CN113777224A - Method and system for generating gridding air quality evaluation data - Google Patents

Method and system for generating gridding air quality evaluation data Download PDF

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CN113777224A
CN113777224A CN202110924759.9A CN202110924759A CN113777224A CN 113777224 A CN113777224 A CN 113777224A CN 202110924759 A CN202110924759 A CN 202110924759A CN 113777224 A CN113777224 A CN 113777224A
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王正
宫正宇
安新国
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Abstract

The application provides a method and a system for generating gridding air quality evaluation data, wherein the method comprises the following steps: carrying out gridding treatment on the selected air quality evaluation area; collecting air quality monitoring index data and meteorological data in each grid of a selected air quality assessment area; calculating a first air mass concentration according to the collected air quality monitoring index data; calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to meteorological data; the convolution matrix is transferred according to the first air mass concentration and the influence degree of the neighborhood grids, and second air mass concentrations of all the grids are calculated; and generating gridding air quality evaluation data according to the second air quality concentrations of all the grids. The method has low requirement on basic data, is suitable for generating refined grid air quality data, and improves the generation efficiency and accuracy of air quality evaluation data.

Description

Method and system for generating gridding air quality evaluation data
Technical Field
The application relates to the technical field of air environment quality monitoring, in particular to a method and a system for generating gridding air quality evaluation data.
Background
The air environment quality is related to the health of people, the gridding air quality data is generally obtained by a mechanism model simulation and spatial interpolation method, the air quality monitoring data is limited by the layout of monitoring sites, the manpower, the financial resources and other factors, the air quality monitoring data can not show the space-time distribution condition of the air quality, and the method has certain limitation on the analysis of the space-time distribution condition of pollutants, the estimation of the exposure risk of people in different regions and the establishment of precautionary measures.
The method for obtaining gridding air quality data based on mechanism model simulation has the following defects: firstly, the requirements on basic data are high, such as pollutant source emission list data; secondly, the spatial scale of the output result of the model is large, and the model is not suitable for generating gridding data in a small area.
The gridding air quality data is obtained by an interpolation algorithm based on mathematical principles such as kriging, nearest neighbor and inverse distance weighting, although the gridding data can be obtained quickly, because the influence of meteorological factors is not considered, and the influence of the meteorological factors on pollutant diffusion is large, the obtained gridding air quality data has a large difference with real data.
The existing air pollutant concentration interpolation method considering wind direction and wind speed has the following defects that a grid interpolation algorithm based on Dijkstra algorithm has the following defects: when the number of the grids is large, the number of the points to be solved is multiplied, the shortest distance between all the points to be solved and the sampling points needs to be calculated, and the algorithm needs to be calculated in a large amount, so that the method is difficult to support a fine grid, and inaccurate gridding data can be caused to a certain extent. In addition to wind speed and direction, humidity, air pressure, etc., meteorological factors can also affect the transmission of pollutants, thereby affecting air quality and further affecting the gridding data. Therefore, a generation method which considers various meteorological conditions, has low requirement on basic data and is suitable for generating refined grid air quality data is needed.
Disclosure of Invention
The application aims to provide a method and a system for generating gridding air quality evaluation data, which have low requirements on basic data, are suitable for generating a method for generating refined gridding 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 gridding air quality assessment data, which includes the following steps: carrying out gridding treatment on the selected air quality evaluation area; collecting air quality monitoring index data and meteorological data in each grid of a selected air quality assessment area; calculating a first air mass concentration according to the collected air quality monitoring index data; calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to meteorological data; transferring the convolution matrix according to the first air mass concentration and the influence degree, and calculating second air mass concentrations of all grids; and generating gridding air quality evaluation data according to the second air quality concentrations of all the grids.
As above, wherein the method of gridding the selected air quality assessment area comprises:
preliminarily carrying out grid division on the selected air quality evaluation area;
acquiring the number of air quality monitoring stations and meteorological stations in a selected air quality assessment area;
and performing grid expansion on the selected air quality evaluation area according to the obtained number of the air quality monitoring stations and the meteorological stations and the preset maximum number of similar stations in the grid.
The method for calculating the influence degree transfer convolution matrix of the neighborhood grid to the central grid comprises the following steps:
acquiring a meteorological data set according to meteorological data;
calculating the influence degree of the neighborhood grids of each grid according to the meteorological data set;
and carrying out normalization processing on the influence degree of the neighborhood grids to obtain an influence degree transfer convolution matrix of the neighborhood grids.
As above, the meteorological data set obtaining method includes:
Figure BDA0003208749630000031
wherein, b is 1.. multidot.l;
wherein G isbA meteorological dataset representing a grid numbered b; gq represents the total number of grids comprising the weather station; s represents the longitude grid number of the selected air quality evaluation area, and l represents the latitude grid number of the selected air quality evaluation area; giWeather data representing an ith grid including weather stations;
wherein,
Figure BDA0003208749630000032
wherein,
Figure BDA0003208749630000033
is a parameter; dbjRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the jth grid comprising the weather station; dbiRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the ith grid comprising the weather station; gq represents the total number of grids that comprise the weather station.
The above, wherein the method for calculating the neighborhood grid influence degree of each grid comprises:
carrying out grid expansion on grids in each initial grid area;
acquiring a neighborhood grid set of grids in an initial grid area and a meteorological data set of neighborhood grids;
and calculating the influence degree of the neighborhood grid to the central grid according to the meteorological data set of the neighborhood grid.
As above, where the influence y of the neighborhood grid on the center gridgfThe calculation formula of (2) is as follows:
Figure BDA0003208749630000041
wherein, ygfRepresenting a neighborhood grid ZgCentering grid GfThe degree of influence of (c); sgfRepresentation grid ZgTo the center grid GfA transmission probability function of (a); λ 0 and λ k are both influencing parameters; ak is a grid ZgCalculating SgfNot included kth other meteorological data, V representing grid ZgCalculating SgfThe total number of other meteorological data not included; k is a parameter.
As above, wherein the probability function S is transmittedgfThe calculation method of (2) is as follows:
Figure BDA0003208749630000042
wherein u represents a grid ZgA wind component in a longitudinal direction; v denotes a grid ZgA wind component in a dimensional direction; ld denotes the grid ZgAnd grid GfThe earth surface distance between the longitude and latitude of the centers of the two grids; c represents a grid ZgCenter and grid GfCenter connecting line vector and grid ZgThe included angle of the wind direction.
As above, the method for calculating the influence degree transfer convolution matrix of the neighborhood grid includes:
Figure BDA0003208749630000051
wherein,
Figure BDA0003208749630000052
expressing the influence degree of the neighborhood grid to transfer a convolution matrix; y isifRepresents the ith neighborhood grid to the center grid GfThe degree of influence of (c); t represents a grid GfThe total number of neighborhood grids.
As above, the second air mass concentration is calculated by:
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:
Figure BDA0003208749630000053
wherein,
Figure BDA0003208749630000054
representation grid GfA second air mass concentration of (d); o isfRepresentation grid GfA first air mass concentration of;
otherwise, the calculation method of the second air mass concentration of the grid is as follows:
Figure BDA0003208749630000055
wherein,
Figure BDA0003208749630000056
representation grid GfA second air mass concentration of (d);
Figure BDA0003208749630000057
representation grid GfThe influence of the ith neighborhood grid of (1) shifts the convolution matrix; o isiRepresentation grid GfThe first air mass concentration of the ith neighborhood grid of (a); t represents a grid GfThe total number of neighborhood grids.
The present application further provides a generation system of grid air quality evaluation data, the system includes: the grid division module is used for carrying out grid processing on the selected air quality evaluation area; the air quality monitoring station is used for acquiring air quality monitoring index data in each grid of the selected air quality assessment area; the meteorological station is used for acquiring meteorological data in each grid of the selected air quality assessment area; the data processor is used for calculating first air quality concentration according to the collected air quality monitoring index data; the data processor is also used for calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data; the data processor is also used for transferring the convolution matrix according to the first air quality concentration and the influence degree of the neighborhood grids and calculating second air quality concentrations of all the grids; and the gridding data generation module is used for generating gridding air quality evaluation data according to the second air quality concentrations of all grids.
The beneficial effect that this application realized is as follows:
(1) according to the method and the device, the quick generation of the air quality gridding accurate numerical value based on the meteorological condition constraint is realized, and the generation efficiency of the air quality assessment data is improved.
(2) This application combines the meteorological condition of multiple influence air quality, no longer limits in wind speed and wind direction, still can consider multiple meteorological conditions such as humidity, atmospheric pressure, and the quantity of meteorological factor only limits the number including air quality monitoring website in the net, improves the formation degree of accuracy of air quality evaluation data.
(3) The method generates the air quality gridding data, firstly considers the influence of meteorological conditions on the transmission of air pollutants, and then utilizes the convolution transfer matrix based on influence degree, 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 more rapid based on the mode of the convolution matrix.
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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 needed to be 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 described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a method for generating grid air quality evaluation data according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for calculating an influence degree transfer convolution matrix of a neighborhood grid on a center grid according to the embodiment of the present application.
Fig. 3 is a flowchart of an impact parameter obtaining method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a system for generating grid air quality evaluation data according to an embodiment of the present application.
Reference numerals: 10-a mesh partitioning module; 20-an air quality monitoring station; 30-a weather station; 40-a data processor; 50-a gridding data generating module; 100-air quality assessment data generation system.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
Example one
As shown in fig. 1, the present application provides a method for generating gridding air quality evaluation data, which includes the following steps:
in step S1, the selected air quality evaluation area is subjected to gridding processing.
Step S1 includes the following sub-steps:
step S110, preliminarily grid division is carried out on the selected air quality evaluation area, the grid number in the longitude direction of the selected air quality evaluation area is S, and the grid number in the latitude direction is l.
And step S120, acquiring the number of the air quality monitoring stations and the meteorological stations in the selected air quality evaluation area.
And step S130, performing grid expansion on the selected air quality evaluation area according to the obtained number of the air quality monitoring stations and the meteorological stations and the preset maximum number of similar stations in the grid.
As an embodiment of the present invention, a method of gridding processing includes: and determining grids corresponding to each air quality monitoring station and each meteorological station, and increasing the number of the grids when the number of the same type of sites in the same grid is more than l, and stopping increasing the number of the grids until the number of the same type of sites in the grid is l.
As another embodiment of the present invention, a method of gridding includes: 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 sites of the single grid, and when the number of the sites in the single grid is more than the average value; increasing the grid number, and stopping increasing the grid number when the number of the sites in the grid is less than or equal to the average value.
Defining the grid number including an air quality monitoring station as ga, and the grid number including a meteorological station as gq, wherein the ga is less than or equal to m, and the gq is less than or equal to n; m is the number of air quality monitoring stations; n is the number of meteorological stations; grid GfIs defined as Cf；f＝1,......,s*l。
And step S2, collecting air quality monitoring index data and meteorological data in each grid of the selected air quality evaluation area.
An air quality monitoring station and a meteorological station are preset in the selected air quality assessment area.
The air quality monitoring station is used for acquiring air quality monitoring index data; the weather station is used for acquiring weather data. The meteorological 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 a first air quality concentration according to the collected air quality monitoring index data.
The first air mass concentration is an air mass concentration without image 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 meteorological data constraint.
And step S4, calculating the influence degree of the neighborhood grid on the central grid to transfer the convolution matrix according to the meteorological data.
As shown in fig. 2, step S4 includes the following sub-steps:
and step S410, acquiring a meteorological data set according to the meteorological data.
Specifically, the method for acquiring the meteorological dataset of the grid comprises the following steps:
Figure BDA0003208749630000091
wherein, b is 1.. multidot.l; (1)
wherein G isbA meteorological dataset representing a grid numbered b; gq represents the total number of grids comprising the weather station; s represents the longitude grid number of the selected air quality evaluation area, and l represents the latitude grid number of the selected air quality evaluation area; giMeteorological data representing the ith grid including meteorological stations.
Wherein,
Figure BDA0003208749630000092
wherein,
Figure BDA0003208749630000093
is a parameter; dbjRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the jth grid comprising the weather station; dbiRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the ith grid comprising the weather station; gq represents the total number of grids that comprise the weather station.
Step S420, calculating the influence degree of the neighborhood grids of each grid according to the meteorological data set.
Step S420 includes the following substeps:
step S421, performing grid expansion on the grid in each initial grid region.
Specifically, q layers of grids are respectively added around the selected grid, and the number of the expanded grids is (s +2 × q) × (l +2 × q), wherein the index data of the air quality data set and the meteorological data set of the newly added grid are both 0.
Step S422, a neighborhood grid set of grids in the initial grid region and a meteorological data set of neighborhood grids are obtained.
Defining grid G for meteorological fieldfIn other words, the grid G is influencedfThe grid neighborhood radius of the air quality is R (for example, when R is 1, that is, 8 grids adjacent to the grid affect the air quality of the grid),the set of grids in the neighborhood of the grid, except the center grid, is defined as the neighborhood grid, denoted Z, for grid GfObtaining its neighborhood grid set as Zf。
Step S423, according to the neighborhood grid ZfComputing a neighborhood grid ZfCentering grid GfThe influence of (c).
In particular, for grid Z in the neighborhood gridg；g＝1,...,t；t＝(2q+1)2-1. If the grid is a newly added grid, defining the influence degree of the grid on the central grid to be 0, otherwise, calculating the grid G of the neighborhood gridfDegree of influence y ofgf。
Specifically, neighborhood grid to center grid GfDegree of influence y ofgfThe calculation formula of (2) is as follows:
Figure BDA0003208749630000101
wherein, ygfRepresenting a neighborhood grid ZgCentering grid GfThe degree of influence of (c); sgfRepresentation grid ZgTo the center grid GfA transmission probability function of (a); λ 0 and λ k are both influencing parameters; ak is a grid ZgThe k-th other meteorological data (e.g. humidity, barometric pressure, etc. calculate S)gfNon-contained or unused meteorological data), V represents a grid ZgThe total number of other meteorological data of (a); k is a parameter.
Wherein a transmission probability function SgfThe calculation method of (2) is as follows:
Figure BDA0003208749630000111
wherein u represents a grid ZgA wind component in a longitudinal direction; v denotes a grid ZgA wind component in a dimensional direction; ld denotes the grid ZgAnd grid GfThe earth surface distance between the longitude and latitude of the centers of the two grids; c represents a grid ZgCenter and grid GfCentral connecting line vector and netLattice ZgThe included angle of the wind direction; exp () is an exponential function; tan represents a function.
As shown in fig. 3, the method for obtaining the influence parameters λ 0 and λ k is:
at step T1, air quality values for all known grids including air monitoring sites are obtained.
And step T2, establishing a plurality of air quality value calculation equations according to the influence degree of the neighborhood grid on the grid.
Defining all grids including air monitoring sites as Iw，w＝1,...,ga。
The air mass value calculation equation is:
Figure BDA0003208749630000112
wherein, P _ IwRepresentation grid IwAn air mass value of (d); t represents a grid IwThe total number of neighborhood grids of (2); pZkRepresentation grid IwThe air quality monitoring index data of the kth neighborhood grid; y iskwRepresentation grid IwOf the kth neighborhood grid pair grid IwThe degree of influence of (c); y isjwRepresentation grid IwJ-th neighborhood grid pair grid IwThe degree of influence of (c); denotes multiplication.
Wherein, ykwThe calculation formula of (a) is as follows:
Figure BDA0003208749630000121
yjwand ykwAre the same as the formula (a), yjwCalculated according to the calculation formula (6).
Mesh IwK-th neighborhood grid to grid IwIs a transmission probability function SkwNeighborhood grid computing SkwSubstituting the uncontained other meteorological data ak into the formula (6) to obtain ykwThe equation of (c); in the same way, y is obtainedjwThe equation of (c); y to be obtainedkwEquation and yjwThe equation is substituted into the formula (5),obtaining an air quality value P _ IwAnd (4) an equation.
And step T3, obtaining 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 by carrying out multivariate linear fitting on the air quality value calculation equation.
Solving for air quality value P _ IwThe unknowns λ 0 and λ k of the equation, and the influence parameters λ 0 and λ k when the error of the air quality value calculated from the air quality value calculation equation with the known true air quality value is minimized are obtained. The influence parameters λ 0 and λ k are used to calculate the influence of the neighborhood grid on the central grid.
Step S430, normalization processing is carried out on the influence degree of the neighborhood grids, and the influence degree transfer convolution matrix of the neighborhood grids is obtained.
Specifically, the calculation method of the influence degree transfer convolution matrix of the neighborhood grid includes:
Figure BDA0003208749630000122
wherein,
Figure BDA0003208749630000123
expressing the influence degree of the neighborhood grid to transfer a convolution matrix; y isifRepresents the ith neighborhood grid to the center grid GfThe degree of influence of (c); t represents a grid GfThe total number of neighborhood grids.
And step S5, calculating second air quality concentrations of all grids according to the first air quality concentrations and the influence degree transfer convolution matrix of the adjacent grids.
Specifically, whether an air quality monitoring station is included in the grid is judged, and if the air quality monitoring station is included in the grid, the calculation method of the second air quality concentration of the grid is as follows:
Figure BDA0003208749630000131
wherein,
Figure BDA0003208749630000132
representation grid GfA second air mass concentration of (d); o isfRepresentation grid GfThe first air mass concentration of (a).
For the grid with the air quality monitoring station at the beginning, the air quality concentration is unchanged, namely the grid air quality concentration after the interpolation of the air quality monitoring index data is carried out by utilizing a Krigin interpolation method.
If the grid does not include the air quality monitoring station, the calculation method of the second air quality concentration of the grid comprises the following steps:
Figure BDA0003208749630000133
wherein,
Figure BDA0003208749630000134
representation grid GfA second air mass concentration of (d);
Figure BDA0003208749630000135
representation grid GfThe influence of the ith neighborhood grid of (1) shifts the convolution matrix; o isiRepresentation grid GfThe first air mass concentration of the ith neighborhood grid of (a); t represents a grid GfThe total number of neighborhood grids.
And step S6, generating gridded air quality evaluation data according to the second air quality concentrations of all the grids.
Specifically, a corresponding second air quality concentration is generated corresponding to each grid, forming the grid air quality assessment data.
Example two
As shown in fig. 4, the present application provides a system 100 for generating gridded air quality assessment data, the system comprising:
the grid division module 10 is used for carrying out grid processing on the selected air quality evaluation area;
the air quality monitoring station 20 is used for collecting air quality monitoring index data in each grid of the selected air quality assessment area;
a weather station 30 for collecting weather data in each grid of the selected air quality assessment area;
the data processor 40 is used for calculating a first air quality concentration according to the collected air quality monitoring index data;
the data processor 40 is further configured to calculate an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data;
the data processor 40 is further configured to transfer the convolution matrix according to the first air quality concentration and the influence of the neighborhood grid, and calculate second air quality concentrations of all grids;
and a gridding data generating module 50, configured to generate gridding air quality evaluation data according to the second air quality concentrations of all grids.
The beneficial effect that this application realized is as follows:
(1) according to the method and the device, the quick generation of the air quality gridding accurate numerical value based on the meteorological condition constraint is realized, and the generation efficiency of the air quality assessment data is improved.
(2) This application combines the meteorological condition of multiple influence air quality, no longer limits in wind speed and wind direction, still can consider multiple meteorological conditions such as humidity, atmospheric pressure, and the quantity of meteorological factor only limits the number including air quality monitoring website in the net, improves the formation degree of accuracy of air quality evaluation data.
(3) The method generates the air quality gridding data, firstly considers the influence of meteorological conditions on the transmission of air pollutants, and then utilizes the convolution transfer matrix based on influence degree, 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 more rapid based on the mode of the convolution matrix.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (10)

1. A method for generating gridding air quality evaluation data is characterized by comprising the following steps:
carrying out gridding treatment on the selected air quality evaluation area;
collecting air quality monitoring index data and meteorological data in each grid of a selected air quality assessment area;
calculating a first air mass concentration according to the collected air quality monitoring index data;
calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to meteorological data;
transferring the convolution matrix according to the first air mass concentration and the influence degree, and calculating second air mass concentrations of all grids;
and generating gridding air quality evaluation data according to the second air quality concentrations of all the grids.
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:
preliminarily carrying out grid division on the selected air quality evaluation area;
acquiring the number of air quality monitoring stations and meteorological stations in a selected air quality assessment area;
and performing grid expansion on the selected air quality evaluation area according to the obtained number of the air quality monitoring stations and the meteorological stations and the preset maximum number of similar stations in the grid.
3. The method for generating gridded air quality assessment data according to claim 1, wherein the method for calculating the influence degree transfer convolution matrix of the neighborhood grid to the center grid comprises:
acquiring a meteorological data set according to meteorological data;
calculating the influence degree of the neighborhood grids of each grid according to the meteorological data set;
and carrying out normalization processing on the influence degree of the neighborhood grids to obtain an influence degree transfer convolution matrix of the neighborhood grids.
4. The method for generating gridded air quality assessment data according to claim 3 wherein the meteorological data set is acquired by:
Figure FDA0003208749620000021
wherein, b is 1.. multidot.l;
wherein G isbA meteorological dataset representing a grid numbered b; gq represents the total number of grids comprising the weather station; s represents the longitude grid number of the selected air quality evaluation area, and l represents the latitude grid number of the selected air quality evaluation area; giWeather data representing an ith grid including weather stations;
wherein,
Figure FDA0003208749620000022
wherein,
Figure FDA0003208749620000023
is a parameter; dbjRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the jth grid comprising the weather station; dbiRepresenting the earth surface distance between the grid with the number b and the longitude and latitude of the center of the ith grid comprising the weather station; gq represents the total number of grids that comprise the weather station.
5. A method of generating gridded air quality assessment data according to claim 3 wherein the method of calculating the neighborhood grid influence level of each grid comprises:
carrying out grid expansion on grids in each initial grid area;
acquiring a neighborhood grid set of grids in an initial grid area and a meteorological data set of neighborhood grids;
and calculating the influence degree of the neighborhood grid to the central grid according to the meteorological data set of the neighborhood grid.
6. The method of claim 5, wherein the influence y of the neighborhood grid on the center grid isgfThe calculation formula of (2) is as follows:
Figure FDA0003208749620000031
wherein, ygfRepresenting a neighborhood grid ZgCentering grid GfThe degree of influence of (c); sgfRepresentation grid ZgTo the center grid GfA transmission probability function of (a); λ 0 and λ k are both influencing parameters; ak is a grid ZgCalculating SgfNot included kth other meteorological data, V representing grid ZgCalculating SgfThe total number of other meteorological data not included; k is a parameter.
7. The method of generating gridded air quality assessment data according to claim 6, wherein a transmission probability function SgfThe calculation method of (2) is as follows:
Figure FDA0003208749620000032
wherein u represents a grid ZgA wind component in a longitudinal direction; v denotes a grid ZgA wind component in a dimensional direction; ld denotes the grid ZgAnd grid GfThe earth surface distance between the longitude and latitude of the centers of the two grids; c represents a grid ZgCenter and grid GfCenter connecting line vector and grid ZgThe included angle of the wind direction.
8. The method for generating gridded air quality assessment data according to claim 3, wherein the method for calculating the influence degree transfer convolution matrix of the neighborhood grid is as follows:
Figure FDA0003208749620000041
wherein,
Figure FDA0003208749620000042
expressing the influence degree of the neighborhood grid to transfer a convolution matrix; y isifRepresents the ith neighborhood grid to the center grid GfThe degree of influence of (c); t represents a grid GfThe total number of neighborhood grids.
9. The method for generating gridded air quality assessment data according to claim 8, wherein the second air quality concentration is calculated by:
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:
Figure FDA0003208749620000043
wherein,
Figure FDA0003208749620000044
representation grid GfA second air mass concentration of (d); o isfRepresentation grid GfA first air mass concentration of;
otherwise, the calculation method of the second air mass concentration of the grid is as follows:
Figure FDA0003208749620000045
wherein,
Figure FDA0003208749620000046
representation grid GfA second air mass concentration of (d);
Figure FDA0003208749620000047
representation grid GfThe influence of the ith neighborhood grid of (1) shifts the convolution matrix; o isiRepresentation grid GfThe first air mass concentration of the ith neighborhood grid of (a); t represents a grid GfThe total number of neighborhood grids.
10. A system for generating gridded air quality assessment data, the system comprising:
the grid division module is used for carrying out grid processing on the selected air quality evaluation area;
the air quality monitoring station is used for acquiring air quality monitoring index data in each grid of the selected air quality assessment area;
the meteorological station is used for acquiring meteorological data in each grid of the selected air quality assessment area;
the data processor is used for calculating first air quality concentration according to the collected air quality monitoring index data;
the data processor is also used for calculating an influence degree transfer convolution matrix of the neighborhood grid on the central grid according to the meteorological data;
the data processor is also used for transferring the convolution matrix according to the first air quality concentration and the influence degree of the neighborhood grids and calculating second air quality concentrations of all the grids;
and the gridding data generation module is used for generating gridding air quality evaluation data according to the second air quality concentrations of all grids.
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