A method for calculating point source carbon emission flux based on mobile FTIR remote sensing

Abstract

The present invention discloses a method for calculating the flux of point source carbon emissions based on mobile FTIR remote sensing, including: using mobile FTIR to carry out closed-loop cruise observation of the concentration of greenhouse gas columns from point sources, accurately tracking the solar spectrum while the vehicle is moving based on a ground-based FTIR installed on a vehicle, and ensuring that the FTIR spectrometer of the system can still continuously record solar spectrum data when the system is in motion; based on a specific spectrum screening and correction pre-processing method, spectra that are severely affected by interference or have poor fitting effects are eliminated to control the quality of the spectrum. The present invention inverts the concentration of greenhouse gas columns based on a nonlinear least squares fitting algorithm to obtain the distribution of greenhouse gas column concentrations from point sources; based on the greenhouse gas concentration results measured by ground-based FTIR in a circle and combined with wind speed and direction information, point source greenhouse gas emission flux calculations are performed to analyze flux calculation errors.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental monitoring, and in particular relates to a point source carbon emission flux measurement method based on mobile FTIR remote sensing. Background Art

[0002] At present, most of the CO2 and CH4 monitoring stations established in China are located in high mountains, islands and coasts, and relatively few high-temporal-resolution monitoring studies are conducted in urban areas. Moreover, the current domestic monitoring of regional emissions from power plants mainly adopts a bottom-up measurement method, using emission factors and statistical data such as the operating conditions of coal and power equipment to calculate greenhouse gas emissions from power plants. However, the greenhouse gas measurement method based on emission factors has large errors, making it difficult to clearly define the changing characteristics of greenhouse gas emissions from power plants, resulting in deviations in the calculated greenhouse gas emissions from power plants.

[0003] The closed-loop flux method is an effective method for assessing atmospheric greenhouse gas emissions from a city or important point sources in the city. The closed-loop flux method mainly uses a mobile observation platform to observe greenhouse gas or pollutant concentrations in a closed loop around the target area, combines wind field information to study greenhouse gas or pollutant emissions in the area, and calculates the regional emission flux based on the difference between the output and input fluxes. Cruise observation based on mobile spectral remote sensing is also an important method for conducting closed-loop flux observations.

[0004] Ground-based Fourier transform infrared spectroscopy (FTIR) can detect atmospheric greenhouse gas column concentrations with high precision and accuracy. Portable FTIR spectrometers are widely used as a supplement to global carbon observations due to their small size and easy deployment. Vehicle-mounted portable FTIR, as a mobile spectral remote sensing cruise observation, is also often used to monitor greenhouse gas emissions from cities and point sources.

[0005] In summary, the point source flux emission calculation method based on mobile FTIR remote sensing is used to obtain the emission flux of point sources. Based on the technology of accurately tracking the solar spectrum while the vehicle is moving, the FTIR spectrometer of the system can continuously record the solar spectrum while in motion. Based on the greenhouse gas column concentration measured by closed-loop cruise combined with wind speed and direction information, the greenhouse gas emission flux calculation results are obtained. The greenhouse gas emission results provide scientific data for clarifying carbon emission reduction. Summary of the invention

[0006] The purpose of the present invention is to provide a method for calculating point source carbon emission flux based on mobile FTIR remote sensing. The method can accurately obtain the emission status of the carbon flux of the point source.

[0007] To achieve the above-mentioned purpose of the invention, the present invention provides a method for calculating point source carbon emission flux based on mobile FTIR remote sensing, the method comprising the following steps:

[0008] (1) conducting closed-loop cruise observations of greenhouse gas column concentrations near the point source, using a vehicle-mounted FTIR spectrometer installed on the vehicle to accurately track the solar spectrum while the vehicle is moving, ensuring that the FTIR spectrometer continuously records solar spectrum data and collects solar spectrum while the vehicle is moving;

[0009] (2) Based on the solar spectrum screening and correction pre-processing method, the solar spectra that are seriously affected by interference or have poor fitting effects are eliminated to control the quality of the solar spectrum; the greenhouse gas column concentration is inverted based on the nonlinear least squares fitting algorithm to obtain the greenhouse gas column concentration distribution in the observation area of ​​the closed-loop cruise observation;

[0010] (3) Based on the greenhouse gas concentration results measured during the closed-loop cruise observation and combined with the wind speed and direction information, the greenhouse gas concentration distribution characteristics in the observation area are analyzed, the carbon emission flux of greenhouse gases is calculated, and the flux calculation error is analyzed.

[0011] Furthermore, the step (1) of accurately tracking the solar spectrum while the vehicle is moving includes using a gyroscope to understand the vehicle's direction of travel in real time, then reading the GPS longitude and latitude information to calculate the sun's zenith angle and azimuth, and transmitting the information to the motor to rotate the reflective lens through the host computer to achieve real-time tracking of the sun.

[0012] Furthermore, the step (2) of eliminating solar spectra that are seriously affected by interference or have poor fitting effects includes screening out solar spectra that are seriously affected by clouds or fluctuations during vehicle movement or have poor fitting effects through spectral screening criteria, thereby ensuring the quality of the inverted spectrum.

[0013] Furthermore, the inversion of greenhouse gas column concentration based on the nonlinear least squares fitting algorithm in step (2) includes:

[0014] (1) Forward modeling, using the simulated greenhouse gas prior gas profiles, the temperature, humidity, and pressure profiles obtained by interpolation of reanalysis data, and the molecular line parameters of CO2 and CH4, the initial state vector xa of the atmosphere is given;

[0015] (2) Substituting the initial state vector xa of the atmosphere as xi into the forward model for calculation, and simulating the radiation brightness F(x) received by the FTIR spectrometer based on the convolution instrument linear function of the atmospheric radiation transfer model;

[0016] (3) Based on the Newton-Gaussian iteration, the solar spectrum is continuously iterated to obtain xi+1, and it is determined whether the difference between xi+1 and xi meets the threshold. If so, xi+1 is the solution, and its covariance matrix is ​​given. If it does not converge, the number of iterations is counted; if the number of iterations is greater than 10, it is discarded, otherwise the state vector and iteration factor are updated according to the cost function, and the updated state vector is used as xi to continue the iteration;

[0017] (4) The calculated atmospheric state vector xr is the result of the atmospheric initial state vector xa as the prior state vector and the true atmospheric state vector x after average kernel smoothing:

[0018] xr=xa+(x-xa) (1)

[0019] where A is the average kernel matrix in the inversion:

[0020] A=(S-1+KTS-1K)-1KTS-1K (2)

[0021] S is the error covariance matrix in the inversion, K is the Jacobian matrix in the inversion; the average kernel represents the influence of concentration changes at different heights on the disturbance of the inversion result.

[0022] Furthermore, the step (3) of calculating the point source greenhouse gas emission flux based on the greenhouse gas concentration results measured during the closed-loop cruise observation combined with the wind speed and direction information includes: closed-loop integration along the travel direction of the closed-loop cruise observation vehicle platform to obtain the greenhouse gas emission flux in the closed-loop cruise area; wherein each group of observations is completed within a period of time, and the closed-loop integral is obtained by adding the flux of each group of observations:

[0023] Flux≈∑VCD(S i )·ω i ·sin(β i )·ΔS i (3)

[0024] Where VCD(Si) is the column concentration of greenhouse gas observed in the i-th segment, ωi is the wind speed in the observation period, βi is the angle between the wind direction clockwise and the driving direction, and ΔSi is the formal distance of the vehicle platform during the i-th observation period;

[0025] When the wind blows from outside the ring to inside the ring at the i-th observation point, βi>180°, and the flux at this point is less than 0, indicating that the wind is input from outside the ring to inside the ring; when the wind blows from inside the ring to outside the ring at the i-th observation point, βi<180°, and the flux at this point is greater than 0, indicating that the wind is output from inside the ring to outside the ring; Flux represents the difference between the output and input fluxes, that is, the emission flux of the area inside the ring; the observations of βi<180° and βi>180° are summed respectively to obtain the input flux Flux,in and the output flux Flux,out.

[0026] Furthermore, the analysis flux calculation error in step (3) includes: according to the emission flux acquisition method, the error of the emission flux within the loop of the closed-loop navigation area comes from the observation interval error and the wind field error.

[0027] Furthermore, the calculation method of the observation interval error is as follows:

[0028] Since the emission flux is calculated using formula (3), within the observation distance of ΔSi, VCDi is used as the column concentration of the target pollutant, and the error ΔF caused by the fluctuation of VCDi on the observation route is calculated using formula (4):

[0029]

[0030] Among them, ΔVCD is the standard deviation of all VCDs measured during the closed-loop cruise process: VCD is the observed atmospheric greenhouse gas concentration distribution result;

[0031] ΔVCD=SD(VCDi) (5)

[0032] Where SD is the standard deviation of VCDi.

[0033] Furthermore, the wind field error is the absolute value of the difference between the average wind field and the calculated emission flux of the local wind field at each observation point.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The method for calculating point source carbon emission flux based on mobile FTIR remote sensing of the present invention adopts a vehicle-mounted ground-based remote sensing FTIR solar tracker, which can obtain the solar zenith angle and azimuth in real time based on the vehicle's direction of travel and longitude and latitude information, making it convenient for the FTIR spectrometer to measure the solar spectrum. According to the vehicle's direction of travel and speed, the solar tracker automatically tracks the sun and imports sunlight into the FTIR spectrometer in real time. The ground-based remote sensing FTIR based on direct sunlight of the present invention can quickly move and observe greenhouse gas emissions, measure the column concentration of greenhouse gases in the atmosphere, and obtain regional emission fluxes in combination with meteorological data, and can calculate the carbon emission flux of point sources based on the vehicle-mounted FTIR closed-loop cruise observation and wind speed and direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 This is a schematic diagram of a closed-loop cruise observation vehicle-mounted platform of the present invention;

[0038] Figure 2 A flux calculation flow chart of regional greenhouse gas closed-loop carbon flux based on mobile FTIR remote sensing according to an embodiment of the present invention;

[0039] Figure 3A flow chart of the sun tracking by the vehicle-mounted sun tracker of the present invention;

[0040] Figure 4 A schematic diagram of the CO2 absorption window and its fitting residual of the portable FTIR spectrometer of the present invention;

[0041] Figure 5 A schematic diagram of the CH4 absorption window and its fitting residual of the portable FTIR spectrometer of the present invention;

[0042] Figure 6 This is a schematic diagram of the CO2 concentration distribution around the Hefei Wanneng Power Plant of the present invention.

[0043] In the figure: master control 1, GPS 2, sun tracker 3, portable Fourier transform infrared spectrometer 4, weather station 5. DETAILED DESCRIPTION

[0044] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0045] like Figure 1 As shown in the figure, the method for calculating the point source carbon emission flux based on mobile FTIR remote sensing of the present invention uses a closed-loop cruise observation vehicle-mounted platform to calculate the greenhouse gas carbon emission flux in the power plant area. It uses a vehicle-mounted FTIR spectrometer, and based on the vehicle-mounted FTIR spectrometer, it circles to obtain the solar absorption spectrum of the power plant area, and uses a nonlinear least squares inversion algorithm to achieve the inversion of greenhouse gas column concentration. Among them, the closed-loop cruise observation vehicle-mounted platform is as follows Figure 1 As shown. The closed-loop cruise observation vehicle-mounted platform includes a master control 1, GPS 2, a sun tracker 3, a portable Fourier transform infrared spectrometer 4 and a weather station 5. The master control 1 is connected to GPS 2, and obtains the vehicle's travel direction and longitude and latitude information from GPS 2, thereby obtaining the real-time solar zenith angle and azimuth; the master control 1 is connected to the sun tracker 3, and the sun tracker 3 automatically tracks the sun according to the real-time solar zenith angle and azimuth obtained by the master control 1; the sun tracker 3 is connected to the portable Fourier transform infrared spectrometer 4, and the sunlight is introduced into the portable Fourier transform infrared spectrometer 4; the master control 1 is connected to the portable Fourier transform infrared spectrometer 4, and the portable Fourier transform infrared spectrometer 4 is adjusted and controlled to continuously record the solar spectrum data; the master control 1 is connected to the weather station 5, and the weather station 5 is adjusted and controlled to record real-time meteorological information. Finally, the point source carbon emission flux information is calculated based on the greenhouse gas column concentration combined with the wind speed and direction information.

[0046] The present invention adopts a portable FTIR spectrometer with a spectral resolution of 0.5cm-1 and a measurement band of 5000-11000cm-1, covering the near-infrared detection bands of CO2 (6180-6260, 6297-6382cm-1) and CH4 (5880-5996, 6007-6145cm-1). The light source of the portable FTIR spectrometer is direct sunlight, and the detector is an indium gallium arsenide (InGaAs) room temperature detector.

[0047] Specifically, the present invention uses a gyroscope to understand the direction of the vehicle in real time, and then reads the GPS longitude and latitude information to calculate the sun's zenith angle and azimuth. The positioning accuracy of the GPS is 7.8m. The information is transmitted to the motor to rotate the reflective lens through the host computer to achieve real-time tracking of the sun. The sun tracker is a vehicle-mounted type with a tracking accuracy of 0.1°. The process of the vehicle-mounted sun tracker tracking the sun is as follows Figure 3 As shown. First, turn on the vehicle-mounted solar tracker to ensure that the instrument can work normally; secondly, obtain the vehicle's direction of travel based on the gyroscope; obtain time, longitude and latitude information based on GPS; calculate the real-time solar azimuth and altitude according to the vehicle's direction of travel and longitude and latitude information; then calculate the motor rotation pulse based on the solar altitude and azimuth; finally drive the motor to rotate the reflective lens. In order to ensure that the vehicle-mounted solar tracker transmits sunlight to the spectrometer, it is also necessary to observe the spectrum data collection through the master control. If the sunlight is transmitted to the spectrometer normally, the spectrometer begins to continuously collect the solar spectrum until it ends. On the contrary, the vehicle-mounted solar tracker starts to obtain information such as the vehicle's direction of travel, time, longitude and latitude information, recalculates the solar azimuth, altitude and motor rotation pulse, and drives the motor to rotate the reflective lens again until it is determined that the sunlight is transmitted to the spectrometer.

[0048] The flow of the flux calculation method of regional greenhouse gas closed-loop carbon flux based on mobile FTIR remote sensing in the embodiment of the present invention is as follows: Figure 2As shown. Among them, for all the collected solar absorption spectra, the spectrum screening criteria shown in Table 1 are used to screen out the spectra that are seriously affected by clouds, fluctuations during vehicle movement, etc. or have poor fitting effects, so as to control the quality of the spectrum. The solar spectrum inversion is based on the nonlinear least squares fitting algorithm. First, forward modeling is performed, and the atmospheric initial state vector xa is given using the greenhouse gas prior gas lines simulated by the World Atmospheric Climate Model (WACCM), the temperature, humidity, and pressure profiles obtained by interpolation of the reanalysis data of the National Center for Environmental Prediction (NCEP), and the CO2 and CH4 molecular line parameters of the HATRAN database. Substitute xa as xi into the forward model for calculation, and simulate the radiation brightness F(x) received by the FTIR spectrometer based on the convolution instrument line function of the atmospheric radiation transfer model. The spectrum is continuously iterated based on the Newton-Gaussian iteration to obtain xi+1, and it is judged whether the difference between xi+1 and xi meets the threshold. If it meets, xi+1 is the solution, and its covariance matrix is ​​given at the same time. If it does not converge, the number of iterations is counted. If the number of iterations is greater than 10, it is discarded, otherwise the state vector and iteration factor are updated according to the cost function, and the updated state vector is used as xi to continue iterating.

[0049] The final calculated atmospheric state vector xr is the result of the atmospheric initial state vector xa as the prior state vector and the true atmospheric state vector x after average kernel smoothing:

[0050] xr=xa+A(x-xa) (1)

[0051] Where A is the average kernel matrix in the inversion:

[0052] A=(S-1+KTS-1K)-1KTS-1K (2)

[0053] Among them, S is the error covariance matrix in the inversion, K is the Jacobian matrix in the inversion; the average kernel represents the influence of concentration changes at different heights on the disturbance of the inversion results.

[0054] The CO2 observation accuracy based on the FTIR spectrometer combined with the inversion algorithm is 0.2ppm, and the CH4 observation accuracy is 1ppb.

[0055] Table 1. Spectral screening criteria

[0056]

[0057] By performing closed-loop integration along the travel direction of the closed-loop cruise observation vehicle platform, the emission flux of greenhouse gas j in the closed-loop area is obtained. In the actual observation process, since each set of observations is completed within a period of time, the closed-loop integral is usually obtained by adding the flux of each set of observations.

[0058] Flux≈∑VCD(Si )·ω i ·sin(β i )·ΔS i (3)

[0059] Among them, VCD(Si) is the column concentration of greenhouse gases observed in the i-th section, ωi is the wind speed in the observation section, βi is the angle between the wind direction clockwise and the driving direction, and ΔSi is the formal distance of the closed-loop cruise observation vehicle platform during the i-th observation period. When the wind blows from outside the ring to inside the ring at the i-th observation point, βi>180°, and the flux at this point is less than 0, indicating that the wind is input from outside the ring to inside the ring; when the wind blows from inside the ring to outside the ring at the i-th observation point, βi<180°, and the flux at this point is greater than 0, indicating that the wind is output from inside the ring to outside the ring. Therefore, Flux represents the difference between the output and input fluxes, that is, the emission flux of the area inside the ring. The input flux Flux,in and the output flux Flux,out are obtained by summing the observations of βi<180° and βi>180° respectively.

[0060] In this embodiment, the error of the emission flux in the closed-loop navigation loop mainly comes from the observation interval error, wind field error, etc.

[0061] The calculation method of the above error is:

[0062] (1) Observation interval error:

[0063] Due to the limited observed beam current, in actual research, the emission flux is calculated using formula (3). i VCD will be used within the observation distance i Column concentration as the target contaminant. i The error ΔF caused by fluctuations on the observation route can be calculated using formula (4):

[0064]

[0065] Among them, ΔVCD is the standard deviation of all VCDs measured during the closed-loop cruise process: VCD is the observed atmospheric greenhouse gas concentration distribution result.

[0066] ΔVCD=SD(VCD i ) (5)

[0067] Where SD is the standard deviation of VCDi.

[0068] (2) Wind field error:

[0069] In order to ensure that each observation point in the loop has the same weight when calculating the emission flux, the average wind field is used for each observation point in the present invention instead of the wind field of each observation point. Since there are certain differences between the wind fields of each observation point in the closed-loop navigation loop, errors may be caused. The present invention will use the average wind field and the local wind field of each observation point to calculate the emission flux respectively, and the absolute value of the difference between the emission fluxes calculated by the two methods is used as the error caused by the wind field.

[0070] Based on the ground-based remote sensing FTIR infrared spectroscopy technology, the present invention successfully inverts the column concentrations and vertical profiles of trace gases such as CO2, CH4, CO, HNO3 and O3 in the atmosphere, and analyzes the influence of different parameters in the spectral inversion on the spectral inversion results, such as Figure 4 and Figure 5 At the same time, the distribution of greenhouse gas concentrations at Hefei point sources was also studied based on the vehicle-mounted FTIR spectrometer, such as Figure 6 As shown. The above-mentioned closed-loop carbon emission flux measurement of greenhouse gases from point sources based on mobile Fourier transform infrared spectroscopy technology uses the technology of accurately tracking the sun while the vehicle is moving to ensure that when the system is in motion, the FTIR spectrometer can still continuously record solar spectrum data, and use solar spectrum screening and greenhouse gas column concentration inversion technology to obtain regional circle greenhouse gas column concentration distribution. Based on the greenhouse gas column concentration distribution results combined with wind speed and direction information, point source greenhouse gas emission information is accurately obtained. Through comprehensive verification and analysis of the results from multiple aspects, the reliability of the technical system proposed by the research institute is ensured, the rationality and reliability of the results are guaranteed, and the feasibility of using the method proposed in the research to develop a point source greenhouse gas observation technology system is demonstrated.

[0071] The specific implementation methods described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating point source carbon emission flux based on mobile FTIR remote sensing, characterized in that: The following steps are involved: (1) conducting closed-loop cruise observations of greenhouse gas column concentrations near the point source, based on an on-board FTIR spectrometer installed on a vehicle, accurately tracking the solar spectrum while the vehicle is moving, and ensuring that the FTIR spectrometer continuously records solar spectrum data and collects solar spectrum while the vehicle is moving; (2) Based on the solar spectrum screening and correction pre-processing method, the solar spectra that are seriously affected by interference or have poor fitting effects are eliminated to control the quality of the solar spectrum; the greenhouse gas column concentration is inverted based on the nonlinear least squares fitting algorithm to obtain the greenhouse gas column concentration distribution in the observation area of ​​the closed-loop cruise observation; The inversion of greenhouse gas column concentrations based on the nonlinear least squares fitting algorithm includes: (2.1) Forward modeling, using the simulated greenhouse gas prior gas profiles, the temperature, humidity, and pressure profiles obtained by interpolation of reanalysis data, and the molecular line parameters of CO2 and CH4, the initial state vector of the atmosphere is given ; (2.2) The initial state vector of the atmosphere is As Substituting into the forward model for calculation, the radiation brightness F(x) received by the FTIR spectrometer is simulated based on the atmospheric radiation transfer model convolution instrument linear function; (2.3) Based on the Newton-Gaussian iteration, the solar spectrum is continuously iterated to obtain ,judge and Does the difference meet the threshold? If so, That is the solution, and its covariance matrix is ​​given at the same time. If it does not converge, the number of iterations is counted; if the number of iterations is greater than 10, it is discarded, otherwise the state vector and iteration factor are updated according to the cost function, and the updated state vector is used as Continue to iterate; (2.4) Calculated atmospheric state vector is the initial state vector of the atmosphere as a priori state vector and the true atmospheric state vector The result after average kernel smoothing: (1) where A is the average kernel matrix in the inversion: (2) Wherein, S is the error covariance matrix in the inversion, K is the Jacobian matrix in the inversion; the average kernel represents the influence of concentration changes at different heights on the disturbance of the inversion result; (3) Based on the greenhouse gas concentration results measured during the closed-loop cruise observation and combined with the wind speed and direction information, analyze the distribution characteristics of greenhouse gas concentrations in the observation area, calculate the carbon emission flux of greenhouse gases, and analyze the flux calculation errors; Based on the greenhouse gas concentration results measured during the closed-loop cruise observation and the wind speed and direction information, the point source greenhouse gas emission flux calculation includes: closed-loop integration along the travel direction of the closed-loop cruise observation vehicle platform to obtain the greenhouse gas emission flux in the closed-loop cruise area; each group of observations is completed within a period of time, and the closed-loop integral is obtained by adding the flux of each group of observations: (3) in, Greenhouse gases The column concentration observed in the segment, is the wind speed in the observation period, is the angle between the clockwise wind direction and the travel direction, For the The form distance of the vehicle platform during the observation period; When the wind is When the air blows from outside the ring to inside the ring at the observation point, , the flux here is less than 0, indicating that the wind is input from outside the ring to inside the ring; when the wind blows from inside the ring to outside the ring at the observation point of the 𝑖th segment, , the flux here is greater than 0, indicating that the output is from inside the ring to outside the ring; It represents the difference between the output and input fluxes, that is, the emission flux in the area inside the ring; and The observations of are summed up to get the input flux and output flux ; The errors in flux calculation are analyzed as follows: according to the emission flux acquisition method, the error of emission flux in the loop of the closed-loop navigation area comes from the observation interval error and wind field error; The calculation method of the observation interval error is as follows: Since the emission flux is calculated using formula (3), Within the observation distance, using VCDi as the column concentration of the target pollutant, the error caused by the fluctuation of VCDi on the observation route Calculate using formula (4): (4) in, is the standard deviation of all VCDs measured during the closed-loop cruise process: VCD is the observed atmospheric greenhouse gas concentration distribution result; (5) Where SD is the standard deviation of VCDi.

2. The method for calculating point source carbon emission flux based on mobile FTIR remote sensing according to claim 1, characterized in that: The step (1) of accurately tracking the solar spectrum while the vehicle is moving includes using a gyroscope to understand the vehicle's direction of travel in real time, then reading the GPS longitude and latitude information to calculate the sun's zenith angle and azimuth, and transmitting the information to the motor to rotate the reflective lens through the host computer to achieve real-time tracking of the sun.

3. The method for calculating point source carbon emission flux based on mobile FTIR remote sensing according to claim 2, characterized in that: The step (2) of eliminating solar spectra that are seriously affected by interference or have poor fitting effects includes filtering out solar spectra that are seriously affected by clouds or fluctuations during vehicle movement or have poor fitting effects through spectral screening criteria, thereby ensuring the quality of the inverted spectrum.

4. The method for calculating point source carbon emission flux based on mobile FTIR remote sensing according to claim 1, characterized in that: The wind field error is the absolute value of the difference between the average wind field and the calculated emission flux of the local wind field at each observation point.

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