Calculation method and system for small-scale greenhouse gas emissions

Abstract

The present invention belongs to the technical field of the calculation of greenhouse gas emissions. Disclosed are a calculation method and system for small-scale greenhouse gas emissions. The method comprises: obtaining real-time meteorological data within the range of a park; using a Lagrangian diffusion model to perform forward diffusion simulation; calculating simulated greenhouse gas concentrations at the locations of receptor sites; acquiring monitored greenhouse gas concentrations at the greenhouse gas receptor sites within the park; and introducing a determination formula, and by means of iterative fitting, continuously adjusting the intensities of greenhouse gas emission sources, such that the simulated concentrations outputted by the model gradually approach the actual monitored concentrations. The present invention can accurately simulate the diffusion and transmission processes of greenhouse gases within a small-scale range; and compared with conventional methods, the present invention can more effectively capture subtle changes in local meteorological conditions and emission sources, thereby improving the calculation accuracy.

Description

Small-scale greenhouse gas emission accounting method and system TECHNICAL FIELD

[0001] The present application relates to a small-scale greenhouse gas emission accounting method and system based on numerical simulation and Lagrangian diffusion model, belonging to the technical field of greenhouse gas emission accounting. BACKGROUND

[0002] With the increasingly prominent global climate change problem, the accurate accounting and monitoring of greenhouse gas emissions have become an important issue in the field of environmental protection. The emission of greenhouse gases, especially carbon dioxide (CO2) and methane (CH4), has a significant impact on the global climate system and has become one of the main driving factors of global warming. The main sources of greenhouse gas emissions include energy production and consumption, agricultural activities, waste disposal, and industrial processes, which are widely distributed in the global range and have significant regional differences.

[0003] In the atmosphere, the propagation and distribution of greenhouse gases are influenced by many factors, including atmospheric dynamics, geographical terrain, and meteorological conditions. These factors make the diffusion process of greenhouse gases in the atmosphere complex and variable, especially in small-scale ranges. Due to the high spatial non-uniformity of greenhouse gases, the relationship between the emission source and the receptor region is complex and difficult to measure directly, which brings great challenges to the accounting of greenhouse gas emissions.

[0004] Traditional greenhouse gas emission accounting methods usually rely on global or regional climate models, which can provide trend predictions for large-scale ranges, but are often difficult to effectively capture local concentration changes of greenhouse gases in small-scale ranges. In addition, although a large-scale monitoring network can provide more detailed data support for global or regional greenhouse gas concentrations, in small-scale ranges, especially in urban, industrial park, or agricultural areas where greenhouse gas emissions are concentrated, the spatial resolution of these methods is often insufficient, making it difficult to accurately depict the characteristics and influence of local emission sources.

[0005] Therefore, in order to more accurately account for the greenhouse gas emissions in small-scale ranges, it is urgent to develop a refined method that can comprehensively consider local meteorological conditions, emission source distribution, and gas diffusion process, capture the non-uniform distribution and variation characteristics of greenhouse gases in small-scale ranges, improve the accuracy of small-scale greenhouse gas emission accounting, and enhance the monitoring and prevention and control capabilities of greenhouse gas emissions. SUMMARY

[0006] In order to capture the non-uniform distribution and variation characteristics of greenhouse gases in a small scale range, improve the accounting accuracy of small-scale greenhouse gas emissions, and improve the monitoring and prevention and control capability of greenhouse gas emissions, the present application provides a small-scale greenhouse gas emission accounting method and system based on numerical simulation and Lagrangian diffusion model, and the technical solution is as follows:

[0007] The small-scale greenhouse gas emission accounting method of the present application comprises:

[0008] Step 1: Collect global meteorological reanalysis data and observation data of meteorological monitoring stations in the park, establish a meteorological data forecasting system, and obtain real-time meteorological data in the park range;

[0009] Step 2: Based on the real-time meteorological data and the location of greenhouse gas emission sources in the park, the forward diffusion simulation is carried out by using the Lagrangian diffusion model;

[0010] Step 3: Obtain the position information of the greenhouse gas receptor stations in the park, and calculate the simulated concentration R of greenhouse gases at the receptor station position based on the forward diffusion simulation result;

[0011] Step 4: Obtain the greenhouse gas monitoring concentration O of the greenhouse gas receptor stations in the park;

[0012] Step 5: Determine whether the simulation source intensity of the Lagrangian diffusion model in step 2 needs to be modified based on the following formula:

[0013] Wherein, M represents the judgment threshold;

[0014] If the above judgment formula is not established, the simulation source intensity is updated, and steps 2 to 5 are repeated, and the update formula of the simulation source intensity is:

[0015] Wherein S n represents the simulation source intensity updated by the n th fitting, R n-1 represents the simulated concentration of greenhouse gases at the receptor station position calculated in the n-1 th fitting process;

[0016] If the above judgment formula is established, the current simulation source intensity S is output as the final greenhouse gas emission source intensity accounting result.

[0017] Optionally, the step 1 comprises:

[0018] Step 11: Collect global meteorological reanalysis data and observation data of meteorological monitoring stations in the park;

[0019] Step 12: using the WRF-ARW numerical prediction model, using the global weather reanalysis data as initial data, using the WRFDA data assimilation model and the 3DVAR assimilation method, real-time assimilation of meteorological monitoring station observation data, using a multi-layer nested grid, establishing an automatic weather data prediction system covering the park;

[0020] Step 13: sorting and verifying the meteorological data output by the automatic weather data prediction system, and archiving into the historical weather database.

[0021] Optionally, the Lagrangian diffusion model in step 2 includes: CALPUFF model, FLEXPART model and HYSPLIT model.

[0022] Optionally, the judgment threshold M=0.01.

[0023] The application provides a small-scale greenhouse gas emission accounting system, comprising:

[0024] A weather data prediction module is configured to collect global weather reanalysis data and observation data of meteorological monitoring stations in the park, establish a weather data prediction system, and obtain real-time weather data in the park area;

[0025] A diffusion simulation module is configured to perform forward diffusion simulation using a Lagrangian diffusion model based on the real-time weather data and the location of greenhouse gas emission sources in the park;

[0026] A greenhouse gas simulation concentration calculation module is configured to obtain the location information of greenhouse gas receptor stations in the park, and calculate the greenhouse gas simulation concentration R at the receptor station location based on the forward diffusion simulation result.

[0027] A greenhouse gas emission source strength accounting module is configured to determine whether the simulation source strength of the Lagrangian diffusion model in the diffusion simulation module needs to be modified based on the following formula:

[0028] Wherein, M represents the judgment threshold;

[0029] If the above judgment formula is not established, the simulation source strength is updated, and the calculation of the diffusion simulation module, the greenhouse gas simulation concentration calculation module and the greenhouse gas emission source strength accounting module is repeated, and the update formula of the simulation source strength is:

[0030] Wherein S n represents the updated simulation source strength in the nth fitting, and R n-1 represents the greenhouse gas simulation concentration at the receptor station location calculated in the (n-1) th fitting process.

[0031] If the judgment formula is established, the current simulation source intensity S is output as the final greenhouse gas emission source intensity accounting result.

[0032] Optionally, the calculation process of the meteorological data prediction module comprises:

[0033] Collecting global meteorological reanalysis data and observation data of meteorological monitoring stations in the park;

[0034] Using the WRF-ARW numerical prediction model, using global meteorological reanalysis data as initial data, using the WRFDA data assimilation model and the 3DVAR assimilation method, real-time assimilating meteorological monitoring station observation data, using multi-layer nested grid, establishing a meteorological data automatic prediction system covering the upper space of the park;

[0035] The meteorological data output by the meteorological data automatic prediction system is sorted and verified, and is archived into a historical meteorological database.

[0036] Optionally, the Lagrangian diffusion model comprises: a CALPUFF model, a FLEXPART model and a HYSPLIT model.

[0037] Optionally, the judgment threshold value M is 0.01.

[0038] The application provides a small-scale greenhouse gas emission accounting device, comprising a memory and a processor.

[0039] The memory is used for storing a computer program.

[0040] The processor is used for realizing the small-scale greenhouse gas emission accounting method according to any one of the above when the computer program is executed.

[0041] The application provides a computer readable storage medium, and the storage medium stores a computer program.

[0042] The application has the following beneficial effects:

[0043] (1) The application can accurately simulate the diffusion and transmission process of greenhouse gases in a small-scale range by combining high-resolution numerical simulation with a Lagrangian diffusion model, so that the emission amount of greenhouse gases can be more accurately calculated.

[0044] (2) The method of the present application adjusts the intensity of the greenhouse gas emission source through iterative fitting, so that the simulated concentration output by the model gradually approaches the actual monitored concentration. This dynamic adjustment mechanism ensures the accuracy of the emission source intensity and can reflect the actual emission situation.

[0045] (3) The present application is suitable for complex geographical and meteorological conditions, such as industrial parks, urban areas and agricultural areas, etc. It can be effectively applied in various environments, providing a flexible solution for greenhouse gas emission accounting in different scenarios. At the same time, through accurate emission accounting, the present application can provide more timely greenhouse gas emission information to help relevant departments quickly take measures to improve the prevention and control ability of greenhouse gas emissions. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 is a schematic diagram of the overall process of the small-scale greenhouse gas emission accounting method based on numerical simulation and Lagrangian diffusion model of the present application.

[0048] Figure 2 is a simulated concentration distribution diagram of greenhouse gas diffusion in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0050] Embodiment one:

[0051] The present application provides a small-scale greenhouse gas emission accounting method based on numerical simulation and Lagrangian diffusion model, which specifically includes the following steps:

[0052] Step S1: Collect global meteorological reanalysis data and observation data of monitoring stations in the park, establish a high-precision meteorological data forecasting system, and obtain real-time meteorological data in the park area. Specifically, it includes the following steps:

[0053] Step S1.1: Collect global meteorological reanalysis data and observation data of monitoring stations in the park.

[0054] Global reanalysis meteorological data is meteorological data obtained by integrating and reanalyzing various observation data worldwide. It integrates data from various observation platforms, including meteorological satellites, ground weather stations, buoys, and aircraft sounding, and can more comprehensively reflect the meteorological conditions of the earth's atmosphere, ocean, and land, providing detailed global meteorological field information. Specifically, the present embodiment selects the global numerical weather prediction data (Global Forecast System, hereinafter referred to as GFS) developed by the National Oceanic and Atmospheric Administration (National Oceanic and Atmospheric Administration, hereinafter referred to as NOAA). GFS data integrates data from various observation platforms, including meteorological satellites, ground weather stations, buoys, and aircraft sounding, and can more comprehensively reflect the meteorological conditions of the earth's atmosphere, ocean, and land. The horizontal resolution of the data reaches 0.25°x0.25°, which can provide high-precision systems, and has the characteristics of stable updating and convenient maintenance. Create a GFS data timing download program to update the data of the current 24 hours at 12:00 every day and archive them.

[0055] The real-time meteorological monitoring data of the monitoring sites in the park include wind speed, wind direction, temperature, relative humidity, air pressure, etc. The sites in the park include standard air stations and air micro stations, which are uniformly distributed in space; both types of stations can measure near-surface meteorological data, including wind speed, wind direction, temperature, relative humidity, air pressure, and other key meteorological parameters.

[0056] Step S1.2: Using the WRF numerical prediction model, using global meteorological reanalysis data as initial data, using the WRFDA data assimilation model, real-time assimilating monitoring site observation data, using multi-layer nested grid, establishing an automatic meteorological data prediction system covering the upper air of the park.

[0057] WRF (Weather Research and Forecasting Model) model is a numerical weather prediction model widely used in meteorology and climate research, with flexible horizontal and vertical grid settings, which can adapt to the simulation needs of different geographical regions and meteorological events. The parameter configuration scheme adopted by the model has particularly excellent effects in the numerical simulation of mesoscale and small-scale regions.

[0058] WRFDA (WRF Data Assimilation) is a data assimilation system of WRF model, which is used to fuse observation data with model output to improve the model's description of atmospheric state. Using the WRFDA data assimilation model, real-time assimilating monitoring site observation data can significantly improve the model's prediction ability in small-scale regions.

[0059] This embodiment uses the WRF numerical prediction model, with GFS data as initial data, using the WRFDA data assimilation model, 3DVAR assimilation method, and assimilating monitoring station observation data to establish a high-precision meteorological data automatic prediction system. The prediction system uses a four-layer bidirectional nested grid, with a horizontal resolution of 100m x 100m in the innermost layer, covering the entire park and its surrounding area, providing high-precision meteorological data within the park.

[0060] Step S1.3: Organize and verify the meteorological data output by the system, and archive it into the historical meteorological database.

[0061] The model automatically performs a prediction every day, and each prediction obtains weather data for 24 hours. The system automatically checks the consistency and spatial and temporal continuity of the output data to ensure that the meteorological data does not have sudden changes or discontinuities during the entire time period. Subsequently, the organized and verified meteorological data is stored in the historical meteorological database to ensure data security and integrity, using appropriate data formats (such as NetCDF or HDF5) for subsequent queries and use, while regularly backing up the meteorological database to prevent data loss or damage.

[0062] Step S2: Obtain the location information of greenhouse gas emission sources within the park, and use a small-scale diffusion model based on the Lagrangian method to perform forward diffusion simulation calculations on the greenhouse gas emission sources. Specifically, the following steps are included:

[0063] Step S2.1: Obtain the precise location information of greenhouse gas emission sources within the park, including the latitude and longitude coordinates of the emission sources, the emission height, and the type and characteristics of the emission sources.

[0064] For known greenhouse gas emission sources, their geographic location information, including latitude and longitude coordinates and emission source height, needs to be collected. This information can be obtained from existing emission source lists, which are usually maintained by environmental monitoring agencies or park management departments and contain all known emission sources and their basic information.

[0065] For unknown greenhouse gas emission sources that have not been identified or recorded, they must be located through a tracing algorithm. The tracing algorithm uses existing greenhouse gas monitoring data, combined with meteorological conditions and geographic information, to reverse the possible emission source location. Common tracing methods include trajectory analysis, spatial interpolation, and model fitting. For unknown emission sources located by the tracing algorithm, further verification and confirmation are needed. This can be done through field surveys, satellite remote sensing data, or other auxiliary means to verify the accuracy of the location results.

[0066] In this embodiment, the fixed greenhouse gas emission source positions within the park are obtained by querying the emission source list, including greenhouse gas emission facilities of enterprises such as coal-fired power plants, chemical plants, and sewage treatment plants; unknown greenhouse gas emission sources within the park, such as traffic emissions and combustion emissions, need to be located and tracked through a tracing algorithm combined with high-precision meteorological data.

[0067] Step S2.2: Obtain real-time meteorological data of the park where the emission source is located by using the high-precision meteorological data prediction system established in step S1.

[0068] Specific meteorological parameters include: wind speed and direction, which affect the diffusion speed and direction of greenhouse gases in the atmosphere and are the most important parameters in diffusion simulation; temperature, which affects the density and diffusion coefficient of the gas and is an important parameter for simulating atmospheric stability and vertical diffusion; humidity, which affects the reaction of gas and water molecules in the atmosphere, as well as the formation and transformation of aerosols; air pressure, which affects the diffusion height and range of the gas in the atmosphere, especially when simulating atmospheric stratification and vertical motion. These data are obtained in real time by the high-precision meteorological data prediction system established in step S1.

[0069] In this embodiment, the high-precision meteorological data prediction system provides real-time meteorological data, the spatial range of the data includes the entire park and its surrounding areas, the horizontal resolution reaches 100m x 100m, and the time resolution reaches 1 minute, providing necessary input conditions for subsequent greenhouse gas diffusion simulation, ensuring that the simulation results can accurately reflect the influence range and concentration distribution of the emission source under specific meteorological conditions.

[0070] Step S2.3: Based on the above meteorological data and emission source position information, select an appropriate Lagrangian diffusion model for forward diffusion simulation, set the initial emission source concentration as S0, and simulate the process of greenhouse gas diffusion from the emission source to the surrounding area.

[0071] The Lagrangian-based dispersion model is a numerical model commonly used to simulate the dispersion, transport, and transformation of atmospheric pollutants. It is based on the concept of tracking individual air parcels or particles as they move through the atmosphere, predicting the spatial and temporal distribution of pollutants by simulating their behavior in the atmosphere. Compared to the Eulerian method, the Lagrangian method is more suitable for handling complex flow fields and non-uniform pollution source distributions, especially in small-scale environments. The core idea of the Lagrangian method is to consider the pollutants in the atmosphere as a large number of small particles or air parcels that move, disperse, and settle in the atmospheric motion field over time. By simulating the trajectories of these particles, the concentration distribution of pollutants at different times and locations can be obtained. Compared to the Gaussian dispersion model, the particle dispersion model based on the Lagrangian method can fully utilize the advantages of high-precision meteorological data to calculate the motion of particles at each time and location, thereby more accurately simulating the diffusion trajectory of pollutants in the atmosphere. Commonly used dispersion models based on the Lagrangian method include CALPUFF, FLEXPART, and HYSPLIT.

[0072] In this example, the HYSPLIT (The Hybrid Single-Particle Lagrangian Integrated Trajectory model) dispersion model is selected for greenhouse gas dispersion simulation. The HYSPLIT model integrates a three-dimensional particle dispersion module based on the Lagrangian method, which can achieve forward and backward dispersion simulation of specified emission sources by calling high-precision meteorological data, and supports customized parameter adjustment. The basic principle of the HYSPLIT particle dispersion module is to release a large number of particles at the specified emission source location to simulate the emission process of pollutants. By calling high-precision meteorological data, the model calculates the motion trajectory of each particle and superimposes the atmospheric turbulence motion component based on this to simulate the dispersion process of pollutants in the atmosphere. The model will automatically calculate the atmospheric stratification conditions based on the input meteorological data to obtain the horizontal and vertical turbulence mixing coefficients, and then calculate and simulate the atmospheric turbulence motion.

[0073] The emission source location information (such as latitude and longitude coordinates, height, emission characteristics, etc.) obtained in step S2.1 and the real-time meteorological data (wind speed, wind direction, temperature, humidity, air pressure, etc.) obtained in step S2.2 are input into the HYSPLIT diffusion model, which will serve as the initial conditions for the model. Set the initial concentration S0 of the emission source, which represents the initial concentration of greenhouse gases from the emission source, which is usually set according to the type of emission source, emission rate, and historical emission data. Start the HYSPLIT diffusion model to simulate the diffusion process of greenhouse gases from the emission source to the surrounding area. The model will track the movement trajectory of the gas particles according to the meteorological conditions and topographic features, and calculate the concentration distribution of the gas at different times and spatial locations. Figure 2 shows the concentration distribution of the greenhouse gas diffusion simulation in this embodiment, where the five-point star indicates the location of the greenhouse gas emission source, the simulation area is the Wuxi city range, and the simulation time is from 12:00 to 18:00 on August 18, 2024 (UTC).

[0074] Step S3: Obtain the location information of the greenhouse gas receptor sites in the park, and calculate the simulated concentration data of the greenhouse gas at the receptor site locations based on the forward diffusion simulation results. Specifically, the following steps are included:

[0075] Step S3.1: Determine the location information of the greenhouse gas receptor sites in the park, including the latitude and longitude coordinates and height of each receptor site, which will be used to determine the concentration of the gas after diffusion in the model.

[0076] The receptor site is usually a monitoring device or a designated environmental monitoring point for monitoring the concentration of greenhouse gases in the air. Obtain the precise location information of all greenhouse gas receptor sites in the park, which includes the latitude and longitude coordinates and height (usually relative to the ground) of each receptor site, which are crucial for simulating the concentration changes during the diffusion of greenhouse gases. Input these location information into the model to ensure that the model can accurately capture the location of each receptor site. These locations will be used to calculate the concentration distribution of the greenhouse gas after diffusion at these points.

[0077] In this embodiment, the greenhouse gas receptor sites mainly include greenhouse gas monitoring micro-stations and standard stations in the park, which are distributed uniformly in space and can monitor the concentration of carbon dioxide, methane and other greenhouse gases in the air in real time. Extract the location information of these sites, including the precise latitude and longitude coordinates and height data of the sites, which are accurate to 10 meters, and input the data into the model for the next calculation.

[0078] Step S3.2: Match the location information of the receptor sites with the simulated concentration distribution in step S2, and calculate the simulated concentration value R0 of the greenhouse gas at the corresponding location of the receptor site.

[0079] By matching the location information of the receptor sites with the simulated concentration distribution obtained in step S2, the model grid or coordinate point corresponding to each receptor site is determined. Using the simulation results of the Lagrangian dispersion model, the simulated concentration value R0 of the greenhouse gas at the location corresponding to the receptor site is calculated. The simulated concentration value R0 represents the concentration of the greenhouse gas when it diffuses from the emission source to the location of the receptor site under specific time and meteorological conditions.

[0080] When calculating the simulated concentration value of the greenhouse gas receptor site, an interpolation algorithm is usually used to estimate the concentration at the site location. This is because the location of the receptor site may not exactly fall on the nodes of the simulation grid, and an interpolation algorithm is needed to calculate the concentration at the site location from the concentration values of the adjacent grid points. Common interpolation algorithms include bilinear interpolation, Kriging difference, nearest neighbor interpolation, etc. According to the specific data characteristics, a suitable interpolation algorithm is selected to ensure that the calculation results of the simulated concentration value are accurate and reliable.

[0081] In this embodiment, based on the greenhouse gas receptor sites and location information, and the simulated concentration distribution obtained in step S2, the simulated concentration value R0 of the greenhouse gas at the location corresponding to the receptor site is calculated. Based on the data characteristics in this embodiment, bilinear interpolation method is selected to calculate the simulated concentration value of the greenhouse gas receptor site. Bilinear interpolation is a commonly used method suitable for two-dimensional grid. It performs linear interpolation on the four grid points adjacent to the location of the receptor site, first in one direction (usually x direction), and then in the other direction (y direction), to obtain the concentration value of the receptor site. The advantage of this algorithm is that it is simple to calculate and has high accuracy in high-resolution grids. Since the horizontal resolution of the grid in this embodiment reaches 100m x 100m, bilinear interpolation method can meet the accuracy requirements of the interpolation algorithm in this embodiment and save computing resources.

[0082] Step S4: Obtain the greenhouse gas monitoring data of the receptor sites in step S3 to obtain the greenhouse gas monitoring concentration O at the time corresponding to the simulated concentration data R0, and calculate the ratio Q0 = R0 / O of the monitoring concentration and the simulated concentration. Specifically, the following steps are included:

[0083] Step S4.1: Obtain the greenhouse gas monitoring data of the receptor sites in step S3, including the concentration data time series of greenhouse gases such as carbon dioxide and methane.

[0084] The receptor sites can be equipped with various types of sensors and monitoring devices that can continuously collect concentration data of greenhouse gases such as carbon dioxide (CO2) and methane (CH4), and the monitoring data covers greenhouse gas concentration values at different time points, usually recorded in time series. The greenhouse gas monitoring data of these receptor sites is collected through a sensor network or an online data platform, and preliminary quality control is performed on the collected data to remove outliers and missing values, and linear interpolation is used to fill in the data gaps to ensure the continuity of the data in time.

[0085] In this embodiment, the receptor monitoring sites record the concentration data of greenhouse gases once an hour, obtain the monitoring data time series of the required period for simulation calculation, and perform quality control on the data to ensure the accuracy of the data.

[0086] Step S4.2: Obtain the monitoring concentration O of the greenhouse gas at the corresponding time of the simulation concentration data R0, and verify the spatio-temporal consistency of the data, and calculate the ratio Q0=R0 / O of the monitoring concentration and the simulation concentration.

[0087] According to the simulation concentration data R0 obtained in step S3, the actual monitoring concentration O of the receptor site at the corresponding time is obtained, and the data O is extracted from the greenhouse gas monitoring time series data of the receptor site. And perform time consistency test to ensure that the monitoring data and the simulation data match in time and space, that is, the monitoring data and the simulation data are compared at the same time and the same place. The time series can be interpolated or aggregated to ensure consistency in the time dimension. After determining the spatio-temporal consistency for each receptor site, the ratio Q0=R0 / O of the simulation concentration R0 and the monitoring concentration O is calculated, which reflects the difference between the model simulation results and the actual monitoring results, and is the core parameter for subsequent fitting calculation.

[0088] In this embodiment, the monitoring concentration O corresponding to the simulation concentration R0 is obtained, and since the monitoring data has the feature of once an hour, it is not necessary to perform interpolation on the time series. The ratio Q0=R0 / O of the simulation concentration R0 and the monitoring concentration O is calculated.

[0089] Step S5: According to the ratio Q0 obtained in step S4, adjust the source intensity data of the greenhouse gas emission source to S1=S0 / Q0, use the adjusted source intensity data S1 to restart the diffusion model, and calculate the simulation concentration data R1 of the receptor site again. Specifically, the following steps are included:

[0090] Step S5.1: According to the ratio Q0 of the simulation concentration and the monitoring concentration obtained in step S4, adjust the source intensity data of the greenhouse gas emission source to S1=S0 / Q0.

[0091] The ratio Q0 represents the proportional relationship between the concentration simulated by the model and the actual monitoring concentration. It can be reasonably inferred that the ratio of the actual source strength of the greenhouse gas emission source to the simulated source strength in the model has the same trend as Q0. If Q0 > 1, it means that the initial emission source strength S0 in the model is overestimated, and the source strength needs to be reduced; if Q0 < 1, it means that the initial emission source strength is underestimated, and the source strength needs to be increased. According to the ratio Q0, the initial source strength S0 is corrected to obtain a new emission source strength S1 = S0 / Q0. Through this adjustment, it is ensured that the updated emission source strength data can be closer to the actual source strength data. This is an iterative correction based on the comparison between the model and the observation, aiming to gradually optimize the fitting results of the source strength.

[0092] In this embodiment, based on the ratio Q0 calculated in step S4, the initial emission source strength S0 is adjusted to obtain a new emission source strength S1.

[0093] Step S5.2: Using the adjusted source strength S1, restart the diffusion model, and based on the new source strength data and the previously obtained high-precision meteorological data, re-simulate the diffusion process of greenhouse gases, and calculate the new simulated concentration data R1 at the location of the receptor site.

[0094] Based on the new source strength data S1, the small-scale Lagrangian diffusion model is re-run, the purpose is to evaluate the greenhouse gas concentration at the receptor site again through the new simulation results, so as to provide the basis for further iterative correction. After the simulation is completed, the new simulation concentration data R1 of each receptor site is calculated again. These simulation concentrations are generated based on the updated source strength data, and therefore should be closer to the actual monitoring concentration O than the initial simulation concentration R0. Through this iterative process, the gap between the model simulation value and the actual monitoring value is gradually narrowed, so as to more accurately estimate the emission amount of greenhouse gases.

[0095] In this embodiment, using the adjusted source strength S1, the HYSPLIT model is used again for diffusion simulation based on the high-precision meteorological data in the park during the same period, and the bilinear interpolation is used to calculate the new simulation concentration data R1 at the location of the receptor site.

[0096] Step S6: Repeat steps S4 and S5 to repeatedly fit and calculate the emission source strength, and store the emission source strength S n , the simulation concentration R n of the receptor site, the ratio Q n ; wherein S n =S n-1 / Q n-1 , Q n =R n / O. Specifically, the following steps are included:

[0097] Step S6.1: Repeat steps S4 and S5 to perform iterative fitting calculation, and update the emission source intensity according to the calculation results of each fitting.

[0098] In each iteration process, the emission source intensity input in this iteration is updated based on the ratio of the simulated concentration and the monitoring concentration in the last iteration process, and then the Lagrangian dispersion model is started again, and the updated receptor site simulated concentration is calculated, and the ratio of the simulated concentration and the monitoring concentration is calculated for the threshold judgment in the next iteration and the next iteration calculation. Through the iteration process, the source intensity of the emission source is constantly adjusted, the accuracy of the model is constantly improved, and the simulation data of the model constantly approaches the actual monitoring data.

[0099] Step S6.2: Store the calculation results of each variable in each fitting: in the nth fitting, the emission source intensity is updated to S n based on the calculation results of the last fitting. n-1 n-1 The simulated concentration corresponding to the actual monitoring data O calculated by the model is R n , the ratio of the monitoring concentration and the simulated concentration is Q n = R n / O.

[0100] In this embodiment, in the nth fitting iteration, the emission source intensity S n-1 in the last iteration process (i.e. the (n-1)th) is updated to S n based on the ratio of the monitoring concentration and the simulated concentration Q n-1 = R n-1 / O. n-1 The Lagrangian dispersion model is started using S n , and then the updated receptor site simulated concentration R n is calculated, and the ratio of the simulated concentration and the monitoring concentration Q n = R n / O. Check whether there is an abnormality in each parameter output by the model, and store the calculation results of each variable in each fitting for the threshold judgment in the next iteration and the next iteration calculation.

[0101] Step S7: When n≥1, compare |1-Q n | with the size of the set threshold M, where M=0.01: if |1-Q n |>M, continue to repeat the above steps to calculate S n+1 ; if |1-Q n |≤M, exit the loop and output S n . The final S n is the source intensity fitting result. Specifically, the following steps are included:

[0102] ​Step S7.1: Set the convergence threshold M = 0.01 for the fitting calculation, i.e., the convergence threshold is set to 1% of the actual monitoring concentration.

[0103] The convergence threshold M is the termination condition of the iterative fitting process, which is used to determine whether the calculation results of the model reach sufficient consistency with the actual monitoring data. In actual situations, due to the measurement instrument precision, model calculation error, and environmental condition fluctuations, even a highly accurate model is difficult to completely match the actual observation data. The simulated data of the model and the actual monitoring data are difficult to completely consistent. Setting a small but non-zero threshold can accommodate these measurement uncertainties and environmental fluctuations. At the same time, the diffusion model and the meteorological model usually simplify or approximate physical phenomena during simulation, especially in small-scale simulation. These simplifications will cause a certain difference between the model output and the actual situation. At this time, setting a too strict convergence requirement may cause the model to be difficult to converge, resulting in excessive iteration times, and seriously increasing the calculation time and resource consumption. Therefore, setting a reasonable threshold can improve the calculation efficiency on the premise of ensuring the result accuracy.

[0104] In this embodiment, the threshold M for judging whether the iteration process converges is set to 0.01, indicating that the allowable error range of the model result is 1% of the actual monitoring concentration. This threshold can ensure that the error between the fitting result of the model and the actual monitoring concentration is within a reasonable range, while balancing the calculation accuracy and the actual monitoring error, model simplification, and the demand for calculation resources, ensuring the accuracy and practicality of the model result.

[0105] Step S7.2: When n ≥ 1, compare |1-Q n | with the convergence threshold M to determine whether to continue the fitting process: if |1-Q n | > M, it means that there is still a large gap between the model and the actual monitoring data, and the model has not reached the required accuracy. The fitting process needs to be continued, and the emission source strength is adjusted to S n+1 , and the diffusion model is re-run to calculate the new simulated concentration; if |1-Q n | ≤ M, it means that the model has reached the expected accuracy, and the difference between the simulated concentration and the actual monitoring data is within an acceptable range. At this time, the fitting process can be exited, and the final obtained emission source strength S n is output.

[0106] The purpose of comparing |1-Q n | with the convergence threshold M is to measure the deviation between the simulated concentration of the model and the actual monitoring concentration, and to determine whether the current model fitting reaches the expected accuracy. Q n is the ratio between the simulated concentration R n and the actual monitoring concentration O at the current n-th fitting, i.e., Q n = Rn / O, according to the formula, Q n The closer to 1, the concentration R n The smaller the deviation between the actual monitoring concentration O, the closer the model simulation to the actual monitoring data. Therefore, when |1-Q n |≤M, that is, the deviation is less than or equal to the convergence threshold, it means that the model has reached sufficient accuracy, at this time the difference between the simulation data and the actual data is within an acceptable range, the model has successfully completed the fitting of the emission source intensity, and the fitting process can stop; on the contrary, when |1-Q n |>M, that is, the deviation is greater than the convergence threshold, it means that the difference between the simulation result and the actual data is still large, and the model has not reached the required accuracy. At this time, it is necessary to continue to adjust the emission source intensity and continue the next iteration fitting to narrow the gap between the model simulation result and the actual monitoring data. Through the convergence judgment of this step, the accuracy of the model simulation result is ensured. When the difference between the model simulation concentration and the actual monitoring concentration reaches the set threshold, the iteration is stopped and the final emission source intensity S n .

[0107] The embodiment can accurately simulate the diffusion and transport process of greenhouse gases in a small scale range by combining high-resolution numerical simulation with the Lagrangian diffusion model, thereby more accurately calculating the emission amount of greenhouse gases. Compared with traditional methods, it can better capture the subtle changes of local meteorological conditions and emission sources, thereby improving the calculation accuracy.

[0108] The embodiment adjusts the intensity of the greenhouse gas emission source through iteration fitting, so that the simulation concentration output by the model gradually approaches the actual monitoring concentration. This dynamic adjustment mechanism ensures the accuracy of the emission source intensity and can reflect the actual emission situation.

[0109] The embodiment is suitable for complex geographical and meteorological conditions, such as industrial parks, urban areas, and agricultural areas, etc. It can be effectively applied in various environments and provides a flexible solution for greenhouse gas emission accounting in different scenarios. At the same time, through accurate emission amount calculation, the method of the embodiment can provide greenhouse gas emission information more timely, helping relevant departments to quickly take measures to improve the prevention and control ability of greenhouse gas emissions.

[0110] Embodiment two:

[0111] The embodiment provides a small-scale greenhouse gas emission amount accounting system, comprising:

[0112] The meteorological data prediction module is configured to collect global meteorological reanalysis data and observation data of meteorological monitoring sites in the park, establish a meteorological data prediction system, and obtain real-time meteorological data in the park;

[0113] The diffusion simulation module is configured to perform forward diffusion simulation based on real-time meteorological data and locations of greenhouse gas emission sources in the park using a Lagrangian diffusion model.

[0114] The greenhouse gas simulated concentration calculation module is configured to obtain location information of greenhouse gas receptor sites in the park, and calculate the simulated concentration R of greenhouse gases at the receptor site locations based on the forward diffusion simulation results.

[0115] The greenhouse gas emission source strength calculation module is configured to determine whether the simulated source strength of the Lagrangian diffusion model in the diffusion simulation module needs to be modified based on the following formula:

[0116] where M represents a judgment threshold;

[0117] If the above judgment formula is not established, the simulated source strength is updated, and the calculation of the diffusion simulation module, the greenhouse gas simulated concentration calculation module, and the greenhouse gas emission source strength calculation module is repeated, and the update formula of the simulated source strength is:

[0118] where S n represents the updated simulated source strength in the nth fitting, and R n-1 represents the simulated concentration of greenhouse gases at the receptor site locations calculated in the (n-1)th fitting process.

[0119] If the above judgment formula is established, the current simulated source strength S is output as the final greenhouse gas emission source strength calculation result.

[0120] The embodiment can accurately simulate the diffusion and transport process of greenhouse gases in a small-scale range by combining high-resolution numerical simulation with the Lagrangian diffusion model, thereby more accurately calculating the emission amount of greenhouse gases. Compared with traditional methods, it can better capture local meteorological conditions and subtle changes in emission sources, thereby improving the calculation accuracy.

[0121] The embodiment adjusts the strength of the greenhouse gas emission source through iterative fitting, so that the simulated concentration output by the model gradually approaches the actual monitoring concentration. This dynamic adjustment mechanism ensures the accuracy of the emission source strength and can reflect the actual emission situation.

[0122] The embodiment is suitable for complex geographical and meteorological conditions, such as greenhouse gas emission intensive areas such as industrial parks, urban areas and agricultural areas. It can be effectively applied in various environments, and provides a flexible solution for greenhouse gas emission accounting in different scenarios. At the same time, through accurate emission accounting, the method can provide greenhouse gas emission information more timely, help relevant departments make response measures quickly, and improve the prevention and control ability of greenhouse gas emission.

[0123] Part of the steps in the embodiment of the application can be realized by software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0124] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for accounting small-scale greenhouse gas emissions, characterized by, The method comprises: Step 1: collecting global meteorological reanalysis data and observation data of meteorological monitoring stations in the park, establishing a meteorological data forecasting system, and obtaining real-time meteorological data in the park; Step 2: based on the real-time meteorological data and the location of the greenhouse gas emission source in the park, using the Lagrangian diffusion model to simulate the forward diffusion; Step 3: obtaining the location information of the greenhouse gas receptor station in the park, based on the forward diffusion simulation result, calculating the simulated concentration R of greenhouse gas at the location of the receptor station; Step 4: obtaining the monitoring concentration O of greenhouse gas at the receptor station in the park; Step 5: Determine if the simulated source strength of the Lagrangian dispersion model in Step 2 needs to be modified based on the following equation: Wherein, M represents the judgment threshold; If the above judgment formula is not established, the simulation source strength is updated, and steps 2 to 5 are repeated, and the update formula of the simulation source strength is: where S n represents the updated simulated source strength after the nth fitting, R n-1 represents the simulated concentration of greenhouse gases at the receptor site position calculated in the n-1th fitting process; If the above judgment formula is established, the current simulation source S is output as the final greenhouse gas emission source strength accounting result.

2. The small-scale greenhouse gas emission accounting method according to claim 1, characterized in that, The step 1 comprises: Step 11: collecting global meteorological reanalysis data and observation data of meteorological monitoring stations in the park; Step 12: using WRF-ARW numerical prediction model, using global meteorological reanalysis data as initial data, using WRFDA data assimilation model and 3DVAR assimilation method, real-time assimilation of meteorological monitoring station observation data, using multi-layer nested grid, establishing meteorological data automatic forecasting system covering the upper air of the park; Step 13: the meteorological data automatic forecasting system outputs the meteorological data for collation and verification, and is archived into the historical meteorological database.

3. The small-scale greenhouse gas emission accounting method according to claim 1, characterized in that, The Lagrangian diffusion model in step 2 comprises: CALPUFF model, FLEXPART model and HYSPLIT model.

4. The small-scale greenhouse gas emission accounting method according to claim 1, characterized in that, The judgment threshold M = 0.

01.

5. A small-scale greenhouse gas emission accounting system, characterized by, The system comprises: A meteorological data forecasting module configured to collect global meteorological reanalysis data and observation data of meteorological monitoring stations in the park, establish a meteorological data forecasting system, and obtain real-time meteorological data in the park; A diffusion simulation module configured to use the Lagrangian diffusion model to simulate the forward diffusion based on the real-time meteorological data and the location of the greenhouse gas emission source in the park; A greenhouse gas simulation concentration calculation module configured to obtain the location information of the greenhouse gas receptor station in the park, based on the forward diffusion simulation result, calculate the simulated concentration R of greenhouse gas at the location of the receptor station; The greenhouse gas emission source strength accounting module is configured to determine whether the simulated source strength in the Lagrangian dispersion model of the dispersion simulation module needs to be modified based on the following formula: Wherein, M represents the judgment threshold; If the above judgment formula is not established, the simulation source intensity is updated, and the calculation of the diffusion simulation module, the greenhouse gas simulation concentration calculation module and the greenhouse gas emission source intensity accounting module is repeated, and the update formula of the simulation source intensity is: where S n represents the updated simulated source strength after the nth fitting, R n-1 represents the simulated concentration of greenhouse gases at the receptor site position calculated in the n-1th fitting process; If the above judgment formula is established, the current simulation source S is output as the final greenhouse gas emission source strength accounting result.

6. The small-scale greenhouse gas emissions accounting system of claim 5, wherein, The calculation process of the meteorological data forecasting module comprises: Collecting global meteorological reanalysis data and observation data of meteorological monitoring stations in the park; Using WRF-ARW numerical prediction model, using global meteorological reanalysis data as initial data, using WRFDA data assimilation model and 3DVAR assimilation method, real-time assimilation of meteorological monitoring station observation data, using multi-layer nested grid, establishing meteorological data automatic forecasting system covering the upper air of the park; The meteorological data automatic forecasting system outputs the meteorological data for collation and verification, and is archived into the historical meteorological database.

7. The small-scale greenhouse gas emissions accounting system of claim 5, wherein, The Lagrangian diffusion model comprises: CALPUFF model, FLEXPART model and HYSPLIT model.

8. The small-scale greenhouse gas emissions accounting system of claim 5, wherein, The judgment threshold M = 0.

01.

9. A small-scale greenhouse gas emission accounting device, comprising: Comprising a memory and a processor; The memory is configured to store a computer program. The processor is configured to implement the small-scale greenhouse gas emission accounting method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the small-scale greenhouse gas emission accounting method according to any one of claims 1 to 4.

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