A carbon emission remote sensing monitoring system and method for cities and parks

Abstract

The present invention belongs to the technical field of greenhouse gas emission monitoring, and discloses a carbon emission remote sensing monitoring system and method for urban and park scales. Based on the spatial autocorrelation of atmospheric CO2 concentration, the method uses the statistical results of the local Moran's I index to automatically and objectively identify the XCO2 enhancement signal caused by human emissions on the satellite observation strip; the carbon emission estimation method simulated by the XCO2 enhancement value constraint model of satellite observation is used to establish a mapping relationship between atmospheric CO2 concentration and human emissions through the WRF-Chem atmospheric transmission model, and quantify the impact of atmospheric transmission on emission estimation; the method framework of top-down quantitative estimation of carbon emissions is simulated by the artificial XCO2 signal constraint model of satellite observation. The carbon emission estimation results of the present invention are objective and reliable, which reduces large-scale calculations and saves calculation time.

Description

Technical Field

[0001] The present invention belongs to the technical field of greenhouse gas emission monitoring, and in particular relates to a carbon emission remote sensing monitoring system and method for city and park scales. Background Art

[0002] At present, urban carbon emission monitoring mainly adopts the traditional "bottom-up" inventory accounting method. Based on different industrial sectors at the national or regional scale, statistical data on energy consumption are multiplied by carbon emission factors for classification calculation, and then the total amount of anthropogenic emissions is redistributed in time and space based on data such as population density, roads, and night lights. However, inaccurate statistical data and time lags will lead to great uncertainty in emission inventories, especially at the urban scale.

[0003] Carbon satellites provide an important data source for monitoring changes in atmospheric CO2 concentrations at the regional scale by directly observing the average dry air mixing ratio of the atmospheric CO2 column (XCO2). The difficulty in estimating emissions using satellite-observed atmospheric CO2 concentrations lies in how to extract the CO2 enhancement signal caused by anthropogenic emissions from high background levels and accurately quantify it as emissions. Existing research on remote sensing monitoring of carbon emissions mainly focuses on responses to changes in XCO2 concentrations, industrial point source emissions, regional carbon emissions assessments and policy discussions, and lacks quantitative estimates of carbon emissions at the city and park scales.

[0004] Prior art 1 provides a carbon emission inventory calculation method based on statistical data. In the process of carbon emission inventory calculation, it is necessary to clarify the inventory scope and boundaries, activity level data, carbon emission factors, geographical location of emission sources and other prerequisite elements. Its calculation can be divided into the following steps: (1) According to the characteristics and needs of a specific city, clarify the inventory scope and boundaries, including industries, activities, geographical areas, etc. involved in different types of emission sources; (2) Collect activity level data of various industries or sectors related to emission sources, such as statistical data on energy consumption, transportation, industrial production, waste treatment, etc.; (3) Use emission factors (emissions generated per unit of activity or energy consumption) to transform activity level data and estimate emissions; (4) According to the geographic information system (GIS) and other relevant data (such as population density, night light, etc.), allocate emission data in time and space.

[0005] The accuracy of the existing technology carbon emission inventory is affected by the quality of statistical data and the uncertainty of emission factors. The defects of this method are as follows: (1) Not covering all emission sources: Carbon emission inventories usually focus on major industries and activities, and may not fully cover some small or scattered emission sources, resulting in incomplete statistical results of anthropogenic emissions; (2) Statistical uncertainty: The compilation of carbon emission inventories relies on multiple data sources, including energy statistics, industrial production and other activity data. Some of these data are reported by enterprises or organizations themselves, and there are problems with the credibility and authenticity of self-reported data; for large cities or complex industrial systems, supervision and verification of emission data may face certain difficulties, especially when the regulatory system in some regions is not sound enough; (3) Data lag: Statistical data usually takes time to collect and process, so the release of the inventory may have a certain lag and fail to reflect the latest actual emission status in a timely manner; (4) Large errors in carbon emission factors: The accuracy of carbon emission factors directly affects the accuracy of emission estimates. Regional differences, different activity levels and technological changes will lead to changes in emission factors.

[0006] Prior art 2 provides a Gaussian plume model simulation method based on atmospheric CO2 satellite observation data. The instantaneous plume generated by CO2 molecules emitted by independent point sources has an irregular shape and a detectable scale of 0.1-1km. The CO2 emission rate can be inferred by relying on the XCO2 increment observation value caused by the local source and the local wind field information. The Gaussian plume model assumes a stable wind field state and an independent known wind speed to drive the plume transmission. The plume distribution near the independent emission point source is taken as a function of wind speed and wind direction. A statistical model is used to linearly fit the XCO2 value observed by the satellite at the emission source to estimate the emission.

[0007] Disadvantages of the second prior art: This method has strict requirements on the satellite observation orbit direction and wind direction, and is only applicable to strip data where the wind direction intersects the orbit;

[0008] Emission plumes are usually sampled by a few pixels, and the detection and fitting of their shapes are also susceptible to cloud contamination and instrument noise. Instantaneous small-scale plumes usually do not follow a steady-state Gaussian distribution, so there are large errors in the calculation;

[0009] The calculation of effective wind speed has a significant impact on the emission estimates. Statistical methods for modeling emission source rates from plume observations require calculations under the assumption of a steady wind state. However, in remote sensing, the instantaneous plume wind speed is not directly measured and may vary in the vertical and horizontal directions of the plume. Summary of the invention

[0010] In order to overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a carbon emission remote sensing monitoring system and method for city and park scales.

[0011] The technical solution is as follows: A carbon emission remote sensing monitoring method for urban and park scales, comprising:

[0012] S1, collect and process atmospheric CO2 satellite observation data to obtain XCO2 abnormal points in the observation points; where XCO2 is the atmospheric CO2 column concentration observed by the satellite;

[0013] S2, based on the spatial autocorrelation of atmospheric CO2 concentration, uses the statistical results of the local Moran's I index to automatically identify the XCO2 enhancement signal caused by anthropogenic emissions in the satellite observation strip;

[0014] S3, using the passive tracer mode of the WRF-Chem atmospheric transport model to couple CO2 emissions, simulate anthropogenic CO2 mixing ratios, and establish a mapping relationship between anthropogenic CO2 concentrations and emissions;

[0015] S4, based on the mapping relationship, takes the CO2 enhancement value observed by satellite as the constraint condition of the atmospheric transport model simulation results, and iteratively optimizes the anthropogenic CO2 concentration simulation results by changing the emission input data.

[0016] In step S1, obtaining XCO2 abnormal points in the observation points includes:

[0017] a. Read the secondary XCO2 data products of satellite observations, including XCO2, orbit, observation time, quality flag, prior CO2 profile, average kernel function, and pressure weight parameter;

[0018] b. Screening of satellite observation points in land areas;

[0019] c. Use the two-step linear regression method to perform linear fitting on the satellite observation data of each orbit, and identify satellite observation points with outliers greater than 2ppm and multiple outlier points within a spatial neighborhood with a radius of 10km;

[0020] d, calculate the standard deviation σ of the fitting error XCO2_local local , filter the satellite observations; meet XCO2 <XCO2_trend+0.5σ local The data of the conditions are regarded as background concentration sampling points; where XCO2_trend is the linear trend of XCO2 changing with latitude;

[0021] e. Perform linear fitting on the background sampling points again to obtain the background line, calculate the difference between XCO2 and the background line, and the observation points with a difference greater than 2ppm are XCO2 abnormal points.

[0022] In step S2, the XCO2 enhancement signal caused by anthropogenic emissions on the satellite observation strip is automatically identified, specifically including:

[0023] S201, determine the spatial scope of the research target and obtain the XCO2 abnormal points;

[0024] S202, using the local Moran's I index as the local spatial association index LISA to calculate the spatial correlation of satellite observation points in the study area, and to perform clustering and anomaly analysis of high CO2 concentration observations;

[0025] S203, removing satellite observation anomalies caused by random noise, calculating the XCO2 mean value every 0.1° latitude band, and obtaining the XCO2 strip profile data of satellite observation; according to the formula of the anthropogenic XCO2 enhancement value detected by satellite observation, extracting the XCO2 enhancement signal caused by the local emission source.

[0026] In step S202, clustering and anomaly analysis are performed on the CO2 high concentration observations, and the expression is:

[0027]

[0028] In the formula, I i Clustering and anomaly analysis of CO2 high concentration observations, s i is the inverse distance of the ith XCO2 observation at different locations, x i , x j are the i-th and j-th XCO2 observations at different locations, is the calculated mean XCO2 of the observation points, ω i，j is the spatial weight matrix, which represents the distance relationship between two points; n is the number of observation points involved in the calculation;

[0029] The inverse distance square method was used to calculate the distance relationship between features, and a significance test was performed with a significance level p-value of 0.05 based on the calculated test statistic.

[0030] Furthermore, in the statistical results of local Moran's I, the XCO2 observations in each period were divided into non-significant correlation points NS and four significant correlation types with p-values ​​less than 0.05, including high-high HH, low-low LL, high-low HL, and low-high LH; among them, H means that the variable value is higher than the average value, L means that the variable value is lower than the average value, HH means that the data points with high values ​​are clustered, LL means that the data points with low values ​​are clustered, and LH and HL both indicate that there are significant statistical differences between the variable values ​​of the observation point and its surrounding observation points.

[0031] In step S203, the formula for the artificial XCO2 enhancement value detected by satellite observation is expressed as:

[0032]

[0033] In the formula, is the anthropogenic XCO2 enhancement value detected by satellite observations, is the XCO2 observation value of type HH, is the band background value calculated from the median value of the profile data.

[0034] In step S3, a mapping relationship between anthropogenic CO2 concentration and emissions is established, including:

[0035] S301, set the parameters of the atmospheric transport model, using the EDGAR emission inventory as prior information, the atmospheric transport model simulation generates output once every hour, providing artificial CO2 concentration data x at different pressure layers m ;

[0036] S302, using the layered atmospheric pressure to calculate the pressure weight, converting the atmospheric transport model simulation results into column concentration data.

[0037] In step S302, the pressure weight is calculated using the layered atmospheric pressure to convert the atmospheric transport model simulation results into column concentration data, expressed as:

[0038]

[0039] In the formula, h m，i is the pressure weight of the i-th layer of atmosphere, p i is the pressure of the i-th layer, p i+1 is the pressure of the i+1th layer, p i-1 is the pressure of the i-1th layer, p surf is the surface pressure, is the anthropogenic CO2 enhancement value simulated by the atmospheric transport model, is the mth atmospheric pressure weight set at the Tth moment, X m are m artificially set enhancement coefficients.

[0040] In step S4, the anthropogenic CO2 concentration simulation results are iteratively optimized, including:

[0041] S401, interpolate the simulation results of the atmospheric transport model to the same number of layers as the satellite prior profile, combine the satellite average kernel function and the pressure weight function, and convert the layered simulation results into column concentrations. The expression is:

[0042]

[0043] In the formula, for The corrected value, hj is the pressure weight function, ak j is the column average kernel function, X mj is the m enhancement coefficients artificially set under the number of layers of vertical distribution of satellite observation inversion, and j is the number of layers of vertical distribution of satellite observation inversion;

[0044] S402, using the average deviation between the atmospheric transport model simulation and the satellite observed XCO2 enhancement, to quantify the error of the atmospheric transport model simulation results, the expression is:

[0045]

[0046] Where σ is the simulation result error, is the anthropogenic XCO2 enhancement value detected by satellite observations;

[0047] S403, constructing a simulation experiment with emission input as an atmospheric transport model variable, and obtaining the optimal emission estimation result by multiplying the prior emission data by the emission coefficient;

[0048] S404, construct a sensitivity experiment with wind speed as a model variable, set meteorological input conditions with different wind speeds, and perform sensitivity tests on the simulation results of the atmospheric transport model.

[0049] Another object of the present invention is to provide a carbon emission remote sensing monitoring system for city and park scales, which implements the carbon emission remote sensing monitoring method for city and park scales, and the system comprises:

[0050] Atmospheric CO2 satellite observation data collection and processing module, used for atmospheric CO2 satellite observation data collection and processing, to obtain XCO2 abnormal points in the observation points;

[0051] Satellite observation XCO2 enhancement signal detection module, based on the spatial autocorrelation of atmospheric CO2 concentration and using the statistical results of the local Moran's I index, automatically identifies XCO2 enhancement signals caused by anthropogenic emissions in satellite observation strips;

[0052] The anthropogenic CO2 concentration simulation module coupled with atmospheric transport processes is used to couple CO2 emissions using the passive tracer mode of the WRF-Chem atmospheric transport model, simulate anthropogenic CO2 mixing ratios, and establish a mapping relationship between anthropogenic CO2 concentrations and emissions;

[0053] The satellite observation-constrained carbon emission estimation and uncertainty assessment module is used to iteratively optimize the anthropogenic CO2 concentration simulation results by changing the emission input data, based on the mapping relationship and taking the satellite-observed CO2 enhancement value as the constraint condition for the atmospheric transport model simulation results.

[0054] In combination with all the above-mentioned technical solutions, the beneficial effects of the present invention are as follows: the present invention provides a carbon emission remote sensing monitoring system and method for urban and park scales. The method uses atmospheric CO2 satellite observation data to automatically identify CO2 concentration enhancement signals caused by human emissions, and uses this as a constraint condition for atmospheric transmission model simulation, thereby realizing a "top-down" quantitative estimation of carbon emissions and objectively evaluating the emission inventory. Compared with existing carbon emission remote sensing estimation technology, the method of the present invention can more objectively and directly identify atmospheric CO2 high concentration observation points that are not affected by background concentrations. Based on the spatial clustering characteristics of these observation points, the method can automatically and effectively detect CO2 concentration enhancement signals caused by human emissions, thereby providing reliable constraints for accurately estimating carbon emissions.

[0055] Compared with the currently popular carbon emission estimation method based on the atmospheric transport model, the method of the present invention uses the atmospheric CO2 enhancement signal simulated by the satellite observation constraint model to iteratively optimize the prior emission information. The carbon emission estimation results are objective and reliable, which reduces large-scale calculations and saves calculation time. The method framework of carbon emission estimation simulated by the satellite observation constraint model proposed in the present invention has undergone observation system simulation tests (OSSEs). Through the sensitivity experiment of the control variables, the influence of the emission inventory and meteorological wind field on the emission estimation results was quantitatively evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0057] Figure 1 This is a flow chart of a carbon emission remote sensing monitoring method for city and park scales provided by an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of a carbon emission remote sensing monitoring method for city and park scales provided by an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of a carbon emission remote sensing monitoring system for city and park scales provided by an embodiment of the present invention;

[0060] In the figure: 1. Atmospheric CO2 satellite observation data collection and processing module; 2. Satellite observation XCO2 enhanced signal detection module; 3. Anthropogenic CO2 concentration simulation module coupled with atmospheric transport process; 4. Carbon emission estimation and uncertainty assessment module constrained by satellite observation. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0062] The innovation of the present invention is that: based on the spatial autocorrelation of atmospheric CO2 concentration, the present invention uses the statistical results of the local Moran's I index to automatically and objectively identify the XCO2 enhancement signal caused by human emissions on the satellite observation strip; the carbon emission estimation method simulated by the XCO2 enhancement value constraint model of satellite observation is used to establish the mapping relationship between atmospheric CO2 concentration and human emissions through the WRF-Chem atmospheric transmission model, and quantify the impact of atmospheric transmission on emission estimation. The present invention uses satellite-observed atmospheric CO2 concentration data, emission inventories, meteorological wind fields and other multi-source data, combined with the spatial autocorrelation method and the atmospheric transmission model, to simulate the "top-down" quantitative estimation of carbon emissions using the artificial XCO2 signal constraint model of satellite observation. The method is oriented to planar emission sources such as cities and parks, such as industrial parks where steel mills, cement plants, and power plants are concentrated.

[0063] Embodiment 1, as Figure 1 The carbon emission remote sensing monitoring method for city and park scales provided by the embodiment of the present invention includes the collection and processing of atmospheric CO2 satellite observation data, the detection of XCO2 enhanced signals from satellite observations, the simulation of anthropogenic CO2 concentration coupled with atmospheric transmission processes, and the estimation and uncertainty evaluation of carbon emissions constrained by satellite observations.

[0064] S1, collecting and processing atmospheric CO2 satellite observation data, obtaining XCO2 abnormal points in the observation points; where XCO2 is the atmospheric CO2 column concentration observed by the satellite; including:

[0065] a. Read the secondary XCO2 data products of satellite observations, including XCO2, orbit, observation time, quality flag, prior CO2 profile, average kernel function, and pressure weight parameter;

[0066] b. Screening of satellite observation points in land areas;

[0067] c. Use the two-step linear regression method to perform linear fitting on the satellite observation data of each orbit, and identify satellite observation points with outliers greater than 2ppm and multiple outlier points within a spatial neighborhood with a radius of 10km;

[0068] XCO2=XCO2_trend+XCO2_local;

[0069] XCO2_trend = a×x+b;

[0070] Among them, XCO2_trend is the linear trend of XCO2 changing with latitude, and XCO2_local is the fitting error, indicating local changes.

[0071] d, calculate the standard deviation σ of the fitting error XCO2_local local , filter the satellite observations; meet XCO2 <XCO2_trend+0.5σ local The data of the conditions are regarded as background concentration sampling points; where XCO2_trend is the linear trend of XCO2 changing with latitude;

[0072] e. Perform linear fitting on the background sampling points again to obtain the background line, calculate the difference between XCO2 and the background line, and the observation points with a difference greater than 2ppm are XCO2 abnormal points.

[0073] S2, based on the spatial autocorrelation of atmospheric CO2 concentration, uses the statistical results of the local Moran's I index to automatically identify the XCO2 enhancement signal caused by anthropogenic emissions in the satellite observation strip;

[0074] Among them, using satellite observation data to detect the signal of anthropogenic CO2 concentration changes, a key problem that needs to be solved is how to distinguish between CO2 observations from emission sources and "clean" observations that are almost unaffected by anthropogenic emissions nearby. The XCO2 values ​​observed by satellites have autocorrelation characteristics in space. The higher the XCO2 observation value is near the emission source, the lower the XCO2 observation value is far away from the emission source.

[0075] Specifically include:

[0076] S201, determine the spatial scope of the research target (city or park) to ensure that it covers the XCO2 anomaly points obtained in step S1;

[0077] S202, using the local Moran's I index as the local spatial association indicator (Local Indicators of Spatial Association, LISA) to calculate the spatial correlation of satellite observation points in the study area, and perform clustering and anomaly analysis on high CO2 concentration observations.

[0078]

[0079] In the formula, I i Clustering and anomaly analysis of CO2 high concentration observations, s i is the inverse distance of the ith XCO2 observation at different locations, xi , x j are the i-th and j-th XCO2 observations at different locations, is the calculated mean XCO2 of the observation points, ω i，j is the spatial weight matrix, which represents the distance relationship between two points; n is the number of observation points involved in the calculation;

[0080] The inverse distance square method was used to calculate the distance relationship between features, and the weighted matrix was row-standardized. Based on the calculated test statistic, a significance level (p-value) of 0.05 was taken for significance testing.

[0081] It can be understood that the present invention applies formula (1) and formula (2) to the atmospheric CO2 concentration data observed by satellite for the first time, and there is no relevant literature in the prior art; the role of the technology is to identify the CO2 enhancement signal caused by anthropogenic emissions by calculating the spatial autocorrelation of atmospheric CO2 concentration through the local Moran's I index.

[0082] Among them, in the local Moran's I statistical results, the XCO2 observations of each period will be divided into non-significant correlation points (NS) and 4 types of significant correlation points (p-value less than 0.05), including high high (HH), low low (LL), high low (HL), and low high (LH). Among them, H means that the variable value is higher than the average, and L means that the variable value is lower than the average. HH and LL indicate that the difference between the observation point and its surrounding observations is small, that is, the data points with high or low values ​​are clustered, while LH and HL indicate that there is a significant statistical difference between the variable values ​​of the observation point and its surrounding observation points.

[0083] S203, remove satellite observation abnormal points caused by random noise, that is, HL and LH type points. The XCO2 mean value calculated every 0.1° latitude band is used to obtain the XCO2 strip profile data of satellite observation.

[0084] according to The XCO2 enhancement signal caused by local emission sources is extracted.

[0085] in, is the anthropogenic XCO2 enhancement value detected by satellite observations, is the XCO2 observation value of type HH, is the band background value calculated from the median value of the profile data.

[0086] It can be understood that compared with the prior art, the formula (3) of the present invention improves Background value and The HH type points calculated by the local Moran's I index are used as enhanced observation points, and the strip background value is calculated using the data without outliers.

[0087] S3, simulation of anthropogenic CO2 concentration coupled with atmospheric transport process: using the passive tracer mode of the WRF-Chem (V3.9.1) atmospheric transport model to couple CO2 emissions, simulate anthropogenic CO2 mixing ratios, and establish a quantitative relationship between anthropogenic emissions and anthropogenic CO2 concentrations; that is, establish a mapping relationship between anthropogenic CO2 concentrations and emissions;

[0088] Specifically include:

[0089] S301, set the parameters of the atmospheric transport model. The 0.1° grid where the center of the research target (city or park) is located is used as the center point of the simulation area. Based on the satellite observation date, the simulation start time is set to 00:00 UTC time 5 days forward (the simulation duration is 108 hours, of which the self-start time is 12 hours). The NCEP FNL data with a 6-hour, 0.25° grid resolution is used as the initial and boundary conditions of the meteorological field, and the model grid is configured as 32 vertical layers to represent the atmospheric column from the surface to 100hPa. Using the EDGAR emission inventory as prior information, the atmospheric transport model simulation generates output once an hour, providing anthropogenic CO2 concentration data for different pressure layers.

[0090] S302, the atmospheric transport model simulation generates output results every hour, providing anthropogenic CO2 concentration data at different pressure layers (X m ). The atmospheric transport model simulation results are converted into column concentration data by calculating the pressure weights using the layered atmospheric pressure.

[0091]

[0092] In the formula, h m,i is the pressure weight of the i-th layer of atmosphere, p i is the pressure of the i-th layer, p i+1 is the pressure of the i+1th layer, p i-1 is the pressure of the i-1th layer, p surf is the surface pressure, is the anthropogenic CO2 enhancement value simulated by the atmospheric transport model, is the mth atmospheric pressure weight set at the Tth moment, X m are m artificially set enhancement coefficients.

[0093] In the formula, the pressure weight calculation of the boundary layer is divided into two terms. When i = 1, only the first term of formula (4) is used. When i is equal to the highest level, only the second term of formula (4) is used

[0094] It can be understood that the function of the techniques of formula (4) and formula (5) of the present invention is to convert the stratified data of atmospheric CO2 concentration into column concentration data.

[0095] S4, Carbon emission estimation and uncertainty assessment constrained by satellite observations: Based on the established mapping relationship between anthropogenic CO2 concentration and emissions, the CO2 enhancement value observed by satellite is used as a constraint on the simulation results of the atmospheric transport model. The simulation results of anthropogenic CO2 concentration are iteratively optimized by changing the emission input data.

[0096] Specifically include:

[0097] S401, the number of layers of vertical distribution of satellite CO2 profiles is different from that of WRF-Chem model simulation results, and the sensitivity to CO2 concentration changes at different heights in the atmospheric column is also different. In order to make direct comparison between satellite observations and atmospheric transport model simulations of anthropogenic CO2 concentrations, the pressure and X m Piecewise linear interpolation was performed to interpolate the atmospheric transport model simulation results to the same number of layers as the satellite prior profile (20 layers). The layered simulation results were converted into column concentrations by combining the satellite average kernel function and the pressure weight function.

[0098]

[0099] In the formula, for The corrected value, h j is the pressure weight function, ak j is the column average kernel function, is the m enhancement coefficients artificially set under the number of layers of vertical distribution of satellite observation inversion, and j is the number of layers of vertical distribution of satellite observation inversion;

[0100] The technical function of formula (6) is to use satellite observation data to correct the model simulation results, so that satellite observations and model simulations can be directly compared, without considering the differences in the sensitivity of satellite sensors to changes in CO2 concentration at different heights in the atmospheric column.

[0101] S402, quantify the error of the atmospheric transport model simulation results using the average deviation between the atmospheric transport model simulation and the satellite observed XCO2 enhancement, that is:

[0102]

[0103] Where σ is the simulation result error, is the anthropogenic XCO2 enhancement value detected by satellite observations;

[0104] S403, construct a simulation experiment with emission input as the atmospheric transport model variable, set different emission coefficients from 0.1 to 2 at equal intervals to change the emission input data simulated by the atmospheric transport model, and obtain multiple groups of control results. Perform least squares fitting on the emission coefficient and its corresponding simulation result error σ to obtain the emission coefficient when σ = 0. By multiplying the prior emission data by the emission coefficient, the optimal emission estimation result is obtained.

[0105] S404: Construct a sensitivity experiment with wind speed as the model variable, set meteorological input conditions with different wind speeds, and conduct sensitivity tests on the simulation results of the atmospheric transmission model. Perform a least squares fit on the wind speed adjustment coefficient and the simulation error of the atmospheric transmission model to quantify the impact of wind speed on the emission estimation results.

[0106] in, Figure 2 The invention provides the principle of the carbon emission remote sensing monitoring method for cities and parks.

[0107] Embodiment 2, as Figure 3 As shown, the carbon emission remote sensing monitoring system for city and park scales provided by the embodiment of the present invention includes:

[0108] Atmospheric CO2 satellite observation data collection and processing module 1 is used for collecting and processing atmospheric CO2 satellite observation data and obtaining XCO2 abnormal points in observation points;

[0109] Satellite-observed XCO2 enhancement signal detection module 2, based on the spatial autocorrelation of atmospheric CO2 concentration and using the statistical results of the local Moran's I index, automatically identifies XCO2 enhancement signals caused by anthropogenic emissions on satellite-observed strips;

[0110] Module 3 of anthropogenic CO2 concentration simulation coupled with atmospheric transport process is used to couple CO2 emissions using the passive tracer mode of the WRF-Chem atmospheric transport model, simulate anthropogenic CO2 mixing ratios, and establish a mapping relationship between anthropogenic CO2 concentrations and emissions;

[0111] The satellite observation-constrained carbon emission estimation and uncertainty assessment module 4 is used to iteratively optimize the anthropogenic CO2 concentration simulation results by changing the emission input data based on the mapping relationship and taking the satellite-observed CO2 enhancement value as the constraint condition of the atmospheric transport model simulation results.

[0112] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Since the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention, their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0114] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment.

[0115] An embodiment of the present invention further provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program.

[0116] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0117] An embodiment of the present invention further provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0118] An embodiment of the present invention further provides a server, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device.

[0119] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0121] To further illustrate the effects of the embodiments of the present invention, the following experiment was conducted.

[0122] (1) Input data.

[0123] The present invention is applicable to atmospheric CO2 concentration data observed by multi-source carbon satellites such as OCO-2, OCO-3, and Tansat, and can process files in different formats including .nc and .hdf. It is also applicable to other greenhouse gases such as CH4.

[0124] (2) Spatial autocorrelation method.

[0125] In order to identify the XCO2 enhancement signal caused by human emissions, the present invention clusters and performs anomaly analysis on high-concentration observation points based on the spatial autocorrelation of atmospheric CO2 concentration observed by satellite. Most existing technologies use arithmetic statistics of median / mean or linear fitting methods, which have subjective definition problems, and insufficient valid data for statistics will also lead to large statistical errors.

[0126] The local Moran's I index used in the present invention is a representative index of spatial autocorrelation analysis. Similar spatial correlation analysis methods such as the global Moran's I index and the spatiotemporal semivariogram can be used to replace the local Moran's I index.

[0127] (3) Atmospheric transmission model.

[0128] The WRF-Chem atmospheric transport model used in the present invention is one of the most widely used meteorological models in the world. The model is driven by basic geographic data, meteorological data, and anthropogenic emissions, simulates the atmospheric molecular diffusion process based on the Euler model, and establishes a mapping relationship between anthropogenic emissions and atmospheric CO2 concentration.

[0129] This method can be replaced by an atmospheric transport model that is also suitable for regional scale simulations, such as WRF-STILT, X-STILT and other atmospheric transport models based on Lagrangian random walk theory. Through the backward running trajectory of air particles driven by turbulence and average wind field, the number of all particles within the boundary layer height of the study area and their residence time are calculated to determine the footprint weight value, and the anthropogenic emission flux is linked to the concentration change at that location.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the protection scope of the present invention.

Claims

1. A carbon emission remote sensing monitoring method for cities and parks, characterized in that: The method includes: S1, collect and process atmospheric CO2 satellite observation data to obtain XCO2 abnormal points in the observation points; where XCO2 is the atmospheric CO2 column concentration observed by the satellite; S2, based on the spatial autocorrelation of atmospheric CO2 concentration, uses the statistical results of the local Moran's I index to automatically identify the XCO2 enhancement signal caused by anthropogenic emissions in the satellite observation strip; S3, using the passive tracer mode of the WRF-Chem atmospheric transport model to couple CO2 emissions, simulate anthropogenic CO2 mixing ratios, and establish a mapping relationship between anthropogenic CO2 concentrations and emissions; S4, based on the mapping relationship, takes the CO2 enhancement value observed by satellite as the constraint condition of the atmospheric transport model simulation results, and iteratively optimizes the anthropogenic CO2 concentration simulation results by changing the emission input data; In step S1, obtaining XCO2 abnormal points in the observation points includes: a. Read the secondary XCO2 data products of satellite observations, including XCO2, orbit, observation time, quality flag, prior CO2 profile, average kernel function, and pressure weight parameter; b. Screening of satellite observation points in land areas; c. Use the two-step linear regression method to perform linear fitting on the satellite observation data of each orbit, and identify satellite observation points with outliers greater than 2ppm and multiple outlier points within a spatial neighborhood with a radius of 10km; d, calculate the standard deviation σ of the fitting error XCO2_local local , filter the satellite observations; meet XCO2 <XCO2_trend+0.5σ local The data of the conditions are regarded as background concentration sampling points; where XCO2_trend is the linear trend of XCO2 changing with latitude; e. Perform linear fitting on the background sampling points again to obtain the background line, calculate the difference between XCO2 and the background line, and the observation point with a difference greater than 2ppm is the XCO2 abnormal point; In step S2, the XCO2 enhancement signal caused by anthropogenic emissions on the satellite observation strip is automatically identified, specifically including: S201, determine the spatial scope of the research target and obtain the XCO2 abnormal points; S202, using the local Moran's I index as the local spatial association index LISA to calculate the spatial correlation of satellite observation points in the study area, and to perform clustering and anomaly analysis of high CO2 concentration observations; S203, removing satellite observation anomalies caused by random noise, calculating the XCO2 mean value every 0.1° latitude band, and obtaining the XCO2 strip profile data observed by the satellite; extracting the XCO2 enhancement signal caused by the local emission source according to the formula of the artificial XCO2 enhancement value detected by satellite observation; In step S202, clustering and anomaly analysis are performed on the CO2 high concentration observations, and the expression is: In the formula, I i Clustering and anomaly analysis of CO2 high concentration observations, s i is the inverse distance of the ith XCO2 observation at different locations, x i ,x j are the i-th and j-th XCO2 observations at different locations, is the calculated mean XCO2 of the observation points, ω i,j is the spatial weight matrix, which represents the distance relationship between two points; n is the number of observation points involved in the calculation; The inverse distance square method was used to calculate the distance relationship between features, and a significance test was performed with a significance level p-value of 0.05 based on the calculated test statistic; In the statistical results of local Moran's I, the XCO2 observations in each period are divided into non-significant correlation points NS and four types of significant correlation points with p-value less than 0.05, including high-high HH, low-low LL, high-low HL, and low-high LH; among them, H means that the variable value is higher than the average value, L means that the variable value is lower than the average value, HH means that the data points with high values ​​are clustered, LL means that the data points with low values ​​are clustered, and LH and HL both indicate that there are significant statistical differences between the variable values ​​of the observation point and its surrounding observation points; In step S203, the formula for the artificial XCO2 enhancement value detected by satellite observation is expressed as: In the formula, is the anthropogenic XCO2 enhancement value detected by satellite observations, is the XCO2 observation value of type HH, is the band background value calculated from the median value of the profile data.

2. The carbon emission remote sensing monitoring method for cities and parks according to claim 1 is characterized in that: In step S3, a mapping relationship between anthropogenic CO2 concentration and emissions is established, including: S301, set the parameters of the atmospheric transport model, using the EDGAR emission inventory as prior information, and simulate the atmospheric transport model to generate output once an hour, providing anthropogenic CO2 concentration data X at different pressure layers m ; S302, using the layered atmospheric pressure to calculate the pressure weight, converting the atmospheric transport model simulation results into column concentration data.

3. The carbon emission remote sensing monitoring method for cities and parks according to claim 2 is characterized in that: In step S302, the pressure weight is calculated using the layered atmospheric pressure to convert the atmospheric transport model simulation results into column concentration data, expressed as: In the formula, h m,i is the pressure weight of the i-th layer of atmosphere, p i is the pressure of the i-th layer, p i+1 is the pressure of the i+1th layer, p i-1 is the pressure of the i-1th layer, p surf is the surface pressure, is the anthropogenic CO2 enhancement value simulated by the atmospheric transport model, is the mth atmospheric pressure weight set at the Tth moment, X m are m artificially set enhancement coefficients.

4. The carbon emission remote sensing monitoring method for cities and parks according to claim 1 is characterized in that: In step S4, the anthropogenic CO2 concentration simulation results are iteratively optimized, including: S401, interpolate the simulation results of the atmospheric transport model to the same number of layers as the satellite prior profile, combine the satellite average kernel function and the pressure weight function, and convert the layered simulation results into column concentrations. The expression is: In the formula, for The corrected value, h j is the pressure weight function, ak j is the column average kernel function, is the m enhancement coefficients artificially set under the number of layers of vertical distribution of satellite observation inversion, and j is the number of layers of vertical distribution of satellite observation inversion; S402, using the average deviation between the atmospheric transport model simulation and the satellite observed XCO2 enhancement, to quantify the error of the atmospheric transport model simulation results, the expression is: Where σ is the simulation result error, is the anthropogenic XCO2 enhancement value detected by satellite observations; S403, constructing a simulation experiment with emission input as an atmospheric transport model variable, and obtaining the optimal emission estimation result by multiplying the prior emission data by the emission coefficient; S404, construct a sensitivity experiment with wind speed as a model variable, set meteorological input conditions with different wind speeds, and perform sensitivity tests on the simulation results of the atmospheric transport model.

5. A carbon emission remote sensing monitoring system for cities and parks, characterized in that: The system implements the carbon emission remote sensing monitoring method for city and park scales as described in any one of claims 1 to 4, and the system includes: The atmospheric CO2 satellite observation data collection and processing module (1) is used for collecting and processing atmospheric CO2 satellite observation data and obtaining XCO2 abnormal points in the observation points; Satellite-observed XCO2 enhancement signal detection module (2) automatically identifies XCO2 enhancement signals caused by anthropogenic emissions in satellite-observed strips based on the spatial autocorrelation of atmospheric CO2 concentration and using the statistical results of the local Moran's I index; The anthropogenic CO2 concentration simulation module coupled with atmospheric transport processes (3) is used to couple CO2 emissions using the passive tracer mode of the WRF-Chem atmospheric transport model, simulate anthropogenic CO2 mixing ratios, and establish a mapping relationship between anthropogenic CO2 concentrations and emissions; The satellite observation-constrained carbon emission estimation and uncertainty assessment module (4) is used to iteratively optimize the anthropogenic CO2 concentration simulation results by changing the emission input data, based on the mapping relationship and taking the satellite-observed CO2 enhancement value as the constraint condition for the atmospheric transport model simulation results.

Images