A method for evaluating greenhouse gas fluxes in a two-dimensional urban canopy

Abstract

The present invention discloses a method for evaluating greenhouse gas fluxes in a two-dimensional urban canopy, which relates to the technical field of evaluating greenhouse gas fluxes at the regional scale and is used to estimate the regional atmospheric greenhouse gas fluxes with high temporal resolution. This technology can solve the problem in the traditional method that the bottom-up estimation of the IPCC is highly dependent on the accuracy and comprehensiveness of the energy statistical data itself, while most cities still do not have the conditions to use this method. The present invention integrates multi-source meteorological environment observation data such as greenhouse gas in-situ observation data, wind profiler radar data, microwave radiometer data, and ground meteorological station data from the perspective of atmospheric observation, conducts quality control, constructs a high-precision data set, estimates the horizontal greenhouse gas concentration gradient and vertical fluxes in the urban canopy based on a two-dimensional box model, and provides a new solution for regional carbon budget detection.

Description

Technical Field

[0001] The present invention relates to the technical field of regional-scale greenhouse gas flux assessment, and specifically to a method for assessing two-dimensional urban canopy greenhouse gas flux. Background Art

[0002] The increase of greenhouse gases such as CO 2 , CH 4 and N 2 O in the atmosphere may cause a series of environmental problems such as global warming, precipitation distribution variation, and even changes in vegetation distribution and productivity, and an increase in extreme climates. In the context of carbon peaking and carbon neutrality, how to accurately monitor and evaluate the emissions of regional atmospheric greenhouse gases has become one of the main challenges faced by governments and scientists around the world.

[0003] From the perspective of traditional methods, both the IPCC bottom-up and top-down emission inventory methods can be used to calculate carbon source emissions, but the IPCC also has some of its own deficiencies compared with atmospheric observations. For the IPCC bottom-up estimation method, due to the characteristics of different cities (such as city size, population, energy consumption structure, land use type, climate type, energy use efficiency, etc.), it may have a large uncertainty in the estimation.

[0004] The atmospheric CO 2 observation experiment based on the top-down approach provides a new idea for the division of urban atmospheric CO 2 sources. Compared with the energy emission inventory, atmospheric observation has the characteristics of no administrative boundary and high time resolution. The IPCC has included the method of atmospheric greenhouse gas monitoring and inversion in one of the ways to account for regional carbon emissions in its 2019 revised version. Due to getting rid of the uncertainty of single traditional energy data and the defects in statistical methods, this approach can better characterize the flux status between the underlying surface and the atmosphere at the regional or local scale. Further, based on the two-dimensional box model, the use of high-precision instruments to estimate the greenhouse gas flux of the urban canopy also provides a technical basis for the present invention.

[0005] From the perspective of traditional methods, the IPCC bottom-up estimation method is highly dependent on the accuracy and comprehensiveness of energy statistical data itself. Most cities still do not have the conditions to use this method. In addition, inconsistent statistical calibers may also cause huge errors; therefore, there is an urgent need in the market to develop a method for assessing two-dimensional urban canopy greenhouse gas flux to help people solve existing problems. Summary of the Invention

[0006] The object of the present invention is to provide a method for evaluating greenhouse gas fluxes in a two-dimensional urban canopy, so as to solve the problems in the traditional method described in the above background technology. In the IPCC bottom-up estimation method, it highly depends on the accuracy and comprehensiveness of energy statistical data itself. Most cities still do not have the conditions to use this method. In addition, the inconsistent statistical caliber may also cause huge errors.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for evaluating greenhouse gas fluxes in a two-dimensional urban canopy, including a data preparation and preprocessing module and a flux calculation module:

[0008] Data preparation and preprocessing module: Integrate online observation data of greenhouse gas gradients, wind profiler radar data, microwave radiometer data, and ground meteorological station data, perform quality control on the data, and construct a high-precision data set, which is specifically outlined as follows:

[0009] (1) Quality control of greenhouse gas gradient monitoring data

[0010] S1: Collect raw data, check the time series of raw data, revise the raw data, and perform quality control by observers and expert-level quality control on the station duty record information to obtain a preliminary effective data set;

[0011] S2: Through an online calibration system, use multiple calibration gases to calibrate and calibrate the atmospheric observation data, and form a secondary observation data set after passing the quality inspection and evaluation;

[0012] S3: Apply the box model, introduce the "greenhouse gas observation tour comparison" method, that is, calibrate 5 stations with the same group of internationally traceable calibration gases, and adopt the "three-point calibration method"; the quality-controlled greenhouse gas data meets the technical requirements standard of the World Meteorological Organization (WMO);

[0013] (2) Quality control of wind profiler radar and microwave radiometer observation data

[0014] Quality control of wind profiler radar data: First, use the smoothing filter algorithm, the minimum value connection method, the ground clutter suppression algorithm, and the spectral peak search algorithm to perform quality control on the power spectrum data. Secondly, use the consistency averaging algorithm, the median test algorithm, and the mode stitching algorithm to perform quality control on the radial wind data generated by spectral moment parameter estimation. Finally, compare and verify with radiosonde data through a large amount of actual data;

[0015] Quality control of microwave radiometer data; quality control of temperature and humidity profiles of microwave radiometer; first, conduct extreme value inspection layer by layer for atmospheric temperature profile and humidity profile, mark the data exceeding the climate extreme value range as suspicious errors. If there is one or more layers of data that cannot pass the inspection, the profile is marked as suspicious error. Second, conduct time consistency inspection layer by layer. For suspicious error profiles with sudden changes in temperature and humidity, the change range of meteorological elements should be within a certain range for adjacent time steps. If it exceeds the set range, it is a suspicious error. If one or more layers of data cannot pass the time consistency inspection, the profile is determined as a suspicious error profile. Third, vertical consistency inspection. For the situation where there may be outliers and large inter-layer temperature changes in the vertical direction, set extreme value inspection and standard deviation inspection for the vertical temperature change rate. When it exceeds a certain set value, the profile is a suspicious error profile.

[0016] Quality control of surface meteorological station data; mainly through the following steps: format inspection, missing measurement inspection, boundary value inspection, main range change inspection, internal consistency inspection, time consistency inspection, spatial consistency inspection, comprehensive analysis of quality control and data quality identification;

[0017] Flux calculation module: This model needs to establish 5 or more stations within the urban scale to form a cross-shaped layout, so as to carry out the calculation of the concentration difference between stations in the dominant wind direction; further combine the boundary layer height, wind direction and wind speed to calculate the vertical flux of atmospheric greenhouse gases; the specific steps are as follows:

[0018] S1: Determination of boundary layer height

[0019] Use wind profiler radar data to determine the boundary layer height: Since the echo signal received by the wind profiler radar mainly comes from the inhomogeneity of the radio refractive index, it can be characterized by the refractive index structure constant and there is a peak of at the top of the boundary layer; this algorithm uses the deviation method to derive to amplify its variable characteristics, and judges the boundary layer height according to the overall continuous mutation or jump characteristics of the profile.

[0020] The resolution of the wind profiler radar is less than 10 minutes, and the spatial resolution is lower than 50 meters, but the blind area is large and it is difficult to capture the lower boundary layer. It is necessary to combine microwave radiometer data for secondary determination. The microwave radiometer has good cloud penetration, less interference from clouds, provides the atmospheric temperature and humidity structure under different weather conditions, and has a higher vertical resolution at low altitudes, which can make up for the defect of the low-altitude blind area of the wind profiler radar. The defect of its low resolution and large inversion error at high altitudes is made up for by the wind profiler radar, and the two are combined to determine the boundary layer height;

[0021] Determining the boundary layer height using microwave radiometer data for the second time: When there is a ground inversion layer detected by the microwave radiometer, the boundary layer height is defined as the top of the ground inversion layer; in other cases, it is defined as the height with the maximum potential temperature gradient.

[0022] S2: Determination of the dominant wind direction and wind speed

[0023] The dominant wind direction refers to the range of wind direction angles with the highest wind frequency; the frequencies of different wind directions in different time periods are statistically analyzed respectively, the dominant wind direction is determined according to the maximum wind direction frequency, and then the mathematical average calculation is carried out based on the wind speed of the dominant wind direction to finally determine the dominant wind speed of the dominant wind direction.

[0024] S3: Determination of the horizontal concentration gradient of greenhouse gases

[0025] Verification and analysis show that the concentration gradient along the wind direction is much larger than the cross-wind gradient, and the cross-wind gradient is relatively small. This result supports the premise of the two-dimensional box model: the concentration gradient mainly exists in the direction along the wind because when the air mass passes through the urban land, the gas accumulates in the air mass.

[0026] Therefore, the greenhouse gas concentration gradient along the wind reveals the intensity of the surface emission source; the horizontal concentration vector gradient is as follows:

[0027]

[0028] Determination of the horizontal concentration gradient: Assume that the concentrations measured at three horizontal points are P 1 , P 2 and P 3 , and these three points form a triangle with the base L and the height H. The horizontal concentration vector gradient is:

[0029]

[0030] S4: Estimation of the greenhouse gas vertical flux

[0031] After calculating the horizontal concentration gradient, the greenhouse gas vertical flux of the regional canopy along the dominant wind direction is:

[0032]

[0033] Where is the concentration difference of the greenhouse gas along the dominant wind direction, u is the wind speed in the dominant wind direction, H is the boundary layer height, the wind speed u in the dominant wind direction is determined by step S2, and the horizontal concentration gradient in the dominant wind direction is determined by step S3;

[0034] In this solution, the observation height of greenhouse gases is designed by combining with the urban canopy height, which is defined as the average height of buildings in the urban area. The estimation method of this vertical flux is applicable to the nocturnal stable boundary layer and is used to quantify the greenhouse gas budget of the urban area surface.

[0035] Preferably, the principle of the three-point calibration method is to use the known high and low concentration calibration gas values and the actual signal response intensities in two adjacent runs of the instrument working sequence, combined with the signal response intensity of atmospheric sampling, to calculate the true mole fraction to be calibrated through linear fitting, and compare the fitted value and the nominal value of the target gas.

[0036] Preferably, the specific practice of the three-point calibration method is based on the two-point gain and offset calibration of the high gas WH and the low gas WL, and the target gas T is used for verification. The sample concentration is linearly fitted and calibrated by the measured values of the adjacent WH and WL and the nominal concentration. The fixed-value target gas T is regarded as a "sample with unknown concentration" and participates in the measurement regularly. If the difference between the calibration value and the nominal concentration is within the range specified by WMO, the system is considered stable and the calibration result is reliable; otherwise, the calibration result is considered unreliable and the data is excluded or marked.

[0037] Preferably, in the determination of the horizontal concentration gradient of greenhouse gases in S3, a horizontal concentration gradient can be measured through a triangle formed by three points not on the same line. Five points are arranged within the urban-scale horizontal range, with 8 triangles or 1 rhombus. The vector components formed by the rhombus are determined by the positions of the opposite vertices; first, calculate the gradient per hour and take the average of 9 estimated values per hour. Spatial averaging can reduce the error of the deviation of each single observation instrument.

[0038] Preferably, in the determination of the dominant wind direction and wind speed in S2, the sum of the wind frequency of the dominant wind direction angle should be greater than or equal to 30%.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. In this invention, a high-precision greenhouse gas observation instrument is adopted and data quality control is realized through an online calibration system, so that the instrumental errors at multiple sites are significantly smaller than the differences in spatial measurements, thereby being able to capture the concentration differences at the regional spatial scale.

[0041] 2. In this invention, by combining multi-source observation data such as greenhouse gas concentration, urban boundary layer height, and wind field, and adopting a two-dimensional box model, the estimation of regional atmospheric greenhouse gas flux with high time resolution is realized. Combining with the model theory, this method is mainly applicable to the conditions of the atmospheric stable boundary layer.

[0042] 3. In this invention, the resolution of the wind profile radar is less than 10 minutes, and the spatial resolution is lower than 50 m. However, it has a large blind area and it is difficult to capture the lower boundary layer. Therefore, it is necessary to combine the microwave radiometer data for secondary determination. The microwave radiometer has good cloud penetration, is less affected by clouds, provides the atmospheric temperature and humidity structures under different weather conditions, and has a high vertical resolution at low altitudes, which can make up for the defects of the low-altitude blind area of the wind profile radar. The defects of its low resolution and large retrieval error at high altitudes are compensated by the wind profile radar. The two are combined to better determine the boundary layer height. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is the technical roadmap of a two-dimensional urban canopy greenhouse gas flux assessment method of the present invention;

[0044] Figure 2 is the schematic diagram of the calibration effect evaluation by the three-point calibration method of the present invention;

[0045] Figure 3 is the flowchart of the wind profile radar data quality control of the present invention;

[0046] Figure 4 is the schematic diagram of the specific process of the microwave radiometer data quality control of the present invention;

[0047] Figure 5 is the schematic diagram of the three-point combination model in the horizontal concentration gradient of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0049] Please refer to Figures 1 - 5 , an embodiment provided by the present invention: a two-dimensional urban canopy greenhouse gas flux assessment method, including a data preparation and preprocessing module and a flux calculation module, characterized in that:

[0050] Data preparation and preprocessing module: integrate the greenhouse gas online observation data, wind profile radar data, microwave radiometer data and ground meteorological station data; and perform quality control on the data to construct a high-precision data set, which is specifically outlined as follows:

[0051] (1) The quality control process of the greenhouse gas monitoring data is

[0052] S1: Collect the original data, check the time series of the original data, revise the original data and load the station duty record information for the quality control of the observer and the expert-level quality control, so as to obtain a preliminary effective data set;

[0053] S2: Through the online calibration system, use multiple calibration gases to calibrate and calibrate the atmospheric observation data. After passing the quality inspection and evaluation, a secondary observation data set is formed;

[0054] S3: Since the box model is applied in this method, it is required that the instrument error between stations needs to be controlled within a high-precision range to reduce or eliminate the uncertain factors caused by regional differences. Therefore, finally, the "Greenhouse Gas Observation Circuit Comparison" method is introduced, that is, the same calibration gas is used to calibrate 5 stations, and the "Three-point Calibration Method" is adopted. Follow the WMO standard to review the calibration results and conduct a secondary confirmation to unify the standards and correct the deviations to obtain the final reliable data. The circuit comparison follows the technical requirement standards of the World Meteorological Organization (WMO) and the International Atomic Energy Agency (IAEA) for the measurement of relevant elements in atmospheric composition observations. The principle of the three-point calibration method is as follows: Use the known high and low concentration calibration gas values and the actual signal response intensities in two adjacent runs of the instrument working sequence, combined with the signal response intensity of the atmospheric sampling, to calculate the true mole fraction to be calibrated through linear fitting, and compare the fitting value and the nominal value of the target gas to evaluate the calibration effect ( Figure 2 ), specific approach: Based on the two-point gain and offset calibration (Two-point delta value gain and offset calibration) of the high gas WH and the low gas WL, and use the target gas T for verification. The sample concentration is linearly fitted and calibrated by the measured values and the nominal concentration of the adjacent WH and WL. The fixed-value target gas T is regarded as a "sample with unknown concentration" and participates in the measurement regularly. If the difference between the calibration value and the nominal concentration is within the range specified by the WMO, the system is considered stable and the calibration result is reliable; otherwise, the calibration result is considered unreliable, and the data is excluded or marked;

[0055] (2) Quality control of wind profiler radar and microwave radiometer observation data

[0056] Quality control of wind profiler radar data: First, use algorithms such as smoothing filtering, minimum value connection method to suppress ground clutter, and spectral peak search to perform quality control on the power spectrum data. Secondly, use algorithms such as consistency averaging, median test, and mode stitching to perform quality control on the radial wind data generated by spectral moment parameter estimation. Finally, compare and verify with radiosonde data through a large amount of actual data (see Figure 3 );

[0057] Quality control of microwave radiometer data: Quality control is carried out on the temperature and humidity profiles of the microwave radiometer. First, extreme value checks are performed layer by layer on the atmospheric temperature profile and the humidity profile. Data exceeding the climate extreme value range are marked as suspect. If there is one or more layers of data that cannot pass the check, the profile is marked as suspect. Second, time consistency checks are carried out layer by layer. For suspect profiles with sudden changes in temperature and humidity, the change range of meteorological elements should be within a certain range for adjacent time steps. If it exceeds the set range, it is suspect. If one or more layers of data cannot pass the time consistency check, the profile is determined to be a suspect profile. Third, vertical consistency checks are carried out. For cases where there may be outliers and large inter-layer temperature changes in the vertical direction, extreme value checks and standard deviation checks of the vertical temperature change rate are set. When exceeding a certain set value, the profile is a suspect profile. (See Figure 4 ) The quality-controlled wind profiler radar and microwave radiometer data both meet the technical requirement standards of the World Meteorological Organization (WMO).

[0058] (3) Quality control of surface meteorological station data: It is mainly carried out through the following steps: format check, missing data check, limit value check, main range change check, internal consistency check, time consistency check, spatial consistency check, comprehensive analysis of quality control, and data quality identification.

[0059] Furthermore, the flux calculation module: This model needs to establish 5 or more stations within the urban scale to form a cross-shaped layout, so as to calculate the concentration difference between stations in the dominant wind direction, and further calculate the atmospheric greenhouse gas flux in combination with the boundary layer height, wind direction and wind speed. The specific steps are as follows:

[0060] S1: Determination of the boundary layer height

[0061] Using wind profiler radar data to determine the boundary layer height: Since the echo signal received by the wind profiler radar mainly comes from the inhomogeneity of the radio refractive index, it can be characterized by the refractive index structure constant . There is a peak of at the boundary layer top. This algorithm uses the deviation method to take the derivative of to amplify its variable characteristics, and judges the boundary layer height according to the overall continuous mutation or jump characteristics of the profile. This method is simple and easy to implement, and avoids the misjudgment that the sudden change of the single height layer of the maximum value may be an abnormal value and simply regarded as the boundary layer top.

[0062] The resolution of the wind profiler radar is less than 10 minutes, and the spatial resolution is lower than 50 m. However, it has a large blind area and it is difficult to capture the lower boundary layer. Therefore, it is necessary to combine the microwave radiometer data for secondary determination. The microwave radiometer has good cloud penetration, is less affected by clouds, provides the atmospheric temperature and humidity structures under different weather conditions, and has a relatively high vertical resolution at low altitudes, which can make up for the defects of the low-altitude blind area of the wind profiler radar. The defects of its low resolution and large inversion error at high altitudes are compensated by the wind profiler radar. The two are combined to better determine the boundary layer height;

[0063] Using the microwave radiometer data to determine the boundary layer height for the second time: When there is a ground inversion layer detected by the microwave radiometer, the boundary layer height is defined as the top of the ground inversion layer, and in other cases, it is defined as the height with the largest potential temperature gradient;

[0064] S2: Determination of the dominant wind direction and wind speed

[0065] The dominant wind direction refers to the range of the wind direction angle with the largest wind frequency. The frequencies of different wind directions in different time periods (hourly / daily / monthly / seasonally / annually) are respectively counted, and the dominant wind direction is determined according to the maximum wind direction frequency. It should be noted that the sum of the wind frequencies of the dominant wind direction angle should be greater than or equal to 30%. Otherwise, it should be determined that the dominant wind direction in this period and this area is not obvious. After determining the main wind direction, the mathematical average calculation is carried out according to the wind speed of the dominant wind direction, and finally the dominant wind speed of the dominant wind direction is determined;

[0066] S3: Determination of the horizontal concentration gradient of greenhouse gases

[0067] The verification analysis shows that the concentration gradient along the wind direction is much larger than the cross-wind gradient, and the cross-wind gradient is relatively small. This result supports the premise of the two-dimensional box model: the concentration gradient mainly exists in the along-wind direction because when the air mass passes through the urban land, the gas accumulates in the air mass;

[0068] The greenhouse gas concentration gradient along the wind reveals the intensity of the surface emission source. The horizontal concentration vector gradient is as follows:

[0069]

[0070] Determination of the horizontal concentration gradient: Assume that the concentrations measured at three horizontal points are P1, P2, and P3 respectively. These three points form a triangle with the base L and the height H (see Figure 5 a)), and the horizontal concentration vector gradient is:

[0071]

[0072] A triangle formed by three non - collinear points can measure a horizontal concentration gradient. When arranging 5 points within the urban - scale horizontal range, there are 8 possible triangles and 1 rhombus. The vector components formed by the rhombus are determined by the positions at opposite vertices (see Figure 5 ). We first calculate the gradient per hour and take the average of 9 hourly estimates. Spatial averaging can reduce the error of the deviation of each individual observation instrument. The schematic diagram shows (a) the geometry for measuring the CO2 concentration gradient vector and (b) 9 possible combinations for determining the CO2 concentration gradient;

[0073] S4: Measurement of vertical flux

[0074] After calculating the horizontal concentration gradient, the vertical flux of greenhouse gases in the regional canopy along the dominant wind direction is:

[0075]

[0076] where is the concentration difference of greenhouse gases along the dominant wind direction, u is the wind speed along the dominant wind direction, H is the height of the boundary layer. The wind speed u along the dominant wind direction is determined by step S2, and the horizontal concentration gradient along the dominant wind direction is determined by S3;

[0077] In this scheme, the observation height of greenhouse gases is designed in combination with the urban canopy height. The urban canopy height is defined as the average height of buildings in the urban area. This estimation method of vertical flux is mainly applicable to the nocturnal stable boundary layer and is used to quantify the greenhouse gas budget of the urban - area surface.

[0078] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above - mentioned exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A method for evaluating greenhouse gas fluxes in a two-dimensional urban canopy, including a data preparation and preprocessing module and a flux calculation module, characterized in that: Data preparation and preprocessing module: Integrate on-line observation data of greenhouse gas gradients, wind profiler radar data, microwave radiometer data and ground meteorological station data, and perform quality control on the data to construct a high-precision data set, which is specifically outlined as follows: (1) Quality control of greenhouse gas gradient monitoring data S1: Conduct quality control by observers and expert-level quality control on the original data collection, time series inspection of the original data, revision of the original data, and station duty record information to obtain a preliminary effective data set; S2: Calibrate and calibrate the atmospheric observation data with multiple calibration gases through an on-line calibration system, and form a secondary observation data set after passing the quality inspection and evaluation; S3: Apply the box model and introduce the "greenhouse gas observation circuit comparison" method, that is, calibrate 5 stations with the same set of internationally traceable calibration gases, and use the "three-point calibration method"; the greenhouse gas data after quality control meets the technical requirement standards of the World Meteorological Organization (WMO). (2) Quality control of wind profiler radar and microwave radiometer observation data Quality control of wind profiler radar data: First, use the smoothing filter algorithm, the minimum value connection method to suppress the ground clutter algorithm and the spectral peak search algorithm to perform quality control on the power spectrum data. Secondly, use the consistency averaging algorithm, the median test algorithm and the mode splicing algorithm to perform quality control on the radial wind data generated by the spectral moment parameter estimation. Finally, compare and verify with radiosonde data through a large amount of actual data; Quality control of microwave radiometer data; Perform quality control on the temperature and humidity profiles of the microwave radiometer; First, perform extreme value checks layer by layer on the atmospheric temperature profile and the humidity profile, and mark the data exceeding the climate extreme range as suspected errors. If there is 1 layer or more of data that cannot pass the inspection, the profile is marked as suspected error; Secondly, perform time consistency checks layer by layer. For suspected error profiles with sudden changes in temperature and humidity, the change range of meteorological elements should be within a certain range for adjacent time steps. If it exceeds the set range, it is a suspected error. If there is 1 layer or more of data that cannot pass the time consistency check, the profile is defined as a suspected error profile; Thirdly, perform vertical consistency checks. For the possible presence of outliers and large temperature interlayer changes in the vertical direction, set the extreme value check and standard deviation check of the temperature vertical change rate. When it exceeds a certain set value, the profile is a suspected error profile; Quality control of ground meteorological station data; mainly through the following steps: format check, missing measurement check, limit value check, main range change check, internal consistency check, time consistency check, spatial consistency check, comprehensive analysis of quality control and data quality identification; Flux calculation module: This model needs to establish 5 or more stations within the urban scale to form a cross-shaped layout, so as to calculate the concentration difference between stations in the dominant wind direction; further combine the boundary layer height, wind direction and wind speed to calculate the vertical flux of atmospheric greenhouse gases. The specific steps are as follows: S1: Determination of the boundary layer height Determining the boundary layer height using wind profiler radar data: Since the echo signals received by the wind profiler radar mainly come from the inhomogeneity of the radio refractive index, it can be characterized by the refractive index structure constant , and there is a peak value of at the boundary layer top; This algorithm uses the deviation method to take the derivative of to amplify its variable characteristics, and judges the boundary layer height according to the continuous mutation or jump characteristics of the overall profile; The resolution of the wind profiler radar is less than 10 min, and the spatial resolution is lower than 50 m. However, it has a large blind area and it is difficult to capture the lower boundary layer. It is necessary to combine the microwave radiometer data for secondary determination. The microwave radiometer has good cloud penetration, is less affected by clouds, provides the atmospheric temperature and humidity structure under different weather conditions, and has a higher vertical resolution at low altitudes, which can make up for the defect of the low-altitude blind area of the wind profiler radar. The defects of its low resolution and large inversion error at high altitudes are made up for by the wind profiler radar. The two are combined to determine the boundary layer height; Using the microwave radiometer data to determine the boundary layer height for the second time: When there is a ground inversion layer detected by the microwave radiometer, the boundary layer height is defined as the top of the ground inversion layer, and in other cases, it is defined as the height with the largest potential temperature gradient; S2: Determination of the dominant wind direction and wind speed The dominant wind direction refers to the range of the wind direction angle with the largest wind frequency; The frequencies of different wind directions in different time periods are statistically analyzed respectively. The dominant wind direction is determined according to the maximum wind direction frequency, and then the mathematical average calculation is carried out according to the wind speed of the dominant wind direction, and finally the dominant wind speed of the dominant wind direction is determined; S3: Determination of the horizontal concentration gradient of greenhouse gases The verification analysis shows that the concentration gradient along the wind direction is much larger than the cross-wind gradient, and the cross-wind gradient is smaller. This result supports the premise of the two-dimensional box model: the concentration gradient mainly exists in the direction along the wind, because when the air mass passes through the urban land, the gas accumulates in the air mass; Therefore, the greenhouse gas concentration gradient along the wind reveals the intensity of the surface emission source; The horizontal concentration vector gradient is as follows: Determination of horizontal concentration gradient: Assume that the concentrations measured at three horizontal points are P 1 , P 2 , and P 3 . These three points form a triangle with base L and height H. The horizontal concentration vector gradient is: S4: Estimation of the vertical flux of greenhouse gases After calculating the horizontal concentration gradient, the vertical flux of greenhouse gases in the regional canopy along the dominant wind direction is: wherein is the concentration difference of greenhouse gases along the prevailing wind direction, u is the wind speed in the prevailing wind direction, H is the height of the boundary layer, the wind speed u in the prevailing wind direction is determined by step S2, and the horizontal concentration gradient is determined by step S3; In this scheme, the observation height of greenhouse gases is designed by combining the urban canopy height. The urban canopy height is defined as the average height of buildings in the urban area. This method for estimating the vertical flux is applicable to the nocturnal stable boundary layer and is used to quantify the greenhouse gas budget of the urban area surface.

2. A two-dimensional urban canopy greenhouse gas flux assessment method according to claim 1, characterized in that: The principle of the three-point calibration method is to use the known high and low concentration calibration gas values and the actual signal response intensity in two adjacent runs of the instrument working sequence, combined with the signal response intensity of the atmospheric sampling, and the true mole fraction to be calibrated can be calculated by linear fitting, and the fitting value of the target gas is compared with the nominal value.

3. A two-dimensional urban canopy greenhouse gas flux assessment method according to claim 1, characterized in that: The specific practice of the three-point calibration method is based on the two-point gain and offset calibration of the high gas WH and the low gas WL, and the target gas T is used for verification. The sample concentration is linearly fitted and calibrated by the measured values and the nominal concentration of the adjacent WH and WL. The fixed-value target gas T is regarded as an "unknown concentration" sample and participates in the measurement regularly. If the difference between the calibration value and the nominal concentration is within the range specified by WMO, it is considered that the system is stable and the calibration result is reliable, otherwise the calibration result is considered unreliable and the data is excluded or marked.

4. A two-dimensional urban canopy greenhouse gas flux assessment method according to claim 1, characterized in that: In the determination of the horizontal concentration gradient of greenhouse gas levels in S3, a horizontal concentration gradient can be measured through a triangle formed by three non-collinear points. Five points are arranged within the urban-scale horizontal range, resulting in eight triangles or one rhombus. The vector components formed by the rhombus are determined by the positions of the opposite vertices. First, calculate the gradient for each hour and take the average of the nine estimated values for each hour. Spatial averaging can reduce the error caused by the deviation of each individual observation instrument.

5. A two-dimensional urban canopy greenhouse gas flux assessment method according to claim 1, characterized in that: In the determination of the dominant wind direction and wind speed in S2, the sum of the wind frequencies of the dominant wind direction angles should be greater than or equal to 30%.

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