CN111912754A - Remote sensing inversion method for near-surface particulate matter concentration - Google Patents

Remote sensing inversion method for near-surface particulate matter concentration Download PDF

Info

Publication number
CN111912754A
CN111912754A CN202010727936.XA CN202010727936A CN111912754A CN 111912754 A CN111912754 A CN 111912754A CN 202010727936 A CN202010727936 A CN 202010727936A CN 111912754 A CN111912754 A CN 111912754A
Authority
CN
China
Prior art keywords
concentration
aerosol
ground
remote sensing
particulate matter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010727936.XA
Other languages
Chinese (zh)
Other versions
CN111912754B (en
Inventor
何彬方
荀尚培
冯妍
刘惠敏
吴文玉
姚筠
霍彦峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui Province Meteorological Science Research Institute
Original Assignee
Anhui Province Meteorological Science Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui Province Meteorological Science Research Institute filed Critical Anhui Province Meteorological Science Research Institute
Priority to CN202010727936.XA priority Critical patent/CN111912754B/en
Publication of CN111912754A publication Critical patent/CN111912754A/en
Application granted granted Critical
Publication of CN111912754B publication Critical patent/CN111912754B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06Investigating concentration of particle suspensions
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/11Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/13Differential equations
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06Investigating concentration of particle suspensions
    • G01N15/075Investigating concentration of particle suspensions by optical means
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Chemical & Material Sciences (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Analytical Chemistry (AREA)
  • Dispersion Chemistry (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

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

Remote sensing inversion method for near-surface particulate matter concentration
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:
Figure BDA0002598650000000021
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:
Figure BDA0002598650000000022
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=AODia,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
Figure BDA0002598650000000031
(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:
Figure BDA0002598650000000032
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:
Figure BDA0002598650000000033
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:
Figure BDA0002598650000000041
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:
Figure BDA0002598650000000042
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
Figure BDA0002598650000000051
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
Figure BDA0002598650000000052
Figure BDA0002598650000000053
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:
Figure BDA0002598650000000054
namely, it is
Figure BDA0002598650000000055
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:
Figure BDA0002598650000000056
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:
Figure BDA0002598650000000061
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:
Figure BDA0002598650000000062
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:
Figure BDA0002598650000000071
σa,03.912/L was then obtained in conjunction with equation (1):
Hi=AODia,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
Figure BDA0002598650000000072
(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:
Figure BDA0002598650000000073
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:
Figure BDA0002598650000000081
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:
Figure BDA0002598650000000082
γ is the Hanel growth coefficient.
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.
Figure BDA0002598650000000083
Taking natural logarithm on both sides to obtain
Figure BDA0002598650000000091
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:
Figure BDA0002598650000000092
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
Figure BDA0002598650000000093
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:
Figure BDA0002598650000000094
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
Figure BDA0002598650000000101
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)
Figure BDA0002598650000000102
Figure BDA0002598650000000103
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:
Figure BDA0002598650000000104
namely, it is
Figure BDA0002598650000000105
Reaching a minimum value.
For the above variational problem, it can be assumed that functional J is constructed*
Figure BDA0002598650000000106
Where λ is the constraint coefficient.
The above formula can be rewritten as
Figure BDA0002598650000000107
Corresponding Euler equation is
Figure BDA0002598650000000108
Wherein
Figure BDA0002598650000000109
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:
Figure BDA00025986500000001010
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:
Figure FDA0002598649990000011
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:
Figure FDA0002598649990000012
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=AODia,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
Figure FDA0002598649990000021
(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:
Figure FDA0002598649990000022
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:
Figure FDA0002598649990000031
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:
Figure FDA0002598649990000032
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:
Figure FDA0002598649990000041
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
Figure FDA0002598649990000042
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
Figure FDA0002598649990000043
Figure FDA0002598649990000044
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:
Figure FDA0002598649990000045
namely, it is
Figure FDA0002598649990000046
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:
Figure FDA0002598649990000051
CN202010727936.XA 2020-07-23 2020-07-23 Remote sensing inversion method for near-surface particulate matter concentration Active CN111912754B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010727936.XA CN111912754B (en) 2020-07-23 2020-07-23 Remote sensing inversion method for near-surface particulate matter concentration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010727936.XA CN111912754B (en) 2020-07-23 2020-07-23 Remote sensing inversion method for near-surface particulate matter concentration

Publications (2)

Publication Number Publication Date
CN111912754A true CN111912754A (en) 2020-11-10
CN111912754B CN111912754B (en) 2023-03-28

Family

ID=73280842

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010727936.XA Active CN111912754B (en) 2020-07-23 2020-07-23 Remote sensing inversion method for near-surface particulate matter concentration

Country Status (1)

Country Link
CN (1) CN111912754B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112378828A (en) * 2020-12-11 2021-02-19 中科三清科技有限公司 Method and device for inverting concentration of atmospheric fine particulate matters based on satellite remote sensing data
CN112484776A (en) * 2020-11-18 2021-03-12 成都信息工程大学 Method for estimating hourly near-ground atmospheric fine particles by using geostationary satellite
CN112525787A (en) * 2020-11-27 2021-03-19 中国气象局广州热带海洋气象研究所(广东省气象科学研究所) Method for inverting PM2.5 all-weather fine grid data based on surface high-density meteorological data

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102539336A (en) * 2011-02-01 2012-07-04 环境保护部卫星环境应用中心 Method and system for estimating inhalable particles based on HJ-1 satellite
CN106124374A (en) * 2016-07-22 2016-11-16 中科宇图科技股份有限公司 Atmospheric particulates remote-sensing monitoring method based on data fusion
CN108426815A (en) * 2018-04-20 2018-08-21 中国科学院遥感与数字地球研究所 A kind of fine particle concentration of component evaluation method near the ground
US20180238789A1 (en) * 2017-02-17 2018-08-23 International Business Machines Corporation Correlation-based determination of particle concentration field
CN108763756A (en) * 2018-05-28 2018-11-06 河南工业大学 A kind of aerosol optical depth and PM2.5 invertings correction method and its system
CN109001091A (en) * 2018-07-18 2018-12-14 北京航天宏图信息技术股份有限公司 Satellite remote-sensing monitoring method, device and the computer-readable medium of atmosphere pollution
CN110411918A (en) * 2019-08-02 2019-11-05 中国科学院遥感与数字地球研究所 A kind of PM2.5 concentration remote-sensing evaluation method based on satellite polarization technology
CN110907319A (en) * 2019-11-07 2020-03-24 中国科学院遥感与数字地球研究所 Attribution analysis method for near-surface fine particulate matters
CN110929228A (en) * 2019-12-13 2020-03-27 成都信息工程大学 Inversion algorithm for moisture absorption growth factor of uniformly mixed aerosol
CN111323352A (en) * 2020-04-09 2020-06-23 中南大学 Regional PM2.5 remote sensing inversion model fusing fine particulate matter concentration data

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102539336A (en) * 2011-02-01 2012-07-04 环境保护部卫星环境应用中心 Method and system for estimating inhalable particles based on HJ-1 satellite
CN106124374A (en) * 2016-07-22 2016-11-16 中科宇图科技股份有限公司 Atmospheric particulates remote-sensing monitoring method based on data fusion
US20180238789A1 (en) * 2017-02-17 2018-08-23 International Business Machines Corporation Correlation-based determination of particle concentration field
CN108426815A (en) * 2018-04-20 2018-08-21 中国科学院遥感与数字地球研究所 A kind of fine particle concentration of component evaluation method near the ground
CN108763756A (en) * 2018-05-28 2018-11-06 河南工业大学 A kind of aerosol optical depth and PM2.5 invertings correction method and its system
CN109001091A (en) * 2018-07-18 2018-12-14 北京航天宏图信息技术股份有限公司 Satellite remote-sensing monitoring method, device and the computer-readable medium of atmosphere pollution
CN110411918A (en) * 2019-08-02 2019-11-05 中国科学院遥感与数字地球研究所 A kind of PM2.5 concentration remote-sensing evaluation method based on satellite polarization technology
CN110907319A (en) * 2019-11-07 2020-03-24 中国科学院遥感与数字地球研究所 Attribution analysis method for near-surface fine particulate matters
CN110929228A (en) * 2019-12-13 2020-03-27 成都信息工程大学 Inversion algorithm for moisture absorption growth factor of uniformly mixed aerosol
CN111323352A (en) * 2020-04-09 2020-06-23 中南大学 Regional PM2.5 remote sensing inversion model fusing fine particulate matter concentration data

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
崔蕾 等: "成都颗粒物吸湿增长特征及订正方法研究", 《环境科学学报》 *
李倩 等: "利用激光雷达和卫星遥感获得城市地面大气悬浮颗粒物浓度分布", 《北京大学学报(自然科学版)》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112484776A (en) * 2020-11-18 2021-03-12 成都信息工程大学 Method for estimating hourly near-ground atmospheric fine particles by using geostationary satellite
CN112525787A (en) * 2020-11-27 2021-03-19 中国气象局广州热带海洋气象研究所(广东省气象科学研究所) Method for inverting PM2.5 all-weather fine grid data based on surface high-density meteorological data
CN112378828A (en) * 2020-12-11 2021-02-19 中科三清科技有限公司 Method and device for inverting concentration of atmospheric fine particulate matters based on satellite remote sensing data
CN112378828B (en) * 2020-12-11 2021-09-17 中科三清科技有限公司 Method and device for inverting concentration of atmospheric fine particulate matters based on satellite remote sensing data

Also Published As

Publication number Publication date
CN111912754B (en) 2023-03-28

Similar Documents

Publication Publication Date Title
CN111912754B (en) Remote sensing inversion method for near-surface particulate matter concentration
CN108763756B (en) Aerosol optical thickness and PM2.5 inversion correction method and system
Harrison et al. Measurements of the physical properties of particles in the urban atmosphere
Ruuskanen et al. Concentrations of ultrafine, fine and PM2. 5 particles in three European cities
KR101609740B1 (en) Vertical distribution calculation method of Aerosol mass concentration
Eichler et al. Hygroscopic properties and extinction of aerosol particles at ambient relative humidity in South-Eastern China
Xu et al. Estimation of ground-level PM2. 5 concentration using MODIS AOD and corrected regression model over Beijing, China
Li et al. Vertical distribution of particulate matter and its relationship with planetary boundary layer structure in Shenyang, Northeast China
Lv et al. Hygroscopic growth of atmospheric aerosol particles based on lidar, radiosonde, and in situ measurements: Case studies from the Xinzhou field campaign
Yu et al. A parameterization for the light scattering enhancement factor with aerosol chemical compositions
Akpinar et al. Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazığ, Turkey
Chauvigné et al. Comparison of the aerosol optical properties and size distribution retrieved by sun photometer with in situ measurements at midlatitude
Xia et al. Observational study of aerosol hygroscopic growth on scattering coefficient in Beijing: A case study in March of 2018
Ma et al. Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia
Nandan et al. Estimation of Aerosol Complex Refractive Index over a tropical atmosphere using a synergy of in-situ measurements
Von Hoyningen-Huene et al. Variability of aerosol optical parameters by advective processes
CN110907318B (en) Near-ground atmospheric total suspended particulate matter mass concentration remote sensing physical estimation method
CN111999268B (en) Atmospheric extinction coefficient humidity correction method
Kushwaha et al. Bias in PM2. 5 measurements using collocated reference-grade and optical instruments
Mao et al. Performance of MODIS aerosol products at various timescales and in different pollution conditions over eastern Asia
Ou et al. Vertical characterization and potential sources of aerosols in different seasons over the Yangtze River Delta using ground-based MAX-DOAS
Schladitz et al. In situ aerosol characterization at Cape Verde: Part 2: Parametrization of relative humidity-and wavelength-dependent aerosol optical properties
CN116466368A (en) Dust extinction coefficient profile estimation method based on laser radar and satellite data
Raoufi et al. Air pollution effects on climate and air temperature of Tehran city using remote sensing data
Li et al. Visibility measurement using multi-angle forward scattering by liquid droplets

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant