CN117538488A - Remote sensing detection-based multi-discharge-source carbon dioxide discharge amount detection method - Google Patents
Remote sensing detection-based multi-discharge-source carbon dioxide discharge amount detection method Download PDFInfo
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Abstract
The invention relates to the technical field of environmental protection, in particular to a method for detecting carbon dioxide discharge amount of a plurality of discharge sources based on remote sensing detection, which comprises the following steps of observing carbon dioxide concentration of a position downwind of a carbon dioxide discharge source by using remote sensing equipment to obtain carbon dioxide concentration increment data in a certain space range of the position downwind of the discharge source; establishing a mathematical model of the discharge rate and the concentration increment of each discharge source by using a flue gas quasi-stable Gaussian discharge model; fitting the carbon dioxide emission rate of each emission source by using a least square estimation method according to the concentration increment data acquired by the remote sensing equipment; performing proper periodic monitoring on the discharge rate in a time range in which the discharge amount needs to be estimated, dividing a large time scale into a plurality of small time scales, and estimating the discharge rate; the average cycle emission rate calculates the emission amount over the total period. The emission quantity of a plurality of emission sources is fitted by utilizing remote sensing data, so that the problems of low efficiency and insufficient objectivity of a bottom-up list calculation method are solved.
Description
Technical Field
The invention belongs to the technical field of environmental protection, relates to a remote sensing monitoring and carbon dioxide discharge estimation technology, and in particular relates to a method for detecting carbon dioxide discharge of a plurality of discharge sources based on remote sensing detection.
Background
The development of greenhouse gas monitoring research is an important means for researching the emission characteristics of greenhouse gases and the influence of the emission characteristics on climate change. Emission source monitoring is a relatively underlying layer in greenhouse gas monitoring research, and is a research type closer to emission sources. The emission source monitoring can realize the accurate control of the greenhouse gas emission source, and the supervision efficiency of the greenhouse gas emission is improved. Traditional studies of greenhouse gas and atmospheric pollutant emissions generally employ "bottom-up" research approaches. The checklist method is a bottom-up method that uses various sources of information, such as crop production surveys and corporate emission information collection, to estimate carbon dioxide emissions covering an area of investigation. Because of the lack of high-precision monitoring and metering means, the bottom-up method is difficult to objectively and accurately reflect the distribution and emission characteristics of greenhouse gases. In addition, the bottom-up approach is inefficient due to the large amount of information that is collected. Compared with the bottom-up method, the top-down method utilizes the remote sensing monitoring equipment to estimate the carbon dioxide discharge capacity, and has the characteristics of stronger objectivity and high efficiency.
The literature "a constrained least square method combining bottom-up and top-down carbon dioxide flux estimation" gives a method combining bottom-up (inventory) and top-down carbon dioxide flux estimation, and attempts to complement the advantages and disadvantages of the two types of methods by combining bottom-up and top-down methods to estimate regional carbon dioxide flux. The study uses a constrained least squares statistical approach to process the output data from both bottom-up and top-down models. The literature "Yangtze river delta human CH4 emission monitoring based on top-down method" describes the comparison of CH in a lake area near the Wuxi city of Jiangsu province in China 4 And carbon dioxide. The study uses the atmospheric tracing method (atmospheric tracermethod) in the literature "North-east United states human mercury discharge to the atmosphere" to achieve CH for Yangtze river delta 4 Quantification of emissions and their annual changes.
The studies represented by the above documents only consider the problem of quantifying carbon dioxide emissions at a large spatial scale, but do not relate to a method for quantifying carbon dioxide emissions at a small scale such as a school business. Aiming at the problem of quantifying carbon dioxide emission under a small scale, the literature 'remote sensing technology for globally monitoring the carbon dioxide emission in the space of a power plant and related application' focuses the monitoring range of carbon dioxide emission on a specific emission source, and provides a remote sensing monitoring method for quantifying the carbon dioxide emission of the power plant. And estimating the discharge rate of the carbon dioxide discharge source by establishing a flue gas transmission model, and calculating the discharge amount.
At present, a carbon dioxide discharge quantification method based on remote sensing monitoring is mostly focused on a single discharge source, but in an actual scene, the main discharge source in a research area is often not unique, so that carbon dioxide discharge of multiple discharge sources is monitored simultaneously, and the problems of low efficiency and insufficient objectivity of a bottom-up list calculation method are the problems to be solved.
Disclosure of Invention
In order to solve the problems of insufficient objectivity and low efficiency of a bottom-up carbon dioxide discharge amount estimation method in the prior art, the method for detecting the carbon dioxide discharge amount of the multiple discharge sources based on remote sensing detection is provided, the top-down estimation of the carbon dioxide discharge amount of the multiple discharge sources is realized, remote sensing data are utilized to simultaneously fit the discharge amounts of the multiple discharge sources, and the problems of low efficiency and insufficient objectivity of the bottom-up inventory calculation method are solved.
In order to achieve the above purpose, the invention provides a method for detecting carbon dioxide emission of a multi-emission source based on remote sensing detection, which comprises the following steps:
monitoring the carbon dioxide concentration of the downwind of the carbon dioxide emission source by using remote sensing equipment, and acquiring carbon dioxide concentration increment data in a certain range of the downwind of the emission source;
according to the obtained carbon dioxide concentration increment data, a mathematical model of the emission rate and the concentration increment of the multiple emission sources is established by using a quasi-stable Gaussian emission model;
fitting the emission rate of carbon dioxide of each emission source by a least square estimation method according to the mathematical model of the emission rate and the concentration increment of the plurality of emission sources and the carbon dioxide concentration increment data of each emission source;
periodically monitoring a plurality of emission rates in a time range in which the emission amount needs to be estimated, and respectively estimating each emission rate after dividing a large time scale into a plurality of small time scales;
after the emission rates of all the small time scales are averaged, the average emission rates are obtained, a plurality of small time scales are integrated to calculate the emission amount in the large time scale, and the total emission amount is obtained.
Further, when the remote sensing device is used for monitoring the concentration of carbon dioxide in the downwind of the carbon dioxide emission source, a rectangular coordinate system is established by taking a certain point in the upwind as an origin, the direction of the wind direction is set as the positive direction of the X axis, and the vertical direction of the wind direction is set as the positive direction of the Y axis.
Further, the method for acquiring the concentration increment data comprises the following steps:
the concentration data of each point obtained by observation of the remote sensing equipment is monitored and obtained at the same time, and the average value of the carbon dioxide concentration in a certain range at the windward position is used as the background concentration which is not influenced by the emission of the emission source;
and subtracting the background concentration data from the concentration data of each point to obtain concentration increment data of each point caused by the influence of emission source emission.
Further, the construction of the mathematical model of concentration increment comprises the following steps:
the quasi-stationary Gaussian emission model represents the influence relation of concentration increment change at the downwind caused by a single emission source, and the carbon dioxide concentration increment is calculated by a mathematical model of the carbon dioxide concentration increment, namely a formula (1),
wherein V (x, y) is the carbon dioxide concentration increase at point (x, y), u is wind speed, F is emission rate, x o 1000m is a specific length, a is an atmospheric stability parameter, a depends on information such as ground wind speed and cloud amount, and the value of a only considers the factor of wind speed and is classified into several grades: u (u)<2m/s (a=213), u=2 to 3m/s (a=213 or 156), u=3 to 5m/s (a=156), u>5m/s(a＝104)。
The construction of the multi-discharge source discharge rate model comprises the following steps:
the quasi-stationary Gaussian emission model simulates the diffusion of the gas emitted by the emission source in the atmosphere, the emission rate F is estimated by using a weighted linear least square method, and the process of estimating the emission rate of the single emission source based on the model is as follows:
the formula (1) is expressed as:
V(x,y)＝α(x,y)F(3)
wherein,
the observation equation for the concentration increment is:
L＝HF+w(5)
wherein L represents a vector formed by the carbon dioxide concentration increment remote sensing observation value, V (x, y) is reduced to one dimension to obtain the vector, H represents a one-dimensional observation matrix, alpha (x, y) is reduced to one dimension to obtain the vector, and w represents noise;
from the least square method, it is known that the estimated value of the discharge rate satisfying the minimum error sum-of-squares index should be such that the objective function as shown in (6) is minimized.
S is a weight matrix, and diagonal elements are inverse numbers of uncertainty quantized values, so that data with large uncertainty have small weights; if the uncertain quantization information is not available, the weight factor related to S can be removed;
since the estimated value of the discharge rate that minimizes the objective function J has the smallest square errorAnd, thus, the objective function is pairedSolving the bias guide, and enabling the bias guide to be zero:
the method can obtain:
is an estimate of the carbon dioxide emission rate in g/s.
The method also comprises the construction of a mathematical model for the concentration increment change caused by the discharge rate of the multi-discharge source, and comprises the following steps: :
expanding the concentration increment in formula (1) to a form of multiple emission sources
F i Is the emission rate of the ith emission source, (x) i ,y i ) Is the coordinates of the ith emission source.
Further, the method for fitting the carbon dioxide emission rate of each emission source by using least square estimation comprises the following steps:
the formula (9) is expressed as:
wherein,
the observation equation for concentration delta can be described as:
wherein H is i Representing a one-dimensional observation matrix by combining alpha i (x, y) decreasing to one dimension;
the objective function is:
objective function is respectively toTo->Solving the bias guide and enabling the bias guide to be zero to obtain an equation set:
and solving an equation set (14) by utilizing a Newton iteration method to obtain the emission rate of each emission source.
Compared with the prior art, the invention has the beneficial effects that: and monitoring the carbon dioxide concentration of the downwind of the carbon dioxide emission source by using remote sensing equipment, and acquiring the concentration increment data of the carbon dioxide in a certain range of the downwind of the emission source. And fitting an estimated value of the carbon dioxide emission rate of each emission source by using a flue gas quasi-stable Gaussian emission model and a least square method, and calculating the carbon dioxide emission amount in a period of time. The method can realize top-down estimation of the carbon dioxide emission of multiple emission sources, simultaneously fit the emission of multiple emission sources by using remote sensing data, and solve the problems of low efficiency and insufficient objectivity of a bottom-up inventory calculation method.
Drawings
FIG. 1 is a schematic illustration of a technical route of the present invention;
FIG. 2 is a schematic view of the observation of incremental information of carbon dioxide concentration;
FIG. 3 is a flow chart of quantifying carbon dioxide emissions from a small emissions source based on unmanned aerial vehicle remote sensing;
fig. 4 is a process flow of quantifying carbon dioxide emissions from a large emissions source based on satellite remote sensing.
Detailed Description
The invention will be further described with reference to the drawings and embodiments.
Example 1
The invention provides a method for detecting carbon dioxide emission of multiple emission sources based on remote sensing detection, which comprises the following steps:
monitoring the carbon dioxide concentration of the downwind of the carbon dioxide emission source by using remote sensing equipment, and acquiring carbon dioxide concentration increment data in a certain range of the downwind of the emission source;
according to the obtained carbon dioxide concentration increment data, a mathematical model of the emission rate and the concentration increment of the multiple emission sources is established by using a quasi-stable Gaussian emission model;
fitting the emission rate of carbon dioxide of each emission source by a least square estimation method according to the mathematical model of the emission rate and the concentration increment of the plurality of emission sources and the carbon dioxide concentration increment data of each emission source;
periodically monitoring a plurality of emission rates in a time range in which the emission amount needs to be estimated, and respectively estimating each emission rate after dividing a large time scale into a plurality of small time scales;
after the emission rates of all the small time scales are averaged, the average emission rates are obtained, a plurality of small time scales are integrated to calculate the emission amount in the large time scale, and the total emission amount is obtained.
Further, when the remote sensing device is used for monitoring the carbon dioxide concentration of the downwind of the carbon dioxide emission source, a rectangular coordinate system is established by taking a certain point of the upwind as an origin, the direction of the wind direction is set as the positive direction of the X axis, and the vertical direction of the wind direction is set as the positive direction of the Y axis.
Further, the method for acquiring the concentration increment data comprises the following steps:
the concentration data of each point obtained by observation of the remote sensing equipment is monitored and obtained at the same time, and the average value of the carbon dioxide concentration in a certain range at the windward position is used as the background concentration which is not influenced by the emission of the emission source;
and subtracting the background concentration data from the concentration data of each point to obtain concentration increment data of each point caused by the influence of emission source emission.
Further, the construction of the mathematical model of concentration increment comprises the following steps:
the quasi-stationary Gaussian emission model represents the influence relation of concentration increment change at the downwind caused by a single emission source, and the mathematical model of carbon dioxide concentration increment is represented by a formula (1):
wherein V (x, y) is the carbon dioxide concentration increase at point (x, y), u is wind speed, F is emission rate, x o 1000m is a specific length, a is an atmospheric stability parameter, a depends on information such as ground wind speed and cloud amount, and the value of a only considers the factor of wind speed and is classified into several grades: u (u)<2m/s (a=213), u=2 to 3m/s (a=213 or 156), u=3 to 5m/s (a=156), u>5m/s(a＝104)。
The construction of the multi-discharge source discharge rate model comprises the following steps:
the quasi-stationary Gaussian emission model simulates the diffusion of the gas emitted by the emission source in the atmosphere, the emission rate F is estimated by using a weighted linear least square method, and the process of estimating the emission rate of the single emission source based on the model is as follows:
the formula (1) is expressed as:
V(x,y)＝α(x,y)F(3)
wherein,
the observation equation for the concentration increment is:
L＝HF+w(5)
wherein L represents a vector formed by the carbon dioxide concentration increment remote sensing observation value, V (x, y) is reduced to one dimension to obtain the vector, H represents a one-dimensional observation matrix, alpha (x, y) is reduced to one dimension to obtain the vector, and w represents noise;
from the least square method, it is known that the estimated value of the discharge rate satisfying the minimum error sum-of-squares index should be such that the objective function as shown in (6) is minimized.
S is a weight matrix, and diagonal elements are inverse numbers of uncertainty quantized values, so that data with large uncertainty have small weights; if the uncertain quantization information is not available, the weight factor related to S can be removed;
since the estimated value of the discharge rate that minimizes the objective function J has the smallest sum of squares error, the objective function is pairedSolving the bias guide, and enabling the bias guide to be zero:
the method can obtain:
is an estimate of the carbon dioxide emission rate in g/s.
Further, the mathematical model of the concentration increment change caused by the discharge rate of the multi-discharge source is established as follows:
expanding the concentration increment in formula (1) to a form of multiple emission sources
F i Is the emission rate of the ith emission source, (x) i ,y i ) Is the coordinates of the ith emission source.
Further, the method for fitting the carbon dioxide emission rate of each emission source by using least square estimation comprises the following steps:
the formula (9) is expressed as:
wherein,
the observation equation for concentration delta can be described as:
wherein H is i Representing a one-dimensional observation matrix by combining alpha i (x, y) decreasing to one dimension;
the objective function is:
objective function is respectively toTo->Solving the bias guide and enabling the bias guide to be zero to obtain an equation set:
and solving an equation set (14) by utilizing a Newton iteration method to obtain the emission rate of each emission source.
Example 2
Based on the embodiment 1 and referring to fig. 1-4, the method for detecting the carbon dioxide emission of the multi-emission source based on remote sensing detection comprises the following steps:
s1: observing the carbon dioxide concentration of the downwind of the carbon dioxide emission source by using remote sensing equipment, and acquiring carbon dioxide concentration increment data in a certain range of the downwind of the emission source;
s2: establishing a mathematical model of the emission rate and the concentration increment of the multiple emission sources by using a quasi-stationary and quasi-stationary Gaussian emission model;
s3: fitting the emission rate of the carbon dioxide of each emission source by using a least square estimation method;
s4: the emission rate is monitored periodically in a time range in which the emission amount needs to be estimated, the large time scale is divided into a plurality of small time scales, and the emission rate is estimated, so that the accuracy of emission amount calculation is improved;
s5: the average emission rate of each cycle calculates the emission amount in the total period to obtain the total emission amount.
Further, in the step S1, when the remote sensing device is used to observe the carbon dioxide concentration, a rectangular coordinate system is established by taking a certain point of the windward position as an origin, the direction of the wind direction is the positive direction of the X axis, and the vertical direction of the wind direction is the positive direction of the Y axis. A schematic diagram of carbon dioxide concentration monitoring is shown in figure 2.
Further, the method for acquiring the concentration increment information in step S1 includes: the concentration information of each point is observed by the remote sensing equipment, and the average value of the carbon dioxide concentration in a certain range at the windward position is required to be monitored and obtained at the same time and used as the background concentration which is not influenced by the emission of the emission source, and the concentration value of each point is subtracted by the background concentration to obtain the concentration increment information of each point caused by the emission influence of the emission source.
Further, the quasi-stationary and quasi-stationary gaussian emission model in step S2 can simulate the diffusion of the gas emitted from the emission source in the atmosphere, and a mathematical model of the carbon dioxide concentration increment is given by the formula (1), wherein the model contains the factor of the emission rate, and the emission rate can be estimated by using the concentration increment observation value, so as to calculate the emission amount within a period of time.
Where V (x, y) is the carbon dioxide concentration increase at point (x, y), in units of: g/m 2 U is wind speed, unit: m/s, F is the discharge rate, unit: g/s, x o The value of =1000m is a specific length, and a is an atmospheric stability parameter, which depends on information such as ground wind speed and cloud. The value of a only considers the wind speed factor and is classified into several classes: u (u)<2m/s (a=213), u=2 to 3m/s (a=213 or 156), u=3 to 5m/s (a=156), u>5m/s(a＝104)。
The model describes the effect of concentration delta changes in the downwind caused by a single emissions source, and the emissions rate F can be estimated using a weighted linear least squares method. The process of estimating the individual emissions source emissions rate based on the model is:
formula (1) is expressed as
V(x,y)＝α(x,y)F(3)
Wherein the method comprises the steps of
The observation equation for concentration delta can be described as
L＝HF+w(5)
Where L represents a vector of remote sensing observations of carbon dioxide concentration increments (obtained by reducing V (x, y) to 1 dimension), H represents a one-dimensional observation matrix (obtained by reducing α (x, y) to 1 dimension), and w represents noise.
From the least square method, it is known that the estimated value of the discharge rate satisfying the minimum error sum-of-squares index should be such that the objective function as shown in (6) is minimized.
Wherein S is a weight matrix, and diagonal elements are inverse of uncertainty quantized values, so that data with large uncertainty has small weight. If uncertainty quantization information is not available, the weighting factor for S is removed.
Since the estimated value of the discharge rate that minimizes the objective function J has the smallest sum of squares error, the objective function is pairedCalculate the bias guide and make it zero
Is available in the form of
Is the estimated value of the carbon dioxide emission rate, and is expressed in g/s
Further, in the step S2, a mathematical model of the discharge rate and the concentration increment of the discharge source is established as follows:
expanding the concentration increment in formula (1) to a form of multiple emission sources
F i Is the emission rate of the ith emission source, (x) i ,y i ) Is the coordinates of the ith emission source.
Further, in the step S3, the method for fitting the carbon dioxide emission rate of each emission source by using least square estimation is as follows:
formula (9) is expressed as
Wherein the method comprises the steps of
The observation equation for concentration delta can be described as
H i Representing a one-dimensional observation matrix (by combining alpha i (x, y) is reduced to 1-dimensional.
The objective function is:
objective function is respectively toTo->Obtaining a system of equations by solving the partial derivative and making the partial derivative zero
And solving an equation set (14) by utilizing a Newton iteration method to obtain the emission rate of each emission source.
Example 3
The following describes in detail the embodiments of the present invention based on example 1, two different embodiments of carbon dioxide emission quantification are given according to the characteristics of different types of remote sensing devices and the scale of the emission source.
Satellite remote sensing is suitable for monitoring large-space-scale emission sources such as towns and industrial areas due to low spatial resolution, and a relatively accurate inversion result cannot be obtained for small emission sources. Unmanned aerial vehicle remote sensing is difficult to apply to the monitoring of large space scale emission source because of limited coverage, but its higher spatial resolution can be applied to the monitoring of little emission source.
1. Small emission source carbon dioxide emission quantification implementation scheme based on unmanned aerial vehicle remote sensing
For quantifying carbon dioxide emission of small emission sources such as schools and enterprises, unmanned aerial vehicle remote sensing is used as a monitoring tool of carbon dioxide concentration in order to ensure accuracy of inversion results, and a specific implementation scheme for quantifying month emission is provided. The unmanned aerial vehicle remote sensing can obtain carbon dioxide concentration data with higher spatial resolution than satellite remote sensing. The higher the spatial resolution of the theoretically obtained carbon dioxide concentration data, the higher the accuracy of the inversion-derived emission rate. Meanwhile, the accuracy of the emission amount is also influenced by the data acquisition times, and the more the data acquisition times, the more accurate the estimation result of the emission amount is. Therefore, it is necessary to collect carbon dioxide concentration data several times in one month when quantifying the amount of emissions in one month. In addition, the influence caused by the emission of other emission sources is considered, and the carbon dioxide concentration is monitored under different wind direction conditions as much as possible when data acquisition is carried out each time, so that the influence of the other emission sources is weakened when the total emission amount of a month is estimated.
Referring to fig. 3, the implementation process specifically includes:
step 1: remote sensing observation data of carbon dioxide concentration in a certain space range of downwind of emission source by using unmanned aerial vehicle
And dividing one month into 6 carbon dioxide concentration data acquisition periods by taking 5 days as a unit, and carrying out acquisition work of carbon dioxide concentration data once in each acquisition period. And when the carbon dioxide concentration data is acquired each time, the unmanned aerial vehicle carries a carbon dioxide sensor to fly in a range which needs to be monitored at the position of the exhaust source downwind (the irradiation track of the sensor is realized to completely cover the area), so that the carbon dioxide concentration data is acquired. This stage obtains carbon dioxide concentration acquisition data in the space range to be monitored at 6 downwind of the emission source within one month.
Step 2: inversion of carbon dioxide emission rate by combining quasi-stationary Gaussian emission model
Subtracting a carbon dioxide background concentration value from the carbon dioxide concentration data collected each time, wherein the background concentration is obtained by averaging carbon dioxide concentration measured values in a certain range of the upwind of the emission source. And obtaining carbon dioxide concentration increment data of 6 times of measurement, and then obtaining the carbon dioxide emission rate in 6 groups of sampling periods by using a least square parameter estimation method and combining quasi-stationary Gaussian emission model inversion based on the data.
Step 3: calculating the discharge amount in each collecting period and estimating the total discharge amount of the month
The inverted carbon dioxide emission rate represents the instantaneous emission rate at the time of data acquisition, considering the finite number of acquisitions and the approximate constant emission rate in a short period of time, we consider the emission rate obtained by inverting each acquisition of data as the average carbon dioxide emission rate over the period of the data acquisition. The discharge amount of 6 cycles was calculated from the carbon dioxide discharge rate. And accumulating the emission of each period to finally obtain the total emission amount of the carbon dioxide in the month.
2. Big emission source carbon dioxide emission quantization implementation scheme based on satellite remote sensing
For monitoring carbon dioxide of large emission sources such as towns, industrial areas and the like, the satellite remote sensing data can be utilized to estimate the carbon dioxide emission in consideration of the flight cost and coverage limitation of the unmanned aerial vehicle. The satellite remote sensing equipment can obtain the spatial concentration data of the carbon dioxide through spectral analysis, and the coverage range is wide. The major greenhouse gas monitoring satellites in the world currently include TanSat, OCO-2, GOSAT, and the like. In view of the limitation of the re-turn period (time resolution), satellite tele-sensing performs displacement inversion of carbon dioxide over a longer period of time than unmanned aerial vehicles.
Referring to fig. 4, taking a TanSat satellite with a 16-day return period as an example, a specific implementation procedure is given:
step 1: remote sensing observation data of carbon dioxide concentration in a certain space range of downwind of emission source by utilizing satellite
Because the satellite has a return period of 16 days, the remote sensing data can be obtained 22-23 times in one year. We take Yang Ling area as an example (about 22km in area) 2 ) And uses it as an emission source. Taking an emission source as a starting point, acquiring 200km of a downwind position of the emission source obtained by satellite detection 2 Carbon dioxide concentration data in the range, the spatial resolution of carbon dioxide concentration is 2km 2 。
Step 2: inversion of carbon dioxide emission rate by combining quasi-stationary Gaussian emission model
Subtracting a carbon dioxide background concentration value from carbon dioxide concentration data obtained by each satellite return, wherein the background concentration is obtained by averaging carbon dioxide concentration measured values in a certain range of the upwind of the emission source. And obtaining carbon dioxide concentration increment data of each re-returning measurement, and then obtaining the carbon dioxide emission rate of each re-returning monitoring by combining a quasi-stationary Gaussian emission model inversion by using a least square parameter estimation method based on the data.
Step 3: estimating annual emissions
The carbon dioxide emission rate obtained by inverting the reentrant data each time is averaged to represent the annual average emission rate, and the annual emission of carbon dioxide is calculated.
The above embodiments are only for illustrating the technical solution of the present invention, but not for limiting the technical solution of the present invention, and any modification or partial replacement without departing from the spirit and scope of the present invention should be covered in the scope of the claims of the present invention according to the actual situation.
It is noted that in the present invention, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Claims (6)
1. The method for detecting the carbon dioxide emission of the multi-emission source based on remote sensing detection is characterized by comprising the following steps of:
monitoring the carbon dioxide concentration of the downwind of the carbon dioxide emission source by using remote sensing equipment, and acquiring carbon dioxide concentration increment data in a certain range of the downwind of the emission source;
according to the obtained carbon dioxide concentration increment data, a mathematical model of the emission rate and the concentration increment of the multiple emission sources is established by using a quasi-stable Gaussian emission model;
fitting the emission rate of carbon dioxide of each emission source by a least square estimation method according to the mathematical model of the emission rate and the concentration increment of the plurality of emission sources and the carbon dioxide concentration increment data of each emission source;
periodically monitoring a plurality of emission rates in a time range in which the emission amount needs to be estimated, and respectively estimating each emission rate after dividing a large time scale into a plurality of small time scales;
after the emission rates of all the small time scales are averaged, the average emission rates are obtained, a plurality of small time scales are integrated to calculate the emission amount in the large time scale, and the total emission amount is obtained.
2. The method for detecting carbon dioxide emissions from multiple emission sources based on remote sensing according to claim 1, wherein when monitoring the concentration of carbon dioxide in the downwind of the carbon dioxide emission sources by using the remote sensing device, a rectangular coordinate system is established by taking a certain point in the upwind as an origin, the direction of the wind direction is set as the positive direction of the X axis, and the vertical direction of the wind direction is set as the positive direction of the Y axis.
3. The method for detecting carbon dioxide emissions from multiple emission sources based on remote sensing according to claim 1, wherein the method for acquiring the concentration increment data of carbon dioxide comprises the following steps:
the concentration data of each point obtained by observation of the remote sensing equipment is monitored and obtained at the same time, and the average value of the carbon dioxide concentration in a certain range at the windward position is used as the background concentration which is not influenced by the emission of the emission source;
and subtracting the background concentration data from the concentration data of each point to obtain concentration increment data of each point caused by the influence of emission source emission.
4. The remote sensing-based multi-emission-source carbon dioxide emission detection method according to claim 1, wherein the mathematical model construction of the concentration increment comprises the following steps:
the quasi-stationary Gaussian emission model represents the influence relation of concentration increment change at the downwind caused by a single emission source, the emission source is taken as an origin, and a mathematical model of concentration increment of carbon dioxide is represented by formula (1):
wherein V (x, y) is the carbon dioxide concentration increase at point (x, y), u is wind speed, F is emission rate, x o 1000m is a specific length, a is an atmospheric stability parameter, a depends on information such as ground wind speed and cloud amount, and the value of a only considers the factor of wind speed and is classified into several grades: u (u)<2m/s (a=213), u=2 to 3m/s (a=213 or 156), u=3 to 5m/s (a=156), u>5m/s(a＝104)；
The construction of the multi-discharge source discharge rate model comprises the following steps:
the quasi-stationary Gaussian emission model simulates the diffusion of the gas emitted by the emission source in the atmosphere, the emission rate F is estimated by using a weighted linear least square method, and the process of estimating the emission rate of the single emission source based on the model is as follows:
the formula (1) is expressed as:
V(x,y)＝α(x,y)F (3)
wherein,
the observation equation for the carbon dioxide concentration increase is:
L＝HF+w (5)
wherein L represents a vector formed by the carbon dioxide concentration increment remote sensing observation value, V (x, y) is reduced to one dimension to obtain the vector, H represents a one-dimensional observation matrix, alpha (x, y) is reduced to one dimension to obtain the vector, and w represents noise;
from the least squares method, it is known that the estimated emission rate satisfying the minimum error sum-of-squares index should minimize the objective function as shown in (6):
s is a weight matrix, and diagonal elements are inverse numbers of uncertainty quantized values, so that data with large uncertainty have small weights; if the uncertain quantitative information cannot be obtained, removing the weight factor related to S;
since the estimated value of the discharge rate that minimizes the objective function J has the smallest sum of squares error, the objective function is pairedSolving the bias guide, and enabling the bias guide to be zero:
the method can obtain:
is an estimate of the carbon dioxide emission rate in g/s.
5. The remote sensing-based multi-emission-source carbon dioxide emission detection method as defined in claim 4, further comprising: the construction of the mathematical model for the concentration increment change caused by the discharge rate of the multi-discharge source comprises the following steps:
expanding the concentration increment in formula (1) to a form of multiple emission sources
F i Is the emission rate of the ith emission source, (x) i ,y i ) Is the coordinates of the ith emission source.
6. The method for detecting carbon dioxide emissions from multiple emissions sources based on remote sensing according to claim 5, wherein said fitting the carbon dioxide emissions rate from each of the emissions sources using least squares estimation comprises the steps of:
the formula (9) is expressed as:
wherein,
the observation equation for the concentration increase is described as:
wherein H is i Representing a one-dimensional observation matrix by combining alpha i (x, y) decreasing to one dimension;
the objective function is:
objective function is respectively toTo->Solving the bias guide and enabling the bias guide to be zero to obtain an equation set:
and solving an equation set (14) by utilizing a Newton iteration method to obtain the emission rate of each emission source.
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