial parks, traffic emission zones, and areas where polluting enterprises are located. During the drawing process, multiple outline information is obtained using different methods, and overlapping or intersecting outlines of the same type are processed by taking their union. The outlines of multiple high-concentration emission areas of air pollutants are combined to obtain multiple preliminary high-value label outlines. Sub-step 2-2 involves drawing the outline of low-concentration emission areas of air pollutants in a high spatial resolution remote sensing image. These low-concentration emission areas include farmland, desert, and forest areas. During the drawing process, multiple outline information is obtained using different methods, and overlapping or intersecting outlines of the same type are processed by taking their union. The outlines of multiple low-concentration emission areas of air pollutants are combined to obtain multiple preliminary median label outlines. Sub-steps 2-3 involve drawing the outlines of areas free from air pollutant emissions in a high spatial resolution remote sensing image. These areas include lakes, reservoirs, and the sea. During the drawing process, multiple outline information is obtained using different methods, and overlapping or intersecting outlines of the same type are processed by taking their union. The outlines of multiple areas with no air pollutant emissions are combined to obtain multiple preliminary low-value label outlines. Sub-steps 2-4 involve selecting a rectangular region within the label outline. The side length of this rectangular region is an integer multiple of the side length of the smallest pixel in the remote sensing image of atmospheric pollutant concentration. The label outline includes a preliminary high-value label outline, a preliminary medium-value label outline, and a preliminary low-value label outline.

2. The method for collaborative identification of high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring according to claim 1, characterized in that, Through the above process of taking the union set, each sub-region has at least one preliminary high-value label profile and at least one preliminary median label profile.

3. The method for collaborative identification of high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring according to claim 1, characterized in that, In sub-steps 2-4, the rectangular region is completely within the label outline. When the label outline is too small to completely cover the smallest pixel, the label outline information is discarded and deleted.

4. The method for collaborative identification of high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring according to claim 1, characterized in that, In step 3, the process of assigning values ​​to the label box is as follows: read the pollutant concentration values ​​represented by all pixels falling within the label box in the remote sensing image, and take their average value as the pollutant concentration value of the label box, thus obtaining a complete label.

5. The method for collaborative identification of high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring according to claim 1, characterized in that, In step 4, within a grid cell, if the value of any high-value label is greater than the value of any median label and also greater than the value of any low-value label, the information in that grid cell is considered normal; otherwise, the information in that grid cell is considered abnormal.

6. The method for collaborative identification of high- and low-altitude pollution emission sources using geostationary satellites and ground monitoring according to claim 1, characterized in that, In S6, the overlapping area between low-altitude and high-altitude pollution emission locations is designated as the key monitoring area.

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