Gas emission flux determination method, apparatus, device, and readable storage medium

Abstract

The application provides a gas emission flux determination method, device, equipment and readable storage medium, and the method comprises the following steps: obtaining preprocessed satellite data corresponding to an observation point; simulating a gas concentration based on an Euler atmospheric transmission model to obtain a gas background concentration; determining a transmission operator according to the preprocessed satellite data and a Lagrangian particle diffusion model; constructing an objective function according to the transmission operator, and determining the gas emission flux corresponding to the observation point according to the objective function. The application solves the problem of inaccurate estimation of the gas emission flux in the existing gas emission flux determination method.

Description

TECHNICAL FIELD

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

[0002] In order to reveal the spatial and temporal characteristics of greenhouse gas emission and transmission characteristics, it is necessary to assimilate the gas observation in the atmosphere and perform source inversion, so as to master the actual situation of gas emission. The current way of estimating gas emission flux has the following two problems: (1) Based on the emission level data and emission factor of different sources, the emission information obtained by statistical method or model is difficult to guarantee the accuracy and timeliness of the emission factor and activity level data, and the uncertainty of the emission inventory is large. (2) The properties of the emission source are inferred using observation data, but the monitoring sites are few, and the data spatial coverage is very sparse. And all are background sites, the data is not representative. SUMMARY

[0003] The present application provides a gas emission flux determination method, device, equipment and readable storage medium, which solves the problem of inaccurate gas emission flux estimation in the existing gas emission flux determination method.

[0004] The present application provides a gas emission flux determination method, comprising:

[0005] Obtaining preprocessed satellite data corresponding to an observation point;

[0006] Simulating gas concentration based on an Eulerian atmospheric transport model to obtain a gas background concentration;

[0007] Determining a transport operator according to the preprocessed satellite data and a Lagrangian particle diffusion model;

[0008] Constructing an objective function according to the transport operator, and determining the gas emission flux corresponding to the observation point according to the objective function.

[0009] According to the gas emission flux determination method provided by the present application, the gas concentration is simulated based on the Eulerian atmospheric transport model to obtain the gas background concentration, which comprises:

[0010] Obtaining gas emission data, inputting the gas emission data into the Eulerian atmospheric transport model to obtain the gas background concentration.

[0011] According to the gas emission flux determination method provided by the present application, the transport operator is determined according to the preprocessed satellite data and the Lagrangian particle diffusion model, which comprises:

[0012] Based on the pre-processed satellite data and the Lagrange particle diffusion model, the atmospheric particle motion is reversely simulated to obtain an observation point concentration, a gas background concentration and a transmission operator between the observation point and a corresponding emission source.

[0013] According to the present application, a gas emission flux determination method is provided, and the construction of a target function according to the transmission operator comprises:

[0014] According to the pre-processed satellite data, the gas background concentration and the transmission operator, a target contribution of a prior emission flux corresponding to the observation point to the observation point concentration is calculated;

[0015] An observation point measured concentration, an observation error and a background concentration error are obtained.

[0016] A target function is constructed according to the observation point concentration, the observation point measured concentration, the observation error and the background concentration error.

[0017] According to the present application, a gas emission flux determination method is provided, and the determination of the gas emission flux corresponding to the observation point according to the target function comprises:

[0018] According to an optimization algorithm and the target function, the prior emission flux is iteratively adjusted to obtain an optimal solution, and the optimal solution is taken as the gas emission flux corresponding to the observation point.

[0019] According to the present application, a gas emission flux determination method is provided, and the pre-processed satellite data corresponding to the observation point comprises:

[0020] The satellite data corresponding to the observation point is subjected to clustering sparse processing, screening processing and normalization processing to obtain the pre-processed satellite data corresponding to the observation point.

[0021] The present application further provides a gas emission flux determination device, comprising:

[0022] A satellite data acquisition module is configured to acquire pre-processed satellite data corresponding to an observation point.

[0023] A gas background concentration determination module is configured to simulate a gas concentration based on an Euler atmospheric transmission model to obtain a gas background concentration.

[0024] A transmission operator determination module is configured to determine a transmission operator according to the pre-processed satellite data and a Lagrange particle diffusion model.

[0025] A gas emission flux determination module is configured to construct a target function according to the transmission operator and determine a gas emission flux corresponding to the observation point according to the target function.

[0026] The application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the gas emission flux determination method according to any one of the above when executing the program.

[0027] The application further provides a non-transitory computer readable storage medium, which stores a computer program, wherein the computer program is executable on a processor to implement the gas emission flux determination method according to any one of the above.

[0028] The application further provides a computer program product, comprising a computer program, wherein the computer program is executable on a processor to implement the gas emission flux determination method according to any one of the above.

[0029] The gas emission flux determination method, device, equipment and readable storage medium provided by the application, by obtaining the preprocessed satellite data corresponding to the observation point, then simulating the gas concentration based on the Euler atmospheric transmission model, obtaining the gas background concentration, and then determining the transmission operator according to the preprocessed satellite data and the Lagrangian particle diffusion model, and finally constructing the target function according to the transmission operator, and determining the gas emission flux corresponding to the observation point according to the target function, solve the problem of inaccurate gas emission flux estimation existing in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of 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 described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0031] Figure 1 is one of the flowcharts of the gas emission flux determination method provided by the application;

[0032] Figure 2 is the principle diagram of the Lagrangian particle diffusion model and the forward model provided by the application;

[0033] Figure 3 is the second flowchart of the gas emission flux determination method provided by the application;

[0034] Figure 4 is the structural schematic diagram of the gas emission flux determination device provided by the application;

[0035] Figure 5 is the structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0036] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0037] The present application provides a gas emission flux determination method, comprising: Figures 1-2 The gas emission flux determination method of the present application is described below.

[0038] The present application provides a gas emission flux determination method, comprising: Figure 1 The present application provides a gas emission flux determination method, comprising:

[0039] In step 100, the preprocessed satellite data corresponding to the observation point is obtained.

[0040] Specifically, the satellite data corresponding to the observation point is obtained, and then the preprocessed satellite data in the present embodiment is obtained based on clustering and sparse. The sparse method is as follows: assuming that the number of observation points in the original single satellite data is N, the observation points are divided into subset 1 and subset 2, subset 1 has L points, and subset 2 has N-L points. A random value R is assigned to each observation point, R is a random value uniformly distributed in [0, 1], and then according to the ratio of the number L of observation points in subset 1 to the number N of all observation points, it is determined which subset the observation point is put into. If the random value R is less than L / N, the observation point is assigned to subset 1, and if the random value R is greater than or equal to L / N, the observation point is assigned to subset 2. Because the serial number of the observation point is a random value, the result obtained after sparse is evenly distributed in space. The density of the sparse point is consistent with the density before sparse. The sparse place is more sparse, and the dense place is more dense.

[0041] In step 200, the gas concentration is simulated based on the Euler atmospheric transmission model to obtain the gas background concentration.

[0042] Specifically, the satellite data after clustering and sparse is obtained, a plurality of emission data is adopted, the CO2 background concentration data under the prior emission is simulated and calculated based on the Euler chemical transmission model (i.e. the Euler atmospheric transmission model in the present embodiment) to obtain the gas background concentration in the present embodiment. In the Euler model, the station measurement value is represented by the average value of the corresponding grid element.

[0043] In step 300, the transmission operator is determined according to the preprocessed satellite data and the Lagrangian particle diffusion model.

[0044] Specifically, based on the Lagrangian particle dispersion atmospheric transport model (i.e., the Lagrangian particle dispersion model in the embodiment) and the preprocessed satellite data corresponding to the observation point, the movement of the atmospheric particles released from the observation point is reversely simulated to obtain the transport operator data (i.e., the transport operator in the embodiment) between the concentration of the observation point and the emission source and between the background concentration.

[0045] In step 400, a target function is constructed according to the transport operator, and the gas emission flux corresponding to the observation point is determined according to the target function.

[0046] Specifically, the contributions of the prior emission flux and the background concentration output by the Eulerian atmospheric transport model to the concentration of the observation point are calculated by the transport operator, and based on the Bayesian theory, the error of the concentration of the observation point simulated according to the prior flux and the measured concentration of the observation point is considered, and the target function (i.e., the target function in the embodiment) is constructed by considering the observation error and the error of the background concentration. According to the optimization algorithm, the prior flux is iteratively adjusted to obtain the optimal solution, i.e., the gas emission flux corresponding to the observation point in the embodiment.

[0047] The embodiment obtains the preprocessed satellite data corresponding to the observation point, then simulates the gas concentration based on the Eulerian atmospheric transport model to obtain the background concentration of the gas, and then determines the transport operator according to the preprocessed satellite data and the Lagrangian particle dispersion model, and finally constructs the target function according to the transport operator, and determines the gas emission flux corresponding to the observation point according to the target function. The present application solves the problem of inaccurate estimation of the gas emission flux existing in the prior art gas emission flux determination method.

[0048] In one embodiment, the gas emission flux determination method provided by the embodiment of the present application can further include:

[0049] In step 210, the gas emission data is obtained, and the gas emission data is input into the Eulerian atmospheric transport model to obtain the background concentration of the gas.

[0050] Specifically, in the Eulerian model, the site measurement value is represented by the average value of the corresponding grid element, the clustered and sparse satellite data is used to obtain the gas emission data, and the CO2 background concentration data under the prior emission is calculated based on the Eulerian atmospheric transport model simulation to obtain the background concentration of the gas in the embodiment.

[0051] The embodiment obtains the background concentration of the gas by the Eulerian atmospheric transport model.

[0052] In one embodiment, the gas emission flux determination method provided by the embodiment of the present application can further include:

[0053] Step 310, based on the pre-processed satellite data and the Lagrangian particle diffusion model, the atmospheric particle motion is reversely simulated to obtain the observation point concentration, the gas background concentration and the transmission operator between the observation point and the corresponding emission source.

[0054] Specifically, based on the Lagrangian particle diffusion model and the pre-processed satellite data corresponding to the observation point, the motion of the atmospheric particles released from the observation point is reversely simulated to obtain the transmission operator between the observation point concentration, the corresponding emission source of the observation point and the gas background concentration. Wherein, the principle of forward simulation is as follows: given the mixing ratio change of atmospheric species and the flux matrix operator correlation, the matrix operator of the mixing ratio can be expressed as formula 1, wherein the vector is the simulated mixing ratio at the space-time M coordinate point, the vector x (N×1) is the space-time discretization of N state variables, H (M×N) is the transmission operator. In addition, the absolute mixing ratio is the sum of the flux and the background field mixing ratio, also known as the state vector.

[0055] y mod =Hx Formula 1

[0056] As Figure 2 shown, Figure 2 is the Lagrangian particle diffusion model and the principle diagram of the forward model provided by the application, wherein the black dot represents the receptor, the solid line box represents the grid flux, and the dashed line box represents the grid mixing ratio output by the global model. The arrow indicates the transmission process inside the nested grid, inside and outside the nested grid, and outside the nested grid. According to the process of atmospheric transmission to each receptor, the matrix H can be constructed from three aspects: the transmission flux H nest in the range of the nested grid region; the transmission flux H out outside the nested grid region; and the initial mixing ratio H bg , H nest and H out are obtained from the source-receptor relationship. Similarly, the vector x can also be constructed from three aspects: the flux f nest in the range of the nested grid region; the flux f out outside the nested grid region; and the initial mixing ratio f bg . Therefore, the above formula 1 is also written as formula 2

[0057] y mod =H nest f nest +H out f out +H bg y bg Formula 2

[0058] The initial mixing ratio refers to the contribution of the mixing ratio observed at the end of the trajectory, and the background field mixing ratio refers to the sum of the initial mixing ratio and the flux outside the nested grid area. The initial mixing ratio is the mixing ratio contribution at the end of the Lagrangian backward trajectory, and there are two calculation methods. The first is to use the value of the backward simulation end mixing ratio to represent, at this time the mixing ratio needs to be obtained from the global model. The second is to approximate the observation value as the background mixing ratio, in which case the initial mixing ratio is not distinguished from the contribution outside the nested grid.

[0059] In the embodiment, the atmospheric particle motion is reversely simulated by using the Lagrangian particle diffusion model to obtain the concentration of the observation point, the background concentration of the gas, and the transport operator between the emission source corresponding to the observation point.

[0060] Reference Figure 3 In one embodiment, the gas emission flux determination method provided by the embodiment of the application can further include:

[0061] Step 410, calculating the target contribution of the prior emission flux of the observation point to the concentration of the observation point according to the preprocessed satellite data, the background concentration of the gas, and the transport operator;

[0062] Step 420, obtaining the measured concentration of the observation point, the observation error, and the background concentration error;

[0063] Step 430, constructing a target function according to the concentration of the observation point, the measured concentration of the observation point, the observation error, and the background concentration error.

[0064] Specifically, the contribution of the prior emission flux to the concentration of the observation point is calculated according to the preprocessed satellite data, the background concentration of the gas, and the transport operator. Based on the Bayesian theory, the target function is constructed according to the error of the observation point concentration simulated by the prior flux and the measured concentration of the observation point (i.e., the measured concentration of the observation point in the embodiment), while considering the observation error and the error of the background concentration (i.e., the background concentration error in the embodiment).

[0065] In the embodiment, the target function is constructed by using the concentration of the observation point, the measured concentration of the observation point, the observation error, and the background concentration error.

[0066] In one embodiment, the gas emission flux determination method provided by the embodiment of the application can further include:

[0067] Step 440, adjusting the prior emission flux iteratively according to the optimization algorithm and the target function to obtain an optimal solution, and taking the optimal solution as the gas emission flux corresponding to the observation point.

[0068] Specifically, according to the optimization algorithm, the prior flux is iteratively adjusted to obtain an optimal solution; the optimal solution is the final CO2 emission flux optimization result (i.e., the gas emission flux corresponding to the observation point in this embodiment), which is transmitted to the user.

[0069] In this embodiment, the prior emission flux is iteratively adjusted by the optimization algorithm and the objective function to obtain an optimal solution, and the optimal solution is taken as the gas emission flux corresponding to the observation point.

[0070] In one embodiment, the gas emission flux determination method provided by the embodiment of the present application can further include:

[0071] In step 110, the satellite data corresponding to the observation point is subjected to clustering and sparse processing, screening processing and normalization processing to obtain the preprocessed satellite data corresponding to the observation point.

[0072] Specifically, the satellite data corresponding to the observation point is obtained, and then the preprocessed satellite data in this embodiment is obtained based on clustering and sparse processing of the daily satellite data. The sparse processing method is as follows: assuming that the number of observation points in the original single satellite data is N, the observation points are divided into subset 1 and subset 2, subset 1 has L points, and subset 2 has N-L points. A random value R is assigned to each observation point, R is a random value uniformly distributed in [0, 1], and then the ratio of the number of observation points in subset 1 to the number of all observation points is used to determine which subset the observation point is put into. If the random value R is less than L / N, the observation point is assigned to subset 1, and if the random value R is greater than or equal to L / N, the observation point is assigned to subset 2. Because the serial number of the observation point is a random value, the result obtained after sparse processing is evenly distributed in space. The density of the points after sparse processing is consistent with the density before sparse processing. The sparse places are more sparse, and the dense places are more dense.

[0073] In this embodiment, the satellite data corresponding to the observation point is obtained by clustering and sparse processing.

[0074] The gas emission flux determination device provided by the present application is described below. The gas emission flux determination device described below can be referred to in correspondence with the gas emission flux determination method described above.

[0075] Please refer to Figure 4 The present application also provides a gas emission flux determination device, which comprises:

[0076] The satellite data acquisition module 401 is configured to acquire the preprocessed satellite data corresponding to the observation point.

[0077] The gas background concentration determination module 402 is configured to simulate the gas concentration based on the Euler atmospheric transmission model to obtain the gas background concentration.

[0078] The transmission operator determination module 403 is configured to determine a transmission operator according to the preprocessed satellite data and a Lagrangian particle diffusion model.

[0079] The gas emission flux determination module 404 is configured to construct a target function according to the transmission operator, and determine the gas emission flux corresponding to the observation point according to the target function.

[0080] Optionally, the gas background concentration determination module comprises:

[0081] The gas background concentration determination unit is configured to acquire gas emission data, input the gas emission data into an Euler atmospheric transmission model, and obtain a gas background concentration.

[0082] Optionally, the transmission operator determination module comprises:

[0083] The transmission operator determination unit is configured to reversely simulate atmospheric particle motion based on the preprocessed satellite data and the Lagrangian particle diffusion model, to obtain an observation point concentration, the gas background concentration, and a transmission operator between the observation point and a corresponding emission source.

[0084] Optionally, the gas emission flux determination module comprises:

[0085] The target contribution calculation unit is configured to calculate a target contribution of a prior emission flux corresponding to the observation point to the observation point concentration according to the preprocessed satellite data, the gas background concentration, and the transmission operator.

[0086] The acquisition unit is configured to acquire an observation point measured concentration, an observation error, and a background concentration error.

[0087] The target function construction unit is configured to construct a target function according to the observation point concentration, the observation point measured concentration, the observation error, and the background concentration error.

[0088] Optionally, the gas emission flux determination module comprises:

[0089] The optimal solution determination unit is configured to iteratively adjust the prior emission flux according to an optimization algorithm and the target function, to obtain an optimal solution, and take the optimal solution as the gas emission flux corresponding to the observation point.

[0090] Optionally, the satellite data acquisition module comprises:

[0091] The clustering and sparse processing unit is configured to perform clustering and sparse processing, screening processing, and normalization processing on satellite data corresponding to an observation point, to obtain preprocessed satellite data corresponding to the observation point.

[0092] Figure 5An example of a schematic diagram of a physical structure of an electronic device is shown in FIG. 1. Figure 5 As shown in FIG. 1, the electronic device can include a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 can communicate with each other through the communications bus 540. The processor 510 can invoke a logic instruction in the memory 530 to execute a gas emission flux determination method.

[0093] In addition, the logic instruction in the memory 530 described above can be implemented in the form of a software function unit and sold or used as an independent product, and 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. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned 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.

[0094] On the other hand, the present application also provides a computer program product, which includes 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 gas emission flux determination method provided by the above-mentioned methods.

[0095] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the gas emission flux determination method provided by the above-mentioned methods.

[0096] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they 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 present embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0097] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0098] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than 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 for some technical features therein; 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 of gas efflux determination, characterized in that, The method comprises the following steps: obtaining preprocessed satellite data corresponding to an observation point; simulating gas concentration based on an Euler atmospheric transmission model to obtain a gas background concentration; determining a transmission operator according to the preprocessed satellite data and a Lagrangian particle diffusion model; constructing an objective function according to the transmission operator, and determining a gas emission flux corresponding to the observation point according to the objective function; the step of determining the transmission operator according to the preprocessed satellite data and the Lagrangian particle diffusion model comprises: reverse simulating atmospheric particle motion based on the preprocessed satellite data and the Lagrangian particle diffusion model to obtain an observation point concentration, the gas background concentration, and a transmission operator between an emission source corresponding to the observation point; the step of constructing the objective function according to the transmission operator comprises: calculating a target contribution of a prior emission flux corresponding to the observation point to the observation point concentration according to the preprocessed satellite data, the gas background concentration, and the transmission operator; obtaining a measured concentration of the observation point, an observation error, and a background concentration error; constructing an objective function according to the observation point concentration, the measured concentration of the observation point, the observation error, and the background concentration error.

2. The gas emission flux determination method according to claim 1, characterized by, the step of simulating the gas concentration based on the Euler atmospheric transmission model to obtain the gas background concentration comprises: obtaining gas emission data, and inputting the gas emission data into the Euler atmospheric transmission model to obtain the gas background concentration.

3. The gas emission flux determination method according to claim 1, characterized by, the step of determining the gas emission flux corresponding to the observation point according to the objective function comprises: iteratively adjusting the prior emission flux according to an optimization algorithm and the objective function to obtain an optimal solution, and taking the optimal solution as the gas emission flux corresponding to the observation point.

4. The gas emission flux determination method according to claim 1, characterized by, the step of obtaining the preprocessed satellite data corresponding to the observation point comprises: performing clustering sparse processing, screening processing, and normalization processing on satellite data corresponding to the observation point to obtain the preprocessed satellite data corresponding to the observation point.

5. A gas efflux determination device, characterized in that The method comprises the following steps: a satellite data acquisition module is configured to obtain preprocessed satellite data corresponding to an observation point; a gas background concentration determination module is configured to simulate gas concentration based on an Euler atmospheric transmission model to obtain a gas background concentration; a transmission operator determination module is configured to determine a transmission operator according to the preprocessed satellite data and a Lagrangian particle diffusion model; a gas emission flux determination module is configured to construct an objective function according to the transmission operator, and determine a gas emission flux corresponding to the observation point according to the objective function; the step of determining the transmission operator according to the preprocessed satellite data and the Lagrangian particle diffusion model comprises: reverse simulating atmospheric particle motion based on the preprocessed satellite data and the Lagrangian particle diffusion model to obtain an observation point concentration, the gas background concentration, and a transmission operator between an emission source corresponding to the observation point; the step of constructing the objective function according to the transmission operator comprises: calculating a target contribution of a prior emission flux corresponding to the observation point to the observation point concentration according to the preprocessed satellite data, the gas background concentration, and the transmission operator; obtaining a measured concentration of the observation point, an observation error, and a background concentration error; constructing an objective function according to the observation point concentration, the measured concentration of the observation point, the observation error, and the background concentration error. A target function is constructed according to the observation point concentration, the observation point measured concentration, the observation error and the background concentration error.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the gas emission flux determination method of any one of claims 1 to 4 when executing the program.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the gas emission flux determination method of any one of claims 1 to 4 when executed by the processor.

8. A computer program product comprising a computer program, characterized in that, The computer program implements the gas emission flux determination method of any one of claims 1 to 4 when executed by the processor. The computer program implements the gas emission flux determination method of any one of claims 1 to 4 when executed by the processor.

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