Methane emission flux analysis method, device, equipment and storage medium

Abstract

The application discloses a kind of methane emission flux analysis method, device, equipment and storage medium, belong to the technical field of atmospheric remote sensing monitoring, the method comprises: obtaining target area under methane vertical spectral data and auxiliary observation data;Based on the methane vertical spectral data and auxiliary observation data, the source methane concentration field in target height range is separated and calculated;The source methane concentration field is analyzed in time and space characteristics, and a plurality of groups of time-space orthogonal characteristics are obtained, and the time-space orthogonal characteristics are used to characterize the methane column concentration background variation characteristics in target area and the dynamic difference of methane column concentration on transmission path;The energy emission source of the plurality of groups of time-space orthogonal characteristics is analyzed, and the methane emission intensity distribution of target area is obtained.The application can realize the analysis of methane concentration by statistical relationship, realize the accurate analysis of methane emission flux and emission attribution.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of atmospheric remote sensing monitoring, and particularly relates to a methane emission flux analysis method, device, equipment and storage medium. BACKGROUND

[0002] Methane is the second largest global greenhouse gas after carbon dioxide. Remote sensing satellites represented by S5P / TROPOMI satellites can obtain methane column concentration (XCH4) data covering the globe once a day by using the absorption spectral characteristics of methane at 1.65 μm or 2.3 μm, and the data can be used to carry out quantitative analysis and attribution analysis of methane emissions from different sources.

[0003] The satellite remote sensing inverted methane column concentration (XCH4) data contains atmospheric background value, natural and human emissions. The forward model posterior estimation method is usually used to quantify the methane emission amount of each influencing factor from the satellite methane column concentration data, but the forward model posterior estimation method needs to use an atmospheric chemical model for analysis, which has a large operation overhead, requires high computing resources, and has a relatively coarse spatial resolution. Meanwhile, in the case of lacking prior inventory or poor prior inventory data, the prior inventory may have a great influence on the results of the methane emission flux analysis and the effect is poor. SUMMARY

[0004] The methane emission flux analysis method, device, equipment and storage medium provided by the embodiments of the application can realize the analysis of methane concentration by statistical relationship, and realize the accurate analysis of methane emission flux and emission attribution.

[0005] In a first aspect, the embodiments of the application provide a methane emission flux analysis method, which comprises:

[0006] Obtaining methane vertical spectral data and auxiliary observation data in a target area;

[0007] Based on the methane vertical spectral data and the auxiliary observation data, a source methane concentration field in a target height range is separated and calculated;

[0008] Performing a spatio-temporal feature analysis on the source methane concentration field to obtain a plurality of groups of spatio-temporal orthogonal features, the spatio-temporal orthogonal features being used to represent the background variation characteristics of the methane column concentration in the target area and the dynamic difference of the methane column concentration on the transmission path;

[0009] Performing energy emission source analysis on the plurality of groups of spatio-temporal orthogonal features to obtain a methane emission intensity distribution of the target area.

[0010] Optionally, the separation and calculation of the source methane concentration field in the target height range based on the methane vertical spectral data and the auxiliary observation data comprises:

[0011] obtaining a latitude zone where the target region is located and real-time meteorological data;

[0012] based on a preset chemical transport model, assimilating the methane vertical spectrum data in the preset latitude zone to obtain an assimilation result;

[0013] determining a methane concentration probability density function of the target region at a target height according to a preset methane concentration vertical probability distribution model;

[0014] based on a preset correction coefficient and the methane concentration probability density function under a height range, performing separation calculation to obtain a source methane concentration field in the target height range.

[0015] Optionally, the auxiliary observation data includes a zenith angle, and before the spatiotemporal characteristic analysis on the source methane concentration field is performed to obtain a plurality of spatiotemporal orthogonal characteristics, the method further includes:

[0016] based on a preset atmospheric scattering modulation factor and the zenith angle, determining a methane concentration bias;

[0017] based on the methane concentration bias, correcting the source methane concentration field to obtain a corrected methane concentration field;

[0018] the spatiotemporal characteristic analysis on the source methane concentration field to obtain a plurality of spatiotemporal orthogonal characteristics includes:

[0019] the spatiotemporal characteristic analysis on the corrected methane concentration field to obtain a plurality of spatiotemporal orthogonal characteristics.

[0020] Optionally, the spatiotemporal orthogonal characteristics include a first orthogonal characteristic and a second orthogonal characteristic, the first orthogonal characteristic is used to represent a variation characteristic of a methane column concentration background concentration, and the second orthogonal characteristic is used to represent a dynamic difference of the methane column concentration on a transmission path, and the spatiotemporal characteristic analysis on the source methane concentration field to obtain a plurality of spatiotemporal orthogonal characteristics includes:

[0021] dividing the target region into a plurality of grid points;

[0022] for each grid point, reorganizing methane concentration data of the grid point into a spatiotemporal matrix;

[0023] performing singular value decomposition on the spatiotemporal matrix to obtain a plurality of first orthogonal characteristics and second orthogonal characteristics, the first orthogonal characteristics include a first mode and a first time coefficient, and the second orthogonal characteristics include a second mode and a second time coefficient.

[0024] Optionally, after the singular value decomposition on the spatiotemporal matrix to obtain a plurality of first orthogonal characteristics and second orthogonal characteristics, the method further includes:

[0025] According to a preset screening condition, the first orthogonal feature and / or the second orthogonal feature are screened, and a transmission mode is obtained;

[0026] The spatio-temporal matrix is corrected based on the transmission mode, and a final spatio-temporal matrix is obtained.

[0027] Optionally, the energy emission source analysis on the plurality of groups of spatio-temporal orthogonal features is performed to obtain a methane emission intensity distribution of the target region, including:

[0028] For the target region, at least one pollutant concentration value in the target region is obtained;

[0029] For any one grid point in the target region, an association matrix of the methane and the pollutant is constructed;

[0030] For each pollutant, source feature region identification is performed based on the association matrix, and at least one energy emission source region is obtained.

[0031] Based on the energy emission source region, a methane emission intensity distribution of the target region is determined.

[0032] Optionally, before the methane emission intensity distribution of the target region is determined based on the energy emission source region, the method further includes:

[0033] Based on a preset radiation transmission correction relationship, the methane column concentration is corrected to obtain a corrected methane column concentration;

[0034] According to a preset mass balance equation and the corrected methane column concentration, an energy methane emission intensity distribution diagram is drawn.

[0035] In a second aspect, an embodiment of the present application provides a methane emission flux analysis device, and the device includes:

[0036] An acquisition module is configured to acquire methane vertical spectrum data and auxiliary observation data of a target region.

[0037] A separation calculation module is configured to separate and calculate a source methane concentration field in a target height range based on the methane vertical spectrum data and the auxiliary observation data.

[0038] An analysis module is configured to perform spatio-temporal feature analysis on the source methane concentration field to obtain a plurality of groups of spatio-temporal orthogonal features, and the spatio-temporal orthogonal features are used to represent methane column concentration background change characteristics and dynamic differences of the methane column concentration on a transmission path in the target region.

[0039] An analysis module is configured to perform spatio-temporal feature analysis on the source methane concentration field to obtain a plurality of groups of spatio-temporal orthogonal features, and the spatio-temporal orthogonal features are used to represent methane column concentration background change characteristics and dynamic differences of the methane column concentration on a transmission path in the target region.

[0040] In a third aspect, an electronic device is provided, and the device comprises a processor and a memory storing computer program instructions;

[0041] The processor executes the computer program instructions to implement the methane emission flux analysis method according to any one of the first aspect.

[0042] In a fourth aspect, a computer storage medium is provided, and the computer readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to implement the methane emission flux analysis method according to any one of the first aspect.

[0043] The methane emission flux analysis method, device, equipment and computer storage medium provided by the present application can realize the separation of the methane column concentration in the target area by obtaining the methane vertical hyperspectral data and auxiliary observation data under the target area, then perform the spatial and temporal characteristic analysis on the methane concentration field, that is, the spatial and temporal signal decoupling, avoid the dependence on the atmospheric chemical transmission model in the prior art, and realize the adaptive correction of the transmission time through the spatial and temporal orthogonal characteristics, and then perform the energy emission source analysis on the spatial and temporal orthogonal characteristics, and the methane emission source type is back calculated, and the interference of other pollutants in the target area on the energy methane emission flux is excluded, so that the methane emission flux can be quickly and accurately analyzed, and the efficient methane emission attribution calculation is realized. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. For those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0045] Figure 1 is a flowchart of the methane emission flux analysis method in the preferred embodiments of the present application;

[0046] Figure 2 is a structural schematic diagram of the methane emission flux analysis device in the preferred embodiments of the present application;

[0047] Figure 3 is a structural schematic diagram of the electronic device in the preferred embodiments of the present application. DETAILED DESCRIPTION

[0048] The features and exemplary embodiments of various aspects of the present application will be described below in detail, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0049] It should be noted that, in this paper, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0050] In order to solve the problems in the prior art, the embodiments of the present application provide a methane emission flux analysis method, device, equipment and storage medium. In the embodiments of the present application, the methane vertical hyperspectral data and auxiliary observation data under the target area are obtained to separate the methane column concentration in the target area, then the methane concentration field is analyzed in time and space, that is, the time and space signal is decoupled, the dependence on the atmospheric chemical transport model in the prior art is avoided, and the transmission time is adaptively corrected through the time and space orthogonal characteristics, then the time and space orthogonal characteristics are analyzed to analyze the energy emission source, the type of methane emission source is back calculated, the interference of other pollutants in the target area on the energy methane emission flux is excluded, so that the methane emission flux can be quickly and accurately analyzed, and the efficient methane emission attribution calculation is realized.

[0051] Firstly, the methane emission flux analysis method provided by the embodiments of the present application will be introduced.

[0052] Figure 1 The flowchart of the methane emission flux analysis method provided by one embodiment of the present application is shown. As shown in Figure 1 The methane emission flux analysis method can include S101-S104:

[0053] S101, obtaining methane vertical spectral data and auxiliary observation data under a target area.

[0054] In the embodiment of the present application, the electronic device can obtain methane vertical spectrum data through an infrared atmospheric sounding interferometer or the like limb observation satellite, wherein the methane vertical spectrum data can include concentration distribution characteristics of different atmospheric layers, and the different atmospheric layers can include the troposphere and the stratosphere. In order to facilitate the understanding of the present application, the troposphere is taken as an example below.

[0055] As an example, because the data of satellite observation presented is different due to different latitudes, different methane concentration vertical probability distribution models need to be established for different latitudes and altitudes, which can be determined through a large amount of experimental data.

[0056] In the embodiment of the present application, the auxiliary observation data can include zenith angle and cloud top height data of the satellite.

[0057] It should be noted that the methane column concentration can be assimilated through the methane vertical spectrum data and a chemical transport model (CTM) to obtain the methane column concentration. It can be understood that the methane column concentration can be a concentration column of different atmospheric layers in the target region.

[0058] In addition, in order to quickly obtain the source methane concentration field of each latitude band, a methane concentration vertical probability distribution model can be constructed according to the latitude band to facilitate the obtaining of the methane column concentration of each latitude band.

[0059] S102, based on the methane vertical spectrum data and the auxiliary observation data, separate calculation of the source methane concentration field in the target height range.

[0060] In the embodiment of the present application, S102 can specifically include:

[0061] Obtaining the latitude band in which the target region is located and real-time meteorological data;

[0062] In the preset latitude band, the methane vertical spectrum data is assimilated based on a preset chemical transport model to obtain an assimilation result;

[0063] According to a preset methane concentration vertical probability distribution model, a methane concentration probability density function of the target region at the target height is determined;

[0064] Based on a preset correction coefficient and the methane concentration probability density function at the height range, separate calculation is performed to obtain the source methane concentration field in the target height range.

[0065] In the embodiment of the present application, in order to be able to realize the separation of the tropospheric methane column concentration, obtain the source methane concentration field of the target area in the target height range, the latitude band where the target area is located and real-time meteorological data can be acquired, and then the methane vertical spectrum data is assimilated through a preset chemical transport model, and the assimilation result is the above-mentioned methane column concentration, and then the methane concentration probability density function is obtained through a preset methane concentration vertical probability distribution model, so as to facilitate the obtaining of the methane column concentration.

[0066] Specifically, the methane concentration probability density function can be determined by the following formula (1):

[0067] (1)

[0068] wherein, is the methane concentration probability density function at height z (unit: ppb / km), is the weight coefficient of the i th Gaussian sub-distribution (determined by least square fitting), is the altitude of the i th concentration peak (unit: km), is the vertical profile parameter of the i th concentration peak (unit: km).

[0069] In the embodiment of the present application, the methane concentration in the target height range can be determined through the above formula (1), and for different latitude bands, different parameter changes can be used, which can be measured through a large amount of experimental data in advance, so as to facilitate the rapid searching of the methane concentration.

[0070] In some other embodiments, after obtaining the methane concentration according to the above formula (1), the tropospheric methane column concentration ratio can be calculated after determining the tropospheric fixed height Ht according to the real-time meteorological data.

[0071] Specifically, the following formula (2) can be used for settlement:

[0072] (2)

[0073] wherein, to is the range of the effective observation boundary height of the atmosphere layer (unit: km), is the seasonal correction coefficient of the latitude .

[0074] It can be understood that the above TCR is the ratio of the methane column concentration to the total column concentration, which represents the source methane concentration field.

[0075] In the embodiment of the present application, the probability distribution model of the tropospheric methane column concentration and the total column concentration is established by using the limb satellite vertical profile data, and the separation of the tropospheric concentration is realized by using the preset methane concentration vertical probability distribution model, thereby avoiding the dependence on the atmospheric chemical transport model (CTM) and realizing the accurate extraction of the tropospheric methane concentration.

[0076] In S103, the source methane concentration field is analyzed in time and space to obtain a plurality of groups of time-space orthogonal characteristics.

[0077] In the embodiment of the present application, the time-space orthogonal characteristics are used to represent the background change characteristics of the methane column concentration in the target area and the dynamic difference of the methane column concentration on the transmission path.

[0078] In order to be more accurate in the time-space characteristic analysis, before S103, the method can further include:

[0079] Based on the preset atmospheric scattering modulation factor and the zenith angle, the methane concentration deviation is determined;

[0080] Based on the methane concentration deviation, the source methane concentration field is corrected to obtain a corrected methane concentration field;

[0081] In the embodiment of the present application, the source methane concentration field can be corrected so as to be more accurate in analyzing the methane emission flux in the subsequent time-space analysis. Specifically, for each latitude band, there is corresponding satellite observation zenith angle and cloud top height data, so the correction equation can be constructed based on the satellite observation and the parameters, such as the following formula (3):

[0082] (3)

[0083] Wherein, is the satellite observation zenith angle (unit: degree), is the height corresponding to the cloud top pressure (unit: hPa), k is the atmospheric scattering modulation factor (laboratory calibration value 0.15-0.35), is the atmospheric altitude (typical value 8km).

[0084] In the embodiment of the present application, the source methane concentration field can be corrected by using the above correction formula to obtain the corrected methane concentration field, and the corrected methane concentration field after correction can more accurately extract the methane concentration, thereby realizing the accurate analysis.

[0085] After obtaining the corrected methane concentration field, S103 can be specifically:

[0086] The time-space characteristic analysis is performed on the corrected methane concentration field to obtain a plurality of groups of time-space orthogonal characteristics.

[0087] In this embodiment, the empirical orthogonal function (EOF) component analysis method can be used to analyze the spatiotemporal characteristics of satellite tropospheric methane column concentration. This technique decomposes the dataset corresponding to the corrected methane concentration field into multiple sets of orthogonal signals, consisting of spatial modes (EOF) and temporal components (PCA). The dominant signal that contributes the most to the total variance of the dataset is selected, representing the unique phenomenon controlling the overall distribution pattern of satellite tropospheric methane column concentration. The first mode EOF1 and its time coefficient PC1 mainly characterize the variation characteristics of the background concentration of satellite tropospheric methane column concentration. The spatial distribution of the second mode EOF2 exhibits a significant dipole structure. This spatial pattern of coexisting positive and negative outlier regions reveals the dynamic differences in satellite tropospheric methane column concentration between the source region and the transport path. Specifically, the positive value region of EOF2 usually corresponds to strong emission sources, while the negative value region characterizes the methane transport path and deposition region. This spatial distribution clearly reflects the transport process of methane from the source region to downstream via atmospheric advection. The extreme points of the PC2 time series have a significant correlation with strong methane transport events. By analyzing the statistical characteristics of PC2, the key time points of these transmission events can be accurately identified.

[0088] Specifically, the spatiotemporal orthogonal features include a first orthogonal feature and a second orthogonal feature. The first orthogonal feature is used to characterize the variation characteristics of the methane column concentration and background concentration, and the second orthogonal feature is used to characterize the dynamic differences of the methane column concentration along the transport path. S103 may specifically include:

[0089] The target area is divided into grids, resulting in multiple grid points;

[0090] For each grid point, the methane concentration data of the grid point is reconstructed into a spatiotemporal matrix;

[0091] Singular value decomposition is performed on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features. The first orthogonal features include the first mode and the first time coefficient, and the second orthogonal features include the second mode and the second time coefficient.

[0092] In this embodiment, the target region is first divided into grids to obtain multiple grid points. Then, the methane column concentration at each grid point is reorganized to obtain an m*n spatiotemporal matrix. The spatiotemporal matrix X can be represented as:

[0093] (4)

[0094] in, It is the first i The spatial lattice point at the th ... j Methane column concentration (in ppbv) for each time slice. m It is the total number of spatial grids. n It is the length of the time series.

[0095] In the embodiment, by decoupling the data, the concentration distribution of methane in the target area and the dynamic difference in the source area and the transmission path can be found out, where the source area can be the area where methane is generated.

[0096] In some other embodiments, when singular value decomposition is performed on the standardized matrix, the following formula (5) can be used for decomposition:

[0097] (5)

[0098] wherein, is the k th spatial mode (EOF) vector, k is the k th time coefficient (PC) vector, is the k th modal singular value (the variance contribution is proportional to ).

[0099] In the embodiment, the first orthogonal feature and the second orthogonal feature can be more accurately extracted by singular value decomposition, so as to analyze the concentration evolution relationship of methane in the target area, thereby laying a foundation for subsequent removal of pollutant concentration.

[0100] It is worth noting that the first modal EOF1 and its first time coefficient PC1 mainly represent the variation characteristics of the background concentration of the satellite tropospheric methane column concentration. The spatial distribution of the second modal EOF2 presents a significant dipole structure, and this spatial pattern of coexistence of positive and negative abnormal values reveals the dynamic difference of the satellite tropospheric methane column concentration in the source area and the transmission path. Specifically, the positive area of EOF2 usually corresponds to a strong emission source, while the negative area represents the transmission path and the settlement area of methane. This spatial distribution characteristic clearly reflects the transportation process of methane from the source area to the downstream through atmospheric advection. The extreme points of the second time coefficient PC2 have a significant corresponding relationship with the strong methane transmission events. By analyzing the statistical characteristics of PC2, the key time nodes of these transmission events can be accurately identified.

[0101] In some other embodiments, the specific path and the influence range of methane transmission can also be further analyzed in combination with the wind field data at that time. For example, when the extreme points of PC2 are identified, the transmission trajectory of methane from the emission source to the downstream area can be accurately tracked by analyzing the matching relationship between the spatial distribution characteristics (such as plume shape and concentration gradient) of the satellite tropospheric methane column concentration data and the wind field (wind direction and wind speed), so as to extract the extraneous transmission methane signal from the satellite tropospheric methane column concentration data.

[0102] It is worth noting that the extraction of extraneous transmission methane information in combination with wind field data can be realized manually, and a neural network model can also be constructed by a large amount of data to perform the extraction, which is not limited herein.​

[0103] In some embodiments, after singular value decomposition is performed on the spatiotemporal matrix to obtain a plurality of first orthogonal features and second orthogonal features, the method further comprises:

[0104] For each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or the second orthogonal features are screened according to a preset screening condition to obtain a transmission mode.

[0105] The spatiotemporal matrix is corrected based on the transmission mode to obtain a final spatiotemporal matrix.

[0106] Specifically, the preset screening condition can screen according to a variance contribution range, for example, the first orthogonal features and / or the second orthogonal features with a variance contribution of 20%-30% can be selected. k =2 mode to construct a transmission signal:

[0107] (6)

[0108] wherein, is a modal confidence weight (through wind field correlation 0.7 <0.9), is a transmission signal matrix to be removed, is the first 2 spatial mode (EOF) vector, is the second time coefficient (PC) vector, is the second modal singular value.

[0109] In this embodiment, after the extraneous methane concentration is removed based on the wind field signal, the final spatiotemporal matrix can be represented as: .

[0110] In the embodiments of the present application, by using the spatiotemporal decoupling and transmission extraction technology, the dipole structure of the EOF decomposition can be used to identify the source area transmission path, the transmission time can be adaptively corrected by matching the PC2 extreme point with the wind field trajectory, and the methane column concentration of the emission source in the target area can be accurately extracted.

[0111] S104, energy emission source analysis is performed on a plurality of spatiotemporal orthogonal features to obtain a methane emission intensity distribution of the target area.

[0112] In some embodiments, since there can be multiple associated pollutants in the target area, in order to be able to determine the methane emission intensity of the emission source, S104 can specifically include:

[0113] For the target area, at least one pollutant concentration value in the target area is obtained;

[0114] For any one grid point in the target area, an associated matrix of methane and pollutants is constructed.

[0115] For each pollutant, source feature area recognition is performed based on the correlation matrix to obtain at least one energy emission source area;

[0116] Based on the energy emission source area, the methane emission intensity distribution of the target area is determined.

[0117] In this embodiment, after the extraneous methane concentration is removed, the remaining methane column concentration is the methane emission flux generated by the emission source, but at this time there are still some associated pollutants emitted by the energy industry, so further correction is still needed to obtain more accurate methane concentration.

[0118] Specifically, the associated pollutants can include carbon monoxide, nitrogen oxides, sulfur dioxide, and light-absorbing aerosols, so the multi-component observation data of TROPOMI / S5P satellite and the high-precision measurement results of ground stations (such as TCCON, EMEP network) can be integrated to establish a multi-species collaborative analysis framework. The methane chemical oxidation process is often accompanied by the coordinated balance conversion process of CO2, CO and other components. Since the emission ratio of methane to CO (CH4 / CO) differs significantly in coal mining (typical ratio > 5), natural gas leakage (pure leakage ratio >> 10) and conventional combustion (ratio ≈ 0.1) and other scenarios, the difference between each pollutant can be determined by the difference to determine the corresponding energy interference.

[0119] Further, the correlation matrix can be constructed according to the concentration value of at least one pollutant in the target area, and the specific formula is as follows:

[0120] (7)

[0121] wherein, is the spatial Pearson correlation coefficient of species x and y, is the concentration anomaly value (measured value minus regional background value) of the i-th grid, and R is the correlation matrix.

[0122] Then the above correlation matrix is used for corresponding judgment, and the judgment basis is as follows:

[0123] (8)

[0124] wherein, Ω is a sub-area of target analysis, is a significant emission threshold (typical value: 100 ppb·km 2 ).

[0125] The above judgment basis can be used to screen the energy industry emission source area to select the methane concentration, so as to facilitate further correction of the methane concentration data and obtain a more accurate energy methane emission intensity distribution map.

[0126] Further, before determining the methane emission intensity distribution of the target region based on the energy emission source area, the method further comprises:

[0127] Based on the preset radiation transmission correction relationship, the methane column concentration is corrected to obtain a corrected methane column concentration;

[0128] According to the preset mass balance equation and the corrected methane column concentration, an energy methane emission intensity distribution map is drawn.

[0129] In this embodiment, the methane column concentration can be further corrected by the preset radiation transmission correction relationship to obtain a more accurate methane emission flux.

[0130] Specifically, the preset radiation transmission correction relationship can be expressed by the following formula:

[0131] \left [ {{CH}_{4}} \right ]_{cor}=\left [ {{CH}_{4}} \right ]_{raw}+\delta \cdot {AOD}_{550}\cdot \frac {\partial \left [ {{CH}_{4}} \right ]} {{\partial}_{\tau}} (9)

[0132] Wherein, is the 550nm aerosol optical depth obtained from the TROPOMI UAVI product, the wavelength-dependent correction factor (0.25±0.05 for the 1.65µm wavelength band), is the atmospheric optical depth.

[0133] Further, the preset mass balance equation can be expressed by the following formula:

[0134] (10)

[0135] Wherein, is the wind field gradient, is the wind field vector, is the chemical reaction consumption.

[0136] In this embodiment, the carbon pollution homologous multi-component separation technology can be used to realize source analysis by using the emission correlation characteristics of pollutants and methane, which eliminates the dependence on low-resolution wind field data, reduces the influence of wind field on concentration calculation, and thus can accurately calculate the methane concentration emission flux, realize physical separation and quantitative evaluation of the methane emission signal of the energy industry, and further improve the spatial and temporal resolution of the emission source analysis.

[0137] Based on the methane emission flux analysis method provided in the above embodiments, correspondingly, the application also provides a specific implementation of a methane emission flux analysis device. Please refer to the following embodiments.

[0138] Firstly, referring to Figure 2 The methane emission flux analysis device 200 provided by the embodiments of the application comprises the following units:

[0139] The acquisition module 201 is configured to acquire methane vertical spectrum data and auxiliary observation data in a target area.

[0140] The separation calculation module 202 is configured to separate and calculate a source methane concentration field in a target height range based on the methane vertical spectrum data and the auxiliary observation data.

[0141] The analysis module 203 is configured to perform a spatio-temporal feature analysis on the source methane concentration field to obtain a plurality of spatio-temporal orthogonal features, which are used to represent the methane column concentration background variation characteristics in the target area and the dynamic differences of the methane column concentration on the transmission path.

[0142] The analysis module 204 is configured to analyze the plurality of spatio-temporal orthogonal features to obtain the methane emission intensity distribution of the target area.

[0143] As an optional implementation, the separation calculation module 202 can be specifically configured to:

[0144] acquire a latitude band and real-time meteorological data in which the target area is located;

[0145] based on a preset chemical transport model, assimilate the methane vertical spectrum data in the preset latitude band to obtain an assimilation result;

[0146] determine a methane concentration probability density function of the target area at the target height according to a preset methane concentration vertical probability distribution model;

[0147] based on a preset correction coefficient and the methane concentration probability density function at the height range, perform separation calculation to obtain the source methane concentration field in the target height range.

[0148] As an optional implementation, the auxiliary observation data comprises a zenith angle separation calculation module, which can be specifically configured to:

[0149] based on a preset atmospheric scattering modulation factor and a zenith angle, determine a methane concentration deviation;

[0150] based on the methane concentration deviation, correct the source methane concentration field to obtain a corrected methane concentration field;

[0151] The analysis module 203 is specifically configured to:

[0152] The time-space characteristics of the corrected methane concentration field are analyzed to obtain a plurality of time-space orthogonal characteristics.

[0153] As an optional implementation, the time-space orthogonal characteristics include first orthogonal characteristics and second orthogonal characteristics, the first orthogonal characteristics are used to represent the variation characteristics of the background concentration of the methane column concentration, and the second orthogonal characteristics are used to represent the dynamic difference of the methane column concentration on the transmission path. The analysis module 203 can be specifically used for:

[0154] The target area is divided into a plurality of grid points;

[0155] For each grid point, the methane concentration data of the grid point is reorganized into a time-space matrix;

[0156] The time-space matrix is singular value decomposed to obtain a plurality of first orthogonal characteristics and second orthogonal characteristics, the first orthogonal characteristics include a first mode and a first time coefficient, and the second orthogonal characteristics include a second mode and a second time coefficient.

[0157] As an optional implementation, the analysis module 203 can be specifically used for:

[0158] For each group of first orthogonal characteristics and / or second orthogonal characteristics, the first orthogonal characteristics and / or the second orthogonal characteristics are screened according to a preset screening condition to obtain a transmission mode;

[0159] The time-space matrix is corrected based on the transmission mode to obtain a final time-space matrix.

[0160] As an optional implementation, the analysis module 204 can be specifically used for:

[0161] For the target area, at least one pollutant concentration value in the target area is obtained;

[0162] For any one grid point in the target area, a correlation matrix of methane and pollutants is constructed;

[0163] For each pollutant, a source feature area is identified based on the correlation matrix to obtain at least one energy emission source area;

[0164] Based on the energy emission source area, a methane emission intensity distribution of the target area is determined.

[0165] As an optional implementation, the analysis module 204 can be specifically used for:

[0166] Based on a preset radiation transmission correction relationship, the methane column concentration is corrected to obtain a corrected methane column concentration;

[0167] According to a preset mass balance equation and the corrected methane column concentration, an energy methane emission intensity distribution diagram is drawn.

[0168] Figure 3 A hardware structure schematic diagram of an electronic device provided by an embodiment of the present application is shown.

[0169] The electronic device can include a processor 301 and a memory 302 having computer program instructions stored therein.

[0170] Specifically, the processor 301 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0171] The memory 302 can include a mass storage for data or instructions. By way of example and not limitation, the memory 302 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In one example, the memory 302 can include a removable or non-removable (or fixed) media, or the memory 302 is a non-volatile solid-state memory. The memory 302 can be internal or external to the integrated gateway disaster recovery device.

[0172] In one example, the memory 302 can be a read-only memory (ROM). In one example, the ROM can be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0173] The memory 302 can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions that, when executed (e.g., by one or more processors), are operable to perform operations described with reference to the methane emission flux analysis method according to the first aspect of the present disclosure.

[0174] The processor 301 implements the functions of the electronic device by reading and executing the computer program instructions stored in the memory 302. Figure 1A methane emission flux analysis method in one of the embodiments.

[0175] In one example, the electronic device can further include a communication interface 303 and a bus 304. Wherein, as shown, the processor 301, the memory 302, the communication interface 303 are connected through the bus 304 and complete the communication between each other. Figure 3

[0176] The communication interface 303 is mainly used to realize the communication between each module, device, unit and / or equipment in the embodiments of the present application.

[0177] The bus 304 includes hardware, software or both to couple components of the electronic device to each other in which, for example and not limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus or other suitable bus or a combination of two or more of the above. In a suitable case, the bus 304 can include one or more buses. Although the embodiments of the present application describe and show a specific bus, the present application contemplates any suitable bus or interconnect.

[0178] The electronic device can execute the methane emission flux analysis method in the embodiments of the present application, thereby realizing the methane emission flux analysis method and device described in combination Figures 1-2 with the above embodiments.

[0179] In addition, in combination with the methane emission flux analysis method in the above embodiments, the embodiments of the present application can provide a computer storage medium to realize. The computer storage medium has computer program instructions stored thereon; the computer program instructions are executed by the processor to realize any one of the methane emission flux analysis methods in the above embodiments.

[0180] ​In an optional embodiment, in combination with the methane emission flux analysis method in the above embodiments, the embodiments of the present application can provide a computer program product to implement, and the instructions in the computer program product are executed by the processor of the electronic device, so that the electronic device can implement any one of the methane emission flux analysis methods in the above embodiments.

[0181] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted herein. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the present application.

[0182] The functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via a computer network such as the Internet, an intranet, etc.

[0183] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0184] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted herein. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the present application.

[0185] The functions noted in the description of the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0186] It is also important to note that the examples described herein can be implemented in a variety of systems, including and not limited to a digital electronic circuit, an analog electronic circuit, a computer hardware, firmware, software, or in combinations of them. The example described herein can be implemented as one or more computer programs running on a computer or other programmable data processing devices, a computer program, a computer program product, a computer, a mobile phone, a portable computer, a portable computer, a server, or other programmable data processing devices. The computer program is a set of instructions that can be used to program a computer or other programmable data processing devices to implement a desired function, logic, or action in a computer or other programmable data processing devices. The program can be implemented in a high level programming language or in a low level programming language, or in a combination of them. The set of instructions can be stored in any type of computer readable medium, or transmitted by a computer or other programmable data processing devices, or a combination of them. The computer readable medium can include, but is not limited to, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0187] The computer program instructions can also be loaded onto a computer or other programmable data processing devices to cause a series of operational steps to be performed on the computer or other programmable data processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing devices provide steps for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0188] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method of methane emission flux analysis, characterized by, The method comprises the following steps: acquiring methane vertical spectrum data and auxiliary observation data in a target area; based on the methane vertical spectrum data and auxiliary observation data, separating and calculating the source methane concentration field in the target height range; spatial and temporal feature analysis is performed on the source methane concentration field to obtain a plurality of groups of spatial and temporal orthogonal features, which are used to represent the background variation characteristics of methane column concentration in the target area and the dynamic difference of methane column concentration in the transmission path; energy emission source analysis is performed on the plurality of groups of spatial and temporal orthogonal features to obtain the methane emission intensity distribution of the target area; wherein, based on the methane vertical spectrum data and auxiliary observation data, separating and calculating the source methane concentration field in the target height range, comprises: acquiring the latitude band and real-time meteorological data of the target area; based on the preset chemical transport model, assimilating the methane vertical spectrum data in the preset latitude band to obtain the assimilation result; determining the methane concentration probability density function of the target area at the target height according to the preset methane concentration vertical probability distribution model; based on the preset correction coefficient and the methane concentration probability density function under the height range, separating and calculating to obtain the source methane concentration field in the target height range; the spatial and temporal orthogonal features include first orthogonal features and second orthogonal features, the first orthogonal features are used to represent the variation characteristics of the background concentration of methane column concentration, and the second orthogonal features are used to represent the dynamic difference of methane column concentration in the transmission path, the spatial and temporal orthogonal features are obtained by performing spatial and temporal feature analysis on the source methane concentration field, comprising: dividing the target area into a plurality of grid points; for each grid point, reorganize the methane concentration data of the grid point into a spatial and temporal matrix; singular value decomposition is performed on the spatial and temporal matrix to obtain a plurality of first orthogonal features and second orthogonal features, the first orthogonal features include a first mode and a first time coefficient, and the second orthogonal features include a second mode and a second time coefficient.

2. The method of claim 1, wherein, The auxiliary observation data includes zenith angle, before the spatial and temporal orthogonal features are obtained by performing spatial and temporal feature analysis on the source methane concentration field, the method further comprises: based on the preset atmospheric scattering modulation factor and the zenith angle, determining the methane concentration deviation; based on the methane concentration deviation, correcting the source methane concentration field to obtain a corrected methane concentration field; the spatial and temporal orthogonal features are obtained by performing spatial and temporal feature analysis on the source methane concentration field, comprising: spatial and temporal feature analysis is performed on the corrected methane concentration field to obtain a plurality of groups of spatial and temporal orthogonal features.

3. The method of claim 1, wherein, After the singular value decomposition is performed on the spatial and temporal matrix to obtain a plurality of first orthogonal features and second orthogonal features, the method further comprises: for each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or the second orthogonal features are screened according to the preset screening condition to obtain a transmission mode; based on the transmission mode, the spatial and temporal matrix is corrected to obtain a final spatial and temporal matrix.

4. The method of claim 1, wherein, the methane emission intensity distribution of the target area is obtained by performing energy emission source analysis on the plurality of groups of spatial and temporal orthogonal features, comprising: For the target area, obtain at least one pollutant concentration value in the target area; For any one grid point in the target area, construct the correlation matrix of the methane and the pollutant; For each pollutant, based on the correlation matrix, source feature area identification is performed to obtain at least one energy emission source area; Based on the energy emission source area, the methane emission intensity distribution of the target area is determined.

5. The method of claim 4, wherein, Before the methane emission intensity distribution of the target area is determined based on the energy emission source area, the method further comprises: Based on a preset radiation transmission correction relationship, the methane column concentration is corrected to obtain a corrected methane column concentration; According to a preset mass balance equation and the corrected methane column concentration, an energy methane emission intensity distribution map is drawn.

6. A methane emission flux analysis device for implementing the methane emission flux analysis method according to claim 1, characterized by, The device comprises: An acquisition module is configured to acquire methane vertical spectrum data and auxiliary observation data of a target area; A separation calculation module is configured to separate and calculate a source methane concentration field in a target height range based on the methane vertical spectrum data and the auxiliary observation data; An analysis module is configured to perform time-space feature analysis on the source methane concentration field to obtain a plurality of time-space orthogonal features, which are used to represent the background change characteristics of the methane column concentration in the target area and the dynamic difference of the methane column concentration in the transmission path; An analysis module is configured to analyze the plurality of time-space orthogonal features to obtain the methane emission intensity distribution of the target area.

7. An electronic device, comprising: The device comprises a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the methane emission flux analysis method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the methane emission flux analysis method of any one of claims 1-5.

Images