CN110261272A - Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution - Google Patents
Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution Download PDFInfo
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Abstract
The invention discloses a kind of based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution comprising obtains PM2.5 monitoring station position, establishes several buffer areas；It obtains the impact factor data in area to be studied and makees sliding-model control；It is corresponding to PM2.5 monitoring station by the impact factor data after sliding-model control, obtain the impact factor data of each PM2.5 monitoring station；Contribution Analysis and association analysis etc. are carried out using geographical detector model to the impact factor data after sliding-model control.The present invention is able to solve the analysis method for lacking can consider the problems of each variable Yu PM2.5 concentration distribution relationship comprehensively in the prior art, and accuracy is strong, analysis is comprehensive, applied widely.
Description
Technical field
The present invention relates to atmosphere pollution studying technological domains, and in particular to one kind is dense to PM2.5 based on geographical detection and PCA
Spend the Key Influential Factors screening technique of distribution.
Background technique
PM2.5 is one of Air Pollutant Discharge, is closely related with haze weather.To PM2.5 pollution condition into
Row assessment needs a large amount of continuous spatial distribution datas, and in the prior art, ground monitoring is to obtain PM2.5 concentration data most may be used
The method leaned on, but existing ground fixed monitoring station point quantity is few, and spatial distribution is uneven, is unable to satisfy requirement.
For the gas detecting instrument of PM2.5 tacheometer one kind, have the time limit short, range is small, and precision is high, but at high cost etc.
Feature can not also meet evaluation requirement well.
Using the monitoring data of the impact factor data combined ground stationary monitoring website of PM2.5, regression equation is constructed, with
Estimation range PM2.5 mass concentration, and its spatial-temporal distribution characteristic is analyzed, it is one for realizing pollutant and continuously being monitored in region
Key technology method.
The key for implementing this method is identification and screening to PM2.5 impact factor, and existing method specifically includes that one
It is factor correlativity analysis, that is, there are problems that certain synteny between the variable screened, it is possible to cause to have to PM2.5 significant related
Explanatory variable be removed；Second is that factor principal component analysis, can solve the problems, such as synteny between variable, but it is easy to ignore different changes
Measure the collaboration and reciprocation to PM2.5.
Summary of the invention
The present invention be directed to above-mentioned deficiency in the prior art, provide one kind be able to solve in the prior art lack can be complete
Face consider the problems of the analysis method of each variable and PM2.5 concentration distribution relationship based on geography detection and PCA to PM2.5 concentration
The Key Influential Factors screening technique of distribution.
In order to solve the above technical problems, present invention employs following technical proposals:
Provide a kind of Key Influential Factors screening technique based on geographical detection and PCA to PM2.5 concentration distribution, packet
Include following steps:
PM2.5 monitoring station position in S1, acquisition area to be studied, establishes several buffer areas；
S2, the impact factor data in area to be studied are obtained and make sliding-model control；
S3, by the impact factor data after sliding-model control, it is corresponding to PM2.5 monitoring station, obtain each monitoring station
Impact factor data；
S4, Contribution Analysis and association are carried out using geographical detector model to the impact factor data after sliding-model control
Property analysis；
S5, will affect the factor and be divided into several relevant groups, and successively the influence to the identical relevant group in each buffer area because
Son carries out correlation analysis；
S6, by geographical detector model analysis and correlation analysis as a result, screened to impact factor, obtain whole
Body Key Influential Factors；
S7, target critical shadow is obtained to whole Key Influential Factors progress principal component transform by principal component analytical method
Ring the factor.
Further, buffer area is that several centered on the PM2.5 monitoring station position have and are sized
Region.
Further, impact factor data include the meteorological datas such as temperature, precipitation, relative humidity, wind speed, air pressure, green
The land uses distributed data such as ground, arable land, building site, waters, density data of population, industrial pollution source, traffic related data.
Further, the method for making sliding-model control to the impact factor data in survey region is to acquisition area to be studied
The meteorological data in impact factor data, density data of population, traffic related data in domain are reclassified, and by this three
Class data are converted into type amount from numerical quantities.
Further, the method for association analysis are as follows:
Meet condition formula as a result, filtering out according to the Contribution Analysis of geographical detector model:
q(X1∩X2)>q(X1)or q(X2)
Impact factor X1、X2, wherein q is the contribution angle value obtained by geographical detector model.
Further, will affect the factor to be divided into the method for several relevant groups is according to the representative object of impact factor by shadow
It rings the factor and is divided into several groupings including meteorological data, demographic data, traffic related data.
Further, the method for correlation analysis is group will to be pressed in each buffer area successively by the influence in same group
The factor and PM2.5 concentration value carry out Pearson correlation analysis, obtain each impact factor in each buffer area and PM2.5 is dense
The relative coefficient and significance of angle value.
Further, impact factor is screened, the method for obtaining whole Key Influential Factors are as follows:
S1, according to correlation analysis as a result, filter out the impact factor of significant relevant setting quantity, it is crucial as a whole
Impact factor；
S2, it is sorted as a result, will affect the factor by relative coefficient according to correlation analysis, filters out relative coefficient highest
Setting quantity impact factor；
S3, by association analysis result that geographical detector model analysis obtains with to screen relative coefficient highest
Impact factor comparison, filters out while meeting relative coefficient and the impact factor with association analysis, also crucial as a whole
Impact factor.
It is provided by the invention above-mentioned based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
The main beneficial effect of method is:
The present invention is by the comprehensive analysis to PM2.5 concentration distribution impact factor data, by relevance and impact factor sheet
Body accounts for the contribution of PM2.5 concentration distribution simultaneously, multicollinearity between the factor that can eliminate the effects of the act and will not
The collaboration of PM2.5 and reciprocation are accounted for impact factor, fully ensures that and PM2.5 concentration distribution impact factor is examined
The accuracy of worry.
By principal component analysis, the multicollinearity between Key Influential Factors is eliminated；By correlation analysis, will affect
Collaboration and reciprocation between the factor account for；By geographical detector model analysis, each impact factor is on the one hand detected
To the influence degree of PM concentration distribution, on the other hand identifies the collaboration and reciprocation of Different Effects factor pair PM2.5 concentration, comment
It can or can not enhance or weaken the influence degree to PM2.5 concentration distribution when estimating Different Effects factor interaction.
Detailed description of the invention
Fig. 1 is the stream the present invention is based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution
Cheng Tu.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings:
As shown in Figure 1, it is that the present invention is based on geographical detections and PCA to sieve to the Key Influential Factors of PM2.5 concentration distribution
The flow chart of choosing method.
Of the invention includes such as to the Key Influential Factors screening technique of PM2.5 concentration distribution based on geographical detection and PCA
Lower step:
PM2.5 monitoring station position in S1, acquisition area to be studied, establishes several buffer areas.
Specifically, buffer area is that several centered on PM2.5 monitoring station position have the area that is sized
Domain, by the way that buffer area is arranged, to embody the scale effect that impact factor influences PM2.5 concentration distribution.
S2, the impact factor data in area to be studied are obtained and make sliding-model control.
Impact factor data include the meteorological datas such as temperature, precipitation, relative humidity, wind speed, air pressure, and greenery patches ploughs, builds
Build the land uses distributed data such as land used, waters, density data of population, industrial pollution source, traffic related data.
Wherein, traffic related data includes the link length data in area to be studied；Industrial pollution source data packet include to
The quantity of atmosphere concerned countries key monitoring enterprise in survey region.
The method for making sliding-model control to the impact factor data in survey region is to the shadow obtained in area to be studied
Ring the meteorological data in factor data, density data of population, traffic related data are reclassified, and by these three types of data from
Numerical quantities are converted into type amount.And land use distributed data, industrial pollution source are type amount, do not need to remake at discretization
Reason.
S3, by the impact factor data after sliding-model control, it is corresponding to PM2.5 monitoring station, obtain each PM2.5 monitoring
The impact factor data of website.
Wherein, include buffer size according to setting to the processing of traffic related data, obtain in each buffer area
Link length data；Processing to industrial pollution source data includes the buffer size according to setting, is obtained in each buffer area
Atmosphere concerned countries key monitoring enterprise quantity.
S4, Contribution Analysis and association are carried out using geographical detector model to the impact factor data after sliding-model control
Property analysis.
The PM2.5 concentration value of impact factor data and each monitoring station after sliding-model control is substituted into geographical detection respectively
After device model, then available each impact factor analyzes the contribution degree of PM2.5 concentration according to being associated property of contribution degree.
The method of association analysis are as follows:
Meet condition formula as a result, filtering out according to the Contribution Analysis of geographical detector model:
q(X1∩X2)>q(X1)or q(X2)
Impact factor X1、X2, wherein q is the contribution angle value obtained by geographical detector model.
S5, will affect the factor and be divided into several relevant groups, and successively the influence to the identical relevant group in each buffer area because
Son carries out correlation analysis.
Will affect the factor to be divided into the method for several relevant groups is that will affect Factor minute according to the representative object of impact factor
Be include meteorological data, demographic data, traffic related data, industrial pollution source data, land use distributed data several
Grouping.
The method of correlation analysis is, by pressed in each buffer area group successively by same group impact factor with
PM2.5 concentration value carries out Pearson correlation analysis, obtains each impact factor and PM2.5 concentration value in each buffer area
Relative coefficient and significance.
S6, by geographical detector model analysis and correlation analysis as a result, screened to impact factor, obtain whole
Body Key Influential Factors.
Specifically, being screened to impact factor, the method for obtaining whole Key Influential Factors are as follows:
S6-1, according to correlation analysis as a result, filter out it is significant it is relevant setting quantity impact factor, close as a whole
Key impact factor.
S6-2, it is sorted as a result, will affect the factor by relative coefficient according to correlation analysis, filters out relative coefficient most
The impact factor of high setting quantity.
S6-3, by association analysis result that geographical detector model analysis obtains and relative coefficient highest is screened
Impact factor comparison, filter out while meeting relative coefficient and the impact factor with association analysis, also close as a whole
Key impact factor.
S7, target critical shadow is obtained to whole Key Influential Factors progress principal component transform by principal component analytical method
Ring the factor.
By principal component analysis, screen to obtain from S6 extracted in whole Key Influential Factors obtain mutually independent influence because
Son simultaneously sorts, as target critical impact factor.
Here is to utilize the above-mentioned crucial effect based on geographical detection with PCA to PM2.5 concentration distribution provided by the invention
The example that factor screening method is analyzed:
S1, certain area to be studied is carried out after adjusting difference, obtains sharing 24 PM2.5 monitoring stations, that investigates is corresponding
Impact factor data include precipitation, temperature, air pressure, humidity, vapour pressure, wind speed, maximum wind velocity, the density of population, traffic in 2 minutes
Source, land used status, key monitoring number of the enterprise and each website PM2.5 concentration value.Buffer area be several with
Border circular areas centered on PM2.5 monitoring station, between radius 100m to 10km.
S2, will to obtain result after the impact factor Data Discretization of area to be studied as follows:
1 impact factor Data Discretization result of table
S3, it is corresponded to PM2.5 monitoring station.
S4 simultaneously carries out Contribution Analysis and pass using geographical detector model to the impact factor data after sliding-model control
The analysis of connection property, as a result as follows:
2 Contribution Analysis result of table
The result of Contribution Analysis and association analysis can be intuitively obtained by table 2.
S5, precipitation, temperature, air pressure, humidity, vapour pressure, 2 minutes wind speed, maximum wind velocity are divided into one group, and carry out phase
The analysis of closing property, it is as follows with significance to obtain relative coefficient:
3 correlation analysis result of table
S6, by screening, to obtain whole Key Influential Factors include traffic route length, industry/greenery patches/arable land/for building
It is precipitation when ground/water surface area, population, temperature on average, 20-20, average gas pressure, average vapour pressure, average relative humidity, average
2 minutes wind speed and maximum wind velocity.
Wherein, maximum wind velocity in correlation analysis the results show that its correlation between PM2.5 concentration is not high, it is theoretical
On should be removed, but from geographical detector analyze result from the point of view of, wind speed and other impact factor interaction after, it is right
PM2.5 impact effect significantly increases, it is contemplated that synergistic effect remains maximum wind velocity.
S7, principal component analysis is carried out to above-mentioned whole Key Influential Factors, obtains its sequence knot influenced on PM2.5 concentration
Fruit is as follows:
4 principal component analysis result of table
| Key Influential Factors | VIF | Principal component component | VIF |
| Traffic | 3.284218 | Y1 | 1.008 |
| Industry | 1.331097 | Y2 | 1.007 |
| Greenery patches | 3.288399 | Y3 | 1.002 |
| Arable land | 3.872414 | Y4 | 1.001 |
| Building site | 4.366028 | Y5 | 1.001 |
| Waters | 1.484241 | Y6 | 1.001 |
| Population | 2.478188 | Y7 | 1.001 |
| Temperature on average (DEG C) | 22.562325 | Y8 | 1.001 |
| Precipitation (millimeter) when 20-20 | 13.903188 | Y9 | 1.001 |
| Average gas pressure (hundred pas) | 10.402263 | Y10 | 1.001 |
| Average vapour pressure (hundred pas) | 16.34602 | Y11 | 1.001 |
| Average relative humidity (percentage) | 7.234171 | Y12 | 1.003 |
| Average 2 minutes wind speed (meter per second) | 36.898927 | Y13 | 1.007 |
| Maximum wind velocity (meter per second) | 12.158731 | Y14 | 1.011 |
Wherein, synteny of the VIF between each impact factor.
A specific embodiment of the invention is described above, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
Claims (8)
1. a kind of based on the geographical Key Influential Factors screening technique detected with PCA to PM2.5 concentration distribution, which is characterized in that
Include the following steps:
PM2.5 monitoring station position in S1, acquisition area to be studied, establishes several buffer areas；
S2, the impact factor data in area to be studied are obtained and make sliding-model control；
S3, by the impact factor data after sliding-model control, it is corresponding to PM2.5 monitoring station, obtain each PM2.5 monitoring station
Impact factor data；
S4, Contribution Analysis and relevance point are carried out using geographical detector model to the impact factor data after sliding-model control
Analysis；
S5, will affect the factor and be divided into several relevant groups, and successively to the impact factor of the identical relevant group in each buffer area into
Row correlation analysis；
S6, by geographical detector model analysis and correlation analysis as a result, screened to impact factor, obtain whole pass
Key impact factor；
S7, by principal component analytical method, principal component transform is carried out to whole Key Influential Factors, obtain target critical influence because
Son.
2. according to claim 1 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that the buffer area is that several centered on the PM2.5 monitoring station position have and are sized
Region.
3. according to claim 2 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that the impact factor data include the meteorological datas such as temperature, precipitation, relative humidity, wind speed, air pressure, green
The land uses distributed data such as ground, arable land, building site, waters, density data of population, industrial pollution source, traffic related data.
4. according to claim 3 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that the method that the impact factor data in survey region make sliding-model control is to be studied to obtaining
The meteorological data in impact factor data, density data of population, traffic related data in region are reclassified, and by this
Three classes data are converted into type amount from numerical quantities.
5. according to claim 4 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that the method for the association analysis are as follows:
Meet condition formula as a result, filtering out according to the Contribution Analysis of geographical detector model:
q(X1∩X2)>q(X1)or q(X2)
Impact factor X1、X2, wherein q is the contribution angle value obtained by geographical detector model.
6. according to claim 5 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that it is described will affect the factor be divided into several relevant groups method be according to the representative object of impact factor will
Impact factor is divided into several groupings including meteorological data, demographic data, traffic related data.
7. according to claim 6 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that the method for the correlation analysis is group will to be pressed in each buffer area successively by the influence in same group
The factor and PM2.5 concentration value carry out Pearson correlation analysis, obtain each impact factor in each buffer area and PM2.5 is dense
The relative coefficient and significance of angle value.
8. according to claim 7 based on the geographical Key Influential Factors screening side detected with PCA to PM2.5 concentration distribution
Method, which is characterized in that described that impact factor is screened, the method for obtaining whole Key Influential Factors are as follows:
S1, according to correlation analysis as a result, filtering out the impact factor of significant relevant setting quantity, as a whole crucial effect
The factor；
S2, it is sorted as a result, will affect the factor by relative coefficient according to correlation analysis, filters out that relative coefficient is highest to be set
The impact factor of fixed number amount；
S3, by association analysis result that geographical detector model analysis obtains and the highest influence of relative coefficient is screened
Factor pair ratio filters out while meeting relative coefficient and the impact factor with association analysis, also crucial effect as a whole
The factor.
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| CN112183962A (en) * | 2020-09-11 | 2021-01-05 | 中国地质大学（武汉） | Basin water pollution risk factor analysis method based on geographic detector |
| CN113836473A (en) * | 2021-08-06 | 2021-12-24 | 中国地质大学（武汉） | Lake eutrophication quantitative analysis method based on geographic detector and storage medium |
| CN114155717A (en) * | 2022-02-10 | 2022-03-08 | 西南交通大学 | Traffic flow data screening method, device, equipment and readable storage medium |
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