Method for dividing weak diffusion area of atmospheric pollution based on extended WRF and CMAQ models

Abstract

The application provides a method for dividing an atmospheric pollution weak diffusion area based on an extended WRF and CMAQ model, which comprises the following steps: collecting data, wherein the data collection comprises collecting local data and satellite remote sensing data of a research area; inverting underlying surface data, wherein the local data of the research area obtained in the data collection stage is used to invert the latest underlying surface condition by using the latest city underlying surface surface cover data, so that the underlying surface data is obtained; constructing an extended WRF-CMAQ chemical model, wherein the extended WRF-CMAQ chemical model comprises a three-dimensional assimilation system, a WRF model and a CMAQ model; optimizing the extended WRF-CMAQ chemical model and performing data simulation; obtaining different index concentration data of air quality; performing normalization processing on the index concentration data to obtain concentration; and determining a weak diffusion area division standard according to the concentration, and dividing the weak diffusion area.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and more particularly to the application of computers in the field of air quality control technology. More specifically, it relates to a method for delineating weak diffusion areas of atmospheric pollution based on extended WRF and CMAQ models. Background Technology

[0002] With rapid economic development, concentrated urban industries, population growth, and massive energy consumption, China's air pollution problem is becoming increasingly serious, gradually hindering urban and rural economic development and endangering people's health and safety.

[0003] The concentration of air pollutants is determined by both pollutant emissions and meteorological conditions. Therefore, to effectively prevent air pollution, in addition to using various engineering technologies such as dust removal and exhaust gas purification devices to control pollutant emissions, it is also necessary to fully utilize the atmospheric turbulent mixing effect to diffuse and dilute pollutants, i.e., the atmosphere's self-cleaning capacity. Due to China's complex topography, the environmental protection industry has historically used foreign global reanalysis datasets (such as GLDAS and FNL reanalysis data) or only ground meteorological observation data from limited observation stations to simulate weak atmospheric diffusion areas. This approach lacks regional representativeness, only covers limited time and space, and is not comprehensive enough to accurately and meticulously describe the spatiotemporal climate characteristics of China.

[0004] The delineation of weak atmospheric diffusion zones is a crucial step in the delineation of key atmospheric environmental control areas. Scientifically identifying these zones contributes to adjustments in regional industrial structure, layout, and scale, and is an important component of the "three lines and one list" (ecological red lines, environmental quality lines, and resource availability lists) in local strategic environmental impact assessments. Current technologies typically consider horizontal diffusion performance primarily related to average wind speed, while vertical diffusion performance depends mainly on the mixing layer height, with the ventilation coefficient often used to evaluate atmospheric diffusion performance. However, wind speeds obtained through WRF simulations at different heights often deviate significantly from actual values. Therefore, current techniques for delineating diffusion zones fail to effectively consider the impact of local topographic conditions on the study area. It is necessary to research methods for delineating weak diffusion zones in the study area under non-anthropogenic emission source conditions, taking into account local meteorological and topographical conditions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method that combines environmental protection and meteorology, based on extended WRF and CMAQ models, and obtains the weak diffusion distribution area of ​​a certain region according to meteorological data, topographic data, and anthropogenic emission data.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for delineating weak diffusion areas of air pollution based on extended WRF and CMAQ models, comprising the following steps: data collection, including collecting local data and satellite remote sensing data of the study area; inverting underlying surface data, using the localized data of the study area obtained in the data collection phase and the latest urban underlying surface cover data to invert the latest underlying surface conditions, thereby obtaining underlying surface data; constructing an extended WRF-CMAQ chemical model, the extended WRF-CMAQ chemical model including a three-dimensional assimilation system, a WRF model, and a CMAQ model; optimizing the extended WRF-CMAQ chemical model and performing data simulation to obtain index concentration data reflecting air quality; normalizing the index concentration data to obtain the base concentration; and determining the criteria for delineating weak diffusion areas based on the base concentration, thereby delineating weak diffusion areas.

[0007] Preferably, the process of constructing the extended WRF-CMAQ chemical model further includes: constructing a WRF-CMAQ model based on a bidirectional feedback mechanism, wherein the WRF model provides simulated meteorological data to the CMAQ model, and the CMAQ model provides chemical data of atmospheric pollution to the WRF model, and the two models achieve bidirectional feedback to improve the simulation accuracy of the model.

[0008] Preferably, the process of constructing the extended WRF-CMAQ chemical model further includes: constructing the three-dimensional variational assimilation system, which is a GIS three-dimensional variational assimilation system, to integrate observational data of different spatiotemporal resolutions such as national control points, city control points, and micro-stations in the study area into the numerical model observation data, so as to find an optimal solution between the model solution and the actual observation using the three-dimensional variational assimilation system, providing an initial field for the numerical model of the WRF model, and making the prediction results of the WRF model more accurate.

[0009] Preferably, the process of constructing the extended WRF-CMAQ chemical model further includes: setting model parameters, including setting the spatial resolution and physical parameters of the model; the WRF mode uses a four-layer nesting, with the outermost layer having a resolution of 27 km; the second layer having a resolution of 9 km; the third layer having a resolution of 3 km; and the innermost layer having a resolution of 1 km.

[0010] Preferably, the process of constructing the extended WRF-CMAQ chemical model further includes: localizing the WRF-CMAQ model, wherein the localization process includes: localizing the surface data and parameter scheme under the WRF model, wherein the surface data localization under the WRF model is to use the latest satellite data to retrieve the latest underlying surface conditions, wherein the underlying surface conditions include topographic conditions, urban distribution, and vegetation distribution, and the underlying surface data is used in the numerical simulation of the extended WRF-CMAQ model.

[0011] Preferably, the parameter scheme localization further includes: setting the boundary layer scheme and setting the physical parameters of the WRF model.

[0012] Preferably, the optimization and data simulation steps include: inputting meteorological observation data of the study area, passing it through the three-dimensional assimilation system, adjusting the operating parameters of the WRF model so that the output of the WRF model is consistent with the actual meteorological observation data; and homogenizing the anthropogenic emission inventory input into the CMAQ model so that the emissions of each grid are consistent, thereby eliminating the influence of emitted pollutants.

[0013] Preferably, the concentration data of the indicators include six conventional pollutants such as SO2, NO2, CO, O3, PM2.5 and PM10, as well as AQI.

[0014] Preferredly, the normalization process for the index concentration data includes: First, selecting any one or more of the index concentration data, and denoting their concentration data as Xi,i = 1, 2, 3, ... m according to their grid numbers, where m is the grid number; Second, extracting the maximum value Xmax = max(Xi); Third, calculating Xii = Xi / Xmax to obtain the secondary concentration Xii; Finally, dividing the secondary concentration value Xii into ten levels: 0%-10%, 10%-20%, 20%-30%, 30%-40%, 40%-50%, 50%-60%, 60%-70%, 70%-80%, 80%-90%, and 90%-100%, representing levels 1-10 of the pollutant aggregation vulnerability assessment.

[0015] Preferably, the division of weak diffusion zones is based on the obtained concentration values, and the weak diffusion levels of different pollutants are divided according to the aggregation vulnerability evaluation index. Levels 8-10 are weak diffusion areas, and respectively represent the general, weak, and extremely weak weak diffusion levels. Attached Figure Description

[0016] Various embodiments or examples (“Examples”) of this disclosure are disclosed in the following detailed description and accompanying drawings. It is not necessary to draw the drawings to scale. Generally, unless otherwise specified in the claims, the operations of the methods disclosed in this invention can be performed in any order. In the drawings:

[0017] Figure 1 A flowchart of the method for delineating weak diffusion areas of atmospheric pollution based on the extended WRF and CMAQ models according to the present invention;

[0018] Figure 2 This is a flowchart illustrating the pollution and meteorological two-way feedback mechanism of the extended WRF and CMAQ models of this invention.

[0019] Figure 3 This is a flowchart illustrating the main operational process of the pollution GIS three-dimensional assimilation system of the present invention.

[0020] Figure 4 This is a schematic diagram comparing the simulated 2m temperature and the observed results using different land cover data after the model was localized;

[0021] Figure 5 This is a schematic diagram comparing the simulated 2m relative humidity and the observation results using different land cover data after the model was localized;

[0022] Figure 6 This is an evaluation map of the weak diffusion zone in the atmospheric environment simulated according to the method of the present invention;

[0023] Figure 7 This is a distribution map of the weak diffusion zone in the atmospheric environment obtained by the method of the present invention. Detailed Implementation

[0024] Before explaining one or more embodiments of this disclosure in detail, it should be understood that the embodiments are not limited to the construction details in their specific applications, and the steps or methods presented in the following embodiments or drawings. The systems and methods of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating the method for delineating weak diffusion areas of air pollution based on the extended WRF and CMAQ models according to the present invention. To illustrate the method of the present invention, the study area is the main urban area of ​​Chongqing, China. The study area specified in this invention can be any city, especially medium-sized cities with environmental protection requirements. Figure 1 As shown, the method of this invention is based on satellite data, meteorological observation data, underlying surface data, and emission data, and applies WRF and CMAQ models to study the pollution characteristics of highly polluted areas and the changing characteristics of weak diffusion zones. According to the method of this invention, it includes:

[0026] 1. Data collection

[0027] Local data and satellite remote sensing data of the study area were collected. Local data included air quality monitoring data and the latest pollutant emission data from national and municipal monitoring stations in the study area, such as Chongqing. Satellite remote sensing data included pollutant data observed by high-resolution satellite remote sensing, as well as three-dimensional spatial field data containing meteorological elements such as wind, temperature, pressure, and humidity.

[0028] 2. Inversion of underlying surface data

[0029] By using the localized data of the study area obtained during the data collection phase, and using the latest urban underlying surface cover data to invert the latest underlying surface conditions, we obtained underlying surface data, including 30-meter resolution DEM, topographic conditions, urban distribution, vegetation distribution, etc.

[0030] 3. Data Input

[0031] The data input module is a data processing module that provides localized data input to the WRF model. This module performs localization processing on the data input to the WRF model, including localization of the WRF model parameter scheme.

[0032] 4. Construct an extended WRF-CMAQ chemical model

[0033] like Figure 1 As shown, the extended WRF-CMAQ chemical model of the present invention includes a three-dimensional assimilation system, a WRF model, and a CMAQ model. The three-dimensional assimilation system is the GSI three-dimensional variational assimilation system, which provides the initial field for the WRF numerical model, making the simulation results of the model more accurate.

[0034] 4.1 Constructing a WRF-CMAQ model based on a two-way feedback mechanism

[0035] This invention employs an extended WRF-CMAQ model to simulate the spatiotemporal variation characteristics of pollutant diffusion in the main urban area of ​​Chongqing. The meteorological model to be used is the latest version of the WRF (Weather Research Forecast) model. CMAQ, developed by the U.S. Environmental Protection Agency, is a third-generation air quality forecasting model that adopts a "one atmosphere" design concept, integrating the complex physicochemical processes among different atmospheric pollutants. It not only simulates the impact of pollutant transport, diffusion, transformation, and migration processes driven by the meteorological model, but also takes into account various chemical processes of pollutants in the atmosphere at regional and urban scales, including liquid-phase chemical processes, heterogeneous chemical processes, aerosol processes, and dry-wet deposition processes on concentration distribution.

[0036] The pollution and meteorological two-way feedback mechanism of WRF and CMAQ models, such as Figure 2 As shown. Figure 2 As shown, the WRF model provides meteorological data for the CMAQ model, while the CMAQ model provides chemical data on atmospheric pollution for the WRF model. The two models achieve bidirectional feedback to improve the simulation accuracy.

[0037] 4.2 Constructing a three-dimensional variational assimilation system

[0038] Figure 3 This is a flowchart illustrating the main operational framework of the pollution GIS three-dimensional assimilation system of the present invention. Figure 3 As shown, a 3D variational assimilation system for a research area, such as Chongqing, is constructed. Observational data from various national control points, municipal control points, and micro-stations in the city are used to fuse observational data from different sources, with different error information and different spatiotemporal resolutions into the numerical model every day. The 3D variational assimilation system is used to find an optimal solution between the model solution and the actual observation value, providing an initial field for the numerical model and making the model's prediction results more accurate.

[0039] The assimilation module can simultaneously assimilate six types of conventional automatic air monitoring data, including SO2, NO2, CO, O3, PM2.5, and PM10. It can also assimilate the local urban meteorological initial field, providing a high-precision chemical and meteorological initial field for numerical model calculations.

[0040] 4.3 Setting Model Parameters

[0041] This includes setting the spatial resolution and physical parameters of the model. The WRF air quality model used here employs a four-layer nesting structure. The outermost layer has a resolution of 27 km, primarily covering western and central China; the second layer has a resolution of 9 km, covering the entire Chongqing municipality and surrounding areas; the third layer has a resolution of 3 km, covering the main urban area of ​​Chongqing; and the innermost layer has a resolution of 1 km, covering the built-up area of ​​the main urban area and industrial clusters. The model's center is located at 32°N, 108°E. WRF-CMAQ uses a four-layer nested grid with resolutions of 27, 9, 3, and 1 km. The first layer, centered on Chongqing, covers most of East Asia; the second layer mainly includes most of central and western China; the third layer mainly includes Chongqing and surrounding areas; and the innermost layer includes the main urban area of ​​Chongqing to meet the needs of refined research. The vertical WRF simulation consists of 28 layers, with a model top height of 50 hPa. The CMAQ model's simulation domain has three fewer grids on each side than the WRF model, and vertically it is compressed from 28 WRF layers to 16 layers, with 7 layers located within the boundary layer and the others in the free convection layer. The physical parameters of the model are shown in Table 1.

[0042] Table 1 Basic parameter settings for the CMAQ model

[0043] Model Options set up Grid nesting method Triple nesting, unidirectional Horizontal resolution 27 / 9 / 3 / 1km Vertical layer number 16 Horizontal advection Quadratic Piecewise Division Method (PPM) Vertical convection PPM Horizontal diffusion Spatial heterogeneity Vertical diffusion eddy diffusion dry sedimentation Pleim and Xiu (1995) improved model Gas phase chemical mechanism SAPRC99 Gas phase chemical algorithm Euler Backward Iteration (EBI) Boundary conditions Global model multi-year average results Initial conditions Global model multi-year average results

[0044] 4.4 Localization of the Expansion Model

[0045] The establishment of meteorological and air quality models involves the selection of important physical and chemical methods or parameters. Localization of the models involves screening and optimizing the parameter settings to ensure that the simulation results reflect the changing characteristics of the meteorological and pollutant concentration fields in the study area. The models consider local pollution factors such as topographic effects and the distribution of the urban canopy over the underlying surface of Chongqing.

[0046] The latest urban underlying surface cover satellite data is used to retrieve the latest underlying surface conditions (including 30-meter resolution DEM, topographic conditions, urban distribution, vegetation distribution, etc.); through simulation experiments, the model's simulation results of near-surface meteorological characteristics are improved, localized parameter schemes are clarified, and the simulation accuracy of the model is improved.

[0047] (1) Localization of mat surface data in WRF mode

[0048] Because the land cover data included with the WRF model is a global remote sensing product from the mid-1990s, and given China's rapid economic development and urbanization in recent decades, particularly in its economically developed regions, this data is no longer sufficient to characterize the current land cover situation, especially the distribution of cities. Land cover, especially urban distribution, can affect the heat, momentum, and energy exchange of the Earth's surface, thus significantly influencing meteorological characteristics within the boundary layer.

[0049] Therefore, the latest satellite data will be used to retrieve the latest underlying surface conditions (including topographical conditions, urban distribution, vegetation distribution, etc.) and incorporated into the numerical model. This can significantly improve the model's simulation results of near-surface meteorological characteristics, clarify localized parameter schemes, and enhance the model's simulation accuracy.

[0050] like Figure 4 and Figure 5 As shown, using high-resolution remote sensing topographic and land cover data significantly improves the simulation results of near-surface meteorological characteristics by the WRF model.

[0051] (2) Parameter scheme localization

[0052] A. Boundary Layer Scheme

[0053] Boundary layer schemes directly influence various meteorological elements at the Earth's surface, including near-surface temperature, humidity, wind, and boundary layer height, thereby directly affecting the transport and diffusion of pollutants and consequently the forecasting of atmospheric pollutant concentration fields. Simulation experiments were conducted on five boundary layer schemes commonly used in WRF: the Yonsei University scheme (YSU), the Mellor-Yamada-Janjic scheme (MYJ), the Quasi-Normal Scale Elimination PBL (QNSE), the Mellor-YamadaNakanishi and Niino Level 2.5 PBL (MYNNN2.5), and the Mellor-YamadaNakanishi and Niino Level 3 PBL (MYNN3.0). The YSU scheme is a nonlocal K-theory scheme that uses an inverse gradient term to represent nonlocal fluxes in the governing equations. It is an improvement on the MRF scheme, increasing the mixing layer height due to thermal convection and decreasing the mixing layer height due to wind shear compared to the MRF scheme. The reduced inverse gradient term makes the boundary layer structure closer to neutral, solving the problem of overly stable stratification caused by an excessively large inverse gradient term in the MRF scheme. The MYJ scheme uses Mellor and Yamada's turbulence closure method to represent turbulence above the surface layer. The turbulent diffusion coefficient is calculated from the turbulent kinetic energy, and the boundary layer height is determined by the turbulent kinetic energy profile. This scheme is applicable to boundary layers under all stable and weakly unstable conditions, but has a larger error in convective boundary layers. Simulations were used to evaluate the effectiveness of different boundary layer schemes, and the scheme closest to the actual local atmospheric conditions was selected.

[0054] B. WRF Physical Parameterization Scheme

[0055] The physical parameterization scheme of the WRF model takes into account that chemical and meteorological processes occur simultaneously in the actual atmosphere and can influence each other. For example, aerosols can affect the radiation balance of the Earth-atmosphere system, and as cloud condensation nuclei, they can affect precipitation. Temperature, clouds, and precipitation also have a strong impact on chemical processes. Therefore, the simulation physical parameterization scheme is evaluated to assess its effectiveness, and the scheme that best approximates the actual local atmospheric conditions is selected to reproduce a more realistic atmospheric environment. Table 2 shows the physical parameter scheme of the WRF model of this invention.

[0056] Table 2 Parameter Configuration of WRF Model

[0057]

[0058] 5. Optimize the extended WRF-CMAQ chemical model and perform data simulations.

[0059] The meteorological data driving the WRF model comes from the US NCEP FNL global reanalysis data, with a temporal resolution of 6 hours and a spatial resolution of 1°×1°. For example... Figure 1 As shown, the data output by the parameter input module is the physical parameter setting scheme shown in Table 2. Model optimization involves adjusting the operating parameters of the WRF model through the GSI three-dimensional assimilation system to make the output of the WRF model more consistent with the actual meteorological observation data. Specifically, the meteorological observation data consists of meteorological observation data obtained from national control points, city control points, and micro-stations in the study area. This data is input into the three-dimensional assimilation system, which finds an optimal solution between the output meteorological values ​​of the WRF model and the actual observation values. This provides initial field data for the numerical model of the WRF model, making the model's forecast results more accurate.

[0060] In the CMAQ model, the inputs come from meteorological data output by the WRF model, initial data provided by the ideal initial field module, and homogenized anthropogenic emission inventory data provided by the homogenized emission field module. To eliminate the impact of emissions on pollutants and obtain weak diffusion areas where pollutants easily accumulate under the influence of only meteorological and topographical factors, this invention adds a homogenized emission field module to the CMAQ model. Its purpose is to homogenize the anthropogenic emission inventory, ensuring consistent emissions across each grid. This allows for the simulation of pollutant concentrations in each grid while excluding anthropogenic interference. The data fed into the homogenized emission field module is a high-precision air pollution emission inventory provided by the gridded inventory module in the invention patent "A Method for Constructing a Networked Platform for Urban Air Pollution" filed by the applicant on the same day.

[0061] 6. Obtain concentration data for different air quality indicators.

[0062] The simulated pollution elements are six conventional pollutants: SO2, NO2, CO, O3, PM2.5 and PM10, as well as AQI.

[0063] By extending the WRF-CMAQ model, based on parameter configuration and operation scheme, and using meteorological data and emission source input data of the study area, the model simulates the transport of six conventional pollutants, namely fine particulate matter (PM2.5), inhalable particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO), as well as the AQI, through the simulation of physical processes such as advection, diffusion, convection, gravity deposition, dry deposition, and wet deposition, and aerosol chemistry (inorganic, SOA), heterogeneous chemistry, gas phase chemistry, and liquid phase chemistry. In other words, the output of CMAQ includes six conventional pollutants, namely SO2, NO2, CO, O3, PM2.5, and PM10, as well as the AQI.

[0064] 7. Normalize the index concentration data to obtain the concentration...

[0065] To quantify the differences in pollutant concentration distribution, the pollutant concentrations in the study area, such as the main urban area of ​​Chongqing, are normalized. The normalized concentration is the concentration specified in this invention. The normalization method is as follows:

[0066] First, select any one or more index concentration data, such as one or more of SO2, NO2, CO, O3, PM2.5, PM10 and AQI, and denot their concentration data as Xi,i=1,2,3,…m according to their grid numbers, where m is the grid number.

[0067] Secondly, extract the maximum value Xmax = max(Xi);

[0068] Next, calculate Xii = Xi / Xmax to obtain the concentration Xii.

[0069] Finally, the concentration values ​​Xi were categorized into ten levels based on normalization results: 0%-10%, 10%-20%, 20%-30%, 30%-40%, 40%-50%, 50%-60%, 60%-70%, 70%-80%, 80%-90%, and 90%-100%. These levels represent the pollutant accumulation vulnerability assessment levels 1-10. A higher vulnerability value (closer to level 10) indicates poorer diffusion conditions, and vice versa. It should be noted that Chongqing has a vast water system, and ozone easily accumulates on water surfaces. Therefore, large water surface areas (the main streams of the Yangtze and Jialing Rivers) were excluded from the ozone accumulation vulnerability assessment to minimize the impact of water bodies.

[0070] 8. Determine the criteria for delineating weak diffusion areas and divide the weak diffusion areas accordingly.

[0071] The weak diffusion zone is divided based on the obtained concentration values. Different pollutants are classified into weak diffusion levels according to the aggregation vulnerability assessment index. Levels 8-10 are weak diffusion areas, and represent the general, weak, and extremely weak weak diffusion levels, respectively.

[0072] [Demonstration Example]

[0073] Taking Zhejiang Province as an example, according to the extended WRF-CMAQ model of the present invention, a model simulation area is set, and then the model simulation is performed. Through numerical simulation using the extended WRF-CMAQ model of the present invention, based on the calculated concentration and according to the aggregation vulnerability evaluation index, the distribution of weak atmospheric pollution diffusion areas is obtained as follows: Figure 6 , Figure 7 This is a map showing the distribution of weak diffusion zones in the atmospheric environment.

[0074] Although the invention has been described with reference to embodiments shown in the accompanying drawings, equivalent or alternative means may be used without departing from the scope of the claims. The components described and illustrated in this invention are merely examples of systems / apparatus and methods that can be used to implement embodiments of this disclosure, and may be replaced with other devices and components without departing from the scope of the claims.

Claims

1. A method for delineating weak diffusion areas of atmospheric pollution based on extended WRF and CMAQ models, comprising: Data collection, which includes collecting local data and satellite remote sensing data of the study area; The underlying surface data is retrieved by inverting the local data and satellite remote sensing data, using the latest land cover data of the study area to retrieve the latest underlying surface conditions. A data input module and a parameter input module are constructed, and the data input module is used to preprocess the underlying surface data; An extended WRF-CMAQ chemical model was constructed, which included a three-dimensional assimilation system, a WRF model, and a CMAQ model. The extended WRF-CMAQ chemical model was optimized and data simulation was performed. The model optimization involved adjusting the operating parameters of the WRF model through the GSI three-dimensional assimilation system so that the output of the WRF model was consistent with the actual meteorological observation data. The data simulation includes: Meteorological data from the WRF model output are fed into the CMAQ model; The initial data provided by the ideal initial field module and the anthropogenic emission inventory homogenized data provided by the uniform emission field module are fed into the CMAQ model to overcome anthropogenic interference and make the emissions of each grid consistent. Numerical simulations were performed using the CMAQ model to obtain concentration data for different air quality indicators; The concentration data of the index are normalized to obtain the concentration; Based on the concentration, the criteria for dividing the weak diffusion region are determined, and the weak diffusion region is divided.

2. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The process of constructing the extended WRF-CMAQ chemical model further includes: A WRF-CMAQ model is constructed based on a bidirectional feedback mechanism. The WRF model provides simulated meteorological data to the CMAQ model, while the CMAQ model provides chemical data on atmospheric pollution to the WRF model. The two models achieve bidirectional feedback to improve the simulation accuracy of the model.

3. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The process of constructing the extended WRF-CMAQ chemical model further includes: The three-dimensional variational assimilation system is constructed. This system is a GIS three-dimensional variational assimilation system. It integrates observational data from national control points, city control points, and micro-stations of different spatiotemporal resolutions in the study area into the numerical model observation data. The three-dimensional variational assimilation system is used to find an optimal solution between the model solution and the actual observations, providing an initial field for the numerical model of the WRF model, thus making the prediction results of the WRF model more accurate.

4. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The process of constructing the extended WRF-CMAQ chemical model further includes: The model parameters are set, including the spatial resolution and physical parameters of the model. The WRF model uses a four-layer nesting structure, with the outermost layer having a resolution of 27km, the second layer 9km, the third layer 3km, and the innermost layer 1km.

5. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The process of constructing the extended WRF-CMAQ chemical model further includes: The WRF-CMAQ model is localized, and the localization process includes: localization of surface data and parameter scheme under WRF mode. The localization of surface data under WRF mode involves using the latest satellite data to retrieve the latest surface conditions, including topographic conditions, urban distribution, and vegetation distribution. The surface data is then used in the numerical simulation of the extended WRF-CMAQ model.

6. The method for delineating weak diffusion areas of air pollution according to claim 5, characterized in that, The parameter scheme localization further includes: setting the boundary layer scheme and setting the physical parameters of the WRF model.

7. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The optimization and data simulation steps include: inputting meteorological observation data of the study area, passing it through the three-dimensional assimilation system, adjusting the operating parameters of the WRF model so that the output of the WRF model is consistent with the actual meteorological observation data; and homogenizing the anthropogenic emission inventory input into the CMAQ model so that the emissions of each grid are consistent, thereby eliminating the influence of emitted pollutants.

8. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The concentration data of the indicators include six conventional pollutants: SO2, NO2, CO, O3, PM2.5 and PM10, as well as AQI.

9. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The normalization process for the index concentration data includes: First, select any one or more of the above-mentioned index concentration data, and denot the concentration data as Xii=1,2,3,…m according to its grid number, where m is the grid number; Secondly, extract the maximum value Xmax = max(Xi); Next, calculate Xii = Xi / Xmax to obtain the concentration Xii; Finally, the concentration values ​​Xi are divided into ten levels: 0%-10%, 10%-20%, 20%-30%, 30%-40%, 40%-50%, 50%-60%, 60%-70%, 70%-80%, 80%-90%, and 90%-100%, representing levels 1-10 of the pollutant aggregation vulnerability assessment.

10. The method for delineating weak diffusion areas of air pollution according to claim 1, characterized in that, The weak diffusion zone is divided based on the obtained concentration values. Different pollutants are classified into weak diffusion levels according to the aggregation vulnerability evaluation index. Levels 8-10 are weak diffusion areas, and represent the general, weak, and extremely weak weak diffusion levels, respectively.

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