AERMOD model-based simulation area secondary PM2.5 prediction result calculation and visualization method

Abstract

The invention discloses a secondary PM2.5 prediction result calculation and visualization method based on an AERMOD model simulation area in the technical field of atmospheric pollutant observation. The method comprises the following steps: acquiring pollution data of pollutants in a pollution source and corresponding meteorological information; according to the method, pollution data and corresponding meteorological information are subjected to simulation calculation through an AIRMOD model to obtain primary PM2.5 concentration prediction data, text vector data obtained through an Aermod model are calculated through a mode of being combined with Arcgis to obtain secondary PM2.5 concentration prediction data, prediction result data of the mass concentration of PM2.5 pollutants are obtained, and the prediction result data of the mass concentration of the PM2.5 pollutants is obtained. The method is used for realizing visualization of regional secondary PM2.5 concentration results corresponding to different time spans, and can quickly obtain regional secondary PM2.5 concentration and visualization results. The defects that an air quality model is large in basic data demand quantity, the model structure is very complex, high-performance calculation is needed, and an AERMOD model can only obtain one-time pollutant concentration result visualization are overcome.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollutant observation technology, specifically to a method for simulating regional secondary PM2.5 based on the AERMOD model. 2.5 Methods for calculating and visualizing prediction results. Background Technology

[0002] With the acceleration of industrialization and urbanization, fine particulate matter (PM2.5) has become increasingly prevalent. 2.5 Pollution has become a global environmental problem. PM2.5 2.5 Based on their formation mechanism, particulate matter can be divided into primary particulate matter (directly emitted) and secondary particulate matter (formed from gaseous precursors such as SO2 and NO). x (These are generated through chemical reactions). my country has also designated particulate matter as a basic pollutant for assessing whether ambient air quality meets standards. Therefore, secondary PM2.5... 2.5 Concentration simulation is crucial for the formulation of pollution control policies.

[0003] Air quality models are an important tool for studying the spatiotemporal distribution characteristics of air pollutants and predicting atmospheric environmental quality, and are widely used in the field of environmental planning and management. AERMOD (American Meteorological Society / Environmental Protection Agency Regulatory Model), as a widely used model for atmospheric diffusion simulation, plays a crucial role in estimating the impact of emissions from various industrial sources on air quality.

[0004] Existing models such as CMAQ and WRF-Chem are typical comprehensive regional-scale models. While they can simulate various complex air quality issues and regional compound air pollution problems, including O3, particulate matter, visibility, acid rain, and even climate change, they require a large amount of basic data and suffer from high computational complexity, demanding hardware, high-performance computing requirements, and long processing times. The Aermod model can only directly output primary pollutant concentrations and visualization results, making it inconvenient to realize regional secondary PM2.5 concentrations. 2.5 The simulation of concentration calculations and visualizations is not very effective. Summary of the Invention

[0005] The purpose of this invention is to provide a method for simulating regional secondary PM based on the AERMOD model. 2.5 The proposed methods for calculating and visualizing prediction results address the issues raised above, such as the need for massive amounts of basic data, high computational complexity, demanding hardware, requiring high-performance computing, and lengthy processing times, making them unsuitable for regional secondary PM implementation. 2.5 The simulation of concentration calculations and visualizations suffers from poor performance.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for simulating regional secondary PM based on the AERMOD model 2.5 The method for calculating and visualizing the prediction results includes the following steps:

[0008] S1: Obtain pollutant emission concentration, pollutant emission rate and corresponding meteorological information from surrounding meteorological stations in the pollution source;

[0009] S2: Establish an AERMOD model, establish a first discrete point distribution map based on the coordinate system of the AERMOD model, input the pollutant emission concentration, pollutant emission rate and meteorological information into the AERMOD model to simulate and calculate the spatial distribution data of primary pollutant concentration for time series analysis, and output text vector data based on the spatial distribution data of primary pollutant concentration.

[0010] S3: Establish a second discrete point distribution map based on the ArcGIS coordinate system, and couple the first discrete point distribution map with the second discrete point distribution map to obtain the final discrete point distribution map;

[0011] S4: Input the text vector data into ArcGIS for processing to obtain the spatial distribution data of secondary pollutant concentrations for time series analysis. Add the spatial distribution data of primary pollutant concentrations to the spatial distribution data of secondary pollutant concentrations to obtain the prediction result data corresponding to the time series analysis. Assign the prediction result data to the final discrete point distribution map.

[0012] S5: Establish a raster map based on the final discrete point distribution map, and interpolate the raster map using the inverse distance weighting method based on the prediction result data to obtain a concentration raster map. Add preset labels to the concentration raster map to generate visualization data for corresponding time series analysis.

[0013] As a further aspect of the present invention: in S1, the pollution source includes point source data, area source data and line source data, wherein the point source data is used to characterize pollutant emission sources distributed in a point manner, the area source data is used to characterize pollutant emission sources distributed in a area manner, and the line source data is used to characterize pollutant emission sources distributed in a line manner.

[0014] The meteorological information includes wind speed, wind direction, temperature, humidity, air pressure, precipitation, and cloud cover around the pollution source. The wind speed and wind direction are used to characterize the direction and speed of pollutant diffusion. The temperature, humidity, and cloud cover are used to characterize atmospheric stability. The air pressure is used to characterize plume lift calculations and the conversion of pollutant concentration units. The precipitation is used to characterize wet deposition.

[0015] As a further aspect of the present invention: the pollutants in the pollution source include PM2.5. 2.5Concentration data, NO2 concentration data, and SO2 concentration data.

[0016] As a further aspect of the present invention: in S2, the AERMOD model includes an AERMOD diffusion module, an AERMOT meteorological preprocessing module, and an AERMAP terrain preprocessing module, wherein the AERMOT meteorological preprocessing module and the AERMAP terrain preprocessing module are electrically connected to the AERMOD diffusion module.

[0017] Geographic data around the pollution source is acquired. The AERMAP terrain preprocessing module establishes a coordinate system. The AERMAP terrain preprocessing module establishes a first discrete point distribution map in the coordinate system based on the geographic data. The first discrete point distribution map has discrete points with several matrix distributions.

[0018] The AERMET meteorological preprocessing module receives wind speed, wind direction, temperature, humidity, air pressure, precipitation and cloud cover for preprocessing to generate meteorological data based on the parameterized planetary boundary layer.

[0019] The AERMOD diffusion module receives a first discrete point distribution map and meteorological data to generate time-series analysis of primary pollutant concentration spatial distribution data. This primary pollutant concentration spatial distribution data includes primary PM2.5 concentration data. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data, based on the aforementioned primary PM2.5 concentration prediction data. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data are used to generate text vector data based on the shapefile format.

[0020] The time series analysis includes hourly, daily, and input time ranges.

[0021] As a further aspect of the present invention: in step S3, the first discrete point distribution map in the coordinate system of the AERMOD model is coupled with the second discrete point distribution map in the coordinate system of ArcGIS, so that the first discrete point distribution map and the second discrete point distribution map coincide to obtain a final discrete point distribution map with unified coordinates.

[0022] As a further aspect of the present invention: in step S4, the text vector data is input into ArcGIS, and ArcGIS loads PM once. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data were used to obtain spatial distribution data of secondary pollutant concentrations based on the field calculator in ArcGIS. This spatial distribution data of secondary pollutant concentrations includes secondary PM2.5 concentrations. 2.5 Concentration prediction data C 二次PM2.5 ;

[0023] The C 二次PM2.5The calculation formula is as follows:

[0024]

[0025] In the formula: C 二次 PM2.5 is a secondary PM2.5 2.5 The mass concentration of the concentration prediction data is in μg / m³. 3 ;

[0026] and Convert SO2 and NO2 concentrations to PM2.5. 2.5 Concentration coefficient;

[0027] C SO2 and C NO2 These are the mass concentrations of predicted SO2 and NO2 concentrations, respectively, in μg / m³. 3 .

[0028] As a further aspect of the present invention: the 0.58 It is 0.44.

[0029] As a further aspect of the present invention: the secondary PM 2.5 Concentration prediction data and primary PM 2.5 The concentration prediction data are accumulated to obtain the PM corresponding to the time series analysis. 2.5 The predicted data of pollutant mass concentration will be used to determine the PM2.5 concentration. 2.5 The predicted pollutant mass concentration data are used to generate the final discrete point distribution map.

[0030] As a further embodiment of the present invention: in step S5, a raster map is obtained by combining several discrete points of the final discrete point distribution map in both directions; the predicted result data is interpolated on the raster map based on the inverse distance weighting method to obtain a concentration raster map; and a preset label is added to the concentration raster map to generate corresponding time series analysis visualization data.

[0031] The concentration raster is given a preset color level to generate corresponding time series analysis and visualized data in PNG / JPG format.

[0032] As a further aspect of the present invention: the time series analysis of the spatial distribution data of the primary pollutant concentration is not less than the daily level and the time resolution is the hour level.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] 1. In this invention, the PM2.5 concentration is obtained by simulating and calculating pollution data and corresponding meteorological information using the AERMOD model. 2.5The method uses concentration prediction data and, by combining it with ArcGIS, calculates secondary PM2.5 concentration prediction data from the text vector data obtained by the Aermod model. This allows for visualization of regional secondary PM2.5 concentration results across different time spans. This method can quickly obtain regional secondary PM2.5 concentrations and visualization results, overcoming the shortcomings of air quality models, such as extremely large basic data requirements, highly complex model structures, the need for high-performance computing, and the fact that the Aermod model can only obtain visualization of primary pollutant concentration results.

[0035] 2. In this invention, PM 2.5 Concentration data, NO2 concentration data, SO2 concentration data, and meteorological information are used as input information and integrated into the AERMOD model to simulate and predict regional secondary PM2.5 concentrations. 2.5 The generation and distribution of PM 2.5 Concentration data directly reflects the level of fine particulate matter pollution in the air, while NO2 and SO2 concentration data serve as precursors for secondary particulate matter formation. By accurately measuring and recording the concentration data of these pollutants and combining them with meteorological information, the AERMOD model can comprehensively consider multiple factors such as atmospheric transport and deposition, thereby more accurately predicting regional secondary PM2.5 concentrations. 2.5 The concentration distribution was determined using PM2.5. 2.5 Concentration data, NO2 concentration data, and SO2 concentration data reduce the overall data processing volume of the model and improve computational efficiency. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0037] Figure 2 This invention provides a schematic diagram of the shapefile output from the model loaded into ArcGIS according to Embodiment 1.

[0038] Figure 3 A schematic diagram of the final calculation results of Embodiment 1 is provided for this invention;

[0039] Figure 4 A schematic diagram of the visualization results of Embodiment 1 of the present invention is provided. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example:

[0042] Please see Figure 1 In this embodiment of the invention, a method for simulating regional secondary PM based on the AERMOD model is described. 2.5 The method for calculating and visualizing the prediction results includes the following steps:

[0043] S1: Obtain pollutant emission concentration, pollutant emission rate and corresponding meteorological information from surrounding meteorological stations in the pollution source;

[0044] S2: Establish an AERMOD model, establish a first discrete point distribution map based on the coordinate system of the AERMOD model, input the pollutant emission concentration, pollutant emission rate and meteorological information into the AERMOD model to simulate and calculate the spatial distribution data of primary pollutant concentration for time series analysis, and output text vector data based on the spatial distribution data of primary pollutant concentration.

[0045] S3: Establish a second discrete point distribution map based on the ArcGIS coordinate system, and couple the first discrete point distribution map with the second discrete point distribution map to obtain the final discrete point distribution map;

[0046] S4: Input the text vector data into ArcGIS for processing to obtain the spatial distribution data of secondary pollutant concentrations for time series analysis. Add the spatial distribution data of primary pollutant concentrations to the spatial distribution data of secondary pollutant concentrations to obtain the prediction result data corresponding to the time series analysis. Assign the prediction result data to the final discrete point distribution map.

[0047] S5: Establish a raster map based on the final discrete point distribution map, and interpolate the raster map using the inverse distance weighting method based on the prediction result data to obtain a concentration raster map. Add preset labels to the concentration raster map to generate visualization data for corresponding time series analysis.

[0048] Specifically, the box model based on the law of mass conservation, the Gaussian model based on the statistical theory of turbulent diffusion, and the Lagrange trajectory model are first-generation air quality models. They employ simple linearization mechanisms when simulating atmospheric physicochemical parameters and are suitable for simulating the long-term average concentrations of inert atmospheric pollutants. Among them, the Gaussian model has advantages such as simple structure, low requirements for input data, and fast computation speed, making it the most widely used model among the first-generation air quality models. Small- and medium-scale models also belong to the first-generation air quality models. The AMS / EPA Regulatory Model (AERMOD) model, jointly developed by the U.S. Environmental Protection Agency and the American Meteorological Society's Regulatory Model Improvement Committee (AERMIC), is an atmospheric diffusion prediction model and is currently one of the most mature and advanced air quality models internationally. In 2008, my country included it in the recommended models of the "Technical Guidelines for Environmental Impact Assessment - Atmospheric Environment" (HJ2.2-2008). This model is a first-generation air diffusion model based on planetary boundary layer theory. It is a steady-state plume diffusion model that can simulate the distribution of short-term (hourly average, daily average) and long-term (annual average) concentrations of pollutants emitted from point sources, area sources, and volume sources within a 50km range based on atmospheric boundary layer data characteristics. It is applicable to both rural and urban areas.

[0049] ArcGIS is a collective term for a suite of Geographic Information System (GIS) software products developed by the Environmental Systems Research Institute (ESRI). It provides a comprehensive set of tools and platforms for creating, managing, analyzing, sharing, and visualizing geospatial data.

[0050] Specifically, this invention uses the AERMOD model to simulate and calculate primary PM2.5 concentration prediction data based on pollution data and corresponding meteorological information. Furthermore, by combining it with ArcGIS, it calculates secondary PM2.5 concentration prediction data from the text vector data obtained by the AERMOD model. This enables visualization of secondary PM2.5 concentration results for different time spans. This method can quickly obtain regional secondary PM2.5 concentrations and visualization results, overcoming the shortcomings of air quality models, such as extremely large basic data requirements, highly complex model structures, the need for high-performance computing, and the limitation that the AERMOD model can only provide visualization of primary pollutant concentration results.

[0051] Preferably, in S1, the pollution sources include point source data, area source data, and line source data. Point source data is used to characterize pollutant emission sources that are distributed in a point-like manner, area source data is used to characterize pollutant emission sources that are distributed in a area-like manner, and line source data is used to characterize pollutant emission sources that are distributed in a line-like manner.

[0052] Meteorological information includes wind speed, wind direction, temperature, humidity, air pressure, precipitation, and cloud cover around the pollution source. Wind speed and wind direction are used to characterize the direction and speed of pollutant diffusion, temperature, humidity, and cloud cover are used to characterize atmospheric stability, air pressure is used to characterize plume lift calculations and the conversion of pollutant concentration units, and precipitation is used to characterize wet deposition.

[0053] Point source: refers to a pollution source that emits pollutants in a concentrated point or a relatively small area, such as a factory chimney or a large boiler. Its emission outlet can be approximated as a point.

[0054] Non-point source pollution refers to pollution sources that emit pollutants over a large area, such as heating boilers in residential areas, or the volatilization of pesticides and fertilizers in farmland, where pollutants are emitted dispersedly throughout the area.

[0055] Line source: A pollution source that emits pollutants along a line, such as cars traveling on a highway. These mobile sources continuously emit pollutants along the road.

[0056] Specifically, wind speed and direction, as key parameters, can directly affect the diffusion path and speed of pollutants, helping models to more accurately predict the spread range of pollutants. Temperature and humidity work together to affect atmospheric stability, influence the vertical diffusion of pollutants and the height of the mixing layer, and thus affect the concentration of pollutants on the ground. The amount of cloud cover also affects solar radiation and surface temperature, indirectly affecting the chemical reaction rate and diffusion conditions of atmospheric pollutants.

[0057] Air pressure plays a crucial role in the conversion of pollutant concentration units and the calculation of plume rise. It determines the concentration distribution of pollutants at different altitudes and the degree of vertical diffusion of pollutants. At the same time, changes in air pressure also affect air density and flow conditions, further influencing the diffusion of pollutants.

[0058] Precipitation is the primary driver of wet deposition, removing some pollutants from the atmosphere and reducing their concentration at the ground. The amount and duration of precipitation directly affect the effectiveness and extent of wet deposition. By comprehensively considering these factors, the AERMOD model can more accurately simulate the spatial distribution of pollutant concentrations, making it applicable to various environments and with a wide range of applications.

[0059] Preferably, the pollutants in the pollution source include PM2.5. 2.5 Concentration data, NO2 concentration data, and SO2 concentration data.

[0060] Specifically, pollutants in pollution sources include PM2.5 2.5PM2.5 concentration data, NO2 concentration data, SO2 concentration data, and meteorological information are used as input information and integrated into the AERMOD model to simulate and predict regional secondary PM2.5 concentrations. 2.5 The generation and distribution of PM 2.5 Concentration data directly reflects the level of fine particulate matter pollution in the air, while NO2 and SO2 concentration data serve as precursors for secondary particulate matter formation. By accurately measuring and recording the concentration data of these pollutants and combining them with meteorological information, the AERMOD model can comprehensively consider multiple factors such as atmospheric transport, chemical reactions, and deposition, thereby more accurately predicting regional secondary PM2.5 levels. 2.5 The concentration distribution was determined using PM2.5. 2.5 Concentration data, NO2 concentration data, and SO2 concentration data reduce the overall data processing volume of the model and improve computational efficiency.

[0061] Preferably, in S2, the AERMOD model includes an AERMOD diffusion module, an AERMET meteorological preprocessing module, and an AERMAP terrain preprocessing module, and the AERMET meteorological preprocessing module and the AERMAP terrain preprocessing module are electrically connected to the AERMOD diffusion module.

[0062] Geographic data around the pollution source is acquired. The AERMAP terrain preprocessing module establishes a coordinate system. Based on the geographic data, the AERMAP terrain preprocessing module establishes a first discrete point distribution map in the coordinate system. The first discrete point distribution map has discrete points with several matrix distributions.

[0063] The AERMET meteorological preprocessing module receives wind speed, wind direction, temperature, humidity, air pressure, precipitation, and cloud cover for preprocessing to generate meteorological data based on a parameterized planetary boundary layer.

[0064] The AERMOD diffusion module receives the first discrete point distribution map and meteorological data to generate spatial distribution data of primary pollutant concentrations for time series analysis. The spatial distribution data of primary pollutant concentrations includes primary PM2.5 concentration prediction data, NO2 concentration prediction data and SO2 concentration prediction data. Based on the primary PM2.5 concentration prediction data, NO2 concentration prediction data and SO2 concentration prediction data, text vector data based on shp format is generated.

[0065] The time series analysis includes hourly, daily, and input time ranges. The input time range can be set by the user according to the actual research needs. For example, short-term simulations can be set to 1 to 7 days, medium-term simulations to 1 to 3 months, and long-term simulations to 1 year or more. The set time range must cover the complete cycle of pollutant concentration changes to ensure the completeness and accuracy of the simulation results.

[0066] Specifically, the AERMET meteorological preprocessing module is responsible for receiving key meteorological parameters such as wind speed, wind direction, temperature, humidity and air pressure, precipitation, and cloud cover, and performing preprocessing to generate meteorological data based on the parameterized planetary boundary layer, providing crucial meteorological condition information for subsequent pollutant diffusion simulation.

[0067] The AERMOD diffusion module receives the first discrete point distribution map and meteorological data. Through complex mathematical models and algorithms, it generates hourly, daily, and preset input time range spatial distribution data of primary pollutant concentrations. These data cover the predicted primary concentrations of key pollutants such as PM2.5, NO2, and SO2, providing a foundation for the subsequent generation of text vector data based on the shp format.

[0068] Based on this data, text vector data in shapefile format can be generated. This data not only contains detailed pollutant concentration information, but is also easy to visualize and analyze on platforms such as GIS.

[0069] Preferably, in S3, the first discrete point distribution map in the coordinate system of the AERMOD model is coupled with the second discrete point distribution map in the coordinate system of ArcGIS, so that the first discrete point distribution map and the second discrete point distribution map coincide to obtain a final discrete point distribution map with unified coordinates.

[0070] Specifically, the first discrete point distribution map is superimposed with the second discrete point distribution map to unify the coordinate system. Through precise superposition, the first and second discrete point distribution maps are made to correspond completely in geographic space, resulting in a final discrete point distribution map with unified coordinates. The advantage of superimposed coordinate systems is that they can eliminate errors and inconveniences caused by differences in coordinate systems between different data sources. In fields such as environmental science and air pollution prediction, accurate spatial positioning is essential to ensure data accuracy. By superimposing coordinate systems, meteorological data and pollutant concentration data from different sources and at different times can be effectively integrated and compared, thereby gaining a deeper understanding of the diffusion patterns and influencing factors of pollutants.

[0071] Furthermore, the final discrete point distribution map with unified coordinates not only facilitates visualization and analysis on platforms such as GIS, but also provides more accurate and reliable basic data for subsequent pollutant diffusion simulation and prediction, which is crucial for improving regional secondary PM2.5 concentration. 2.5 The accuracy and practicality of the prediction results are of great significance.

[0072] Preferably, in S4, text vector data is input into ArcGIS, and ArcGIS loads PM once. 2.5Predicted concentration data for NO2 and SO2 were used, and spatial distribution data of secondary pollutant concentrations were obtained based on the field calculator in ArcGIS. This spatial distribution data included secondary PM2.5 concentrations. 2.5 Concentration prediction data C 二次PM2.5 ;

[0073] C 二次PM2.5 The calculation formula is as follows:

[0074]

[0075] In the formula: C 二次 PM2.5 is a secondary PM2.5 2.5 The mass concentration of the concentration prediction data is in μg / m³. 3 ;

[0076] and Convert SO2 and NO2 concentrations to PM2.5. 2.5 Concentration coefficient;

[0077] C SO2 and C NO2 These are the mass concentrations of predicted SO2 and NO2 concentrations, respectively, in μg / m³. 3 .

[0078] Preferred, 0.58 It is 0.44.

[0079] Specifically, the precursor conversion rate can be referenced from the secondary PM2.5 ratio in the "Technical Guidelines for Environmental Impact Assessment - Atmospheric Environment" (HJ 2.2-2018). 2.5 The method for calculating contribution concentration should be explained, taking into account regional applicability. For cases where the conversion rates of precursors such as SO2 and NO2 cannot be obtained, [the following method can be used]. 0.58 The secondary PM was calculated to be 0.44. 2.5 Contribution concentration.

[0080] Preferably, secondary PM 2.5 Concentration prediction data and primary PM 2.5 The concentration prediction data are accumulated to obtain the PM corresponding to the time series analysis. 2.5 The predicted data of pollutant mass concentration will include PM2.5. 2.5 The predicted pollutant mass concentration data are used to generate the final discrete point distribution map.

[0081] When text vector data is input into ArcGIS, ArcGIS loads PM once. 2.5The concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data are used as examples of 24-hour simulation results, and are respectively named 24hPM. 2.5 ArcGIS software calculates 24hNO2 and 24hSO2 from text vector data in shapefile format. The calculation process is as follows:

[0082] (1) Calculate the conversion results of NO2 and SO2 according to the conversion ratio. Select the 24hNO2 file, open the attribute table, add a field, name it "2NO2", select double precision for the type, and click "OK". Right-click the "2NO2" field, select "Field Calculator", calculate "FVALUE * 0.44", and click "OK". Calculate the conversion of SO2 to secondary PM using the same method. 2.5 The concentration results are named as "2SO2".

[0083] (2) Connect the file. Connect the calculated shapefile to the input 24hPM file. 2.5 In the forecast results file, right-click PM 2.5 File - Select "Connections and Associations" - "Connect", select the fields and layers you want to connect, and click "OK".

[0084] (3) Calculate the discrete point quadratic PM 2.5 Concentration results. 24hPM was selected. 2.5 File "Open Attribute Table" - "Add Field" - with "2PM" 2.5 In the "Naming" and "Type" fields, select Double Precision and click "OK". Right-click "2PM". 2.5 Use the "Field Calculator" to calculate "2NO2+2SO2", and then click "OK".

[0085] (4) Calculate PM 2.5 Concentration. Select 24hPM. 2.5 File "Open Attribute Table" - "Add Field" - with "2PM" 2.5 In the "Naming" and "Type" fields, select Double and click "OK". Right-click "FinallyPM". 2.5 "Field - Field Calculator" calculates "FVALUE + 2PM" 2.5 Click "OK" to get the prediction results data.

[0086] Preferably, in S5, a raster map is obtained by combining several discrete points of the final discrete point distribution map in both directions. The predicted result data is interpolated on the raster map based on the inverse distance weighting method to obtain a concentration raster map. The concentration raster map is added with preset markers to generate corresponding time series analysis visualization data. Further, the preset markers include preset color levels, and the preset color levels are from level one to level nine.

[0087] Each color level corresponds to a different PM2.5 concentration range, and the distribution of PM2.5 concentration in different areas is intuitively displayed through the shade of color;

[0088] Furthermore, as needed, the preset markers also include map elements such as legends, scale bars, and titles. By adding legends, scale bars, and titles, the visualization data of PNG / JPG format images can be displayed more intuitively and clearly, making it easier for users to quickly understand and analyze the prediction results.

[0089] Add preset color levels to the concentration raster to generate corresponding time series analysis and visualize the data in PNG / JPG format.

[0090] Preferably, the time series analysis of the spatial distribution data of pollutant concentration is at least daily and has a time resolution of hourly.

[0091] Specifically, the spatial distribution data of primary pollutant concentrations is at least one day in duration, with a time resolution of hourly levels. This ensures data integrity and accuracy, providing a solid foundation for subsequent secondary PM2.5 predictions. This setup helps capture the diurnal variation characteristics of pollutant concentrations, such as morning and evening peaks and daytime troughs, thereby more accurately simulating and predicting the generation and distribution of secondary PM2.5. Simultaneously, the hourly time resolution ensures high-frequency data acquisition, allowing prediction results to reflect the dynamic changes in pollutant concentrations more meticulously, improving prediction accuracy and reliability. Output results can be provided by hour, day, or the input time range. This continuous visualization data helps users better understand the trends and distribution of PM2.5 concentrations, providing strong data support for environmental protection and governance.

[0092] In addition, users can adjust the time span setting as needed to adapt to different application scenarios and requirements. For example, in short-term air quality warnings, a shorter time span may be required to detect and respond to changes in air quality in a timely manner; while in long-term air quality assessments, a longer time span may be required to comprehensively assess the changing trends and influencing factors of air quality.

[0093] Example 1:

[0094] Taking point source as an example;

[0095] Please see Figures 2-4 To obtain pollution data and corresponding meteorological information of pollutants in a point source;

[0096] Output shp files from the AERMOD model;

[0097] After loading a shapefile using ArcGIS, the following can be obtained: Figure 2The distribution of each discrete point is shown;

[0098] Based on the conversion ratio of precursors, the secondary PM was obtained. 2.5 Concentration results, compared with Aermod's prediction of a single PM2.5 concentration. 2.5 The concentration results are added together to obtain P. M2.5 The prediction results data, such as Figure 3 As shown;

[0099] The predicted data is assigned to the final discrete point distribution map. A raster map is created based on the final discrete point distribution map. The predicted data is then interpolated onto the raster map using the inverse distance weighting method to obtain a concentration raster map. Preset colors from level one to nine are added to the concentration raster map to generate corresponding PNG / JPG format visualization images. Figure 3 As shown; where, Figure 3 The plus sign indicates the emission source, and the triangle indicates the receiver point. Receiver points are environmentally sensitive or points of concern. These receiver points can be places where people gather, such as schools, hospitals, and residential areas, or areas with high environmental quality requirements, such as nature reserves and scenic spots. By predicting the PM2.5 concentration at these receiver points using the AERMOD model, the impact of pollutants on the environment and human health can be assessed.

[0100] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for simulating regional secondary PM based on the AERMOD model 2.5 The method for calculating and visualizing prediction results is characterized by, Includes the following steps: S1: Obtain pollutant emission concentration, pollutant emission rate and corresponding meteorological information from surrounding meteorological stations in the pollution source; S2: Establish an AERMOD model, establish a first discrete point distribution map based on the coordinate system of the AERMOD model, input the pollutant emission concentration, pollutant emission rate and meteorological information into the AERMOD model to simulate and calculate the spatial distribution data of primary pollutant concentration for time series analysis, and output text vector data based on the spatial distribution data of primary pollutant concentration. S3: Establish a second discrete point distribution map based on the ArcGIS coordinate system, and couple the first discrete point distribution map with the second discrete point distribution map to obtain the final discrete point distribution map; S4: Input the text vector data into ArcGIS for processing to obtain the spatial distribution data of secondary pollutant concentrations for time series analysis. Add the spatial distribution data of primary pollutant concentrations to the spatial distribution data of secondary pollutant concentrations to obtain the prediction result data corresponding to the time series analysis. Assign the prediction result data to the final discrete point distribution map. S5: Establish a raster map based on the final discrete point distribution map, and interpolate the raster map using the inverse distance weighting method based on the prediction result data to obtain a concentration raster map. Add preset labels to the concentration raster map to generate visualization data for corresponding time series analysis.

2. The method for simulating regional secondary PM based on the AERMOD model according to claim 1 2.5 The method for calculating and visualizing prediction results is characterized by: In step S1, the pollution sources include point source data, area source data, and line source data. The point source data is used to characterize pollutant emission sources that are distributed in a point-like manner, the area source data is used to characterize pollutant emission sources that are distributed in a area-like manner, and the line source data is used to characterize pollutant emission sources that are distributed in a line-like manner. The meteorological information includes wind speed, wind direction, temperature, humidity, air pressure, precipitation, and cloud cover around the pollution source. The wind speed and wind direction are used to characterize the direction and speed of pollutant diffusion. The temperature, humidity, and cloud cover are used to characterize atmospheric stability. The air pressure is used to characterize plume lift calculations and the conversion of pollutant concentration units. The precipitation is used to characterize wet deposition.

3. The method for simulating regional secondary PM based on the AERMOD model according to claim 2 2.5 The method for calculating and visualizing prediction results is characterized by: The pollutants in the pollution source include PM2.5 2.5 Concentration data, NO2 concentration data, and SO2 concentration data.

4. The method for simulating regional secondary PM based on the AERMOD model according to claim 3 2.5 The method for calculating and visualizing prediction results is characterized by: In S2, the AERMOD model includes an AERMOD diffusion module, an AERMOT meteorological preprocessing module, and an AERMAP terrain preprocessing module. The AERMOT meteorological preprocessing module and the AERMAP terrain preprocessing module are both electrically connected to the AERMOD diffusion module. Geographic data around the pollution source is acquired. The AERMAP terrain preprocessing module establishes a coordinate system. The AERMAP terrain preprocessing module establishes a first discrete point distribution map in the coordinate system based on the geographic data. The first discrete point distribution map has discrete points with several matrix distributions. The AERMET meteorological preprocessing module receives wind speed, wind direction, temperature, humidity, air pressure, precipitation and cloud cover for preprocessing to generate meteorological data based on the parameterized planetary boundary layer. The AERMOD diffusion module receives a first discrete point distribution map and meteorological data to generate time-series analysis of primary pollutant concentration spatial distribution data. This primary pollutant concentration spatial distribution data includes primary PM2.5 concentration data. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data, based on the aforementioned primary PM2.5 concentration prediction data. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data are used to generate text vector data based on the shapefile format. The time series analysis includes hourly, daily, and input time ranges.

5. The method for simulating regional secondary PM based on the AERMOD model according to claim 4 2.5 The method for calculating and visualizing prediction results is characterized by: In step S3, the first discrete point distribution map in the coordinate system of the AERMOD model is coupled with the second discrete point distribution map in the coordinate system of ArcGIS, so that the first discrete point distribution map and the second discrete point distribution map coincide to obtain a final discrete point distribution map with unified coordinates.

6. The method for simulating regional secondary PM based on the AERMOD model according to claim 5 2.5 The method for calculating and visualizing prediction results is characterized by: In step S4, the text vector data is input into ArcGIS, and ArcGIS loads PM once. 2.5 Concentration prediction data, NO2 concentration prediction data, and SO2 concentration prediction data were used to obtain spatial distribution data of secondary pollutant concentrations based on the field calculator in ArcGIS. This spatial distribution data of secondary pollutant concentrations includes secondary PM2.5 concentrations. 2.5 Concentration prediction data C 二次PM2.5 ; The C 二次PM2.5 The calculation formula is as follows: In the formula: C 二次 PM2.5 is a secondary PM2.5 2.5 The mass concentration of the concentration prediction data is in μg / m³. 3 ; and Convert SO2 and NO2 concentrations to PM2.

5. 2.5 Concentration coefficient; C SO2 and C NO2 These are the mass concentrations of predicted SO2 and NO2 concentrations, respectively, in μg / m³. 3 .

7. The method for simulating regional secondary PM based on the AERMOD model according to claim 6 2.5 The method for calculating and visualizing prediction results is characterized by: The 0.58 It is 0.

44.

8. The method for simulating regional secondary PM based on the AERMOD model according to claim 7 2.5 The method for calculating and visualizing prediction results is characterized by: The secondary PM 2.5 Concentration prediction data and primary PM 2.5 The concentration prediction data are accumulated to obtain the PM corresponding to the time series analysis. 2.5 The predicted data of pollutant mass concentration will be used to determine the PM2.5 concentration. 2.5 The predicted pollutant mass concentration data are used to generate the final discrete point distribution map.

9. The method for simulating regional secondary PM based on the AERMOD model according to claim 8 2.5 The method for calculating and visualizing prediction results is characterized by: In step S5, a raster map is obtained by combining several discrete points of the final discrete point distribution map in both directions. The predicted result data is interpolated on the raster map using the inverse distance weighting method to obtain a concentration raster map. Preset labels are added to the concentration raster map to generate visualization data for the corresponding time series analysis. The concentration raster is given a preset color level to generate corresponding time series analysis and visualized data in PNG / JPG format.

10. The method for simulating regional secondary PM based on the AERMOD model according to claim 9 2.5 The method for calculating and visualizing prediction results is characterized by: The time series analysis of the spatial distribution data of pollutant concentrations is no less than the daily level and the time resolution is the hour level.

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