Greenhouse gas emission flux determination method and device, electronic equipment and storage medium

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for determining greenhouse gas emission fluxes. The method includes: providing a background gas concentration field as a reference for determining gas emission fluxes; simulating the actual diffusion process using a Lagrange particle diffusion model to determine the transport operator between the average concentration of the gas column at the observation point and the ground emission flux; and decomposing the transport operator into three parts: a first transport operator for a first flux within a nested grid, a second transport operator for a second flux outside the nested grid, and a third transport operator for the background gas concentration field. This decomposition not only refines the spatial resolution and distinguishes emission sources at different spatial scales but also comprehensively considers the influence of the background concentration field. Based on the first, second, and third transport operators, an objective function is constructed, and optimizing the objective function determines the gas emission flux corresponding to the observation point, significantly improving the accuracy of gas emission flux determination.

Description

Technical Field

[0001] This invention relates to the field of gas emission flux determination technology, and in particular to a method, apparatus, electronic device and storage medium for determining greenhouse gas emission flux. Background Technology

[0002] With increasing global concern about climate change, accurately determining carbon emission fluxes has become a critical task. Currently, the main methods employed are bottom-up and top-down atmospheric inversion methods. Bottom-up methods estimate carbon emissions through energy consumption and emission factors, but suffer from inconsistencies in energy statistics, reliance on global default values, and data update lags, leading to significant uncertainty in the estimation results. Top-down methods, on the other hand, utilize atmospheric concentration observations to invert surface fluxes. Their Eulerian atmospheric transport models suffer from fixed resolution and complex adjoint model construction. While Lagrange models have advantages, their accompanying inversion framework, FLEXINVERT, is only applicable to limited ground station monitoring and cannot assimilate greenhouse gas column concentration observation data, making it difficult to fully utilize high spatiotemporal resolution observation information. Furthermore, ensemble filtering methods are computationally intensive, variational methods are difficult to develop, and Bayesian methods are computationally too costly in high-resolution observation scenarios, losing their advantage when using a limited number of observations.

[0003] Therefore, existing technologies are insufficient to meet the requirements for high-precision, high-spatiotemporal-resolution carbon emission monitoring. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining greenhouse gas emission fluxes, in order to address the shortcomings of existing technologies that cannot meet the requirements for high-precision, high-spatiotemporal resolution carbon emission monitoring.

[0005] This invention provides a method for determining gas emission flux, comprising the following steps.

[0006] Obtain the background concentration field of gases in the atmosphere; The gas concentration at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located is averaged to obtain the average gas column concentration corresponding to the observation point. Based on the Lagrange particle diffusion model, the transport operator between the average concentration of the gas column and the ground emission flux corresponding to the observation point is determined, and the transport operator is decomposed into a first transport operator for the first flux within the nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the gas background concentration field. Based on the first transport operator, the second transport operator, and the third transport operator, an objective function is constructed, and the gas emission flux corresponding to the observation point is determined according to the objective function.

[0007] According to a method for determining gas emission flux provided by the present invention, the step of constructing an objective function based on the first transport operator, the second transport operator, and the third transport operator includes: Based on the first transport operator, the second transport operator, and the third transport operator, the simulated mixing ratio of greenhouse gases at the observation point is determined; The greenhouse gas column average mixing ratio is determined based on the simulated mixing ratio and the prior column average mixing ratio of greenhouse gases. The objective function is constructed based on the average mixing ratio of the greenhouse gas column.

[0008] According to a method for determining gas emission flux provided by the present invention, the step of determining the simulated greenhouse gas mixing ratio at the observation point based on the first transport operator, the second transport operator, and the third transport operator includes: Based on the first transmission operator and the first flux, a first mixing ratio is determined; The second mixing ratio is determined based on the second transport operator and the second flux; Based on the third transport operator and the gas background concentration field, a third mixing ratio is determined, and based on the first mixing ratio, the second mixing ratio, and the third mixing ratio, the simulated greenhouse gas mixing ratio is determined.

[0009] According to a method for determining gas emission flux provided by the present invention, constructing the objective function based on the average mixing ratio of the greenhouse gas column includes: Obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector; The objective function is constructed based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column.

[0010] According to a method for determining gas emission flux provided by the present invention, the observation vector is determined based on the transport operator and the observation error vector.

[0011] According to a method for determining gas emission flux provided by the present invention, the step of determining the gas emission flux corresponding to the observation point based on the objective function includes: Determine the feature matrix from the state vector to the observation vector in the objective function; The feature matrix is ​​iteratively solved to obtain the optimal solution, which is then used as the gas emission flux corresponding to the observation point.

[0012] According to a method for determining gas emission flux provided by the present invention, the step of obtaining the gas background concentration field includes: Obtain the original gas background concentration field; Determine the deviation correction factor between the original gas background concentration field and the observations from global background stations; Based on the deviation correction factor, the original gas background concentration field is corrected to obtain the gas background concentration field.

[0013] The present invention also provides a gas emission flux determination device, comprising the following units: Acquisition unit, used to acquire the background concentration field of gases in the atmosphere; The processing unit is used to average the gas concentration at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located, so as to obtain the average gas column concentration corresponding to the observation point. The decomposition unit is used to determine the transport operator between the average concentration of the gas column and the ground emission flux corresponding to the observation point based on the Lagrange particle diffusion model, and decompose the transport operator into a first transport operator of the first flux within the nested grid, a second transport operator of the second flux outside the nested grid, and a third transport operator of the gas background concentration field. The construction unit is used to construct an objective function based on the first transport operator, the second transport operator, and the third transport operator, and to determine the gas emission flux corresponding to the observation point according to the objective function.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the gas emission flux determination method as described above.

[0015] 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 gas emission flux determination method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the gas emission flux determination method as described above.

[0017] The invention provides a method, apparatus, electronic device, and storage medium for determining greenhouse gas emission fluxes. First, it acquires the atmospheric background gas concentration field to provide a benchmark reference for determining gas emission fluxes and eliminates interference from natural factors. Second, it uses a Lagrange particle diffusion model to simulate the actual diffusion process. This model can adapt to complex terrain and environmental conditions and accurately determine the transport operator between the average column concentration of gas at the observation point and the ground emission flux. Third, it decomposes the transport operator into three parts: a first transport operator for the first flux within a nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the background gas concentration field. This decomposition not only refines the spatial resolution and distinguishes emission sources at different spatial scales but also comprehensively considers the influence of the background concentration field. Finally, based on the first, second, and third transport operators, an objective function is constructed, and the gas emission flux corresponding to the observation point is determined by optimizing the objective function, thereby significantly improving the accuracy and precision of gas emission flux determination. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for determining gas emission flux provided by the present invention.

[0020] Figure 2 This is a schematic diagram of the gas emission flux determination device provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The terms "first," "second," etc., used in this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and that the objects distinguished by "first," "second," etc., are generally of the same class.

[0024] Figure 1 This is a flowchart illustrating the gas emission flux determination method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 110: Obtain the background concentration field of gases in the atmosphere.

[0025] Specifically, firstly, the background concentration field of gases in the atmosphere is obtained. The background concentration field can be obtained by correcting the original background concentration field with a deviation correction factor between the original background concentration field and the observations of global background stations.

[0026] Here, the original background greenhouse gas concentration field can be generated by simulating physical processes such as atmospheric advection, convection, and boundary layer mixing. During the simulation, the effects of near-surface greenhouse gas emission sources and sinks need to be considered, driven by the global meteorological field. Through the simulation of these comprehensive factors, a three-dimensional dynamic distribution simulation of greenhouse gases in the global atmosphere can be achieved, and key diagnostic quantities can be derived. The resulting global background greenhouse gas concentration field can provide fundamental data support for carbon emission monitoring.

[0027] However, the greenhouse gas mixing ratios in the simulated global background concentration field (gas background concentration field) may be significantly affected by short-term surface emissions. To eliminate this effect, this embodiment of the invention uses observations from global background stations obtained through AirCore observations to correct the bias in the three-dimensional greenhouse gas mixing ratios simulated by the model. In this way, upper-level greenhouse gas values, independent of surface influences, can be extracted, thereby ensuring that the influence of short-term near-surface emissions is removed from the background concentration field, improving the accuracy and reliability of the gas background concentration field.

[0028] Step 120: Average the gas concentration at multiple points corresponding to the longitudinal section of the nested grid where the observation point is located to obtain the average gas column concentration corresponding to the observation point.

[0029] Specifically, considering that the gas concentration at a single observation point may be affected by local factors (such as local emission sources, topography, and meteorological conditions), it cannot accurately represent the gas concentration situation of the entire region. Averaging can reduce the impact of local anomalies on the overall assessment, making the results more representative. Furthermore, atmospheric gas concentrations are spatially continuous, and single-point measurements cannot reflect this continuity. Averaging can better simulate this continuity, providing a more accurate picture of the regional gas concentration distribution.

[0030] Accordingly, the gas concentrations at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located are averaged to obtain the average gas column concentration corresponding to the observation point. Here, the average gas column concentration represents the average gas concentration along a certain vertical path. It reflects the overall gas distribution along that path, rather than the local concentration at a specific height.

[0031] It is understood that, in order to accurately assess the average gas column concentration at the observation point, this embodiment of the invention employs an innovative averaging method. Specifically, this embodiment focuses on the longitudinal profile of the nested grid where the observation point is located, collecting gas concentration data at multiple points at different altitudes on this profile. By averaging these multi-point gas concentration data, the average gas column concentration corresponding to the observation point is calculated. This method has broad applicability, applicable to both near-ground observations and satellite-based whole-layer observations. However, the core breakthrough and innovation of this embodiment lies in its successful application to the field of satellite-based whole-layer observations. In satellite-based whole-layer observations, this method can effectively integrate a large amount of gas concentration information from the ground to the top of the atmosphere acquired by satellite remote sensing, thereby providing strong support for comprehensively and accurately retrieving the whole-layer distribution of gases in the atmosphere. This greatly expands the application scope of average gas column concentration in atmospheric monitoring and research, laying a solid foundation for in-depth exploration in related fields.

[0032] Step 130: Based on the Lagrange particle diffusion model, determine the transport operator between the average concentration of the gas column and the surface emission flux corresponding to the observation point, and decompose the transport operator into a first transport operator for the first flux within the nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the gas background concentration field. Specifically, simulation and retrospective analysis of greenhouse gases are conducted based on the Lagrange particle diffusion model. The open-source FLEXPART model can only use older meteorological data, and its spatial resolution is limited to 1°, which is a problem with most open-source models. In this embodiment of the invention, the spatial resolution of the meteorological field read into the model is improved, and the maximum number of computational grid points fixed in the model is expanded, enabling it to use a reanalysis data-driven mode with a spatial resolution of 0.25° to simulate the sensitivity relationship between the gas concentration at the observation point and the surface emission flux. In other words, the transport operator between the gas concentration at the observation point and the surface emission flux can be determined based on the Lagrange particle diffusion model.

[0033] In this embodiment of the invention, the Lagrange particle diffusion model uses a simple "zero acceleration" scheme to calculate the trajectory of greenhouse gas particles over time, as shown in the following formula: In the formula, For time, For time increments, For position vectors, This represents the initial position of the greenhouse gas particle model. The first guessed position, For grid-scale wind Turbulent wind fluctuations and mesoscale wind fluctuations The wind vector formed.

[0034] Understandably, when simulating greenhouse gas movement using a Lagrange particle diffusion model, atmospheric losses over time can be neglected. Therefore, the source-acceptor relationship can be expressed as the relationship between the source and acceptor cells in the considered grid cells. The average dwell time of each reverse trajectory is proportional to the average dwell time of the reverse trajectory. For a given spatiotemporal grid cell... Given an observation point and an emission flux grid, the source-receptor relationship is expressed as follows: In the formula, the unit of source-receptor relationship is residence time × volume per unit mass. It is the concentration of the grid cells. It is the number of reverse trajectories. It is a trajectory In the spatiotemporal grid cell The time spent in the middle, This represents the air density within the grid cell. From this, the source-receptor relationship matrix at the observation point can be obtained.

[0035] Here, the source-receptor relationship is used to describe the transport and transformation of pollutants from emission sources to receptors (such as observation points or affected areas).

[0036] Considering only the case where atmospheric transport and chemical reactions are linear, i.e., the average column concentration of greenhouse gases is linearly related to surface emission fluxes, then the average column concentration of greenhouse gases... and surface emission flux It can be represented as: In the formula, This represents the measured concentration vector (observation vector) at each observation point at each observation time. This is the transport operator between the average column concentration of greenhouse gases at the observation points and the surface emission flux; it represents the contribution coefficient of different grid points of greenhouse gas emissions to the concentration at the observation points. A 3D matrix This is the time series observed at the observation point. It is the total number of grid points emitted; the value to be determined is... yes A 3D state vector, physically representing the ground arrangement at each grid point; yes A dimensional vector, physically representing the simulated concentration at each observation point at each observation time, This represents the observation error vector.

[0037] It is understandable that in the above formula This is the source-receptor relationship matrix, used to describe the relationship between surface emission fluxes and the average column concentration of gases at observation points. Specifically, the transport operator... It is a matrix whose elements Indicates the first The unit emission flux of the emission grid point affects the first... The contribution of the average concentration of the gas column at each observation point.

[0038] Here, the observation point can be located on the ground, a tower, at sea, on an airplane, or on a satellite. The specific location depends on the monitoring target and the application scenario. Ground observation points can be in cities, rural areas, mountains, or background stations, etc. This embodiment of the invention does not make specific limitations on this.

[0039] Specifically, since greenhouse gases are mostly long-lived gases, the observed concentration y of greenhouse gases is not solely related to recent surface emissions. Therefore, the above relationship can be further decomposed into three sources: the background concentration of greenhouse gases in the atmosphere, the emission contribution concentration within the study area, and the emission contribution concentration outside the study area.

[0040] Accordingly, the region corresponding to the observation point can be taken as a nested grid, and the transport operator can be decomposed into a first transport operator of the first flux within the nested grid, a second transport operator of the second flux outside the nested grid, and a third transport operator of the gas background concentration field.

[0041] Here, the first flux refers to the gas flux generated by emission sources within the nested grid, and the second flux refers to the gas flux generated by emission sources outside the nested grid.

[0042] Step 140: Based on the first transport operator, the second transport operator, and the third transport operator, construct an objective function, and determine the gas emission flux corresponding to the observation point according to the objective function.

[0043] Specifically, after obtaining the first transport operator, the second transport operator, and the third transport operator, an objective function can be constructed based on the first transport operator, the second transport operator, and the gas emission flux corresponding to the observation point can be determined according to the objective function.

[0044] The method provided in this invention first acquires the atmospheric background gas concentration field to provide a benchmark reference for determining gas emission fluxes and eliminates interference from natural factors. Second, it uses a Lagrange particle diffusion model to simulate the actual diffusion process. This model can adapt to complex terrain and environmental conditions and accurately determine the transport operator between the average gas column concentration and the ground emission flux at the observation point. Third, it decomposes the transport operator into three parts: a first transport operator for the first flux within a nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the gas background concentration field. This decomposition not only refines the spatial resolution and distinguishes emission sources at different spatial scales but also comprehensively considers the influence of the background concentration field. Finally, based on the first, second, and third transport operators, an objective function is constructed, and the gas emission flux corresponding to the observation point is determined by optimizing the objective function, thereby significantly improving the accuracy and precision of gas emission flux determination.

[0045] Based on the above embodiments, step 140, which involves constructing the objective function based on the first transmission operator, the second transmission operator, and the third transmission operator, includes: Step 141: Based on the first transport operator, the second transport operator, and the third transport operator, determine the simulated greenhouse gas mixing ratio at the observation point; Step 142: Based on the simulated mixing ratio of greenhouse gases and the a priori column average mixing ratio of greenhouse gases, determine the column average mixing ratio of greenhouse gases; Step 143: Construct the objective function based on the average mixing ratio of the greenhouse gas column.

[0046] Specifically, the simulated mixing ratio of greenhouse gases at the observation point can be determined based on the first, second, and third transport operators. Here, the simulated mixing ratio of greenhouse gases refers to the concentration value of greenhouse gases at the observation point calculated through a mathematical model.

[0047] Specifically, the first mixing ratio can be determined based on the first transport operator and the first flux, and the first mixing ratio can be expressed as: Then, based on the second transport operator and the second flux, the second mixing ratio is determined, which can be expressed as: Finally, the third mixing ratio can be determined based on the third transport operator and the gas background concentration field. The third mixing ratio can be expressed as: Based on the first mixing ratio, the second mixing ratio, and the third mixing ratio, the simulated mixing ratio of greenhouse gases is determined.

[0048] The formula for determining the simulated greenhouse gas mixing ratio based on the first mixing ratio, the second mixing ratio, and the third mixing ratio is as follows: (1) in, Indicates the simulated mixing ratio of greenhouse gases. This represents the first transport operator for the first flux within the nested grid. Indicates the first flux. The second transport operator represents the second flux outside the nested mesh. Indicates the second flux. The third transport operator represents the gas background concentration field. This represents the background concentration field.

[0049] However, in the next step of greenhouse gas flux inversion, it is necessary to compare the forward modeling value with the observed column-mean mixing ratio. Therefore, it is necessary to further perform atmospheric column-mean calculation on the forward modeling value. That is, after obtaining the simulated greenhouse gas mixing ratio, the column-mean mixing ratio of greenhouse gases can be determined based on the simulated greenhouse gas mixing ratio and the prior column-mean mixing ratio of greenhouse gases, as shown in the following formula: + (2) Substituting formula (1) into formula (2), we get: in, This represents the average mixing ratio of greenhouse gases in the column. Indicates the simulated mixing ratio of greenhouse gases. This represents the a priori column average mixing ratio of greenhouse gases. Indicates the a priori stratified mixing ratio of greenhouse gases; The average kernel function reflects the influence of atmospheric composition at different altitudes on the inversion, and its value fluctuates around 1.0. The pressure weighting function reflects the pressure weights at different altitudes, and their sum is approximately 1.0. The column convolution method is applicable to greenhouse gas products solved by various optimization algorithms with average kernels and full pressure functions.

[0050] Here, the simulated greenhouse gas mixing ratio refers to the concentration of greenhouse gases in the atmosphere calculated by the model. The a priori column average mixing ratio refers to the average mixing ratio of greenhouse gases in the atmospheric column calculated based on prior knowledge (such as background concentration, emission source distribution, etc.) before performing the greenhouse gas column average concentration inversion. The greenhouse gas column average mixing ratio refers to the average concentration of greenhouse gases in the atmospheric column.

[0051] Finally, after obtaining the average mixing ratio of the greenhouse gas column, an objective function can be constructed based on the average mixing ratio of the greenhouse gas column.

[0052] The method provided in this invention firstly improves the simulation accuracy by accurately describing the impact of different emission sources on observation points through a transmission operator. Secondly, it determines the greenhouse gas column average mixing ratio based on the simulated greenhouse gas mixing ratio and the prior column average mixing ratio, which can reduce uncertainties in the inversion process. Especially when sufficient observation data is lacking, the prior information reflected by the greenhouse gas column average mixing ratio provides reasonable initial values, helping the inversion algorithm converge better. Furthermore, constructing and optimizing the objective function based on the greenhouse gas column average mixing ratio can reduce computational complexity and improve computational efficiency.

[0053] Based on the above embodiments, step 143 includes: Step 1431: Obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector; Step 1432: Construct the objective function based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column.

[0054] Specifically, the core of retrieving greenhouse gas fluxes from observed atmospheric greenhouse gas mixing ratios is solving for the state vector in the forward simulation. However, because the number of effective observations is less than the dimension of the state vector (i.e., the number of grid points), the number of governing equations is greater than the number of unknowns, meaning the inversion equations are overdetermined. To solve the overdetermined equation problem, Bayesian theory is introduced to achieve the optimal solution.

[0055] First, obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector. The prior flux error covariance matrix reflects the prior flux. The uncertainty describes the error distribution and correlation of the prior flux estimate. The observation error covariance matrix is ​​used to reflect the observation vector. The uncertainty describes the distribution and correlation of observation errors.

[0056] Then, based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of greenhouse gases, the objective function can be constructed as follows: In the formula, Describe the objective function. It is a cost function constructed based on a Bayesian framework. This represents the state vector that needs to be solved, i.e. Indicates greenhouse gas flux, This represents the prior flux error covariance matrix. It is the observation error covariance matrix. It is the observation vector, i.e. This represents the measured concentration vector at each observation point at each observation time. Represents the observation operator. This represents the average mixing ratio of the greenhouse gas column obtained above. This represents the a priori flux, which is a measure of the prior flux. Prior estimates.

[0057] Here, based on the preceding description, the observation vector is determined based on the transport operator and the observation error vector.

[0058] It should be noted that prior flux is the preprocessing of emission data that will ultimately be used for carbon flux distribution optimization inversion. This preprocessing includes monthly variable flux calculation, spatiotemporal interpolation, and unit conversion, and the final output is data in a file format readable by carbon flux distribution optimization inversion.

[0059] The method provided in this invention constructs an objective function based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column, which can significantly improve the accuracy and reliability of greenhouse gas emission flux inversion. This construction method combines prior knowledge and observational data, thereby reducing errors and improving the model's adaptability and computational efficiency.

[0060] Based on the above embodiments, step 140 includes: Step 140-1: Determine the feature matrix from the state vector to the observation vector in the objective function; Step 140-2: Iteratively solve the feature matrix to obtain the optimal solution, and use the optimal solution as the gas emission flux corresponding to the observation point.

[0061] Specifically, the objective function is rearranged using its first derivative: in, for The inverse matrix can be obtained from the matrix using Cholesky decomposition. The state vector can be obtained through iterative solution. That is, the optimal solution for the posterior greenhouse gas flux distribution. This indicates that the prior state vector The value mapped to the observation space represents the prior simulated observation value.

[0062] However, directly solving for the eigenvectors leads to a huge computational burden, and an increase in the number of observations causes a dramatic increase in computation time. Therefore, this embodiment of the invention optimizes the matrix operations in solving for the eigenvectors. The iterative solution of the original eigenvectors is as follows: Optimized to: in, This represents the principal eigenvectors of the prior flux error covariance matrix. express The eigenvalue diagonal matrix is ​​used to reflect the magnitude of the variance along the principal direction. express The square root, This represents the state perturbation vector, indicating a change in a certain direction. This represents an intermediate matrix. express Each eigenvector is scaled by the square root of its eigenvalue. This represents the mapping result. Indicates the projection amount, indicating Weighted projection in principal component space Indicates calculation exist Projection in space This indicates that the projection result is scaled according to the square root of the eigenvalue. This indicates that the scaled projection is calculated in... Transpose in space.

[0063] While ensuring computational accuracy, this invention achieves significant performance improvements by optimizing matrix storage and computation processes. Specifically, the matrix storage size only doubles, while the computational load is drastically reduced to one-thousandth of its original size. Furthermore, by introducing coordinate mapping relationships into variable indices, this invention enhances matrix reusability, further improving computational efficiency. In test cases, the zwork variable calculation results remain consistent before and after optimization, but the computation time is significantly reduced by 8 times. In the calculation of the error covariance matrix, by explicitly recoding the complex four-loop and conditional branch logic, the computation time is reduced by 68 times. These optimizations not only improve computational efficiency but also maintain the accuracy of the results, significantly enhancing the overall system performance.

[0064] Understandably, the optimized formula iteratively solves the characteristic matrix to obtain the optimal solution, which is then used as the gas emission flux corresponding to the observation point. This reduces unnecessary intermediate steps and allows for direct calculation. exist The projection in space is then scaled and transposed. This optimization can improve computational efficiency and reduce computational complexity.

[0065] In summary, (1) the present invention uses meteorological field data with higher spatial resolution (0.25°) to drive the Lagrange particle diffusion model, which effectively improves the simulation accuracy of source-acceptor relationship.

[0066] (2) This invention proposes a flux inversion method that can assimilate greenhouse gas column concentration observation data, which is applicable to satellite remote sensing observation and ground-based high-precision observation networks (such as column concentration data provided by TCCON).

[0067] (3) By optimizing the eigenvector solution process and combining variable memory reuse with explicit calculation of the error covariance matrix, this invention significantly reduces the computational overhead and memory requirements in the inversion process, and achieves efficient computation of flux-optimized inversion.

[0068] In one embodiment, the present invention was applied based on ground-based Picarro data and OCO2 satellite remote sensing data from XX City from August 8th to August 17th, 2023. The results showed that the CO2 flux distribution based on satellite observations was generally consistent with the ground-based data, and the values ​​were quite close. Compared with the assimilated in-situ observation data, the CO2 flux distribution of the assimilated satellite observation data for XX City better captured the hotspots of industrial power emissions, exhibiting more accurate spatial heterogeneity. Comparing the inversion results with existing inventories, it was found that the emission inversion results based on satellite observations showed good consistency with the EDGAR inventory, with the inverted value being 95% of the EDGAR emissions, falling within a ±5% error range. In contrast, the ground-based observation inversion result was 86% of the EDGAR. The present invention can assimilate satellite remote sensing data to invert greenhouse gas flux distribution with improved accuracy. Therefore, the method of the present invention has significant scalability.

[0069] The gas emission flux determination apparatus provided by the present invention is described below. The gas emission flux determination apparatus described below can be referred to in correspondence with the gas emission flux determination method described above.

[0070] Based on any of the above embodiments, the present invention provides a gas emission flux determination device. Figure 2 This is a schematic diagram of the gas emission flux determination device provided by the present invention, as shown below. Figure 2 As shown, the device includes: Acquisition unit 210 is used to acquire the background concentration field of gases in the atmosphere; Processing unit 220 is used to average the gas concentration at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located, so as to obtain the average gas column concentration corresponding to the observation point. The decomposition unit 230 is used to determine the transport operator between the average concentration of the gas column and the ground emission flux corresponding to the observation point based on the Lagrange particle diffusion model, and decompose the transport operator into a first transport operator of the first flux within the nested grid, a second transport operator of the second flux outside the nested grid, and a third transport operator of the gas background concentration field. The construction unit 240 is used to construct an objective function based on the first transport operator, the second transport operator, and the third transport operator, and to determine the gas emission flux corresponding to the observation point according to the objective function.

[0071] The apparatus provided in this invention first acquires the atmospheric background gas concentration field to provide a benchmark reference for determining gas emission flux and eliminates interference from natural factors. Second, it uses a Lagrange particle diffusion model to simulate the actual diffusion process. This model can adapt to complex terrain and environmental conditions and accurately determine the transport operator between the average gas column concentration and the ground emission flux at the observation point. Third, it decomposes the transport operator into three parts: a first transport operator for the first flux within a nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the gas background concentration field. This decomposition not only refines the spatial resolution and distinguishes emission sources at different spatial scales but also comprehensively considers the influence of the background concentration field. Finally, based on the first, second, and third transport operators, an objective function is constructed, and the gas emission flux corresponding to the observation point is determined by optimizing the objective function, thereby significantly improving the accuracy and precision of gas emission flux determination.

[0072] Based on any of the above embodiments, the construction unit 240 specifically includes: A simulated mixing ratio determination unit is used to determine the simulated mixing ratio of greenhouse gases at the observation point based on the first transport operator, the second transport operator, and the third transport operator. The average mixing ratio determination unit is used to determine the greenhouse gas column average mixing ratio based on the simulated greenhouse gas mixing ratio and the prior average mixing ratio of the greenhouse gas column. Construct sub-units to build the objective function based on the average mixing ratio of the greenhouse gas column.

[0073] Based on any of the above embodiments, the unit for determining the simulated mixing ratio is specifically used for: Based on the first transmission operator and the first flux, a first mixing ratio is determined; The second mixing ratio is determined based on the second transport operator and the second flux; Based on the third transport operator and the gas background concentration field, a third mixing ratio is determined, and based on the first mixing ratio, the second mixing ratio, and the third mixing ratio, the simulated greenhouse gas mixing ratio is determined.

[0074] Based on any of the above embodiments, the construction subunit is specifically used for: Obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector; The objective function is constructed based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column.

[0075] Based on any of the above embodiments, the observation vector is determined based on the transmission operator and the observation error vector.

[0076] Based on any of the above embodiments, the construction unit 240 is specifically used for: Determine the feature matrix from the state vector to the observation vector in the objective function; The feature matrix is ​​iteratively solved to obtain the optimal solution, which is then used as the gas emission flux corresponding to the observation point.

[0077] Based on any of the above embodiments, a gas background concentration field acquisition unit is further included, wherein the gas background concentration field acquisition unit is specifically used for: Obtain the original gas background concentration field; Determine the deviation correction factor between the original gas background concentration field and the observations from global background stations; Based on the deviation correction factor, the original gas background concentration field is corrected to obtain the gas background concentration field.

[0078] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a method for determining greenhouse gas emission fluxes. This method includes: acquiring a background gas concentration field in the atmosphere; averaging the gas concentrations at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located to obtain the average gas column concentration corresponding to the observation point; determining a transport operator between the average gas column concentration corresponding to the observation point and the ground emission flux based on a Lagrange particle diffusion model, and decomposing the transport operator into a first transport operator for a first flux within the nested grid, a second transport operator for a second flux outside the nested grid, and a third transport operator for the background gas concentration field; constructing an objective function based on the first, second, and third transport operators, and determining the gas emission flux corresponding to the observation point according to the objective function.

[0079] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the greenhouse gas emission flux determination method provided by the above methods. The method includes: obtaining the background concentration field of gases in the atmosphere; averaging the gas concentrations at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located to obtain the average concentration of the gas column corresponding to the observation point; determining the transport operator between the average concentration of the gas column corresponding to the observation point and the ground emission flux based on the Lagrange particle diffusion model, and decomposing the transport operator into a first transport operator for a first flux within the nested grid, a second transport operator for a second flux outside the nested grid, and a third transport operator for the background concentration field of gases; constructing an objective function based on the first transport operator, the second transport operator, and the third transport operator, and determining the gas emission flux corresponding to the observation point according to the objective function.

[0081] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for determining greenhouse gas emission flux provided by the methods described above. The method includes: acquiring a background gas concentration field in the atmosphere; averaging the gas concentrations at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located to obtain the average gas column concentration corresponding to the observation point; determining a transport operator between the average gas column concentration corresponding to the observation point and the ground emission flux based on a Lagrange particle diffusion model, and decomposing the transport operator into a first transport operator for a first flux within the nested grid, a second transport operator for a second flux outside the nested grid, and a third transport operator for the background gas concentration field; constructing an objective function based on the first transport operator, the second transport operator, and the third transport operator, and determining the gas emission flux corresponding to the observation point according to the objective function.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining gas emission flux, characterized in that, include: Obtain the background concentration field of gases in the atmosphere; The gas concentration at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located is averaged to obtain the average gas column concentration corresponding to the observation point. Based on the Lagrange particle diffusion model, the transport operator between the average concentration of the gas column and the ground emission flux corresponding to the observation point is determined, and the transport operator is decomposed into a first transport operator for the first flux within the nested grid, a second transport operator for the second flux outside the nested grid, and a third transport operator for the gas background concentration field. Based on the first transport operator, the second transport operator, and the third transport operator, an objective function is constructed, and the gas emission flux corresponding to the observation point is determined according to the objective function. The construction of the objective function based on the first transmission operator, the second transmission operator, and the third transmission operator includes: Based on the first transport operator, the second transport operator, and the third transport operator, the simulated mixing ratio of greenhouse gases at the observation point is determined; Based on the simulated mixing ratio of greenhouse gases and the prior average mixing ratio of greenhouse gas columns, the average mixing ratio of greenhouse gases columns is determined; The objective function is constructed based on the average mixing ratio of the greenhouse gas column. The objective function, constructed based on the average mixing ratio of the greenhouse gas column, includes: Obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector; The objective function is constructed based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column.

2. The method for determining gas emission flux according to claim 1, characterized in that, The step of determining the simulated greenhouse gas mixing ratio at the observation point based on the first transport operator, the second transport operator, and the third transport operator includes: Based on the first transmission operator and the first flux, a first mixing ratio is determined; The second mixing ratio is determined based on the second transport operator and the second flux; Based on the third transport operator and the gas background concentration field, a third mixing ratio is determined, and based on the first mixing ratio, the second mixing ratio, and the third mixing ratio, the simulated greenhouse gas mixing ratio is determined.

3. The method for determining gas emission flux according to claim 1, characterized in that, The observation vector is determined based on the transmission operator and the observation error vector.

4. The method for determining gas emission flux according to any one of claims 1 to 3, characterized in that, The step of determining the gas emission flux corresponding to the observation point based on the objective function includes: Determine the feature matrix from the state vector to the observation vector in the objective function; The feature matrix is ​​iteratively solved to obtain the optimal solution, which is then used as the gas emission flux corresponding to the observation point.

5. The method for determining gas emission flux according to any one of claims 1 to 3, characterized in that, The steps for obtaining the gas background concentration field include: Obtain the original gas background concentration field; Determine the deviation correction factor between the original gas background concentration field and the observations from global background stations; Based on the deviation correction factor, the original gas background concentration field is corrected to obtain the gas background concentration field.

6. A device for determining gas emission flux, characterized in that, include: Acquisition unit, used to acquire the background concentration field of gases in the atmosphere; The processing unit is used to average the gas concentration at multiple points corresponding to the longitudinal profile of the nested grid where the observation point is located, so as to obtain the average gas column concentration corresponding to the observation point. The decomposition unit is used to determine the transport operator between the average concentration of the gas column and the ground emission flux corresponding to the observation point based on the Lagrange particle diffusion model, and decompose the transport operator into a first transport operator of the first flux within the nested grid, a second transport operator of the second flux outside the nested grid, and a third transport operator of the gas background concentration field. The construction unit is used to construct an objective function based on the first transmission operator, the second transmission operator, and the third transmission operator, and to determine the gas emission flux corresponding to the observation point according to the objective function; The building unit specifically includes: A simulated mixing ratio determination unit is used to determine the simulated mixing ratio of greenhouse gases at the observation point based on the first transport operator, the second transport operator, and the third transport operator. The average mixing ratio determination unit is used to determine the average mixing ratio of greenhouse gases based on the simulated mixing ratio of greenhouse gases and the prior average mixing ratio of the greenhouse gas column; A function unit is constructed to construct the objective function based on the average mixing ratio of the greenhouse gas column. The construction function unit is specifically used for: Obtain the prior flux error covariance matrix, the observation error covariance matrix, and the observation vector; The objective function is constructed based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the average mixing ratio of the greenhouse gas column.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the gas emission flux determination method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the gas emission flux determination method as described in any one of claims 1 to 5.

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