Local scale atmospheric pollution tracing method based on big data

Abstract

The invention provides a local-scale atmospheric pollution tracing method based on big data, and belongs to the technical field of atmospheric pollution tracing. The method comprises the following steps: S1, acquiring environmental basic information; s2, grid division is carried out on an area to be subjected to atmospheric pollution traceability, the distance x from each grid point to a pollution source in the wind direction axis direction is calculated, and the distance y from each grid point to the pollution source in the direction perpendicular to the wind direction axis is calculated; s3, calculating source intensity, a transverse diffusion coefficient and a longitudinal diffusion coefficient based on the environment basic information, the distance x and the distance y; s4, calculating the pollution contribution concentration of each pollution source to the grid points based on the environmental basic information, the distance y, the source intensity, the transverse diffusion coefficient and the longitudinal diffusion coefficient through a Gaussian diffusion model; and S5, carrying out local-scale atmospheric pollution tracing based on the pollution contribution concentration. The method has the advantages that the accuracy and comprehensiveness of local scale atmospheric pollution tracing are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollution source tracing technology, and in particular to a local-scale atmospheric pollution source tracing method based on big data. Background Technology

[0002] While traditional air pollution control efforts have yielded significant results and overall air quality has improved, air quality in some areas has rebounded due to frequent spring dust storms caused by global warming and increased emissions from industrial resumption of production. Furthermore, the main pollutants vary considerably among different monitoring stations within the same region, indicating that key stations still have significant room for emission reduction. Against this backdrop, implementing refined management has become a crucial breakthrough for continuously improving ambient air quality and has also created an urgent need for tracing the sources of air pollution.

[0003] Currently, the main traditional technical solutions for tracing the sources of air pollution are as follows: 1. Air Quality Monitoring at Monitoring Stations: This refers to the fixed-point, continuous, or timed sampling and measurement analysis of atmospheric pollutants (such as PM2.5, PM10, SO2, NO2, CO, O3, TSP, etc.) and meteorological parameters (such as wind speed, wind direction, temperature, humidity, etc.). Monitoring stations are equipped with multi-parameter automatic monitoring instruments to achieve continuous automatic monitoring and to store and analyze the monitoring data in real time, generating relevant monitoring results. This method serves as the infrastructure for air quality assessment and control, providing data support for urban atmospheric environmental protection.

[0004] 2. Air Pollution Source Emission Monitoring: This refers to the real-time monitoring and data recording of pollutant emissions from stationary pollution sources such as industrial enterprises and energy production facilities. Through environmental monitoring methods, the source, flow rate, concentration, temperature, type of pollutant, and emission volume of pollutants are determined. Data acquisition and transmission equipment is used to upload the monitoring data to a management platform, enabling real-time monitoring of pollution conditions and providing a basis for environmental management and policy formulation.

[0005] 3. Video AI Monitoring: This technology applies computer vision to process, analyze, and understand video signals, enabling automatic identification, location, and tracking of targets in monitored scenes. It also intelligently identifies and alarms for pollution behaviors (such as illegal discharges by enterprises, black smoke from burning loose coal, and open-air garbage burning) without human intervention. This technology can assist environmental protection personnel in responding promptly to abnormal emission events.

[0006] 4. Air Pollution Source Tracing Based on Gaussian Diffusion Model: This model is a mathematical model of atmospheric diffusion based on Gaussian probability distribution theory. It is applicable to the simulation of pollutant diffusion at medium and small scales (such as urban or industrial areas) under stable meteorological conditions. By inputting pollution source intensity, meteorological parameters, and diffusion coefficients, the spatial distribution of pollutants and their contribution to pollution can be calculated. Due to its simplicity, ease of use, and wide applicability, this model is widely used in air pollution source tracing and assessment.

[0007] However, the above-mentioned traditional technical solutions still have the following shortcomings: 1. Air quality monitoring at monitoring stations: can only reflect the current pollution level of the monitored factors, and cannot directly identify the source of pollution or associate it with specific pollution scenarios.

[0008] 2. Monitoring of air pollution sources: Although it can accurately grasp the emission status of pollution sources, it cannot assess the actual impact on the air quality of surrounding monitoring stations, and it also lacks the ability to detect sudden pollution events such as open burning, resulting in an incomplete pollution assessment.

[0009] 3. Video AI monitoring: It can identify visible pollution events such as fire plumes, loose coal burning, and open burning in real time, but it cannot monitor the actual emission data of pollution sources, nor can it quantify their impact on air quality, and its assessment capabilities are limited.

[0010] 4. Source tracing based on Gaussian diffusion models: Although it can locate suspected pollution sources and estimate their pollution contribution by combining emission and meteorological data, it lacks a mechanism for comparison and verification with real-time monitoring data, making it difficult to assess the accuracy of the model; at the same time, it usually does not incorporate pollution event information discovered by video AI, failing to effectively supplement pollution source monitoring.

[0011] Therefore, how to provide a big data-based method for tracing the source of local air pollution, and improve the accuracy and comprehensiveness of tracing the source of local air pollution, has become an urgent technical problem to be solved. Summary of the Invention

[0012] The technical problem to be solved by this invention is to provide a method for tracing the source of local atmospheric pollution based on big data, so as to improve the accuracy and comprehensiveness of tracing the source of local atmospheric pollution.

[0013] This invention is implemented as follows: A method for tracing the source of local-scale air pollution based on big data, comprising the following steps: Step S1: Collect basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and information on sensitive points; Step S2: Divide the area to be traced for air pollution sources into grids based on the preset segmentation size, and calculate the distance x from each grid point to the pollution source along the wind direction axis and the distance y from each grid point to the pollution source perpendicular to the wind direction axis. Step S3: Based on the preset source tracing period, and based on the environmental basic information, distance x, and distance y, calculate the source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient. Step S4: Using the Gaussian diffusion model, calculate the pollution contribution concentration of each pollution source to the grid point based on the environmental basic information, distance y, source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient. Step S5: Based on the pollution contribution concentration, perform local-scale air pollution source tracing, generate pollution source tracing results, and display the pollution source tracing results on a GIS map.

[0014] Furthermore, in step S1, the regional meteorological information includes at least weather, wind speed, and wind direction; The monitoring station information includes at least the monitoring station name, monitoring station latitude and longitude, monitoring time, concentration value of preset parameters, monitoring station wind speed, and monitoring station wind direction; The basic information of the pollution source includes at least the name of the pollution source, the latitude and longitude of the pollution source, and the location of the pollution source; The pollutant emission information includes at least the emission outlet number, the pollution source to which the emission outlet belongs, the type of emission outlet, the height of the exhaust stack, the inner diameter of the outlet, the emitted pollutants, the average flue gas velocity, the average flue gas temperature, the average flue gas humidity, the average flue gas pressure, the exhaust gas emission volume, the pollutant emission monitoring concentration, and the pollutant emission amount. The alarm information includes at least the alarm name, alarm time, alarm type, alarm latitude and longitude, and alarm address; The sensitive point information includes at least the sensitive point name, sensitive point type, sensitive point latitude and longitude, and sensitive point address.

[0015] Furthermore, step S2 specifically includes: Centered on the pollution source, the area to be traced for air pollution is divided into grids based on a 20km*20km segmentation size. The distance x from each grid point along the wind direction axis to the pollution source and the distance y from each grid point perpendicular to the wind direction axis to the pollution source are calculated. x=ABS((offset East ^2+offset North ^2)^0.5*(COS(ABS(grid bearing -θ) / 180*PI()))); y=ABS((offset East ^2+offset North^2)^0.5*(SIN(ABS(grid bearing -θ) / 180*PI()))); Where ABS() represents the absolute value function; offset East This represents the east-west offset of a grid point relative to the center of the pollution source; offset North This indicates the north-south offset of a grid point relative to the center of the pollution source; grid bearing θ represents the angle between the vector pointing from the center of the pollution source to the grid point and due north; θ represents the wind direction; PI() represents the function of pi.

[0016] Furthermore, in step S3, the source strength is calculated using the pollutant emission rate, or based on the following formula: Source strength = pollutant emission monitoring concentration * outlet cross-sectional area / emission duration; the outlet cross-sectional area is calculated based on the outlet inner diameter; The calculation process for the lateral diffusion coefficient is as follows: Based on the current time, match the solar altitude angle from the solar altitude angle correspondence table; based on the solar altitude angle and the current weather, match the solar radiation level from the radiation level correspondence table; based on the solar radiation level and the current wind speed, match the atmospheric stability from the atmospheric stability correspondence table; based on the atmospheric stability and distance x, calculate the lateral diffusion coefficient: σy=IFS(x<=1000,Dh2*x^Dh1,t D <=30,Dh2*x^Dh1,t D <=60,Dh2*(1800*u)^Dh1(t D / 30)^0.2,t D >60,Dh2*(1800*u)^Dh1(t D / 30)^0.3); t D =x / u / 60; Where σy represents the transverse diffusion coefficient; Dh1 represents the first initial transverse diffusion coefficient, obtained by looking up a table; Dh2 represents the second initial transverse diffusion coefficient, obtained by looking up a table; t D Indicates diffusion time; u represents current wind speed; The formula for calculating the longitudinal diffusion coefficient is: σz=Dz2*x^Dz1; Where σz represents the longitudinal diffusion coefficient; Dz1 represents the first initial longitudinal diffusion coefficient, which is obtained by looking up a table; and Dz2 represents the second initial longitudinal diffusion coefficient, which is obtained by looking up a table.

[0017] Furthermore, in step S4, the formula for calculating the pollution contribution concentration is: ; in, Indicates the concentration of pollution contribution; Indicates source strength; Indicates the current wind speed; Indicates the lateral diffusion coefficient; Represents the longitudinal diffusion coefficient; exp() represents the exponential operation with the natural constant e as the base. Indicates the height of the monitoring point above the ground; Indicates the effective emission height.

[0018] The advantages of this invention are: 1. Collect basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information; divide the area to be traced for air pollution sources into grids based on a preset segmentation size; calculate the distance x from each grid point along the wind direction axis to the pollution source and the distance y from each grid point perpendicular to the wind direction axis to the pollution source; based on a preset tracing period, and using the basic environmental information, distance x, and distance y, calculate the source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient; then, using a Gaussian diffusion model, calculate the pollution contribution concentration of each pollution source to the grid points based on the basic environmental information, distance y, source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient. This method generates pollution source tracing results at the local scale based on pollution contribution concentrations, and displays these results on a GIS map. Specifically, it utilizes multi-source big data, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information. A source tracing grid is dynamically constructed based on real-time wind direction. Simultaneously, key model parameters are dynamically calculated according to real-time meteorological conditions to drive an atmospheric diffusion model (Gaussian diffusion model) to achieve refined and quantitative simulation of pollution contribution. Finally, the pollution source tracing results are visually displayed on a GIS map, thus systematically improving the accuracy and comprehensiveness of local-scale atmospheric pollution source tracing in terms of data foundation, model adaptability, and result presentation.

[0019] 2. By collecting a variety of basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information, this comprehensive data integration utilizes big data technology to ensure the diversity and completeness of the input data for the source tracing model, thereby significantly improving the accuracy and reliability of pollution source tracing; for example, by combining meteorological data and pollution source emission information, the diffusion path of pollutants can be simulated more accurately.

[0020] 3. The region is divided into grids using a preset segmentation size (e.g., 20km*20km), and the distances x and y of each grid point relative to the pollution source are calculated. This gridding process allows for finer operation at the local scale, enabling better capture of small-scale pollution characteristics, avoiding the coarseness of traditional large-scale models, and improving the spatial resolution of source tracing.

[0021] 4. Based on basic environmental information and distance data, the source strength, lateral diffusion coefficient and longitudinal diffusion coefficient are dynamically calculated. The source strength calculation takes into account the pollutant emission monitoring concentration and the cross-sectional area of ​​the discharge outlet, while the diffusion coefficient calculation incorporates factors such as solar altitude angle, radiation level, and atmospheric stability, and combines wind speed and distance. This dynamic adjustment enables the model to adapt to real-time changes in meteorological conditions and pollution source status, thereby improving the robustness and applicability of the model.

[0022] 5. Using the Gaussian diffusion model, combined with the calculated source strength, diffusion coefficient, and environmental information, the pollution contribution concentration of each pollution source to the grid points is calculated. This model is a validated standard model, but it has been optimized by integrating big data and dynamic parameters, thereby improving the accuracy of concentration calculation. The formula takes into account the effective emission height and the height of the monitoring point, further reducing the error. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] Figure 1 This is a flowchart of a local-scale atmospheric pollution source tracing method based on big data according to the present invention.

[0025] Figure 2 This is a schematic diagram of the monitoring station and pollutants of this invention. Detailed Implementation

[0026] The overall approach of the technical solution in this application is as follows: By using multi-source big data such as regional meteorological information, monitoring station information, basic information of pollution sources, pollutant emission information, alarm information, and sensitive point information, and dynamically constructing a source tracing grid based on real-time wind direction, while dynamically calculating key model parameters according to real-time meteorological conditions, the Gaussian diffusion model is driven to achieve refined and quantitative simulation of the pollution contribution. Finally, the pollution source tracing results are displayed intuitively through a GIS map to improve the accuracy and comprehensiveness of local-scale atmospheric pollution source tracing.

[0027] Please refer to Figures 1 to 2 As shown, a preferred embodiment of the present invention, a local-scale air pollution source tracing method based on big data, includes the following steps: Step S1: Collect basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and information on sensitive points; Step S2: Divide the area to be traced for air pollution sources into grids based on the preset segmentation size (e.g., divide it into 40,000 grids), and calculate the distance x from each grid point to the pollution source along the wind direction axis and the distance y from each grid point to the pollution source perpendicular to the wind direction axis. Step S3: Based on the preset source tracing period (e.g., a 1-hour source tracing period, with source tracing triggered when the time reaches the hour), calculate the source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient based on the environmental basic information, distance x, and distance y. Step S4: Using the Gaussian diffusion model, calculate the pollution contribution concentration of each pollution source to the grid point based on the environmental basic information, distance y, source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient. Step S5: Based on the pollution contribution concentration, perform local-scale air pollution source tracing, generate pollution source tracing results, and display the pollution source tracing results on a GIS map.

[0028] This invention takes monitoring stations as the main body and conducts a comprehensive analysis of the impact of air pollution at the station level. Based on the station's air quality profile, the station's pollution source profile, and the air pollution source tracing model (Gaussian diffusion model), it statistically analyzes the correlation between ambient air quality at the monitoring points and pollution sources, and gradually expands and progresses from "point-line-area" to improve the level of digitalization, enhance the comprehensive air pollution control capabilities, and further promote the continuous improvement of air quality.

[0029] This invention interconnects and integrates multi-source data from air quality monitoring stations, including pollution sources, alarm content, and sensitive point information related to each station. It enables refined management based on the station level, uncovering the improvement potential of specific stations, and precisely planning to gradually expand from "points to lines to areas," accelerating the improvement of regional pollution problems, continuously promoting comprehensive air pollution control, and improving overall regional air quality. The invention compares pollution contribution concentrations with detailed air quality monitoring data from monitoring stations, allowing for timely analysis of the simulation accuracy of local-scale models to further improve the accuracy of results. Pollution source emission monitoring data is input into a Gaussian diffusion model for pollution diffusion simulation analysis, facilitating continuous correction of the diffusion model and improving the accuracy of model predictions. The discovery of new pollution sources through video AI alarm monitoring also expands the scope of pollution sources within the analysis range. Analysis of the surrounding areas of monitoring stations within the region, and the overlay analysis of multiple results, helps in the supervision of monitoring blind spots.

[0030] In step S1, the regional meteorological information includes at least weather, wind speed, and wind direction; The monitoring station information includes at least the monitoring station name, monitoring station latitude and longitude, monitoring time, concentration value of preset parameters, monitoring station wind speed, and monitoring station wind direction; The GIS coordinates (latitude and longitude) of the monitoring stations are recorded as a1, a2, a3, etc.; the wind direction monitored by the monitoring stations is recorded as L1. The basic information of the pollution source includes at least the name of the pollution source, the latitude and longitude of the pollution source, and the location of the pollution source; The GIS coordinates (latitude and longitude of the pollution source) are recorded as b1, b2, b3... respectively; The pollutant emission information includes at least the emission outlet number, the pollution source to which the emission outlet belongs, the type of emission outlet, the height of the exhaust stack, the inner diameter of the outlet, the emitted pollutants, the average flue gas velocity, the average flue gas temperature, the average flue gas humidity, the average flue gas pressure, the exhaust gas emission volume, the pollutant emission monitoring concentration, and the pollutant emission amount. The alarm information includes at least the alarm name, alarm time, alarm type, alarm latitude and longitude, and alarm address; the alarm type includes video AI alarms, which are recorded as c1, c2, c3, etc. The sensitive point information includes at least the sensitive point name, sensitive point type, sensitive point latitude and longitude, and sensitive point address.

[0031] The GIS coordinates (latitude and longitude of the sensitive points) are recorded as d1, d2, d3, ...; Step S2 specifically involves: Centered on the pollution source, the area to be traced for air pollution is divided into grids based on a 20km*20km segmentation size. The distance x from each grid point along the wind direction axis to the pollution source and the distance y from each grid point perpendicular to the wind direction axis to the pollution source are calculated. x=ABS((offset East ^2+offset North ^2)^0.5*(COS(ABS(grid bearing -θ) / 180*PI()))); y=ABS((offset East ^2+offset North ^2)^0.5*(SIN(ABS(grid bearing -θ) / 180*PI()))); Where ABS() represents the absolute value function; offset East This represents the east-west offset of a grid point relative to the center of the pollution source; offset North This indicates the north-south offset of a grid point relative to the center of the pollution source; grid bearing θ represents the angle between the vector pointing from the center of the pollution source to the grid point and due north; θ represents the wind direction; PI() represents the function of pi.

[0032] In step S3, the source strength is calculated using the pollutant emission rate or based on the following formula: Source strength = pollutant emission monitoring concentration * outlet cross-sectional area / emission duration; the outlet cross-sectional area is calculated based on the outlet inner diameter; The calculation process for the lateral diffusion coefficient is as follows: Based on the current time, match the solar altitude angle from the solar altitude angle correspondence table; based on the solar altitude angle and the current weather, match the solar radiation level from the radiation level correspondence table; based on the solar radiation level and the current wind speed, match the atmospheric stability from the atmospheric stability correspondence table; based on the atmospheric stability and distance x, calculate the lateral diffusion coefficient: σy=IFS(x<=1000,Dh2*x^Dh1,t D <=30,Dh2*x^Dh1,t D <=60,Dh2*(1800*u)^Dh1(t D / 30)^0.2,t D >60,Dh2*(1800*u)^Dh1(t D / 30)^0.3); t D =x / u / 60; Where σy represents the transverse diffusion coefficient; Dh1 represents the first initial transverse diffusion coefficient, obtained by looking up a table; Dh2 represents the second initial transverse diffusion coefficient, obtained by looking up a table; t D Indicates diffusion time; u represents current wind speed; An example of a table showing the correspondence between solar altitude angles is as follows:

[0033] An example of a radiation level correspondence table is as follows:

[0034] An example of an atmospheric stability correspondence table is as follows:

[0035] The table for selecting the initial transverse diffusion coefficient is as follows:

[0036] The formula for calculating the longitudinal diffusion coefficient is: σz=Dz2*x^Dz1; Where σz represents the longitudinal diffusion coefficient; Dz1 represents the first initial longitudinal diffusion coefficient, which is obtained by looking up a table; and Dz2 represents the second initial longitudinal diffusion coefficient, which is obtained by looking up a table.

[0037] In step S4, the formula for calculating the pollution contribution concentration is: ; in, Indicates the concentration of pollution contribution; Indicates source strength; Indicates the current wind speed; Indicates the lateral diffusion coefficient; Represents the longitudinal diffusion coefficient; exp() represents the exponential operation with the natural constant e as the base. Indicates the height of the monitoring point above the ground; Indicates the effective emission height (exhaust stack height).

[0038] The selection table for the initial longitudinal diffusion coefficient is as follows:

[0039] In specific implementation, the execution process of step S5 is as follows: Let the monitoring station to be analyzed be abbreviated as a1. Based on a1, obtain the pollution sources b1, b2, and b3 that contribute to the pollution of a1, and their corresponding pollution contribution concentrations s1, s2, and s3 (if b1 has multiple emission outlets p1, p2, and p3, and the pollution contribution of all pollutants from each emission outlet is sp1, sp2, and sp3 respectively, then s1 = sp1 + sp2 + sp3); if there are no relevant pollution sources, then no statistics will be collected. The pollution contribution concentration calculated in step S4 is the pollution contribution grid concentration of one pollutant in the gas emitted from a certain outlet under a specific pollution source. If it is necessary to calculate the overall pollution contribution of the outlet, the pollution contribution grid of all pollutants at the outlet needs to be summed.

[0040] Next, confirm the upwind area of ​​a1 and identify the video AI alarms c1 and c2 within the upwind area of ​​a1 in the past 3 hours; if there are none, they will not be counted. Upwind video AI alarm statistics area: Taking the wind direction monitored by a1 as the center line and a1 as the starting point, the fan-shaped area with a total of 135° on both sides of the center line, 67.5° to the left and 67.5° to the right of the center line, is the upwind video AI alarm statistics area.

[0041] Next, identify the sensitive points d1, d2, and d3 within the analysis range surrounding a1; if there are none, then no statistics will be collected. The above results are then visualized and rendered on a GIS map. For example... Figure 2 The diagram shown is a schematic diagram of the area surrounding the monitoring station. 1) Draw a circle with a radius of 10 kilometers, centered at a1.

[0042] 2) Draw a straight line L1 with the wind direction monitored by the monitoring station as the direction to indicate the direction of pollution spread in the current monitoring station area.

[0043] 3) Identify the pollution sources that cause pollution to a1. Based on the longitude and latitude of the pollution sources, determine the locations of b1, b2, and b3, and display the corresponding pollution source icons. You can also choose to display the corresponding pollution contribution concentrations s1, s2, and s3 in a specific way.

[0044] 3) Based on the longitude and latitude of the upwind video AI alarm, determine the alarm locations c1, c2, and c3, and display the corresponding alarm icons.

[0045] 5) Based on the latitude and longitude of the sensitive points, determine the sensitive points d1, d2, and d3, and display the sensitive point icons.

[0046] In practice, when conducting analysis and visualization of the area surrounding the monitoring station based on a GIS map, a statistical analysis section should also be provided for the flat and intuitive display of analysis results. The content includes, but is not limited to, "basic information of the monitoring station, monitoring details of the monitoring station, pollution source information, alarm information, and sensitive point information".

[0047] After presenting the results of the surrounding analysis based on the above steps, the pollution contributions of the traced pollution sources b1, b2, and b3 can be summed (s1, s2, and s3), and compared with the air quality monitoring details of the monitoring stations. The simulation accuracy of the local scale model can be analyzed in a timely manner to further improve the accuracy of the results.

[0048] In summary, the advantages of this invention are: 1. Collect basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information; divide the area to be traced for air pollution sources into grids based on a preset segmentation size; calculate the distance x from each grid point along the wind direction axis to the pollution source and the distance y from each grid point perpendicular to the wind direction axis to the pollution source; based on a preset tracing period, and using the basic environmental information, distance x, and distance y, calculate the source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient; then, using a Gaussian diffusion model, calculate the pollution contribution concentration of each pollution source to the grid points based on the basic environmental information, distance y, source strength, lateral diffusion coefficient, and longitudinal diffusion coefficient. This method generates pollution source tracing results at the local scale based on pollution contribution concentrations, and displays these results on a GIS map. Specifically, it utilizes multi-source big data, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information. A source tracing grid is dynamically constructed based on real-time wind direction. Simultaneously, key model parameters are dynamically calculated according to real-time meteorological conditions to drive an atmospheric diffusion model (Gaussian diffusion model) to achieve refined and quantitative simulation of pollution contribution. Finally, the pollution source tracing results are visually displayed on a GIS map, thus systematically improving the accuracy and comprehensiveness of local-scale atmospheric pollution source tracing in terms of data foundation, model adaptability, and result presentation.

[0049] 2. By collecting a variety of basic environmental information, including regional meteorological information, monitoring station information, basic information on pollution sources, pollutant emission information, alarm information, and sensitive point information, this comprehensive data integration utilizes big data technology to ensure the diversity and completeness of the input data for the source tracing model, thereby significantly improving the accuracy and reliability of pollution source tracing; for example, by combining meteorological data and pollution source emission information, the diffusion path of pollutants can be simulated more accurately.

[0050] 3. The region is divided into grids using a preset segmentation size (e.g., 20km*20km), and the distances x and y of each grid point relative to the pollution source are calculated. This gridding process allows for finer operation at the local scale, enabling better capture of small-scale pollution characteristics, avoiding the coarseness of traditional large-scale models, and improving the spatial resolution of source tracing.

[0051] 4. Based on basic environmental information and distance data, the source strength, lateral diffusion coefficient and longitudinal diffusion coefficient are dynamically calculated. The source strength calculation takes into account the pollutant emission monitoring concentration and the cross-sectional area of ​​the discharge outlet, while the diffusion coefficient calculation incorporates factors such as solar altitude angle, radiation level, and atmospheric stability, and combines wind speed and distance. This dynamic adjustment enables the model to adapt to real-time changes in meteorological conditions and pollution source status, thereby improving the robustness and applicability of the model.

[0052] 5. Using the Gaussian diffusion model, combined with the calculated source strength, diffusion coefficient, and environmental information, the pollution contribution concentration of each pollution source to the grid points is calculated. This model is a validated standard model, but it has been optimized by integrating big data and dynamic parameters, thereby improving the accuracy of concentration calculation. The formula takes into account the effective emission height and the height of the monitoring point, further reducing the error.

[0053] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for local-scale atmospheric pollution source apportionment based on big data, characterized in that: The method comprises the following steps: Step S1, collecting environmental basic information including regional meteorological information, monitoring station information, pollution source basic information, pollutant emission information, alarm information and sensitive point information; Step S2, performing grid division on the region to be traced for air pollution based on a preset division size, and calculating the distance x of each grid point to the pollution source along the wind direction axis and the distance y of the grid point to the pollution source perpendicular to the wind direction axis; Step S3, based on a preset tracing period, calculating the source strength, lateral diffusion coefficient and longitudinal diffusion coefficient based on the environmental basic information, distance x and distance y; Step S4, calculating the pollution contribution concentration of each pollution source to the grid point based on the environmental basic information, distance y, source strength, lateral diffusion coefficient and longitudinal diffusion coefficient through a Gaussian diffusion model; Step S5, performing air pollution tracing at a local scale based on the pollution contribution concentration to generate a pollution tracing result and displaying the pollution tracing result on a GIS map.

2. The method of claim 1, wherein: In the step S1, the regional meteorological information at least includes weather, wind speed and wind direction; The monitoring station information at least includes monitoring station name, monitoring station longitude and latitude, monitoring time, concentration value of preset parameter, monitoring station wind speed and monitoring station wind direction; The pollution source basic information at least includes pollution source name, pollution source longitude and latitude and pollution source location; The pollutant emission information at least includes emission port number, emission port belonging to pollution source, emission port type, exhaust pipe height, outlet inner diameter, emitted pollutant, average flue gas flow rate, average flue gas temperature, average flue gas humidity, average flue gas pressure, waste gas emission volume, pollutant emission monitoring concentration and pollutant emission volume; The alarm information at least includes alarm name, alarm time, alarm type, alarm longitude and latitude and alarm address; The sensitive point information at least includes sensitive point name, sensitive point type, sensitive point longitude and latitude and sensitive point address.

3. The method of claim 1, wherein: The step S2 specifically comprises: Performing grid division on the region to be traced for air pollution based on a 20km*20km division size with the pollution source as the center, calculating the distance x of each grid point to the pollution source along the wind direction axis and the distance y of the grid point to the pollution source perpendicular to the wind direction axis; x = ABS((offset East ^2+offset North ^2)^0.5*(COS(ABS(grid bearing -θ) / 180*PI()))) y = ABS((offset East ^2+offset North ^2)^0.5*(SIN(ABS(grid bearing -θ) / 180*PI()))) where ABS() represents the absolute value function; offset East represents the east-west direction offset of the grid point relative to the pollution source center; offset North represents the north-south direction offset of the grid point relative to the pollution source center; grid bearing represents the angle between the vector from the pollution source center to the grid point and the north direction; θ represents the wind direction; PI() represents the circular constant function.

4. The method of claim 1, wherein: In the step S3, the source strength adopts the pollutant emission rate or is calculated based on the following formula: Source strength = pollutant emission monitoring concentration * emission port cross-sectional area / emission time length; the emission port cross-sectional area is calculated based on the outlet inner diameter; The calculation process of the lateral diffusion coefficient is as follows: Based on the current time, match the solar elevation angle from the solar elevation angle corresponding table; based on the solar elevation angle and the current weather, match the solar radiation level from the radiation level corresponding table; Based on the solar radiation level and the current wind speed, match the atmospheric stability from the atmospheric stability corresponding table; based on the atmospheric stability and the distance x, calculate the lateral diffusion coefficient: σy = IFS(x <= 1000, Dh2 * x^Dh1, t D <= 30, Dh2 * x^Dh1, t D <= 60, Dh2 * (1800 * u)^Dh1, t D / 30)^0.2, t D > 60, Dh2 * (1800 * u)^Dh1, t D / 30)^0.3); t D = x / u / 60; wherein σy represents a transverse diffusion coefficient; Dh1 represents a first transverse initial diffusion coefficient, obtained by table lookup; Dh2 represents a second transverse initial diffusion coefficient, obtained by table lookup; t D represents a diffusion time; u represents a current wind speed; The calculation formula of the longitudinal diffusion coefficient is as follows: σz=Dz2*x^Dz1; Wherein, σz represents the longitudinal diffusion coefficient; Dz1 represents the first longitudinal initial diffusion coefficient, which is obtained by table lookup; Dz2 represents the second longitudinal initial diffusion coefficient, which is obtained by table lookup.

5. The method of claim 1, wherein: In the step S4, the calculation formula of the pollution contribution concentration is: ; wherein, represents the pollution contribution concentration; represents the source strength; represents the current wind speed; represents the lateral dispersion coefficient; represents the longitudinal dispersion coefficient; exp() represents the exponential operation with the natural constant e; represents the monitoring point height above ground; represents the effective emission height.

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