CN113777224B - Method and system for generating grid air quality evaluation data - Google Patents
Method and system for generating grid air quality evaluation data Download PDFInfo
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Abstract
The application provides a generation method and a system of gridding air quality evaluation data, wherein the method comprises the following steps: gridding the selected air quality assessment area; collecting air quality monitoring index data and air image data in each grid of the selected air quality assessment area; calculating a first air mass concentration according to the collected air mass monitoring index data; calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data; calculating second air mass concentrations of all grids according to the first air mass concentrations and influence transfer convolution matrixes of the neighborhood grids; and generating meshed air quality assessment data according to the second air quality concentrations of all the grids. The method has low requirement on basic data, is suitable for a generating method for generating the air quality data of the fine grid, and improves the generating efficiency and accuracy of the air quality evaluation data.
Description
Technical Field
The application relates to the technical field of air environment quality monitoring, in particular to a method and a system for generating grid air quality evaluation data.
Background
The air environment quality is related to the physical health of people, the meshed air quality data is generally obtained through a mechanism model simulation and spatial interpolation method, and is limited by factors such as arrangement of monitoring stations, manpower, financial resources and the like, the air quality monitoring data cannot show the space-time distribution situation of the air quality, so that the method has certain limitation on analyzing the space-time distribution situation of pollutants, estimating the exposure risk of people in different areas and formulating precautionary measures.
The mode of obtaining the gridded air quality data based on the mechanism model simulation has the following defects: firstly, the requirements on basic data are high, such as pollution source emission list data; secondly, the result output by the model has large spatial scale and is not suitable for generating gridding data in a small area.
The method for obtaining the gridding air quality data based on the mathematical principle interpolation algorithms such as Kerling, nearest neighbor, inverse distance weighting and the like can quickly obtain the gridding air quality data, but because the influence of meteorological factors is not considered, the influence of the meteorological factors on pollutant diffusion is great, and therefore the difference between the obtained gridding air quality data and real data is great.
The existing gridding interpolation algorithm based on the Dijie Tesla algorithm provided in the air pollutant concentration interpolation method considering wind direction and wind speed has the following defects: when the number of grids is large, the number of points to be solved is multiplied, the shortest distance between all the points to be solved and the sampling points is required to be calculated, and the algorithm is required to be calculated in a large amount, so that the method is difficult to support finer grids, and inaccurate grid data is caused to a certain extent. Besides wind speed and direction, humidity, air pressure and the like, meteorological factors also influence the transmission of pollutants, so that the air quality is influenced, and the meshing data is influenced. Therefore, there is a need for a method of generating air quality data suitable for generating fine grid with low requirements for the underlying data in view of various meteorological conditions.
Disclosure of Invention
The application aims to provide a generation method and a generation system of grid air quality evaluation data, which have low requirements on basic data, are suitable for generating the generation method of the fine grid air quality data, and improve the generation efficiency and accuracy of the air quality evaluation data.
In order to achieve the above object, the present application provides a method for generating meshed air quality assessment data, the method comprising the steps of: gridding the selected air quality assessment area; collecting air quality monitoring index data and air image data in each grid of the selected air quality assessment area; calculating a first air mass concentration according to the collected air mass monitoring index data; calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data; calculating second air mass concentrations of all grids according to the first air mass concentration and the influence transfer convolution matrix; and generating meshed air quality assessment data according to the second air quality concentrations of all the grids.
As above, the method for gridding the selected air quality assessment area includes:
primarily meshing the selected air quality assessment area;
acquiring the number of air quality monitoring stations and weather stations in a selected air quality assessment area;
And (3) expanding the grid of the selected air quality assessment area according to the number of the acquired air quality monitoring stations and weather stations and the preset maximum number of similar stations in the grid.
As above, the method for calculating the influence degree transfer convolution matrix of the neighborhood grid on the center grid comprises the following steps:
Acquiring a meteorological data set according to meteorological data;
Calculating the influence degree of the neighborhood grid of each grid according to the meteorological data set;
and carrying out normalization processing on the influence degree of the neighborhood grid to obtain an influence degree transfer convolution matrix of the neighborhood grid.
As above, the method for acquiring the meteorological data set includes:
wherein b=1,..s.l;
Wherein G b represents the weather dataset of grid number b; gq represents the total number of grids including weather stations; s represents the number of longitudinal grids of the selected air quality assessment area, and l represents the number of latitudinal grids of the selected air quality assessment area; g i denotes the ith weather data for the grid including weather stations;
Wherein,
Wherein,Is a parameter; d bj denotes the surface distance between the center longitude and latitude of the grid numbered b and the jth grid including the weather station; d bi denotes the surface distance between the center longitude and latitude of the grid numbered b and the ith grid including the weather station; gq represents the total number of grids including the weather station.
As above, the method for calculating the influence degree of the neighborhood grid of each grid includes:
performing grid expansion on grids in each initial grid area;
acquiring a neighborhood grid set of grids in an initial grid region and a meteorological data set of the neighborhood grids of the neighborhood grid set;
and calculating the influence degree of the neighborhood grid on the central grid according to the meteorological data set of the neighborhood grid.
As above, the calculation formula of the influence degree y gf of the neighborhood grid on the center grid is as follows:
Wherein y gf represents the influence of the neighborhood grid Z g on the center grid G f; s gf denotes the transmission probability function of grid Z g to the central grid G f; λ0 and λk are both influencing parameters; ak is the k other meteorological data not included in the calculation S gf of the grid Z g, and V represents the total number of other meteorological data not included in the calculation S gf of the grid Z g; k is a parameter.
As above, the calculation method of the transmission probability function S gf is as follows:
where u represents a wind component in the longitudinal direction of grid Z g; v represents the wind component in the direction of the dimension Z g of the grid; ld represents the surface distance between the longitude and latitude of the center of the grid Z g and the grid G f; c represents the angle between the vector of the line connecting the center of grid Z g and the center of grid G f and the wind direction of grid Z g.
As above, the method for calculating the influence degree transfer convolution matrix of the neighborhood grid comprises the following steps:
Wherein, Representing influence degree transfer convolution matrix of neighborhood grid; y if represents the influence of the ith neighbor mesh on the center mesh G f; t represents the total number of neighbor meshes of mesh G f.
As above, the second air mass concentration calculating method includes:
Judging whether the grid comprises an air quality monitoring station, if so, calculating the second air quality concentration of the grid by the following steps:
Wherein, A second air mass concentration representing grid G f; o f represents the first air mass concentration of grid G f;
Otherwise, the calculation method of the second air mass concentration of the grid is as follows:
Wherein, A second air mass concentration representing grid G f; /(I)An influence degree transfer convolution matrix representing an ith neighborhood grid of grid G f; o i represents the first air mass concentration of the ith neighborhood mesh of mesh G f; t represents the total number of neighbor meshes of mesh G f.
The application also provides a generation system of the gridding air quality evaluation data, which comprises the following steps: the grid division module is used for carrying out grid treatment on the selected air quality assessment area; the air quality monitoring station is used for collecting air quality monitoring index data in each grid of the selected air quality assessment area; the weather station is used for collecting weather data in each grid of the selected air quality assessment area; the data processor is used for calculating a first air mass concentration according to the collected air mass monitoring index data; the data processor is also used for calculating an influence degree transfer convolution matrix of the neighborhood grid on the center grid according to the meteorological data; the data processor is also used for transferring a convolution matrix according to the first air mass concentration and the influence degree of the neighborhood grid and calculating second air mass concentrations of all grids; and the gridding data generating module is used for generating gridding air quality evaluation data according to the second air quality concentrations of all grids.
The beneficial effects achieved by the application are as follows:
(1) According to the application, rapid generation of the air quality gridding precise numerical value based on the meteorological condition constraint is realized, and the generation efficiency of the air quality evaluation data is improved.
(2) The application combines various meteorological conditions affecting the air quality, is not limited to wind speed and wind direction, can also consider various meteorological conditions such as humidity, air pressure and the like, and the number of meteorological factors is limited to the number of air quality monitoring stations included in the grid, thereby improving the generation accuracy of air quality evaluation data.
(3) According to the method, the air quality gridding data are generated, firstly, the influence of meteorological conditions on air pollutant transmission is considered, and then the convolution transfer matrix based on influence degree is utilized, so that the generated result is more accurate and more accords with the real air quality condition, and the calculation of the air quality evaluation data is faster based on a convolution matrix mode.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings to those skilled in the art.
Fig. 1 is a flowchart of a method for generating gridding air quality evaluation data according to an embodiment of the present application.
FIG. 2 is a flowchart of a method for calculating a influence degree transfer convolution matrix of a neighborhood grid on a center grid according to an embodiment of the present application.
Fig. 3 is a flowchart of a method for obtaining an influence parameter according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a system for generating meshed air quality assessment data according to an embodiment of the present application.
Reference numerals: 10-a grid dividing module; 20-an air quality monitoring station; 30-a weather station; 40-a data processor; 50-a gridding data generating module; 100-generating air quality assessment data.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in fig. 1, the present application provides a method for generating meshed air quality assessment data, the method comprising the steps of:
Step S1, gridding the selected air quality assessment area.
Step S1 comprises the following sub-steps:
step S110, primarily meshing the selected air quality assessment area, wherein the number of longitudinal grids of the selected air quality assessment area is S, and the number of latitudinal grids is l.
Step S120, the number of air quality monitoring stations and weather stations in the selected air quality assessment area is obtained.
And step S130, expanding the grid of the selected air quality assessment area according to the number of the acquired air quality monitoring stations and weather stations and the preset maximum number of similar stations in the grid.
As one embodiment of the present invention, the meshing process includes: and determining the corresponding grids of each air quality monitoring station and each meteorological station, and when the types of the same type of stations in the same grid are more than l, increasing the grid number until the number of the same type of stations in the grid is l, and stopping increasing the grid number.
As another embodiment of the present invention, the method of meshing processing is: calculating an average of the number of sites in the grid of the selected air quality assessment area, the average being equal to the number of sites divided by the number of grids; taking the average value as the maximum number of the corresponding type of stations of the single grid, wherein the number of the stations in the single grid is more than the average value; the number of grids is increased and when the number of stations in the grid is less than or equal to the average value, the increase of the number of grids is stopped.
Defining the grid number including the air quality monitoring station as ga, and the grid number including the weather station as gq, wherein ga is less than or equal to m, gq is less than or equal to n; m is the number of air quality monitoring stations; n is the number of weather stations; the center of grid G f is defined as C f; f=1..the term "is used, s.i.
And S2, acquiring air quality monitoring index data and air image data in each grid of the selected air quality assessment area.
The selected air quality assessment area is pre-provided with an air quality monitoring station and a weather station.
The air quality monitoring station is used for acquiring air quality monitoring index data; the weather station is used for acquiring weather data. The weather data includes u-wind (wind in the longitudinal direction), v_wind (wind in the latitudinal direction), humidity, air pressure, and the like.
And step S3, calculating the first air mass concentration according to the collected air mass monitoring index data.
The first air mass concentration is an air mass concentration without weather data constraints.
Specifically, the air quality monitoring index data (the concentration of the air quality monitoring index) is interpolated by using the existing kriging interpolation method to obtain the air quality concentration without considering the constraint of meteorological data.
And S4, calculating an influence degree transfer convolution matrix of the neighborhood grid on the center grid according to the meteorological data.
As shown in fig. 2, step S4 includes the following sub-steps:
step S410, acquiring a meteorological data set according to meteorological data.
Specifically, the method for acquiring the meteorological data set of the grid comprises the following steps:
wherein b=1,..s.l; (1)
Wherein G b represents the weather dataset of grid number b; gq represents the total number of grids including weather stations; s represents the number of longitudinal grids of the selected air quality assessment area, and l represents the number of latitudinal grids of the selected air quality assessment area; g i denotes weather data of the ith grid including weather stations.
Wherein,
Wherein,Is a parameter; d bj denotes the surface distance between the center longitude and latitude of the grid numbered b and the jth grid including the weather station; d bi denotes the surface distance between the center longitude and latitude of the grid numbered b and the ith grid including the weather station; gq represents the total number of grids including the weather station.
Step S420, calculating the influence degree of the neighborhood grid of each grid according to the meteorological data set.
Step S420 includes the following sub-steps:
step S421, grid expansion is performed on the grids in each initial grid region.
Specifically, q layers of grids are respectively added around the selected grid, and the number of the grids after expansion is (s+ 2*q) × (l+ 2*q), wherein index data of an air quality data set and an air image data set of the newly added grid are all 0.
Step S422, a neighborhood grid set of grids in the initial grid region and a meteorological data set of the neighborhood grids thereof are obtained.
Definition for grid G f in the ambient image field, the grid neighborhood radius affecting the air quality of this grid G f is R (e.g. when r=1, i.e. 8 grids adjacent to the grid affect the air quality of this grid), the set of grids in the grid neighborhood except the center grid is defined as the neighborhood grid, denoted by Z, and for grid G f, the neighborhood grid set is obtained as Z f.
In step S423, the influence of the neighborhood grid Z f on the center grid G f is calculated from the meteorological data set of the neighborhood grid Z f.
Specifically, for grid Z g;g＝1,...,t;t＝(2q+1)2 -1 in the neighborhood grid. If the grid is a newly added grid, defining the influence degree of the grid on the central grid as 0, otherwise, calculating the influence degree y gf of the neighborhood grid on the grid G f.
Specifically, the calculation formula of the influence degree y gf of the neighborhood grid on the center grid G f is as follows:
wherein y gf represents the influence of the neighborhood grid Z g on the center grid G f; s gf denotes the transmission probability function of grid Z g to the central grid G f; λ0 and λk are both influencing parameters; ak is the kth other weather data of grid Z g (e.g., humidity, barometric pressure, etc. calculation S gf does not include or use the weather data), and V represents the total number of other weather data of grid Z g; k is a parameter.
The calculation method of the transmission probability function S gf is as follows:
Where u represents a wind component in the longitudinal direction of grid Z g; v represents the wind component in the direction of the dimension Z g of the grid; ld represents the surface distance between the longitude and latitude of the center of the grid Z g and the grid G f; c represents the included angle between the connecting line vector of the center of the grid Z g and the center of the grid G f and the wind direction of the grid Z g; exp () is an exponential function; tan represents a function.
As shown in fig. 3, the method for obtaining the influence parameters λ0 and λk is as follows:
step T1, obtaining known air quality values of all grids comprising the air monitoring station.
And step T2, establishing a plurality of air quality value calculation equations according to influence of the neighborhood grid on the grid.
All grids including air monitoring sites were defined as I w, w=1.
The air quality value calculation equation is:
Wherein P_I w represents the air quality value of grid I w; t represents the total number of neighbor grids of grid I w; p Zk represents the air quality monitoring index data of the kth neighborhood mesh of mesh I w; y kw represents the influence of the kth neighbor mesh of mesh I w on mesh I w; y jw represents the influence of the jth neighborhood mesh of mesh I w on mesh I w; * Representing multiplication.
Wherein, the calculation formula of y kw is as follows:
y jw is the same as the calculation formula of y kw, and y jw is calculated according to the calculation formula (6).
Substituting the transmission probability function S kw from the kth neighborhood grid of the grid I w to the grid I w and other meteorological data ak which are not contained in the neighborhood grid calculation S kw into a formula (6) to obtain an equation of y kw; similarly, an equation of y jw is obtained; substituting the obtained y kw equation and y jw equation into the formula (5) to obtain an air quality value P_I w equation.
And step T3, performing multiple linear fitting on the air quality value calculation equation to obtain an influence parameter set which enables the error between the air quality value calculated according to the air quality value calculation equation and the known real air quality value to be minimum.
And solving unknowns lambda 0 and lambda k of the equation of the air quality value P_I w to obtain influence parameters lambda 0 and lambda k for minimizing errors between the air quality value calculated according to the air quality value calculation equation and the known real air quality value. The influence parameters λ0 and λk are used to calculate the influence of the neighborhood grid on the center grid.
And step S430, carrying out normalization processing on the influence degree of the neighborhood grid to obtain an influence degree transfer convolution matrix of the neighborhood grid.
Specifically, the calculation method of the influence degree transfer convolution matrix of the neighborhood grid comprises the following steps:
Wherein, Representing influence degree transfer convolution matrix of neighborhood grid; y if represents the influence of the ith neighbor mesh on the center mesh G f; t represents the total number of neighbor meshes of mesh G f.
And S5, calculating second air mass concentrations of all grids according to the first air mass concentrations and the influence degree transfer convolution matrix of the neighborhood grids.
Specifically, whether the grid comprises an air quality monitoring station or not is judged, and if the grid comprises the air quality monitoring station, the calculation method of the second air quality concentration of the grid is as follows:
Wherein, A second air mass concentration representing grid G f; o f represents the first air mass concentration of grid G f.
For grids with air quality monitoring stations at the beginning, the air quality concentration is unchanged, namely the grid air quality concentration after interpolation of air quality monitoring index data by using a kriging interpolation method.
If the grid does not comprise the air quality monitoring station, the calculation method of the second air quality concentration of the grid comprises the following steps:
Wherein, A second air mass concentration representing grid G f; /(I)An influence degree transfer convolution matrix representing an ith neighborhood grid of grid G f; o i represents the first air mass concentration of the ith neighborhood mesh of mesh G f; t represents the total number of neighbor meshes of mesh G f.
And S6, generating meshed air quality assessment data according to the second air quality concentrations of all the grids.
Specifically, corresponding second air mass concentrations are generated corresponding to each grid to form grid-type air mass evaluation data.
Example two
As shown in fig. 4, the present application provides a system 100 for generating meshed air quality assessment data, the system comprising:
a meshing module 10 for meshing the selected air quality assessment area;
An air quality monitoring station 20 for collecting air quality monitoring index data within each grid of the selected air quality assessment area;
A weather station 30 for collecting weather data within each grid of the selected air quality assessment area;
A data processor 40 for calculating a first air mass concentration from the collected air mass monitoring index data;
The data processor 40 is further configured to calculate, according to the meteorological data, a influence degree transfer convolution matrix of the neighborhood grid on the center grid;
The data processor 40 is further configured to calculate a second air mass concentration of all grids according to the first air mass concentration and the influence degree transfer convolution matrix of the neighboring grids;
The gridding data generating module 50 is configured to generate gridding air quality evaluation data according to the second air quality concentrations of all grids.
The beneficial effects achieved by the application are as follows:
(1) According to the application, rapid generation of the air quality gridding precise numerical value based on the meteorological condition constraint is realized, and the generation efficiency of the air quality evaluation data is improved.
(2) The application combines various meteorological conditions affecting the air quality, is not limited to wind speed and wind direction, can also consider various meteorological conditions such as humidity, air pressure and the like, and the number of meteorological factors is limited to the number of air quality monitoring stations included in the grid, thereby improving the generation accuracy of air quality evaluation data.
(3) According to the method, the air quality gridding data are generated, firstly, the influence of meteorological conditions on air pollutant transmission is considered, and then the convolution transfer matrix based on influence degree is utilized, so that the generated result is more accurate and more accords with the real air quality condition, and the calculation of the air quality evaluation data is faster based on a convolution matrix mode.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.
Claims (4)
1. A method of generating meshed air quality assessment data, the method comprising the steps of:
Gridding the selected air quality assessment area;
Collecting air quality monitoring index data and air image data in each grid of the selected air quality assessment area;
calculating a first air mass concentration according to the collected air mass monitoring index data;
Calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data;
Calculating second air mass concentrations of all grids according to the first air mass concentration and the influence transfer convolution matrix;
generating gridding air quality assessment data according to the second air quality concentrations of all grids;
the second air mass concentration calculating method comprises the following steps:
Judging whether the grid comprises an air quality monitoring station, if so, calculating the second air quality concentration of the grid by the following steps:
Wherein, A second air mass concentration representing grid G f; o f represents the first air mass concentration of grid G f;
Otherwise, the calculation method of the second air mass concentration of the grid is as follows:
Wherein, A second air mass concentration representing grid G f; /(I)An influence degree transfer convolution matrix representing an ith neighborhood grid of grid G f; o i represents the first air mass concentration of the ith neighborhood mesh of mesh G f; t represents the total number of neighbor meshes of mesh G f;
The method for calculating the influence degree transfer convolution matrix of the neighborhood grid on the center grid comprises the following steps:
Acquiring a meteorological data set according to meteorological data;
Calculating the influence degree of the neighborhood grid of each grid according to the meteorological data set;
normalizing the influence degree of the neighborhood grid to obtain an influence degree transfer convolution matrix of the neighborhood grid;
The method for calculating the influence degree of the neighborhood grid of each grid comprises the following steps:
performing grid expansion on grids in each initial grid area;
acquiring a neighborhood grid set of grids in an initial grid region and a meteorological data set of the neighborhood grids of the neighborhood grid set;
calculating the influence degree of the neighborhood grid on the central grid according to the meteorological data set of the neighborhood grid;
the calculation formula of the influence degree y gf of the neighborhood grid on the center grid is as follows:
Wherein y gf represents the influence of the neighborhood grid Z g on the center grid G f; s gf denotes the transmission probability function of grid Z g to the central grid G f; λ0 and λk are both influencing parameters; ak is the k other meteorological data not included in the calculation S gf of the grid Z g, and aV represents the total number of other meteorological data not included in the calculation S gf of the grid Z g; k is a parameter;
The calculation method of the transmission probability function S gf is as follows:
Where u represents a wind component in the longitudinal direction of grid Z g; v represents the wind component in the direction of the dimension Z g of the grid; ld represents the surface distance between the longitude and latitude of the center of the grid Z g and the grid G f; c represents the included angle between the connecting line vector of the center of the grid Z g and the center of the grid G f and the wind direction of the grid Z g;
the influence degree transfer convolution matrix of the neighborhood grid is calculated by the following steps:
Wherein, Representing influence degree transfer convolution matrix of neighborhood grid; y if represents the influence of the ith neighbor mesh on the center mesh G f; t represents the total number of neighbor meshes of mesh G f.
2. The method of generating gridded air quality assessment data according to claim 1, wherein the method of gridding the selected air quality assessment area comprises:
primarily meshing the selected air quality assessment area;
acquiring the number of air quality monitoring stations and weather stations in a selected air quality assessment area;
And (3) expanding the grid of the selected air quality assessment area according to the number of the acquired air quality monitoring stations and weather stations and the preset maximum number of similar stations in the grid.
3. The method for generating meshed air quality assessment data according to claim 1, wherein the method for acquiring the meteorological data set is as follows:
wherein b=1,..s.l;
Wherein G b represents the weather dataset of grid number b; gq represents the total number of grids including weather stations; s represents the number of longitudinal grids of the selected air quality assessment area, and l represents the number of latitudinal grids of the selected air quality assessment area; g i denotes the ith weather data for the grid including weather stations;
Wherein,
Wherein,Is a parameter; d bj denotes the surface distance between the center longitude and latitude of the grid numbered b and the jth grid including the weather station; d bi denotes the surface distance between the center longitude and latitude of the grid numbered b and the ith grid including the weather station; gq represents the total number of grids including the weather station.
4. A system for generating meshed air quality assessment data, wherein the generating method of any one of claims 1-3 is performed, the system comprising:
the grid division module is used for carrying out grid treatment on the selected air quality assessment area;
The air quality monitoring station is used for collecting air quality monitoring index data in each grid of the selected air quality assessment area;
the weather station is used for collecting weather data in each grid of the selected air quality assessment area;
The data processor is used for calculating a first air mass concentration according to the collected air mass monitoring index data;
the data processor is also used for calculating an influence degree transfer convolution matrix of the neighborhood grid on the center grid according to the meteorological data;
The data processor is also used for transferring a convolution matrix according to the first air mass concentration and the influence degree of the neighborhood grid and calculating second air mass concentrations of all grids;
And the gridding data generating module is used for generating gridding air quality evaluation data according to the second air quality concentrations of all grids.
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