DPEC emission list preprocessing method and system suitable for WRF-Chem

Abstract

The invention provides a DPEC emission list preprocessing method suitable for WRF-Chem, and relates to the technical field of atmosphere pollution source list processing, and the method comprises the steps: obtaining original DPEC emission list data of a simulation region based on a DPEC list platform; integrating the data according to the types of emissions; preprocessing the integrated original DPEC emission list data, and matching a unit required by the chemical species emission amount of the WRF-Chem model; according to the WRF-Chem model configuration parameters, interpolating the chemical species emission data of the WRF-Chem model to a simulation region according to the area; and carrying out space-time distribution on the chemical species emission data of the simulation area according to the emission factors, and generating chemical species emission list data which can be read and used by the WRF-Chem model according to a set standard. According to the invention, through preprocessing and fine distribution, the processed data can reflect the characteristics of the pollution source more truly, and the simulation effect is improved.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollution source inventory processing technology, and in particular to a DPEC emission inventory preprocessing method and system applicable to WRF-Chem. Background Technology

[0002] This invention relates to the field of atmospheric pollution source inventory processing technology, and in particular to emission inventory preprocessing technology for chemical transport models such as WRF-Chem (Weather Research and Forecasting Model with Chemistry, an online coupled meteorological-chemical model).

[0003] Air quality models are crucial tools for atmospheric environmental science research and operational forecasting, and high-quality emission inventory data is a prerequisite for ensuring model accuracy. Currently, the main technical approaches for preparing emission inventory data for air quality models are as follows:

[0004] The first approach is to adopt mature emission inventory processing systems from abroad, the most representative of which is the SMOKE model (Sparse Matrix Operator Kernel Emissions Modeling system) developed by the U.S. Environmental Protection Agency (EPA). The SMOKE model is powerful, capable of handling various inventory formats and chemical mechanisms, and is a widely used standardized tool internationally. However, the SMOKE model has significant shortcomings when dealing with localized applications in my country: First, its configuration process is extremely complex, requiring high technical expertise from users, resulting in significant learning and usage costs; second, its built-in processing parameters and allocation profiles are primarily developed based on North American characteristics, leading to incompatibility issues when applied to China. Obtaining accurate results requires extensive localization and parameter calibration, a cumbersome and inefficient process.

[0005] The second method is the "scripted" or "semi-manual" processing approach commonly used by domestic research and operational units. This method typically relies on fragmented scripts written by researchers in languages ​​such as NCL, Python, or Fortran to perform operations such as format conversion, spatiotemporal allocation, and species matching on the original emission inventory. The advantage of this method is its high flexibility, but its disadvantages are also significant: First, it suffers from low automation and inefficiency. The entire processing flow involves a large amount of manual intervention, making it prone to errors and difficult to reproduce, consuming a significant amount of researchers' time and energy. Second, it lacks processing precision. In terms of time allocation, it often uses simple monthly / daily / hourly averages, failing to reflect the true hourly variation patterns of pollution sources. In terms of spatial allocation, limitations in data and algorithms often lead to inaccurate spatial positioning of emission sources, resulting in the "averaging" of pollution hotspots. Third, it lacks standardization and universality. Different researchers use different processing methods and parameters, leading to a lack of comparability between different research results due to differences in emission source treatment.

[0006] In summary, existing technical solutions are either too complex and unsuitable for China's national conditions, or too rudimentary, inefficient, and lacking in accuracy. Neither can adequately meet the urgent need for rapid, accurate, and automated processing of highly time-sensitive local emission inventories such as DPEC (Dynamic Platform for Emissions in China). Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a DPEC emission inventory preprocessing method and system suitable for WRF-Chem. It utilizes encapsulated Python code to process the DPEC emission inventory, managing parameters and paths through configuration files. Users only need to modify the configuration content, without altering the main program, to flexibly control the execution of the Python code. This approach not only improves the system's flexibility and maintainability but also facilitates subsequent expansion and upgrades, making it suitable for various application scenarios. WRF-Chem model users can more accurately and easily allocate various pollutants from the original emission inventory to the user-defined simulation regions, simulation strata, and simulation times.

[0008] To achieve the above objectives, this invention provides a DPEC emission inventory pretreatment method suitable for WRF-Chem, comprising:

[0009] Obtain raw DPEC emission inventory data for the simulated region based on the DPEC inventory platform;

[0010] The original DPEC emission inventory data were integrated according to the type of emissions;

[0011] The integrated raw DPEC emission inventory data was preprocessed to match the units required for chemical species emissions in the WRF-Chem model.

[0012] Based on the WRF-Chem model configuration parameters, the WRF-Chem model chemical species emission data are interpolated to the simulation area by area.

[0013] The chemical species emission data of the simulated region are spatially and temporally allocated according to emission factors;

[0014] The processed data of pollutants in the DPEC inventory are mapped to chemical species that can be identified by the WRF-Chem model, and chemical species emission inventory data that can be read and used by the WRF-Chem model are generated according to the set standards.

[0015] As a further improvement of the present invention, the original DPEC emission inventory data is integrated according to the emission type, including:

[0016] Emission data for the same emitter in the same month will be merged into a single nc file.

[0017] As a further improvement of the present invention, the integrated original DPEC emission inventory data is preprocessed, including:

[0018] Pretreatment formulas include pretreatment formulas for inorganic gaseous pollutants, pretreatment formulas for organic gaseous pollutants, and pretreatment formulas for particulate pollutants;

[0019] The pretreatment formula for inorganic gaseous pollutants is:

[0020]

[0021] The pretreatment formula for organic gaseous pollutants is:

[0022]

[0023] The pretreatment formula for particulate matter pollutants is:

[0024]

[0025] In the formula,

[0026] e InorganicGases E OrganicGases E Aerosol These represent the emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants input into the WRF-Chem model, respectively.

[0027] These represent the original emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants from the original DPEC emission inventory;

[0028] S represents the area of ​​a single grid cell in the latitude and longitude grid;

[0029] M gas This represents the molecular weight of the corresponding gas.

[0030] The emissions of various pollutants input into the WRF-Chem model are obtained based on the preprocessing formula.

[0031] As a further improvement to the present invention, the preprocessing of the integrated original DPEC emission inventory data further includes:

[0032] The formula for calculating the area projected onto the latitude and longitude grid in the original DPEC emissions inventory data is as follows:

[0033] S = R 2 ·(Φ2-Φ1)·[sin(θ2)-sin(θ1)]

[0034] In the formula:

[0035] R is the Earth's radius;

[0036] Φ1 is the longitude of the left boundary. To convert degrees to radians, Φ1 = lon.

[0037] Φ2 is the longitude of the right boundary. Convert degrees to radians, Φ2 = lon + Δ.

[0038] θ1 is the latitude of the southern boundary; convert degrees to radians.

[0039] θ2 is the latitude of the northern boundary; convert degrees to radians.

[0040] Δ represents the resolution of latitude and longitude.

[0041] As a further improvement of the present invention, the chemical species emission data of the WRF-Chem model are interpolated to the simulation region by area according to the WRF-Chem model configuration parameters; including:

[0042] Obtain the WRF-Chem model configuration parameters, including grid resolution, latitude and longitude of the simulation area, and simulation time period data;

[0043] The emissions of various pollutants input into the WRF-Chem model are interpolated from the DPEC grid to the simulation region of the WRF-Chem model by area according to the WRF-Chem model configuration parameters.

[0044] As a further improvement of the present invention

[0045] For any target WRF grid in the WRF-Chem model, identify all DPEC grids that overlap with the area of ​​the target WRF grid, and obtain the emissions of each DPEC grid accordingly.

[0046] Calculate the overlap area between each DPEC grid and the target WRF;

[0047] The formula for calculating the emission value of the target WRF grid after interpolation is as follows:

[0048]

[0049] In the formula,

[0050] P x P y Indicates the latitude and longitude of the target WRF grid point;

[0051] emis[i,j] represents the emissions corresponding to the i-th row and j-th column of the DPEC grid;

[0052] S wrf This represents the actual area of ​​the target WRF mesh, in km².

[0053] Si,j represents the overlap area between the DPEC mesh emis[i,j] and the target WRF mesh, in km2;

[0054] E interp (P x P y ) represents the emission value of the target WRF grid after interpolation.

[0055] As a further improvement of the present invention, the chemical species emission data of the simulated region are spatiotemporally allocated according to emission factors, including:

[0056] The allocation factor for each emission source per day during the simulation period in the simulated area is determined based on the time period, wherein the emission sources include five categories of emission sources in the DPEC emission inventory: agriculture, residential, transportation, energy, and industry.

[0057] The emissions of various emission sources at any level in any hour of any day during the simulation time are obtained based on the allocation factors for each emission source per day, hour, and vertical allocation factors.

[0058] The emission values ​​of various emission sources of the same pollutant in the same layer at the same hour are added together to obtain the emission amount of the pollutant in the simulated area at that time and on that date in that layer.

[0059] As a further improvement of the present invention, the allocation factor for each emission source per day during the simulation period of the simulated area is determined according to the time period, and the formula is:

[0060]

[0061] In the formula,

[0062] d k Indicates the day of the week for the k-th day;

[0063] Indicates emission source s per week per day k Factors;

[0064] F s,k This represents the daily allocation factor of emission source s on day k after normalization.

[0065] n represents the number of days in each month during the simulation period;

[0066] Normalized daily allocation factor F for all days of a month for emission source s s,k They add up to 1.

[0067] As a further improvement of the present invention, the emissions of various emission sources at any level in any hour of any day during the simulation time are obtained based on the allocation factor, hourly allocation factor, and vertical allocation factor for each emission source per day, as follows:

[0068]

[0069] In the formula,

[0070] r represents the time;

[0071] l indicates the layer;

[0072] E r,l This represents the emission of a chemical species at time k in layer l;

[0073] 's' represents the emission source, which includes five categories: agriculture, housing, transportation, energy, and industry.

[0074] m represents the number of emission sources;

[0075] E s This represents the pre-processed chemical species emission data input into the WRF-Chem model from each emission source;

[0076] This represents the hourly emission allocation factor of a chemical species at emission source s;

[0077] This represents the normalized daily emission allocation factor for a chemical species at emission source s;

[0078] This represents the vertical layer emission allocation factor of a chemical species at emission source s.

[0079] As a further improvement of the present invention, the data of pollutants processed from the DPEC inventory are mapped to chemical species recognizable by the WRF-Chem model, and chemical species emission inventory data that can be read and used by the WRF-Chem model are generated according to a set standard, including:

[0080] Based on the selected chemical mechanism (such as CBM-Z or SAPRC), the total pollutants (such as NOx, VOCs) in the DPEC inventory are allocated and transformed into specific chemical species (NO, NO2, OLE, PAR, etc.) that can be directly identified by the WRF-Chem model.

[0081] The generated data is stored hourly in the file wrfchemi_d01_{year}_{month}_{date}.nc, generating hourly chemical species emission inventory data for input into the WRF-Chem model.

[0082] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0083] This invention automates the entire process, reducing complex processing steps that previously took days and relied on manual labor to minutes, significantly improving efficiency. More importantly, through an innovative localized, spatiotemporally refined allocation scheme, it effectively overcomes the core pain points of existing technologies, such as fuzzy emission source hotspots and distorted temporal changes. This allows the processed data to more accurately reflect the characteristics of pollution sources, significantly improving the simulation effect of downstream air quality models and making them more consistent with measured data.

[0084] As an efficient, accurate, and easy-to-use standardized tool, this invention not only greatly lowers the technical threshold for atmospheric science research, but also provides a unified benchmark for comparing different research results, thus powerfully promoting collaborative innovation and development in the industry.

[0085] This invention addresses the automated and refined preprocessing of the DPEC (Dynamic Platform for Emissions in China) inventory, resolving the issues of complex configuration and poor localization adaptability of foreign models (such as SMOKE). It provides an out-of-the-box, easy-to-use solution to the pain points of cumbersome processes, parameter mismatches, and extensive secondary development required when processing Chinese local inventories using foreign models. Furthermore, it solves the problems of low automation and inefficiency in existing domestic script-based processing methods. Addressing the reliance on manual operation, fragmented processes, susceptibility to errors, and time-consuming nature of existing methods, it offers a highly efficient, fully automated, one-click processing solution. Finally, it addresses the insufficient accuracy of existing technologies in spatiotemporal allocation. Resolving the distortion of emission spatiotemporal characteristics caused by the use of average allocation or general profiles in traditional methods, it provides a refined allocation scheme that incorporates high-resolution localized Chinese data to improve the simulation realism of emission sources.

[0086] This invention utilizes pre-packaged Python code to process the DPEC emission inventory. Parameters and paths are managed through configuration files, allowing users to flexibly control the Python code's execution by simply modifying the configuration without altering the main program. This approach not only enhances system flexibility and maintainability but also facilitates subsequent expansion and upgrades, making it suitable for various application scenarios. In WRF-Chem mode, users can more accurately and easily allocate various pollutants from the original emission inventory to the user-defined simulation regions, strata, and time periods. Attached Figure Description

[0087] Figure 1 This is a schematic diagram of a DPEC emission inventory pretreatment method applicable to WRF-Chem disclosed in an embodiment of the present invention;

[0088] Figure 2 This is a map of PM2.5 concentration in Beijing in January 2022 obtained by inputting the preprocessed DPEC emission inventory into the WRF-Chem model, as disclosed in one embodiment of the present invention. Detailed Implementation

[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0090] The present invention will now be described in further detail with reference to the accompanying drawings:

[0091] like Figure 1 As shown, the present invention provides a DPEC emission inventory preprocessing method applicable to WRF-Chem, comprising:

[0092] S1. Obtain the original DPEC emission inventory data for the simulated region based on the DPEC inventory platform;

[0093] in,

[0094] Download the original DPEC emissions inventory data from the official DPEC website.

[0095] S2. Integrate the original DPEC emission inventory data according to the type of emissions;

[0096] in,

[0097] Emission data for the same emitter in the same month will be merged into a single nc file.

[0098] S3. Preprocess the integrated original DPEC emission inventory data to match the units required for chemical species emissions in the WRF-Chem model.

[0099] in,

[0100] Pretreatment formulas include pretreatment formulas for inorganic gaseous pollutants, pretreatment formulas for organic gaseous pollutants, and pretreatment formulas for particulate pollutants;

[0101] The pretreatment formula for inorganic gaseous pollutants is:

[0102]

[0103] The pretreatment formula for organic gaseous pollutants is:

[0104]

[0105] The pretreatment formula for particulate matter pollutants is:

[0106]

[0107] In the formula,

[0108] E lnorganicGases E OrganicGases E Aerosol These represent the emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants input into the WRF-Chem model, respectively.

[0109] These represent the original emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants from the original DPEC emission inventory;

[0110] S represents the area of ​​a single grid cell in the latitude and longitude grid;

[0111] M gas This represents the molecular weight of the corresponding gas.

[0112] The emissions of various pollutants input into the WRF-Chem model are obtained based on the preprocessing formula.

[0113] Furthermore,

[0114] The formula for calculating the area projected onto the latitude and longitude grid in the original DPEC emissions inventory data is as follows:

[0115] S = R 2 ·(Φ2-Φ1)·[sin(θ2)-sin(θ1)]

[0116] In the formula:

[0117] R is the Earth's radius (unit: meters, commonly R = 6,371,000 meters);

[0118] Φ1 is the longitude of the left boundary. Convert degrees to radians, Φ1 = lon;

[0119] Φ2 is the longitude of the right boundary. Convert degrees to radians, Φ2 = lon + Δ.

[0120] θ1 is the latitude of the southern boundary; convert degrees to radians.

[0121] θ2 is the latitude of the northern boundary; convert degrees to radians.

[0122] Δ represents the resolution of latitude and longitude, in degrees.

[0123] S4. Based on the WRF-Chem model configuration parameters, interpolate the WRF-Chem model chemical species emission data to the simulation area by area.

[0124] in,

[0125] Obtain the WRF-Chem model configuration parameters, including grid resolution, latitude and longitude of the simulation area, and simulation time period data;

[0126] The emissions of various pollutants input into the WRF-Chem model are interpolated from the DPEC grid to the simulation region of the WRF-Chem model by area according to the WRF-Chem model configuration parameters.

[0127] Furthermore,

[0128] For any target WRF grid in the WRF-Chem model, identify all DPEC grids that overlap with the area of ​​the target WRF grid, and obtain the emissions of each DPEC grid accordingly.

[0129] Calculate the overlap area between each DPEC grid and the target WRF;

[0130] The formula for calculating the emission value of the target WRF grid after interpolation is as follows:

[0131]

[0132] In the formula,

[0133] P x P y Indicates the latitude and longitude of the target WRF grid point;

[0134] emis[i,j] represents the emissions corresponding to the i-th row and j-th column of the DPEC grid;

[0135] Swrf represents the actual area of ​​the target WRF mesh, in km².

[0136] Si,j represents the overlap area between the DPEC mesh emis[i,j] and the target WRF mesh, in km2;

[0137] E interp (P x P y ) represents the emission value of the target WRF grid after interpolation.

[0138] S5. Allocate the chemical species emission data of the simulated area in time and space according to the emission factors, and generate chemical species emission inventory data that can be read and used by the WRF-Chem model according to the set standards.

[0139] in,

[0140] The allocation factor for each emission source per day during the simulation period is determined based on the time period. The emission sources include five categories of emission sources in the DPEC emission inventory: agriculture, residential, transportation, energy, and industry.

[0141] The emissions of various emission sources at any level in any hour of any day during the simulation time are obtained based on the allocation factors for each emission source per day, hour, and vertical allocation factors.

[0142] The emission values ​​of various emission sources of the same pollutant in the same layer at the same hour are added together to obtain the emission amount of that pollutant in the simulated area at that time and in that layer on that date.

[0143] Furthermore,

[0144] The allocation factor for each emission source per day within the simulation period is determined based on the time period, using the following formula:

[0145]

[0146] In the formula,

[0147] d k Indicates the day of the week for the k-th day;

[0148] Indicates emission source s per week per day k Factors;

[0149] F s,k This represents the daily allocation factor of emission source s on day k after normalization.

[0150] n represents the number of days in each month during the simulation period;

[0151] Normalized daily allocation factor F for all days of a month for emission source s s,k They add up to 1.

[0152] The emissions from each emission source at any given hour and at any given floor on any given day during the simulation period are obtained based on the daily allocation factor, hourly allocation factor, and vertical allocation factor. The formula is as follows:

[0153]

[0154] In the formula,

[0155] r represents the time;

[0156] l indicates the layer;

[0157] E r,l This represents the emission of a chemical species at time k in layer l;

[0158] 's' represents the emission source, which includes five categories: agriculture, housing, transportation, energy, and industry.

[0159] m represents the number of emission sources;

[0160] E s This represents the pre-processed chemical species emission data input into the WRF-Chem model from each emission source;

[0161] This represents the hourly emission allocation factor of a chemical species at emission source s;

[0162] This represents the normalized daily emission allocation factor for a chemical species at emission source s;

[0163] This represents the vertical layer emission allocation factor of a chemical species at emission source s.

[0164] at last,

[0165] Based on the selected chemical mechanism (such as CBM-Z or SAPRC), the total pollutants (such as NOx, VOCs) in the DPEC inventory are allocated and transformed into specific chemical species (such as NO, NO2, OLE, PAR, etc.) that can be directly identified by the WRF-Chem model.

[0166] The generated data is stored hourly in the file wrfchemi_d01_{year}_{month}_{date}.nc, generating hourly chemical species emission inventory data for input into the WRF-Chem model.

[0167] Example:

[0168] Based on Beijing's PM2.5 in January 2020 2.5 Taking the processing of emission inventories as an example, we will demonstrate the effectiveness of the entire processing flow. First, we extract the PM2.5 concentration for the Beijing area in January 2020 from the DPEC inventory platform. 2.5 The DPEC raw inventory data. This data is stored in a 0.25° × 0.25° latitude and longitude grid, in tons per month. To adapt to the WRF-Chem simulation platform, we also prepared a 9km resolution WRF grid configuration file for Beijing, as well as PM2.5 data for the region. 2.5 The program first uses an area-conserving interpolation algorithm to distribute the raw emission data to a 9km WRF grid, ensuring that the total emissions on the new grid are not distorted. Then, preprocessing is performed, using an area formula to preprocess the emissions of each raw grid for subsequent calculations. Next, in the spatiotemporal allocation stage, the program automatically identifies the day of the week for each day and calls the January universal hourly and daily factors for all emission sources, further breaking down the monthly emissions into hourly and layered data. Finally, the NetCDF emission file output by the program can be directly read by WRF-Chem. The specific steps are as follows:

[0169] Step 1: Obtain the original DPEC emissions inventory data;

[0170] In January 2020, the coordinates of Beijing's central point A were 116.3333° longitude and 39.9333° latitude, corresponding to PM emissions in the DPEC emissions data's latitude and longitude grid. 2.5 Emissions (in tons per month) are categorized into five emission sources, or five sectors: Agriculture: 0; Industry: 216.208; Energy: 1.27998; Residential: 221.552; Transportation: 154.027.

[0171] Step 2: Merge emission data for the same pollutant in the same month into a single nc file for easier subsequent processing;

[0172] Step 3: The formula for calculating the area of ​​the DPEC inventory's latitude and longitude grid projection is as follows:

[0173] S = R 2 ·(Φ2-Φ1)·[sin(θ2)-sin(θ1)]

[0174] Convert latitude and longitude to radians and then substitute them into the formula:

[0175] S = 6371.392 2 ×0.00436×0.00277=492.86km 2

[0176] Step 4, PM 2.5 The preprocessing formula is,

[0177]

[0178] Taking the industrial sector as an example:

[0179]

[0180] Similarly, calculate the calculations for other departments:

[0181] Energy sector (pow): E pow =0.7214ug / (m 2 ·s)

[0182] Residential sector (rdt): E rdt =124.86ug / (m 2 ·s)

[0183] Transportation Department (tpt): E tpt =86.81ug / (m 2 ·s)

[0184] Agricultural sector (apt): E apt =0ug / (m 2 ·s)

[0185] Step 5: The WRF grid containing the latitude and longitude of Beijing's city center falls within a DPEC grid, according to the formula:

[0186]

[0187] That is, the PM in the WRF corresponding grid for industry, energy, residential and transportation. 2.5 The emissions were 121.855 ug / (m³). 2 ·s), 0.7214ug / (m 2 ·s), 124.86ug / (m 2 ·s) and 86.81ug / (m2 ·s), agriculture is 0ug / (m 2 ·s).

[0188] Step 6: Distribute the preprocessed data by day, hour, and vertically.

[0189] Step 6.1, Daily Allocation Factor

[0190] For industrial emission sources, the weekly allocation is 0.162 for weekdays, 0.112 for Saturdays, and 0.078 for Sundays. January 2020 had 23 weekdays, 4 weekends, and 4 Sundays, i.e.:

[0191]

[0192] For example, on Saturday (January 4th):

[0193]

[0194] Step 6.2, Hourly Allocation Factor

[0195] Step 6.3, Vertical Allocation Factor

[0196] Finally, by multiplying the results step by step, we can obtain the emissions of a certain industry at a certain time on a certain day and at a certain floor. Then, we add up the emission values ​​of the same pollutant from multiple industries at the same time and on the same floor to obtain the emission amount of that pollutant at a certain time on a certain day and at a certain floor.

[0197] For example, 8 PM on January 4, 2020 2.5 Emissions are:

[0198]

[0199] Step 7: Based on the selected chemical mechanism CBM-Z, the total pollutants (such as NOx, VOCs, PM2.5) in the DPEC inventory are... 2.5 The allocations are transformed into specific chemical species that can be directly identified by the WRF-Chem model (such as NO, NO2, OLE, PAR, PM2.5). i PM25 j The generated data is stored hourly in wrfchemi_d01_{year}_{month}_{date}.nc, generating hourly emission inventory data for input to WRF-Chem for simulation use.

[0200] Step 8: Input the processed WRF-Chem emission inventory into the model and run it. The final simulated PM2.5 concentration map for January 2020 is shown below. Figure 2As shown in the figure, compared with the PM2.5 data from the monitoring stations, the model's simulated mean R value is 0.818498, and the MFB and MFE are 27.3586% and 30.1919% respectively, indicating that the model's simulation effect can well represent the actual situation and the model's simulation performance is good.

[0201] Advantages of this invention:

[0202] This invention automates the entire process, reducing complex processing steps that previously took days and relied on manual labor to minutes, significantly improving efficiency. More importantly, through an innovative localized, spatiotemporally refined allocation scheme, it effectively overcomes the core pain points of existing technologies, such as fuzzy emission source hotspots and distorted temporal changes. This allows the processed data to more accurately reflect the characteristics of pollution sources, significantly improving the simulation effect of downstream air quality models and making them more consistent with measured data.

[0203] As an efficient, accurate, and easy-to-use standardized tool, this invention not only greatly lowers the technical threshold for atmospheric science research, but also provides a unified benchmark for comparing different research results, thus powerfully promoting collaborative innovation and development in the industry.

[0204] This invention addresses the automated and refined preprocessing of the DPEC (Dynamic Platform for Emissions in China) inventory, resolving the issues of complex configuration and poor localization adaptability of foreign models (such as SMOKE). It provides an out-of-the-box, easy-to-use solution to the pain points of cumbersome processes, parameter mismatches, and extensive secondary development required when processing Chinese local inventories using foreign models. Furthermore, it solves the problems of low automation and inefficiency in existing domestic script-based processing methods. Addressing the reliance on manual operation, fragmented processes, susceptibility to errors, and time-consuming nature of existing methods, it offers a highly efficient, fully automated, one-click processing solution. Finally, it addresses the insufficient accuracy of existing technologies in spatiotemporal allocation. Resolving the distortion of emission spatiotemporal characteristics caused by the use of average allocation or general profiles in traditional methods, it provides a refined allocation scheme that incorporates high-resolution localized Chinese data to improve the simulation realism of emission sources.

[0205] This invention utilizes pre-packaged Python code to process the DPEC emission inventory. Parameters and paths are managed through configuration files, allowing users to flexibly control the Python code's execution by simply modifying the configuration without altering the main program. This approach not only enhances system flexibility and maintainability but also facilitates subsequent expansion and upgrades, making it suitable for various application scenarios. In WRF-Chem mode, users can more accurately and easily allocate various pollutants from the original emission inventory to the user-defined simulation regions, strata, and time periods.

[0206] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A DPEC emission inventory pretreatment method applicable to WRF-Chem, characterized in that: include: Obtain raw DPEC emission inventory data for the simulated region based on the DPEC inventory platform; The original DPEC emission inventory data were integrated according to the type of emissions; The integrated raw DPEC emission inventory data was preprocessed to match the units required for chemical species emissions in the WRF-Chem model. Based on the WRF-Chem model configuration parameters, the WRF-Chem model chemical species emission data are interpolated to the simulation area by area. The chemical species emission data of the simulated region are spatially and temporally allocated according to emission factors; The processed data of pollutants in the DPEC inventory are mapped to chemical species that can be identified by the WRF-Chem model, and chemical species emission inventory data that can be read and used by the WRF-Chem model are generated according to the set standards.

2. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: The original DPEC emission inventory data is integrated according to emission type, including: Emission data for the same emitter in the same month will be merged into a single nc file.

3. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: The integrated raw DPEC emissions inventory data is preprocessed, including: Pretreatment formulas include pretreatment formulas for inorganic gaseous pollutants, pretreatment formulas for organic gaseous pollutants, and pretreatment formulas for particulate pollutants; The pretreatment formula for inorganic gaseous pollutants is: The pretreatment formula for organic gaseous pollutants is: The pretreatment formula for particulate matter pollutants is: In the formula, E InorganicGases E OrganicGases E Aerosol These represent the emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants input into the WRF-Chem model, respectively. These represent the original emissions of inorganic gaseous pollutants, organic gaseous pollutants, and particulate pollutants from the original DPEC emission inventory; S represents the area of ​​a single grid cell in the latitude and longitude grid; M gas This represents the molecular weight of the corresponding gas. The emissions of various pollutants input into the WRF-Chem model are obtained based on the preprocessing formula.

4. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: The preprocessing of the integrated raw DPEC emissions inventory data also includes: The formula for calculating the area projected onto the latitude and longitude grid in the original DPEC emissions inventory data is as follows: S=R 2 ·(Φ2-Φ1)·[sin(θ2)-sin(θ1)] In the formula: R is the Earth's radius; Φ1 is the longitude of the left boundary. To convert degrees to radians, Φ1 = lon. Φ2 is the longitude of the right boundary. Convert degrees to radians, Φ2 = lon + Δ. θ1 is the latitude of the southern boundary; convert degrees to radians. θ2 is the latitude of the northern boundary; convert degrees to radians. Δ represents the resolution of latitude and longitude.

5. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: Based on the WRF-Chem model configuration parameters, the WRF-Chem model chemical species emission data are interpolated to the simulation region by area; including: Obtain the WRF-Chem model configuration parameters, including grid resolution, latitude and longitude of the simulation area, and simulation time period data; The emissions of various pollutants input into the WRF-Chem model are interpolated from the DPEC grid to the simulation region of the WRF-Chem model by area according to the WRF-Chem model configuration parameters.

6. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 5, characterized in that: For any target WRF grid in the WRF-Chem model, identify all DPEC grids that overlap with the area of ​​the target WRF grid, and obtain the emissions of each DPEC grid accordingly. Calculate the overlap area between each DPEC grid and the target WRF; The formula for calculating the emission value of the target WRF grid after interpolation is as follows: In the formula, P x P y Indicates the latitude and longitude of the target WRF grid point; emis[i,j] represents the emissions corresponding to the i-th row and j-th column of the DPEC grid; S wrf This represents the actual area of ​​the target WRF mesh, in km². S i,j The area of ​​overlap between the DPEC mesh emis[i,j] and the target WRF mesh is expressed in km². E interp (P x P y ) represents the emission value of the target WRF grid after interpolation.

7. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: The chemical species emission data of the simulated region are spatially and temporally allocated according to emission factors, including: The allocation factor for each emission source per day during the simulation period in the simulated area is determined based on the time period, wherein the emission sources include five categories of emission sources in the DPEC emission inventory: agriculture, residential, transportation, energy, and industry. The emissions of various emission sources at any level in any hour of any day during the simulation time are obtained based on the allocation factors for each emission source per day, hour, and vertical allocation factors. The emission values ​​of various emission sources of the same pollutant in the same layer at the same hour are added together to obtain the emission amount of the pollutant in the simulated area at that time and on that date in that layer.

8. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 7, characterized in that: The allocation factor for each emission source per day within the simulation period of the simulated area is determined based on the time period, using the following formula: In the formula, d k Indicates the day of the week for the k-th day; Indicates emission source s per week per day k Factors; F s,k This represents the daily allocation factor of emission source s on day k after normalization. n represents the number of days in each month during the simulation period; Normalized daily allocation factor F for all days of a month for emission source s s,k They add up to 1.

9. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 7, characterized in that: The emissions from each emission source at any given hour and at any given floor on any given day during the simulation period are obtained based on the daily allocation factor, hourly allocation factor, and vertical allocation factor. The formula is as follows: In the formula, r represents the time; l indicates the layer; E r,l This represents the emission of a chemical species at time k in layer l; 's' represents the emission source, which includes five categories: agriculture, housing, transportation, energy, and industry. m represents the number of emission sources; E s This represents the pre-processed chemical species emission data input into the WRF-Chem model from each emission source; This represents the hourly emission allocation factor of a chemical species at emission source s; This represents the normalized daily emission allocation factor for a chemical species at emission source s; This represents the vertical layer emission allocation factor of a chemical species at emission source s.

10. The DPEC emission inventory pretreatment method applicable to WRF-Chem according to claim 1, characterized in that: The processed pollutant data from the DPEC inventory is mapped to chemical species recognizable by the WRF-Chem model, and emission inventory data of chemical species readable and usable by the WRF-Chem model is generated according to set standards, including: Based on the selected chemical mechanisms, the overall pollutant allocation in the DPEC inventory is transformed into specific chemical species that can be directly identified by the WRF-Chem model; The generated data is stored hourly in the file wrfchemi_d01_{year}_{month}_{date}.nc, generating hourly chemical species emission inventory data for input into the WRF-Chem model.

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