Pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution

Abstract

The present invention relates to the technical field of atmospheric pollution source tracing, and specifically to a pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution, comprising: constructing a deep neural network simulation model for the spatial distribution of fine particulate matter concentration; determining the sites and time periods where the fine particulate matter monitoring concentration exceeds the standard according to the air quality monitoring standard, and generating a gridded fine particulate matter concentration distribution within the exceeding time period by the deep neural network model for the spatial distribution of fine particulate matter concentration; cyclically stepping to generate pollution transmission trajectories, and when the tracing termination condition is met, superimposing the pollution source coordinates in the emission inventory data to achieve the final pollution tracing. The present invention, from the perspective of geographic spatial association, based on the gridded fine particulate matter concentration in continuous time periods, iteratively traces back the pollution diffusion path time by time period, obtains the fine particulate matter transmission and diffusion trajectory, intuitively reflects the atmospheric pollution diffusion process, provides directional guidance for fine particulate matter pollution source tracing, and after superimposing the spatial distribution of pollution sources, can lock the atmospheric pollution emission source.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric pollution source tracing, and in particular to a pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution. Background Art

[0002] Seasonal and sudden air pollution is influenced by a combination of factors, including pollution emissions, meteorology, and the geographical environment. Pollution emissions are the most critical factor, and the one most susceptible to human intervention. Therefore, accurately identifying the emission sources that contribute to air pollution is a key technical challenge in the current phase of air quality control.

[0003] The existing technology is mainly based on the concentration data of monitoring stations, generates backward trajectories according to the atmospheric pollution diffusion model, and then combines the receptor model to lock the location of the suspected pollution source. The existing atmospheric pollution diffusion models are mainly mature diffusion models such as CALPUFF and HYSPLIT and backward trajectory models. For details, please refer to patent application CN202410515361.3, which is used to reversely lock the spatial location of the suspected pollution source. However, the existing mature diffusion model requires the input of emission inventory data, and assumes that all pollution emission sources in the emission inventory data have contributions, ignoring the situation that some pollution sources may not emit pollution sources in certain periods of time, resulting in the atmospheric pollution diffusion process simulated by the atmospheric pollution diffusion model being inconsistent with the actual pollution concentration, resulting in inaccurate pollution tracing. Summary of the Invention

[0004] The present invention aims to provide a pollution source tracing method based on high-temporal and spatial resolution pollution concentration distribution to solve the technical problem that the emission inventory data input into the atmospheric pollution diffusion model in the prior art assumes that all pollution emission sources have contributions, resulting in the atmospheric pollution diffusion process simulated by the atmospheric pollution diffusion model being inconsistent with the actual pollution concentration. The specific technical solution is as follows:

[0005] The present invention provides a pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution, comprising the following steps:

[0006] S1. Collect fine particulate matter concentration monitoring data from fine particulate matter stations, meteorological station monitoring data, land use data, terrain elevation data, emission inventory data, and environmental petition data. Perform Kriging spatial interpolation on the meteorological station monitoring data to generate gridded meteorological data. Conduct transmission buffer area analysis based on the Pearson correlation coefficient to determine the optimal spatial scale for the proportion of land use types in the land use data.

[0007] S2. Taking the spatial location of the fine particulate matter station as a benchmark, first find the gridded meteorological data values ​​and terrain elevation data values ​​corresponding to the spatial location of the fine particulate matter station. Then, set the buffer area size according to the optimal spatial scale and calculate the land use type area ratio at the spatial location of the fine particulate matter station. Finally, use the fine particulate matter concentration monitoring data values, gridded meteorological data values, terrain elevation data values, and land use type area ratio at the spatial location of the fine particulate matter station as input data to train a deep neural network and construct a deep neural network simulation model for the spatial distribution of fine particulate matter concentration.

[0008] S3. Determine the sites and time periods where fine particulate matter concentrations exceed the standard based on air quality monitoring standards. Then, use the deep neural network model of fine particulate matter concentration spatial distribution to generate a gridded distribution of fine particulate matter concentrations during the period of exceeding the standard.

[0009] S4. Generate pollution transmission trajectories based on the gridded fine particulate matter concentration distribution during the exceeding-standard period obtained in S3, and superimpose the pollution source coordinates in the emission inventory data to achieve the ultimate pollution source tracing.

[0010] A further improvement of the pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention is that the meteorological station monitoring data includes wind speed, wind direction, temperature, air pressure and humidity.

[0011] A further improvement of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention is that S4 specifically includes the following steps:

[0012] S401, the location of the station where the fine particulate matter concentration exceeds the standard is T n Time reference point, determine T according to wind speed n The size of the transmission buffer area at any time; T is determined according to the wind direction n Transmit the buffer area position at any time and extract T n The pollution concentration exceeds the standard grid in the buffer zone at the moment, and the center coordinates of the exceeding grid are calculated to generate T n Transmit trajectory points at all times and record T n The pollution concentration C at the transmission trajectory point at the moment n ; n is a natural number from 1 to N, where N is the maximum value of the monitoring time period;

[0013] S402, take n=n+1, if C n Less than C N , then the traceability termination condition is considered to be met and the process goes to S403; if C n Greater than or equal to C N , then return to S401 with T n The center coordinates of the exceeding grid at the moment are used as the reference point, and T is established according to the method in S1. n+1 Tick ​​transmission buffer area, calculate T n+1The center coordinates of the grid that exceeds the limit at the moment generate the transmission trajectory point and record T n+1 Pollution concentration C at the transmission trajectory point at the moment n+1 ;

[0014] S403. Determine whether there is a pollution source at the transmission trajectory point in combination with the emission inventory data. If so, output the pollution source information to achieve final pollution tracing. If not, end the loop and identify the transmission trajectory point as a suspected pollution source area. Further combine the field investigation to determine whether there is a pollution source omitted in the emission inventory or a temporary pollution emission source.

[0015] The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention is further improved in that, when determining T according to wind speed, n When the buffer area size is transmitted at any moment, the following expression is used for calculation:

[0016]

[0017] Where D is the buffer side length grid number, Ws is the wind speed, T is the duration, and abs is the rounding operator.

[0018] The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention is further improved in that, when determining T according to wind direction, n When transmitting the buffer zone position at any time, the fine particulate matter station is used as the reference position. The buffer zone position is determined by the wind direction and is divided into the following eight situations:

[0019] When the wind direction is northwest, the reference position is located at the northwest corner of the transmission buffer zone;

[0020] When the wind direction is north, the reference position is located due north of the transmission buffer zone;

[0021] When the wind direction is northeast, the reference position is located in the northeast corner of the transmission buffer zone;

[0022] When the wind direction is westerly, the reference position is located due west of the transmission buffer zone;

[0023] When the wind direction is south, the reference position is located due south of the transmission buffer zone;

[0024] When the wind direction is easterly, the reference position is located due east of the transmission buffer zone;

[0025] When the wind direction is southwest, the reference position is located in the southwest corner of the transmission buffer zone;

[0026] When the wind direction is southeast, the reference position is located in the southeast corner of the transmission buffer zone.

[0027] The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention is further improved in that the center coordinates of the excessive grid are calculated to generate Tn When the trajectory points are transmitted at any moment, the expression of the exceeded grid center is as follows:

[0028]

[0029] Among them, Lat is the horizontal coordinate of the center of the exceeding standard grid, Lon is the vertical coordinate of the center of the exceeding standard grid ... i is the horizontal coordinate of the i-th exceeding standard grid, lon i is the vertical coordinate of the i-th exceeding standard grid, and e is the number of exceeding standard grids.

[0030] A further improvement of the pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention is that the land use data includes cultivated land, forest land, road land, water bodies and construction land.

[0031] A further improvement of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention is that when Kriging spatial interpolation is performed on meteorological monitoring data, the expression of the Kriging spatial interpolation is as follows:

[0032]

[0033] Among them, b i (i=1,2,3,···,n) is the weight of the meteorological station monitoring value; γ(ω i ,ω j ) represents the meteorological monitoring station ω i (i=1,2,3,···,n) and meteorological monitoring station ω j (j=1,2,3,···,n); γ(ω i ,V) represents the meteorological monitoring station ω i The spatial correlation between λ and the estimated region V; λ is the Lagrange multiplier.

[0034] The pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention is further improved in that, when the transmission buffer area analysis is carried out on the optimal spatial scale of the land use type proportion in the land use data based on the Pearson correlation coefficient, the calculation formula of the Pearson correlation coefficient r is as follows:

[0035]

[0036] Among them, X i represents the observed concentration of fine particulate matter at the i-th monitoring station; Y i,m represents the proportion of land use types within the buffer zone with a side length of m at the i-th monitoring station; It represents the average value of the observed concentration of fine particulate matter at the monitoring station; It represents the average proportion of land use types within the buffer zone with a side length of m at the monitoring station.

[0037] The present invention further improves the pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution in that the deep neural network model of fine particulate matter concentration spatial distribution is as follows:

[0038]

[0039] Among them, WS is wind speed, TEMP is air temperature, PRS is air pressure, SHU is specific humidity, LD is land use type area ratio, and DEM is terrain elevation; It is the site monitoring value of fine particulate matter concentration.

[0040] The application of the technical solution of the present invention has the following beneficial effects:

[0041] The present invention is based on a pollution source tracing method with high spatiotemporal resolution pollution concentration distribution. From the perspective of geographic spatial correlation, based on the gridded fine particulate matter concentration for continuous time periods, the pollution diffusion path is iteratively traced back time by time period to obtain the transmission and diffusion trajectory of fine particulate matter. This intuitively reflects the atmospheric pollution diffusion process and provides directional guidance for fine particulate matter pollution source tracing. After superimposing the spatial distribution of pollution sources, the atmospheric pollution emission sources can be locked, solving the technical problem in the existing atmospheric pollution diffusion model that the emission inventory data input into the atmospheric pollution diffusion model assumes that all pollution emission sources have contributions, resulting in the atmospheric pollution diffusion process simulated by the atmospheric pollution diffusion model being inconsistent with the actual pollution concentration.

[0042] The present invention directly obtains the spatial distribution of fine particulate matter concentration by establishing a deep neural network model of the fine particulate matter concentration at the fine particulate matter station and the related meteorological, land use and terrain elevation factors that affect the distribution of fine particulate matter concentration. It abandons the simple reliance on site monitoring numerical spatial interpolation or distribution simulation based on the atmospheric pollution diffusion numerical simulation model, improves the accuracy of spatial distribution simulation while not relying on high computing resources.

[0043] This method establishes a deep neural network model that integrates fine particulate matter concentration site monitoring data with meteorological monitoring data, land use, and terrain elevation. This model can perform spatial predictions of fine particulate matter concentration in areas without monitoring sites, effectively improving the spatial coverage of fine particulate matter concentrations and objectively reflecting the spatial differentiation characteristics of fine particulate matter concentrations. This model can provide a spatial data foundation for tracing the source of fine particulate matter pollution. Compared to existing technologies, the spatial resolution of the method described in this paper can reach 100 meters. While existing atmospheric pollution diffusion models such as CALPUFF, CMAQ, and WRF-CHEM all have kilometer-level spatial resolutions, this finer spatial resolution ensures the accuracy of pollution tracing.

[0044] Based on the geographic and spatial movement characteristics of atmospheric pollution diffusion, this paper establishes a spatiotemporal, step-by-step pollution source tracing model for fine particulate matter using reverse engineering. This model can intuitively trace the atmospheric pollution diffusion process and determine the pollution source trajectory at each time period, providing guidance for tracing atmospheric pollution sources and significantly improving the accuracy of fine particulate matter pollution source tracing.

[0045] In addition, the present invention does not require high computing resources, while the existing technology is very dependent on computing resources because it involves solving complex partial differential equations. Therefore, the method of the present invention is more efficient and convenient.

[0046] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a flow chart of the pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention;

[0049] Figure 2 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention when the wind is northwest (the arrow N in the figure indicates the north);

[0050] Figure 3 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention when the wind is north (the arrow N in the figure indicates the north);

[0051] Figure 4 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention during northeasterly winds (arrow N in the figure indicates north);

[0052] Figure 5 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention during westerly winds (arrow N in the figure indicates north);

[0053] Figure 6 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention when the wind is southerly (the arrow N in the figure indicates the north);

[0054] Figure 7This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution in the easterly wind (the arrow N in the figure indicates the north);

[0055] Figure 8 This is a schematic diagram of the transmission buffer area orientation of the pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution of the present invention when the wind is southwesterly (the arrow N in the figure indicates the north);

[0056] Figure 9 This is a schematic diagram of the orientation of the transmission buffer area when the wind is southeasterly according to the pollution source tracing method based on the high spatiotemporal resolution pollution concentration distribution of the present invention (the arrow N in the figure indicates the north). DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] See also Figures 1 to 9 As shown in FIG, a pollution source tracing method based on high spatiotemporal resolution (spatial resolution of 100 meters, time resolution of 1 hour) pollution concentration distribution includes the following steps:

[0059] S1, collecting fine particulate matter (PM 2.5 , particulate matter (particles with a diameter of 2.5 microns or less) concentration monitoring data, meteorological station monitoring data, land use data, terrain elevation data, emission inventory data, and environmental petition data were used to perform Kriging spatial interpolation on the meteorological station monitoring data to generate gridded meteorological data; based on the Pearson correlation coefficient, a transmission buffer area analysis was conducted on the optimal spatial scale of the land use type area ratio in the land use data to determine the optimal buffer zone scale corresponding to the area ratio of different land use types;

[0060] The fine particulate matter concentration monitoring data collected from fine particulate matter sites include the hourly monitoring values ​​of fine particulate matter concentration collected from four-level monitoring sites: national, provincial, actual and street-level monitoring sites; the meteorological site monitoring data include wind speed, wind direction, temperature, air pressure and humidity; the land use data include cultivated land, forest land, road land, water bodies and construction land; the emission inventory contains the name, longitude and latitude information of the pollution source; the petition data include the name, longitude and latitude information of the pollution source complained about.

[0061] Furthermore, when Kriging spatial interpolation is performed on meteorological monitoring data, the expression of Kriging spatial interpolation is as follows:

[0062]

[0063] Among them, b i (i=1,2,3,···,n) is the weight of the meteorological station monitoring value; γ(ω i ,ωj ) represents the meteorological monitoring station ω i (i=1,2,3,···,n) and meteorological monitoring station ω j (j=1,2,3,···,n); γ(ω i ,V) represents the meteorological monitoring station ω i The spatial correlation between λ and the estimated region V; λ is the Lagrange multiplier.

[0064] Preferably, the original land use data does not have numerical attributes and cannot be used in the construction of deep neural network models. Therefore, buffer analysis is required to convert the land use data into land use area proportions. Specifically, the Pearson correlation coefficient is used to determine the optimal buffer size for each land use type. When conducting transmission buffer area analysis based on the Pearson correlation coefficient for the optimal spatial scale of land use type proportions in land use data, the Pearson correlation coefficient r is calculated as follows:

[0065]

[0066] Among them, X i represents the observed concentration of fine particulate matter at the i-th monitoring station; Y i,m represents the proportion of land use types within the buffer zone with a side length of m at the i-th monitoring station; It represents the average value of the observed concentration of fine particulate matter at the monitoring station; It represents the average proportion of land use types within the buffer zone with a side length of m at the monitoring station.

[0067] Due to the spatial distance between monitoring stations, to avoid setting the buffer area too roughly so that the calculated land use area percentages of adjacent stations are the same, the buffer area size cannot exceed the minimum distance between adjacent stations. The optimal buffer area size is shown in Table 1.

[0068] Table 1 Pearson correlation coefficient between fine particulate matter concentration and land use area ratio:

[0069]

[0070] Therefore, in this embodiment, the optimal buffer area sizes for cultivated land, forest land, water body, construction land, and road land are 300m, 300m, 100m, 300m, and 200m, respectively.

[0071] S2. Taking the spatial location of the fine particulate matter station as a benchmark, first find the gridded meteorological data values ​​and terrain elevation data values ​​corresponding to the spatial location of the fine particulate matter station, then set the buffer area size according to the optimal spatial scale to calculate the land use type area ratio at the spatial location of the fine particulate matter station, and finally use the fine particulate matter concentration monitoring data values, gridded meteorological data values, terrain elevation data values, and land use type area ratio of the spatial location of the fine particulate matter station as input data to train a deep neural network and construct a deep neural network (DNN) simulation model of fine particulate matter concentration spatial distribution.

[0072] Specifically, the Extract Values ​​to Points (ArcGIS) tool was used to spatially match gridded meteorological data, terrain elevation values, and the proportion of land use types at the optimal buffer zone scale with the fine particulate matter concentrations at the monitoring stations. The data were normalized using the "maximum-minimum" method to generate the input dataset. The "maximum-minimum" method calculation formula is as follows:

[0073]

[0074] Where, X * is the normalized input sample, X is the original input data sample, X min is the minimum sample value in the original input data sample, X max is the maximum sample value in the original input data samples.

[0075] Preferably, a deep neural network is used to construct a mapping relationship between fine particulate matter concentration and meteorological data, terrain elevation data, and land use type area ratio. The deep neural network model for the spatial distribution of fine particulate matter concentration is as follows:

[0076]

[0077] Among them, WS is wind speed, TEMP is temperature, PRS is air pressure, SHU is specific humidity, LD is land use type area ratio, and DEM is terrain elevation; the spatial resolution of WS, TEMP, PRS, and SHU is 100m; the spatial resolution of DEM is 30m; It is the site monitoring value of fine particulate matter concentration.

[0078] The fitting coefficient R2 and root mean square error (RMSE) were used to evaluate the model accuracy to ensure the reliability of the model accuracy.

[0079] S3. Determine the sites and time periods where the fine particulate matter monitoring concentration exceeds the standard based on the air quality monitoring standards, and then use the deep neural network model of the fine particulate matter concentration spatial distribution to generate the gridded fine particulate matter concentration distribution during the period of exceeding the standard.

[0080] Specifically, according to the "Ambient Air Quality Standard" (GB3095-2012), fine particulate matter (PM 2.5 Using the secondary concentration limit of 75 μg / m³ as the standard, monitoring stations exceeding this value were identified as exceeding the standard, and the corresponding period of exceeding the standard was recorded. Gridded meteorological data, land use type area ratio, and terrain elevation data for this period were then extracted, and the deep neural network established in the above steps was used to simulate the gridded fine particulate matter concentration distribution.

[0081] S4. Generate pollution transmission trajectories based on the gridded fine particulate matter concentration distribution during the exceeding-standard period obtained in S3, and superimpose the pollution source coordinates in the emission inventory data to achieve the ultimate pollution source tracing.

[0082] S401, the location of the station where the fine particulate matter concentration exceeds the standard is T n Time reference point, determine T according to wind speed n The size of the transmission buffer area at any time; T is determined according to the wind direction n Transmit the buffer area position at any time and extract T n The pollution concentration exceeds the standard grid in the buffer zone at the moment, and the center coordinates of the exceeding grid are calculated to generate T n Transmit trajectory points at all times and record T n The pollution concentration C at the transmission trajectory point at the moment n , and record T n+1 Pollution concentration C at the transmission trajectory point at the moment n+1 ; n is a natural number from 1 to N, where N is the maximum value of the monitoring time period;

[0083] S402, take n=n+1, if C n Less than C N , then the traceability termination condition is considered to be met and the process goes to S403; if C n Greater than or equal to C N , then return to S401 with T n The center coordinates of the exceeding grid at the moment are used as the reference point, and T is established according to the method described in S1. n+1 Tick ​​transmission buffer area, calculate T n+1 The center coordinates of the grid that exceeds the limit at the moment generate the transmission trajectory point and record T n+1 Pollution concentration C at the transmission trajectory point at the moment n+1 ;

[0084] S403. Determine whether there is a pollution source at the transmission trajectory point in combination with the emission inventory data. If so, output the pollution source information to achieve final pollution tracing. If not, end the loop and identify the transmission trajectory point as a suspected pollution source area. Further combine the field investigation to determine whether there is a pollution source omitted in the emission inventory or a temporary pollution emission source.

[0085] Table 2 is the pollution source tracing result of a certain place in this embodiment:

[0086]

[0087]

[0088] Specifically, this embodiment passed through a total of 5 transmission trajectory points and traced back to 7 pollution emission sources. Among them, the pollution sources of trajectory point 1 and trajectory point 2 are restaurants, the pollution source of trajectory point 3 is a gas station, there are 3 pollution sources at trajectory point 4, all of which are auto repair shops, and trajectory point 5 is an advertising production store, and all pollution emission sources are in the production contribution period. Pollution transmission is mainly driven by wind. Driven by the prevailing wind, pollutants will be transmitted and diffused from upwind to downwind. The latitude and longitude coordinates of the tracing trajectory points in this embodiment are generally in the "northwest-southeast" direction, and the current prevailing wind direction is northwest wind. The two are consistent, indicating that the method of the present invention can effectively determine the direction of the pollution source and can accurately find the pollution source.

[0089] Preferably, when determining T according to wind speed n When the buffer area size is transmitted at any moment, the following expression is used for calculation:

[0090]

[0091] Where D is the buffer side length grid number, Ws is the wind speed, T is the duration, and abs is the rounding operator.

[0092] Preferably, when determining T according to the wind direction n When transmitting the buffer zone orientation at any time, the fine particulate matter station is used as the reference position, and the buffer zone orientation is determined by the wind direction, such as Figures 2 to 9 As shown in the figure, the gray area is the buffer area, the black area is the reference position, and the arrow points to the north. It is divided into the following eight situations:

[0093] like Figure 2 As shown, when the wind direction is northwest, the reference position is located in the northwest corner of the transmission buffer zone;

[0094] like Figure 3 As shown, when the wind direction is north, the reference position is located due north of the transmission buffer zone;

[0095] like Figure 4 As shown, when the wind direction is northeast, the reference position is located in the northeast corner of the transmission buffer zone;

[0096] like Figure 5 As shown, when the wind direction is westerly, the reference position is located due west of the transmission buffer zone;

[0097] like Figure 6As shown, when the wind direction is south, the reference position is located due south of the transmission buffer zone;

[0098] like Figure 7 As shown, when the wind direction is easterly, the reference position is located due east of the transmission buffer zone;

[0099] like Figure 8 As shown, when the wind direction is southwesterly, the reference position is located in the southwest corner of the transmission buffer zone;

[0100] like Figure 9 As shown, when the wind direction is southeast, the reference position is located in the southeast corner of the transmission buffer zone.

[0101] Preferably, when calculating the center coordinates of the excess grid, T n When the trajectory points are transmitted at any moment, the expression of the exceeded grid center is as follows:

[0102]

[0103] Among them, Lat is the horizontal coordinate of the center of the exceeding standard grid, Lon is the vertical coordinate of the center of the exceeding standard grid, lat i is the horizontal coordinate of the i-th exceeding standard grid, lon i is the vertical coordinate of the i-th exceeding standard grid, and e is the number of exceeding standard grids.

[0104] Figure 1 This is a flow chart of the pollution source tracing method based on high spatiotemporal resolution pollution concentration distribution of the present invention. The specific process is: first, the wind speed, wind direction, temperature, air pressure and humidity are subjected to Kriging spatial interpolation to obtain gridded meteorological raster data; at the same time, the land use data is subjected to buffer area analysis to obtain the land use area ratio; then the PM 2.5 The concentration data of fine particulate matter, DEM (terrain elevation) data, meteorological grid data and land use area ratio are matched temporally and spatially to analyze PM 2.5 DNN (deep neural network) simulation of the spatial distribution of (fine particulate matter) concentration determines the location of the exceeding site and the exceeding period, thereby generating a gridded PM 2.5 The concentration of fine particulate matter is determined, and the size and orientation of the transmission area at the exceeding site are determined based on wind speed and direction. The grids with exceeding pollution concentration in the transmission area are calculated, and spatial clustering of grids with exceeding pollution concentration is performed to generate pollution transmission trajectories. The pollution emission inventory is then nested to generate the tracing results.

[0105] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution, characterized in that: The steps include: S1. Collect fine particulate matter concentration monitoring data from fine particulate matter stations, meteorological station monitoring data, land use data, terrain elevation data, emission inventory data, and environmental petition data. Perform Kriging spatial interpolation on the meteorological station monitoring data to generate gridded meteorological data. Conduct transmission buffer area analysis based on the Pearson correlation coefficient to determine the optimal spatial scale for the proportion of land use types in the land use data. S2. Taking the spatial location of the fine particulate matter station as a reference, first find the gridded meteorological data values ​​and terrain elevation data values ​​corresponding to the spatial location of the fine particulate matter station, then set the buffer area size according to the optimal spatial scale to calculate the land use type area ratio at the spatial location of the fine particulate matter station, and finally use the fine particulate matter concentration monitoring data values, gridded meteorological data values, terrain elevation data values, and land use type area ratio at the spatial location of the fine particulate matter station as input data to train a deep neural network and construct a deep neural network simulation model of fine particulate matter concentration spatial distribution; S3. Determine the sites and time periods where fine particulate matter concentrations exceed the standard based on air quality monitoring standards. Then, use the deep neural network model of fine particulate matter concentration spatial distribution to generate a gridded distribution of fine particulate matter concentrations during the period of exceeding the standard. S4. Generate pollution transmission trajectories based on the gridded fine particulate matter concentration distribution during the exceeding-standard period obtained in S3, and superimpose the pollution source coordinates in the emission inventory data to achieve the ultimate pollution source tracing; The meteorological station monitoring data includes wind speed, wind direction, temperature, air pressure and humidity; S4 specifically includes the following steps: S401, the location of the station where the fine particulate matter concentration exceeds the standard is T n Time reference point, determine T according to wind speed n The size of the transmission buffer area at any time; T is determined according to the wind direction n Transmit the buffer area position at any time and extract T n The pollution concentration exceeds the standard grid in the buffer zone at the moment, and the center coordinates of the exceeding grid are calculated to generate T n Transmit trajectory points at all times and record T n The pollution concentration C at the transmission trajectory point at the moment n ; n is a natural number from 1 to N, where N is the maximum value of the monitoring time period; S402, take n=n+1, if C n Less than C N , then the traceability termination condition is considered to be met and the process goes to S403; if C n Greater than or equal to C N , then return to S401 with T n The center coordinates of the exceeding grid at the moment are used as the reference point, and T is established according to the method described in S1. n+1 Tick ​​transmission buffer area, calculate T n+1 The center coordinates of the grid that exceeds the limit at the moment generate the transmission trajectory point and record T n+1 Pollution concentration C at the transmission trajectory point at the moment n+1 ; S403. Determine whether there is a pollution source at the transmission trajectory point in combination with the emission inventory data. If so, output the pollution source information to achieve final pollution tracing. If not, end the loop and identify the transmission trajectory point as a suspected pollution source area. Further combine the field investigation to determine whether there is a pollution source omitted in the emission inventory or a temporary pollution emission source.

2. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: Determine T according to wind speed n When the buffer area size is transmitted at any moment, the following expression is used for calculation: Where D is the buffer side length and number of grids, Ws is the wind speed, T is the duration, and abs is the rounding operator.

3. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: Determine T according to wind direction n When transmitting the buffer zone position at any time, the fine particulate matter station is used as the reference position. The buffer zone position is determined by the wind direction and is divided into the following eight situations: When the wind direction is northwest, the reference position is located at the northwest corner of the transmission buffer zone; When the wind direction is north, the reference position is located due north of the transmission buffer zone; When the wind direction is northeast, the reference position is located in the northeast corner of the transmission buffer zone; When the wind direction is westerly, the reference position is located due west of the transmission buffer zone; When the wind direction is south, the reference position is located due south of the transmission buffer zone; When the wind direction is easterly, the reference position is located due east of the transmission buffer zone; When the wind direction is southwest, the reference position is located in the southwest corner of the transmission buffer zone; When the wind direction is southeast, the reference position is located in the southeast corner of the transmission buffer zone.

4. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: When calculating the center coordinates of the excess grid, generate T n When the trajectory points are transmitted at any moment, the expression of the exceeded grid center is as follows: Among them, Lat is the horizontal coordinate of the center of the exceeding standard grid, Lon is the vertical coordinate of the center of the exceeding standard grid, lat i is the horizontal coordinate of the i-th exceeding standard grid, lon i is the vertical coordinate of the i-th exceeding standard grid, and e is the number of exceeding standard grids.

5. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: The land use data includes cultivated land, forest land, road land, water bodies and construction land.

6. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: When performing Kriging spatial interpolation on meteorological monitoring data, the expression of Kriging spatial interpolation is as follows: Among them, b i (i=1,2,3,···,n) is the weight of the meteorological station monitoring value; γ(ω i ,ω j ) represents the meteorological monitoring station ω i (i=1,2,3,···,n) and meteorological monitoring station ω j (j=1,2,3,···,n); γ(ω i ,V) represents the meteorological monitoring station ω i The spatial correlation between λ and the estimated region V; λ is the Lagrange multiplier.

7. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: When conducting transmission buffer area analysis on the optimal spatial scale of land use type proportion in land use data based on the Pearson correlation coefficient, the calculation formula of the Pearson correlation coefficient r is as follows: Among them, X i represents the observed concentration of fine particulate matter at the i-th monitoring station; Y i,m represents the proportion of land use types within the buffer zone with a side length of m at the i-th monitoring station; It represents the average value of the observed concentration of fine particulate matter at the monitoring station; It represents the average proportion of land use types within the buffer zone with a side length of m at the monitoring station.

8. The pollution source tracing method based on high temporal and spatial resolution pollution concentration distribution according to claim 1 is characterized in that: The deep neural network model of the spatial distribution of fine particulate matter concentration is as follows: Among them, WS is wind speed, TEMP is air temperature, PRS is air pressure, SHU is specific humidity, LD is land use type area ratio, and DEM is terrain elevation; It is the site monitoring value of fine particulate matter concentration.

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