CN110160924B - Particulate matter concentration detection method - Google Patents
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- CN110160924B CN110160924B CN201910566835.6A CN201910566835A CN110160924B CN 110160924 B CN110160924 B CN 110160924B CN 201910566835 A CN201910566835 A CN 201910566835A CN 110160924 B CN110160924 B CN 110160924B
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- 239000013618 particulate matter Substances 0.000 title claims abstract description 75
- 238000001514 detection method Methods 0.000 title claims description 11
- 239000000443 aerosol Substances 0.000 claims abstract description 155
- 230000003287 optical effect Effects 0.000 claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 30
- 239000004071 soot Substances 0.000 claims abstract description 23
- 239000000428 dust Substances 0.000 claims abstract description 12
- 238000010521 absorption reaction Methods 0.000 claims abstract description 10
- 238000003331 infrared imaging Methods 0.000 claims abstract description 7
- 239000002245 particle Substances 0.000 claims description 27
- 230000000694 effects Effects 0.000 claims description 24
- 238000000275 quality assurance Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000003384 imaging method Methods 0.000 description 4
- 238000013179 statistical model Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 3
- 239000002028 Biomass Substances 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000002485 combustion reaction Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- Y—GENERAL 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Abstract
The invention provides a method for detecting concentration of particulate matters, which solves the problems that the existing method is poor in estimation precision and difficult to popularize in a large range. A method for detecting the concentration of particulate matter, comprising the steps of: carrying out custom classification on the aerosol types to obtain the aerosol custom types: dust type, urban type, soot type, indeterminate type, the urban type comprises urban clean type, urban pollution type, the soot type comprises soot low absorption type and soot high absorption type; constructing a data set by using a visible infrared imaging radiometer according to the aerosol custom type, wherein the data set comprises the following parameters: aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density; and constructing a particulate matter concentration estimation formula through the data set. The invention realizes large-scale and accurate estimation of the concentration of the particulate matters.
Description
Technical Field
The invention relates to the field of satellite remote sensing, in particular to a method for detecting the concentration of particulate matters.
Background
Existing estimation of atmospheric PM based on aerosol optical thickness 2.5 The concentration method comprises the following steps: using aerosol optical thickness (Aerosol Optical Depth, AOD) and PM 2.5 Statistical model estimation region PM with simple linear and non-simple linear relationships between 2.5 Concentration; AOD and PM are discussed using mid-resolution imaging spectrometer sensor (Moderate Resolution Imaging Spectroradiometer, MODIS) data 2.5 The relationship between different regions and seasons, both of which do not take into account the influence of aerosol type, makes the estimation result inaccurate. Additionally, classifying aerosol types based on global automatic observational network data for Xuzhou and Beijing stations has the disadvantage of discussing aerosol type pair estimation PM based on site data 2.5 Is difficult to popularize in a large scale.
Disclosure of Invention
The invention provides a method for detecting concentration of particulate matters, which solves the problems that the existing method is poor in estimation precision and difficult to popularize in a large range.
The embodiment of the invention provides a method for detecting the concentration of particulate matters, which comprises the following steps: carrying out custom classification on the aerosol types to obtain the aerosol custom types: a dust type, a city type, a soot type, an uncertainty type, the city type comprising a city cleaning type, a city pollution type, the soot type comprising a soot low absorption type, a soot high absorption type, the uncertainty type being that during a day adjacent panels are identified as different types of aerosol types at the same pixel location; constructing a data set by using a visible infrared imaging radiometer according to the aerosol custom type, wherein the data set comprises the following parameters: aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density; constructing a particulate matter concentration estimation formula by the data set, wherein the particulate matter concentration estimation formula comprises:
ln(PM 2.5,st,i )＝(α+ω)+(β 1,i +u 1,i )×ln(AOD st,i )+(β 2,i +u 2,i )×TMP st +
(β 3,i +u 3,i )×RH st +(β 4,i +u 4,i )×ln(PBLH st )+(β 5,i +u 5,i )×SP st +
(β 6,i +u 6,i )×ln(WS st )+(β 7,i +u 7,i )×WD st +β 8,i ×ELEV s +β 9,i ×Pop s +ε st,i
wherein i is the aerosol custom type, PM 2.5,st,i 、AOD st,i TMP (total thickness) respectively corresponding to the estimated value of the concentration of the particulate matters and the optical thickness of the aerosol of the ith aerosol custom type st For the temperature, RH st For the relative humidity, PBLH st For boundary layer height, SP st For the surface strength, WS st For the wind speed, WD st For the wind direction, ELEV s For the elevation, pop s For the population density, α is the fixed effect intercept, ω is the random effect intercept, β 1,i ～β 9,i First to ninth fixed effect slopes, u, corresponding to the ith aerosol custom type 1,i ～u 7,i First to seventh random effect slopes, ε, corresponding to the ith aerosol custom type st,i Random errors corresponding to the ith aerosol custom type.
Further, the method further comprises: and according to the aerosol type, classifying and counting the optical thickness of the ground observation aerosol and the ground observation particulate matter concentration, and verifying the accuracy of the particulate matter concentration estimated value.
Further, the aerosol optical thickness in the dataset is high quality data with a quality assurance value of 3.
Preferably, the step of classifying and counting the optical thickness of the aerosol observed on the basis and the concentration of the particles observed on the basis according to the customized type of the aerosol and verifying the accuracy of the estimated value of the concentration of the particles further comprises the steps of: classifying the optical thickness of the aerosol observed by the foundation and the concentration of particles observed by the foundation according to the aerosol custom type; and calculating a correlation coefficient between the estimated value of the particulate matter concentration of each aerosol custom type and the observed particulate matter concentration of the foundation according to the classification result.
Preferably, the optical thickness of the ground observation aerosol is an average value of the optical thickness of the ground observation aerosol 1 hour before and after the satellite transit time.
Preferably, the ground observation particulate matter concentration is an average value of the ground observation particulate matter concentration 1 hour before and after the satellite transit time.
Further, a correlation coefficient of the estimated value of the particulate matter concentration of the urban aerosol and the ground observation particulate matter concentration is 0.82, a correlation coefficient of the estimated value of the particulate matter concentration of the soot aerosol and the ground observation particulate matter concentration is 0.85, and a correlation coefficient of the estimated value of the particulate matter concentration of the uncertain aerosol and the ground observation particulate matter concentration is 0.82.
The beneficial effects of the invention include: according to the invention, four aerosol types are customized according to aerosol types provided by the authorities of the visible light infrared imaging radiometer (Visible Infrared Imager Radiometer Suite, VIIRS), and the relationship between the particle concentration and the aerosol optical thickness is classified and described in the calculation of the particle concentration statistical model for the first time in the space range, so that the estimation precision of the particle concentration is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of an embodiment of a method for detecting particulate matter concentration;
FIG. 2 is a flowchart of an embodiment of a method for detecting a concentration of particulate matter including accuracy verification;
fig. 3 is an example of a scatter plot of the results of the particulate concentration detection, wherein (a) is not classified as an aerosol type, (b) is an aerosol type soot type, (c) is an aerosol type city type, and (d) is an aerosol type uncertainty type.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Early researchers utilized aerosol optical thickness (Aerosol Optical Depth, AOD) and PM 2.5 Statistical models of simple linear and non-simple linear relationships between them estimate the regional PM2.5 concentration (Wang et al, 2003; engel-Cox et al, 2004;Hutchison et al, 2005;Schaap et al, 2009), introduce various relevant parameters into the model to improve the accuracy of the estimation, but none take into account the effect of aerosol type. Aerosol scattering characteristics of different components in particulate matter are different to investigate their PM 2.5 Influence of estimation accuracy Sun et al (2017) propose Local aerosol concept (Local AOD) to distinguish artificial aerosol from natural sources, use medium resolution imaging spectrometer sensor (Moderate Resolution Imaging Spectroradiometer, MODIS) data to investigate the relationship of AOD and PM in different regions and seasons, study the tableThe apparent AOD-PM2.5 relationship is related to season and region. Wang et al 2017 obtain global automatic observational network (AErosol robotics NETwork, AERONET) data from Xuzhou and Beijing stations, and divide the AErosol into urban, continental, dust-type and biomass combustion type estimated PM 2.5 The results indicate that the aerosol-type separation method helps to improve PM 2.5 The estimation accuracy, i.e. the correlation coefficient, is increased from 0.25 to 0.34, with the urban and biomass combustion type being the highest in estimation accuracy and the dust type being the worst. It can be seen that from this, aerosol type pair estimates PM 2.5 The accuracy impact is large, but aerosol type is discussed on estimated PM based on site data 2.5 Is difficult to popularize in a large scale range. In 2011, the NASA team has judged the corresponding aerosol type by using the visible infrared imaging radiometer (Visible Infrared Imager Radiometer Suite, VIIRS) data through the corresponding algorithm to make up for the defect of the foundation observation in space, but is not widely applied to estimating PM at present 2.5 . Estimating PM in view of discussion of aerosol type pairs 2.5 Is less effective and aerosol types have not been considered on a spatial scale in statistical model studies.
The following describes in detail the technical solutions provided by the embodiments of the present invention with reference to the accompanying drawings.
Fig. 1 is a flowchart of an embodiment of a method for detecting a concentration of particulate matter, in which a type of an aerosol optical thickness is introduced into calculation of a model of a concentration of particulate matter, as an embodiment of the present invention, a method for detecting a concentration of particulate matter specifically includes the following steps:
step 101, performing custom classification on aerosol types to obtain aerosol custom types, wherein the aerosol custom types are as follows: dust type, urban type, soot type, and indeterminate type.
In step 101, in order to consider the feasibility of constructing the model data samples, the aerosol types are reclassified, and in the aerosol classification method disclosed in the prior art VIIRS, the aerosol types include: sand dust type, city cleaning type, city pollution, low absorption type of smoke dust and high absorption type of smoke dust. In the embodiment of the invention, the dust type refers to the dust type in the prior art, and the urban type comprises the urban clean type and the urban pollution type; the soot type comprises the soot low-absorption type and soot high-absorption type; by uncertainty it is meant that during the day adjacent panels are identified as being of different aerosol types at the same pel position. It should be noted that the uncertainty is due to a certain difference in the acquisition time of two adjacent images, and may be caused by weather, such as wind speed, wind direction, precipitation, etc., so that the aerosol composition changes in the period or different aerosol types are identified for the same pixel in the AOD inversion process.
In step 101, classifying the aerosol optical thickness according to the aerosol optical thickness custom type can avoid large deviation of estimating accuracy of the particulate matter concentration caused by unclassified aerosol optical thickness, and can also avoid too many accidental factors caused by too small data volume of the data set due to too fine classification, thereby influencing estimating accuracy of the particulate matter concentration.
It should be noted that, in the embodiment of the present invention, the uncertainty aerosol is introduced, and in the prior art, the uncertainty is classified into a city type or other known types, so that an uncertain accidental factor is added when the concentration of the particulate matter is calculated, and the embodiment of the present invention classifies the uncertainty alone, so that accidental data of the city type, the soot type and the dust type aerosol types can be reduced, and the estimation precision of the concentration of the particulate matter is further increased.
Step 102, constructing a data set by using a visible infrared imaging radiation instrument according to the aerosol custom type, wherein the data set comprises the following parameters: aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density.
As an embodiment of the present invention, the high-quality data in which the aerosol optical thickness in the data set is 3 as the quality assurance value (quality assurance, QA), it should be noted that the aerosol optical thickness in the data set may be high-quality data with qa=3, or may be other high-quality data, and is not particularly limited herein.
In step 102, according to the VIIRS disclosed aerosol classification, the following parameters based on the VIIRS aerosol classification may be obtained: the aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density, i.e., each aerosol type based on the VIIRS aerosol classification, can be correspondingly matched to a set of the above parameters. Therefore, according to the classification rule of the aerosol custom type, the above parameters need to be re-matched, so that the following parameters based on the aerosol custom type can be obtained: the aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, ground surface strength, boundary layer height, elevation and population density can be obtained for each aerosol custom type and each corresponding group of aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, ground surface strength, boundary layer height, elevation and population density.
In step 102, all data of the dataset is data matched according to time information and location information, for example, the time information is 2015-2016. The surface strength, boundary layer height, elevation, population density data are resampled data consistent with VIIRS resolution.
Step 103, constructing a particulate matter concentration estimation formula according to the data set, wherein the particulate matter concentration estimation formula comprises:
wherein i is the aerosol custom type, PM 2.5,st,i 、AOD st,i TMP (total thickness) respectively corresponding to the estimated value of the concentration of the particulate matters and the optical thickness of the aerosol of the ith aerosol custom type st For the temperature, RH st For the relative humidity, PBLH st For boundary layer height, SP st For the surface strength, WS st For the wind speed, WD st For the wind direction, ELEV s For the elevation, pop s For the population density, α is the fixed effect intercept, ω is the random effect intercept, β 1,i ～β 9,i First to ninth fixed effect slopes, u, corresponding to the ith aerosol custom type 1,i ～u 7,i Self-ordering for the ith aerosolFirst to seventh random effect slopes, ε, corresponding to sense type st,i Random errors corresponding to the ith aerosol custom type. In step 103, the unit of each parameter in the dataset is as follows: temperature in degrees celsius, wind speed in (m/s), wind direction in (°), relative humidity in (%), surface pressure in (hPa), boundary layer height in (m), elevation in (m), population density in (th bond/km) 2 )。
In step 103, it should be noted that, the temperature, the relative humidity, the boundary layer height, the surface strength, the customs, the wind direction, the elevation, and the population density are irrelevant to the aerosol custom types, and the particulate matter concentration, the aerosol optical thickness, the first to ninth fixed effect slopes, the first to seventh random effect slopes, and the random error are relevant to the aerosol custom types, that is, each aerosol custom type corresponds to a group of the particulate matter concentration, the aerosol optical thickness, the first to ninth fixed effect slopes, the first to seventh random effect slopes, and the random error.
In step 103, u 1,i 、u 2,i 、u 3,i 、u 4,i 、u 5,i 、u 6,i 、u 7,i First, second, third, fourth, fifth, sixth, seventh random effect slopes, beta, respectively corresponding to the ith aerosol custom type 1,i 、β 2,i 、β 3,i 、β 4,i 、β 5,i 、β 6,i 、β 7,i 、β 8,i 、β 9,i The first to seventh random effect slopes can be obtained by inputting the aerosol optical thickness, temperature, relative humidity, boundary layer height, ground surface strength, wind speed, wind direction, elevation and population density into an LME model of R statistical language, and the first to seventh random effect slopes can be obtained by inputting the random errors into the LME model of R statistical language, wherein the first to ninth fixed effect slopes are respectively the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth fixed effect slopes corresponding to the ith aerosol custom typeFixed effect slope, random error.
The particle concentration detection method provided by the embodiment of the invention reclassifies the types of the aerosol optical thickness, and the modeling indicates PM 2.5 The relationship with the type of the aerosol optical thickness shows that the aerosol optical thickness of different types has different effects on the concentration of the particulate matters, and the estimation accuracy of the concentration of the particulate matters is improved.
Fig. 2 is a flowchart of an embodiment of a method for detecting a concentration of particulate matter, which includes accuracy verification, and the accuracy verification is performed on an estimation result, as an embodiment of the present invention, the method for detecting a concentration of particulate matter specifically includes the following steps:
step 101, performing custom classification on aerosol types to obtain aerosol custom types, wherein the aerosol custom types are as follows: dust type, urban type, soot type, and indeterminate type.
Step 102, constructing a data set by using a visible infrared imaging radiation instrument according to the aerosol custom type, wherein the data set comprises the following parameters: aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density.
And 103, constructing a particulate matter concentration estimation formula according to the data set.
And 104, classifying and counting the optical thickness of the ground observation aerosol and the ground observation particulate matter concentration according to the aerosol type, and verifying the accuracy of the particulate matter concentration estimated value.
Further, step 104 comprises: step 104A, step 104B.
And 104A, classifying the optical thickness of the foundation observation aerosol and the concentration of the foundation observation particulate matters according to the aerosol custom type.
In step 104A, the ground observation aerosol optical thickness, the ground observation particulate concentration is the ground observation aerosol optical thickness of the aerosol type, sand type, city type, soot type, indeterminate type, and the corresponding ground observation particulate concentration.
In step 104A, the optical thickness of the ground observation aerosol is an average value of the optical thickness of the ground observation aerosol 1 hour before and after the satellite transit time.
In step 104A, the particulate matter concentration data is an average value of the ground-based observed particulate matter concentration 1 hour before and after the satellite transit time, and further, the ground-based observed particulate matter concentration data eliminates the observed outliers caused by the instrument or other factors.
And 104B, calculating a correlation coefficient between the estimated value of the particulate matter concentration of each aerosol custom type and the ground-based observed particulate matter concentration according to the classification result.
Wherein i is the aerosol custom type, r i For the correlation coefficient of the ith aerosol custom type, N is the total number of observed pixels of the ith aerosol custom type, PM 2.5,i,n 、The estimated value of the particle concentration of the ith aerosol custom type nth pixel, the ground-based observed particle concentration, and +.>The estimated value of the particle concentration of the ith aerosol custom type and the average value of the particle concentration observed by the foundation are respectively obtained.
In step 104B, the root mean square error value between the estimated value of the particulate matter concentration and the ground observation particulate matter concentration data is calculated by using the ground observation particulate matter concentration data as a base reference value. In step 104B, it should be noted that the larger the correlation coefficient is, the closer the estimated value of the particulate matter concentration is to the ground observation particulate matter concentration, that is, the higher the estimation accuracy is.
The particle concentration detection method provided by the embodiment of the invention further verifies that the AOD types are different, the corresponding particle concentrations are different, and the particle concentration estimation accuracy is verified, so that the particle concentration estimation accuracy can be further improved.
Fig. 3 is an embodiment of a scatter diagram of the results of the concentration detection of the particulate matter, for verifying the accuracy of the concentration detection of the particulate matter, wherein (a) is an unclassified aerosol type, (b) is an aerosol type soot type, (c) is an aerosol type city type, and (d) is an aerosol type uncertainty type.
In the embodiment of the invention, the data source of the scatter diagram is satellite observation results of 5-10-year in 2016 in Jing Ji area.
As an embodiment of the present invention, the abscissa represents the ground-based observed particulate matter concentration, the ordinate represents the particulate matter concentration estimated value, and the scattering point in the figure is the value of each observation pixel, and it should be noted that each observation pixel refers to a pixel with resolution of 3*3, and may also be other pixels without any particular limitation.
As an embodiment of the invention, the scatter diagram comprises a straight line with a slope of 45 degrees, wherein the physical meaning is a straight line representing that the estimated value of the particulate matter concentration is completely equal to the concentration of the ground observation particulate matter, the scatter diagram also comprises a fitting straight line, the physical meaning is a linear relation representing that the estimated value of the particulate matter concentration is equal to the concentration of the ground observation particulate matter, and the closer the fitting straight line is to the straight line with the slope of 45 degrees, the closer the estimated value of the particulate matter concentration is to the concentration of the ground observation particulate matter, namely the higher the estimated accuracy is.
When aerosol types are not classified, the estimated value of the particle concentration is that the ground observation particle concentration is multiplied by 0.76 and added by 12, and the correlation coefficient is 0.78; when the aerosol type is soot type, the estimated value of the particulate matter concentration is that the ground observation particulate matter concentration is multiplied by 0.78 and added with 17, and the correlation coefficient is 0.85; when the aerosol type is urban, the estimated value of the particle concentration is that the ground observation particle concentration is multiplied by 0.78 and added with 12, and the correlation coefficient is 0.82; when the aerosol type is indeterminate, the estimated particulate matter concentration is the ground based observed particulate matter concentration multiplied by 0.79 plus 9.7, and the correlation coefficient is 0.82.
Because the larger the correlation coefficient is, the higher the detection precision of the concentration of the particulate matters is, compared with the method without classifying the optical thickness of the aerosol, the method can be used for carrying out the self-defined classification on the type of the aerosol, and can effectively improve the detection precision of the concentration of the particulate matters. It should be noted that 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (6)
1. A method for detecting a concentration of particulate matter, comprising the steps of:
carrying out custom classification on the aerosol types to obtain the aerosol custom types: a dust type, a city type, a soot type, an uncertainty type, the city type comprising a city cleaning type, a city pollution type, the soot type comprising a soot low absorption type, a soot high absorption type, the uncertainty type being that during a day adjacent panels are identified as different types of aerosol types at the same pixel location;
constructing a data set by using a visible infrared imaging radiometer according to the aerosol custom type, wherein the data set comprises the following parameters: aerosol optical thickness, temperature, wind speed, wind direction, relative humidity, surface strength, boundary layer height, elevation, population density;
constructing a particulate matter concentration estimation formula by the data set, wherein the particulate matter concentration estimation formula comprises:
ln(PM 2.5,st,i )＝(α+ω)+(β 1,i +u 1,i )×ln(AOD st,i )+(β 2,i +u 2,i )×TMP st +
(β 3,i +u 3,i )×RH st +(β 4,i +u 4,i )×ln(PBLH st )+(β 5,i +u 5,i )×SP st +
(β 6,i +u 6,i )×ln(WS st )+(β 7,i +u 7,i )×WD st +β 8,i ×ELEV s +β 9,i ×Pop s +ε st,i
wherein i is the aerosol custom type, PM 2.5,st,i 、AOD st,i The estimated value of the particle concentration, the optical thickness of the aerosol and the TMP corresponding to the ith aerosol custom type are respectively st For the temperature, RH st For the relative humidity, PBLH st For boundary layer height, SP st For the surface strength, WS st For the wind speed, WD st For the wind direction, ELEV s For the elevation, pop s For the population density, α is the fixed effect intercept, ω is the random effect intercept, β 1,i ～β 9,i First to ninth fixed effect slopes, u, corresponding to the ith aerosol custom type 1,i ～u 7,i First to seventh random effect slopes, ε, corresponding to the ith aerosol custom type st,i Random errors corresponding to the ith aerosol custom type.
2. The particulate matter concentration detection method according to claim 1, characterized in that the method further comprises:
and according to the aerosol custom type, classifying and counting the optical thickness of the aerosol observed by the foundation and the concentration of particles observed by the foundation, and verifying the precision of the estimated value of the concentration of the particles.
3. The particulate matter concentration detection method according to any one of claims 1 to 2, wherein the aerosol optical thickness in the data set is high-quality data having a quality assurance value of 3.
4. The method for detecting the concentration of the particulate matter according to claim 2, wherein the step of classifying and counting the optical thickness of the aerosol observed on the basis and the concentration of the particulate matter observed on the basis according to the type of the aerosol custom, and verifying the accuracy of the estimated value of the concentration of the particulate matter further comprises:
classifying the optical thickness of the aerosol observed by the foundation and the concentration of particles observed by the foundation according to the aerosol custom type;
according to the classification result, calculating a correlation coefficient between the estimated value of the particulate matter concentration of each aerosol custom type and the observed particulate matter concentration of the foundation:
wherein i is the aerosol custom type, ri for the correlation coefficient of the ith aerosol custom type, N is the total number of observed pixels of the ith aerosol custom type, PM 2.5,i,n 、The estimated value of the particle concentration of the ith aerosol custom type nth pixel, the ground-based observed particle concentration, and +.>The estimated value of the particle concentration of the ith aerosol custom type and the average value of the particle concentration observed by the foundation are respectively obtained.
5. The method for detecting the concentration of particulate matter according to claim 2, wherein the optical thickness of the ground observation aerosol is an average value of the optical thickness of the ground observation aerosol 1 hour before and after the satellite transit time.
6. The method for detecting the concentration of particulate matter according to claim 2, wherein the ground-based observed particulate matter concentration is an average value of ground-based observed particulate matter concentrations 1 hour before and after a satellite transit time.
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| CN112577866A (en) * | 2020-12-02 | 2021-03-30 | 石家庄学院 | Urban particulate matter concentration estimation model |
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