Industrial park ozone concentration simulation prediction method and device coupling diffusion model and zero-dimensional chemical box model

Abstract

A method and apparatus for simulating and predicting ozone concentration in industrial parks using a coupled diffusion model and a zero-dimensional chemical box model are disclosed. The method includes: using upper-air and surface meteorological data and land use data as inputs to the AERMET model; using the AERMET output file and the emission inventory of pollution sources in the petrochemical park as input data to the AERMED model; running the AERMED model to obtain hourly concentration variation data of ozone precursors; and then using the model output results to input into the AtChem box model, combining the MCM mechanism to achieve ozone concentration prediction. This invention significantly improves the efficiency of ozone concentration prediction by coupling the atmospheric diffusion model and the box model, reducing the pressure of acquiring complex meteorological data using the three-dimensional model method and the pressure of measuring VOCs precursor components using the chemical box model observation method.

Description

Technical Field

[0001] This invention relates to the field of air pollution control, specifically to a rapid simulation and prediction method and device for ozone concentration in the atmosphere of industrial parks by coupling the AERMOD diffusion model and the AtChem zero-dimensional chemical box model. Background Technology

[0002] The ongoing battle to protect blue skies has placed higher demands on air pollution prevention and control, and the early prevention of ozone pollution has received increasing national attention. Currently, ozone concentration prediction faces challenges such as complex preliminary preparations, low prediction accuracy, and limitations on large and medium-scale predictions, resulting in high costs and low efficiency. Severe ozone pollution can harm the ecological environment, agriculture, and public health; therefore, accurate and efficient ozone prediction is a crucial element in ozone pollution control.

[0003] Currently, atmospheric diffusion models are mainly used for ozone concentration prediction. Air quality models such as CMAQ (Community Multiscale Air Quality), CALPUFF (The California Puff model), and NAQPMS (Nested Air Quality Prediction Modeling System) are computationally intensive, require high-quality basic data, and are suitable for large-scale applications due to their three-dimensional nature. These models simulate ozone concentration using data on various pollution source strengths, meteorological conditions, and atmospheric physical and chemical processes. The AERMOD (Air Quality Dispersion Modeling) model is widely applicable to small-scale atmospheric simulations, but because its design does not include chemical reactions, it cannot be used as a standalone ozone simulation and prediction tool. In in-situ photochemical simulation, the AtChem chemical box model has wide applications. Its programming and calculation processes are simple and efficient; it incorporates detailed chemical mechanisms (MCM) and is easily modified; it can predict ozone concentration, but its actual prediction efficiency is limited by the high requirements for ozone precursor component data and the difficulty in obtaining such data.

[0004] With the increasing demands for regional ozone pollution control, the requirements for the efficiency and accuracy of ozone pollution prediction are equally stringent. Currently, there is a lack of ozone concentration prediction models for specific small-scale environments such as industrial parks. Directly applying medium- to large-scale prediction models such as CALPUFF and CMAQ involves complex document preparation, high costs, and long processing times, which cannot meet the needs of efficient, accurate, and scientific control. Summary of the Invention

[0005] This invention primarily addresses the lack of methods for simulating and predicting small-scale ozone concentrations in industrial parks, and provides a prediction method and apparatus based on the coupling of AERMOD and the AtChem chemical box model.

[0006] This invention utilizes upper-air and surface meteorological data to compile meteorological input files; determines corresponding land types, and constructs an AERMET meteorological model by dividing land use parameters according to season. Then, the AERMET execution result file, regional geographic information file, receptor sensitive point information, and source strength dataset of the pollution source emission inventory of the study area are input into the AERMET atmospheric diffusion model to predict the concentration distribution of VOCs and NOx at the receptor points; furthermore, the prediction results are used to set an O3 precursor limit file, which is then input into the AtChem chemical box model to predict O3 concentration, thereby achieving ozone concentration prediction at the receptor points.

[0007] The first aspect of this invention relates to a method for simulating and predicting ozone concentration in industrial parks using a coupled diffusion model and a zero-dimensional chemical box model.

[0008] Includes the following steps:

[0009] S1. Simulation and optimization of ozone precursor concentration using the AERMOD model;

[0010] S1.1, Create AERMET upper-air and surface meteorological files. Use the WRF meteorological forecasting model to extract upper-air and surface meteorological information for the simulated date and create the AERMET input file.

[0011] S1.2, Run AERMET. In AERMET, enter the file names and parameters in the upper-air data, ground data, and land use data fields respectively. Run AERMET to obtain SFC and PFL format result files.

[0012] S1.3, AERMAP Geographic File Creation. Using automated tools, select the prediction area and create the AERMAP input file.

[0013] S1.4, Configure AERMOD. Configure the projection and control options in AERMOD, input the source intensity information according to the source emission inventory, and set the corresponding receptor point and output options.

[0014] S1.5, AERMAP is run. Select the DEM input file of the predicted area in AERMAP and run to collect information on pollution source strength and receptor point elevation.

[0015] S1.6, run AERMAP. After the AERMAP run is complete, verify the corresponding pollution source strength and receptor site elevation information, and save the file after confirmation; run the AERMAP model to obtain the simulated predicted concentrations of receptor site precursors (VOCs and NOx).

[0016] S1.7, Model Parameter Optimization. The model simulation values ​​are fitted to the observed values. By adjusting parameters such as the background concentration of precursors and the actual emission rate of pollution sources, the accuracy of the model simulation is improved.

[0017] Specifically, during step S1.2 of running AERMET, if the meteorological station number in the ground data column is unknown, enter 99999; the anemometer height is fixed at 10m; and the time difference with the local standard time is set to 0. If the meteorological station number in the upper-air data column is unknown, enter 99999; and the time difference with the local standard time is set to -8.

[0018] Specifically, in step S1.2, in the land use data section, select to use custom surface characteristic parameters, select by season for frequency, select dry, average, or wet for soil conditions based on actual conditions, and select 12 sectors.

[0019] Specifically, in step S1.2, the surface roughness list in the land use data column is collected using a Python automation tool, and the land category of each sector is set to water body, urban, arable land, etc. The collected land information is entered into an Excel calculation tool to obtain albedo, Baun ratio, and surface roughness parameters, which are then input into the site characteristic parameter list.

[0020] Specifically, in the projection settings described in step S1.4, a user-defined map projection is selected, Universal_Transverse_Mercator is selected for the map projection, WGS_84 is selected for the geodetic datum, and the UTM partition is set according to the actual geographic information of the predicted area.

[0021] Specifically, in step S1.4, the pollutant selection is user-defined and the name of the pollutant to be predicted is entered. The simulation type is set to concentration calculation, and the reduction option is set to no reduction. The output option is to output a detailed record file and select 1-hour concentration data according to the prediction needs.

[0022] Specifically, when step S1.7 yields a simulated value that is close to the observed value, the AERMOD model is considered to have been optimized and can be applied to local ozone precursor prediction work.

[0023] S2. Simulation and optimization of ozone concentration using the AtChem box model, including the following steps:

[0024] S2.1, Selection of Dominant VOC Species. Using monitored VOC component data, the average concentrations of different VOC species during ozone pollution periods were calculated, and the top 20 VOC species were selected for input into the model.

[0025] S2.2, MCM mechanism file extraction. Based on the official website (https: / / mcm.york.ac.uk / MCM / ), search for NOx and corresponding VOC species in the export section and extract the mechanism file to usr / AtChem2 / mcm.

[0026] S2.3, Create a precursor concentration change file. Based on the precursor concentration predicted by AERMOD, create a 1-hour resolution precursor concentration change file.

[0027] S2.4, Model Run Parameter File Settings. Navigate to the model.parameters file located in the usr / AtChem2 / model / configuration folder to set the model run step size, prediction location latitude and longitude, and date.

[0028] S2.5, Model Environment Variables File Settings. Navigate to the environmentVariables.config file located in the usr / AtChem2 / model / configuration folder and set the environment variables, including temperature (TEMP), humidity (RH), and air pressure (PRESS).

[0029] S2.6, Model Output Species Settings. Navigate to the `outputspecies.config` file located in the `usr / AtChem2 / model / configuration` folder and input the target species as O3. Navigate to the `outputrate.config` file and set the output hourly reaction rate species to O3, NO2, NO, and OH.

[0030] S2.7, Set restrictions on precursor species. Go to the speciesconstrained file in the usr / AtChem2 / model / configuration folder and enter the names of the precursors to be restricted.

[0031] S2.8, Set the species to limit the photolysis rate. Go to the photolysisConstrained.config file in the usr / AtChem2 / model / configuration folder and enter the name of the precursor whose photolysis rate needs to be limited.

[0032] S2.9, Input the environment and precursor restriction files. Navigate to the environment, photolysis, and species folders under usr / AtChem2 / model / constr-aint s and input the prepared restriction files.

[0033] S2.10, compile the model reaction mechanism and run the model.

[0034] S2.11, O3 Simulation Concentration Fitting. The model simulates the O3 concentration and fits it to the observed values. By adjusting the photolysis modification factor and the photolysis rate of VOCs species during the simulation period, the model simulation accuracy is improved, and the AtChem box model optimization is completed.

[0035] Specifically, in step S2.2, the MCM mechanism file extraction is performed by selecting "Include inorganic reactions", "Include generic rate coefficients", and "Output file format is FACSIMILE".

[0036] Specifically, the time unit for creating the precursor concentration change file in step S2.3 should be the model setting step size in seconds, and the file time should be the model recognition UTM time, which is 8 hours different from Beijing time. The model start time of 0 seconds is 8:00 AM Beijing time. Before 8:00 AM Beijing time, it is represented by a negative number, that is, the time column starts from -28000 seconds and increases by 3600 seconds.

[0037] Specifically, in the model environment variable settings described in step S2.4, the temperature, humidity, and air pressure information obtained from the meteorological files can be directly applied to the model to create hourly resolution time-varying files. The corresponding lines for environment variables in the environmentVariables.config file should be changed to CONSTRAINED. What parameters should be modified to CONSTRAINED?

[0038] Specifically, in the model environment variable settings described in step S2.4, if RH is set to CONSTRAINED in the environmentVariables.config file, then H2O must be set to CALC on the next line.

[0039] Specifically, in the model environment variable settings described in step S2.4, the solar tilt angle (DEL) is set to 0.41 by default, and the photolysis rate adjustment factor (JFAC) is set to 0.41 by default. These can be modified according to the specific situation of the day (specific photolysis rate being monitored).

[0040] Specifically, in the model environment variable settings described in step S2.4, the photolysis switch (ROOF) must be set to OPEN; the boundary layer height (BLHEIGHT), dilution rate (DILUTE), and aerosol surface area (ASA) should all be set to the default value NOTUSED. When the model considers non-chemical processes, such as compound deposition, chemical substance dilution, and heterogeneous chemical reactions, they can be changed to CONSTRAINED, and the corresponding time change file input should be set.

[0041] In particular, in the species settings for the hourly reaction rates of O3, NO2, NO, and OH described in step S2.6, the output results can be used for ozone sensitivity analysis.

[0042] Specifically, the restricted species name mentioned in step S2.7 should correspond to the species name in the search CAS number and input MCM extraction mechanism.

[0043] S3. Couple the AERMOD atmospheric diffusion model with the AtChem box model to predict ozone concentration, including the following steps:

[0044] S3.1, Extracting meteorological files for the forecast date. The WRF meteorological forecasting model is used to obtain the corresponding upper-air and surface meteorological files for the forecast period.

[0045] S3.2, Optimize the AERMOD model. Follow steps S2-S6 above to set up and run the AERMOD model to obtain the precursor hourly concentration change file.

[0046] S3.3 Formatting of AERMOD Simulation Results. Extract the corresponding data from the AERMOD output meteorological files and precursor concentration change files, and organize them into the AtChem box model input file format.

[0047] S3.4 Optimize the AtChem box model operation. Set up and run the model according to steps S8-S16 above to complete the coupled simulation and obtain the ozone concentration at the receptor point.

[0048] Specifically, in step S3.3, temperature, air pressure and humidity parameters are extracted from the meteorological file and compiled into a text document (.txt) file as per step S2.5.

[0049] Specifically, in step S3.3, the hourly NOx concentration is extracted and compiled into a text document (.txt) file.

[0050] Specifically, in step S3.3, the concentrations of the top 20 VOC species with the highest average concentrations are extracted and compiled into a text document (.txt) file.

[0051] Specifically, in step S3.3, the AtChem box model actually runs on a Linux system. If the input file is imported from another system, the ".txt" extension needs to be removed.

[0052] A second aspect of the present invention relates to an industrial park ozone concentration simulation and prediction device based on a coupled diffusion model and a zero-dimensional chemical box model, comprising a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement the industrial park ozone concentration simulation and prediction method based on the coupled diffusion model and the zero-dimensional chemical box model of the present invention.

[0053] A third aspect of the invention relates to a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model of the present invention.

[0054] This invention significantly improves the efficiency of ozone concentration prediction by coupling atmospheric diffusion models with box models, and reduces the pressure of acquiring complex meteorological data using three-dimensional model methods and the pressure of measuring VOCs precursor components using chemical box model observation methods.

[0055] The advantages of this invention are: it couples the AERMOD atmospheric diffusion model for simulating the diffusion of ozone precursors with the AtChem box model for simulating the ozone oxidation process, establishing a full-process simulation from precursor source emission to ozone pollution formation, and realizing small-scale ozone concentration prediction in industrial parks. Attached Figure Description

[0056] Figure 1 A schematic diagram of the overall technical roadmap for the coupled model.

[0057] Figure 2 This document describes how to extract a schematic diagram of land use in a simulated area using Python automation tools.

[0058] Figure 3 A schematic diagram for setting up the projection bar for the AERMOD model.

[0059] Figure 4 A schematic diagram showing the settings for the AERMOD model control options bar.

[0060] Figure 5 A diagram showing the settings for AERMOD output options.

[0061] Figure 6 This is a schematic diagram of the daily variation of VOCs concentration simulated by AERMOD.

[0062] Figure 7 A schematic diagram for creating the VOCs limitation file for the AtChem model.

[0063] Figure 8 This is a schematic diagram illustrating the extraction of VOCs mechanisms using the MCM official website.

[0064] Figure 9 A diagram illustrating the localization parameter settings for the AtChem model.

[0065] Figure 10 This is a schematic diagram illustrating the dominant species limitation of VOCs in the AtChem model.

[0066] Figure 11 This is a schematic diagram of the model's running interface.

[0067] Figure 12 This is a schematic diagram illustrating the daily variation of ozone concentration. Detailed Implementation

[0068] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0069] Example 1

[0070] This example utilizes the AERMOD atmospheric diffusion model and the AtChem box model to optimize the coupling of precursors and ozone concentrations during the ozone pollution period (May 3, 2022) at Zhejiang Petrochemical Co., Ltd. (30.30115N 121.96074E).

[0071] The specific steps of the method for simulating and predicting ozone concentration in industrial parks using a coupled diffusion model and a zero-dimensional chemical box model are as follows:

[0072] 1. AERMET running

[0073] The WRF meteorological forecasting software was used to obtain upper-air and surface meteorological files for the simulation period, which were then sequentially input into the AERMET model. In the upper-air data column, the time difference with the local time was set to -8, and the meteorological station number to 99999. In the surface data column, the meteorological station altitude was set to 4.5m, the anemometer height to 10m, and the surface meteorological station number to 99999. The land use column was set to seasonal frequency, average soil conditions, and 12 sectors. For land use parameters, a Python automation tool was used to obtain the land use information of Zhejiang Petrochemical Co., Ltd., determining that the land category of sector 12 was urban, and inputting the data into an Excel worksheet to calculate the corresponding site characteristic parameters under each season, as shown in Table 1. The site characteristic parameter column was set to manual modification, and the calculation results were input into the corresponding locations. After saving the files, AERMET was run to obtain upper-air (.PFL) and surface (.SFC) forecast files.

[0074] Table 1

[0075]

[0076] 2. Running AERMAP

[0077] The geographic file (.DEM) of Zhejiang Industrial Park was obtained using automated tools and entered into the AERMAP model. Point and area sources were selected as the pollution source inputs. Source strength information was collected from the industrial park's environmental impact assessment report and formatted as shown in Table 2. The pollutant monitoring station within the park (30.30115N121.96074E) was selected as the receptor point. The AERMAP analysis toolbar was used to run the program, and the calculation of pollution source elevation and receptor elevation was selected. The geographic file was input, and after running AERMAP, the output source strength elevation information was verified.

[0078] Table 2

[0079]

[0080] 3. AERMOD running

[0081] Enter the AERMOD operation interface. In the Projection Projection section, select "User-defined map projection," and choose Universal_Transverse_Mercator for the map projection. Select WGS_84 for the geodetic datum, and UTM zone 51 (Northern Hemisphere). In the Control Options section, select EPA parallel for the calculation program, use version 19191, select concentration calculation for the simulation type, and use the default "no reduction" option for reduction. In the Meteorology section, enter the AERMOD results file, select the start and end dates for the calculation period, and enter the corresponding ozone pollution period dates. In the Model Output section, select Detailed Record File, and in the Extension section, select 1-hour resolution. Select the default file format for the model. After saving the file, run the AERMOD model to obtain the hourly NMHC variation file and analyze the variation trend in the 3D Analyst model analysis tool to obtain a simulation effect similar to the observed changes.

[0082] 4. Advantages of the AtChem model: VOCs species and environmental limitation document creation.

[0083] Based on the AERMOD model prediction results, the average concentration of VOCs during the pollution period was calculated, and hourly rate of change limits were created for 20 VOC species with high average concentrations, such as ethanol (C2H5OH), isopropylbenzene (IPBENZ), and n-decane (NC10H22). At the same time, hourly rate of change limits for corresponding environmental variables such as temperature, pressure, and humidity were created based on the AERMOD meteorological field prediction results.

[0084] 5. Extraction of VOCs through chemical mechanisms

[0085] Go to the MCM mechanism website (https: / / mcm.york.ac.uk / MCM / ), search for NOx and the 20 VOC species selected, such as ethanol (C2H5OH), isopropylbenzene (IPBENZ), and n-decane (NC10H22), in the export column. Check "Include inorganic reactions", "Include generic rate coefficients" and "Output file format is FACSIMILE", and extract the mechanism file to usr / AtChem2 / mcm.

[0086] 6. AtChem model localization parameter settings

[0087] Navigate to the model.parameters file located in the usr / AtChem2 / model / configuration folder. Set the model run steps to 134, the step size to 3600, the predicted location latitude to 30.301, the longitude to -121.961, the date to April 29, 2022, and the reaction rate output interval to 3600s. Use the default model settings for the rest.

[0088] Navigate to the environmentVariables.conf-ig file located in the usr / AtChem2 / model / configuration folder. Set temperature (TEMP), humidity (RH), and air pressure (PRESS) to CONSTRAINE D; set H2O to CALC; set DEC to 0.41; use the model default of 0.41 for the photolysis correction factor; set the photolysis switch to OPEN; and use the model default of N OTUSED for the remaining environmental variables BLHEIGHT, DILUTE, and ASA.

[0089] Enter the speciesconstrained file under usr / AtChem2 / model / configuration folder, and input the names of NO, NO2, and the 20 VOC species selected, including ethanol (C2H5OH), isopropylbenzene (IPBENZ), and n-decane (NC10H22).

[0090] Navigate to the initialConcetrations file located in the usr / AtChem2 / model / configuration folder and enter the initial concentration of O3.

[0091] The simulation example requires limiting the NO2 photolysis rate. Go to the photolysisConstrained.config file in the usr / AtChem2 / model / configuration folder and enter NO to use field monitoring data to limit the NO photolysis rate.

[0092] Navigate to the environment, photolysis, and sp-ecies folders under usr / AtChem2 / model / constraints, and input the prepared environment, photolysis rate, and precursor constraint files.

[0093] 7. Compile the mechanism file and run the model.

[0094] Enter the compilation command to parameterize the VOCs mechanism, and after compilation, enter the running model to obtain the ozone concentration prediction results.

[0095] Example 2

[0096] This embodiment relates to an ozone concentration simulation and prediction device for industrial parks using a coupled diffusion model and a zero-dimensional chemical box model. The device includes a memory and one or more processors. The memory stores executable code. When the one or more processors execute the executable code, they are used to implement the ozone concentration simulation and prediction method for industrial parks using a coupled diffusion model and a zero-dimensional chemical box model as described in Embodiment 1.

[0097] Example 3

[0098] This embodiment relates to a computer-readable storage medium storing a program that, when executed by a processor, implements the method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model of Embodiment 1.

[0099] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for simulating and predicting ozone concentration in industrial parks using a coupled diffusion model and a zero-dimensional chemical box model, comprising the following steps: S1. Simulate ozone precursor concentrations using the AERMOD model and optimize the model, including: S1.1, Create AERMET upper-air and surface meteorological files; The WRF meteorological forecasting model was used to extract upper-air and surface meteorological information for the simulated date, and an AERMET input file was created. S1.2, Run AERMET; Enter AERMET, input the files and parameters in the upper-air data, ground data, and land use data columns respectively, run AERMET, and obtain SFC and PFL format result files; S1.3, AERMAP geographic file creation; using automated tools to select the prediction area and create the AERMAP input file; S1.4, Set AERMOD; Set the projection and control options in AERMOD respectively, input the source strength information according to the source emission inventory, and set the corresponding receptor point and output options; S1.5, AERMAP is run. Select the DEM input file of the predicted area in AERMAP and run to collect information on pollution source strength and receptor point elevation. S1.6, run AERMAP; after the AERMAP run is complete, check the corresponding pollution source strength and receptor point elevation information, and save the file after confirmation; run the AERMAP model to obtain the simulated predicted concentrations of VOCs and NOx precursors at the receptor point. S1.7, Model parameter optimization; Fit the model simulation values ​​with the observed values, and improve the model simulation accuracy by adjusting the background concentration of the corresponding precursors and the actual emission rate parameters of the pollution sources; S2 simulates ozone concentration using the AtChem box model and optimizes the model, including: S2.1, Selection of dominant VOC species: Using the monitored VOC component data, the average concentration of different VOC species during ozone pollution periods is calculated, and the top 20 VOC species are selected for input into the model. S2.2, MCM mechanism file extraction; S2.3, Create a precursor concentration change and environmental variable file; based on the AERMOD prediction of precursor concentration and environmental variables, create a 1-hour resolution precursor concentration change file. S2.4, Model running parameter file settings; Go to the model.parameters file in the usr / AtChem2 / model / configuration folder and set the model running step size, prediction location latitude and longitude, and date; S2.5, Model Environment Variables File Settings; Go to the environmentVariables.config file in the usr / AtChem2 / model / configuration folder and set the environment variables, including temperature (TEMP), humidity (RH), and air pressure (PRESS); S2.6, Model Output Species Settings; Go to the outputspecies.config file in the usr / AtChem2 / model / configuration folder, and input the object to be predicted as O3; Go to the outputrate.config file and set the output hourly reaction rate species to O3, NO2, NO, OH; S2.7, Set the restricted precursor species; Go to the speciesconstrained file under usr / AtChem2 / model / configuration folder and enter the name of the precursor species to be restricted; S2.8, Set the species to limit the photolysis rate; Go to the photolysisConstrained.config file in the usr / AtChem2 / model / configuration folder and enter the name of the precursor whose photolysis rate needs to be limited; S2.9, Input the environmental and precursor constraint files. Navigate to the environment, photolysis, and species folders under usr / AtChem2 / model / constraints and input the prepared constraint files. S2.10, Compile the model reaction mechanism and run the model; S2.11, O3 simulated concentration fitting; The model simulates the O3 concentration and fits it with the observed value. By adjusting the photolysis modification factor and the photolysis rate of VOCs species during the simulation period, the model simulation accuracy is improved, and the AtChem box model optimization is completed. S3. Couple the AERMOD atmospheric diffusion model with the AtChem box model to predict ozone concentration, including the following steps: S3.1, Extracting meteorological files for the forecast date; using the WRF meteorological forecasting model to obtain the corresponding upper-air and surface meteorological files for the forecast period; S3.2, Optimize the AERMOD model operation; Set up and run the AERMOD model according to the above steps S1.1-S1.7 to obtain the precursor hourly concentration change file; S3.3 Formatting of AERMOD simulation results; Extract the corresponding data from the AERMOD output meteorological file and precursor concentration change file, and organize them into the AtChem box model input file format; S3.4 Optimize the AtChem box model operation; set up and run the model according to the above steps S2.1-S2.11 to complete the coupled simulation and obtain the ozone concentration at the receptor point.

2. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that... In step S1.2, during the AERMET process, if the meteorological station number in the ground data column is unknown, enter 99999; the anemometer height is fixed at 10 m, and the time difference with the local standard time is set to 0; if the meteorological station number in the upper-air data column is unknown, enter 99999, and the time difference with the local standard time is set to -8.

3. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that... In step S1.2, select "Use custom surface characteristic parameters" in the land use data column, select "by season" for frequency, select "dry," "average," or "wet" for soil conditions based on actual conditions, and select 12 sectors for the sector.

4. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that, Step S1.2: Collect the surface roughness list in the land use data column using Python automation tools and set the land category of each sector to water body, urban, and cultivated land types; input the collected land information into an Excel calculation tool to obtain albedo, Baun ratio, and surface roughness parameters, and input them into the site characteristic parameter list.

5. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that, When a simulated value close to the observed value is obtained in step S1.7, the AERMOD model is considered to have been optimized and can be applied to local ozone precursor prediction work.

6. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that, In step S2.2, the MCM mechanism file extraction is performed by selecting "Include inorganic reactions", "Include generic rate coefficients", and "Output file format is FACSIMILE".

7. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that, In the model environment variable settings described in step S2.5, the temperature, humidity, and air pressure information obtained from the meteorological files can be directly applied to the model and converted into hourly resolution time variation files; the corresponding environmental variable lines in the environmentVariables.config file should be changed to CONSTRAINED; if RH is set to CONSTRAINED in the environmentVariables.config file, then H2O must be set to CALC in the next line; the solar tilt angle is 0.41 by default, and the photolysis rate adjustment factor is 0.41 by default, which can be modified according to the specific photolysis rate monitored on the day; the photolysis switch must be set to OPEN; the boundary layer height, dilution rate, and aerosol surface area are all set to the default value NOTUSED. When the model considers non-chemical processes, such as compound deposition, chemical dilution, and heterogeneous chemical reactions, they can be changed to CONSTRAINED, and the corresponding time variation file input should be set.

8. The method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in claim 1, characterized in that, In step S3.3, temperature, air pressure and humidity parameters are extracted from the meteorological file and compiled into a text document (.txt) file as in step S12; the hourly NOx concentration is extracted and compiled into a text document (.txt) file; the concentrations of the top 20 VOC species with the highest average concentration are extracted and compiled into a text document (.txt) file; and the AtChem box model actually runs on a Linux system. If the input file is imported from Windows, the ".txt" extension needs to be removed.

9. An industrial park ozone concentration simulation and prediction device based on a coupled diffusion model and a zero-dimensional chemical box model, characterized in that it includes a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement the industrial park ozone concentration simulation and prediction method based on the coupled diffusion model and the zero-dimensional chemical box model as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the method for simulating and predicting ozone concentration in industrial parks using the coupled diffusion model and the zero-dimensional chemical box model as described in any one of claims 1-8.

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