CN111912754A - Remote sensing inversion method for near-surface particulate matter concentration - Google Patents
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Abstract
The invention discloses a remote sensing inversion method of near-ground particulate matter concentration, which comprises the following steps: s100, inverting the satellite remote sensing data to obtain an extinction coefficient close to the ground; s200, carrying out moisture absorption correction on the concentration of the particles of the actually measured station to obtain the concentration of the particles of the near-ground surface with the corrected moisture absorption; s300, carrying out linear fitting on the extinction coefficient of the near-ground obtained in the step S100 and the moisture absorption corrected near-ground particulate matter concentration obtained in the step S200, and establishing a dynamic linear regression model to obtain a near-ground particulate matter concentration field; and S400, performing variational correction on the near-ground particle concentration field linearly regressed in the step S300 by combining the actually-measured site particle concentration to obtain the near-ground particle concentration field with high precision. According to the invention, the satellite remote sensing inversion data and the ground meteorological and environmental site observation data are fused, so that the point-to-surface monitoring of the ground particulate matter concentration is realized, the advantage of high resolution of satellite remote sensing information is exerted, and the accuracy and the coverage of air quality monitoring are greatly improved.
Description
Technical Field
The invention relates to the technical field of air quality monitoring, in particular to a remote sensing inversion method for near-ground particulate matter concentration.
Background
Along with the rapid development of national economy, the urban scale is continuously enlarged, the social economy is rapidly developed by industrialization, and a large amount of resource consumption and severe damage to the atmospheric environment are brought. Particularly, in recent years, heavily polluted weather events frequently occur in the eastern region of our country. The regional atmospheric environment problem taking inhalable particles (PM10) and fine particles (PM2.5) as characteristic pollutants is increasingly prominent, the health of people is damaged, and the social harmony and stability are influenced.
Although many cities establish ground environment monitoring stations in main zones of the cities to monitor particulate matters, pollutant gas concentration and the like, the stations are often sparse and concentrated in the cities, the spatial distribution of aerosol particles is difficult to reflect comprehensively, and macroscopic monitoring cannot be performed. Satellite remote sensing can provide regional distribution of aerosols over a wide range, and has wide application in pollutant monitoring, determination of pollution events, pollutant source analysis, and regional delivery of pollutants. The Aerosol remote sensing data, particularly the Aerosol Optical Depth (AOD), reflects the attenuation degree of Aerosol in the atmosphere and the like to incident solar electromagnetic radiation, is widely applied to atmospheric pollution monitoring, realizes the point-by-point and surface monitoring of the ground particulate matter concentration, can greatly make up the defects of a ground monitoring station, and simultaneously performs variation fusion with the particulate matter concentration data monitored by the ground station, thereby improving the monitoring precision.
Disclosure of Invention
The invention aims to provide a remote sensing inversion method of near-ground particulate matter concentration with high precision.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a remote sensing inversion method for the concentration of near-surface particulate matters comprises the following steps:
s100, inverting the satellite remote sensing data, and performing height correction based on vertical distribution of the aerosol extinction coefficients to obtain the near-ground aerosol extinction coefficients;
s200, carrying out moisture absorption correction on the concentration of the particulate matters of the actually measured station, and carrying out humidity correction on the basis of a Hanel growth coefficient to obtain the concentration of the moisture absorption corrected near-ground particulate matters;
s300, carrying out linear fitting on the extinction coefficient of the near-ground obtained in the step S100 and the moisture absorption corrected near-ground particulate matter concentration obtained in the step S200, and establishing a dynamic linear regression model to obtain a near-ground particulate matter concentration field;
and S400, performing variational correction on the near-ground particle concentration field linearly regressed in the step S300 by combining the actually-measured site particle concentration to obtain the near-ground particle concentration field with high precision.
Further, in step S100, the aerosol optical thickness obtained by satellite remote sensing is converted into a vertical distribution of aerosol extinction coefficients according to the height and stability type of the mixed layer, the vertical distribution of the aerosol extinction coefficients is in a form of a negative index, and the aerosol optical thickness can be obtained by the following equation:
wherein the content of the first and second substances,
AOD is aerosol optical thickness;
z is the vertical height;
σa,0is the near-surface aerosol extinction coefficient;
h is the aerosol elevation, the thickness of the optically active aerosol layer in the atmosphere, and the boundary layer height can be approximated.
Still further, using near-surface aerosol extinction coefficient σa,0Establishing an empirical relationship with visibility L:
wherein the content of the first and second substances,
l is near-ground visibility;
σa,0is the near-surface aerosol extinction coefficient;
is a contrast threshold, determined as 0.02 based on the wavelength 0.55um and vision to which the human eye is most sensitive;
combining equation (1) yields:
Hi＝AODi/σa，0＝(AOD×L)/3.912 (2)
the aerosol level changes smoothly in the region and can pass through a station HiAnd (4) interpolating to obtain a spatial distribution diagram of the aerosol elevation H, wherein i represents different test stations.
Particularly, the spatial distribution diagram of the aerosol elevation H is interpolated by adopting a thin-disc smooth spline surface fitting method, and the theoretical statistical model of the local thin-disc smooth spline is expressed as follows:
Zi＝f(xi)+bTyi+ei (3)
wherein the content of the first and second substances,
Ziis a dependent variable at a space i point;
xid-dimensional spline independent variable vectors;
f is the value to be estimated with respect to xiAn unknown smooth function;
yiis a p-dimensional independent covariate vector;
b is yiT is a transpose;
eito have an expectation of 0 and a variance of
(ii) an independent variable random error of (d); w is aiIs a known local relative coefficient of variation as a weight; w is a2A constant over all data points for error variance;
the function f and the coefficient b are estimated by the least squares method:
wherein the content of the first and second substances,
Jm(f) as a function f (x)i) A roughness measure function of;
ρ is the smoothing parameter.
Further, according to the spatial distribution diagram of H obtained by interpolation, the spatial distribution of the near-ground extinction coefficient is obtained:
σa，0＝AODi/Hi。
further, in step S200, the Hanel growth coefficient is first calculated according to the formula:
wherein the content of the first and second substances,
σa,iis the near-ground aerosol extinction coefficients of different PM2.5 stations;
PM2.5,ithe concentration of particulate matters is actually measured by stations of different PM2.5 stations;
αext,ithe extinction efficiency of the mass of the fine particles of different PM2.5 measurement stations is obtained;
f is the proportion of fine particles in the aerosol at different PM2.5 measuring stations;
γiis the Hanel growth factor;
RH0the relative humidity in the dry state is 40 percent;
RH is the measured relative humidity of the station;
linear fitting is carried out on each station, and the Hanel growth coefficient gamma of each station can be obtainedi(ii) a Then carrying out moisture absorption correction on the site particulate matter concentration:
wherein the content of the first and second substances,
σa，drythe relative humidity in the dry state of RH0 is 40% and the relative humidity measured at the station is rho (PM) for the concentration of particulate matter after moisture absorption and correction2.5) And (4) actually measuring the concentration of the particulate matters for the site, wherein gamma is a Hanel growth coefficient obtained by fitting.
Further, in step S300, it can be approximately considered that each influence factor is relatively stable in a small area and in a short time range, the chemical composition and the spectral distribution of the aerosol are fixed, the average extinction efficiency of the particulate matter, the effective radius of the particulate matter, the average mass density of the particulate matter, and the proportion of the fine particles in the aerosol can be regarded as constants, and a linear model between the optical thickness of the aerosol and the concentration of the particulate matter is fitted:
wherein the content of the first and second substances,
PM2.5is the fine particle concentration;
αext，athe extinction efficiency of the mass of the fine particles of different PM2.5 measurement stations is obtained;
f is the proportion of fine particles in the aerosol at different PM2.5 measuring stations;
AOD is aerosol optical thickness;
h is the boundary layer height.
To obtain a concentration field of particulate matter of
Further, in step S400, the score change correction method is as follows:
according to the actual measurement station PM2.5 concentration element field as Su (x, y), on the actual measurement PM2.5 concentration point coordinate (x, y), the difference field of the two, namely the error field is
Because the number of the coordinate (x, y) points of the PM2.5 concentration observation station is limited, a correction factor field function CR (x, y) in a wider whole field is constructed, a variational method is adopted to search for the CR (x, y) function, and the following conditions are required to be met:
namely, it is
Reaching a minimum value;
solving the numerical solution of the equation by an iteration method to obtain a new variation correction factor field CR (x, y), so that the PM2.5 particulate matter concentration field after variation correction is obtained as follows:
has the advantages that: the embodiment of the invention provides a ground particulate matter concentration remote sensing inversion method based on multiple correction methods, which realizes the point-to-surface monitoring of the ground particulate matter concentration by fusing satellite remote sensing inversion data with ground meteorological and environmental site observation data, exerts the advantage of high resolution of satellite remote sensing information, has high precision and continuous spatial distribution of the inverted ground particulate matter concentration through multiple correction, and greatly improves the accuracy and the coverage of air quality monitoring.
Compared with the prior art, when the concentration and humidity of the particulate matter observed at the station are determined, the aerosol in different areas has different chemical components and different moisture absorption characteristics, so that the station-by-station humidity correction of different areas is realized, and the problem that one area adopts one correction coefficient when the humidity correction is performed in the past is solved; meanwhile, the inversion result and the actually measured ground station particulate matter concentration are subjected to variation fusion correction, and the inversion precision is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow diagram of the process of the present invention;
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
As further explained in connection with fig. 1: a remote sensing inversion method of near-surface particulate matter concentration comprises
The following steps:
s100, inverting the satellite remote sensing data, and performing height correction based on vertical distribution of the aerosol extinction coefficients to obtain the near-ground aerosol extinction coefficients;
the AOD is the integral of the extinction coefficient of the aerosol in the vertical direction, describes the attenuation effect of the aerosol on light, is the content of a column, is the extinction coefficient of the whole layer under the condition of the plane parallel to the atmosphere, is vertically decreased along with the height, is a single-layer model commonly used, is assumed that the extinction coefficient of the aerosol is decreased along with the height in an e index manner, the optical thickness of the aerosol obtained by satellite remote sensing is converted into the vertical distribution of the extinction coefficient of the aerosol according to the height and stability types of a mixed layer, the vertical distribution of the extinction coefficient of the aerosol is in a negative index manner, and the optical thickness of the aerosol can be obtained by the following equation:
wherein H represents the aerosol elevation, represents that the aerosol extinction coefficient is reduced to 1/e of the ground value, is equivalent to the thickness of an optically active aerosol layer (optically active aerosol layer) in the atmosphere, and can approximate the boundary layer height. The physical meaning of atmospheric aerosol elevation is the height which can be reached by the uniform distribution of particles in the whole atmospheric column in the vertical direction according to the ground concentration, namely the equivalent thickness of the aerosol layer when the aerosol concentration is assumed to be constant along with the height.
Aerosol level HiAverage MODIS/AOD capable of being centered at station by 10kmiEstimation in conjunction with visibility, using near-surface aerosol extinction coefficient sigmaa,0Establishing an empirical relationship with visibility L:
wherein, the contrast threshold is determined to be 0.02 according to the most sensitive wavelength of human eyes of 0.55um and vision;
obtaining:
σa,03.912/L was then obtained in conjunction with equation (1):
Hi＝AODi/σa，0＝(AOD×L)/3.912 (2)
the aerosol level changes smoothly in the region and can pass through a station HiAnd (3) obtaining a spatial distribution map of the aerosol elevation H by interpolation, wherein i represents different test stations, the spatial distribution map of the aerosol elevation H is interpolated by adopting a thin-disc smooth spline surface fitting method, and a theoretical statistical model of a local thin-disc smooth spline is expressed as follows:
Zi＝f(xi)+bTyi+ei (3)
wherein the content of the first and second substances,
Ziis a dependent variable at a space i point; x is the number ofiD-dimensional spline independent variable vectors; f is the value to be estimated with respect to xiAn unknown smooth function; y isiIs a p-dimensional independent covariate vector; b is yiT is a transpose; e.g. of the typeiTo have an expectation of 0 and a variance of
(ii) an independent variable random error of (d); w is aiIs a known local relative coefficient of variation as a weight; w is a2A constant over all data points for error variance;
the function f and the coefficient b are estimated by the least squares method:
wherein, Jm(f) As a function f (x)i) A roughness measure function of; ρ is the smoothing parameter.
And finally, obtaining the spatial distribution of the near-ground extinction coefficient according to the spatial distribution graph of H obtained by interpolation:
σa，0＝AODi/Hi。
s200, carrying out moisture absorption correction on the concentration of the particulate matters of the actually measured station, and carrying out humidity correction on the basis of a Hanel growth coefficient to obtain the concentration of the moisture absorption corrected near-ground particulate matters;
the measurement of the ground PM2.5 concentration is generally carried out weighing measurement in a drying process, the AOD inverted by satellite remote sensing is obtained by inversion under the current environment (humidity), and the influence of Relative Humidity (RH) on the extinction coefficient of particulate matters is obvious. When the relative humidity is high, the water-soluble aerosol particles absorb moisture and expand, the particle size increases, and the extinction coefficient can be increased by several times, so that the concentration of PM2.5 must be corrected to eliminate the system difference between the two. The moisture absorption growth characteristics of aerosol particles obviously influence the birefringence index, extinction cross section and other optical properties of aerosol, and the correlation between the aerosol extinction coefficient and the particle concentration PM is different due to the chemical composition of the particles and the ambient air humidity. The influence of relative humidity on the scattering ability of the aerosol is generally described by a scattering moisture absorption growth factor, which is a key factor determining the scattering property of the aerosol and is defined as the ratio of the extinction coefficient of the aerosol in a certain wavelength and a wet state to the extinction coefficient of the aerosol in a dry state (RH is less than or equal to 40%) at the wavelength:
and finally, the humidity correction adopts an empirical formula:
σa，dry＝ρ(PMx)×f(RH)
for the aerosol extinction moisture absorption growth factor f (RH), a plurality of fitting forms exist internationally, and Kasten proposes a classical single-parameter model according to moisture absorption growth observation data in 1969 and adopts the following model through improvement:
In the model, the Hanel growth coefficient needs to be fitted first, and the fitting method is derived according to the following theory.
Aerosol extinction coefficient under dry conditions: sigmadry＝αext,a·PM＝αext·PM2.5+α'ext·PM>2.5
αext,aRepresents the average mass extinction efficiency (α), αextRepresents the extinction efficiency of the fine particle mass (<2.5um)，α'extIndicating the coarse particle mass extinction efficiency. Especially in east asia, the predominant type of aerosol is urban/industrial aerosol, where the fine particle contribution is predominant in aerosol extinction and F is the fine particle fraction. At a certain place alphaext,iAnd F can be considered constant.
Taking natural logarithm on both sides to obtain
Parameter alphaext,iAnd gammaiUnknown, i represents a different PM2.Extinction coefficient sigma of 5 measuring station and stationa,i3.912/L. Under the conditions of relative humidity, PM2.5 concentration and visibility of each station, linear fitting is carried out on each station, and the Hanel growth coefficient gamma of each station can be obtainedi
And finally, the moisture absorption correction of the concentration of the particles at the station is as follows:
σa，dryconcentration of particulate matter after moisture absorption and correction, RH0The relative humidity in the dry state is 40%, and RH is the measured relative humidity of the station, rho (PM)2.5) And (4) actually measuring the concentration of the particulate matters for the site, wherein gamma is a Hanel growth coefficient obtained by fitting.
S300, carrying out linear fitting on the extinction coefficient of the near-ground obtained in the step S100 and the moisture absorption corrected near-ground particulate matter concentration obtained in the step S200, and establishing a dynamic linear regression model to obtain a near-ground particulate matter concentration field;
according to theoretical derivation
Can approximately consider that all the influencing factors are relatively stable in a small area and a short time range, the chemical composition and the spectral distribution of the aerosol are certain, and QextR and rho can be regarded as constants, so that positive correlation exists between the optical thickness of the aerosol and the concentration of the particulate matters, and a linear model between the optical thickness of the aerosol and the concentration of the particulate matters can be fitted through a large amount of observation sample data, as shown in a formula:
and (3) carrying out humidity correction on the concentration of the particulate matters observed by the station, carrying out height correction on the optical thickness of the aerosol obtained by satellite inversion to obtain an extinction coefficient close to the ground, carrying out linear regression according to the theory, and establishing a dynamic linear regression model to obtain a particulate matter concentration field.
And S400, performing variational correction on the near-ground particle concentration field linearly regressed in the step S300 by combining the actually-measured site particle concentration to obtain a particle concentration field with high near-ground precision.
Let the element field of PM2.5 concentration obtained by linear regression of AOD be
The actual measurement PM2.5 concentration element field of the corresponding finite point is Su (x, y), and on the coordinate (x, y) of the actual measurement PM2.5 concentration point, the difference field of the two, namely the error field is Su (x, y)
In fact, because the number of the coordinate (x, y) points of the PM2.5 concentration observation station is limited, a correction factor field function CR (x, y) in a wider whole field needs to be constructed, and the following conditions need to be satisfied when a variational method is adopted to search for the CR (x, y) function:
namely, it is
Reaching a minimum value.
For the above variational problem, it can be assumed that functional J is constructed*
Where λ is the constraint coefficient.
The above formula can be rewritten as
Corresponding Euler equation is
Wherein
Are constraint coefficients. Solving the numerical solution of the equation by an iteration method to obtain a new variation correction factor field CR (x, y), so that the PM2.5 particulate matter concentration field after variation correction is obtained as follows:
the above-mentioned embodiments are only for convenience of description, and are not intended to limit the present invention in any way, and those skilled in the art will understand that the technical features of the present invention can be modified or changed by other equivalent embodiments without departing from the scope of the present invention.
Claims (8)
1. A remote sensing inversion method of near-surface particulate matter concentration is characterized by comprising the following steps:
s100, inverting the satellite remote sensing data to obtain aerosol optical thickness data, and performing height correction based on aerosol extinction coefficient vertical distribution to obtain a near-ground aerosol extinction coefficient;
s200, carrying out moisture absorption correction on the concentration of the particulate matters of the actually measured station, and carrying out humidity correction on the basis of a Hanel growth coefficient to obtain the concentration of the moisture absorption corrected near-ground particulate matters;
s300, carrying out linear fitting on the extinction coefficient of the near-ground obtained in the step S100 and the moisture absorption corrected near-ground particulate matter concentration obtained in the step S200, and establishing a dynamic linear regression model to obtain a near-ground particulate matter concentration field;
and S400, performing variational correction on the near-ground particle concentration field linearly regressed in the step S300 by combining the actually-measured site particle concentration to obtain the near-ground particle concentration field with high precision.
2. The remote sensing inversion method for the concentration of near-surface particles according to claim 1, characterized in that: in the step S100, the aerosol optical thickness obtained by satellite remote sensing is converted into a vertical distribution of the aerosol extinction coefficient according to the height and stability type of the mixed layer, the vertical distribution of the aerosol extinction coefficient is in a negative index form, and the aerosol optical thickness can be obtained by the following equation:
wherein the content of the first and second substances,
AOD is aerosol optical thickness;
z is the vertical height;
σa,0is the near-surface aerosol extinction coefficient;
h is the aerosol elevation, the thickness of the optically active aerosol layer in the atmosphere, and the boundary layer height can be approximated.
3. The remote sensing inversion method for the concentration of near-surface particles according to claim 2, characterized in that: using extinction coefficient sigma of near-surface aerosola,0Establishing an empirical relationship with visibility L:
wherein the content of the first and second substances,
l is near-ground visibility;
σa,0is a near-surface aerosol extinction systemCounting;
is a contrast threshold, determined as 0.02 based on the wavelength 0.55um and vision to which the human eye is most sensitive;
combining equation (1) yields:
Hi＝AODi/σa，0＝(AOD×L)/3.912 (2)
the aerosol level changes smoothly in the region and can pass through a station HiAnd (4) interpolating to obtain a spatial distribution diagram of the aerosol elevation H, wherein i represents different test stations.
4. The remote sensing inversion method for the concentration of near-surface particles according to claim 3, characterized in that: the spatial distribution diagram of the aerosol elevation H adopts a surface fitting method of local thin disc smooth splines to carry out interpolation, and the theoretical statistical model of the local thin disc smooth splines is expressed as follows:
Zi＝f(xi)+bTyi+ei (3)
wherein the content of the first and second substances,
Ziis a dependent variable at a space i point;
xid-dimensional spline independent variable vectors;
f is the value to be estimated with respect to xiAn unknown smooth function;
yiis a p-dimensional independent covariate vector;
b is yiT is a transpose;
eito have an expectation of 0 and a variance of
(ii) an independent variable random error of (d); w is aiIs a known local relative coefficient of variation as a weight; w is a2A constant over all data points for error variance;
the function f and the coefficient b are estimated by the least squares method:
wherein the content of the first and second substances,
Jm(f) as a function f (x)i) A roughness measure function of;
ρ is the smoothing parameter.
5. The remote sensing inversion method for the concentration of near-surface particles as claimed in claim 4, wherein the remote sensing inversion method comprises the following steps: and obtaining the spatial distribution of the near-ground extinction coefficient according to the spatial distribution graph of H obtained by interpolation:
σa，0＝AODi/Hi。
6. the remote sensing inversion method for the concentration of near-surface particles according to claim 1, characterized in that: in step S200, the Hanel growth coefficient is fitted first, according to the formula:
wherein the content of the first and second substances,
σa,iis the near-ground aerosol extinction coefficients of different PM2.5 stations;
PM2.5,ithe concentration of particulate matters is actually measured by stations of different PM2.5 stations;
αext,ithe extinction efficiency of the mass of the fine particles of different PM2.5 measurement stations is obtained;
f is the proportion of fine particles in the aerosol at different PM2.5 measuring stations;
γiis the Hanel growth factor;
RH0the relative humidity in the dry state is 40 percent;
RH is the measured relative humidity of the station;
linear fitting is carried out on each station, and the Hanel growth coefficient gamma of each station can be obtainedi(ii) a Then carrying out moisture absorption correction on the site particulate matter concentration:
wherein the content of the first and second substances,
σa，dryconcentration of particulate matter after moisture absorption and correction, RH0The relative humidity in the dry state is 40%, and RH is the measured relative humidity of the station, rho (PM)2.5) And (4) actually measuring the concentration of the particulate matters for the site, wherein gamma is a Hanel growth coefficient obtained by fitting.
7. The remote sensing inversion method for the concentration of the near-surface particulate matters according to any one of claims 1 to 6, characterized by comprising the following steps: in step S300, a linear model between the optical thickness of the aerosol and the concentration of the particulate matter is fitted:
wherein the content of the first and second substances,
ρ(PM2.5) Is the fine particle concentration;
αext，athe extinction efficiency of the mass of the fine particles of different PM2.5 measurement stations is obtained;
f is the proportion of fine particles in the aerosol at different PM2.5 measuring stations;
AOD is aerosol optical thickness;
h is the boundary layer height.
To obtain a near-surface particulate matter concentration field of
8. The remote sensing inversion method for the concentration of near-surface particles according to claim 7, characterized in that: in step S400, the score change correction method includes:
according to the actual measurement station PM2.5 concentration element field as Su (x, y), on the actual measurement PM2.5 concentration point coordinate (x, y), the difference field of the two, namely the error field is
Because the number of the coordinate (x, y) points of the PM2.5 concentration observation station is limited, a correction factor field function CR (x, y) in a wider whole field is constructed, a variational method is adopted to search for the CR (x, y) function, and the following conditions are required to be met:
namely, it is
Reaching a minimum value;
solving the numerical solution of the equation by an iteration method to obtain a new variation correction factor field CR (x, y), so that the PM2.5 particulate matter concentration field after variation correction is obtained as follows:
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