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Noname manuscript No.(will be inserted by the editor)

Analyzing the Influence ofPrecipitation and Temperature onAir Quality by GTWR Approach

Haiyan Xuan Qi Li Anqi Zhang YoumingGuo

Received: date / Accepted: date

Abstract The paper studies the influence of precipitation

and temperature on air quality by geographically and tempo-

rally weighted regression (GTWR) approach, examines the

relations between the indices and the given climatic condi-

tions in Chinese 67 cities. Results from the case study indi-

cate satisfactory performance of the GTWR model in han-

dling the relations among precipitation, temperature and air

quality. It is found that when the level of the monthly total

precipitation and the average monthly temperature are con-

trolled at one station, the average monthly air quality indexvaries with spatio-temporal position obviously, generally the

highest in North China, followed by the southeast coast, the

lowest in the southern region of China. In the case of the

average monthly temperature remaining unchanged, the av-

erage monthly air quality index varies with monthly mean

temperature in each city. When the monthly total precipita-

tion increased by one unit, the cities in North China, the east-

ern coastal cities ,the northeast cities and Urumqi city have

H. Xuan Q. LiA. ZhangCollege of Science,

Lanzhou University of Technology,

Lanzhou, 730050, China

H. Xuan

E-mail: [email protected]

Q. Li


A. Zhang


Y. Guo (Corresponding author)

College of Science,

Guilin University of Technology,

Guilin, 541004, China


the maximum rate of change. And if the monthly total pre-

cipitation keeps unchanged, with the increase in the monthly

average temperature , the monthly average air quality index

of most cities in Anhui province, Zhejiang province, Jiang-

su province et al. decreases the most. The monthly aver-age temperature increases by 1 degree Celsius, the average

monthly air quality index decreases between 0.152484 and

0.228629.

Keywords Air quality GTWR Modeling Significant

1 Introduction

Urbanization in China accompanies industrialization and m-

odernization and improves societal development with bene-

fits to the population. But at the same time, it puts substan-tial pressure on public facilities and natural resources. Cities

consume natural resources and produce a large quantity of

wastes to be digested within and outside the cities that re-

sults in large-scale environmental problems. Along with the

unprecedented high-speed economic growth, many cities in

China suffer from various environmental problems, such as

air-quality degradation, water pollution and water shortage,

excess solid waste, resource depletion, and so on. Urban air

pollution is one of the major environmental issues (Hao et

al. 2005).

Meteorological experts and medical experts believe that

air pollution is mainly harmful to the respiratory system and

cardiovascular system, and can lead to chronic bronchitis,

cardiac arrhythmia, nonfatal heart attack, lung and heart dis-

ease patients premature death. Studies have found that the

increased mortality of heart and lung diseases patients is as-

sociated with air pollution. If the concentration of PM2.5

is higher than 10g m3 in the long term, it will result inthe death risk of heart and lung diseases patients increased

(Pope et al. 2002; Santosa et al. 2008).

As we enter an era of rapid climate change, the impli-

cations for air quality need to be better understood, both for

the purpose of air quality management and as one of the

societal consequences of climate change. We review here

current knowledge of this issue. Air pollution continues to

pose a significant threat to health worldwide. The associa-

tion between ambient pollutant concentrations and excesses

in mortality and morbidity has been the subject of a num-

ber of studies conducted around the world ( Sichletidis et al.

2005; Grigoropoulos et al. 2008; Grigoropoulos et al. 2009;

Nastos et al. 2010). An Air Quality Index (AQI) is a useful

tool to characterize pollution levels in an area and to inform

the citizens about the levels of pollution in an adequate and

understandable way and also a tool to be used by the rele-

vant authorities to take a series of predetermined measures

to protect the health of the population.


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2 Haiyan Xuan et al.

A number of air quality indices are in use throughout

the world. One of the most commonly used is the AQI intro-

duced by the US Environmental Protection Agency (EPA)

which is calculated for five major air regulated pollutants:

ground-level ozone, particle pollution (also known as par-ticulate matter), carbon monoxide, sulfur dioxide, and nitro-

gen dioxide (EPA 2009). In Europe, different countries have

introduced national AQIs (Belgium, UK, France (ATMO in-

dex), and Germany). Kassomenos et al. (1999) proposed air

quality indices based on the health effects and the subse-

quent European Union (EU) directives for air quality. Mure-

na (2004) has developed and implemented an Air Pollution

Index in Naples, Italy. Kyrkilis et al. (2007) developed an

aggregate Air Quality Index for Athens, Greece. The Com-

mon Air Quality Index (CAQI) was developed in the course

of the CITEAIR project and has been operational on the web

since 2006. The index was made for the purpose of compar-ing the air quality in European cities in real time (van den

Elshout and Leger 2008).

In China, there have been previous studies analyzing and

evaluating the factors (Zhang et al. 2014; Zhou et al. 2014)

what affect air quality and distribution characteristics of Chi-

nese cities air pollution index in an urban environment (Xi-

ang et al. 2009; Ren et al. 2013). However, these studies ei-

ther focused mainly on the large cities, such as Beijing city,

Lanzhou city and so on, or just analysed through text cap-

tions. Also, almost previous studies in China using AQI in

order to understand the air pollution refer mainly to text cap-

tions (Li et al. 2012). While existing published scientific re-sults for Chinese urban cities using mathematical statistical

methods are very limited (Wang et al. 2011; Li 2013). The

resulting improvements in air quality may be modulated by

changes in climate. The existing published scientific results

for Chinese urban air pollution show that changes in climate

affect air quality by perturbing ventilation rates (wind speed,

precipitation, temperature, convection), precipitation scav-

enging, dry deposition, chemical production and loss rates,

natural emissions, and background concentrations. The po-

tential importance of this effect can be appreciated by con-

sidering the observed monthly changes in air quality. Whats

more, analyzing the influence of precipitation and tempera-

ture on air quality by GTWR approach has no one involved.

The main objective of the present study is to analyzing

the influence of precipitation and temperature on air qual-

ity by GTWR approach, to examine the relations between

the indices and the given climatic conditions in the study

area. In Section 2, GTWR model and its estimation method

are given. In Section 3, the methodologies applied and the

observational data used are presented. In the following sec-

tion, Simulation and analysis results about the influence of

precipitation and temperature on air quality by GTWR ap-

proach are shown. Finally, Section 5 summarizes the con-

clusions of the study.

2 GTWR model

With embedding the spatial and temporal characteristics of

the data into the regression model, Huang et al. (2010) pro-

posed geographically and temporally weighted regressionmodel of the form

yi=0(ui,i,ti) +p

j=1

j(ui,i, ti)xi j+ i, i=1, 2, ,n.

(1)where(yi;xi1,xi2, ,xip) are observations of the responsevariableYand explanatory variables X1,X2, ,Xp at loca-tion (ui,i,ti) in the studied region, i(i= 1, 2, , n) areerror terms with mean zero and common variance 2, and

j(ui,i,ti)(j= 0, 1, 2, , p) are p+ 1 unknown functionsof geographical locations and observation times.

Geographically and temporally weighted regression mod-el is the promotion of geographically weighted regression

(GWR) model. Geographically and temporally weighted re-

gression model assumes that the regression coefficients are

the functions of geographical locations and observation times

in varying-coefficent model. Spatial and temporal character-

istics of data are involved in geographically and temporally

weighted regression model, which set the stage for explor-

ing the spatial nonstationarity and temporal nonstationari-

ty of the regression relation. Huang et al. (2010) gave the

fitting method of geographically and temporally weighted

regression model, provided the related select principle of

weight function and cross-validation for fixing bandwidthparameter. In order to test its improved performance, GTWR

was compared with global ordinary least squares, temporal-

ly weighted regression (TWR) model and GWR in terms of

goodness-of-fit and other statistical measures by a case s-

tudy of residential housing sales. The results showed that

there were substantial benefits in modeling both spatial and

temporal nonstationarity simultaneously (Huang et al. 2010).

2.1 Local Linear Estimation

In order to express conveniently, geographically and tem-porally weighted regression model in (1) is rewritten in the

following form:

yi=p

j=0

j(ui,vi, ti)xi j+ i, i=1, 2, , n (2)

Just assume thatxi0 1, can make the model (2) containspatial and temporal variability intercept term.(ui,vi, ti) isany space-time coordinate point in ellipsoidal coordinates.

Set each regression coefficient functionj(u,v, t)(j=0, 1,

, p) in the model (2) regarding the space position coor-dinatesu, v and time coordinates t have continuous partial

derivations. (u0,v0,t0)is any given point in the study area.


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Analyzing the Influence of Precipitation and Temperature on Air Quality by GTWR Approach 3

For each j= 0, 1, ,p, by the Taylor formula, in theneighborhood of(u0,v0,t0), we have

j(u,v,t)j(u0,v0, t0) +(u)j (u0,v0, t0)(uu0)

+(v)j (u0,v0,t0)(vv0) +(t)j (u0,v0,t0)(t t0).

where(u)j (u0,v0,t0),

(v)j (u0,v0, t0) and

(t)j (u0,v0,t0) re-

spectively represent partial derivatives ofj(u,v,t)aboutu,

vand tin(u0,v0,t0). According to local linear fitting method

in varying coefficient model, minimization of the following

formula,

n

i=1

{yip

j=0

j(u0,v0,t0) +

(u)j (u0,v0,t0)(uiu0)

+(v)j (u0,v0,t0)(viv0) +

(t)j (u0,v0,t0)(ti t0)

xi j}

2

i(u0,

0,

t0).

(3)set

W(u0,0, t0) =diag[1(u0,0,t0),2(u0,0,t0),

,n(u0,0,t0)],

Y= (y1,y2, ,yn)T

,

X1(u0,v0,t0) =

x10 x1p x10(u1u0) x1p(u1u0)

x20 x2p x20(u2u0) x2p(u2u0)

.... . .

......

. . ....

xn0 xnp xn0(unu0) xnp(unu0)

,

X2(u0,v0, t0) =x10(v1v0) x1p(v1v0) x10(t1t0) x1p(t1t0)x20(v2v0) x2p(v2v0) x20(t2t0) x2p(t2t0)

.... . .

......

. . ....

xn0(vnv0) xnp (vnv0) xn0(tnt0) xnp (tnt0)

,

then

X(u0,v0, t0) = [X1(u0,v0,t0),X2(u0,v0,t0)] .

P(u0,v0,t0) =0(u0,v0, t0), , p(u0,v0, t0),

(u)0 (u0,v0,t0), ,

(u)p (u0,v0, t0),

(v)0 (u0,v0, t0), ,

(v)p (u0,v0, t0),

(t)

0 (u0,

v0,

t0),

,

(t)

p (u0,

v0,

t0)T.

so the solution of above least squares problem can be ex-

pressed by matrix as

P(u0,v0,t0) =

0(u0,v0, t0), , p(u0,v0,t0),

0(u)

(u0,v0,t0), , p(u)

(u0,v0,t0),

0(v)

(u0,v0,t0), , p(v)

(u0,v0, t0),

0(t)

(u0,v0,t0), , p(t)

(u0,v0,t0)T

=XT(u0,v0, t0)W(u0,v0, t0)X(u0,v0,t0)

1XT(u

0,v

0,t

0)W(u

0,v

0,t

0)Y.

(4)

where set

(u0,v0,t0) =

0(u0,v0, t0), 1(u0,v0, t0), ,

p(u0,v0,t0)T

,

(5)

(u)(u0,v0,t0) =

0(u)

(u0,v0,t0), 1(u)

(u0,v0,t0), ,

p(u)

(u0,v0, t0)T

,

(6)

(v)(u0,v0,t0) =

0(v)

(u0,v0, t0), 1(v)

(u0,v0, t0), ,

p(v)

(u0,v0, t0)T

,

(7)

(t)(u0,v0, t0) =

0(t)

(u0,v0,t0), 1(t)

(u0,v0,t0), ,

p(t)

(u0,v0,t0)T

.

(8)

(5) is the column vector composed by the estimated val-

ues of each regression coefficient functionj(u,v,t)(j = 0, 1,

, p) in (u0,v0, t0) and (6-8) respectively are the colum-n vectors composed by the estimated values of the partial

derivatives aboutu,v andt. From (4), we can obtain

(u0,v0, t0) = (Ip+1, 0p+1, 0p+1, 0p+1)XT(u0,v0, t0)

W(u0,v0,t0)X(u0,v0, t0)1

XT(u0,v0,t0)W(u0,v0,t0)Y(9)

(u)(u0,v0, t0) = (0p+1,Ip+1, 0p+1, 0p+1)XT(u0,v0,t0)

W(u0,v0,t0)X(u0,v0,t0)1

XT(u0,v0,t0)W(u0,v0,t0)Y(10)

(v)(u0,v0,t0) = (0p+1, 0p+1,Ip+1, 0p+1)XT(u0,v0,t0)

W(u0,v0, t0)X(u0,v0,t0)1

XT(u0,v0, t0)W(u0,v0, t0)Y(11)

(t)(u0,v0, t0) = (0p+1, 0p+1, 0p+1,Ip+1)XT(u0,v0,t0)

W(u0,v0, t0)X(u0,v0,t0)1

XT(u0,v0,t0)W(u0,v0,t0)Y(12)

whereIp+1 and 0p+1 respectively represent the p+ 1 order

unit matrix and p+ 1 order zero matrix. The above methodis called local linear estimation of geographically and tem-

porally weighted regression model. This estimation methodcan not only get the estimated values of the coefficient func-

tions but obtain the estimated values of the coefficient func-

tions partial derivatives aboutu,v andt.

Respectively let (u0,v0,t0)=(ui,vi, ti)(i= 1, 2, , n), itis easy to get an estimation of the coefficient function at each

observation position by (9).

(ui,vi,ti) = ( 0(ui,vi,ti), 1(ui,vi,ti), , p(ui,vi, ti))T

= (Ip+1, 0p+1, 0p+1, 0p+1)XT(ui,vi,ti)

W(ui,vi, ti)X(ui,vi,ti)1

XT(ui,v

i,ti)W(u

i,v

i,ti)Y, i=1, 2, ,n.

(13)


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Thus, the fitted values of dependent variables in (ui,vi,ti)are

Yi=p

j=0

j(ui,vi, ti)Xi j=Xi j(ui,vi,ti)

= (Xi, 0p+1, 0p+1, 0p+1)XT(ui,vi, ti)W(ui,vi, ti)

X(ui,vi, ti)1

XT(ui,vi,ti)W(ui,vi, ti)Y, i=1, 2, ,n.(14)

where Xi = (1,xi1, ,xip)T are the column vectors com-

posed by X0i and the ith group observations ofX1, ,Xp.Thereby, the fitting values of the dependent variablesY at

the observation positions are

Y= (Y1,Y2, , Yn)T =LY. (15)

set

Ai= [XT(ui,vi, ti)W(ui,vi,ti)X(ui,vi,ti)]1, i=1, 2, , n.

where

L=

(XT1 ,0p+1,0p+1,0p+1)A1XT(u1,v1,t1)W(u1,v1,t1)

(XT2 ,0p+1,0p+1,0p+1)A2XT(u2,v2,t2)W(u2,v2,t2)

...(XTn ,0p+1,0p+1,0p+1)AnX

T(un,vn,tn)W(un,vn,tn)

.

By the (10) shows that the local linear estimation is lin-

ear estimation asL smooth matrix. Further the residual vec-

tor of local linear estimation is

= ( 1,2, , n)T = Y Y= (IL)Y, (16)

Residual sum of squares are

RSS= T= YT(IL)T(IL)Y.

Then we can obtain the estimations of error variances

Var(i) =2 as followed

2 = T

tr((IL)T(IL))

=YT(IL)T(IL)Y

tr((IL)T(IL)) .

(17)

2.2 Choosing an appropriate bandwidth

In the process of calibrating a GTWR model, the weighting

model should first be decided. This can be done by cross-

validation (Fan et al. 1996; Zhang et al. 2000). Suppose that

the predicted value ofyi from GTWR is denoted as a func-

tion of h by y(i)(h), the sum of the squared error may then

be written as

CV(h1,h2) =n

i=1

yiy(i)(h1,h2)

2(18)

Whileh1= h2

andh2= h2

respectively are space and

time bandwidth parameters.

3 Data and Methods

3.1 Data and pre-analysis

There are lots of factors influence on quality of air, such asprecipitation, sunshine, temperature, atmospheric pressure,

wind force, relative humidity and pollutant emission. Gen-

erally, when there has not much change in pollutant emission

the air quality index will be reduced with increment of tem-

perature, atmospheric pressure, wind force, relative humidi-

ty. In this chapter, we only consider the relationship among

monthly total precipitation, mean monthly temperature and

mean monthly air quality index.

We choose the 67 key environmental protection cities

in China as the site and collect a total of 12 months of the

relevant data which include the average monthly air qual-

ity index, monthly total precipitation(mm), mean monthlytemperature(C), latitude and longitude. The data of average

monthly air quality index get from China National Environ-

mental Monitoring Centre, the data of monthly total precipi-

tation and mean monthly temperature get from China Mete-

orological Data Sharing Service System. We use Google to

find the each citys latitude and longitude which indicate the

location of cities.

Fig. 1 The average monthly air quality index of 67 cities in January

Jan. Feb. Mar. Apr. May June July Aug.Sept. Oct. Nov.Dec.0

2

4

6

8

10

12

14

16

18

Month

AirQualityIndexofBeijing

Fig. 2 The average monthly air quality index of Beijing city


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As shown in Fig. 1, in January the cities which located in

the southeast coast and most of southern cities in China have

a lower average monthly air quality index, the air quality is

good in these cities. The average monthly air quality index

of the city which located in east-cental and northern China ishigher, the air quality is very worrying. Fig. 2 shown that in

January in Beijing, compared to other months, the air quality

is much worse .

Fig. 3 The monthly total precipitation of 67 cities in February


50

100

150

200

250

300

350

Month

MonthlyTotalPrecipitationofHa

ngzhou

Fig. 4 The monthly total precipitation of Hangzhou city

Fig. 5 The average monthly temperature of 67 cities in February


10

5

0

5

10

15

20

25

Month

MonthlyAverageTempera

tureofLanzhou

Fig. 6 The average monthly temperature of Lanzhou city

As shown in Fig. 3, the monthly total precipitations in

most of cities of southern China are larger, but the monthly

total precipitation of inland cities is less. Fig. 4 indicates that

the rainfall of Hangzhou city is relatively more in June and

August, and less in the other months. We can find the aver-

age monthly temperature of 67 key environmental protection

cities in China increases from north to south and inland to

coastal in Fig. 5. Fig. 6 is the average monthly temperature

of Lanzhou city in each month.

3.2 Modeling

LetYrepresent the average monthly air quality index (AQI),

X1 represent the sum of monthly precipitation (mm) andX2represent the average monthly temperature( C). Make an

geographically and temporally weighted regression model

with the observational data for 12 months of 67 cities ,mod-

el as follows

Yi=0(ui,vi,ti) +1(ui,vi,ti)Xi1+2(ui,vi,ti)Xi2+ i,

i=1, 2, , 67.(19)

Where 0(ui,vi,ti) is the basis of the average monthly air

quality index, 1(ui,vi,ti) represents the average monthlyrate of the air quality index with the sum of monthly precipi-

tation, and2(ui,

vi,

ti)indicates the average monthly rate ofthe air quality index with the monthly average temperature.

4 Simulation and analysis

According to the significance test method which was pro-

posed in the literatures Clevelandet al. (1988), Mei et al.

(2012) and Xuan et al. (2013), we construct the test statis-

tic, and we respectively obtain the global non-stationary test

p-value and the significance test p-value of changes in each

coefficient function in model 19 through the third moment

2 approximation. As shown in Table 1.p is the global non-

stationary test p-value of regression model. p0, p1 and p2,


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Table 1 bandwidths and p values with the local linear estimation

hS hT p

67.73km 0.6801 1.31E-307

p0 p1 p2

0.0000958 0.0002781 0.000233

respectively, are the significance test p-values of the regres-

sion coefficient functions 0, 1 and 2 which reflect the

significant changes of the regression coefficients by using

the local linear estimation method.

The kernel function of this section is Guass kernel func-

tion and the bandwidths are all determined by cross-validation

method in section 2.2. The results of significance test in Ta-

ble 1 show that the global non-stationary test p-value of re-

gression model and the significance test p-values of the re-

gression coefficient functions are all very small (approxi-

mately 0). It shows that there are obviously significant d-

ifference among the monthly average air quality index and

the sum of monthly precipitation and the monthly average

temperature in the 67 key environmental protection cities in

2013. In other words, it has significant influence of the sum

of monthly precipitation and monthly average temperature

on the monthly average air quality index.

According to the spatio-temporal data set, based on the

results calculated by SAS software, we use Surfer software

to draw the distribution maps of0,1and 2of 67 cities in

February. They are shown in Fig. 7-9.

Fig. 7 The0 distribution of 67 cities in February

Fig. 7 is the distribution map of0of 67 cities in Febru-

ary. As can be seen the range of the value of each city is

between 3.535 and 9.972. And the values of city are (6.458-

9.972), mostly in Northern regions, In particular, the most

concentrated area are in Hebei province, there are five of 11

points in Hebei province. As shown in Fig. 7, we can also

see that the value of0

in the city is less than 6 is mainly

in southern China, southeast coastal areas, These cities gen-

erally are sufficient rainfall, higher average temperatures or

forest cover large cities. Thus, the distribution of benchmark

average monthly air index 0is the lowest of the south coast,

the southeast coast and southwest border, extending inland

gradually increased, north China achieving the highest val-ue.

Fig. 8 The1distribution of 67 cities in February

Fig. 9 The2distribution of 67 cities in February

Fig. 8 shows that 1 of the 67 cities in February are

mostly negative, only six cities (Nanning city, Hohhot city,

Lanzhou city et al.) are positive. This because of the six c-

ities have the high forest coverage (such as Nanning) or wind

has greater impact on the city (such as Lanzhou). While the

chapter does not consider the impact of these factors on the

air quality index. It leads February precipitation to have an

anomalous influence on average of these six cities air qual-

ity index, and the situation that k value is positive appears.

It is easy to see that the precipitation has the largest impact

on Northeast China cities, North China cities and cities of

Jiangsu province and Zhejiang province and has less impact

on the Yangtze River cities and most of the southern cities.

This is mainly because industrial is relatively developed,

precipitation is relatively small and uneven in North China

and the Northeast cities, while industrial is developed, rain-


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fall is more abundant but more obvious seasonal variation in

Jiangsu province and Zhejiang province cities, therefore in-

fluence by precipitation is large. Cities in the Yangtze River

and most of the southern cities are development in the third

industries or high-tech industries, better air quality and ad-equate monthly rainfall, therefore influence by precipitation

is smaller.

Fig. 9 is the distribution map of2 of Chinese 67 cities

in February, the cities in which the monthly average tem-

perature has the largest impact on monthly air quality index

are in the eastern region of China, the Yangtze River Re-

gion and Tibet and Urumqi regions (temperature changes

are more obvious in these cities), followed by the eastern

coast and the southeast coastal areas. That the cities in the

southern area and Sichuan province in which the monthly

average temperature has the less impact on average monthly

air quality index is mainly because that the area temperaturechanges are not very obvious. North China and Northeast

Chinas cities are greatly influenced by rainfall and the aver-

age monthly temperature of these cities has little impact on

average monthly air quality index (greater than -0.079) but

the index value remains negative. While the values of2 in

the Lanzhou, Hohhot and other four cities are positive, indi-

cating that in these four cities monthly average humidity is

not the major factor what affectes air quality index.

Conjunction with Fig. 8, we find that the impact of the

monthly total precipitations and the average monthly tem-

peratures for these four cities are small, and these two are

not the main factors. Wind and other factors may be the mainfactors that we have not considered.

5 Conclusions

As most previous studies have demonstrated that precipita-

tion and temperature are significant factors on air quality of

city. This study took a nonstationary approach for analyzing

the influence of precipitation and temperature on air qual-

ity. Our analysis reveals that spatio-temporal heterogeneity

prevails in the real air quality data that evolve over both time

and space in the sample area. GTWR approach can deal with

both spatial and temporal heteroscedasticity simultaneously.

GTWR achieved good results in handling the the relations

among precipitation, temperature and air quality. For this s-

tudy, we find that when we control the level of the monthly

total precipitation and the average monthly temperature at

one station, the average monthly air quality index varies ob-

viously with spatio-temporal position, generally the highest

in North China, followed by the southeast coast, the low-

est in the southern region in China. The average monthly

air quality index is the highest in North China. The ten c-

ities in which air quality is worst in China, among which

there are 7 in the Hebei province, which is consistent with

our simulation results. In the case of the average month-

ly temperature remaining unchanged, the average monthly

air quality index varies with monthly mean temperature in

each city. When the monthly total precipitation increased by

one unit, the cities in North China, the eastern coastal c-ities ,the northeast cities and Urumqi city have the maximum

rate of change. And if the monthly total precipitation keeps

unchanged, with the increase in the monthly average tem-

perature , the monthly average air quality index of most c-

ities in Anhui province, Zhejiang province, Jiangsu province

et al. decreases the most. The monthly average tempera-

ture increases by 1 degree Celsius, the average monthly air

quality index decreases 0.152484-0.228629. Thus we know

that the simulation results of using the actual data set is ba-

sically consistent with the results we get in real life and

pre-analysis. Thus, geographically and temporally weight-

ed regression model can help us very well with simulationand analysis of the distribution characteristics of Chinese air

quality index. Geographically and temporally weighted re-

gression can provide an additional useful methodology for

analyzing the influence of meteorological factors (precipi-

tation, temperature, wind power et al.) on air quality. Geo-

graphically and temporally weighted regression in the prac-

tical application of our research is a very important tool and

has practical significance.

Acknowledgement

The authors wish to thank the referees for their many valu-

able suggestions. This work was supported by National Nat-

ural Science Foundation of China (11261031).

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