CN108763756B - Aerosol optical thickness and PM2.5 inversion correction method and system - Google Patents
Aerosol optical thickness and PM2.5 inversion correction method and system Download PDFInfo
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Abstract
The embodiment of the invention discloses an aerosol optical thickness and PM2.5 inversion correction method and a system thereof, relates to the field of environmental protection, can provide a basis for the accuracy and precision of the inversion of PM2.5 in a research area, and can indirectly judge the influence degree of other weather conditions on the inversion result. The method comprises the following steps: and (3) data analysis step: analyzing the trend that the relative humidity and the aerosol elevation of a preset area in a preset time period approximately change along with seasons; an inflection point judgment step: judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend; calculating and determining: and determining the contribution rates of the relative humidity and the aerosol elevation to the optical thickness of the aerosol and the PM2.5 respectively by using a cumulant slope change rate comparison method.
Description
Technical Field
The invention relates to the field of ecological environment, in particular to an aerosol optical thickness and PM2.5 inversion correction method and system.
Background
With the rapid development of global industrial economy, atmospheric pollution is becoming more serious, and more attention is paid to hot-spot environmental problems such as dust haze and the like. Pollutants such as dust haze and the like not only influence normal working life and traveling of people and influence urban atmospheric environment, but also can cause respiratory system diseases and cardiovascular diseases due to fine particle aerosol (such as inhalable fine particulate matter PM2.5 and the like), and are extremely harmful to human health.
However, the fine particulate matter PM2.5 and the aerosol optical thickness (AOD) serve as important indicator factors of the urban atmospheric environmental quality, so that the effective monitoring and analysis of the fine particulate matter PM2.5 and the aerosol optical thickness (AOD) are particularly important. Researchers in various countries in the world widely research and establish the relationship between AOD and PM2.5 in different countries and regions, so that the pollution condition of the near ground of a research area is indirectly reflected by the mass concentration of the reversed PM 2.5. These further illustrate the important role that satellite remote sensing plays in air quality monitoring. The causes and characteristics of the near-ground particles are complex, and the causes and characteristics of the near-ground particles are greatly related to local environmental climate conditions, surface types, seasons, pollution conditions and other meteorological conditions.
These studies show that the most important factor affecting the correlation between AOD and PM2.5 is the relative vertical profile of the aerosol extinction coefficient, and the addition of an environmental climate factor can serve to increase the correlation level between the two. The researches further show that satellite remote sensing has certain feasibility in air quality monitoring, and the large-scale and wide-area air quality monitoring has certain practical application value.
In the past, many scholars mostly select a certain research area, introduce a humidity correction method and a vertical correction method, establish and compare a simple linear regression model, a multiple linear regression model and the like of PM2.5-AOD by utilizing aerosol AOD data, however, the contribution rates of relative humidity and vertical elevation are difficult to determine when humidity correction and elevation correction are carried out in the PM2.5 inversion process.
Disclosure of Invention
In view of this, the embodiment of the present invention provides an aerosol optical thickness and PM2.5 inversion correction method and system, and the method and system analyze and calculate the contribution rate of relative humidity and vertical elevation to AOD or PM2.5, and disclose the sensitivity demonstration of AOD to relative humidity and vertical elevation and the sensitivity demonstration of PM2.5 to relative humidity, so as to have an important guiding effect on the accuracy of AOD and PM2.5 mass concentration inversion, and provide a reference basis for further inverting more accurate near-ground PM2.5 mass concentration data.
In a first aspect, an embodiment of the present invention provides an aerosol optical thickness and PM2.5 inversion correction method, including:
and (3) data analysis step: analyzing the trend that the relative humidity and the aerosol elevation of a preset area in a preset time period approximately change along with seasons;
an inflection point judgment step: judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend;
calculating and determining: and determining the contribution rates of the relative humidity and the aerosol elevation to the optical thickness of the aerosol and the PM2.5 respectively by using a cumulant slope change rate comparison method.
With reference to the first aspect, in a first implementation manner of the first aspect, before the data analyzing step, the method further includes:
a model establishing step: in a preset time period, a CALIPO laser radar secondary aerosol data product is utilized, an aerosol humidity correction method and an aerosol vertical elevation correction method are introduced, the optical thickness of the aerosol below the ground preset height is used as an independent variable, and the PM2.5 mass concentration data near the ground is used as a dependent variable, so that five regression fitting models including linearity, unitary quadratic, power, logarithm and exponent are established.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the aerosol humidity correction method specifically includes the steps of:
and (3) dividing the near-ground extinction coefficient of the corresponding date by the moisture absorption growth factor by combining the relative humidity data of the observation station to obtain the aerosol extinction coefficient after humidity correction, and further inverting the mass concentration of PM2.5 by using the aerosol extinction coefficient.
With reference to the first implementation manner of the first aspect, in a third implementation manner of the first aspect, the aerosol humidity correction method specifically includes the steps of:
and (3) multiplying the mass concentration of PM2.5 at the corresponding date by a moisture absorption growth factor by using the daily average relative humidity data of the observation site to perform humidity correction, so that the mass concentration of fine particles is reduced to the mass concentration in a wet state, and further, inverting the mass concentration of PM2.5 after the humidity correction by using the optical thickness of the aerosol after vertical elevation correction.
With reference to the first implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the aerosol vertical level correction method specifically includes:
and obtaining the extinction coefficient of the aerosol close to the ground by using the obtained visibility data, and realizing the correction of the vertical elevation of the aerosol.
With reference to the first implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the aerosol vertical elevation correction method specifically includes:
utilize laser radar to acquire aerosol elevation and atmospheric boundary layer height, the extinction coefficient that utilizes laser radar to survey is in the ratio of the integral of predetermineeing the interval of height and the extinction coefficient of radar near the ground to obtain the aerosol elevation, divide with the atmospheric aerosol optical thickness of satellite remote sensing the aerosol elevation is in order to carry out the correction of the perpendicular elevation of aerosol.
With reference to the first implementation manner of the first aspect, in a sixth implementation manner of the first aspect, after the model establishing step, the method further includes:
model screening: by a correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
With reference to the sixth implementation manner of the first aspect, in a seventh implementation manner of the first aspect, the inflection point determining step specifically includes:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
With reference to the seventh implementation manner of the first aspect, in an eighth implementation manner of the first aspect, the calculating and determining step specifically includes:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
In a second aspect, an embodiment of the present invention provides an aerosol optical thickness and PM2.5 inversion correction system, including:
the data analysis module is used for analyzing the approximate variation trend of the relative humidity and the aerosol elevation of a preset area in a preset time period along with seasons;
the inflection point judgment module is used for judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend;
and the calculation and determination module is used for determining the contribution rates of the relative humidity and the aerosol elevation to the optical aerosol thickness and the PM2.5 respectively by using a cumulant slope change rate comparison method.
With reference to the second aspect, in a first implementation manner of the second aspect, the system further includes:
and the model establishing module is used for introducing an aerosol humidity correction method and an aerosol vertical elevation correction method by using a CALIPO laser radar secondary aerosol data product in a preset time period, and establishing a linear, unitary and quadratic regression, power, logarithmic and exponential regression fitting model by using the optical thickness of the aerosol below the ground preset height as an independent variable and the mass concentration data of PM2.5 near the ground as a dependent variable.
With reference to the first implementation manner of the second aspect, in a second implementation manner of the second aspect, the system further includes:
a model screening module for passing the correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
With reference to the second implementation manner of the second aspect, in a third implementation manner of the second aspect, the inflection point determining module is specifically configured to:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
With reference to the third implementation manner of the second aspect, in a fourth implementation manner of the second aspect, the calculation determination module is specifically configured to:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
According to the aerosol optical thickness and PM2.5 inversion correction method and system provided by the embodiment of the invention, the approximate change trend of relative humidity and aerosol vertical elevation along with seasons in a preset time period of a research area is analyzed, the change inflection point of the relative humidity and aerosol elevation is judged by using an accumulation distance-leveling method, and the contribution rate of the relative humidity and the vertical elevation to PM2.5 and AOD is determined by using a new calculation method, namely a cumulative slope change rate comparison method. According to the embodiment of the invention, not only is the CALIPO laser radar secondary layer data product AOD below 3KM near the ground and the PM2.5 data measured near the ground inverted, but also the relative contribution rate after the correction of the relative humidity and the vertical elevation is calculated by newly adopting a cumulant slope change rate comparison method, the embodiment of the invention provides a basis for the accuracy and precision of the inversion of the PM2.5 in a research area, and meanwhile, the influence degree of other weather conditions (such as temperature, precipitation, wind speed, wind direction and other meteorological factors) on the inversion result can be indirectly judged.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an aerosol optical thickness and PM2.5 inversion correction method according to the present invention;
FIG. 2 is a schematic diagram of a one-dimensional quadratic equation model of AOD and PM2.5 after correction of vertical elevation and humidity according to the present invention;
FIG. 3 is a schematic diagram of a one-dimensional quadratic equation model of AOD after vertical elevation correction and PM2.5 after humidity correction according to the present invention;
FIG. 4 is a schematic diagram illustrating the variation trend of the average daily relative humidity according to the present invention;
FIG. 5 is a schematic diagram illustrating the trend of 5-point moving average change in daily average relative humidity according to the present invention;
FIG. 6 is a schematic view of the variation trend of the daily aerosol level according to the present invention;
FIG. 7 is a schematic diagram illustrating the trend of the daily aerosol level 5-point moving average according to the present invention;
FIG. 8 is a schematic diagram of the cumulative distance to average relative humidity level of the present invention;
FIG. 9 is a schematic diagram of the PM2.5 cumulative distance level after humidity correction according to the present invention;
FIG. 10 is a graph illustrating cumulative average daily relative humidity in accordance with the present invention;
FIG. 11 is a PM2.5 schematic of the invention after cumulative humidity correction;
FIG. 12 is a schematic view of the aerosol level accumulation distance of the present invention;
FIG. 13 is a schematic view of AOD cumulative distance after elevation correction and humidity correction according to the present invention;
FIG. 14 is a schematic illustration of the cumulative aerosol level of the present invention;
FIG. 15 is a schematic view of the accumulated post elevation correction and humidity correction AOD of the present invention;
fig. 16 is a schematic structural diagram of an aerosol optical thickness and PM2.5 inversion correction system 10 according to the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment provides an aerosol optical thickness and PM2.5 inversion correction method, which can provide a basis for the accuracy and precision of the inversion of the PM2.5 in a research area, and can indirectly judge the degree of influence of other weather conditions on the inversion result.
Fig. 1 is a schematic flow chart of an aerosol optical thickness and PM2.5 inversion correction method according to an embodiment of the present invention.
In step S1, the model creation step: in a preset time period, a CALIPO laser radar secondary aerosol data product is utilized, an aerosol humidity correction method and an aerosol vertical elevation correction method are introduced, the optical thickness of the aerosol below the ground preset height is used as an independent variable, and the PM2.5 mass concentration data near the ground is used as a dependent variable, so that five regression fitting models including linearity, unitary quadratic, power, logarithm and exponent are established.
In this embodiment, a orthogonal polarization cloud/aerosol lidar (calepo) carried by a calepo satellite is the first satellite-borne polarization lidar capable of providing continuous observation, and can receive echo signals of horizontal polarization and vertical polarization at 532nm and 1064nm simultaneously, so that not only can different types of clouds and aerosols be distinguished, but also three-dimensional structure information of the clouds and the aerosols in the global range can be inverted with high resolution. The CALIPO Lida data product of CALIPO Level2 can provide vertical layered space characteristic information of aerosol and cloud, wherein the aerosol layered data product can acquire eight layers of aerosol related characteristic information including information of layer top height, layer bottom height, detected layer number, layer top pressure, layer low pressure, layer top temperature, layer low temperature, relative humidity and the like.
In the embodiment of the invention, the Data of CAL _ LID _ L2_05kmALAy-Standard-V4-10 aerosol layered products in CALIPO space-borne laser radar Level2 version are mainly utilized, the horizontal resolution is 5km, the vertical resolution is 30m, and the Data formats are HDF (high resolution Data Format).
PM2.5 refers to particles with an aerodynamic equivalent diameter of less than or equal to 2.5 μm in ambient air, also called fine particles. The higher the PM2.5 value, the more serious the air pollution. The index of fine particulate matter has become an important index for measuring and controlling the degree of air pollution. Three automatic monitoring methods for PM2.5 are determined by technical indicators and requirements (trial) of PM2.5 automatic monitoring equipment issued by the chinese environmental monitoring central office in 2012 at month 5. The PM2.5 data used in the embodiment of the invention is from a Chinese environmental monitoring central station (http:// www.cnemc.cn /), wherein the daily average PM2.5 concentration data is the result of calculating and calculating an arithmetic mean value according to the PM2.5 data measured by the Chinese environmental monitoring central station at each monitoring station every hour on the day of Zheng city.
Zheng city of province of Henan province is an important central city in the middle region of China, an important comprehensive transportation hub of China, and a core city of the central economic region of China, and is between 112 degrees 42-114 degrees 14 'of east longitude and 34 degrees 16-34 degrees 58' of northern latitude. Zheng Zhou city in Henan province is used as an inland center city, the power demand is mainly thermal power generation, and dust of coal and discharged sulfide gas are important reasons for forming haze.
In the embodiment of the invention, the method obtains the information of the Zheng state urban area within the range of 200 kilometers (latitude:
33.31212615966797 ° N-36.21814727783203 ° N, longitude: 112.10957336425781 ° E-115.20195007324219 ° E) from CAL _ LID _ L2_05kmALay-Standard-V4-10 aerosol layered product data in the calmpo space-borne lidar Level2 version. The study period ranged from 12 months 1 days in 2013 to 11 months 30 days in 2014.
The CALISPOLEvel 2 data product is calculated by a Levell product data by adopting an inversion algorithm, and parameters such as inverted aerosol optical thickness, extinction coefficient, backscattering coefficient and the like have certain errors in the inversion process due to the uncertainty of the algorithm. Therefore, the CALIPO Level2 data needs to be filtered to reduce errors. Therefore, in the embodiment of the present invention, a basic quality screening technology is adopted to screen the callpo Level2 data, and the quality control parameter settings are shown in table 2.
Table 2 data quality control main parameter screening setup
The basic quality screening technology mainly utilizes several main indexes such as The closed-aerosol characterization Score (CAD Score for short) and The extraction quality control (extraction QC for short) in CALISPOlevel 2 data to extract The effective optical thickness of The aerosol.
Calipso secondary data is mainly screened by combining CAD Score, extraction QC and related uncertainty parameters thereof, and specific variation ranges can be given to each specific parameter aiming at different research areas, so that aerosol characteristic data meeting requirements can be obtained. Because the influence of high-rise atmospheric components on near-ground air pollution index data is small, in order to reduce the influence of high-rise atmospheric on near-ground data, aerosol data with the layer top height not more than 3KM in CALIPO layer product data is mainly utilized in the embodiment of the invention, and the arithmetic mean value of the aerosol optical thickness (AOD) of near-ground preset height (for example, below 3 KM) of each rail CALIPO data is obtained by combining basic quality screening technology and IDL program design.
In this embodiment, the screened callpo AOD data is used as an independent variable, the daily average PM2.5 mass concentration data on the date corresponding to the calpo AOD data is used as a dependent variable, five regression models including a linear function, a unitary quadratic function, an exponential function, a power function and a logarithmic function are selected, and a regression fitting model of AOD and PM2.5 mass concentration is established, as shown in table 3. With respect to the correlation coefficient R2In terms of the correlation coefficient R of the five regression fitting models2This is not high, which means that the fitting degree of PM2.5 and AOD is not high, and therefore, the correlation degree needs to be improved by vertical elevation correction, humidity correction, and the like.
TABLE 3 AOD and PM2.5 regression fitting model before correction
In this implementation, the aerosol vertical elevation correction method specifically includes the steps of:
and obtaining the extinction coefficient of the aerosol close to the ground by using the obtained visibility data, and realizing the correction of the vertical elevation of the aerosol.
In another embodiment of this embodiment, the aerosol vertical elevation correction method specifically includes the steps of:
utilize laser radar to acquire aerosol elevation and atmospheric boundary layer height, the extinction coefficient that utilizes laser radar to survey is in the ratio of the integral of predetermineeing the interval of height and the extinction coefficient of radar near the ground to obtain the aerosol elevation, divide with the atmospheric aerosol optical thickness of satellite remote sensing the aerosol elevation is in order to carry out the correction of the perpendicular elevation of aerosol.
Specifically, the atmospheric aerosol elevation refers to the height which can be reached by the uniform distribution of the particulate matters in the whole gas 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 distribution. The optical thickness of the aerosol is the integral of the near-surface extinction coefficient in the vertical direction from the surface to the top of the atmospheric layer, and the mass concentration of the fine particles only represents the mass concentration of the fine particles monitored near the surface and is closely related to the near-surface atmospheric extinction degree. Therefore, when the optical thickness of the aerosol is used for estimating the concentration of the fine particles on the near ground, the optical thickness of the aerosol of the whole atmospheric vertical air column detected by the CALIPO satellite needs to be converted into the extinction coefficient of the aerosol on the near ground.
The Peterson model is based on the Koschmieder model, and the contrast threshold is adjusted from 0.02 to 0.05 so as to adapt to the climate characteristics of different research areas. Further, the influence of Rayleigh scattering and ozone absorption of atmospheric molecules in the horizontal direction is considered by adding 0.0146. Therefore, the Peterson model is a true model describing the relationship between the optical thickness of the atmospheric aerosol and the horizontal meteorological stadia. Namely:
T＝H×(3.0/V-0.0146) (1)
in equation (1): t represents the aerosol optical thickness (AOD), H is the aerosol elevation, and the unit is KM; v denotes visibility. Wherein, the visibility data is provided by China meteorological data network (http:// data. cma. cn /). And then dividing the AOD by the aerosol elevation, and obtaining the aerosol extinction coefficient close to the ground by using the obtained visibility data to realize the correction of the aerosol vertical elevation.
There are two ways to calculate the aerosol elevation: the first method is to calculate the aerosol elevation by using visibility, the second method is to obtain the aerosol elevation and the atmospheric boundary layer height by using a laser radar, and the aerosol elevation H can be obtained by using the ratio of the integral (AOD) of the extinction coefficient detected by the laser radar in a certain height interval to the extinction coefficient of the radar near the ground, so that the aerosol elevation H can be divided by the atmospheric aerosol optical thickness AOD remotely sensed by a satellite, and the correction of the vertical elevation can be carried out. Thereby further improving the accuracy of the inverted optical thickness of the aerosol. The embodiment of the invention mainly adopts daily visibility data to calculate the aerosol elevation.
In this implementation, the aerosol humidity correction method specifically includes the steps of:
and (3) dividing the near-ground extinction coefficient of the corresponding date by the moisture absorption growth factor by combining the relative humidity data of the observation station to obtain the aerosol extinction coefficient after humidity correction, and further inverting the mass concentration of PM2.5 by using the aerosol extinction coefficient.
In another implementation manner of this embodiment, the aerosol humidity correction method specifically includes the steps of:
and (3) multiplying the mass concentration of PM2.5 at the corresponding date by a moisture absorption growth factor by using the daily average relative humidity data of the observation site to perform humidity correction, so that the mass concentration of fine particles is reduced to the mass concentration in a wet state, and further, inverting the mass concentration of PM2.5 after the humidity correction by using the optical thickness of the aerosol after vertical elevation correction.
Specifically, when the environmental protection agency measures the concentration of PM2.5 by using a ground monitoring station, air is generally dried, and the measurement result generally represents the concentration of PM2.5 under an environmental condition with relatively fixed relative humidity. The remote sensing inversion aerosol optical thickness is detected in a background environment without being dried, the influence of relative humidity on the extinction coefficient of aerosol particles is large, when the relative humidity is high, water-soluble aerosol particles absorb and expand, so that the extinction coefficient is increased by multiple times, and errors of experimental results are caused, so that the CALIPO secondary data needs to be subjected to humidity correction, and the following moisture absorption growth factor f (RH) is adopted according to the following formula:
f(RH)＝1/(1-RH/100) (2)
there are two main approaches to the use of a humidity correction factor. The method comprises the steps of combining relative humidity data of an observation station, dividing a near-ground extinction coefficient (namely AOD after vertical elevation correction) of a corresponding date by f (RH) to obtain an aerosol extinction coefficient after humidity correction, and then inverting the mass concentration of PM2.5 by using the dry extinction coefficient. And the second method is to use the daily average relative humidity data of the observation station to multiply the mass concentration of PM2.5 at the corresponding date by f (RH) to carry out humidity correction, so as to reduce the mass concentration of fine particles to the mass concentration in a wet state, and further to use the AOD (near-surface aerosol extinction coefficient) after vertical elevation correction to invert the mass concentration of PM2.5 after humidity correction. In the embodiment of the invention, the humidity correction is mainly carried out by adopting the two methods.
After the correction of the vertical elevation and the humidity, a relation model of the optical thickness data of the 92 aerosols after the processing and screening and the corresponding date-day-average PM2.5 data provided by the national environmental monitoring central station in the environmental protection department is further established. Taking the AOD after the vertical elevation correction as an independent variable and taking the PM2.5 after the humidity correction as a dependent variable, and establishing five models such as linear, logarithmic, unitary and quadratic, power and exponential and the like as shown in a table 4. Five models of linearity, logarithm, unitary and quadratic, power, exponent and the like are established by taking the AOD after vertical elevation correction and humidity correction as an independent variable and taking PM2.5 as a dependent variable, as shown in Table 5.
TABLE 4 AOD after correction of vertical elevation and PM2.5 regression fitting model table after correction of humidity
TABLE 5 regression model Table of AOD and PM2.5 after correction of vertical elevation and humidity
As shown in tables 4 and 5, the correlation coefficients R of the five regression fitting models are obtained no matter the results of the model fitting between the AOD data after the vertical elevation correction and the PM2.5 data after the humidity correction are obtained, or the results of the model fitting between the AOD data after the vertical elevation correction and the PM2.5 data after the humidity correction are obtained2All have great improvement and are correlation coefficients R of a quadratic equation model of a unitary2And highest, 0.7063 and 0.7037, respectively, as shown in fig. 2 and 3. As shown in Table 4, the correlation coefficient R of the one-dimensional quadratic equation model2Lifting from 0.1845 to 0.7063, correlation coefficient R of linear model2Lifting to 0.7037 from 0.0048, multiplying by correlation coefficient R of curtain model2The correlation coefficient R of the exponential model is improved from 0.0103 to 0.66662Lifting from 0.0010 to 0.6583, correlation coefficient R of logarithmic model2From 0.0101 to 0.5947. As shown in Table 5, the correlation coefficient R of the one-dimensional quadratic equation model2Lifting from 0.1845 to 0.7059, correlation coefficient R of linear model2Lifting to 0.7033 from 0.0048, multiplying by correlation coefficient R of curtain model2Lifting from 0.0103 to 0.6654, and the correlation coefficient R of the exponential model2Lifting from 0.0010 to 0.6575, correlation coefficient R of logarithmic model2From 0.0101 to 0.5943.
After the model building step, in step S2, a model screening step: by a correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
In the embodiment, the one-dimensional quadratic equation model is the best choice of the AOD-PM 2.5 relationship model, and further proves that the PM2.5 data inversion is effective and feasible by using the calepo satellite aerosol data.
In step S3, the data analysis step: and analyzing the general variation trend of the relative humidity and the aerosol elevation of the preset area in a preset time period along with seasons.
In the present embodiment, the season data division table is shown in table 6 below.
Table 6 season data separating table
In order to scientifically analyze the change characteristics of the relative humidity and the aerosol elevation in the last year, the whole year is divided into 4 seasons, namely winter (12 months, one year and 2 months), spring (3-5 months), summer (6-8 months) and autumn (9-11 months), based on 92 data screened out by CALIPO secondary data, as shown in Table 6. The characteristics of the change of the average daily relative humidity and the change of the 5-point sliding average value thereof in the zheng city region of he south province from 12 months in 2013 to 11 months in 2014 are shown in fig. 4 and 5, respectively. As can be seen from fig. 4 and 5, the total trend of the daily relative humidity is steadily increased, and the relative humidity shows a certain seasonal regularity, which is approximately a trend of increasing seasons by seasons in the winter, spring, summer and autumn, and is identical to the four-season change law in the zhengzhou city, wherein the average values of the spring, summer, autumn and winter relative humidity are 50.50%, 60.77%, 70.09% and 49.10%, respectively.
And the change of the daily aerosol level and the change of the 5-point sliding average value thereof during the period from 12 months in 2013 to 11 months in 2014 in zheng city, areas of south and Henan province are respectively shown in fig. 6 and 7. As can be seen from fig. 6 and 7, the daily aerosol elevation generally shows a trend of steadily rising first and then steadily falling down from season to season in winter, autumn, summer and spring, and is consistent with the seasonal variation characteristics of the aerosol elevation, wherein the average values of the aerosol elevations in spring, summer, autumn and winter are 0.77KM, 0.62KM, 0.49KM and 0.39KM, respectively.
In step S4, an inflection point determining step: and judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend.
In this embodiment, the inflection point determining step specifically includes:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
In step S5, the calculation determination step: and determining the contribution rates of the relative humidity and the aerosol elevation to the optical thickness of the aerosol and the PM2.5 respectively by using a cumulant slope change rate comparison method.
In this embodiment, the calculating and determining step specifically includes:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
At present, basically, research on the contribution rate of relative humidity and vertical elevation in the process of inverting PM2.5 by satellite remote sensing aerosol optical thickness (AOD) data is not carried out on the basis of a research area, and in order to solve the problem, a new analysis method is adopted in the embodiment of the invention: namely, the cumulative distance flat method and the cumulative quantity slope rate comparison method.
The slope of the linear relation between the cumulative average daily relative humidity and the corresponding date of the whole year is assumed to be K respectively in two periods before and after the inflection pointRbAnd KRa. The slope of the linear relation between the PM2.5 after the correction of the accumulated humidity and the date corresponding to the whole year is Kpb and Kpa respectively in two periods before and after the inflection point. The rate of change of the daily average relative humidity slope HR(unit:%) is:
HR＝100×(KRa-KRb)/KRb (3)
PM2.5 slope change rate M after cumulative humidity correctionp(unit:%) is:
Mp＝100×(Kpa-Kpb)/Kpb (4)
in the above formulas (5) and (6), HR、MPThe value is positive indicating an increase in slope and negative indicating a decrease in slope. Rate of contribution (C) of relative humidity change to PM2.5 changeP: unit: %) can be expressed as:
CP＝100×HR/MP (5)
as can be seen from fig. 8 and 9, the inflection points in the daily relative humidity cumulative distance flat variation sequence are approximately before and after the nodes of the winter season, the spring season, the summer season, and the fall season, and the inflection points in the PM2.5 cumulative distance flat variation sequence after humidity correction are approximately before and after the nodes of the winter season, the spring summer season, and the fall season. Therefore, for convenience of description, the common inflection point of the cumulative day-averaged relative humidity and the PM2.5 after the cumulative humidity correction is divided into three periods of winter, spring and summer, and autumn.
As is clear from the actual calculation results of expressions (3), (4) and (5) with reference to fig. 10 and 11, the contribution rate of the relative humidity to PM2.5 is 31.60% in the spring and summer period and 68.40% in the temperature and other factors to PM2.5 in the winter period. In the autumn period, compared with the spring and summer period, the contribution rate of the relative humidity to the PM2.5 is 48.40%, and the contribution rate of other factors such as the air temperature to the PM2.5 is 51.60%.
Similarly, assume that the slope of the linear relation between the vertical elevation of the accumulated day and the corresponding date of the whole year is K respectively in two periods before and after the inflection pointZbAnd KZa. The slope of the linear relation between AOD and date corresponding to the whole year after accumulated vertical elevation correction and humidity correction is K respectively in two periods before and after the inflection pointsbAnd KsaThe rate of change of slope G of the daily vertical elevationR(unit:%) is:
GR＝100×(KZa-KZb)/KZb (6)
accumulated AOD slope change rate (W) after vertical elevation correction and after humidity correctionS: in%) are:
WS＝100×(Ksa-Ksb)/Ksb (7)
in the above formulas (8), (9), GR、WsThe value is positive indicating an increase in slope and negative indicating a decrease in slope. Contribution rate C of vertical elevation change to AOD changed(unit:%) can be expressed as:
Cd＝100×GR/Ws (8)
as can be seen from fig. 12 and 13, the inflection points in the daily vertical elevation change cumulative distance level change sequence are approximately before and after the nodes of the winter season, the spring season, the summer season, and the fall season, and the inflection points in the AOD cumulative distance level change sequence after the vertical elevation correction and the humidity correction are approximately before and after the nodes of the winter season, the spring summer season, and the fall season. Therefore, for convenience of description, the common inflection point of the accumulated daily vertical elevation, the AODs after the accumulated vertical elevation is corrected, and after the humidity is corrected is divided into three periods of winter, spring and summer, and autumn. As shown by the actual calculation results of equations (6), (7) and (8) with reference to fig. 14 and 15, the AOD contribution rate of the day vertical altitude is 72.28% in the spring and summer period compared with the winter period. In the autumn period, the contribution rate of the day vertical elevation to the AOD was 40.23% compared to the spring and summer period.
Similarly, it is assumed that the slope of the linear relation between the cumulative average daily relative humidity and the corresponding date of the whole year is K respectively before and after the inflection pointRbAnd KRa. The slope of the linear relation between AOD and date corresponding to the whole year after accumulated vertical elevation correction and humidity correction is K respectively in two periods before and after the inflection pointsbAnd KsaThe rate of change of the daily average relative humidity slope (H)R: in%) are:
HR＝100×(KRa-KRb)/KRb (9)
accumulated AOD slope change rate (W) after vertical elevation correction and after humidity corrections: in%) are:
WS＝100×(Ksa-Ksb)/Ksb (10)
in the above formulas (11), (12), HR、WsThe value is positive indicating an increase in slope and negative indicating a decrease in slope. Rate of contribution C of changes in relative humidity to changes in AODS(unit:%) can be expressed as:
CS＝100×HR/WS (11)
as can be seen from fig. 8 and 13, the inflection points in the daily relative humidity cumulative distance level variation sequence are approximately before and after the nodes of the winter season, the spring season, the summer season, and the fall season, and the inflection points in the AOD cumulative distance level variation sequence after the vertical elevation correction and the humidity correction are approximately before and after the nodes of the winter season, the spring summer season, and the fall season. Therefore, for convenience of description, the common inflection point of the AODs after the cumulative day-averaged relative humidity and the cumulative vertical elevation are corrected and after the humidity correction is divided into three periods of winter, spring and summer, and autumn. As can be seen from the actual calculation results of equations (9), (10) and (11) with reference to fig. 10 and 15, the AOD contribution rate of the daily average relative humidity is 24.59% in the spring and summer period as compared with the winter period. In the autumn period, the AOD contribution rate of the average relative humidity is 26.23% compared with the spring and summer period. Wherein the total relative contribution rates of the relative humidity and the vertical elevation to the AOD are 96.87% and 66.46% during spring summer and autumn, respectively, while the relative contribution rates of meteorological factors such as temperature, precipitation and wind speed to the AOD are 3.13% and 33.54% during spring summer and autumn, respectively.
Fig. 16 is a schematic structural diagram of an aerosol optical thickness and PM2.5 inversion correction system 10 according to an embodiment of the present invention.
In this embodiment, an aerosol optical thickness and PM2.5 inversion correction system 10 includes:
the data analysis module 103 is used for analyzing the approximate variation trend of the relative humidity and the aerosol elevation of the preset area in the preset time period along with the seasons;
the inflection point judgment module 104 is used for judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend;
and the calculation and determination module 105 is used for determining the contribution rates of the relative humidity and the aerosol elevation to the optical aerosol thickness and the PM2.5 respectively by using a cumulative slope change rate comparison method.
In this embodiment, the system further includes:
the model establishing module 101 is used for introducing an aerosol humidity correction method and an aerosol vertical elevation correction method by using a CALIPO laser radar secondary aerosol data product in a preset time period, and establishing a linear, unitary and quadratic regression, power, logarithm and exponential regression fitting model by using the optical thickness of the aerosol below the ground preset height as an independent variable and the mass concentration data of PM2.5 near the ground as a dependent variable.
In this embodiment, the system further includes:
a model screening module 102 for passing the correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
In this embodiment, the inflection point determining module 104 is specifically configured to:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
In this embodiment, the calculation determining module 105 is specifically configured to:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
According to the aerosol optical thickness and PM2.5 inversion correction method and system provided by the embodiment of the invention, the approximate change trend of relative humidity and aerosol vertical elevation along with seasons in a preset time period of a research area is analyzed, the change inflection point of the relative humidity and aerosol elevation is judged by using an accumulation distance-leveling method, and the contribution rate of the relative humidity and the vertical elevation to PM2.5 and AOD is determined by using a new calculation method, namely a cumulative slope change rate comparison method. According to the embodiment of the invention, not only is the CALIPO laser radar secondary layer data product AOD below 3KM near the ground and the PM2.5 data measured near the ground inverted, but also the relative contribution rate after the correction of the relative humidity and the vertical elevation is calculated by newly adopting a cumulant slope change rate comparison method, the embodiment of the invention provides a basis for the accuracy and precision of the inversion of the PM2.5 in a research area, and meanwhile, the influence degree of other weather conditions (such as temperature, precipitation, wind speed, wind direction and other meteorological factors) on the inversion result can be indirectly judged.
Embodiments of the present invention also provide an application program executed to implement the aerosol optical thickness and PM2.5 inversion correction method provided in any embodiment of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments.
In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
For convenience of description, the above systems are described separately with the functions divided into various units/modules. Of course, the functionality of the units/modules may be implemented in one or more software and/or hardware implementations of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An aerosol optical thickness and PM2.5 inversion correction method, comprising:
a model establishing step: introducing an aerosol humidity correction method and an aerosol vertical elevation correction method by using a CALIPO laser radar secondary aerosol data product in a preset time period, and establishing a linear, unitary and quadratic, power, logarithmic and exponential five regression fitting model by using the optical thickness of the aerosol below a preset height close to the ground as an independent variable and the PM2.5 mass concentration data close to the ground as a dependent variable;
dividing the near-ground aerosol extinction coefficient of the corresponding date by the moisture absorption growth factor by combining the relative humidity data of the observation station to obtain the aerosol extinction coefficient after humidity correction, and further inverting the mass concentration of PM2.5 by using the aerosol extinction coefficient;
acquiring aerosol elevation and atmospheric boundary layer height by using a laser radar, obtaining the aerosol elevation by using the ratio of the integral of an extinction coefficient detected by the laser radar in a preset height interval to the extinction coefficient of the radar on the ground, and dividing the optical thickness of the atmospheric aerosol remotely sensed by a satellite by the aerosol elevation to correct the vertical elevation of the aerosol;
and (3) data analysis step: analyzing the trend that the relative humidity and the aerosol elevation of a preset area in a preset time period approximately change along with seasons;
an inflection point judgment step: judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend;
calculating and determining: and determining the contribution rates of the relative humidity and the aerosol elevation to the optical thickness of the aerosol and the PM2.5 respectively by using a cumulant slope change rate comparison method.
2. The aerosol optical thickness and PM2.5 inversion correction method according to claim 1, wherein the aerosol humidity correction method specifically comprises the steps of:
and (3) multiplying the mass concentration of PM2.5 at the corresponding date by a moisture absorption growth factor by using the daily average relative humidity data of the observation site to perform humidity correction, so that the mass concentration of fine particles is reduced to the mass concentration in a wet state, and further, inverting the mass concentration of PM2.5 after the humidity correction by using the optical thickness of the aerosol after vertical elevation correction.
3. The aerosol optical thickness and PM2.5 inversion correction method according to claim 1, wherein the aerosol vertical elevation correction method specifically comprises the steps of:
and obtaining the extinction coefficient of the aerosol close to the ground by using the obtained visibility data, and realizing the correction of the vertical elevation of the aerosol.
4. The aerosol optical thickness and PM2.5 inversion correction method of claim 1, wherein after said modeling step, said method further comprises:
model screening: by a correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
5. The aerosol optical thickness and PM2.5 inversion correction method according to claim 4, wherein the inflection point determining step specifically includes:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
6. The aerosol optical thickness and PM2.5 inversion correction method according to claim 5, wherein the calculating and determining step specifically comprises:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
7. An aerosol optical thickness and PM2.5 inversion correction system, comprising:
the model establishing module is used for introducing an aerosol humidity correction method and an aerosol vertical elevation correction method by using a CALIPO laser radar secondary aerosol data product in a preset time period, and establishing a linear, unitary and quadratic regression, power, logarithm and exponential regression fitting model by taking the optical thickness of the aerosol below the ground preset height as an independent variable and taking the PM2.5 mass concentration data near the ground as a dependent variable;
the data analysis module is used for analyzing the approximate variation trend of the relative humidity and the aerosol elevation of a preset area in a preset time period along with seasons;
the inflection point judgment module is used for judging the inflection point of the variation of the relative humidity and the aerosol elevation by utilizing an accumulative distance leveling method according to the variation trend;
and the calculation and determination module is used for determining the contribution rates of the relative humidity and the aerosol elevation to the optical aerosol thickness and the PM2.5 respectively by using a cumulant slope change rate comparison method.
8. The aerosol optical thickness and PM2.5 inversion correction system of claim 7, further comprising:
a model screening module for passing the correlation coefficient R2And screening out an optimal aerosol optical thickness and PM2.5 mass concentration estimation model as a unitary quadratic model.
9. The aerosol optical thickness and PM2.5 inversion correction system of claim 8, wherein the inflection point determination module is specifically configured to:
after the trend that the relative humidity and the aerosol elevation change along with seasons is analyzed by adopting a 5-point sliding average method and an accumulative distance leveling method, the relative humidity and the aerosol elevation are judged to change by taking change nodes in winter, spring, summer and autumn as inflection points;
the method comprises the steps of respectively obtaining linear relations between the accumulated daily relative humidity and the periods of winter, spring and summer and autumn in the three periods of winter, spring and summer and autumn, linear relations between PM2.5 and the periods of winter, spring and summer and autumn after accumulated humidity correction, linear relations between the accumulated daily aerosol elevation and the periods of winter, spring and summer and autumn, and linear relations between the aerosol optical thickness and the periods of winter, spring and summer and autumn after vertical elevation correction and humidity correction by an accumulated vertical elevation correction method.
10. The aerosol optical thickness and PM2.5 inversion correction system of claim 9, wherein the calculation and determination module is specifically configured to:
and calculating the relative contribution rate of the relative humidity to the PM2.5 in the spring and summer period and the autumn period, the relative contribution rate of the aerosol elevation to the optical thickness in the spring and summer period and the autumn period, and the relative contribution rate of the relative humidity to the optical thickness in the spring and summer period and the autumn period in the inversion process aiming at a preset research area by adopting a cumulant slope change rate comparison method and taking the winter as a reference period without considering the influence of other factors.
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| CN109799193A (en) * | 2019-02-19 | 2019-05-24 | 北京英视睿达科技有限公司 | Pollution distribution stereoscopic monitoring method and system |
| CN110046771B (en) * | 2019-04-25 | 2021-04-16 | 河南工业大学 | PM2.5 concentration prediction method and device |
| CN110411918B (en) * | 2019-08-02 | 2020-09-08 | 中国科学院遥感与数字地球研究所 | PM2.5 concentration remote sensing estimation method based on satellite polarization technology |
| CN110738354B (en) * | 2019-09-18 | 2021-02-05 | 北京建筑大学 | Method and device for predicting particulate matter concentration, storage medium and electronic equipment |
| CN112816373A (en) * | 2019-11-15 | 2021-05-18 | 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） | Unmanned aerial vehicle monitoring system and correction method for black carbon vertical profile |
| CN110929228B (en) * | 2019-12-13 | 2023-01-31 | 成都信息工程大学 | Inversion algorithm for moisture absorption growth factor of uniformly mixed aerosol |
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| CN111912754B (en) * | 2020-07-23 | 2023-03-28 | 安徽省气象科学研究所 | Remote sensing inversion method for near-surface particulate matter concentration |
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