Urban greenhouse gas flux monitoring method based on high-density observation network

Abstract

The invention provides an urban greenhouse gas flux monitoring method based on a high-density observation network, and relates to the technical field of greenhouse gas monitoring. The method comprises the following steps: dividing a target area into a plurality of grids, wherein each grid contains concentration observation data of two heights; wherein a plurality of sites are arranged on the earth surface, and the earth surface greenhouse gas concentration is obtained through summarizing and calibration; arranging at least one station at the reference height, and calibrating to obtain the greenhouse gas concentration at the reference height; based on the ground surface greenhouse gas concentration and the reference height greenhouse gas concentration, considering aerodynamic impedance, and calculating to obtain local greenhouse gas flux caused by turbulence. According to the method provided by the invention, the urban greenhouse gas emission characteristics are quantified based on high-density observation, large-scale monitoring can be implemented by using a high-density observation network, and the method can be flexibly deployed on a vehicle or an unmanned aerial vehicle to implement encrypted emission monitoring on a key area, so that a greenhouse gas flux distribution diagram is drawn with higher grid resolution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of greenhouse gas monitoring, and particularly relates to a city greenhouse gas flux monitoring method based on a high-density observation network. BACKGROUND

[0002] Cities are hotspots of anthropogenic greenhouse gas emissions. IPCC reports show that urban CO2 emissions account for about 70% of the total global fossil fuel emissions (IPCC, 2022). Reliable urban greenhouse gas flux observations can not only verify the reliability of emission inventories, but also provide constraints for optimizing the assimilation of inventories.

[0003] The eddy correlation method (EC) is the main means to obtain urban greenhouse gas fluxes, but its installation conditions are harsh and technical requirements are high. Moreover, the spatial representativeness of EC observation is limited, and it is difficult to cover the entire city and characterize the characteristics of urban greenhouse gas emissions. SUMMARY

[0004] The present application relates to the technical field of greenhouse gas monitoring, and particularly relates to a city greenhouse gas flux monitoring method based on a high-density observation network.

[0005] The city greenhouse gas flux monitoring method based on a high-density observation network provided by the present application comprises the following steps: Divide the target area into a plurality of grids, each grid containing concentration observation data at two heights, wherein a plurality of stations are arranged on the ground to collect and calibrate the ground greenhouse gas concentration ; at least one station is arranged at a reference height to calibrate the reference height greenhouse gas concentration ; Based on the ground greenhouse gas concentration , the reference height greenhouse gas concentration , and considering the aerodynamic resistance, the local greenhouse gas flux caused by turbulence is calculated .

[0006] In further embodiments, the reference height is above 2 times the height of the urban canopy.

[0007] In further embodiments, if concentration observation at two heights cannot be satisfied simultaneously, mobile observation is supplemented, specifically including: In the grid lacking ground stations, vehicle-mounted observation is used to supplement the ground greenhouse gas concentration data; In the grid lacking reference height stations, airborne observation is used to achieve, or the greenhouse gas concentration data of adjacent grids is used instead.

[0008] In further embodiments, the ground surface sites are arranged in the following way: Wind direction data of the target area is obtained, and 8 wind direction sectors are divided according to the wind direction azimuth angle; The ΔCO2 concentration of each ground surface site is time-matched with the wind direction data, the ΔCO2 concentration data is divided according to the 8 wind direction sectors, and the average ΔCO2 concentration in each wind direction sector is calculated; The average ΔCO2 concentration data of each wind direction sector is matched with the underlying surface parameters in the corresponding sector upwind, the artificial source site takes the data in winter daytime, and the Pearson correlation coefficient between ΔCO2 and the impervious surface ratio is calculated ; the natural source site takes the data in summer daytime, and the Pearson correlation coefficient between ΔCO2 and the vegetation coverage is calculated ; The impervious surface ratio of the artificial source site is positively correlated with ΔCO2, and the buffer radius range of the correlation coefficient is taken as the main footprint range of the artificial source site The buffer radius at the maximum value of the correlation coefficient is taken as the main footprint range of the artificial source site The buffer radius at the maximum value of the correlation coefficient is taken as the maximum footprint range of the artificial source site The vegetation coverage of the natural source site is negatively correlated with ΔCO2, and the buffer radius range of the correlation coefficient is taken as the main footprint range of the natural source site The buffer radius at the minimum value of the correlation coefficient is taken as the main footprint range of the natural source site The buffer radius at the minimum value of the correlation coefficient is taken as the maximum footprint range of the natural source site The footprint ranges of all ground surface sites are drawn with the artificial source site or the natural source site as the center and and as the radii, so that the maximum footprint ranges of the ground surface sites overlap and can cover the target area with a predetermined coverage rate, so that the data of the high-density observation network can reflect the concentration change characteristics of the entire target area; otherwise, the site position is adjusted.

[0009] In further embodiments, the wind direction azimuth angle is divided into 8 wind direction sectors according to NE: 22.5-67.5°, E: 67.5-112.5°, SE: 112.5-157.5°, S: 157.5-202.5°, SW: 202.5-247.5°, W: 247.5-292.5°, NW: 292.5-337.5°, and N: 337.5-360° / 0-22.5°.

[0010] In a further embodiment, the local greenhouse gas flux caused by the turbulence is proportional to the concentration difference of the greenhouse gas in the vertical direction and inversely proportional to the aerodynamic resistance, i.e.

[0011] wherein p is the molar density of the air.

[0012] In a further embodiment, if there is only 1 ground station and 1 reference height station in each grid, the original data is calibrated in concentration, and the abnormal values exceeding 3 times of the standard deviation are removed, and then the hourly average is performed to obtain the hourly data of and In a further embodiment, if the number of ground stations or reference height stations in each grid exceeds 1, the spatial average is performed according to the height to obtain the hourly data of and In a further embodiment, if there is a vehicle-mounted mobile observation, the spatial average is performed according to the grid, and the hourly data of is time-matched. In a further embodiment, if there is an airborne mobile observation, the spatial average is performed according to the grid, and the hourly data of is time-matched. In a further embodiment, the aerodynamic resistance is calculated by using the heat flux H in each grid and the potential temperature difference between the ground and the reference height :

[0013]

[0014] wherein H is the heat flux; and are the potential temperatures of the ground and the reference height, respectively; is the specific heat capacity.

[0015] In addition, the present application also discloses an electronic device, which comprises a processor and a memory storing computer program instructions; the processor implements the urban greenhouse gas flux monitoring method based on a high-density observation network according to the first aspect when executing the computer program instructions.

[0016] In addition, the present application also discloses a computer readable storage medium, which stores at least one executable instruction, and the executable instruction makes the electronic device execute the urban greenhouse gas flux monitoring method based on a high-density observation network according to the first aspect when running on the electronic device.

[0017] ​​​​Beneficial effects: The urban greenhouse gas flux monitoring method provided by the application quantifies the emission characteristics of urban greenhouse gases based on high-density observation, which can not only implement large-scale monitoring by using a high-density observation network, but also implement encrypted monitoring of emissions in key areas by flexibly deploying on vehicles or unmanned aerial vehicles, and even draw a greenhouse gas flux distribution map with a grid resolution of 100m. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 Schematic diagram of the CO2 and heat transfer process in the near-surface layer in the embodiment.

[0019] Figure 2 Schematic diagram of the grid design and site layout of high-density concentration observation of CO2 flux observation in A city in the embodiment.

[0020] Figure 3 Time series diagram of the difference between the CO2 concentration at the surface and the reference height from October to December 2023 in the embodiment.

[0021] Figure 4 Diurnal variation diagram of the difference between the CO2 concentration at the surface and the reference height from October to December 2023 in the embodiment.

[0022] Figure 5 Time series diagram of the aerodynamic resistance from October to December 2023 in the embodiment.

[0023] Figure 6 Diurnal variation diagram of the aerodynamic resistance from October to December 2023 in the embodiment.

[0024] Figure 7 Time series diagram of the local CO2 flux from October to December 2023 in the embodiment.

[0025] Figure 8 Diurnal variation diagram of the local CO2 flux from October to December 2023 in the embodiment.

[0026] Figure 9 Schematic diagram of the values of the main footprint range (R main ) and the maximum footprint range (R max ) of the anthropogenic source site in the embodiment.

[0027] Figure 10 Schematic diagram of the values of the main footprint range (R main ) and the maximum footprint range (R max ) of the natural source site in the embodiment.

[0028] Figure 11 Schematic diagram of the values of the site height, the main footprint range (R main ) and the maximum footprint range (Rmax Relationship diagram.

[0029] Figure 12 The main footprint range (R) of all stations plotted in the example is shown. main (Diagram)

[0030] Figure 13 The maximum footprint range (R) of all stations plotted in the example is shown. max (Diagram) Detailed Implementation

[0031] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0032] Example 1 To address the issues of insufficient spatial representativeness and limited number of existing urban greenhouse gas flux monitoring stations, this embodiment proposes a method for monitoring urban greenhouse gas fluxes, quantifying urban greenhouse gas emission characteristics based on high-density observations. The specific technical solution is as follows: (1) Construction of a high-density observation network The city is divided into several grids, with the grid size adjusted according to the sensor's accuracy and the user's spatial resolution requirements. If the high-density observation network primarily uses low-cost sensors, then the average values ​​from different stations within the same grid need to be used to reduce the uncertainty of the results.

[0033] Within each grid, at least two concentration observation heights are required: one near the surface and the other at a reference height, more than twice the height of the urban canopy. In grids that cannot meet these requirements, mobile observations must be used to supplement the data. In grids lacking ground stations, vehicle-mounted observations can be conducted to supplement ground data. If observations at a reference height are lacking, airborne observations or concentration data from adjacent grids can be used as substitutes. For greenhouse gas emission hotspots, a double-layer nesting approach can be used on top of the above grid, employing a combination of high-density and mobile observations to obtain greenhouse gas concentrations in each sub-grid.

[0034] (2) Calculation of greenhouse gas flux According to the turbulent diffusion theory, the local greenhouse gas flux caused by turbulence ( μmol·m -2 ·s -1 ), on the one hand, the concentration difference of the gas in the vertical direction It is directly proportional to, on the other hand, it is related to, aerodynamic impedance. is inversely proportional to.

[0035]

[0036] where ρ is the molar density of air. and are calculated from the data of high-density observation network. If there is only 1 ground station and 1 reference height observation in each grid, the original data is calibrated by concentration, and the abnormal values exceeding 3 times the standard deviation are removed, and then the hourly average is obtained to obtain the hourly data of and ; if the number of ground or reference height stations in each grid exceeds 1, spatial averaging is performed according to the height to obtain the hourly data of and ; if there is a vehicle mobile observation, spatial averaging is performed according to the grid, and time matching is performed with the hourly data of ; if there is an airborne mobile observation, spatial averaging is performed according to the grid, and time matching is performed with the hourly data of .

[0037] In the grid with the vortex correlation observation, can be obtained from the vortex correlation observation of the target area.

[0038] Example 2 On the basis of example 1, this embodiment discloses the specific details of the urban greenhouse gas flux monitoring method.

[0039] The process of CO2 and heat transfer in the near-surface layer is shown in Figure 1 . A city is divided into several grids with a spatial resolution of 12 km x 12 km, and the grid at the position of “☆” is selected as the target area. A high-density observation network of atmospheric CO2 concentration in A city (the specific station positions are shown in Figure 2 ) is built to quantify the hourly changes of CO2 flux. The observation network has 25 medium-precision stations, all near the ground, about 1.0-2.0 m away from the ground, for quantifying the ground CO2 concentration. These stations are mostly located on the top floors of buildings, and some are located on the roadside and in parks, and low-cost CO2 analyzers are used to observe the concentration. The observation network also has 1 high-precision station located at an atmospheric observation field on a mountain in A city, for observing the atmospheric CO2 concentration at the reference height. The air inlet of the instrument is installed on a high tower, about 25 m away from the ground. The station uses a CO2 / CH4 / H2O analyzer based on the cavity ring-down laser spectroscopy technology (CRDS), and observes according to the World Meteorological Organization / Global Atmospheric Observation Program (WMO / GAW) standard. The uncertainty of CO2 concentration observation is <0.2 μmol·mol -1 .

[0040] After calibrating the raw data from the observation network of City A by concentration, outliers exceeding three standard deviations were removed, and then hourly averaged. Hourly data from high-precision stations were used to represent... Calculate using hourly data from medium-precision stations When the number of valid data points from a precision station exceeds seven at a given moment, the hourly data from those precision stations at that moment is averaged to obtain the result. Otherwise, make that moment It is NaN. and After time matching, the concentration difference in the vertical direction was calculated. . Figure 3 and Figure 4 It is the difference in CO2 concentration between the surface and reference altitude within the target area from October to December 2023. The hourly and daily variations.

[0041] Assuming that turbulence has the same capacity to transport scalars such as heat, water vapor, and greenhouse gases, the heat flux and temperature gradient observations within each grid are used to inversely calculate... ,Right now:

[0042] In the formula, H is the heat flux (W·m). -2 ); and These are the potential temperatures (K) at the Earth's surface and at a reference altitude, respectively; ρ is the molar density of air (mol·m³). -3 ); Specific heat capacity (J·mol) -1 ·K -1 Since eddy covariance observations have not yet been conducted in the CO2 concentration observation area, the WRF model is used here for quantification. Based on the spatial resolution of CO2 concentration observations, set the resolution of the innermost nested grid in the WRF. Extract the HFX from the WRF output file (wrfout) and substitute it into the heat flux (H) in equation (1). When H < 2 W·m 2 At that time, because the denominator was too small, The uncertainty of the calculation results increases, therefore, this time period is excluded. Value; extract the air temperature (T2) at a height of 2 m from the WRF output and substitute it into the surface temperature in equation (1). Temperature at reference altitude (); The temperature ( ) is obtained using the interplevel function included in the WRF-Python package. , After performing spatiotemporal matching of flux (H) time series, the aerodynamic impedance is calculated in reverse using equation (1). and to Diagnose the sign of H to ensure that the inverse calculation yields... It conforms to the consistency of thermodynamics and turbulent dynamics. Figure 5 and Figure 6 It is the aerodynamic impedance of the target area from October to December 2023. The hourly and daily variations of ).

[0043] Finally, the above calculations are obtained With the observed Time matching is performed, and the local greenhouse gas flux caused by turbulence is calculated using the following equation (3). μmol·m -2 ·s -1 ):

[0044] In the formula, ρ is the molar density of air (mol·m³). -3 ), and Greenhouse gas concentrations (μmol·mol⁻¹) at the Earth's surface and at a reference height, respectively. -1 ), aerodynamic drag (s·m) -1 ). Figure 7 and Figure 8 This refers to the local CO2 flux within the target region from October to December 2023. The hourly and daily variations of ).

[0045] Example 3 Further research revealed that a reasonable sensor deployment scheme can avoid redundant sensor deployment, reduce equipment investment, improve network observation capabilities, and lower network maintenance costs. However, due to the complex underlying surface structure, strong spatial heterogeneity, and diverse and widely distributed emission sources in urban areas, high-density network observation still faces challenges in practical applications, including uneven spatial coverage, a lack of unified standards for site layout schemes, and insufficient adaptability. Traditional deployment schemes use footprint range to quantify the spatial representativeness of observational data, but footprint calculation relies on detailed meteorological observations or simulations. In urban areas with complex underlying surfaces, both observation and simulation struggle to accurately characterize atmospheric diffusion and transport processes.

[0046] Based on the above problems, this embodiment designs a simple layout method for a high-density observation network, eliminating the need for complex meteorological observations and minimizing the investment in observation instruments while ensuring comprehensive station coverage. The specific technical solution is as follows: Under the existing station conditions, a high-density observation network is initially established. The station properties are divided by the impervious surface ratio and vegetation coverage within a 2 km range around the station. The impervious surface ratio > 50% is defined as the anthropogenic source station, and the vegetation coverage > 50% is defined as the natural source station.

[0047] A pre-test of high-density network observation is carried out, and at least 3 months of continuous observation is carried out. Among them, the anthropogenic source station is observed in winter (December-February of the next year), and the natural source station is observed in summer (June-August). Hourly CO2 concentration high-density network observation data set is obtained.

[0048] In order to eliminate the influence of background concentration difference under different wind direction conditions on observation, the nearby upwind meteorological station is selected as the background station. The difference (ΔCO2) between the CO2 concentration of each high-density station and the background concentration is used for the next calculation.

[0049] Wind direction data of the target area is obtained, and the wind direction azimuth is divided into 8 wind direction sectors according to NE: 22.5-67.5°, E: 67.5-112.5°, SE: 112.5-157.5°, S: 157.5-202.5°, SW: 202.5-247.5°, W: 247.5-292.5°, NW: 292.5-337.5°, N: 337.5-360° / 0-22.5°.

[0050] The ΔCO2 concentration of each station is time-matched with the wind direction data, and the ΔCO2 concentration data is divided according to the 8 wind direction sectors. The average ΔCO2 concentration in each wind direction sector is calculated.

[0051] Taking each high-density station as the center, a buffer radius is taken every 100 m to form n concentric circles. In each concentric circle, 8 sector areas are divided according to the wind direction azimuth, a total of 8n buffer areas are divided, and the underlying surface parameters of each buffer area are calculated, including the impervious surface ratio and the vegetation coverage.

[0052] The average ΔCO2 concentration in each sector area is matched with the underlying surface parameters in the corresponding sector area upwind. The anthropogenic source station takes the daytime data in winter (December-February) to calculate the Pearson correlation coefficient between ΔCO2 and the impervious surface ratio. The natural source station takes the daytime data in summer (June-August) to calculate the Pearson correlation coefficient between ΔCO2 and the vegetation coverage.

[0053] There is a positive correlation between the impervious surface ratio of the anthropogenic source station and ΔCO2. The buffer radius range with a correlation coefficient r≥0 is taken, and the buffer radius with the maximum correlation coefficient is taken as the main footprint range R of the anthropogenic source station. main The buffer radius with a correlation coefficient r=0 is taken as the maximum footprint range R of the anthropogenic source station.max .

[0054] The vegetation coverage of the natural source site is negatively correlated with the ΔCO2, and the buffer radius range with the correlation coefficient r≤0 is taken as the main footprint range R of the natural source site with the buffer radius at the minimum value of the correlation coefficient main The buffer radius at the correlation coefficient =0 is taken as the maximum footprint range R of the natural source site max .

[0055] The footprint ranges of all the sites are drawn with the site as the center and R main and R max as the radius, the maximum footprint range of the ground site is slightly overlapped and can basically cover the target region, so that the data of the high-density observation network can reflect the concentration change characteristics of the entire target region, otherwise the site position is adjusted.

[0056] Embodiment 4 On the basis of embodiment 3, this embodiment takes the site layout optimization of the CO2 concentration high-density observation network in A city as an example to continue the detailed description.

[0057] Figure 2 The right partial enlargement of the figure shows the specific site positions of the high-density observation network of the atmospheric CO2 concentration in A city which has been established. The network has 27 medium-precision sites, which are located in the main urban area of A city, mostly on the top floor of the building, and also on the roadside and in the park. Among them, HZ05, HZ07, HZ09 and HZ26 sites are natural source sites, and HZ01, HZ03, HZ04, HZ08, HZ12, HZ13, HZ14, HZ15, HZ17, HZ18, HZ19, HZ20, HZ21, HZ22, HZ23, HZ25, HZ27 and HZ30 sites are artificial sources.

[0058] The A city national climate benchmark site is taken as the background site, which is located at the target region of the Mantaoshan position, and a CO2 / CH4 / H2O analyzer (model G2301, Picarro, USA) based on the optical cavity ring-down laser spectroscopy technology (CRDS) is used to observe the atmospheric CO2 concentration according to the standard of the World Meteorological Organization / Global Atmospheric Observation Program (WMO / GAW). The air inlet of the instrument is installed on a high tower about 25 m away from the ground.

[0059] The high-density observation network of the CO2 concentration in A city has carried out continuous observation for more than 3 years. After the observation data is quality controlled, the hourly average value is obtained to obtain the hourly CO2 concentration high-density networking observation data set.

[0060] The CO2 concentration observed at the A city climate benchmark station is taken as the background concentration, and the difference (ΔCO2) between the CO2 concentration of each high-density station and the background concentration is used for the next calculation.

[0061] Wind direction data of the A city climate benchmark station is obtained, and the wind direction azimuth is divided into 8 wind direction sectors according to NE: 22.5-67.5°, E: 67.5-112.5°, SE: 112.5-157.5°, S: 157.5-202.5°, SW: 202.5-247.5°, W: 247.5-292.5°, NW: 292.5-337.5°, N: 337.5-360° / 0-22.5°.

[0062] The ΔCO2 concentration of each station is time-matched with the wind direction data, and the ΔCO2 concentration data is divided into 8 wind direction sectors, and the average ΔCO2 concentration in each wind direction sector is calculated.

[0063] Taking each high-density station as the center, every 100m takes a buffer radius, forms n concentric circles, and divides each concentric circle into 8 sectors according to the wind direction azimuth, a total of 8n buffer zones, and calculates the underlying surface parameters of each buffer zone, including the impervious surface ratio and the vegetation coverage.

[0064] The average ΔCO2 concentration in each sector is matched with the underlying surface parameters in the corresponding sector upwind, and the data of the artificial source station in winter (December-February) is taken, and the Pearson correlation coefficient of ΔCO2 and the impervious surface ratio is calculated; the data of the natural source station in summer (June-August) is taken, and the Pearson correlation coefficient of ΔCO2 and the vegetation coverage is calculated.

[0065] As shown in Figure 9 , the impervious surface ratio of the artificial source station is positively correlated with ΔCO2, and the buffer radius range with a correlation coefficient r≥0 is taken, and the buffer radius at the maximum value of the correlation coefficient is taken as the main footprint range R main of the artificial source station. max .

[0066] As shown in Figure 10 , the vegetation coverage of the natural source station is negatively correlated with ΔCO2, and the buffer radius range with a correlation coefficient r≤0 is taken, and the buffer radius at the minimum value of the correlation coefficient is taken as the main footprint range R main of the natural source station. max . Figure 11is the footprint range of the high-density observation network of atmospheric CO2 concentration in city A, which has a linear correlation with the height of the site; the higher the observation height, the larger the footprint range.

[0067] The footprint ranges of all sites are drawn with the site as the center and R main and R max as the radius, and the coverage ranges are shown in Figure 12 and Figure 13 respectively. The maximum footprint ranges of the sites slightly overlap and basically cover the target region, so that the data of the high-density observation network can reflect the concentration variation characteristics of the entire target region; if the above conditions are not met, the site position is adjusted.

[0068] The technical process of the urban greenhouse gas flux monitoring method based on the high-density observation network disclosed in the above embodiments can be realized by software, hardware, firmware or any combination thereof in whole or in part. When implemented by software, the above embodiments can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs.

[0069] When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing a set of one or more available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor medium can be a solid-state disk.

[0070] It should be understood that in various embodiments of the present application, the size of the serial number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] As described above, although the present application has been shown and described with reference to specific preferred embodiments, it should not be construed as a limitation on the present application itself. Various changes in form and details can be made without departing from the spirit and scope of the present application defined in the appended claims.

Claims

1. A method for monitoring urban greenhouse gas fluxes based on a high-density observation network, characterized by, Comprising the following steps: The target area is divided into several grids, and each grid contains concentration observation data of two heights; wherein, a plurality of sites are arranged on the ground to collect and calibrate the ground surface greenhouse gas concentration ; at least one site is arranged at the reference height to calibrate the reference height greenhouse gas concentration ; based on the surface greenhouse gas concentration , the reference height greenhouse gas concentration , taking into account the aerodynamic resistance, the local greenhouse gas flux caused by the turbulence is calculated . 2.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 1, wherein, The reference height is above 2 times the height of the urban canopy. 3.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 1, characterized in that, If the concentration observation of the two heights cannot be satisfied simultaneously, it is supplemented by mobile observation, specifically including: In the grid lacking ground sites, the ground greenhouse gas concentration data is supplemented by vehicle-mounted observation; In the grid lacking reference height sites, it is realized by airborne observation, or replaced by the greenhouse gas concentration data of adjacent grids. 4.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 1, wherein, The ground sites are arranged in the following way: Obtain the wind direction data of the target area, and divide 8 wind direction sectors according to the wind direction azimuth angle; Time-match the ΔCO2 concentration of each ground site with the wind direction data, divide the ΔCO2 concentration data according to the 8 wind direction sectors, and calculate the average ΔCO2 concentration in each wind direction sector; The average CO2 concentration data of each wind direction sector is matched with the underlying surface parameters in the corresponding sector of the upwind direction, the data of the artificial source site in winter daytime is taken to calculate the Pearson correlation coefficient of ΔCO2 and the impervious surface ratio ; the data of the natural source site in summer daytime is taken to calculate the Pearson correlation coefficient of ΔCO2 and the vegetation coverage ; The ratio of impervious surface area of anthropogenic source sites to delta CO2 is positively correlated with the buffer radius range with a correlation coefficient The maximum buffer radius with a correlation coefficient of 0.75 is the primary footprint range of anthropogenic source sites The buffer radius with a correlation coefficient of 0.75 is the maximum footprint range of anthropogenic source sites ; The vegetation cover rate at natural source sites is negatively correlated with ΔCO2; the correlation coefficient is taken. The buffer radius range, with correlation coefficient The buffer radius at its minimum value is the main footprint range of the natural source site. With correlation coefficient The buffer radius at that time is the maximum footprint range of the natural source site. ; All surface sites are plotted with a radius of and the maximum footprint of each site, ensuring that the maximum footprints of the surface sites overlap and cover the target area with a predetermined coverage, otherwise the site locations are adjusted. 5.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 4, characterized in that, Divide the wind direction azimuth angle into 8 wind direction sectors according to NE: 22.5-67.5°, E: 67.5-112.5°, SE: 112.5-157.5°, S: 157.5-202.5°, SW: 202.5-247.5°, W: 247.5-292.5°, NW: 292.5-337.5°, N: 337.5-360° / 0-22.5°. 6.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 1, wherein, Local greenhouse gas fluxes caused by turbulence are proportional to the concentration difference of the greenhouse gas concentration in the vertical direction are proportional to the aerodynamic impedance are inversely proportional to the aerodynamic impedance, i.e.: In the formula, ρ is the molar density of air. 7.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 1, wherein, If there is only one surface site and one reference height site in each grid, the original data is calibrated by concentration, and the abnormal values exceeding 3 times of the standard deviation are removed, and then the hourly average is obtained and hourly data. If the number of surface stations or reference height stations in each grid is more than 1, then spatially average by height to obtain and hourly data; If there are mobile observations on board, then spatially average over the grid, and time match with the hourly data of ; If there are onboard mobile observations, they are spatially averaged over the grid cells and time-matched with the hourly data. of the hourly data. 8.The urban greenhouse gas flux monitoring method based on high-density observation network according to claim 6, characterized in that, The aerodynamic resistance is calculated using the heat flux H within each grid and the difference in potential temperature between the surface and the reference height : where H is the heat flux; and Tb is the potential temperature at the surface and reference height, respectively; Cp is the specific heat capacity.

9. An electronic device, comprising: The electronic device comprises a processor and a memory storing computer program instructions; the processor executes the computer program instructions to realize the urban greenhouse gas flux monitoring method based on high-density observation network according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, and the executable instruction runs on the electronic device to make the electronic device execute the urban greenhouse gas flux monitoring method based on high-density observation network according to any one of claims 1-8.

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