Point source emission intensity prediction method based on ground remote sensing measurement

Abstract

The present invention provides a method for predicting point source emission intensity based on ground remote sensing measurement. The solar spectrum reaching the surface is measured at the plume measurement point and the background measurement point, and the measured concentration value of the specific pollutant gas in the point source emission plume in the atmosphere is inverted. The Gaussian diffusion model is used to simulate and calculate the three-dimensional structure of the emission plume of the specific pollutant gas, and the simulated concentration value of the specific pollutant gas at the plume measurement point is obtained. The measured concentration value and the simulated concentration value are used for inversion to calculate the emission intensity and wind speed of the point source. According to the inverted emission intensity and wind speed, the Gaussian diffusion model is recalculated to reconstruct the three-dimensional structure of the plume. The present invention uses portable ground remote sensing equipment to carry out column concentration measurement of the plume around the point source, and uses multi-angle measurement data to carry out inversion and reconstruction of the three-dimensional structure of the plume, which can not only synchronously calculate the emission intensity, but also reduce the influence of wind field errors in traditional emission intensity calculation methods.

Description

Technical Field

[0001] The invention relates to the field of ground remote sensing monitoring of point source emission intensity, and in particular to a point source emission intensity prediction method based on ground remote sensing measurement. Background Art

[0002] Accurately quantifying the emission intensity of point sources (for example, industrial point sources) is an important task in emission monitoring. Monitoring industrial point source emissions through remote sensing is the current mainstream method, and it is especially effective for monitoring unorganized emissions. For example, optical remote sensing measurement of CO2 emission plumes from power plants is an effective method for quantifying the CO2 emission intensity of power plants. The traditional remote sensing monitoring method is to first use precision ground-based lidar equipment to scan the area around the point source emission port, or to use drones to perform imaging sampling at each altitude layer, measure the 3D structure of the plume, and then calculate the emission intensity based on the law of conservation of mass. In other words, the previous remote sensing measurement method of point source emission intensity was based on the measurement of the 3D structure of the plume.

[0003] However, previous remote sensing measurement methods have the following problems: LiDAR is extremely large in scale, expensive, and has high requirements for the measurement environment, making it difficult to popularize measurement; drones are not only limited by their safety and urban air control, but also by their stability in airflow, making them difficult to popularize and their accuracy faces challenges. Summary of the invention

[0004] Technical problem to be solved by the invention

[0005] The present invention is made to solve the above-mentioned problems existing in the prior art. The purpose of the present invention is to provide a point source emission intensity prediction method based on ground remote sensing measurement, which does not require the use of large-scale equipment, has low measurement cost, low requirements on the measurement environment and high measurement accuracy.

[0006] Means for solving technical problems

[0007] To achieve the above object, the present invention provides the following technical solutions.

[0008] The present invention provides a method for predicting point source emission intensity based on ground remote sensing measurement, which is characterized by comprising:

[0009] Step S1, using a meteorological instrument to measure and obtain meteorological data of the surrounding area of ​​a point source emitting pollutant gas, using a rangefinder to measure and obtain the height of the point source, using a locator to measure and obtain the longitude and latitude of the point source, estimating the range of the emission plume of the point source according to the wind direction and wind speed in the meteorological data, selecting multiple plume measurement points in the plume projection area formed on the ground by the emission plume of the point source through sunlight, selecting at least one background measurement point on the ground outside the plume projection area, using a spectrometer to measure and obtain spectral data of the solar spectrum reaching the ground for each of the multiple plume measurement points and the at least one background measurement point and recording the corresponding measurement time, using a locator to measure and obtain the longitude and latitude of the measurement point, and using a rangefinder to measure and obtain the distance between the measurement point and the point source;

[0010] Step S2, for each of the measurement points in step S1, using the longitude and latitude of the measurement point and the measurement time, calculating the solar altitude angle and solar azimuth angle of the measurement point at the measurement time;

[0011] Step S3, using the spectral data of each measuring point obtained in step S1, the meteorological data obtained synchronously, the measurement time, and the solar altitude angle and solar azimuth angle calculated in step S2, an inversion calculation is performed according to a spectral inversion algorithm to calculate the measured concentration value of a specific pollutant gas in the pollutant gas emitted by the point source measured at each measuring point in the atmosphere;

[0012] Step S4, establishing a three-dimensional coordinate system with the intersection of a vertical line drawn from the point source to the ground in the vertical direction and the ground as the origin, the wind direction in the meteorological data as the x-axis direction, the height direction of the point source, i.e. the vertical direction, as the z-axis direction, and the direction perpendicular to the x-axis direction and the z-axis direction as the y-axis direction, and according to the wind speed in the meteorological data obtained in step S1 and the emission time, height and emission intensity of the point source as prior emission data, using a Gaussian diffusion model to perform simulation in the three-dimensional coordinate system to obtain a three-dimensional simulation structure of the emission plume;

[0013] Step S5, in the three-dimensional coordinate system established in step S4, based on the difference between the longitude and latitude of the multiple smoke plume measurement points obtained in step S1 and the longitude and latitude of the point source and the distance between the multiple measurement points and the point source, locate the multiple smoke plume measurement points in step S1, for each of the smoke plume measurement points, construct a measurement light path according to the solar altitude angle and solar azimuth angle at the measurement time, calculate the concentration integral value of the specific pollutant gas in the smoke plume portion of the measurement light path, and obtain the simulated concentration values ​​of the specific pollutant gas at the multiple smoke plume measurement points; and

[0014] Step S6, using the measured concentration value of the specific pollutant gas inverted and calculated in step S3 and the simulated concentration value corresponding to the measured concentration value simulated and calculated in step S5 to perform inversion, and calculate the inverted emission intensity and inverted wind speed of the point source as the inversion result.

[0015] The point source in the present invention can be any point source that emits pollutant gases, for example, it can be an industrial emission point source or a man-made emission point source.

[0016] In step S1, the method for estimating the range of the emission plume of the point source is not particularly limited, and an estimation method known in the art can be used. For example, it can be estimated according to the Gaussian diffusion model (Formula (1) described later), in which x is the downwind transmission distance and y is the perpendicular wind transmission distance. The downwind transmission distance and perpendicular wind transmission distance of the plume can be estimated according to the wind speed.

[0017] In step S6, the inverted emission intensity and inverted wind speed of the point source calculated by inversion can be regarded as the predicted value of the emission intensity of the point source and the predicted value of the wind speed in the plume, respectively. The wind speed measured by the meteorological instrument in step S1 is the near-ground wind speed where the measurement system is located, and the wind speed (especially the wind speed in the city) changes significantly with height and spatial position. Therefore, the near-ground wind speed measured by the meteorological instrument will be very different from the wind speed at the plume, and it is currently difficult to have equipment to directly measure the wind speed in the plume. Inaccurate wind speed will lead to inaccurate calculated emission intensity. In the present invention, multi-angle measurement values ​​are used to synchronously invert the emission intensity and wind speed, and the obtained inverted emission intensity and inverted wind speed are close to the actual emission intensity and wind speed.

[0018] In the present invention, by performing multi-angle measurements on the entire smoke plume and using Gaussian diffusion model simulation and inversion calculation, the emission intensity and wind speed close to the actual situation can be calculated in real time using the measurement data to obtain the characteristics of the entire smoke plume.

[0019] In some embodiments, in step S1, when a plane parallel to the wind direction and passing through the point source is set as a first plane, a plane perpendicular to the wind direction and passing through the point source is set as a second plane, a line segment where the first plane intersects the smoke plume projection area is set as a first line segment, and a line segment where the second plane intersects the smoke plume projection area is set as a second line segment, multiple positions on the first line segment and the second line segment are selected as the multiple smoke plume measurement points, and at least one position upwind away from industrial and anthropogenic emission sources is selected as the at least one background measurement point.

[0020] In some implementations, in step S2, the calculation formula for the solar altitude angle h0 is as follows:

[0021] h0=sin-1 [sinψsinσ+cosψcosσcos(15t0+λ-300)]

[0022] Where ψ is latitude in degrees, λ is longitude in degrees, t0 is observation time in hours, σ is solar azimuth in degrees, and h0 is in degrees.

[0023] The calculation formula for the solar azimuth is as follows:

[0024]

[0025] Where θ0 = 360d n / 365, the unit is radians, where d n The order of the selected date in this year, ranging from 0 to 364.

[0026] In some embodiments, in step S3, the spectral data of the multiple smoke plume measurement points and the at least one background measurement point obtained in step S1 are subjected to an optimal estimation method, and the Tikhonov-Philips constraint condition is utilized in combination with nonlinear successive iterations to construct an inversion calculation formula. The spectral data of each measurement point obtained in step S1 is converted into logarithmic spectral data, and the data are input into the inversion calculation formula together with the synchronously obtained meteorological data, the measurement time, and the solar altitude angle and the solar azimuth angle calculated in step S2 to calculate the column concentration of the specific pollutant gas. The column concentration of each specific pollutant gas corresponding to each smoke plume measurement point is subtracted from the average value of the column concentration of the specific pollutant gas corresponding to the at least one background measurement point to obtain the measured concentration value of the specific pollutant gas corresponding to each smoke plume measurement point.

[0027] When constructing the inversion calculation formula, an inversion calculation formula known in the art may be used. For example, a representative inversion calculation formula is as follows:

[0028] x i+1 =x i +(K i T S y -1 K i +γB T B) -1 K i T S y -1 (y meas -y(x i )+γB T B(x a -x i )).

[0029] In the formula, x represents the atmospheric state parameter including the parameters to be inverted, the subscript i represents the i-th iteration, and x i+1 is the result of the i+1th iteration, x a is the set of prior values ​​of the variable parameters, B is the regularization matrix, T represents the matrix transpose, γ is the regularization parameter, the Jacobian matrix K represents the sensitivity of the forward model to the inversion parameters, y meas is the actual spectrum value measured by the spectrometer, y represents the simulated spectrum value, S y is the measurement error covariance matrix.

[0030] In some embodiments, in step S4, when the wind speed is μ, the emission time is t, the height is H, and the emission intensity is Q, the Gaussian diffusion model for calculating the 3D simulation structure of the emission plume is represented by the following formula (1):

[0031]

[0032] Wherein, (x, y, z) is the coordinate of the smoke plume position in the three-dimensional coordinate system, C(x, y, z) is the concentration of the specific pollutant gas at the position (x, y, z) in the three-dimensional coordinate system, σ x , σ y , σ z are the diffusion coefficients in the x-axis, y-axis, and z-axis directions, respectively.

[0033] The unit of wind speed μ is m / s, the unit of emission time t is second, the unit of height H is m, the unit of emission intensity Q is g / s, and the unit of concentration C(x,y,z) is g / m 2 ,

[0034] The diffusion coefficient σ x , σ y , σ z It is a value calculated based on an empirical formula corresponding to the atmospheric stability level, and its unit is meter.

[0035] Diffusion coefficient σ x , σ y , σ z For example, the atmospheric stability level can be determined based on the solar altitude angle, ground wind speed and cloud cover, and the corresponding Pasquill-Gifford or McElroy-Pooler diffusion coefficient empirical calculation formula can be selected according to different atmospheric stability levels.

[0036] In some embodiments, in step S5, in the measurement optical path constructed for each of the plume measurement points, the first concentration integral value of the specific pollutant gas in the plume path and the second concentration integral value of the specific pollutant gas in the non-plume path are calculated respectively, and the first concentration integral value and the second concentration integral value are added together to obtain the column concentration of the specific pollutant gas, and the column concentration is used as the simulated concentration value of the specific pollutant gas.

[0037] In some embodiments, in step S6, when performing inversion, the measured concentration value of the specific pollutant gas inverted and calculated in step S3 and the simulated concentration value corresponding to the measured concentration value calculated in step S5 are used, and the emission intensity and wind speed of the specific pollutant gas are inverted using Gauss-Newton iteration using the optimal estimation method, and the solution formula is as follows:

[0038] u i+1 =u a +(K i T S ε -1 K i +S a -1 ) -1 K i T S ε -1 [vF(u i )-K i (u i -u a )] (2)

[0039] Where u represents the emission intensity and wind speed of the specific pollutant gas, the subscript i represents the i-th iteration, and u i+1 is the result of the i+1th iteration, u a is the initial guess of the emission intensity and wind speed of the specific pollutant gas, is the weight function, T represents the matrix transpose, S ε is the measurement error covariance of the specific pollutant gas, S a is the prior error covariance matrix of the emission intensity and wind speed of the specific pollutant gas, v represents the measured concentration value of the specific pollutant gas at the multiple smoke plume measurement points, and F(u) represents the simulated concentration value of the specific pollutant gas at the multiple smoke plume measurement points. In the iterative calculation process, when the updated value of u is less than one ten-thousandth of the initial guess value, it is considered that the iteration converges. At this time, u i+1 The value of is the inverted emission intensity and inverted wind speed of the point source as the inversion result.

[0040] In some embodiments, in step S6, the Gaussian diffusion model in step S4 is recalculated based on the inverted emission intensity and the inverted wind speed to reconstruct the 3D structure of the emission plume.

[0041] By recalculating the Gaussian diffusion model using the inverted emission velocity and inverted wind speed, it is possible to reconstruct the 3D structure of the emission plume that is close to the actual situation and obtain the characteristics of the entire plume.

[0042] In some embodiments, the specific pollutant gas is any one selected from SO2, nitrogen oxides, CO2 and CO.

[0043] The specific pollutant gas in the present invention may be any gas component, for example, various gas components in the smoke plume emitted from a chimney.

[0044] In some embodiments, the specific pollutant gas is CO2.

[0045] Effects of the Invention

[0046] By adopting the point source emission intensity prediction method based on ground remote sensing measurement of the present invention, it is possible to use portable ground remote sensing equipment to carry out column concentration measurement of the smoke plume around the point source (for example, an industrial point source), and to carry out inversion and reconstruction of the 3D smoke plume structure through multi-angle measurement data, which can not only synchronously calculate the emission intensity, but also reduce the influence of wind field errors in traditional emission intensity calculation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of a measurement system used in a method for predicting point source emission intensity based on ground remote sensing measurement according to an embodiment of the present invention.

[0048] Figure 2 It is a technical flow chart of a method for predicting point source emission intensity based on ground remote sensing measurement, which represents an implementation mode of the present invention.

[0049] Figure 3 It is a schematic diagram showing the design of measurement points for a method for predicting point source emission intensity based on ground remote sensing measurement according to an embodiment of the present invention.

[0050] Figure 4 (a) and (b) are observation schematic diagrams of a spectrometer used in ground remote sensing measurements in an embodiment of the present invention.

[0051] Figure 5It is a horizontal distribution diagram of different height layers of the emission plume in the point source emission intensity prediction method based on ground remote sensing measurement according to an implementation manner of the present invention, wherein (a) represents the initially constructed plume profiles at different heights, and (b) represents the reconstructed plume profiles at different heights. In (a) and (b), the heights of the three height layers from left to right are 50m, 150m, and 300m, respectively. DETAILED DESCRIPTION

[0052] Next, taking a method for predicting the CO2 emission intensity of a power plant based on ground remote sensing measurement as an example, the specific implementation of the method for predicting point source emission intensity based on ground remote sensing measurement of the present invention will be described with reference to the accompanying drawings.

[0053] In order to implement the point source emission intensity prediction method based on ground remote sensing measurement of the present invention, it is necessary to first construct a measurement system.

[0054] Figure 1 Schematic diagram of a measurement system used in a method for predicting point source emission intensity based on ground remote sensing measurement according to an embodiment of the present invention. Figure 1 As shown in the figure, the measurement system mainly includes the following parts:

[0055] (1) Visible and near-infrared high-resolution spectrometer: It measures the spectrum of the sun reaching the earth’s surface to obtain the content of specific pollutants (such as CO2) in the atmosphere in the light path (concentration data of specific pollutants (such as CO2) in the atmosphere);

[0056] (2) Meteorological instrument: measures the atmospheric temperature, humidity, pressure, wind direction and wind speed in the vicinity of the point source of pollutant emissions;

[0057] (3) Distance meter: obtains the distance between the measurement system (the measurement point where the measurement system is located) and the point source (such as a chimney) and the height of the point source (such as a chimney);

[0058] (4) High-precision positioning instrument: obtains the longitude and latitude of the measurement system (the measurement point where the measurement system is located) for calculation of the solar altitude angle and azimuth;

[0059] (5) Data control and processing platform: A computer platform that includes measurement control software, data recording software, and data processing software. The measurement control software is used to control the measurements of the spectrometer and meteorological instrument. The data recording software is used to record the measurement results of all equipment in the measurement system. The data processing software includes a spectrometer specific pollutant gas inversion calculation module (e.g., a spectrometer CO2 gas inversion calculation module), which inverts the spectrum measured by the spectrometer into a specific pollutant gas column concentration (e.g., CO2 column concentration); a solar geometry calculation module, which calculates the corresponding solar altitude angle and azimuth angle based on the longitude and latitude of the measurement system and the measurement time; a plume simulation module, which simulates and calculates the 3D structure of the point source emission plume corresponding to the measurement time; and a plume inversion reconstruction and emission intensity inversion module, which inverts the emission intensity of the point source through measurement data and simulation data and reconstructs the 3D plume structure.

[0060] The method for predicting point source emission intensity based on ground remote sensing measurement of the present invention comprises the following steps:

[0061] Step S1 (obtaining measurement data)

[0062] First, design a measurement plan: choose sunny weather, use a meteorological instrument to measure temperature, humidity, pressure, wind direction and wind speed data, use a rangefinder to measure the height of the power plant chimney, and use a positioning instrument to measure the longitude and latitude of the chimney; use the chimney and wind direction to form a main plane, and select multiple smoke plume measurement points at multiple different positions away from the chimney on the main plane and the two planes perpendicular to the main plane, in the ground projection area where sunlight passes through the smoke plume; at the same time, select at least one background measurement point upwind, away from industrial and artificial emission sources.

[0063] Then, measurements are carried out point by point, using a visible near-infrared high-resolution spectrometer to measure the solar spectrum passing through the plume and obtain multi-directional measurement spectral data. At the same time, a rangefinder is used to obtain the straight-line distance between the chimney and the location of the measurement system, and a locator is used to obtain the longitude and latitude of the measurement system.

[0064] Step S2 (solar geometry calculation)

[0065] According to the longitude and latitude of the measurement system measured by the locator (the longitude and latitude of the measurement point where the measurement system is located), combined with the time corresponding to the multi-phase measurement, the solar geometry calculation module is input to calculate the solar altitude angle and azimuth corresponding to each measurement.

[0066] Step S3 (Inversion calculation of spectrometer observations)

[0067] The spectral data observed in step S1, combined with the synchronously observed meteorological data, the observation time, and the corresponding solar altitude angle and azimuth angle calculated in step S2, are input into the CO2 gas inversion calculation module. According to the spectral inversion algorithm, the atmospheric CO2 measurement concentration value corresponding to the measured spectral data is inverted and obtained.

[0068] Step S4 (simulation calculation of 3D structure of CO2 emission plume)

[0069] Using the wind speed μ obtained in step S1, combined with prior emission data such as emission time t, chimney height H, emission intensity Q, etc., the 3D simulation structure diagram of the power plant CO2 emission plume is calculated according to the following formula (1):

[0070]

[0071] Among them, (x, y, z) is the coordinate of the smoke plume position in a three-dimensional coordinate system with the chimney location (the intersection of a vertical line drawn from the chimney outlet to the ground in the vertical direction and the ground) as the origin, wind direction as the x-axis direction, vertical direction as the z-axis direction, and the direction perpendicular to the x-axis and z-axis directions as the y-axis direction. C(x, y, z) is the concentration of a specific pollutant gas (such as CO2) at the position (x, y, z) in the above three-dimensional coordinate system. The unit of wind speed μ is m / s, the unit of emission time t is second, the unit of height H is m, the unit of emission intensity Q is g / s, and the unit of concentration C(x, y, z) is g / m 2 , σ x , σ y , σ z are the diffusion coefficients in the x-axis direction, y-axis direction, and z-axis direction, respectively, and the diffusion coefficient σ x , σ y , σ z The atmospheric stability level can be determined based on the solar altitude angle, wind speed and cloud cover. The corresponding empirical calculation formula for the diffusion coefficient can be selected according to different atmospheric stability levels. The diffusion coefficient σ x , σ y , σ z The unit is meter.

[0072] Step S5 (calculation of simulation value corresponding to measured value)

[0073] The position of the measurement system (the position of the measurement point where the measurement system is located) and the chimney position obtained in step S1 are located in the above three-dimensional coordinate system. The observation light path is constructed according to the solar geometric angle calculated in step S2, and the CO2 concentration integral value of the part of the light path passing through the smoke plume and the CO2 concentration integral value of the remaining part of the light path are calculated respectively, and the two are added to obtain the CO2 column concentration, which is used as the simulation concentration value corresponding to the measured concentration value.

[0074] Step S6 (smoke plume reconstruction and emission intensity simultaneous inversion)

[0075] The inversion is performed using the atmospheric CO2 measured concentration value calculated by inversion in step S3 and the simulated concentration value corresponding to the measured concentration value calculated by simulation in step S5. Specifically, according to the atmospheric CO2 measured concentration value in step S3 and the atmospheric CO2 simulated concentration value in step 5, the optimal estimation method is adopted, and the CO2 emission intensity and wind speed are inverted by Gauss-Newton iteration. The solution formula is designed as follows:

[0076] u i+1 =u a +(K i T S ε -1 K i +S a -1 ) -1 K i T S ε -1 [vF(u i )-K i (u i -u a )] (2)

[0077] Where u represents the CO2 emission intensity and wind speed, the subscript i represents the i-th iteration, and u i+1 is the result of the i+1th iteration, u a is the initial guess of CO2 emission intensity and wind speed, is the weight function, T represents the matrix transpose, S ε is the CO2 measurement error covariance, S a is the prior error covariance matrix of CO2 emission intensity and wind speed, v represents the CO2 measured concentration value of the above multiple smoke plume measurement points, F(u) represents the CO2 simulated concentration value of the above multiple smoke plume measurement points. In the iterative calculation process, when the updated value of u is less than one ten-thousandth of the initial guess value, the iteration is considered to converge. At this time, u i+1 The value of is the CO2 emission intensity and wind speed of the chimney as the inversion result.

[0078] The three-dimensional plume structure is reconstructed and calculated based on the above inversion results. Specifically, the inversion results of CO2 emission intensity and wind speed obtained by inversion are input into formula (1) and recalculated to reconstruct the three-dimensional plume structure.

[0079] Below, a specific example is given to further illustrate in detail the method for predicting point source emission intensity based on ground remote sensing measurement according to an embodiment of the present invention.

[0080] The ground remote sensing monitoring of CO2 emission intensity of a power plant in an urban area is carried out. The specific steps are as follows.

[0081] (1) The measurement target is a power plant, and the measurement time is 1:00 p.m. on January 4, 2022, when the weather is fine. The emission intensity of CO2 emitted from the chimney of the power plant is set to Q0 = 1.75*10 5 g / s, continuous discharge.

[0082] (2) The meteorological instrument measured a wind speed of 2 m / s, a northerly wind direction, a temperature of 5°C, a humidity of 45%, and a pressure of 1003 hPa.

[0083] (3) Measurement point design

[0084] According to the wind direction, the direction of smoke plume diffusion is determined to be south of the chimney. At 1 pm, the sun is located south of the chimney and slightly west. The smoke plume projection area is located north of the chimney and slightly east. With the chimney as the center, the chimney spans 4km in the north-south direction and 2km in the east-west direction. 12 smoke plume measurement points are planned. At the same time, a background measurement point is selected in the upwind direction away from industrial and man-made emission sources. Measurement point design, for example, Figure 3 As shown, the specific number and location of measurement points can be determined based on the actual executable situation after the surrounding environment survey.

[0085] (4) Conduct measurement point by point

[0086] For each measuring point, a spectrometer is used to obtain one piece of spectral data, a locator is used to obtain the longitude and latitude of the point, a rangefinder is used to obtain the distance between the point and the chimney, and the measurement time is recorded.

[0087] (5) Calculate the solar geometry angle corresponding to each point measurement

[0088] Using the latitude and longitude of each point and the measurement time, the following formula is used to calculate the solar altitude angle and azimuth angle of the 13 points. The average solar altitude angle of the 13 points is 40°, and the average azimuth angle is 135°.

[0089] The calculation formula of the solar altitude angle h0 is as follows:

[0090] h0=sin -1 [sinψsinσ+cosψcosσcos(15t0+λ-300)]

[0091] Where ψ is latitude in degrees, λ is longitude in degrees, t0 is observation time in hours, σ is solar azimuth in degrees, and h0 is in degrees.

[0092] The calculation formula for the solar azimuth is as follows:

[0093]

[0094] Where θ0 = 360d n / 365, the unit is radians, where d n The order of the selected date in this year, ranging from 0 to 364.

[0095] (6) Inversion to obtain the observed CO2 concentration enhancement value of the smoke plume

[0096] The 13 observed spectra were inverted and calculated. The optimal estimation method was used, and the Tikhonov-Philips constraint conditions were used in combination with nonlinear successive iterations to construct an inversion calculation formula. The spectra observed by the spectrometer were converted into logarithmic spectra, and the meteorological data, observation time, and solar geometry were input into the inversion calculation formula to calculate the CO2 column concentration. The 12 plume CO2 column concentration values ​​were subtracted from the background CO2 column concentration to obtain 12 plume CO2 concentration enhancement values, which were 30, 35, 38, 42, 31, 24, 26, 21, 23, 36, 33, and 35 (all in ppm).

[0097] The visible near-infrared high-resolution spectrometer is the main equipment in the measurement system. The spectrometer can obtain the atmospheric CO2 column concentration by observing the solar spectrum reaching the surface. The raw data observed by the spectrometer is the spectrum. Through the inversion calculation in the above step S3, the atmospheric CO2 column concentration corresponding to the observed spectrum data is inverted.

[0098] Figure 4 (a) and (b) are observation diagrams of the spectrometer. The parallel lines in (a) and (b) represent the sunlight observed by the spectrometer, and (b) shows a schematic diagram of the CO2 plume at different altitudes. When the position of the spectrometer is different, the CO2 concentration corresponding to the observed light path is different, because different light paths pass through different positions of the plume, and the CO2 at different positions of the plume is different, so the CO2 concentration integral value of the entire light path is also different. The CO2 concentration integral value of the entire light path is referred to as the CO2 column concentration. By subtracting the background CO2 column concentration from each plume CO2 column concentration value, a plume CO2 concentration enhancement value is obtained. The background CO2 column concentration can be the background CO2 column concentration obtained by measuring one background measurement point, or it can be the average value of multiple background CO2 column concentrations obtained by measuring multiple background measurement points.

[0099] (7) Simulation calculation of 3D plume structure

[0100] A 3D coordinate system is established with the chimney as the origin, the wind direction as the x-axis, the vertical wind direction as the y-axis, and the chimney direction as the z-axis. The atmospheric stability is determined to be B based on the solar altitude angle of 40°, the cloud cover of 0, and the ground wind speed of 2m / s. The corresponding McElroy-Pooler urban area diffusion coefficient empirical formula is σ x =σ y =0.32x(1+0.0004x) -1 / 2 , σ z =0.24x(1+0.001x). Combined with wind speed μ=2m / s, emission time t=5 hours (emission starts at 8 am and measurement time is 1 pm), chimney height H=150 meters, emission intensity Q=1.81*10 5 g / s. Input the above data and formula into the following formula to calculate the 3D plume:

[0101]

[0102] (8) Calculate the CO2 concentration enhancement value of the simulated smoke plume

[0103] According to the latitude and longitude differences of the 12 plume measurement points relative to the chimney, the measurement points are located in the 3D coordinate system established in (7). The observation light path is constructed according to the solar geometry at the measurement time, and the path integral value of the CO2 concentration of the light path passing through the plume is calculated to obtain 12 simulated plume CO2 concentration enhancement values, which are 28, 32, 36, 41, 30, 22, 23, 18, 21, 34, 31 and 32 (all in ppm).

[0104] (9) Reflection emission intensity

[0105] First, prepare the data, including: the observation vector v composed of 12 observed CO2 concentration enhancement values ​​of the smoke plume; the simulation vector F(u) composed of 12 simulated CO2 concentration enhancement values ​​of the smoke plume; the CO2 emission intensity 1.81*10 5 g / s and wind speed 2m / s form the initial guess value u of the state vector a ; According to formula (1), the change in the simulation data after the unit disturbance of emission intensity and wind speed is calculated to form a weight function S ε is the CO2 concentration observation error, set to (12ppm) 2 ; S a is the error covariance matrix of emission intensity and wind speed, and the diagonal element values ​​are (0.5*650) 2 and (0.4*2) 2 Then, the above data is substituted into the following formula, which uses the optimal estimation method and Gauss-Newton iteration to invert the CO2 emission intensity and wind speed:

[0106] u i+1 =u a +(K i T S ε -1 K i +S a -1 ) -1 K i T S ε -1 [vF(u i )-K i (u i -u a )] (2)

[0107] Where u represents the CO2 emission intensity and wind speed, the subscript i represents the i-th iteration, and u i+1 is the result of the i+1th iteration. During the iterative calculation process, when the updated value of u is less than one ten-thousandth of the initial guess, the iteration is considered to have converged. At this time, u i+1 The values ​​are the inversion results of CO2 emission intensity and wind speed, which are 1.70*10 5 g / s and 1.2m / s.

[0108] The CO2 emission intensity (1.70*10 5 g / s) and the actual CO2 emission intensity (1.75*10 5 g / s) shows that the error of the CO2 emission intensity calculated by inversion is within 3% relative to the actual CO2 emission intensity, which is very close to the actual CO2 emission intensity.

[0109] (10) The inversion results of CO2 emission intensity and wind speed are input into formula (1), recalculated, and the 3D plume structure is reconstructed.

[0110] According to the above results, the emission intensity and wind speed obtained by inversion calculation using ground remote sensing measurement data and simulation data are 1.70*10 5 g / s and 1.2m / s, both are smaller than the initial guess, so according to Figure 5 Comparison of the cross-sectional views at different heights of the initially constructed plume and the reconstructed plume shows that, compared with the initially constructed plume, the diffusion degree of the reconstructed plume is reduced, the shape becomes smaller, and the concentration value becomes lower.

[0111] Compared with the drone sampling at each altitude layer in the prior art, the point source emission intensity prediction method based on ground remote sensing measurement of the present invention has the significant advantages of simple measurement method, less manual operation, low measurement cost, etc. It is not restricted by flight and is easy to carry out routine measurements. Compared with the lidar scanning measurement in the prior art, the measurement system is simple and can carry out measurements in many key emission areas.

[0112] The point source emission intensity prediction method based on ground remote sensing measurement of the present invention adopts portable ground remote sensing equipment to carry out column concentration measurement of the smoke plume around the point source, and uses multi-angle measurement data to carry out 3D smoke plume inversion and reconstruction, which can not only synchronously calculate the emission intensity, but also reduce the influence of wind field errors in traditional emission intensity calculation methods.

[0113] The contents described in the implementation mode of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the implementation mode. The protection scope of the present invention also includes equivalent technical solutions that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for predicting point source emission intensity based on ground remote sensing measurement, characterized in that: include: Step S1, using a meteorological instrument to measure and obtain meteorological data of the surrounding area of ​​a point source emitting pollutant gas, using a rangefinder to measure and obtain the height of the point source, using a locator to measure and obtain the longitude and latitude of the point source, estimating the range of the emission plume of the point source according to the wind direction and wind speed in the meteorological data, selecting multiple plume measurement points in the plume projection area formed on the ground by the emission plume of the point source through sunlight, selecting at least one background measurement point on the ground outside the plume projection area, using a spectrometer to measure and obtain spectral data of the solar spectrum reaching the ground for each of the multiple plume measurement points and the at least one background measurement point and recording the corresponding measurement time, using a locator to measure and obtain the longitude and latitude of the measurement point, and using a rangefinder to measure and obtain the distance between the measurement point and the point source; Step S2, for each of the measurement points in step S1, using the longitude and latitude of the measurement point and the measurement time, calculating the solar altitude angle and solar azimuth angle of the measurement point at the measurement time; Step S3, using the spectral data of each measuring point obtained in step S1, the meteorological data obtained synchronously, the measurement time, and the solar altitude angle and solar azimuth angle calculated in step S2, an inversion calculation is performed according to a spectral inversion algorithm to calculate the measured concentration value of a specific pollutant gas in the pollutant gas emitted by the point source measured at each measuring point in the atmosphere; Step S4, establishing a three-dimensional coordinate system with the intersection of a vertical line drawn from the point source to the ground in the vertical direction and the ground as the origin, the wind direction in the meteorological data as the x-axis direction, the height direction of the point source, i.e. the vertical direction, as the z-axis direction, and the direction perpendicular to the x-axis direction and the z-axis direction as the y-axis direction, and according to the wind speed in the meteorological data obtained in step S1 and the emission time, height and emission intensity of the point source as prior emission data, using a Gaussian diffusion model to perform simulation in the three-dimensional coordinate system to obtain a three-dimensional simulation structure of the emission plume; Step S5, in the three-dimensional coordinate system established in step S4, based on the difference between the longitude and latitude of the multiple smoke plume measurement points obtained in step S1 and the longitude and latitude of the point source and the distance between the multiple measurement points and the point source, locate the multiple smoke plume measurement points in step S1, for each of the smoke plume measurement points, construct a measurement light path according to the solar altitude angle and solar azimuth angle at the measurement time, calculate the concentration integral value of the specific pollutant gas of the smoke plume portion of the measurement light path, and obtain the simulated concentration values ​​of the specific pollutant gas at the multiple smoke plume measurement points; and Step S6, performing inversion using the measured concentration value of the specific pollutant gas inverted and calculated in step S3 and the simulated concentration value corresponding to the measured concentration value inverted and calculated in step S5, and calculating the inverted emission intensity and inverted wind speed of the point source as the inversion result, In step S2, the calculation formula of the solar altitude angle h0 is as follows: h0=sin -1 [sinψsinσ+cosψcosσcos(15t0+λ-300)] Where ψ is latitude in degrees, λ is longitude in degrees, t0 is the observation time in hours, σ is the solar azimuth in degrees, and h0 is in degrees. The calculation formula for the solar azimuth is as follows: Where θ0 = 360d n / 365, the unit is radians, where d n The order of the selected date in this year, ranging from 0 to 364. In step S3, the spectral data of the multiple smoke plume measurement points and the at least one background measurement point obtained in step S1 are subjected to an optimal estimation method, and Tikhonov-Philips constraints are used in combination with nonlinear successive iterations to construct an inversion calculation formula. The spectral data of each measurement point obtained in step S1 is converted into logarithmic spectral data, and the data is input into the inversion calculation formula together with the synchronously obtained meteorological data, the measurement time, and the solar altitude angle and the solar azimuth angle calculated in step S2 to calculate the column concentration of the specific pollutant gas. The column concentration of each specific pollutant gas corresponding to each smoke plume measurement point is subtracted from the average value of the column concentration of the specific pollutant gas corresponding to the at least one background measurement point to obtain the measured concentration value of the specific pollutant gas corresponding to each smoke plume measurement point. In step S4, when the wind speed is μ, the emission time is t, the height is H, and the emission intensity is Q, the Gaussian diffusion model for calculating the 3D simulation structure of the emission plume is expressed by the following formula (1): Wherein, (x, y, z) is the coordinate of the smoke plume position in the three-dimensional coordinate system, C(x, y, z) is the concentration of the specific pollutant gas at the position (x, y, z) in the three-dimensional coordinate system, σ x , σ y , σ z are the diffusion coefficients in the x-axis, y-axis, and z-axis directions, respectively. The unit of wind speed μ is m / s, the unit of emission time t is second, the unit of height H is m, the unit of emission intensity Q is g / s, and the unit of concentration C(x,y,z) is g / m 2 , The diffusion coefficient σ x , σ y , σ z It is a value calculated based on the empirical formula corresponding to the atmospheric stability level, and the unit is meter. In step S6, when performing inversion, the measured concentration value of the specific pollutant gas inverted and calculated in step S3 and the simulated concentration value corresponding to the measured concentration value calculated in step S5 are used, and the emission intensity and wind speed of the specific pollutant gas are inverted by Gauss-Newton iteration using the optimal estimation method. The solution formula is as follows: u i+1 =u a +(K i T S ε -1 K i +S a -1 ) -1 K i T S ε -1 [v-F(u i )-K i (u i -u a )] (2) Where u represents the emission intensity and wind speed of the specific pollutant gas, the subscript i represents the i-th iteration, and u i+1 is the result of the i+1th iteration, u a is the initial guess of the emission intensity and wind speed of the specific pollutant gas, is the weight function, T represents the matrix transpose, S ε is the measurement error covariance of the specific pollutant gas, S a is the prior error covariance matrix of the emission intensity and wind speed of the specific pollutant gas, v represents the measured concentration value of the specific pollutant gas at the multiple smoke plume measurement points, and F(u) represents the simulated concentration value of the specific pollutant gas at the multiple smoke plume measurement points. In the iterative calculation process, when the updated value of u is less than one ten-thousandth of the initial guess value, it is considered that the iteration converges. At this time, u i+1 The value of is the inverted emission intensity and inverted wind speed of the point source as the inversion result.

2. The method for predicting point source emission intensity based on ground remote sensing measurement according to claim 1, characterized in that: In step S1, when a plane parallel to the wind direction and passing through the point source is set as a first plane, a plane perpendicular to the wind direction and passing through the point source is set as a second plane, a line segment where the first plane intersects the smoke plume projection area is set as a first line segment, and a line segment where the second plane intersects the smoke plume projection area is set as a second line segment, multiple positions on the first line segment and the second line segment are selected as the multiple smoke plume measurement points, and at least one position upwind away from industrial and artificial emission sources is selected as the at least one background measurement point.

3. The method for predicting point source emission intensity based on ground remote sensing measurement according to claim 1, characterized in that: In step S5, in the measurement optical path constructed for each of the plume measurement points, the first concentration integral value of the specific pollutant gas in the plume path and the second concentration integral value of the specific pollutant gas in the non-plume path are calculated respectively, and the first concentration integral value and the second concentration integral value are added together to obtain the column concentration of the specific pollutant gas, and the column concentration is used as the simulated concentration value of the specific pollutant gas.

4. The method for predicting point source emission intensity based on ground remote sensing measurement according to claim 1, characterized in that: In step S6, the Gaussian diffusion model in step S4 is recalculated according to the inverted emission intensity and the inverted wind speed to reconstruct the three-dimensional structure of the emission plume.

5. The method for predicting point source emission intensity based on ground remote sensing measurement according to claim 1, characterized in that: The specific pollutant gas is any one selected from SO2, nitrogen oxides, CO2 and CO.

6. The method for predicting point source emission intensity based on ground remote sensing measurement according to claim 5, characterized in that: The specific pollutant gas is CO2.

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