Park micro-domain air quality precise simulation system

Abstract

The invention provides a park micro-domain air quality precise simulation system, and relates to the technical field of environment supervision, and the system comprises an AIRMOD model which comprises a terrain processor which is used for generating a terrain data file according to a terrain data elevation file inputted by a user and a simulation range specified by the user; inserting the topographic data file into a main program input control file; the meteorological processor is used for processing geophysical and meteorological output fields of the mesoscale model and the forecast weather model, converting an output result of the forecast weather model into parameters and formats required by the main program, and inputting the parameters and formats into the main program; the main program is used for generating a meteorological variable profile map according to an output result of the forecast meteorological model; based on the topographic data file and the meteorological variable profile map and according to the park point source emission list, the concentration diffusion condition of various pollutants is calculated, and simulation results including the hourly concentration values of PM2.5 and O3 are output. The method makes up for the deficiency of the resolution of a regional air quality model, and improves the accuracy of an air quality forecasting system.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental supervision, and in particular to a precise simulation system for air quality in a park micro-domain. Background Art

[0002] The air quality model is a mathematical tool that is based on human scientific understanding of atmospheric physics and chemistry, uses meteorological principles and mathematical methods to simulate air quality horizontally and vertically, and reproduces the processes of pollutant transport, reaction, and removal in the atmosphere. It is an important technical method for analyzing the temporal and spatial evolution of atmospheric pollution, its internal mechanisms, and causes, establishing a quantitative relationship between "pollution reduction" and "quality improvement", and promoting the transition of my country's environmental planning and management to quantification and refinement.

[0003] In general, there are two main types of air quality models: source-based diffusion models and Eulerian-based chemical transport models. Source-based diffusion models use plumes, soot, or particles to represent emitted pollutants. They typically do not consider chemical reactions in the atmosphere or use simplified representations, such as first-order pollutant decay. At the local scale, most methods used to estimate concentrations for individual sources or groups of sources are steady-state Gaussian plume models, and the typical range of such models is within 50 kilometers of the source. Eulerian-based models, such as the Community Multiscale Air Quality Model (CMAQ), are used to simulate the transport and formation of ozone, acid rain, particulate matter (PM), and other pollutants that are formed by chemical reactions of precursors emitted by hundreds or thousands of emission sources. Typically, regional-scale models use arrays of grid cells with horizontal grid resolutions ranging from a few kilometers to tens of kilometers, applicable to scales of hundreds of kilometers.

[0004] The numerical prediction model of air quality is now developing from a single scale to a full-scale (global-regional-city-park) integrated simulation. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Aeronautics and Space Administration (NASA) have successively made breakthroughs in global-regional integrated numerical prediction technology and achieved a global 30-40 km resolution forecast. ECMWF has developed a global integrated forecast system with a forecast period of 5 days, a spatial resolution of 0.4°×0.4°, a temporal resolution of 3 hours, and forecast species mainly including PM1, PM2.5, PM3.5, PM4.6, PM5.8, PM6.9, PM7.1, PM8.1, PM9.2, PM10.3, PM11. 2.5 、PM 10 , ozone, nitric oxide, nitrogen dioxide, carbon monoxide, sulfur dioxide, etc. In addition, the European Federation of Helmholtz National Research Centers has developed a city-park scale air quality numerical simulation technology, and achieved a 100-meter resolution simulation of a single town in Germany.

[0005] At present, as the trend of polluting enterprises from dispersed distribution to concentrated industrial parks becomes increasingly obvious, the pollution pattern has undergone new changes. This change has brought new challenges to air quality forecasting and pollution process control. At present, the existing numerical air quality forecasting models at home and abroad are limited by resolution in application. For the refined simulation of industrial park scale, the lack of computing power and sub-grid scale simulation has exposed the defects of existing regional and urban scale grid models.

[0006] Existing studies have shown that at the industrial park scale, the pollutant concentrations calculated by existing models are often lower than the monitoring data, and the deviation is large. In addition, in terms of temporal and spatial resolution, existing models are difficult to meet the needs of fine-grained control of small-scale industrial parks. In order to meet this challenge, it is urgent to develop a new simulation technology to improve the accuracy and temporal and spatial resolution of air quality forecasts at the industrial park scale. Summary of the invention

[0007] In response to the above problems, the present invention provides a precise simulation system for the micro-domain air quality of a park. By combining high-resolution geographic information data and a pollution source emission inventory, based on the typical emission species and point source / area source emissions of different park types (chemical industry, steel industry, etc.), the present invention makes full use of grid monitoring and dynamic emission information obtained by various sensors in the park to perform a refined simulation of the air quality at the park scale. In the process of system development, the AERMOD model is secondary developed to improve the model's ability to characterize changes in small-scale atmospheric pollutant concentrations from large point sources.

[0008] To achieve the above object, the present invention provides a precise simulation system of micro-domain air quality in a park, comprising: an AERMOD model, wherein the AERMOD model comprises an independently running AERMAP terrain processor, an AERMET meteorological processor and an AERMOD main program, wherein the AERMET meteorological processor comprises a WRF weather forecast model and an MMIF meteorological preprocessor, and the AERMOD main program comprises a meteorological program INTERFACE;

[0009] The AERMAP terrain processor is used to:

[0010] Generates terrain data files based on the terrain data elevation file input by the user and the simulation range specified by the user, and is used by the AERMOD main program;

[0011] Inserting the terrain data file into the AERMOD input control file;

[0012] The MMIF meteorological preprocessor is used to:

[0013] Processing the geophysical and meteorological output fields of the mesoscale model MM5 and the WRF forecast weather model, converting the output results of the WRF forecast weather model into the parameters and format required by the AERMOD main program, and inputting them into the AERMOD main program;

[0014] The AERMOD main program is used to:

[0015] The meteorological program INTERFACE generates a profile of required meteorological variables based on the output results of the WRF forecast meteorological model;

[0016] Based on the terrain data file, the profile of the required meteorological variables and the input park point source emission inventory, the concentration and diffusion of various pollutants are calculated, and the simulation results, including PM 2.5 Hourly concentration values ​​of pollutants such as oxygen and oxygen.

[0017] As a further improvement of the present invention, in the AERMOD model, based on the AERMOD main program and the GRSM mechanism, the high spatiotemporal resolution simulation results of O3 are obtained by modifying the internal subroutine codes.

[0018] As a further improvement of the present invention, in the AERMOD model, by modifying the calling of the calculation subroutine and the programming of the result output, adjusting the core algorithm, and reconfiguring the output data structure, the AERMOD model calculates and outputs the PM per hour. 2.5 Concentration data.

[0019] As a further improvement of the present invention, the simulation results of the AERMOD main program are hourly concentration values ​​of different air pollutants in the park at a resolution of 200 meters.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention can make up for the lack of resolution of the regional air quality model and improve the accuracy of the air quality forecast system by developing the AERMOD model. Compared with the original AERMOD model, the secondary development of the AERMOD main program has achieved functional upgrades based on the original model. 2.5 The concentration results have broken the limitation of only being able to output the 24-hour average concentration, and can smoothly output the hourly average; the O3 simulation function of the AERMOD model has been improved, solving the problem that the standard version of the AERMOD model does not output O3 simulation results.

[0022] The present invention can break through the resolution limitations of previous air quality models and conduct refined simulation of air quality at the park scale, thereby meeting environmental supervision and regulation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a precise simulation system for air quality in a micro-domain of a park disclosed in an embodiment of the present invention;

[0024] Figure 2 The PM output by the accurate simulation system of the micro-domain air quality in a park disclosed in an embodiment of the present invention is 2.5 Schematic diagram of hourly concentration simulation results;

[0025] Figure 3 This is a schematic diagram of the O3 hourly concentration simulation results output by the park micro-domain air quality precision simulation system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0028] like Figure 1 As shown, the present invention provides a precise simulation system for the air quality of a park micro-domain, including: an AERMOD model, the AERMOD model includes an independently running AERMAP terrain processor, an AERMET meteorological processor and an AERMOD main program, the AERMET meteorological processor includes a WRF weather forecast model and an MMIF meteorological preprocessor, and the AERMOD main program includes a meteorological program INTERFACE;

[0029] AERMAP terrain processor for:

[0030] Generate a terrain data file dedicated to the AERMOD input control file based on the terrain data elevation file (DEM file) input by the user and the simulation range specified by the user, and send it to the AERMOD main program;

[0031] Among them, the terrain data of the point source and the receptor point are processed according to the terrain data elevation file input by the user and the simulation range specified by the user.

[0032] Further, the terrain data elevation file includes gridded elevation data;

[0033] The AERMAP terrain processor calculates the terrain height scale for each prediction point based on the gridded elevation data for the simulation range specified by the user, characterizes the terrain, passes the receptor location, height above mean sea level, and receptor-specific terrain height scale information to the AERMOD main program, and generates a grid of prediction points for the AERMOD main program.

[0034] Insert the terrain data file into the AERMOD input control file to ensure that the model fully considers the influence of terrain factors during the simulation process, thereby improving the accuracy and reliability of the simulation results;

[0035] MMIF meteorological preprocessor for:

[0036] Process the geophysical and meteorological output fields of the mesoscale model MM5 and the WRF forecast weather model, convert the output results of the WRF forecast weather model into the parameters and format required by the AERMOD main program, and input them into the AERMOD main program;

[0037] AERMOD main program, used to:

[0038] The meteorological program INTERFACE generates profiles of required meteorological variables based on the output of the WRF forecast meteorological model;

[0039] Based on the terrain data file, the profile of the required meteorological variables and the input park point source emission inventory, the concentration and diffusion of various pollutants are calculated and the simulation results, including PM 2.5 And hourly concentration values ​​of O3.

[0040] in,

[0041] Set up different data input files for the park point source emission inventory according to different pollutant types, insert the data input files into the AERMOD input control file, and input into the AERMOD main program;

[0042] The AERMOD main program runs according to the input AERMOD input control file to obtain the pollutant diffusion results at the park scale.

[0043] in,

[0044] The pollutant categories simulated and calculated by the AERMOD model include O3, SO2, NO2, CO, PM 2.5 、PM 10 ,TSP.

[0045] In the AERMOD model, the present invention obtains the high spatiotemporal resolution simulation results of O3 by modifying the internal subroutine code based on the AERMOD main program and the GRSM mechanism.

[0046] In the AERMOD model, the present invention modifies the calling of the calculation subroutine and the programming of the result output, adjusts the core algorithm, and reconfigures the output data structure, so that the AERMOD model calculates and outputs the PM per hour. 2.5 Concentration data.

[0047] The AERMOD main program in the present invention includes a convection and stable boundary layer diffusion calculation algorithm, a plume lift and buoyancy algorithm, a plume penetration into the upper non-inversion layer algorithm, a vertical profile calculation algorithm of wind turbulence intensity and temperature, an urban night boundary layer algorithm, various terrain prediction point algorithms, a building wake algorithm, a boundary layer parameterization algorithm, and a plume bending process algorithm.

[0048] The simulation results of the AERMOD main program in the present invention are the hourly concentration values ​​of different air pollutants in the park at a resolution of 200 meters.

[0049] In the present invention, the AERMOD model is secondary developed during the system development process to improve the model's ability to characterize changes in the concentration of small-scale atmospheric pollutants from large point sources. In the secondary development of AERMOD, the AERMOD model source code is deconstructed to master the internal code calculation logic and various functional options of AERMOD as a whole; multiple model parameter options are set, case simulations and parallel comparisons of simulation prediction results are carried out, and the degree of influence of each parameter option setting on the prediction results is evaluated; the influence of different underlying surface conditions on the prediction results of smoke plume diffusion is comprehensively evaluated, and different land use types, different seasons, building wash, terrain following and other model options are tested to fully evaluate the ability to carry out high-resolution simulation prediction in complex park scenes. After fully mastering the simulation prediction capabilities and application scope of the AERMOD model, we modified the internal code and running script of the main program to address its shortcomings in hourly simulation of pollutants, and generated a new executable file, whose terrain and meteorological preprocessors remain unchanged. Therefore, in the subsequent use of the technology of this invention, it only needs to be run according to the standard version of the AERMOD model user manual. In order to ensure the accuracy and reliability of the output data of the modified model, and to add new functions without affecting the overall performance and accuracy of the model, we conducted verification and testing. In the unit test, it was confirmed that the input of different parameters would not destroy the output results, and the model could run stably under various predetermined conditions. In the integration test, the focus was on the collaborative work of the model modules including data input, processing logic and output, and it was confirmed that the model after secondary development could maintain the integrity and accuracy of the data during the entire simulation, especially during data interaction and processing.

[0050] The present invention adds simulated species O3: the pollutant categories simulated and calculated by the AERMOD model include SO2, NO2, CO, PM 2.5 、PM10 , TSP, etc., but does not include O3, an important pollutant. Through in-depth analysis of the model code, we found that some chemical mechanisms for calculating NO2 concentrations take into account the impact of O3, such as PVMRM, OLM, TTRM, TTRM2, GRSM, etc. PVMRM and OLM estimate the conversion of initial NOx emissions to NO2 based on the O3 level and plume characteristics in the environment, and assume that the reaction between NO and O3 in the environment to form NO2 occurs instantaneously. GRSM takes into account the balance between NO, NO2 and O3 in the atmosphere and adds a reaction rate based on solar radiation and the transmission time from the pollution source to the receptor. After a comprehensive analysis and comparison of the above chemical mechanisms, we chose to modify the model source code based on the GRSM mechanism, and output the O3 concentration value involved in the NO2 concentration calculation in the GRSM subroutine with code, successfully achieving high temporal and spatial resolution simulation of O3 concentration.

[0051] The present invention realizes PM 2.5 Hourly simulation of PM 2.5 The National Ambient Air Quality Standard (NAAQS) includes two standards: annual average and 24-hour average. In order to meet regulatory requirements, assess health risks, and provide information that is compatible with existing monitoring data, the AERMOD model has used daily averages as output since 2014, which is also a key requirement in the Prevention of Significant Deterioration (PSD) permit and NAAQS compliance assessment. However, in this way, the temporal resolution of the model cannot be unified to the hourly level. Without destroying the original calculation principle and structural framework of the model, we conducted an in-depth analysis at the code level to understand the structure and function of the existing code, and finally referred to the model's calculations for other pollutants (such as SO2, PM 10 ) processing method, and made key modifications to the programming of the calculation subroutine call and result output. By adjusting the PM 2.5 The data flow direction of hourly concentration values ​​is turned off. 2.5 The daily average calculation module ensures that the model can accurately calculate and output the PM per hour. 2.5 Concentration data.

[0052] The present invention is based on the study of typical comprehensive regional scale models such as CMAx model, CMAQ model, NAQPMS model and WRF-Chem model, and found that they have played a huge role in simulating various complex air quality problems and regional complex air pollution problems such as ozone, particulate matter, acid rain and even climate change. However, due to the limitations of the mesoscale calculation formula, the highest grid resolution (about 1 km) using these modeling tools is not enough to capture urban morphology (obstacles) and thermal characteristics, resulting in airflow and pollutants diffusing within a unified scale, mainly near densely urbanized areas and places with many pollution sources. And large grid units cannot fully resolve the main point source plumes. When large point source emissions are instantly diluted into large grid units together with other anthropogenic regional emissions, these dynamic and chemical processes of the plumes are ignored. Existing studies have shown that at the regional scale, the pollutant concentrations calculated by existing models often deviate greatly from monitoring data. In addition, it is also difficult to meet the refined control needs of small scales in terms of time and space resolution. The AERMOD model is a new generation of atmospheric model developed by the Statutory Model Improvement Committee (AERMIC) formed by the US Environmental Protection Agency and the American Meteorological Society. It aims to replace the ISCST3 model in order to introduce the planetary boundary layer theory into the diffusion model and consider the vertical heterogeneity of the boundary layer. The AERMOD model can simulate the hourly average, daily average, and long-term concentration distribution of atmospheric pollutants emitted by point sources, surface sources, and volume sources. It is suitable for rural or urban areas and simple or complex terrain. The model uses hourly continuous pre-processed meteorological data to simulate the concentration distribution of an average time greater than or equal to 1h, and is suitable for evaluation projects with a simulation range of less than or equal to 50km. Tracer tests show that the results of AERMOD simulation are relatively ideal, especially for the accuracy of predictions in a small range of 50km×50km, which is unmatched by other models. A major improvement of AERMOD in diffusion modeling is that it can describe the characteristics of PBL by scaling the surface and mixed layers. AERMOD can infer these measurements based on measurement results and use similarity relationships to construct a vertical profile of the required meteorological variables. Vertical profiles of wind speed, direction, turbulence, temperature, and temperature gradient are estimated using all available meteorological observations.

[0053] The present invention uses the latest version of the AERMOD model developed by the U.S. Environmental Protection Agency EPA. Compared with ISC3, it has the following new or improved algorithms: ① diffusion calculations can be performed in both convective and stable boundary layers; ② plume lift and buoyancy; ③ plume penetration into the upper inversion layer; ④ vertical profile calculations of wind, turbulence intensity and temperature; ⑤ nighttime boundary layer in urban areas; ⑥ the ability to handle prediction points on various types of terrain, including ground points to points on terrain higher than the plume height; ⑦ the ability to handle building wakes; ⑧ improvements to the basic boundary layer parameterization method; and ⑨ the plume bending process is taken into account.

[0054] Example:

[0055] Using the system of the present invention, we simulated the pollution process of a thermal power plant within 15km of Handan City from June 6, 2019 to June 9, 2019. The AERMOD model after secondary development was used, in which AERMAP was used as a terrain preprocessor to process the terrain data of point sources and receptor points, and MMIF was used as the meteorological preprocessor of the model to convert the output results of the forecast meteorological model WRF into the parameters and format required for direct input into the AERMOD model. The specific processing and calculation process is as follows Figure 1 shown.

[0056] In the simulation process of this case, the technology of this invention is mainly responsible for the core calculation part, that is, running the AERMOD main program to simulate the pollution concentration at the receptor point at a resolution of 200 meters. The AERMOD model that has undergone secondary development does not require additional operations. Just prepare the input file and run the executable file according to the user manual of the standard version. By visualizing the simulation results, we can find that the AERMOD model after secondary development has a more detailed depiction of the diffusion of pollutants based on the simulation results of the regional model, especially the plume morphology near large point sources. The accuracy of the simulation results has been improved. In addition, the technology of this invention has broken through the limitations of the standard version of the AERMOD model in simulating species and time resolution, and successfully output PM 2.5 and O3 hourly concentration values ​​(such as Figure 2 , 3 As shown in the figure, high-resolution air quality forecasting at the park scale is achieved.

[0057] Advantages of the present invention:

[0058] The present invention is developed based on the new version of AERMOD model, which can make up for the lack of resolution of regional air quality model and improve the accuracy of air quality forecast system. Although the output features of AERMOD model are very rich, there are still some limitations. Through our secondary development of AERMOD main program, we have realized the function upgrade on the basis of the original model. PM 2.5 The concentration results have broken the limitation of only being able to output the 24-hour average concentration, and can smoothly output the hourly average; the O3 simulation function of the AERMOD model has been improved, solving the problem that the standard version of the AERMOD model does not output O3 simulation results.

[0059] The present invention can break through the resolution limitations of previous air quality models and conduct refined simulation of air quality at the park scale, thereby meeting environmental supervision and regulation needs.

[0060] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A precise simulation system for micro-domain air quality in a park, characterized in that: include: AERMOD model, the AERMOD model includes an independently running AERMAP terrain processor, an AERMETS meteorological processor and an AERMOD main program, the AERMETS meteorological processor includes a WRF weather forecast model and an MMIF meteorological preprocessor, and the AERMOD main program includes a meteorological program INTERFACE; The AERMAP terrain processor is used to: Generates terrain data files based on the terrain data elevation file input by the user and the simulation range specified by the user, and is used by the AERMOD main program; Inserting the terrain data file into the AERMOD input control file; The MMIF meteorological preprocessor is used to: Processing the geophysical and meteorological output fields of the mesoscale model MM5 and the WRF forecast weather model, converting the output results of the WRF forecast weather model into the parameters and format required by the AERMOD main program, and inputting them into the AERMOD main program; The AERMOD main program is used to: The meteorological program INTERFACE generates a profile of required meteorological variables based on the output results of the WRF forecast meteorological model; Based on the terrain data file, the profile of the required meteorological variables and the input park point source emission inventory, the concentration and diffusion of various pollutants are calculated, and the simulation results, including PM 2.5 And hourly concentration values ​​of O3.

2. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: Set different data input files for the park point source emission inventory according to different pollutant types, and insert the data input files into the AERMOD input control file; The AERMOD main program runs according to the input AERMOD input control file to obtain the pollutant diffusion results at the park scale.

3. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: Generate terrain data files based on the terrain data elevation file input by the user and the simulation range specified by the user, including: The terrain data of point sources and receptor points are processed according to the terrain data elevation file input by the user and the simulation range specified by the user.

4. The park micro-domain air quality precision simulation system according to claim 3 is characterized by: The terrain data elevation file includes gridded elevation data; The AERMAP terrain processor calculates the terrain height scale of each prediction point based on the gridded elevation data of the simulation range specified by the user, characterizes the terrain, and passes the receptor location, height above mean sea level, and receptor-specific terrain height scale information to the AERMOD main program, and generates a prediction point grid for the AERMOD main program.

5. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: The pollutant categories simulated and calculated by the AERMOD model include O3, SO2, NO2, CO, PM 2.5 、PM 10 ,TSP.

6. The park micro-domain air quality precision simulation system according to claim 5 is characterized by: In the AERMOD model, based on the AERMOD main program and the GRSM mechanism, the high temporal and spatial resolution simulation results of O3 are obtained by modifying the internal subroutine code.

7. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: In the AERMOD model, by modifying the calculation subroutine call and the result output program design, adjusting the core algorithm, and reconfiguring the output data structure, the AERMOD model calculates and outputs the PM per hour. 2.5 Concentration data.

8. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: The AERMOD main program includes algorithms for calculating convection and stable boundary layer diffusion, algorithms for plume lift and buoyancy, algorithms for plume penetration into the upper non-inversion layer, algorithms for calculating vertical profiles of wind turbulence intensity and temperature, algorithms for urban night boundary layers, algorithms for various terrain prediction points, algorithms for building wakes, algorithms for boundary layer parameterization, and algorithms for plume bending processes.

9. The park micro-domain air quality precision simulation system according to claim 1 is characterized by: The simulation results of the AERMOD main program are the hourly concentration values ​​of different air pollutants in the park at a resolution of 200 meters.

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