Air quality simulation effect optimization method, computer program product and terminal applicable to cities with complex terrain

Abstract

The present invention discloses a method, computer program product, and terminal for optimizing air quality simulation effects applicable to cities with complex terrain, belonging to the field of air quality forecasting technology. The method comprises: performing air quality simulation based on meteorological data and hourly emission inventories by industry / species / vertical stratification; comparing the air quality simulation results with the corresponding monitoring data of the emission inventory region, correcting the industry / region allocation coefficient based on the comparison results, and then performing air quality simulation based on the corrected industry / region adjustment coefficient. Utilizing regional atmospheric pollutant monitoring data and air quality simulation, the atmospheric pollution source emission inventory is rapidly iteratively adjusted, achieving automatic optimization of the atmospheric pollution source emission inventory, and improving the accuracy of the atmospheric pollution source emission inventory applicable to the city scale. This reduces the deviation between the meteorological model simulation results and the corresponding monitoring data of the emission inventory region, and improves the simulation effect of the atmospheric pollution source emission inventory.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality forecasting, and in particular to an air quality simulation effect optimization method, a computer program product, and a terminal applicable to cities with complex terrain. Background Art

[0002] Predictions and forecasts of ambient air quality have significant practical value, allowing them to assess air quality over the coming hours, days, and even months. Based on these predictions, policymakers and society can make proactive decisions to address potential pollution events. These predictions and forecasts can provide timely and effective support for emergency emission reductions during heavy pollution events, thereby mitigating the impact of air pollution on public health and transportation. Research on air quality early warning and forecasting technologies has significant practical significance for improving pollution response capabilities.

[0003] Numerical air quality models are an important tool for conducting air quality warnings and forecasts, and meteorological conditions and pollution emissions are two key inputs to these models. High-precision atmospheric pollution source emission inventory data can help improve the accuracy of air quality simulations and forecasts. However, on the one hand, it is difficult to obtain atmospheric pollution source emission inventories from surrounding cities. On the other hand, the most widely used public emission inventory data is the Multi-scale Emission Inventory of China (MEIC) published by Tsinghua University. This inventory data has a maximum resolution of 10 km, which is insufficient for city-scale applications and has poor simulation effects. Therefore, it is not suitable for direct application at the city scale. In summary, how to improve the accuracy of atmospheric pollution source emission inventories is a core issue facing all air quality numerical forecasting teams. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problems of the prior art and provide an air quality simulation effect optimization method, computer program product and terminal suitable for cities with complex terrain.

[0005] The purpose of the present invention is to achieve the following technical solution: a method for optimizing air quality simulation effect applicable to cities with complex terrain, the method comprising the following steps:

[0006] Adjust the sectoral model emission inventory based on the sectoral model emission inventory, emission inventory regional mapping file, sectoral / regional / time allocation coefficients, and meteorological data to obtain the hourly emission inventory by sector / species / vertical layer;

[0007] Conduct air quality simulation based on meteorological data and hourly emission inventories by industry, species, and vertical stratification to obtain air quality simulation results;

[0008] Compare the air quality simulation results with the corresponding monitoring data of the emission inventory area, correct the industry / region allocation coefficient based on the comparison results, and then perform air quality simulation based on the corrected industry / region adjustment coefficient.

[0009] In one example, the method further includes creating an emissions inventory regional mapping file:

[0010] Based on the layout of atmospheric environmental monitoring points in the area to be simulated, the simulation area where monitoring data can be obtained is divided into administrative regions, and the boundary files of the corresponding administrative regions are collected. The grid points of the administrative region boundaries in the CMAQ model are determined based on the boundary files of the administrative regions. The array values ​​corresponding to the CMAQ model grid points within the administrative regions are set to 1, and the other grid points are set to 0 to obtain the emission inventory regional mapping file.

[0011] In one example, the method further includes updating the hourly emissions inventory by sector / species / vertical stratification, including:

[0012] Road dust emissions are calculated based on gridded graded road length data and meteorological data, and biogenic emissions are calculated based on meteorological data and land use type data. An updated hourly emission inventory by industry / species / vertical layer is formed based on the road dust emission results, biogenic emission results, and the hourly emission inventory by industry / species / vertical layer.

[0013] In one example, the method further includes a meteorological data optimization step:

[0014] Use digital elevation data combined with meteorological model grids to calculate terrain variance data within grid points and perform localization processing on terrain variance data;

[0015] Orthogonalize the parameter schemes of the meteorological model to obtain combinations of different parameter schemes;

[0016] Conduct meteorological simulation according to different parameter schemes to obtain meteorological simulation results;

[0017] Evaluate and analyze the meteorological simulation results of different parameter schemes based on meteorological observation data, and select the optimal parameter scheme with the best simulation effect based on the evaluation and analysis results;

[0018] Meteorological simulation is carried out according to the optimal parameter scheme to obtain optimized meteorological data.

[0019] In one example, the terrain variance The calculation expression is:

[0020]

[0021] in, is the elevation data within the meteorological model grid; is the mean of the elevation data; is the number of elevation grid points within the meteorological model grid.

[0022] In one example, the parameter scheme includes a microphysics scheme, a longwave radiation scheme, a shortwave radiation scheme, a near-surface layer scheme, a land surface process scheme, a boundary layer scheme, and a boundary layer scheme.

[0023] In one example, a meteorological simulation is performed according to different parameter scenarios, including:

[0024] Obtain meteorological field driven data link, meteorological field driven data variable table link, set model simulation time, decompress meteorological field driven data, set model simulation nested grid, generate static data, spatially interpolate meteorological driven data, and output formatted meteorological driven data;

[0025] At the same time, the model parameter scheme, vertical level of meteorological driving data, four-dimensional assimilation of meteorological model, and level in the vertical coordinate system of meteorological model are set, and the initial field and boundary field are generated in combination with the formatted meteorological driving data, and then meteorological simulation is carried out to obtain meteorological simulation results.

[0026] In one example, the calculation expression for evaluating and analyzing the meteorological simulation results of different parameter schemes is:

[0027]

[0028]

[0029] in, Indicates the quantitative scoring results; represents the correlation coefficient; represents the normalization function; represents the root mean square error; represents the normalized mean deviation; represents the normalized mean error; Represents a variable, which is the root mean square error value, normalized mean deviation value, or normalized mean error value.

[0030] It should be further explained that the technical features corresponding to the above examples can be combined or replaced with each other to form a new technical solution.

[0031] The present invention also includes a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for optimizing air quality simulation effects applicable to cities with complex terrain, formed by any one of the above examples or a combination of multiple examples.

[0032] The present invention also includes a terminal, including a memory and a processor, wherein the memory stores computer instructions that can be run on the processor, and when the processor runs the computer instructions, it executes the steps of the method for optimizing the air quality simulation effect applicable to complex terrain cities formed by any one or more of the above examples.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In one example, the present invention utilizes regional atmospheric pollutant monitoring data and air quality simulation to rapidly iteratively adjust the atmospheric pollution source emission inventory, thereby achieving automatic optimization of the atmospheric pollution source emission inventory and improving the accuracy of the atmospheric pollution source emission inventory applicable to the urban scale. This reduces the deviation between the meteorological model simulation results and the corresponding monitoring data in the emission inventory area, thereby improving the simulation effect of the atmospheric pollution source emission inventory.

[0035] 2. In one example, using topographic wind (topo_wind) simulation technology as the core, a terrain variance dataset that conforms to local terrain characteristics was generated. A parameter scheme orthogonalization simulation system was established, the simulation results were evaluated, and the parameter scheme most suitable for urban meteorological simulation was automatically selected. The meteorological data was then optimized. Based on the optimized meteorological data, the urban meteorological simulation results can be further improved. At the same time, combined with the optimization of meteorological data and emission inventories, the problem of poor air quality simulation effects during urban air quality simulation and forecasting was solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to represent the same or similar parts. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0037] Figure 1 A flow chart of a method provided as an example of the present invention;

[0038] Figure 2 A schematic diagram of gridded area determination provided by an example of the present invention;

[0039] Figure 3 An FMEmT emission inventory processing flow chart provided as an example of the present invention;

[0040] Figure 4 A schematic diagram of WRF model grid DEM terrain data extraction provided as an example of the present invention;

[0041] Figure 5A schematic diagram of an orthogonal combination of parameter schemes provided as an example of the present invention;

[0042] Figure 6 This is a flowchart of the WRFRUN operation provided as an example of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is described clearly and completely below with reference to the accompanying drawings. It is apparent that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be noted that the directions or positional relationships indicated by "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the directions or positional relationships described in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the use of ordinal numbers (for example, "first and second", "first to fourth", etc.) is for the purpose of distinguishing objects and is not limited to this order, and cannot be understood as indicating or implying relative importance.

[0045] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention.

[0046] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] In one example, if Figure 1 As shown, a method for optimizing air quality simulation effects applicable to cities with complex terrains comprises the following steps:

[0048] S1: Adjust the sectoral model emission inventory based on the sectoral model emission inventory, emission inventory regional mapping file, sectoral / regional / time allocation coefficients, and meteorological data to obtain the hourly emission inventory by sector / species / vertical layer.

[0049] like Figure 2As shown, this step is implemented using the Fast Model Gridded Emission Inventory Processing Tool (FMEmT). Specifically, the tool reads downscaled and regridded MEIC-HR (Multiresolution Emission Inventory for China – High Resolution) emissions data as input to generate a sector-specific model emission inventory. The emission inventory regional mapping file divides the simulation area into administrative divisions based on the distribution of atmospheric environmental monitoring stations and data availability within the desired simulation area. The SHP boundary files for the corresponding administrative divisions are collected. Based on the locational relationship between the simulation grid center's longitude and latitude and the administrative division, the simulation grids are matched to the administrative division boundary files to create the simulation grid administrative division file, or emission inventory regional mapping file, to identify the administrative division to which different simulation grids belong. The sector / region / time allocation coefficients include monthly / weekly / hourly time allocation coefficients, species allocation coefficients, and vertical allocation coefficients. The sectoral model emission inventory is used as input to the FMEmT tool, and the time allocation coefficient, species allocation coefficient, and vertical allocation coefficient are read to achieve time allocation, species allocation, and vertical allocation of the emission inventory. Simultaneously, the emission inventory is rapidly adjusted using the aforementioned regional mapping file and the corresponding regional emission adjustment coefficients, resulting in an hourly emission inventory by sector / species / vertically layered. Preferably, the method further includes updating the hourly emission inventory by sector / species / vertically layered using the FMEmT tool. In this case, the FMEmT tool calculates road dust emissions based on gridded graded road length data and meteorological data, and calculates biogenic (natural) emissions based on meteorological data and land use type data. An updated hourly emission inventory by sector / species / vertically layered is generated based on the road dust emission results, biogenic emission results (natural VOCs and NO emissions), and the hourly emission inventory by sector / species / vertically layered. Specifically, the three types of emission data, hourly, species-by-species, and vertically layered, are combined into a four-dimensional NetCDF file that can be used in air quality model simulations such as CMAQ.

[0050] S2: The air quality model simulates air quality based on meteorological data and hourly emissions inventories by industry, species, and vertical stratification to produce air quality simulation results. The air quality model used is CMAQ, but CAMx or WRF-CHEM can also be used.

[0051] S3: Compare the air quality simulation results with the corresponding monitoring data of the emission inventory area, calculate the emission inventory adjustment coefficients in different administrative divisions based on the comparison results, correct the emission inventory data in different regions, and then perform air quality simulation based on the corrected industry / regional adjustment coefficients. It is preferred to iterate steps S1-S3 in a loop until the predetermined number of iterations is reached or the simulation results reach the target value.

[0052] Specifically, when the administrative divisions are detailed and there are corresponding pollutant concentration observation data for each administrative division, the pollutant concentration simulated by the model in the corresponding administrative division is extracted and compared with the measured pollutant concentration in the corresponding area, and the correction coefficient is calculated. After adjusting the pollutant emissions in each administrative division, the simulation deviation of the global pollutant concentration is simultaneously reduced to optimize the air quality simulation effect. Calculate the industry / regional adjustment coefficients of the emission inventories in different administrative divisions, adjust the emission data, and perform air quality simulation again. Repeat the cycle to make the simulation results close to the measured values. Through iterative approximation, the automatic optimization of the atmospheric pollution source emission inventory is achieved, thereby achieving the optimization of the numerical simulation effect of urban air quality, which has great practical value. Among them, the calculation expression of the adjustment coefficient of the emission inventory in different administrative divisions is:

[0053]

[0054] in, represents the adjustment factor; represents the pollutant concentration simulated by the meteorological model; Indicates the measured pollutant concentration.

[0055] Furthermore, in this example, the iteration is stopped when the number of iterations is greater than or equal to the preset maximum number of iterations (such as 10 times). The obtained emission data is the emission data close to the actual concentration level in the simulation area. After completing the iterative optimization simulation of the emission inventory on a monthly basis, the emission data is extracted monthly and merged into annual emission data, which can be used for air quality simulation and forecasting.

[0056] In one example, the method further includes creating an emissions inventory region mapping file:

[0057] The administrative boundaries are determined based on the layout of atmospheric environmental monitoring points in the area to be simulated, and then the grid points within the administrative boundaries in the CMAQ model are determined. The array values ​​corresponding to the CMAQ model grid points within the administrative boundaries are set to 1, and the other grid points are set to 0 to obtain the emission inventory area mapping file.

[0058] Specifically, if Figure 3As shown in the figure, based on the distribution of acquired pollution source monitoring data and the corresponding SHP administrative division boundaries, an inclusion relationship is determined between the latitude and longitude of the CMAQ model grid point center and the SHP administrative division boundaries. The CMAQ model grid points within the SHP administrative division are determined. After performing grid inclusion relationship determination for all administrative divisions with monitoring data, an array of dimensions X, Y, and R is established, where X is the number of CMAQ model grid points in the X direction, Y is the number of CMAQ model grid points in the Y direction, and R is the number of SHP administrative divisions with monitoring data. The array value corresponding to the CMAQ model grid point within a certain SHP administrative division is set to 1, and the other grid points are set to 0. The array is then stored as an emission inventory regional mapping file in NetCDF format, which is used to determine the spatial application range of the adjustment coefficient in the subsequent optimization process of the atmospheric pollution source emission inventory. The SHP file is a geographic information system data format used to store geospatial data of administrative divisions.

[0059] In one example, the method further includes a meteorological data optimization step:

[0060] Use digital elevation data combined with meteorological model grid to calculate terrain variance data within grid points, and realize localized processing of terrain variance data;

[0061] Orthogonalize the parameter schemes of the meteorological model to obtain combinations of different parameter schemes;

[0062] Conduct meteorological simulation according to different parameter schemes to obtain meteorological simulation results;

[0063] Evaluate and analyze the meteorological simulation results of different parameter schemes based on meteorological observation data, and select the optimal parameter scheme with the best simulation effect based on the evaluation and analysis results;

[0064] Meteorological simulation is carried out according to the optimal parameter scheme to obtain optimized meteorological data.

[0065] In one example, if Figure 4 As shown in the figure, a polygon is established by the longitude and latitude of the four vertices of the WRF model grid, and the DEM (Digital Elevation Model) data grid is judged by the central longitude and latitude, and the DEM data within the WRF model grid is extracted to calculate the terrain variance Var within the grid. sso DEMs use digital data to represent a model of the Earth's surface terrain. In meteorology, DEM data are particularly important for numerical weather forecast models like WRF because they provide topographic information, which significantly impacts the simulation of valley winds, precipitation distribution, and temperature changes. Therefore, this example introduces localized terrain variance data to help the WRF model more accurately simulate the interaction between the atmosphere and the Earth's surface.

[0066] terrain variance The calculation expression is:

[0067]

[0068] in, is the elevation data within the WRF model grid; is the mean of the elevation data; is the number of DEM elevation grid points within the WRF model grid. After the variance calculation is completed, the localized terrain variance Varsso data is written into the WRF model geo_em.d*.nc file through the program, replacing the data of the VARSSO variable (that is, replacing the original terrain data of the WRF model), thus localizing the terrain variance data in the WRF base data.

[0069] In one example, the parameter schemes include microphysics scheme, longwave radiation scheme, shortwave radiation scheme, near-surface scheme, land process scheme, boundary layer scheme, and boundary layer scheme. Among them, the microphysics scheme is used to simulate the formation process of clouds and precipitation; the longwave radiation scheme is used to simulate the absorption and emission of longwave radiation by the earth's atmosphere; the shortwave radiation scheme is used to deal with the scattering and absorption of solar radiation in the atmosphere; the near-surface scheme is used to simulate the exchange process within the atmospheric boundary layer, including the vertical mixing of heat, humidity and momentum; the land process scheme is used to simulate the surface hydrology and energy balance process; the boundary layer scheme is used to simulate the internal process of the atmospheric boundary layer; and the cumulus scheme is used to simulate the convection process. Each of the above categories often has multiple optional parameters. In order to analyze the impact of different parameter scheme combinations on WRF meteorological simulation and to select the optimal parameter scheme, the parameter schemes must first be orthogonalized and combined. Taking three categories of schemes with three options in each category as an example, the orthogonalization combination process is as follows: Figure 5 As shown. In this invention, since topo_wind (the phenomenon of wind direction and wind speed changing due to the uneven terrain) is used to optimize complex terrain meteorological simulation, the MYJ boundary layer scheme needs to be fixedly selected. Therefore, this invention uses a total of 11 microphysics schemes, 2 near-surface layer schemes, and 7 cumulus cloud parameter schemes. Other parameter schemes are shown in Table 1:

[0070] Table 1 Parameter scheme information table

[0071]

[0072] After orthogonalization, 352 parameter scenario scenarios can be obtained. The generated parameter scenario scenarios are arranged in the order of microphysics scenario-longwave radiation scenario-shortwave radiation scenario-near-surface scenario-land surface process scenario-boundary layer scenario-cumulus parameter scenario-urban canopy scenario, such as 2-3-5-1-1-1-0-0.

[0073] In one example, the present invention develops the WRFRUN program to realize the automatic operation and data management of the WRF model, and then performs meteorological simulation based on different parameter schemes. The WRFRUN operation process is as follows: Figure 6 Shown, including:

[0074] Obtain meteorological field driven data link, meteorological field driven data variable table link, set model simulation time, decompress meteorological field driven data, set model simulation nested grid, generate static data, spatially interpolate meteorological driven data, and output formatted meteorological driven data;

[0075] At the same time, the model parameter scheme, the vertical level of meteorological driving data, the four-dimensional data assimilation setting in the meteorological model (grid_fdda), and the level in the vertical coordinate system in the meteorological model (eta_level) are set, and the initial field and boundary field are generated in combination with the formatted meteorological driving data, and then meteorological simulation is carried out to obtain meteorological simulation results.

[0076] Furthermore, the WRFRUN program implements the configuration and management of weather simulation through the weather simulation project file (WRF PROJECT FILE, WPF). The WPF file format is as follows:

[0077] RUNMODE:FNL

[0078] RUNTIME:20191227-20200201

[0079] RUNDATA:202001

[0080] BACKUPD:FNL2001

[0081] RUNPHYS: 2-3-5-1-1-1-0-0

[0082] METLEVL:AUTO

[0083] IFGFDDA:0,0

[0084] T-STEPS:18

[0085] GEOPATH:OPERFNL

[0086] E-LEVEL:40

[0087] GEOLUTP:T|lcwmodis18fg+9s|20

[0088] In the WPF file, RUNMODE defines the meteorological driver data types used by the WRF model. The program defines filename conventions for different meteorological data types, the number of vertical layers for upper-air meteorological elements, the number of vertical layers for soil data, and Vtable information. This allows the invocation of ungrib.exe and metgrid.exe to decompress and spatially interpolate the meteorological driver data. RUNTIME defines the model run time range, which is set using the setwrftime module in the WRFRUN program. RUNDATA defines the storage path for the meteorological driver data. BACKUPD defines the storage path for the wrfout file after the simulation is completed. Parameter schemes are set using RUNPHYS, which reads orthogonalized parameter scheme scenarios. METLEVL can be used to set the number of upper-air meteorological data layers and override the number of layers automatically determined by the program. IFGFDDA enables the WRF model's grid_fdda function, which approximates the model simulation results to the driver data. T-STEP defines the model integration step size. GEOPATH sets the file path for the model simulation grid. This file contains information such as the number of nested grids, their relative relationships, the number of grid points, and spatial resolution. E-LEVEL sets the number of vertical levels in the model. GEOLUTP sets the model's static data source and resolution. Its format specifies whether to regenerate the static data source and resolution of the geo_em.d0x.nc file.

[0089] In one example, the calculation expression for evaluating and analyzing the meteorological simulation results of different parameter schemes is:

[0090]

[0091]

[0092] in, Indicates the quantitative scoring results; represents the correlation coefficient; represents the normalization function; represents the root mean square error; represents the normalized mean deviation; represents the normalized mean error; Represents a variable, which is the root mean square error value, normalized mean deviation value, or normalized mean error value.

[0093] This example proposes a scoring standard for comparing the simulation effects of WRF parameter schemes. Considering several statistical indicators involved in the model evaluation process, the average deviation NMB, normalized mean error NMGE and root mean square error RMSE are normalized by the normalization function, and then quantitative scoring is performed according to the quantitative scoring result formula. This example selects wind speed, wind direction, temperature and precipitation as the scoring indicators for evaluating the simulation effect of the meteorological model, and selects PM 10 、PM 2.5 , NO2 and O3 are used as scoring indicators to evaluate the simulation effect of the air quality model. The scores of different simulation indicators are averaged and converted into a percentage system, so as to select simulation scenarios with higher correlation coefficients, lower normalized mean deviation, normalized mean error and root mean square error, and realize comprehensive evaluation of simulation results, so as to select parameter solutions with better simulation effects and generate more accurate meteorological data.

[0094] Combining the above examples, a preferred embodiment of the present invention is obtained, in which the method comprises the following steps:

[0095] S10: Calculate terrain variance data within grid points using digital elevation data combined with a meteorological model grid, and perform localization processing on the terrain variance data;

[0096] S20: orthogonalizing the parameter schemes of the meteorological model to obtain combinations of different parameter schemes and form a parameter scheme scenario library;

[0097] S30: Performing automatic meteorological simulation according to the orthogonalized parameter scheme, that is, based on the development of the WRF meteorological model driver, automatically modifying the model parameter scheme by reading the parameter scheme scenario parameter setting field, automatically selecting the corresponding Vtable description file according to the meteorological data selected for simulation, and conducting numerical simulation for the corresponding time period to obtain meteorological simulation results;

[0098] S40: Analyze and evaluate the meteorological data obtained from the simulation of different parameter schemes in combination with meteorological observation data. Establish a scoring method based on four statistical indicators: normalized mean deviation (NMB), normalized mean error (NMGE), root mean square error (RMSE), and correlation coefficient (R). This method can quantitatively analyze the simulation effects of different scenarios. The optimal parameter scheme is determined through a comprehensive judgment based on the obtained scores and the time series simulation effects.

[0099] S50: Performing meteorological simulation according to the optimal parameter scheme to obtain optimized meteorological data.

[0100] S60: Based on the layout of atmospheric environment monitoring points in the area to be simulated, the simulation area where monitoring data can be obtained is divided into administrative regions, and the boundary files of the corresponding administrative regions are collected. The grid points of the administrative region boundaries in the CMAQ model are determined based on the boundary files of the administrative regions. The array values ​​corresponding to the CMAQ model grid points within the administrative regions are set to 1, and the other grid points are set to 0, thereby obtaining the emission inventory region mapping file.

[0101] S70: Adjust the industry-specific model emission inventory based on the industry-specific model emission inventory, the emission inventory regional mapping file, the industry-specific / regional / time allocation coefficients, and meteorological data to obtain the industry-specific / species-specific / vertical-layered hourly emission inventory;

[0102] S80: Perform air quality simulation based on optimized meteorological data and hourly emission inventories by industry / species / vertical stratification to obtain air quality simulation results;

[0103] S90: Extract simulated concentrations from the corresponding grids of atmospheric environmental monitoring stations, compare them with measured data, calculate emission inventory adjustment coefficients for the corresponding administrative divisions, adjust the emission inventory based on the emission inventory adjustment coefficients, and simulate air quality. The model emission inventory adjustment process is iterated, and after 5-10 iterations, a one-month emission inventory optimization process can be completed.

[0104] S100: After completing the rapid iterative optimization of the 12-month model emission inventory, read the adjusted model emission inventory for each month and merge them into the model annual emission inventory, which can be used for operational air quality simulation and forecasting work.

[0105] This paper establishes a set of automatic optimization methods for meteorological parameter schemes suitable for urban air quality simulation and forecasting. Through orthogonal combination, it establishes a parameter scheme scenario library, automatically simulates the scenarios, performs data analysis, and scores the results. This method determines the optimal parameter scheme combination, improves the simulation effect and accuracy of the meteorological model, and thus enhances the effectiveness of air quality simulation and forecasting. In practice, only the parameters to be simulated need to be selected. The automated orthogonal combination and simulation process greatly reduces manual operation, achieves complete coverage of the selected parameter combinations, and has high practicality.

[0106] At the same time, based on the current situation that the quality and availability of atmospheric pollution source emission inventories are one of the bottlenecks for cities to carry out air quality simulation and forecasting, the present invention realizes the optimization of urban atmospheric pollution source emission inventories through rapid iteration technology. Under the most unfavorable conditions, automatic iterative optimization can be carried out based on atmospheric pollutant monitoring data from different regions and the China High-Resolution Emission Inventory (MEIC-HR) published by Tsinghua University, thereby improving the simulation effect of atmospheric pollutant concentrations, meeting the data requirements of urban air quality numerical simulation and forecasting, and solving the problem that cities have difficulty in obtaining their own or surrounding cities' atmospheric pollution source emission inventories, and has high practicality.

[0107] An example of the present invention further provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for optimizing air quality simulation effects applicable to cities with complex terrain, as described in any one or a combination of the above examples. The processor may be a single-core or multi-core central processing unit, a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0108] An example of the present invention also provides a storage medium, which has the same inventive concept as the method for optimizing air quality simulation effects suitable for cities with complex terrain formed by any one of the above examples or a combination of multiple examples, and stores computer instructions thereon. When the computer instructions are run, the steps of the method for optimizing air quality simulation effects suitable for cities with complex terrain formed by any one of the above examples or a combination of multiple examples are executed.

[0109] Based on this understanding, the technical solution of this embodiment, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0110] An example of the present invention further provides a terminal having the same inventive concept as any example or combination of examples corresponding to the aforementioned method for optimizing air quality simulation effects in cities with complex terrain, comprising a memory and a processor. The memory stores computer instructions executable on the processor, and the processor executes the steps of the aforementioned method for optimizing air quality simulation effects in cities with complex terrain when executing the computer instructions. The processor can be a single-core or multi-core central processing unit, a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0111] In one example, the terminal, i.e., the electronic device, is presented in the form of a general-purpose computing device, and the components of the electronic device may include but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0112] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit can perform the aforementioned method for optimizing air quality simulation effects applicable to cities with complex terrain.

[0113] The storage unit may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit, and may further include a read-only memory unit (ROM).

[0114] The storage unit may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0115] The bus can represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0116] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0117] Through the above description, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method of the exemplary embodiment of the present application.

[0118] The above specific implementation methods are detailed descriptions of the present invention. It cannot be considered that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, they can make several simple deductions and substitutions without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for optimizing air quality simulation effects in cities with complex terrain, characterized in that: The following steps are involved: Adjust the sectoral model emission inventory based on the sectoral model emission inventory, emission inventory regional mapping file, sectoral / regional / time allocation coefficients, and meteorological data to obtain the hourly emission inventory by sector / species / vertical layer; The Fast Model Gridded Emission Inventory Processing Tool (FMEmT) reads downscaled and re-gridded China's high-resolution emission inventory data as input to obtain a sector-specific model emission inventory. The sector / region / time allocation coefficients include monthly / weekly / hourly time allocation coefficients, species allocation coefficients, and vertical allocation coefficients. The sector-specific model emission inventory is used as input to the FMEmT tool, which reads the time allocation coefficients, species allocation coefficients, and vertical allocation coefficients to achieve time allocation, species allocation, and vertical allocation of the emission inventory. At the same time, the emission inventory can be quickly adjusted through regional mapping files and the emission adjustment coefficients of the corresponding regions, resulting in an hourly emission inventory with sector / species / vertical stratification. The methodology also includes updating the hourly emissions inventory by sector / species / vertical stratification, including: Calculate road dust emissions based on gridded, graded road length data and meteorological data, and calculate biogenic emissions based on meteorological data and land use type data. Based on the road dust emission results, biogenic emission results, and hourly emissions inventories by industry / species / vertical stratification, an updated hourly emissions inventory by industry / species / vertical stratification is generated. Conduct air quality simulations based on meteorological data and updated hourly emission inventories by industry, species, and vertical stratification to obtain air quality simulation results; Compare the air quality simulation results with the corresponding monitoring data of the emission inventory area, revise the industry / region allocation coefficient based on the comparison results, and then conduct air quality simulation based on the revised industry / region adjustment coefficient; The method further comprises a meteorological data optimization step: Use digital elevation data combined with meteorological model grids to calculate terrain variance data within grid points and perform localization processing on terrain variance data; Orthogonalize the parameter schemes of the meteorological model to obtain combinations of different parameter schemes; Conduct meteorological simulation according to different parameter schemes to obtain meteorological simulation results; Evaluate and analyze the meteorological simulation results of different parameter schemes based on meteorological observation data, and select the optimal parameter scheme with the best simulation effect based on the evaluation and analysis results; Carry out meteorological simulation according to the optimal parameter scheme to obtain optimized meteorological data; Meteorological simulations were performed according to different parameter scenarios, including: Obtain meteorological field driven data link, meteorological field driven data variable table link, set model simulation time, decompress meteorological field driven data, set model simulation nested grid, generate static data, spatially interpolate meteorological driven data, and output formatted meteorological driven data; At the same time, the model parameter scheme, vertical level of meteorological driving data, four-dimensional assimilated data of meteorological model, and level of vertical coordinate system in meteorological model are set, and the initial field and boundary field are generated by combining the formatted meteorological driving data, and then meteorological simulation is carried out to obtain meteorological simulation results; The calculation expression for evaluating and analyzing the meteorological simulation results of different parameter schemes is: Among them, S est represents the quantitative scoring result; R represents the correlation coefficient; abnm represents the normalization function; RMSE represents the root mean square error; NMB represents the normalized mean deviation; NMGE represents the normalized mean error; x represents the variable, which is the root mean square error value, normalized mean deviation value, or normalized mean error value.

2. The air quality simulation effect optimization method applicable to cities with complex terrain according to claim 1 is characterized in that: The method further includes creating an emissions inventory regional mapping file: Based on the layout of atmospheric environmental monitoring points in the area to be simulated, the simulation area where monitoring data can be obtained is divided into administrative regions, and the boundary files of the corresponding administrative regions are collected. The grid points of the administrative region boundaries in the CMAQ model are determined based on the boundary files of the administrative regions. The array values ​​corresponding to the CMAQ model grid points within the administrative regions are set to 1, and the other grid points are set to 0 to obtain the emission inventory regional mapping file.

3. The air quality simulation effect optimization method applicable to cities with complex terrain according to claim 1 is characterized in that: Terrain variance Var sso The calculation expression is: Among them, h i is the elevation data within the meteorological model grid; is the mean of the elevation data; n is the number of elevation grid points in the meteorological model grid.

4. The air quality simulation effect optimization method applicable to cities with complex terrain according to claim 1 is characterized in that: The parameter schemes include microphysics scheme, longwave radiation scheme, shortwave radiation scheme, near-surface layer scheme, land surface process scheme, boundary layer scheme, and boundary layer scheme.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for optimizing air quality simulation effects applicable to cities with complex terrain as described in any one of claims 1 to 4 are implemented.

6. A terminal comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, characterized in that: When the processor runs the computer instructions, the processor performs the steps of the air quality simulation effect optimization method applicable to complex terrain cities as described in any one of claims 1 to 4.

Images