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

Abstract

The invention provides a greenhouse gas emission flux determination method and device, electronic equipment and a storage medium. The method comprises the steps that a gas background concentration field provides a datum reference for determination of gas emission flux; the Lagrange particle diffusion model simulates a real diffusion process, and a transmission operator between the average gas column concentration of an observation point and the ground emission flux is determined; a transmission operator is decomposed into three parts: a first transmission operator of a first flux in a nested grid, a second transmission operator of a second flux outside the nested grid, and a third transmission operator of a gas background concentration field. According to the decomposition mode, the spatial resolution is refined, emission sources of different spatial scales are distinguished, and the influence of a background concentration field is comprehensively considered. Based on the first transmission operator, the second transmission operator and the third transmission operator, the objective function is constructed, the objective function is optimized to determine the gas emission flux corresponding to the observation point, and the accuracy of determining the gas emission flux is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas emission flux determination, and in particular to a greenhouse gas emission flux determination method and device, electronic equipment and a storage medium. BACKGROUND

[0002] With the increasing global concern about climate change, accurately determining carbon emission flux has become a key task. At present, the "bottom-up" method and the "top-down" atmospheric inversion method are mainly used. The "bottom-up" method estimates carbon emissions through energy consumption and emission factors, but there are problems such as inconsistent energy statistics, dependence on global default values, and lagging data updates, resulting in large uncertainty in the estimated results. The "top-down" method uses atmospheric concentration observations to invert the surface flux, and the Euler model atmospheric transmission model has the defects of resolution solidification and complex model construction. Although the Lagrangian model has advantages, the supporting inversion framework FLEXINVERT is only applicable to limited ground station monitoring, cannot assimilate greenhouse gas column concentration observation data, and cannot fully utilize high temporal and spatial resolution observation information. In addition, the ensemble filter method has large computational load, the variational method has high development difficulty, and the Bayesian method has high computational cost in high-resolution observation scenarios, losing its advantage in a small amount of observation.

[0003] Therefore, the prior art cannot meet the demand for high-precision, high-temporal and spatial resolution carbon emission monitoring. SUMMARY

[0004] The present application provides a greenhouse gas emission flux determination method, device, electronic equipment and storage medium to solve the defect that the prior art cannot meet the demand for high-precision, high-temporal and spatial resolution carbon emission monitoring.

[0005] The present application provides a gas emission flux determination method, comprising the following steps.

[0006] Obtaining a gas background concentration field in the atmosphere; Averaging the multi-point gas concentration corresponding to the longitudinal profile of the nested grid where the observation point is located to obtain the gas column average concentration corresponding to the observation point; Based on the Lagrangian particle diffusion model, determining the transport operator between the gas column average concentration corresponding to the observation point and the ground emission flux, and decomposing the transport operator into a first transport operator of a first flux within the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background concentration field; Based on the first transport operator, the second transport operator and the third transport operator, constructing an objective function, and determining the gas emission flux corresponding to the observation point according to the objective function.

[0007] The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps:

[0008] The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps:

[0009] The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps:

[0010] The method for determining the gas emission flux provided by the application comprises the following steps:

[0011] The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps:

[0012] The method for determining the gas emission flux provided by the application comprises the following steps: The method for determining the gas emission flux provided by the application comprises the following steps: Determining a deviation correction factor between the original gas background concentration field and the global background station observation value; The original gas background concentration field is corrected based on the deviation correction factor to obtain the gas background concentration field.

[0013] The present invention also provides a gas emission flux determination device, comprising the following units: An acquisition unit, used for acquiring the gas background concentration field in the atmosphere; a processing unit for averaging the gas concentrations at multiple points corresponding to the longitudinal section of the nested grid where the observation point is located, to obtain an average gas column concentration corresponding to the observation point; a decomposition unit, configured to determine a transmission operator between the average gas column concentration corresponding to the observation point and the ground emission flux based on a Lagrangian particle diffusion model, and decompose the transmission operator into a first transmission operator of the first flux within the nested grid, a second transmission operator of the second flux outside the nested grid, and a third transmission operator of the gas background concentration field; A 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 determine the gas emission flux corresponding to the observation point according to the objective function.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for determining the gas emission flux as described above is implemented.

[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 any of the above-described methods for determining gas emission flux.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for determining gas emission flux.

[0017] The greenhouse gas emission flux determination method, device, electronic equipment and storage medium provided by the present application first acquire the gas background concentration field in the atmosphere, which provides a reference for the determination of the gas emission flux and eliminates the interference of natural factors. Secondly, the real diffusion process is simulated by using the Lagrangian particle diffusion model, which can adapt to complex terrain and environmental conditions and accurately determine the transmission operator between the gas column average concentration of the observation point and the ground emission flux. Thirdly, the transmission operator is divided into three parts: the first transmission operator of the first flux in the nested grid, the second transmission operator of the second flux outside the nested grid, and the third transmission operator of the gas background concentration field. This decomposition method not only refines the spatial resolution and distinguishes the emission sources of different spatial scales, but also considers the influence of the background concentration field. Finally, based on the first transmission operator, the second transmission operator and the third transmission operator, a target function is constructed, and the corresponding gas emission flux of the observation point is determined by optimizing the target function, thereby significantly improving the accuracy and precision of the determination of the gas emission flux. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 is a flowchart of the gas emission flux determination method provided by the present application.

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

[0021] Figure 3 is a structural schematic diagram of the electronic equipment provided by the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present application more clear, the technical scheme in the present application will be described clearly and completely in the following combined with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0023] The terms "first", "second", and the like in the present disclosure are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind.

[0024] Figure 1 is a flowchart of the gas emission flux determination method provided by the present application, as shown in Figure 1 The method comprises the following steps: Step 110, obtaining a gas background concentration field in the atmosphere.

[0025] Specifically, first, a gas background concentration field in the atmosphere is obtained, wherein the gas background concentration field can be a bias correction factor between an original gas background concentration field and a global background station observation value, and the original gas background concentration field is corrected to obtain.

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

[0027] However, the greenhouse gas mixing ratio in the simulated greenhouse gas global background concentration field (gas background concentration field) may be greatly affected by short-term ground emissions. In order to eliminate this influence, the global background station observation value obtained by AirCore observation is needed in the embodiment of the present application to correct the bias of the three-dimensional greenhouse gas mixing ratio obtained by model simulation. In this way, the high-level greenhouse gas value that is independent of ground influence can be extracted, so as to ensure that the background concentration field is free from the influence of short-term near-surface emissions, and the accuracy and reliability of the gas background concentration field are improved.

[0028] Step 120, averaging the multi-point gas concentration corresponding to the longitudinal section of the nested grid where the observation point is located to obtain the gas column average concentration corresponding to the observation point.

[0029] Specifically, considering that the gas concentration of a single observation point can be affected by local factors (such as local emission sources, terrain effects, meteorological conditions, etc.), it cannot well represent the gas concentration conditions of the entire region. Through the averaging process, the influence of local anomalies on the overall evaluation can be reduced, making the results more representative. Moreover, the gas concentration in the atmosphere is continuously distributed in space, and single-point measurement cannot reflect this continuity. The averaging process can better simulate this continuity and provide more accurate regional gas concentration distribution.

[0030] Correspondingly, the multi-point gas concentrations corresponding to the longitudinal profile of the nested grid where the observation point is located are averaged to obtain the gas column average concentration corresponding to the observation point. Here, the gas column average concentration represents the average value of the gas concentration on a certain vertical path. It reflects the overall distribution of the gas on the path, rather than the local concentration at a certain height.

[0031] It can be understood that in the embodiments of the present application, in order to accurately evaluate the gas column average concentration of the observation point, an innovative averaging method is adopted. Specifically, in the embodiments of the present application, the longitudinal profile of the nested grid where the observation point is located is focused on, and the gas concentration data of multiple points at different heights on the longitudinal profile is collected. By averaging the multi-point gas concentration data, the gas column average concentration corresponding to the observation point is calculated. This method has wide applicability and can be used for near-surface observation and satellite whole-layer observation. However, the core breakthrough and innovation in the embodiments of the present application is that it can be successfully applied to the field of satellite whole-layer observation. In satellite whole-layer observation, this method can effectively integrate a large amount of gas concentration information obtained from the ground to the top of the atmosphere by satellite remote sensing, thereby providing strong support for comprehensively and accurately inverting the whole-layer distribution of gases in the atmosphere, greatly expanding the application range of gas column average concentration in atmospheric monitoring and research, and laying a solid foundation for in-depth exploration in related fields.

[0032] At step 130, a transport operator between the average concentration of the gas column corresponding to the observation point and the ground emission flux is determined based on the Lagrangian particle diffusion model, and the transport operator is decomposed into a first transport operator of a first flux in the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background concentration field. Specifically, the Lagrangian particle diffusion model is used to simulate and trace back the greenhouse gas. The open source FLEXPART can only use old meteorological data, and the spatial resolution is limited to 1°, which is a problem of most open source models. In the embodiment of the present application, the spatial resolution of the meteorological field read by the model is improved, and the maximum number of calculation grid points solidified by the model is expanded, so that the model can be driven by reanalysis data with a spatial resolution of 0.25°, and the simulation of the sensitive relationship between the gas concentration of the observation point and the ground emission flux is realized. That is, the transport operator between the gas concentration of the observation point and the ground emission flux can be determined based on the Lagrangian particle diffusion model.

[0033] In the embodiment of the present application, the Lagrangian particle diffusion model is used to calculate the running trajectory of the greenhouse gas particle model over time by using a simple "zero acceleration" scheme, and the formula is as follows: In the formula, is the time, is the time increment, is the position vector, is the initial position of the greenhouse gas particle model, is the first guessed position, is the wind vector composed of the grid scale wind , the turbulent wind fluctuation and the mesoscale wind fluctuation .

[0034] It can be understood that, by simulating the movement of the greenhouse gas by the Lagrangian particle diffusion model, the atmospheric loss on the time scale can be ignored, so the source-receptor relationship can be represented as being proportional to the average residence time of the reverse trajectory in the grid cell considered. For an observation point in a given spatio-temporal grid cell and an emission flux grid, the expression of the source-receptor relationship is as follows: In the formula, the unit of the source-receptor relationship is residence time x volume per unit mass, is the concentration of the grid cell, is the number of reverse trajectories, is the residence time of the trajectory in the spatio-temporal grid cell . is the air density in the grid cell. Thus, 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 process between the emission source and the receptor (such as the observation point or the affected area).

[0036] Only considering the linear case of atmospheric transport and chemical reaction, i.e. the column-averaged concentration of greenhouse gas is linearly related to the surface emission flux. Then, the column-averaged concentration of greenhouse gas and the surface emission flux can be expressed as: In the formula, represents the measured concentration vector (observation vector) of each observation time of the observation point, is the transport operator between the column-averaged concentration of the observation point and the surface emission flux, i.e. the contribution coefficient of different grid points of greenhouse gas emission to the concentration of the observation point, which is a dimensional matrix, is the time sequence observed by the observation point, is the total number of emission grid points; the to-be-solved is a dimensional state vector, and the physical meaning is the surface emission of each grid point; is a dimensional vector, and the physical meaning is the simulated concentration of each observation time of the observation point, represents the observation error vector.

[0037] It can be understood that in the above formula is the source-receptor relationship matrix, which is used to describe the relationship between the surface emission flux and the column-averaged concentration of the observation point. Specifically, the transport operator is a matrix, and the element of the matrix represents the contribution of the unit emission flux of the i-th emission grid point to the column-averaged concentration of the j-th observation point.

[0038] Here, the observation point can be located on the ground, a high tower, the sea, an airplane or a satellite, and the specific location depends on the monitoring target and the application scenario. The ground observation point can be a city, a rural area, a mountain or a background station, and the embodiments of the present application do not make specific limitations.

[0039] Specifically, since the greenhouse gas is mostly a long-lived gas, the observed concentration y of the greenhouse gas is not only related to the recent surface emission. Therefore, the above relationship can be further decomposed into three sources, which are the background concentration of the greenhouse gas in the atmosphere, the emission contribution concentration in the research area and the emission contribution concentration outside the research area.​​

[0040] Correspondingly, the area 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 a first flux in the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of a gas background concentration field.

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

[0042] Step 140, based on the first transport operator, the second transport operator and the third transport operator, constructing a target function, determining the gas emission flux corresponding to the observation point according to the target function.

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

[0044] The method provided by the embodiment of the application first acquires the gas background concentration field in the atmosphere, provides a benchmark reference for the determination of the gas emission flux, and eliminates the interference of natural factors. Second, the real diffusion process is simulated by using the Lagrangian particle diffusion model, which can adapt to complex terrain and environmental conditions, and accurately determine the transport operator between the gas column average concentration of the observation point and the ground emission flux. Third, the transport operator is decomposed into three parts: the first transport operator of the first flux in the nested grid, the second transport operator of the second flux outside the nested grid, and the third transport operator of the gas background concentration field. This decomposition not only refines the spatial resolution and distinguishes the emission sources of different spatial scales, but also considers the influence of the background concentration field. Finally, based on the first transport operator, the second transport operator and the third transport operator, the target function is constructed, and the gas emission flux corresponding to the observation point is determined by optimizing the target function, thereby significantly improving the accuracy and precision of the determination of the gas emission flux.

[0045] Based on the above embodiment, in step 140, the target function is constructed based on the first transport operator, the second transport operator and the third transport operator, which includes: Step 141, based on the first transport operator, the second transport operator and the third transport operator, determining the greenhouse gas simulation mixing ratio of the observation point; Step 142, based on the greenhouse gas simulation mixing ratio and the prior column average mixing ratio of the greenhouse gas, determining the greenhouse gas column average mixing ratio; Step 143, based on the greenhouse gas column average mixing ratio, constructing the target function.

[0046] Specifically, the greenhouse gas simulation mixing ratio of the observation point can be determined based on the first transport operator, the second transport operator and the third transport operator. Here, the greenhouse gas simulation mixing ratio refers to the concentration value of the greenhouse gas at the observation point calculated by the 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 The second mixing ratio can be determined based on the second transport operator and the second flux, and the second mixing ratio can be expressed as The third mixing ratio can be determined based on the third transport operator and the gas background concentration field, and the third mixing ratio can be expressed as And the greenhouse gas simulation mixing ratio is determined based on the first mixing ratio, the second mixing ratio and the third mixing ratio.

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

[0049] However, in the next step of greenhouse gas flux inversion, the forward simulation value needs to be compared with the observed column average mixing ratio. Therefore, further atmospheric column average operation needs to be performed on the forward simulation value. That is, after obtaining the greenhouse gas simulation mixing ratio, the greenhouse gas column average mixing ratio can be determined based on the greenhouse gas simulation mixing ratio and the greenhouse gas prior column average mixing ratio, and the formula is as follows: + (2) Substitute formula (1) into formula (2) to obtain: Wherein, represents the greenhouse gas column average mixing ratio, represents the greenhouse gas simulation mixing ratio, represents the greenhouse gas prior column average mixing ratio, represents the greenhouse gas prior layered mixing ratio; The average kernel function reflects the influence of the atmospheric composition of different altitudes on the inversion, and its value fluctuates around 1.0; The pressure weight function reflects the pressure weight of different altitudes, and the sum is about 1.0. The column convolution method can be applied to the greenhouse gas products solved by various optimization algorithms with average kernel and pressure weight functions.

[0050] Here, the greenhouse gas simulation mixing ratio refers to the concentration value of the greenhouse gas in the atmosphere calculated by the model. The priori column average mixing ratio of the greenhouse gas refers to the average mixing ratio of the greenhouse gas in the atmospheric column calculated according to the priori knowledge (such as background concentration, emission source distribution, etc.) before the inversion of the column average concentration of the greenhouse gas. The column average mixing ratio of the greenhouse gas refers to the average concentration value of the greenhouse gas in the atmospheric column.

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

[0052] The method provided by the embodiment of the present application first accurately describes the influence of different emission sources on the observation point through the transmission operator, thereby improving the simulation accuracy. Secondly, the column average mixing ratio of the greenhouse gas is determined based on the greenhouse gas simulation mixing ratio and the priori column average mixing ratio of the greenhouse gas, which can reduce the uncertainty in the inversion process. Especially when there is a lack of sufficient observation data, the priori information reflected by the priori column average mixing ratio of the greenhouse gas provides a reasonable initial value, which helps the inversion algorithm to converge better. In addition, the target function is constructed based on the column average mixing ratio of the greenhouse gas and optimized, which can reduce the calculation complexity and improve the calculation efficiency.

[0053] Based on the above embodiment, step 143 comprises: Step 1431, acquiring a priori flux error covariance matrix, an observation error covariance matrix and an observation vector; Step 1432, constructing the target function based on the priori flux error covariance matrix, the observation error covariance matrix, the observation vector and the column average mixing ratio of the greenhouse gas.

[0054] Specifically, the core of the inversion of the greenhouse gas flux from the observed mixing ratio of the greenhouse gas in the atmosphere is to solve the state vector However, since the number of effective observations is less than the dimension of the state vector (i.e. the number of emission grid points), the number of control equations is greater than the number of unknowns, that is, the inversion equation is an overdetermined equation. In the face of the problem of solving the overdetermined equation, the optimal solution is realized by introducing the Bayesian theory.

[0055] Firstly, a priori flux error covariance matrix, an observation error covariance matrix and an observation vector are acquired, wherein the priori flux error covariance matrix is used to reflect the priori flux The uncertainty of the prior flux error covariance matrix describes the error distribution and correlation of the prior flux estimation. The observation error covariance matrix is used to reflect the uncertainty of the observation vector The observation error covariance matrix is used to reflect the uncertainty of the observation vector

[0056] Then, the objective function can be constructed based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the greenhouse gas column average mixing ratio, and the formula is as follows: In the formula, denotes the objective function, is a cost function constructed based on a Bayesian framework, denotes a state vector to be solved, that is, denotes a greenhouse gas flux, denotes the prior flux error covariance matrix, is the observation error covariance matrix, is an observation vector, that is, denotes a measured concentration vector of each observation time of the observation point, denotes an observation operator, that is, the greenhouse gas column average mixing ratio obtained above, denotes a prior flux, which is a prior estimation of .

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

[0058] It should be noted that the prior flux is to preprocess the emission data finally used for carbon flux distribution optimization inversion, and to perform monthly variable flux calculation, spatial and temporal interpolation, unit conversion and other processing on the emission data, and finally output data in a file format readable by carbon flux distribution optimization inversion.

[0059] The method provided by the embodiment of the present application can significantly improve the accuracy and reliability of greenhouse gas emission flux inversion by constructing an objective function based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector, and the greenhouse gas column average mixing ratio. This construction method combines prior knowledge and observation data, thereby reducing errors and improving the adaptability and computational efficiency of the model.

[0060] Based on the above embodiment, step 140 includes: Step 140-1, determining a feature matrix of the state vector to the observation vector in the objective function; Step 140-2, iteratively solving the feature matrix to obtain an optimal solution, and taking the optimal solution as the gas emission flux corresponding to the observation point.

[0061] Specifically, the objective function is rearranged by the first-order derivative to obtain: in, for The inverse matrix of the matrix can be obtained by Cholesky decomposition. By iterative solution, the state vector can be obtained , which is the optimal solution of the posterior greenhouse gas flux distribution. Represents the prior state vector The value after mapping to the observation space represents the prior simulated observation value.

[0062] However, directly solving the eigenvector will result in a huge amount of calculation, and the increase in the number of observations will lead to a sharp increase in the calculation time. Therefore, in the embodiment of the present invention, the matrix operation in solving the eigenvector is optimized. The iterative solution in the original eigenvector is: Optimized to: in, represents the principal eigenvector of the prior flux error covariance matrix, express The eigenvalue diagonal matrix is ​​used to reflect the variance in the main direction. express The square root of Represents the state disturbance vector, indicating a change in a certain direction, represents an intermediate matrix, express Each eigenvector of is scaled by the square root of its eigenvalue, Indicates the mapping result, Represents the projection amount, Weighted projection in principal component space, Represents calculation exist Projection in space, Indicates that the projection result is scaled by the square root of the eigenvalue. Indicates that the projection after calculation of scaling is Transpose in space.

[0063] Under the premise of ensuring the calculation accuracy, the application realizes significant performance improvement by optimizing the matrix storage and calculation process. Specifically, the matrix storage size only increases by two times, while the calculation amount is greatly reduced to one thousandth of the original. In addition, by introducing the coordinate mapping relationship in the variable index, the application enhances the reusability of the matrix, further improving the calculation efficiency. In the test example, the calculation results of the zwork variable before and after optimization remain consistent, but the calculation time is significantly shortened by 8 times. In the calculation of the error covariance matrix, by explicitly recoding the complex 4-way loop and conditional branch logic, the calculation time is greatly reduced by 68 times. These optimization measures not only improve the calculation efficiency, but also maintain the accuracy of the results, significantly improving the overall performance of the system.

[0064] It can be understood that the optimized formula iteratively solves the characteristic matrix to obtain the optimal solution, and the optimal solution is taken as the gas emission flux corresponding to the observation point, thereby reducing unnecessary intermediate steps and directly calculating In space, and then scaling and transposing. This optimization can improve the calculation efficiency and reduce the calculation complexity.

[0065] In summary, (1) the application uses higher spatial resolution meteorological field data (0.25°) to drive the Lagrangian particle diffusion model, effectively improving the simulation accuracy of the source-receptor relationship.

[0066] (2) The application proposes a flux inversion method that can assimilate greenhouse gas column concentration observation data, which is suitable for satellite remote sensing observation and ground-based high-precision observation network (such as column concentration data provided by TCCON).

[0067] (3) The application optimizes the eigenvector solving process, combines variable memory reuse and explicit calculation of the error covariance matrix, significantly reduces the calculation overhead and memory requirement in the inversion process, and realizes efficient calculation of the flux optimization inversion.

[0068] In an embodiment, the application is based on the application of Picarro data and OCO2 satellite remote sensing data from August 8, 2023 to August 17, 2023 in XX City. The results show that the CO2 flux distribution based on satellite observation is consistent with the ground observation as a whole, and the numerical value is close. Compared with the assimilated in-situ observation data, the CO2 flux distribution of the assimilated satellite observation data in XX City better captures the hot spot of industrial dynamic emission, and presents more accurate spatial heterogeneity. Compared with the existing inventory, it is found that the emission inversion result based on satellite observation is consistent with the EDGAR inventory, and the inversion value is 95% of the EDGAR emission, which falls within the error range of ± 5%. In contrast, the ground observation inversion result is 86% of EDGAR. The application can assimilate satellite remote sensing data to invert the greenhouse gas flux distribution, and the accuracy is improved. Therefore, the method of the application has great generalizability.

[0069] The gas emission flux determination device provided by the application is described below, and the gas emission flux determination device described below can be mutually corresponding with the gas emission flux determination method described above.

[0070] Based on any of the above embodiments, the application provides a gas emission flux determination device, Figure 2 is a structural schematic diagram of the gas emission flux determination device provided by the application, as Figure 2 shown, the device comprises: An acquisition unit 210 is configured to acquire a gas background concentration field in the atmosphere. A processing unit 220 is configured to average the gas concentration of the longitudinal section corresponding to the observation point of the nested grid to obtain the gas column average concentration corresponding to the observation point. A decomposition unit 230 is configured to determine the transport operator between the gas column average concentration corresponding to the observation point and the ground emission flux based on the Lagrangian particle diffusion model, and decompose the transport operator into a first transport operator of a first flux in the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background concentration field. A construction unit 240 is configured to construct a target function based on the first transport operator, the second transport operator and the third transport operator, and determine the gas emission flux corresponding to the observation point according to the target function.

[0071] The device provided by the embodiment of the present application firstly acquires the gas background concentration field in the atmosphere, provides a reference for the determination of the gas emission flux, and eliminates the interference of natural factors. Secondly, the real diffusion process is simulated by using the Lagrangian particle diffusion model, the model can adapt to complex terrain and environmental conditions, and accurately determine the transmission operator between the gas column average concentration of the observation point and the ground emission flux. Thirdly, the transmission operator is divided into three parts: the first transmission operator of the first flux in the nested grid, the second transmission operator of the second flux outside the nested grid, and the third transmission operator of the gas background concentration field. This decomposition not only refines the spatial resolution and distinguishes the emission sources of different spatial scales, but also comprehensively considers the influence of the background concentration field. Finally, based on the first transmission operator, the second transmission operator and the third transmission operator, the objective function is constructed, and the corresponding gas emission flux of the observation point is determined by optimizing the objective function, thereby significantly improving the accuracy and precision of the determination of the gas emission flux.

[0072] Based on any of the above embodiments, the construction unit 240 specifically comprises: A determination simulation mixing ratio unit is configured to determine a greenhouse gas simulation mixing ratio of the observation point based on the first transmission operator, the second transmission operator and the third transmission operator. A determination average mixing ratio unit is configured to determine a greenhouse gas column average mixing ratio based on the greenhouse gas simulation mixing ratio and a greenhouse gas prior column average mixing ratio. A construction subunit is configured to construct the objective function based on the greenhouse gas column average mixing ratio.

[0073] Based on any of the above embodiments, the determination simulation mixing ratio unit is specifically configured to: Determine a first mixing ratio based on the first transmission operator and the first flux. Determine a second mixing ratio based on the second transmission operator and the second flux. Determine a third mixing ratio based on the third transmission operator and the gas background concentration field, and determine the greenhouse gas simulation mixing ratio based on the first mixing ratio, the second mixing ratio and the third mixing ratio.

[0074] Based on any of the above embodiments, the construction subunit is specifically configured to: Obtain a prior flux error covariance matrix, an observation error covariance matrix and an observation vector. Construct the objective function based on the prior flux error covariance matrix, the observation error covariance matrix, the observation vector and the greenhouse gas column average mixing ratio.

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

[0076] According to any one of the above embodiments, the construction unit 240 is specifically used for: determining a feature matrix of a state vector in the target function to the observation vector; iteratively solving the feature matrix to obtain an optimal solution, and taking the optimal solution as the gas emission flux corresponding to the observation point.

[0077] According to any one of the above embodiments, further comprising a gas background concentration field acquisition unit, which is specifically used for: acquiring an original gas background concentration field; determining a bias correction factor between the original gas background concentration field and a global background station observation value; correcting the original gas background concentration field based on the bias correction factor to obtain the gas background concentration field.

[0078] Figure 3 An example of an entity structure diagram of an electronic device is shown in Figure 3 The electronic device can include a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can invoke the logical instructions in the memory 330 to execute a greenhouse gas emission flux determination method, which includes: acquiring a gas background concentration field in the atmosphere; averaging the gas concentration of the longitudinal profile corresponding to the multi-point gas concentration of the observation point in the nested grid to obtain the gas column average concentration corresponding to the observation point; determining the transport operator between the gas column average concentration corresponding to the observation point and the ground emission flux based on the Lagrangian particle diffusion model, and decomposing the transport operator into a first transport operator of a first flux in the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background concentration field; constructing a target 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 target function.

[0079] In addition, the logic instructions in the memory 330 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0080] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the greenhouse gas emission flux determination method provided by the above-mentioned methods, the method comprising: obtaining a gas background concentration field in the atmosphere; performing averaging processing on the corresponding multi-point gas concentration of the longitudinal profile of the observation point in the nested grid to obtain the gas column average concentration corresponding to the observation point; determining the transport operator between the gas column average concentration corresponding to the observation point and the ground emission flux based on the Lagrangian particle diffusion model, and decomposing the transport operator into a first transport operator of a first flux in the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background 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.

[0081] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements a greenhouse gas emission flux determination method provided by any of the above methods, the method comprising: obtaining a gas background concentration field in the atmosphere; performing averaging processing on a plurality of gas concentrations corresponding to a longitudinal profile of a nested grid in which an observation point is located, to obtain a gas column average concentration corresponding to the observation point; determining a transport operator between the gas column average concentration corresponding to the observation point and a ground emission flux based on a Lagrangian particle diffusion model, and decomposing the transport operator into a first transport operator of a first flux within the nested grid, a second transport operator of a second flux outside the nested grid, and a third transport operator of the gas background 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, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0083] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment 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 application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining gas emission flux, characterized in that: include: Obtain the gas background concentration field in the atmosphere; Averaging the gas concentrations 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; Determine, based on a Lagrangian particle diffusion model, a transmission operator between the average gas column concentration corresponding to the observation point and the ground emission flux, and decompose the transmission operator into a first transmission operator of the first flux within the nested grid, a second transmission operator of the second flux outside the nested grid, and a third transmission operator of the gas background concentration field; An objective function is constructed based on the first transmission operator, the second transmission operator, and the third transmission operator, and the gas emission flux corresponding to the observation point is determined according to the objective function.

2. The method for determining gas emission flux according to claim 1, characterized in that: The constructing of an objective function based on the first transmission operator, the second transmission operator, and the third transmission operator includes: determining a simulated greenhouse gas mixing ratio at the observation point based on the first transmission operator, the second transmission operator, and the third transmission operator; determining a greenhouse gas column average mixing ratio based on the simulated greenhouse gas mixing ratio and the greenhouse gas priori column average mixing ratio; The objective function is constructed based on the greenhouse gas column average mixing ratio.

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

4. The method for determining gas emission flux according to claim 2, wherein: The constructing of the objective function based on the greenhouse gas column average mixing ratio includes: Obtain the prior flux error covariance matrix, observation error covariance matrix and 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 greenhouse gas column average mixing ratio.

5. The method for determining gas emission flux according to claim 4, characterized in that: The observation vector is determined based on the transmission operator and an observation error vector.

6. The method for determining gas emission flux according to any one of claims 1 to 5, characterized in that: Determining the gas emission flux corresponding to the observation point according to the objective function includes: Determine a characteristic matrix from the state vector to the observation vector in the objective function; The characteristic matrix is ​​iteratively solved to obtain an optimal solution, and the optimal solution is used as the gas emission flux corresponding to the observation point.

7. The method for determining gas emission flux according to any one of claims 1 to 5, characterized in that: The step of acquiring the gas background concentration field comprises: Obtain the original gas background concentration field; Determining a deviation correction factor between the original gas background concentration field and the global background station observation value; The original gas background concentration field is corrected based on the deviation correction factor to obtain the gas background concentration field.

8. A gas emission flux determination device, characterized in that: include: An acquisition unit, used for acquiring the gas background concentration field in the atmosphere; a processing unit for averaging the gas concentrations at multiple points corresponding to the longitudinal section of the nested grid where the observation point is located, to obtain an average gas column concentration corresponding to the observation point; a decomposition unit, configured to determine a transmission operator between the average gas column concentration corresponding to the observation point and the ground emission flux based on a Lagrangian particle diffusion model, and decompose the transmission operator into a first transmission operator of the first flux within the nested grid, a second transmission operator of the second flux outside the nested grid, and a third transmission operator of the gas background concentration field; A 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 determine the gas emission flux corresponding to the observation point according to the objective function.

9. 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, the gas emission flux determination method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the gas emission flux determination method according to any one of claims 1 to 7 is implemented.

Images