CN106407633A - Method and system for estimating ground PM2.5 based on space-time regression Kriging model - Google Patents
Method and system for estimating ground PM2.5 based on space-time regression Kriging model Download PDFInfo
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Abstract
The invention provides a method and a system for estimating ground PM2.5 based on a space-time regression Kriging model. The method comprises the steps of re-sampling ground PM2.5 observation data of a to-be-estimated region to a created mesh, and performing matching, wherein the matching process comprises the steps of averaging the ground PM2.5 observation data monitored in the same day by all PM2.5 stations in a mesh unit corresponding to the to-be-estimated region in the created mesh, and then assigning the averaged data to the corresponding mesh unit; calculating an experimental variance function of a residual error according to the ground PM2.5 observation data of the matched to-be-estimated region, and determining a space-time variance function model according to the experimental variance function of the residual error; performing fitting on the space-time variance function model by adopting a least square method; and estimating a ground PM2.5 concentration value of the to-be-estimated region by adopting the space-time regression Kriging model according to a fitting result of the space-time variance function model. Through the method and the system, the PM2.5 estimation precision can be improved.
Description
Technical Field
The invention relates to the technical field of aerosol monitoring, in particular to a method and a system for estimating ground PM2.5 based on a spatio-temporal regression Krigin model.
Background
With the rapid development of economy and the rapid increase of harmful gas emissions from industrial activities and motor vehicle exhaust, air quality is continuously deteriorated. PM2.5 refers to particles with aerodynamic particle size less than 2.5 microns in air, compared with particles with large particle size, the particles with PM2.5 particle size are small, rich in a large amount of toxic and harmful substances, long in retention time in the atmosphere and long in conveying distance, so that the influence on the quality of human bodies and atmospheric environment is great. A large number of epidemiological studies prove that PM2.5 has certain relevance to asthma, respiratory tract infection, lung cancer, cardiovascular diseases and the like. Therefore, monitoring of PM2.5 becomes one of the key points for studying the atmospheric environment.
Since the air monitoring data is often data with a certain time sequence, the air monitoring data has strong space-time variation characteristics, and the spatial interpolation method is the simplest and most effective method for acquiring the pollutant distribution at a place provided with a measuring station.
Many scholars currently use spatial interpolation methods to estimate the distribution of pollutants. For example, although the spatial interpolation method is applied to the research of pollutants such as nitrogen dioxide, ozone, and PM10, the spatial interpolation method is rarely applied to the research of PM2.5, and even though some researchers use the spatial interpolation method to research the distribution characteristics of PM2.5, the spatial interpolation method does not consider the time distribution characteristics of PM2.5, and when the coverage of a ground monitoring station is insufficient, the estimation accuracy of PM2.5 is not high, so that a spatial interpolation model with higher accuracy needs to be provided.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for estimating a ground PM2.5 based on a spatio-temporal regression kriging model, so as to solve the problem that the estimation accuracy of the ground PM2.5 by the existing spatial interpolation method is not high.
According to one aspect of the invention, a method for estimating ground PM2.5 based on a spatio-temporal regression Kriging model is provided, which comprises the following steps:
creating a grid according to the latitude and longitude range and the resolution of the region to be estimated, resampling ground PM2.5 observation data of the region to be estimated into the created grid, and matching; the matching process comprises the steps of averaging ground PM2.5 observation data monitored in the same day by all PM2.5 sites in a grid unit corresponding to an area to be estimated in a grid, and assigning the averaged data to the corresponding grid unit;
calculating an experimental variation function of a residual error according to the matched ground PM2.5 observation data of the region to be estimated, and then determining a space-time variation function model according to the experimental variation function of the residual error;
fitting the space-time variation function model by adopting a least square method;
and according to the fitting result of the space-time variation function model, estimating the ground PM2.5 concentration value of the area to be estimated by adopting a space-time regression Krigin model.
Resampling auxiliary variable data into the created grid and matching; the auxiliary variable data comprises meteorological data, DEM and land utilization data.
In the process of resampling and matching the auxiliary variable data into the created grid, comparing the resolution of the grid unit corresponding to the area to be estimated with the resolution of the auxiliary variable data corresponding to the grid unit, averaging the auxiliary variable data higher than the resolution of the corresponding grid unit and assigning the auxiliary variable data to the corresponding grid unit, and assigning the auxiliary variable data not higher than the resolution of the corresponding grid unit to the corresponding grid unit by adopting an interpolation method with inverse distance weighting.
The method comprises the steps of extracting auxiliary variable data corresponding to each PM2.5 station of an area to be estimated, establishing a multiple linear regression model between PM2.5 observation data of each PM2.5 station and each auxiliary variable data, and obtaining a ground PM2.5 residual value of the area to be estimated according to a calculation result of the multiple linear regression model and the PM2.5 observation data.
And summing and summarizing an interpolation result obtained by estimating by adopting a space-time regression Kriging model and a ground PM2.5 residual value to obtain a ground PM2.5 concentration value of the area to be estimated.
In another aspect, the present invention provides a system for estimating ground PM2.5 based on a spatio-temporal regression kriging model, comprising:
the PM2.5 data matching unit is used for resampling ground PM2.5 observation data of the area to be estimated to the created grid and matching the data; the grid is established according to the latitude and longitude range and the resolution of the area to be estimated, and the matching process comprises the steps of averaging ground PM2.5 observation data monitored in the same day by all PM2.5 sites in a grid unit corresponding to the area to be estimated in the grid, and then assigning the averaged data to the corresponding grid unit;
the space-time variation function model determining unit is used for calculating an experimental variation function of the residual error according to the matched ground PM2.5 observation data of the region to be estimated, and then determining a space-time variation function model according to the experimental variation function of the residual error;
the space-time variation function model fitting unit is used for fitting the space-time variation function model by adopting a least square method;
and the PM2.5 concentration value estimation unit is used for estimating the ground PM2.5 concentration value of the area to be estimated by adopting a space-time regression Krigin model according to the fitting result of the space-time variation function model.
According to the method and the system for estimating the ground PM2.5 based on the spatio-temporal regression Kriging Model, provided by the invention, the spatio-temporal correlation characteristic between PM2.5 station data is verified by adopting the spatial variation function and the time autocorrelation function, and the spatio-temporal correlation characteristic is high, so that the data of the monitored station is suitable for utilizing the spatio-temporal regression Kriging interpolation Model, firstly, a multivariate linear regression Model between the PM2.5 data and an auxiliary variable is established by adding different meteorological parameters, DEM (digital elevation Model), land utilization parameters and other information, and the influence factor of the PM2.5 can be reflected; and then, a proper space-time variation function model is selected by calculating the space-time variation function of the residual error after regression, and time characteristic information is added into the model, so that the estimation precision of PM2.5 can be better improved.
To the accomplishment of the foregoing and related ends, one or more aspects of the invention comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Further, the present invention is intended to include all such aspects and their equivalents.
Drawings
Other objects and results of the present invention will become more apparent and more readily appreciated as the same becomes better understood by reference to the following description and appended claims, taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 is a first flow diagram of a method for estimating surface PM2.5 based on a spatio-temporal regression Crigikin model according to an embodiment of the invention;
FIG. 2 is a second flow diagram of a method for estimating surface PM2.5 based on a spatio-temporal regression Crigikin model according to an embodiment of the invention;
fig. 3 is a block diagram of a logical structure of a system for estimating the ground PM2.5 based on a spatio-temporal regression kriging model according to an embodiment of the present invention.
The same reference numbers in all figures indicate similar or corresponding features or functions.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Aiming at the problem that the estimation accuracy of the existing spatial interpolation method for the ground PM2.5 is not high, the method establishes a multiple linear regression model between the PM2.5 data and the auxiliary variable data by adding different auxiliary variable data (such as meteorological parameters, DEM, land utilization parameters and the like), then selects a space-time variation function model by calculating a space-time variation function of a regression residual error, and estimates the ground PM2.5 concentration by adopting the space-time regression Kriging model according to ground PM2.5 observation data with high space-time correlation monitored by a PM2.5 station through the space-time variation function model, thereby improving the estimation accuracy of the PM 2.5.
Before explaining the present invention, the time-space regression kriging model will be explained.
Since the spatial interpolation method is the simplest and most effective method for obtaining pollutant distribution at a place where a measurement site is arranged, a spatio-temporal regression kriging model (which is one of the spatial interpolation methods) is applied to local-scale ozone and particulate matter interpolation estimation due to the characteristics of the optimal linear unbiased estimation. The invention is provided on the basis of the above.
In addition, PM2.5 in the present invention can also be written as PM2.5。
The method for estimating the ground PM2.5 based on the spatio-temporal regression Kriging model provided by the invention is explained in detail below.
Fig. 1 shows a first flow of a method of estimating a ground PM2.5 based on a spatio-temporal regression kriging model according to an embodiment of the present invention.
As shown in fig. 1, the method for estimating the ground PM2.5 based on the spatio-temporal regression kriging model provided by the present invention includes:
s110: creating a grid according to the latitude and longitude range and the resolution of the region to be estimated, resampling ground PM2.5 observation data of the region to be estimated into the created grid, and matching; the matching process comprises the steps of averaging ground PM2.5 observation data monitored in the same day by all PM2.5 sites in a grid unit corresponding to an area to be estimated in the created grid, and then assigning the averaged data to the corresponding grid unit.
S120: and calculating an experimental variation function of the residual error according to the matched ground PM2.5 observation data of the region to be estimated, and determining a space-time variation function model according to the experimental variation function of the residual error.
S130: and fitting the space-time variation function model by adopting a least square method.
S140: and according to the fitting result of the space-time variation function model, estimating the ground PM2.5 concentration value of the area to be estimated by adopting a space-time regression Krigin model.
Further, fig. 2 shows a second process of the method for estimating the ground PM2.5 based on the spatio-temporal regression kriging model according to the embodiment of the present invention, so as to further improve the estimation accuracy of the ground PM 2.5.
As shown in fig. 2, the method for estimating the ground PM2.5 based on the spatio-temporal regression kriging model provided by the present invention may include the following steps:
(1) s210: and calculating the space-time correlation of PM2.5 observation data monitored by each PM2.5 station in the region to be estimated by adopting a space variation function and a time correlation function, and investigating the distribution characteristics of the PM2.5 stations.
And if the calculation result shows that certain space-time correlation characteristics exist among PM2.5 observation data monitored by the PM2.5 site, estimating the ground PM2.5 concentration of the region by using a space-time regression Crigin interpolation model.
Specifically, the spatial variation feature calculation method is shown in formula 1, and the temporal correlation feature calculation method is shown in formula 2 and formula 3.
(formula 1)
(formula 2)
(formula 3)
Wherein, in the formula 1, gamma (h) is a regionalized variable in siAnd siValue Z(s) at + hi) And Z(s)iHalf the variance of the difference of + h); h is the distance between two points, also known as the lag distance, and N (h) is the sample logarithm of the function of the variation between the distances h.
In the formulas 2 and 3, n is a time series variable ynNumber of (2), yn-tIs and ynThe variation of the distance of the time interval t,is an autocorrelation function of the time distance t,is the mean value.
S220: and resampling and matching the collected ground PM2.5 data, meteorological data, DEM and land utilization data of the region to be estimated into the created grid.
Specifically, the process of resampling the data to the created grid for matching includes: and determining the resolution of the grid corresponding to the area to be estimated. The matching mode of the PM2.5 data is as follows: averaging ground PM2.5 observation data monitored by all PM2.5 sites in a grid unit corresponding to an area to be estimated at the same day, and assigning the average data to the corresponding grid unit; the matching mode of the auxiliary variable data (including meteorological data, DEM and land utilization data) is as follows: and comparing the resolution of the grid unit corresponding to the area to be estimated with the resolution of the auxiliary variable data corresponding to the grid unit, averaging the auxiliary variable data higher than the resolution of the corresponding grid unit, assigning the auxiliary variable data to the corresponding grid unit, and assigning the auxiliary variable data not higher than the resolution of the corresponding grid unit to the corresponding grid unit by adopting an interpolation method with inverse distance weighting.
It should be noted that, in the matching process, the PM2.5 data is from an environmental monitoring department, the meteorological data is from a RAMS (Regional Atmospheric Modeling System) mode simulation, the DEM is from SRTM (near Radar mapping Mission) 90-meter resolution elevation data, and the land utilization data is from a MODISLand Cover product; and interpolating meteorological data in the RAMS into a grid with the resolution of 3km, and resampling DEM and land utilization data to obtain the grid with the resolution of 3 km.
S230: extracting auxiliary variable data corresponding to each PM2.5 station in the area to be estimated, establishing a multiple linear regression model between PM2.5 observation data of each PM2.5 station and each auxiliary variable data, and acquiring a ground PM2.5 residual value of the area to be estimated according to a calculation result of the multiple linear regression model and the PM2.5 observation data monitored by the PM2.5 station.
Specifically, a multivariate linear regression model is used to establish the relationship between PM2.5 and the auxiliary variables, and a least square method is used for fitting:
PM2.5＝β+α1*RH+α2*PBL+α3*Wind+α4*DEM+α5LandUse (equation 4)
Wherein RH represents relative humidity, PBL represents boundary layer height, DEM represents elevation, LandUse represents land use type, β represents intercept, α represents land use type1～α5Representing the slope of each auxiliary variable. The model obtained by the formula 4 is preliminarily used for calculating the trend of PM2.5, and the residual between the observed value of PM2.5 and the trend of PM2.5 (i.e. the calculation result of the observed data of PM2.5 and the multiple linear regression model) is calculated.
S240: the experimental variation function of the residual was calculated.
In particular, it is assumed that a spatial point s is present in the region to be investigated (i.e. the region to be estimated)iThe observed value (i.e., PM2.5 observed data) at (i ═ 1, …, n) is z(s)i) A point to be estimated (i.e., a certain point in the region to be estimated) s0Estimate of an attribute ofIs a weighted sum of the attribute values of n known sample points around, and the formula is expressed as:
(formula 5)
In the formula ofi(i-1, … n) is the pending weight coefficient,is a spatial point s0An estimated value of z(s)i) Is a sample point siThe attribute value of (2). The spatio-temporal regression Kriging model is established under the condition of unbiased and minimum estimation variance, and a Kriging equation set can be established according to the condition.
(formula 6)
Wherein gamma(s)i,sj) The variance function between the ith point and the jth point is expressed, and as can be seen from equation 6, the kriging weight λ can be solved by calculating the variance function, and then the point value to be estimated (i.e., the estimated value at the point to be estimated) and the estimation error (the error between the estimated value at the point to be estimated and the actual observed value at the point to be estimated) can be obtained.
In the invention, the experimental variation function of the residual error is calculated by using the formula 7, and a suitable space-time variation function model is selected according to the distribution condition of the experimental variation function of the residual error.
γst(hs,ht)＝0.5Var[Z(s+hs,t+ht)-Z(s,t)](formula 7)
In the formula of gammast(hs,ht) For regionalized variables at spatio-temporal locations (s, t) and (s + h)s,t+ht) The values Z (s, t) and Z (s + h) ofs,t+ht) Half of the variance of the difference of (a); h issIs the distance of two points in space, htIs the distance in time.
S250: and constructing and selecting a space-time variation function model capable of describing the set space-time characteristics of the PM2.5 observation data according to the calculation result of the step S240, and fitting the space-time variation function model by using a least square method to calculate space-time variation function model parameters.
Wherein, the parameters of the space-time variation function model comprise: the space part is divided into a gold value, a base station value and a variable range; the time is divided into a lump value, a base value and a variable range.
S260: and (3) performing space-time regression kriging interpolation calculation by using the fitted space-time variation function model, summing the kriging interpolation result and the trend part in the step (S230) (namely summing the interpolation result obtained by estimating by using the space-time regression kriging model and the ground PM2.5 residual value obtained by calculating in the step (S230)), and estimating the ground PM2.5 concentration value of the area to be estimated.
Further, a ten-fold cross validation method can be adopted to carry out cross validation on the interpolation result of the spatio-temporal regression Krigin model, the model meeting the precision requirement is applied to PM2.5 estimation, and the final result is output to form grid data in the tff format.
It should be noted that in the present invention, the auxiliary variable data (i.e., meteorological data, DEM, and land use data) are added to further improve the estimation accuracy of PM2.5, but the auxiliary variable data may not be included, that is: in the above step S220, the meteorological data, the DEM, and the land use data of the region to be estimated may not be resampled into the created grid and matched, so that the step S230 does not need to be executed, and the process directly proceeds to the step S240, which is the process shown in fig. 1.
Corresponding to the method, the invention provides a system for estimating the ground PM2.5 based on a space-time regression Krigin model. FIG. 3 illustrates a logical structure of a system for estimating surface PM2.5 based on a spatio-temporal regression Kriging model according to an embodiment of the present invention.
As shown in fig. 3, the system 300 for estimating the ground PM2.5 based on the spatio-temporal regression kriging model provided by the present invention includes a PM2.5 data matching unit 310, a spatio-temporal variation function model determining unit 320, a spatio-temporal variation function model fitting unit 330, and a PM2.5 concentration value estimating unit 340.
The PM2.5 data matching unit 310 is configured to resample ground PM2.5 observation data of an area to be estimated into a created grid and perform matching; the grid is established according to the latitude and longitude range and the resolution of the area to be estimated, and the matching process comprises the steps of averaging ground PM2.5 observation data monitored in the same day by all PM2.5 sites in a grid unit corresponding to the area to be estimated in the established grid, and then assigning the averaged data to the corresponding grid unit.
The spatio-temporal variation function model determining unit 320 is configured to calculate an experimental variation function of the residual according to the matched ground PM2.5 observation data of the area to be estimated, and determine a spatio-temporal variation function model according to the experimental variation function of the residual.
The spatio-temporal variation function model fitting unit 330 is configured to fit the spatio-temporal variation function model by using a least square method.
The PM2.5 concentration value estimation unit 340 is configured to estimate a ground PM2.5 concentration value of the area to be estimated by using a spatio-temporal regression kriging model according to a fitting result of the spatio-temporal variation function model.
Further, in order to improve the estimation accuracy, the system 300 for estimating the ground PM2.5 based on the spatio-temporal regression kriging model provided by the present invention may further include an auxiliary data matching unit 350 and a PM2.5 residual value obtaining unit 360. The auxiliary data matching unit 350 is configured to resample the auxiliary variable data into the created grid and perform matching; the auxiliary variable data comprise meteorological data, DEM and land utilization data; the PM2.5 residual value obtaining unit 360 is configured to establish a multiple linear regression model between the PM2.5 observation data of each PM2.5 station and each auxiliary variable data according to the extracted auxiliary variable data corresponding to each PM2.5 station in the area to be estimated, and obtain a ground PM2.5 residual value of the area to be estimated according to a calculation result of the multiple linear regression model and the PM2.5 observation data.
Specifically, when the auxiliary data matching unit 350 matches auxiliary variable data, the PM2.5 residual value obtaining unit 360 is used to obtain a ground PM2.5 residual value of the area to be estimated, and then the interpolation result obtained by using the spatio-temporal regression kriging model estimation and the ground PM2.5 residual value obtaining unit 360 to obtain the ground PM2.5 residual value of the area to be estimated are summed and summarized in the PM2.5 concentration value estimating unit 340 according to the fitting result of the spatio-temporal variation function model fitting unit 330, so as to estimate the PM2.5 concentration value of the area to be estimated.
According to the method and the system for estimating the ground PM2.5 based on the spatio-temporal regression Kriging model, provided by the invention, the multivariate linear regression model between the PM2.5 data and the auxiliary variables is established by adding different auxiliary variable data, so that the influence factor of the PM2.5 can be reflected; and then, a proper space-time variation function model is selected by calculating the space-time variation function of the residual error after regression, and time characteristic information is added into the model, so that the estimation precision of PM2.5 can be better improved.
A method and system for estimating surface PM2.5 based on a spatio-temporal regression kriging model according to the present invention is described by way of example above with reference to the accompanying drawings. However, it will be appreciated by those skilled in the art that various modifications can be made to the method and system for estimating the surface PM2.5 based on the spatio-temporal regression kriging model of the present invention without departing from the scope of the invention. Therefore, the scope of the present invention should be determined by the contents of the appended claims.
Claims (10)
1. A method for estimating ground PM2.5 based on a spatio-temporal regression Kriging model comprises the following steps:
creating a grid according to the latitude and longitude range and the resolution of the region to be estimated, resampling ground PM2.5 observation data of the region to be estimated into the created grid, and matching; wherein the matching process comprises: in the grid, ground PM2.5 observation data monitored by all PM2.5 sites in a grid unit corresponding to an area to be estimated on the same day are averaged, and then the averaged data is assigned to the corresponding grid unit;
calculating an experimental variation function of a residual error according to the matched ground PM2.5 observation data of the region to be estimated, and then determining a space-time variation function model according to the experimental variation function of the residual error;
fitting the space-time variation function model by adopting a least square method;
and according to the fitting result of the space-time variation function model, estimating the ground PM2.5 concentration value of the area to be estimated by adopting a space-time regression Krigin model.
2. The method for estimating surface PM2.5 based on a spatio-temporal regression kriging model as claimed in claim 1, further comprising:
resampling auxiliary variable data to the created grid and matching; wherein the auxiliary variable data comprises meteorological data, DEM and land use data.
3. The method for estimating ground PM2.5 based on a spatio-temporal regression Krigin model of claim 2, wherein in resampling and matching the auxiliary variable data into the created grid,
and comparing the resolution of the grid unit corresponding to the area to be estimated with the resolution of the auxiliary variable data corresponding to the grid unit, averaging the auxiliary variable data higher than the resolution of the corresponding grid unit, assigning the auxiliary variable data to the corresponding grid unit, and assigning the auxiliary variable data not higher than the resolution of the corresponding grid unit to the corresponding grid unit by adopting an interpolation method with inverse distance weighting.
4. The method for estimating surface PM2.5 based on a spatio-temporal regression Crigit model of claim 3, further comprising,
extracting auxiliary variable data corresponding to each PM2.5 station of the area to be estimated, establishing a multiple linear regression model between PM2.5 observation data of each PM2.5 station and each auxiliary variable data, and acquiring a ground PM2.5 residual value of the area to be estimated according to a calculation result of the multiple linear regression model and the PM2.5 observation data.
5. The method according to claim 4, wherein interpolation results obtained by the spatio-temporal regression kriging model estimation are summed and summarized with the ground PM2.5 residual value to obtain a ground PM2.5 concentration value of the area to be estimated.
6. The method for estimating surface PM2.5 based on a spatio-temporal regression kriging model as claimed in claim 1, further comprising:
calculating the time-space correlation of PM2.5 observation data monitored by each PM2.5 station in the region to be estimated according to the space variation function and the time correlation function; wherein,
and when the PM2.5 observation data monitored by each PM2.5 station have space-time correlation, estimating the ground PM2.5 of the region to be estimated by adopting a space-time regression Krigin model.
7. The method for estimating surface PM2.5 based on a spatio-temporal regression kriging model as claimed in claim 1, wherein the fitting result of the spatio-temporal regression kriging model is cross-validated using a ten-fold cross-validation method.
8. A system for estimating ground PM2.5 based on a spatio-temporal regression kriging model, comprising:
the PM2.5 data matching unit is used for resampling ground PM2.5 observation data of the area to be estimated to the created grid and matching the data; the grid is established according to the latitude and longitude range and the resolution of the area to be estimated, and the matching process comprises the steps of averaging ground PM2.5 observation data monitored in the same day by all PM2.5 sites in a grid unit corresponding to the area to be estimated in the grid, and then assigning the averaged data to the corresponding grid unit;
the space-time variation function model determining unit is used for calculating an experimental variation function of the residual error according to the matched ground PM2.5 observation data of the region to be estimated, and then determining a space-time variation function model according to the experimental variation function of the residual error;
the space-time variation function model fitting unit is used for fitting the space-time variation function model by adopting a least square method;
and the PM2.5 concentration value estimation unit is used for estimating the ground PM2.5 concentration value of the area to be estimated by adopting a space-time regression Krigin model according to the fitting result of the space-time variation function model.
9. The system for estimating surface PM2.5 based on a spatio-temporal regression kriging model as claimed in claim 8, further comprising:
the auxiliary data matching unit is used for resampling auxiliary variable data into the created grid and matching the auxiliary variable data; wherein the auxiliary variable data comprises meteorological data, DEM and land use data.
10. The system for estimating surface PM2.5 based on a spatio-temporal regression kriging model as claimed in claim 9, further comprising:
and the PM2.5 residual value obtaining unit is used for establishing a multiple linear regression model between the PM2.5 observation data of each PM2.5 station and each auxiliary variable data according to the extracted auxiliary variable data corresponding to each PM2.5 station of the area to be estimated, and obtaining the ground PM2.5 residual value of the area to be estimated according to the calculation result of the multiple linear regression model and the PM2.5 observation data.
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