Nitrogen oxide emission accounting method based on satellite remote sensing

Abstract

The invention provides a nitrogen oxide emission accounting method based on satellite remote sensing, and belongs to the field of air pollution prevention and control. According to the method, real-time accurate accounting of NOX emission is realized by utilizing high-temporal-spatial-resolution satellite remote sensing observation and combining a prior emission list and an atmospheric chemical mode. According to the method, the nonlinear response relationship between NOX emission and simulated NO2 column concentration and the nonlinear mapping relationship between simulated NO2 and satellite remote sensing NO2 column concentration are researched, the interaction of the two nonlinear response relationships is comprehensively considered, and a backtracking quantitative model is established, so that a high-resolution NOX emission list is constructed in real time based on satellite remote sensing NO2 data. According to the method, the problems of subjectivity, difference and time delay in the traditional emission list making process are solved, and the accuracy and timeliness of the emission list are improved. In addition, the method also enhances the reliability of simulation and prediction of atmospheric pollutants, and provides powerful support for accurate prevention and control of atmospheric pollution.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric pollutant monitoring, and in particular to a nitrogen oxide (NOx) emission accounting method based on satellite remote sensing. Background Art

[0002] Nitrogen oxides (NO X =NO 2 +NO) are important pollutants in the troposphere, which are widely produced in industrial combustion, transportation and natural phenomena. These gases not only directly damage the environment, but also indirectly affect air quality and human health by participating in complex atmospheric chemical reactions. First, NO X It is one of the main factors in the formation of acid rain, causing soil and water acidification and destroying the ecological balance. X is ozone (O 3 ), aerosols and other secondary pollutants. X Increased concentrations can irritate the human respiratory system and increase the risk of disease. 3 It also has damaging effects on crops and forests, affecting agricultural production and the health of forest ecosystems. X The generation, transformation and removal of NO are crucial to controlling air pollution. Through continuous monitoring and data analysis, NO X Provide a scientific basis for the control and management of

[0003] Air quality mode in NO X It is widely used in research. It has high coverage and spatiotemporal resolution in both horizontal and vertical directions. It is an effective tool for reproducing atmospheric pollution processes and predicting future air quality. It can help accurately regulate the atmospheric environment from a four-dimensional space. Atmospheric pollutant emission data is one of the important inputs for air quality simulation, analysis and prediction. However, air quality models are not suitable for studying NO X When it comes to processes such as emission, transmission and atmospheric chemical reactions, the emission inventory compiled by traditional methods is often outdated and has great uncertainty. The core idea of ​​this method is to start from the most basic emission units (such as individual enterprises, vehicles, agricultural activities, etc.), collect specific emission activity data, and then summarize them step by step to finally obtain the total emissions of the entire country or region. At present, the emission inventory establishment system at the city scale is still imperfect, there are differences in the standards for setting emission factors in various places, and there is subjectivity in the emission data reported by enterprises, which brings great challenges to the accurate and complete compilation of emission inventories. Especially in recent years, with the implementation of a series of pollution prevention and control measures and the occurrence of special events such as major air quality assurance activities, NO XThe dramatic changes in emission conditions have further magnified the uncertainty of air quality model simulation results. Outdated emission inventories, when used to drive chemical transport models, will lead to large errors in the simulation of pollutant concentrations and evolution processes. Re-collecting activity rate and emission factor data is time-consuming and laborious. Emission inventories compiled uniformly using traditional methods have a time lag of at least one year, which makes it impossible to meet real-time research needs.

[0004] Atmospheric environment remote sensing technology is a key means of monitoring environmental quality. When the gases observed by satellites are less affected by transmission and chemical reactions, or these effects can be reduced by other means, the pollutant emission rate in a specific area can be effectively estimated based on satellite observation data. Based on this, a method that integrates satellite observations, prior emission data and air quality models has been developed to quantify regional pollutant emissions. Among them, the widely used mass balance method uses the ratio between the tropospheric vertical column densities (TVCDs) of pollutants observed by satellite and simulated by the prior emission inventory. According to the linear relationship between pollutant emissions and TVCDs, the pollutant emission rate is adjusted to reflect the actual emission situation. This method assumes that the atmospheric components TVCDs in a specific grid in the atmosphere are linearly related to their corresponding emissions. The ratio of the atmospheric components TVCDs observed by satellite to the TVCDs simulated by the atmospheric chemistry model is multiplied by the prior emissions. However, due to NO X Chemical reactions with OH radicals will indirectly affect NO X Life cycle, so this method is NO X The assumption that emissions and concentrations are linearly related results in large errors. In addition, this method ignores the phenomenon of gas transport between grids, which will further amplify the error in emission estimates when the research problem requires higher resolution. There have also been advances in optimizing nonlinear models of chemical reactions, but this improvement does not solve the problem of NO in satellite inversion. 2 The problem of dependence on prior information. This dependence may lead to errors in the inversion results, which will eventually affect the accuracy of the emission inventory and the effectiveness of the correction. X Issues such as the subjectivity, variability and delay of emission inventories are of great value and significance to existing air pollution simulations and forecasts. Summary of the invention

[0005] In order to quickly obtain more real and objective emissions, the present invention provides a nitrogen oxide emission accounting method based on satellite remote sensing, which improves NO X The emission inventory solves the subjectivity, variability and delay issues of traditional methods in conventional emission reduction measures and special events. XEmissions and simulations of NO 2 The nonlinear response relationship of column concentration and simulated NO 2 With satellite remote sensing NO 2 The nonlinear mapping relationship of column concentration is established based on high-resolution satellite remote sensing NO 2 Build NO X The retrospective quantification model of the emission inventory can greatly improve the accuracy and timeliness of the emission inventory, and improve the accuracy of the model's simulation and prediction of atmospheric pollutants, thereby providing a scientific basis for emission reduction measures and pollution prevention and control.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A method for calculating nitrogen oxide emissions based on satellite remote sensing comprises the following steps: Step 101: Using the improved WRF-Chem simulation method, fine parameter optimization is performed for daily emission fluctuations of different emission sources, and the emission height of the power plant emission source is accurately calibrated to ensure high accuracy of the simulation results; Step 102: Based on the simulation in step 101, obtain the prior NO 2 The concentration profile is obtained by inverting the satellite-observed NO using high-precision satellite spectral information. 2 TVCDs data; Step 103: Establish a priori NO according to step 101 and step 102 X Emission changes and simulated prior NO 2 Nonlinear response model for concentration changes, simulating NO 2 With satellite observation NO 2 The nonlinear mapping model of TVCDs was used to analyze the interaction between the two nonlinear models, iteratively adjust the response parameters, and calculate NO X emission; Step 104: construct a satellite observation back-tracing quantitative model, optimize the model by iteratively adjusting the response parameters, and obtain high-precision NO in real time. X Emission inventory, realizing accurate quantification of regional NO by satellite remote sensing X Emission dynamics.

[0007] Furthermore, the step 101 includes: Prior NO X The emission inventory is used as input to generate simulation prior NO 2 Spatial and temporal distribution data of concentration; The a priori NO X The emission inventory takes into account the impact of daily emission changes from agriculture, industry, transportation, power plants and residents' lives on daily pollutant changes; daily emission variation coefficients are introduced in the simulation process and these coefficients are optimized to improve simulation accuracy; A priori list NO X The emission inventory considers the contribution of pollutants emitted by power plants at different altitudes to more accurately simulate the diffusion and deposition of high-altitude pollutants in the atmosphere.

[0008] Further, the step 101 includes: WRF-Chem simulation output NO 2 The product is a four-dimensional array, indicating NO 2 The change of three-dimensional distribution in the time dimension; for a single longitude and latitude, the model outputs a 44-layer NO 2 The concentration profile has a total height range of 1013 hPa to 10 hPa. The stratification rule follows the vertical distribution law of pollutant concentration. The pollutant concentration near the ground is higher and the stratification is denser. The pollutant concentration near the top of the troposphere is lower and the stratification is sparser. 2 The concentration profile is integrated vertically to the top of the troposphere to obtain the simulated NO 2 TVCDs.

[0009] Furthermore, the step 102 includes: First, the spectral data were accurately fitted to determine the NO 2 Total diagonal column concentration ; Secondly, calculate the atmospheric quality factor , including the atmospheric mass factor of the stratospheric AMF and the atmospheric mass factor of the tropospheric AMF Calculation; Finally, estimate the stratospheric column concentration , from NO 2 Total column concentration The stratospheric contribution is deducted from the tropospheric column concentration to accurately reflect the tropospheric column concentration. , as shown in (1-1) and (1-2): (1-1) (1-2).

[0010] Furthermore, the step 103 includes: First, based on the prior NO X Emission inventory, two sets of simulations were performed, one set of simulated inventory was derived from the prior NO X emission Another set of simulated inventories was based on a priori inventories with 30% reduction in NO X emissions, called the perturbed emission inventory; then, two key nonlinear response parameters Б and Э were determined, where Б represents NO 2 TVCDs to NO X The nonlinear response of emission changes, through NO XAdjustment of the chemical reaction rate between OH radicals and NO 2 NO to satellite observation 2 The nonlinear mapping of TVCDs is measured by the proximity between the prior profile and the actual concentration. Finally, the interaction between the two nonlinear response relationships is comprehensively considered and fine-tuned in an iterative manner to accurately calculate the satellite remote sensing NO X Emissions : (2-1) Among them, the intermediate parameters (2-2) The nonlinear response parameters Б and Э are calculated as follows: (3-1) Among them, the intermediate parameters (3-2) Intermediate parameters (3-3) Intermediate parameters (3-4) (4-1) Intermediate parameters (4-2) Intermediate parameters (4-3) in, and T are the simulated radiation intensity and temperature of the study area, respectively, which are derived from the model calculation output; Based on the prior NO X Simulated NO after 30% disturbance reduction based on emission inventory 2 TVCDs; based on Simulate NO 2 Satellite NO from profile information inversion 2 TVCDs.

[0011] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned nitrogen oxide emission calculation method based on satellite remote sensing when executing the program.

[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for calculating nitrogen oxide emissions based on satellite remote sensing.

[0013] Beneficial effects: 1. Social benefits: (1) Implementation of air pollution control policies: In the face of the continuous advancement of the battle against air pollution, policy makers urgently need to re-evaluate air pollutants and their changing trends. X The emission inventory improves the timeliness and accuracy of atmospheric composition monitoring and forecasting, thereby evaluating pollution prevention and control effects and implementing emission reduction responsibilities.

[0014] (2) Air quality assurance for major events: In major sports events, high-standard air quality is the key to ensuring the smooth progress of the event. The present invention dynamically monitors NO in real time with high precision. X Emissions can be precisely controlled to achieve precise control of air pollution sources, ensuring the health and safety of athletes and spectators and providing a solid environmental guarantee for the successful hosting of the event.

[0015] (3) Protection of people’s life and health: In view of long-term exposure to high concentrations of NO 2 The environment can cause respiratory and cardiovascular diseases. X Emissions data is essential for evaluating environmental health benefits. It supports the refined management of air pollution, helps reduce potential damage to public health, and improves the overall well-being of the environment and health.

[0016] 2. Ecological and environmental benefits: Provide strong monitoring and supervision technical support for ecological civilization construction, quantitatively evaluate NO X The actual emission reduction effect, analysis of the temporal and spatial variation trend of pollutants, discovery and control of pollutant emissions in hot spots and typical transmission channels, and thus improvement of the environmental quality of the entire region. In addition, obtaining a real-time pollutant emission inventory can timely evaluate the effectiveness of emission reduction, evaluate the control effect of industrial emissions, traffic pollution, etc. during major events, and timely adjust and optimize emission reduction strategies.

[0017] 3. Technological innovation benefits: Pollutant emission inventories are very important for pollutant simulation. However, the traditional emission inventory preparation methods have problems of subjectivity, variability and delay, and there are large errors in the estimation of pollutant emissions. The mass balance method can effectively reduce the errors caused by outdated emission inventories, but the method assumes that NO X The relationship between emission and concentration is linear, and the NO in the atmosphere is ignored. X The lifetime of NO and the gas transmission phenomenon between grids will further amplify the uncertainty of the estimation results when studying high-resolution emission inventories. X Emissions and simulations of NO 2 The nonlinear response relationship of column concentration and simulated NO 2 With satellite remote sensing NO 2The nonlinear mapping relationship between column concentration and satellite observation is used to establish a quantitative model based on satellite observation. There is no need to recompile the real-time emission inventory. Instead, the real-time dynamic NO X Emissions are of great significance to the efficient and refined management of air pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for calculating nitrogen oxide emissions based on satellite remote sensing is provided in accordance with an embodiment of the present invention.

[0019] Figure 2 A satellite observation backtracking quantification model for calculating NO is provided in an embodiment of the present invention X Schematic diagram of the emissions method. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] The present invention utilizes high-resolution satellite NO 2 TVCDs, combined with optimized atmospheric chemistry model simulations and a priori emission inventories MEIC produced by traditional methods, studied NO X Emissions and simulations of NO 2 The nonlinear response relationship of column concentration and simulated NO 2 With satellite remote sensing NO 2 The nonlinear mapping relationship of column concentration was established to calculate NO based on satellite remote sensing TVCDs. X The retrospective quantification model realizes the high timeliness and high temporal and spatial resolution NO based on satellite remote sensing observations. X Accurately quantify emissions.

[0022] The present invention uses high temporal and spatial resolution satellite spectra (the spectra in this embodiment are from the Tropospheric Monitoring Instrument, https: / / scihub.copernicus.eu) to invert NO 2 Products, comprehensive prior NO XEmission inventory data (from the 2017 version of the Multi-resolution Emission Inventory model for Climate and air pollution research, referred to as MEIC, http: / / meicmodel.org.cn) and optimized WRF–Chem (Weather Research and Forecastingmodel coupled to Chemistry, https: / / www2.mmm.ucar.edu / wrf / users) simulation were used to establish a NOx model based on satellite remote sensing. 2 TVCDs calculation NO X The retrospective quantification model of emissions can achieve accurate quantification of NO in the study area using satellite remote sensing. X Emissions change in real time, Figure 1 NO based on satellite remote sensing provided according to an embodiment of the present invention X Schematic diagram of the emission accounting method flow chart, such as Figure 1 As shown, the following steps may be included: Step 101: Using the improved WRF-Chem simulation method, fine parameter optimization is performed for daily emission fluctuations of different emission sources, and the emission height of the power plant emission source is accurately calibrated to ensure high accuracy of the simulation results.

[0023] Prior NO X The emission inventory is used as input to generate simulation prior NO 2 Spatial and temporal distribution data of concentration; Preferably, in one embodiment of the present invention, the impact of daily emission variations of five major anthropogenic emission sectors (agriculture, industry, transportation, power plants and residents' lives) on daily pollutant variations is fully considered. Daily emission variation coefficients are introduced in the simulation process, and these coefficients are optimized to improve simulation accuracy.

[0024] Based on the measured empirical data, the daily variation coefficients of the five emission sectors from 0 to 23 hours are obtained. The daily variation coefficients of each industry are as follows: agriculture: 0.408, 0.408, 0.432, 0.432, 0.432, 0.432, 0.72, 0.72, 1.68,1.68, 1.68, 2.184, 2.184, 1.56, 1.56, 0.6, 0.6, 0.408,0.408, 0.408.

[0025] industry: 0.7488, 0.7488, 0.7776, 0.8232, 0.888, 0.9552, 1.0296,1.0896, 1.164, 1.2192, 1.272, 1.3032, 1.2192, 1.2384, 1.2456, 1.1592, 1.0824,1.008, 0.9432, 0.8976, 0.8496, 0.8112, 0.7776.

[0026] transportation: 0.4836, 0.4344, 0.384, 0.36, 0.3552, 0.3732, 0.4716, 0.6096, 0.8052,0.9888, 1.2156, 1.392, 1.5156, 1.6164, 1.638, 1.638, 1.6296, 1.59, 1.4976,1.3752, 1.1808, 0.9876, 0.8196, 0.6384.

[0027] Power Plant: 1.0865, 1.0544, 1.0248, 1.0344, 1.0248, 1.0152, 0.9672.

[0028] Resident life: 1.0428, 0.9768, 0.3576, 0.36, 0.3576, 0.3696, 0.4992, 1.1928, 1.5312,1.572, 1.56, 1.3584, 1.1544, 1.0728, 1.0608, 1.0032, 0.9792, 0.996, 1.1184,1.4208, 1.5048, 1.3848, 1.3584, 0.9888.

[0029] These coefficients reveal the changes in emission intensity of different industries during the day. Among them, the daily emission fluctuations of agriculture, residents' lives and transportation are significantly higher than those of industry and power generation. The daily variation coefficient of transportation emissions combines the emission characteristics of ordinary cars and industrial vehicles.

[0030] Preferably, the MEIC defaults to all emission heights being near the ground, while the actual emission height of a power plant is much higher than the adjacent buildings. In order to more accurately simulate the vertical distribution structure of pollutants, the present invention considers the proportion of pollutants emitted by power plants at different heights. X The emissions from 0 to 76 meters, 76 to 153 meters, 153 meters to 308 meters, 308-547 meters, and 547 to 871 meters account for 5%, 40%, 25%, 25%, and 5% respectively. X The total emissions are divided into the model-specific altitudes according to the above ratios and then the simulation is carried out.

[0031] Therefore, the WRF-Chem simulation after optimizing the daily variation of pollution emissions from various emission sectors and the pollution emission height of power plants can not only obtain high-resolution NO 2 The concentration profile data can also more accurately simulate and predict the formation and evolution of polluted weather. The simulation process of the present invention assimilates global reanalysis data, ground meteorological observation data and high-altitude meteorological observation data to improve the simulation accuracy. 2 The product is a four-dimensional array, indicating NO 2 The change of three-dimensional distribution in the time dimension. For a single longitude and latitude, the model outputs a 44-layer NO 2 The total height of the concentration profile is in the range of 1013 hPa to 10 hPa. The stratification rule follows the vertical distribution law of pollutant concentration. The pollutant concentration near the ground is higher and the stratification is denser, while the pollutant concentration near the top of the troposphere is lower and the stratification is sparser. 2 The concentration profile is integrated vertically to the top of the troposphere to obtain the simulated NO 2 TVCDs.

[0032] Step 102: Based on the simulation in step 101, obtain the prior NO 2 The concentration profile is obtained by inverting the satellite-observed NO using high-precision satellite spectral information. 2 TVCDs data; Preferably, in this embodiment, the satellite observation NO 2 TVCDs are based on the study area NO 2 The specially developed NO 2 Inversion algorithm. First, the spectral data are accurately fitted using QDOAS software to determine the NO2 Total diagonal column concentration ; Secondly, calculate the air mass factor (AMF) , including the atmospheric mass factor of the stratospheric AMF and the atmospheric mass factor of the tropospheric AMF ; Finally, estimate the stratospheric column concentration , from NO 2 Total column concentration The stratospheric contribution is deducted from the tropospheric column concentration to accurately reflect the tropospheric column concentration. The main calculation formulas are shown in (1-1) and (1-2): (1-1) (1-2) Specifically, the following steps are included: Step 1: Select the 405–465 nm band and use QDOAS software for spectral analysis. The principle is nonlinear least squares method and the parameters follow QA4ECV NO. 2 Settings in the project.

[0033] Step 2: High-resolution prior NO based on step 101 2 Concentration profiles, calculated at 437.5 nm for tropospheric and stratospheric NO using the Vector Linearized Discrete Ordinate Radiative Transfer model, version 2.7. 2 AMF.

[0034] Step 3: Use the STR atmospheric estimation algorithm from Mainz to estimate and deduct the stratospheric NO 2 TVCDs, and then obtain the tropospheric NO observed by satellite 2 TVCDs.

[0035] Step 103: Establish a priori NO according to step 101 and step 102 X Emission changes (changes from emissions to perturbed emissions) vs. simulated a priori NO 2 A nonlinear response model for concentration changes (based on emission and perturbed emission simulations) simulating NO 2 With satellite observation NO 2 The nonlinear mapping model of TVCDs was used to analyze the interaction between the two nonlinear models, iteratively adjust the response parameters, and calculate NOX emission; The present invention not only introduces nonlinear relationships, but also optimizes the previous nonlinear mapping to more realistically reflect the NO X Emissions and NO 2 The dynamic response relationship between TVCDs. X Emissions and simulations of NO 2 TVCDs, analog NO 2 With satellite observation NO 2 The complex response mechanism of TVCDs is also considered. In addition, the interaction between the two nonlinear models is fully considered, and the satellite inversion process is deeply evaluated for NO 2 The degree of reliance on prior information to improve the accuracy and reliability of the accounting process.

[0036] Satellite observations with high temporal and spatial resolution enable the monitoring of NO X The steps for real-time and accurate emission accounting are as follows: Figure 2 As shown, first, based on a publicly available emission inventory, such as the MEIC inventory, we refer to it as the prior NO X Emission inventory, two sets of simulations were performed, one set of simulated inventory was derived from the prior NO X emission Another set of simulated inventories was based on a priori inventories with 30% reduction in NO X Disturbance Emission Inventory ; Then, two key nonlinear response parameters Б and Э were determined, where Б represents NO 2 TVCDs to NO X The nonlinear response of emission changes is mainly through NO X The chemical reaction rate between OH radicals is adjusted. 2 NO to satellite observation 2 The nonlinear mapping of TVCDs is mainly measured by the proximity between the prior profile and the actual concentration. Finally, the interaction between the two nonlinear response relationships is comprehensively considered and they are fine-tuned in an iterative manner to accurately calculate the satellite remote sensing NO X Emissions .

[0037] (2-1) Among them, the intermediate parameters (2-2) NO for satellite 2 TVCDs, Based on the prior NO X NOx emission inventory simulation 2 TVCDs; The nonlinear response parameters Б and Э are calculated as follows: (3-1) Among them, the intermediate parameters (3-2) Intermediate parameters (3-3) Intermediate parameters (3-4) (4-1) Intermediate parameters (4-2) Intermediate parameters (4-3) in, and T are the simulated radiation intensity and temperature of the study area, respectively, which are derived from the model calculation output; Based on the prior NO X Simulated NO after 30% disturbance reduction based on emission inventory 2 TVCDs; based on Simulate NO 2 Satellite NO from profile information inversion 2 TVCDs.

[0038] Step 104: construct a satellite observation back-tracing quantitative model, optimize the model by iteratively adjusting the response parameters, and obtain high-precision NO in real time. X Emission inventory, realizing accurate quantification of regional NO by satellite remote sensing X Emission dynamics.

[0039] In the present invention, NO X The accuracy of emissions calculation is mainly restricted by two major error sources: on the one hand, the model simulation and satellite observation of NO 2 The process of obtaining TVCDs is uncertain; on the other hand, when simulating NO 2 Concentration and NO X When the emissions are nonlinearly correlated, certain errors will also occur. X The total error of the emission inventory is about 30%, which is within an acceptable and reasonable range, confirming that the present invention has a significant impact on NO X Validity and reliability of emission estimates.

[0040] To achieve the objectives of the aforementioned embodiments, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the aforementioned nitrogen oxide emission accounting method based on satellite remote sensing are implemented.

[0041] An embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for calculating nitrogen oxide emissions based on satellite remote sensing are implemented.

[0042] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for calculating nitrogen oxide emissions based on satellite remote sensing, characterized in that: The steps include: Step 101: Using the improved WRF-Chem simulation method, fine parameter optimization is performed for daily emission fluctuations of different emission sources, and the emission height of the power plant emission source is accurately calibrated to ensure high accuracy of the simulation results; Step 102: Based on the a priori NO2 concentration profile obtained by simulation in step 101, satellite-observed NO2 TVCDs data are obtained by inverting high-precision satellite spectral information; Step 103: Establish a priori NO according to step 101 and step 102 X The nonlinear response model of emission changes and simulated a priori NO2 concentration changes, the nonlinear mapping model of simulated NO2 and satellite observed NO2 TVCDs, and the in-depth analysis of the interaction between the two nonlinear models, iterative adjustment of response parameters, and calculation of NO X emission; Step 104: construct a satellite observation back-tracing quantitative model, optimize the model by iteratively adjusting the response parameters, and obtain high-precision NO in real time. X Emission inventory, realizing accurate quantification of regional NO by satellite remote sensing X Emission dynamics.

2. The method for calculating nitrogen oxide emissions based on satellite remote sensing according to claim 1, characterized in that: The step 101 comprises: Prior NO X The emission inventory is used as input to generate the temporal and spatial distribution data of simulated a priori NO2 concentration; The a priori NO X The emission inventory takes into account the impact of daily emission changes from agriculture, industry, transportation, power plants and residents' lives on daily pollutant changes; daily emission variation coefficients are introduced in the simulation process and these coefficients are optimized to improve simulation accuracy; A priori list NO X The emission inventory considers the contribution of pollutants emitted by power plants at different altitudes to more accurately simulate the diffusion and deposition of high-altitude pollutants in the atmosphere.

3. The method for calculating nitrogen oxide emissions based on satellite remote sensing according to claim 2, characterized in that: The step 101 includes: the NO2 product output by the WRF-Chem simulation is a four-dimensional array, which represents the change of the three-dimensional distribution of NO2 in the time dimension; for a single longitude and latitude, the model outputs a 44-layer NO2 concentration profile, and the total height is in the range of 1013 hPa to 10 hPa. The stratification rule follows the vertical distribution law of pollutant concentration. The pollutant concentration in the near-ground area is higher and the stratification is denser, while the pollutant concentration in the area close to the top of the troposphere is lower and the stratification is sparser; the NO2 concentration profile is integrated vertically to the top of the troposphere to obtain the simulated NO2 TVCDs.

4. The method for calculating nitrogen oxide emissions based on satellite remote sensing according to claim 2, characterized in that: The step 102 includes: First, the spectral data are accurately fitted to determine the total oblique column concentration of NO2 along the observed light path. ; Secondly, calculate the atmospheric quality factor , including the atmospheric mass factor of the stratospheric AMF and the atmospheric mass factor of the tropospheric AMF Calculation; Finally, estimate the stratospheric column concentration , from the total column concentration of NO2 The stratospheric contribution is deducted from the tropospheric column concentration to accurately reflect the tropospheric column concentration. , as shown in (1-1) and (1-2): （1-1） （1-2）。 5. The method for calculating nitrogen oxide emissions based on satellite remote sensing according to claim 4, characterized in that: The step 103 comprises: First, based on the prior NO X Emission inventory, two sets of simulations were performed, one set of simulated inventory was derived from the prior NO X emission Another set of simulated inventories was based on a priori inventories with 30% reduction in NO X emissions, called the perturbed emission inventory; then, two key nonlinear response parameters Б and Э were determined, where Б represents the effect of NO2 TVCDs on NO X The nonlinear response of emission changes, through NO X The chemical reaction rate between the simulated NO2 and the OH radical is adjusted; Э reflects the nonlinear mapping of the simulated NO2 to the satellite observed NO2 TVCDs, measured by the proximity of the prior profile to the actual concentration; finally, the interaction between these two nonlinear response relationships is comprehensively considered, and fine adjustments are made through iteration to accurately calculate the satellite remote sensing NO X Emissions : （2-1） Among them, the intermediate parameters (2-2) For satellite NO2 TVCDs, Based on the prior NO X NO2TVCDs simulated from emission inventories; The nonlinear response parameters Б and Э are calculated as follows: （3-1） Among them, the intermediate parameters (3-2) Intermediate parameters (3-3) Intermediate parameters (3-4) （4-1） Intermediate parameters (4-2) Intermediate parameters (4-3) in, and T are the simulated radiation intensity and temperature of the study area, respectively, which are derived from the model calculation output; Based on the prior NO X The simulated NO2TVCDs after 30% disturbance reduction based on the emission inventory; based on Satellite NO2TVCDs derived from simulated NO2 profile information.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the nitrogen oxide emission calculation method based on satellite remote sensing described in any one of claims 1-5 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a nitrogen oxide emission calculation method based on satellite remote sensing as described in any one of claims 1 to 5 are implemented.

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