xu2013

Environmental Pollution 178 (2013) 102e114
Contents lists available at SciVerse ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
Correlation analysis of the urban heat island effect and the spatial and
temporal distribution of atmospheric particulates using TM images in
Beijing
L.Y. Xu a, *, X.D. Xie a, S. Li b
a
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai
Street, Haidian District, Beijing 100875, PR China
Environmental Information Center, Ministry of Environmental Protection, No. 1 Yuhuinanlu, Chaoyang District, Beijing 100029, PR China
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 8 January 2013
Received in revised form
28 February 2013
Accepted 1 March 2013
This study combines the methods of observation statistics and remote sensing retrieval, using remote
sensing information including the urban heat island (UHI) intensity index, the normalized difference
vegetation index (NDVI), the normalized difference water index (NDWI), and the difference vegetation
index (DVI) to analyze the correlation between the urban heat island effect and the spatial and temporal
concentration distributions of atmospheric particulates in Beijing. The analysis establishes (1) a direct
correlation between UHI and DVI; (2) an indirect correlation among UHI, NDWI and DVI; and (3) an
indirect correlation among UHI, NDVI, and DVI. The results proved the existence of three correlation
types with regional and seasonal effects and revealed an interesting correlation between UHI and DVI,
that is, if UHI is below 0.1, then DVI increases with the increase in UHI, and vice versa. Also, DVI changes
more with UHI in the two middle zones of Beijing.
Ó 2013 Elsevier Ltd. All rights reserved.
Keywords:
Urban heat island
Atmospheric particulates
NDWI
NDVI
Beijing
1. Introduction
The constant increase in human activities and their influence on
the climate, the ecological environment and human health resulting from the increase in atmospheric particulates has drawn a
growing amount of attention. The atmospheric particulate pollution in Beijing has recently received intense international attention.
Meanwhile, the urban island heat effect is considered an important
issue for urban climatology and urban environmental science, with
great effects on urban ecological environments and atmospheric
environments. At present, some previous studies have demonstrated a significant correlation between the urban heat island effect and the concentration distribution of atmospheric particulates
(Li et al., 2007; Liu et al., 2009; Zhou et al., 2008; Jin et al., 2011;
Pandey et al., 2012). However, these studies have not clearly
explained this relationship. Therefore, further research is needed to
fill this gap, and it intends to study the Beijing area to provide useful
suggestions for the prevention and control of atmospheric particulate pollution.
According to the newly research results about atmospheric
particulates and urban heat island effect (Taha, 1997; Sarrat et al.,
* Corresponding author.
E-mail addresses: linyuxy@163.com, xly@bnu.edu.cn (L.Y. Xu).
0269-7491/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.envpol.2013.03.006
2006; Woods et al., 2008), it can be found that the relationship
between the urban heat island effect and the concentration distribution of atmospheric particulates can be classified into three
main types: (1) the direct correlation affected by thermal circulation; (2) the indirect correlation affected by the meteorological
condition (including local wind patterns and humidity); (3) the
indirect correlation affected by the land surface condition
(including the vegetation coverage). In addition, as local wind
patterns are so complex in the urban areas that their effect may be
changeable and uncertainly (Landsberg, 1981; Malek et al., 2006),
this study didn’t make quantitative analysis of this correlation type.
With the development of remote sensing technology, a growing
number of scholars have begun to apply thermal infrared remote
sensing data for urban climate research. Based on research methods
at different stages of development, methods to study the urban heat
island effect or the spatial and temporal distribution of atmospheric
particulates can be classified into two main types (Rizwan et al.,
2008; Mirzaei and Haghighat, 2010): (1) the method of observing
statistics collected from stable or mobile monitoring stations (Zhou
et al., 2008), leading to a temporally continuous but spatially
discontinuous dataset; and (2) the method of remote sensing
retrieval with thermal infrared remote sensing data and some
monitoring data (Li et al., 2007; Pandey et al., 2012), which produces
a spatially continuous but temporally discontinuous dataset. For the
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
method of observation statistics, the observation time, observation
methods, environmental temperature, and even the air temperature at the position where the probe is installed could lead to uncertainty in the observation results and then influence the accuracy
of the final results (Mirzaei and Haghighat, 2010). The accuracy of
the remote sensing retrieval method is limited by the number of
monitoring spots (Li et al., 2007; Rajasekar and Weng, 2009).
Therefore, this study obtains TM images in different seasons of
the same year and in the same season of different years to address
the temporal discontinuity of the remote sensing retrieval method,
and adopts remote sensing information to replace monitoring data
to address the insufficient number of monitoring positions. Remote
sensing information was obtained to reveal this relationship between urban heat islands and the concentration distribution of
atmospheric particulates, including the urban heat island (UHI)
103
Table 1
Classification of the urban heat island intensity.
Relative brightness temperature UHI intensity levels
<0
0e0.1
0.1e0.2
0.2e0.4
>0.4
Green island
Weak heat island
Medium heat island
Strong heat island
Extremely strong heat island
intensity index, NDVI (normalized difference vegetation index),
NDWI (normalized difference water index) and DVI (difference
vegetation index). Then, correlation analyses among the UHI intensity index, NDWI, NDVI, and DVI are used to establish a correlation model to further understand this relationship.
Fig. 1. Distribution of the brightness temperature in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
104
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
Fig. 2. Distribution of the UHI intensity index in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
2. Methodology
2.1. Remote sensing data
As it is difficult to obtain the clear TM images of all four seasons in one year, and
there is the heating period in Beijing which affects the spatial and temporal distribution of atmospheric particulates seriously, this study divided the whole year into
two seasons, that is, the non-heating season (from April to October) and the heating
season (from November to March).
TM images were obtained from the remote sensing data sharing website (http://
ids.ceode.ac.cn/) maintained by the earth observation and Digital Earth Science
Center of the Chinese Academy of Sciences. In this study, TM images from May 28,
2007, May 30, 2008, and March 14 and September 22, 2009 were selected to
represent the changes between different seasons of the same year and in the same
season between different years.
2.2. Information extraction methods
Recent research results have shown that the relationship between temperature
and plant growth is much closer than that between precipitation and plant growth
(Woods et al., 2008), and that the correlation between the surface temperature and
NDVI is clearly negative in urban areas (Lo et al., 1997; Wilson et al., 2003). Meanwhile, vegetation has a purifying action with respect to atmospheric particulates.
Similarly, the surface temperature has a significant negative relationship with NDWI
as in the study in the Pearl River delta region (Chen et al., 2006). Meanwhile, after
local wind patterns, the humidity is the main factor affecting the concentration
distribution of atmospheric particulates (Chan et al., 1997). Therefore, NDVI and
NDWI can be used to respectively indicate the effect of the vegetation coverage and
the humidity on the indirect correlation between the urban heat island effect and
the concentration distribution of atmospheric particulates.
Also, the concentration of atmospheric particulates exhibits a significant negative relationship with DVI. Meanwhile, the spatial and temporal distribution of the
inversion results and real observation data tend to be basically the same (Zhou et al.,
2009; Tang et al., 2011). Therefore, the distribution of DVI can be used to indicate the
concentration distribution of atmospheric particulates.
According to the remote sensing retrieval, this study extracts remote sensing
information, including UHI intensity index, NDWI, NDVI, and DVI, in different seasons of the same year and in the same season of different years. And then it divide
the study area into 4 annular zones based on the degree of urban development, and
then to analyze the spatial distribution of UHI intensity index, NDWI, NDVI, and DVI
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
105
Fig. 3. Distribution of UHI intensity levels in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
and make correlation analyses to reveal the relationship between the urban heat
island effect and the concentration distribution of atmospheric particulates from
three main aspects based on the above theoretical analysis, including (1) the direct
correlation between the UHI intensity index and DVI; (2) the indirect correlation
among the UHI intensity index, NDWI and DVI; (3) the indirect correlation among
the UHI intensity index, NDVI, and DVI. Finally, a correlation model is established to
further understand this relationship.
In addition, the influence of atmospheric particulates from outside of the study area
is considered under the assumption that all atmospheric particulates in the study area
are distributed in the same way under the influence of the urban heat island effect.
According to above analysis, the key step of this study is the extraction of remote
sensing information, including the UHI intensity index, NDWI, NDVI, and DVI. These
data have been obtained using appropriate methods.
(1) UHI intensity index calculation
The surface temperature and temperature patterns in urban areas are so
different that infrared temperature cannot be used to replace the surface
temperature (Eliasson, 1996). However, many studies have established a good correlation between these temperatures (Voogt and Oke, 2003). Meanwhile, although
the brightness temperature obtained from the TM images is not equal to the surface
temperature, these values are also strongly correlated. Thus, this research uses the
brightness temperature to represent the urban heat island effect.
In this study, the mono-window algorithm was used to retrieve the surface
temperature in urban areas from Landsat TM data (Qin et al., 2001). First, the DN
value of band 6 of the Landsat TM data was converted into the radiation brightness
(Lb), calculated as:
Lb ¼ Lmin þ
L
Lmin
*DN
DNmax
max
(1)
where Lmax and Lmin represent the maximum and minimum radiation intensities
received by the Landsat 5 remote sensor. DN represents the gray value of band 6
from Landsat 5. For Landsat 5, Lmin ¼ 0.1238 mW cm2 sr1 mm1,
Lmax ¼ 1.56 mW cm2 sr1 mm1, and DNmax ¼ 255.
Then, the radiation brightness was converted to the brightness temperature (Tb),
according to the following formula:
106
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
Fig. 4. Distribution of NDVI in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
Tb ¼
K2
lnðK1 =Lb þ 1Þ
(2) NDVI calculation
(2)
where K1 and K2 are constants: K1 ¼ 60.776 mW cm2 sr1mm1, K2 ¼ 1260.56 K.
As the urban heat island effect is a relative concept, its degree indicates the
temperature difference between the downtown and the suburbs. This study introduces a relative brightness temperature to indicate the urban heat island intensity. The relative brightness temperature (TR) is the ratio of the brightness
temperature at one site (Ti) minus the average brightness temperature divided by
the average brightness temperature (Ta), which can also be called the UHI intensity
index at that site. This value can be calculated as:
TR
Ti Ta
Ta
(3)
The normalized difference vegetation index (NDVI) is one of the most widely
used vegetation indexes and has been used to indicate the state of vegetation growth
and vegetation coverage over the past two decades (Leprieur et al., 2000). NDVI is
defined as
NDVI ¼
NIR R
NIR þ R
(4)
where R and NIR represent surface reflectances averaged over the visible
(l w 0.6 mm) and near infrared (NIR) (l w 0.8 mm) regions of the spectrum,
respectively (Jiang et al., 2006).
(3) NDWI calculation
Finally, Formula 3 is used to calculate the relative brightness temperature to
represent the spatial distribution of UHI intensity. UHI intensity in the study area can
be divided into five different levels according to the relative brightness temperature
(Table 1).
Along with the development of quantitative remote sensing analytical technology, the normalized difference water index (NDWI), the normalized difference
moisture index (NDMI), and other indicators have been used to represent spatial
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
107
Fig. 5. Distribution of NDWI in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
differences in land surface water content (Gao, 1996; Owen et al., 1998). NDWI is
defined as
NDWI ¼
NIR MIR
NIR þ MIR
(5)
3. Results and discussion
where TM 4 and TM 5 represent the surface reflectances averaged over the near
infrared (NIR) (l w 0.8 mm) and middle infrared (MIR) (l w 1.65 mm) regions of the
spectrum, respectively.
(4) DVI calculation
The difference vegetation index (DVI) highlights the spectrum information on
atmospheric particulate pollution, and partially eliminates the influence of solar
elevation angle and atmospheric radiation (Yu et al., 2004). DVI is defined as
DVI ¼ NIR R
respectively (Jiang et al., 2006). For the Landsat TM data, NIR and R also represent the
gray values for the fourth band and the third band, respectively.
(6)
where R and NIR represent surface reflectances averaged over the visible
(l w 0.6 mm) and near infrared (NIR) (l w 0.8 mm) regions of the spectrum,
3.1. UHI intensity index, NDVI, NDWI and DVI in Beijing
Based on the above Formulas (1e6), TM images from May 28,
2007, May 30, 2008, and March 14 and September 22, 2009 were
used to extract the remote sensing information for the whole city of
Beijing, including the brightness temperature, UHI intensity index
(UHI intensity levels), NDVI, NDWI and DVI (Figs. 1e6).
Fig. 1aed represent the spatial distribution of the brightness
temperature at midnight on May 28, 2007, May 30, 2008, March 14
and September 22, 2009 across the whole city of Beijing. Fig. 3a
clearly reveals that temperatures over the downtown area of
108
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
Fig. 6. Distribution of DVI in Beijing on (a) March 14, 2009; (b) September 22, 2009; (c) May 30, 2008; (d) May 28, 2007.
Beijing were significantly greater than those over the suburbs of
Beijing on March 14. However, Fig. 3bed reveal that temperatures
over the downtown area of Beijing are significantly lower than
those over the suburbs of Beijing on May 28, 2007, May 30, 2008,
and September 22, 2009, demonstrating the existence of an “urban
cool island”(Pandey et al., 2012).
Then, UHI intensity levels over the whole city of Beijing were
obtained by calculating the UHI intensity index (Fig. 2) according to
Formula 3. Fig. 3a clearly shows that the highest UHI intensity is
distributed in the downtown, northwest and southeast areas of
Beijing, and that lower UHI intensities are distributed in the west,
north and northeast areas of Beijing. However, Fig. 3bed show the
opposite situation.
Fig. 4aed show the spatial distribution of NDVI values at
midnight on May 28, 2007, May 30, 2008, and March 14 and
September 22, 2009, respectively, over the whole city of Beijing.
Fig. 4aed all reveal that NDVI values are significantly lower over the
downtown area of Beijing than those over the suburbs of Beijing
except in the Miyun Reservoir, illustrating that vegetation coverage
is lower in the downtown than in the suburbs.
Fig. 5aed report the spatial distribution of NDWI values at
midnight on May 28, 2007, May 30, 2008, and March 14 and
September 22, 2009 over the whole city of Beijing. Fig. 5a and b all
reveal that the NDWI values over the downtown area of Beijing are
significantly lower than those over the suburbs of Beijing, illustrating that the land surface water content in the downtown area is
lower than that in the suburbs. Fig. 5a and b shows that the NDWI
values over the center of Beijing are significantly greater than those
around the center, which may result from the urban heat island
effect. However, in Fig. 5c and d the NDWI values are very low over
the whole city of Beijing, illustrating that land surface water content is at a relatively lower level in May.
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
109
Fig. 7. Distribution of administrative division and “sampling points” in Beijing.
Fig. 6aed show the spatial distribution of DVI values at midnight
on May 28, 2007, May 30, 2008, and March 14 and September 22,
2009 over the whole city of Beijing. Fig. 6aed all reveal that the DVI
values over the downtown of Beijing are significantly lower than
those over the suburbs of Beijing, except in the Miyun Reservoir,
illustrating that the atmospheric particulate concentration is higher
in the downtown area than in the suburbs.
3.2. Correlation analysis of UHI intensity index, NDVI, NDWI and DVI
Figs. 1e6 shows that the properties examined in this study
generally increase or decrease from the center of the city to the
suburbs. Beijing’s urbanization process is also expanding from the
center of the city to the suburbs. Thus, the study area was divided
into 4 annular zones based on the latest Beijing administrative divisions: (1) the core functional zone of the capital, including the
Dongcheng and Xicheng Districts, which is also the traditional inner city; (2) the expanding urban zone, including the Haidian,
Fengtai, Chaoyang and Shijingshan Districts, which are also identified as part of the downtown; (3) the zone of new urban development, including the Tongzhou, Daxing, Shunyi, Fangshan and
Changping Districts; and (4) the ecological conservation
development zone, including Huairou, Pinggu, and Mengtougou
Districts and Miyun and Yanqing counties.
Then, equally distributed grids were constructed in those four
annular zones through the function “Create Fishnet” of Arc GIS 9.3
to obtain 30e50 sampling points for each annular zone (Fig. 7). A
sampling point means that remote sensing information was obtained from that point and averaged with data from other points in
the same annular zone. Finally, remote sensing information,
including the UHI intensity index, NDVI, NDWI and DVI, were
extracted for the correlation analysis.
The correlation analysis has been divided into three types according to the above analysis of the effect of the urban heat island
on the concentration distribution of atmospheric particulates: (1)
UHI intensity index and DVI (DVI: UHI); (2) UHI intensity index,
NDVI and DVI (DVI: NDVI: UHI); (3) UHI intensity index, NDWI and
DVI (DVI: NDWI: UHI). The results are described below.
(1) UHI intensity index and DVI
After deleting obviously incorrect sampling points, a correlation
analysis was conducted between the UHI intensity index and DVI
based on the relevant data extracted from TM images on May 28,
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L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
Table 2
Evaluation equations between NDVI and DVI.
Zones
Time
Regression equations
Correlation types
Core functional
zone
May 2007
y [ 76.42x D 18.10(*)
y ¼ 72.47xþ17.557
y ¼ 65.20xþ16.831
y [ 70.45x D 26.52(*)
y ¼ 64.85x þ 25.468
y [ L4.885x L 2.370 (*)
y [ 82.04x D 9.189 (*)
y ¼ 76.54x þ 8.882
y ¼ 80.73x þ 9.131
y ¼ 244.5x þ 43.30
y [ 246.26x D 43.384 (*)
y ¼ 201.61x þ 39.146
y ¼ 261.5x þ 65.27
y [ 262.69x D 65.353 (*)
y ¼ 167.44x þ 51.310
y ¼ 8.521x 1.989
y [ 9.718x 2.140 (*)
y ¼ 8.343x 1.957
y ¼ 202.6x þ 22.87
y [ 212.6x D 23.19 (*)
y ¼ 149.1x þ 21.16
y ¼ 179.5x þ 40.99
y [ 182.56x D 41.177 (*)
y ¼ 100.28x þ 36.307
y ¼ 153.2x þ 55.52
y [ 155.00x D 55.588 (*)
y ¼ 75.32x þ 48.041
y ¼ 5.537x þ 2.754
y [ L6.235x D 2.851 (*)
y [ 179.1x D 29.00 (*)
y ¼ 176.8x þ 28.95
y [ 44.71x D 39.88 (*)
y ¼ 36.13x þ 40.425
y [ L31.45x D 55.05 (*)
y ¼ 30.55xþ54.882
y ¼ 2.992x þ 2.694
y [ 2.991x D 2.678 (*)
y [ 106.3x D 28.42 (*)
y ¼ 102.5x þ 28.46
y ¼ 67.39x þ 28.97
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:UHI
DVI:NDVI:UHI
DVI:NDWI:UHI
May 2008
March 2009
September
2009
Expanding
urban
functional
zone
May 2007
May 2008
March 2009
September
2009
New urban
development
zone
May 2007
May 2008
March 2009
Ecological
conservation
development
zone
September
2009
May 2007
May 2008
March 2009
September
2009
negative in all annular zones except for the expanding urban
functional zone, which means that higher UHI intensity indexes are
correlated with lower DVI values and greater atmospheric particulate concentrations.
Therefore, for measurements from the same month in 2007 and
2008, the correlations were the same, while different months in
2009 exhibited different correlations. In addition, considering that
the UHI intensity index is mostly below zero on May 28, 2007, May
30, 2008, and September, 2009, the urban heat island effect will
aggravate atmospheric particulate pollution, while a UHI intensity
index below zero, indicating a “cool island”, will exhibit the
opposite effect.
(2) UHI intensity index, NDVI, and DVI
After deleting obviously incorrect sampling points, a correlation
analysis was conducted among UHI intensity index, NDVI and DVI
based on the relevant data extracted from TM images on May 28,
2007, May 30, 2008, March 14 and September 22, 2009. The results
show that the UHI intensity index and NDVI have a remarkable
degree of correlation in March except in the core functional zone,
and that NDVI and DVI also have a remarkable positive correlation
in all annular zones (Table S2 and 3).
On May 28, 2007, May 30, 2008, and September, 2009, the
correlation between UHI intensity index and NDVI is positive in all
annular zones, meaning that considering the positive correlation
between NDVI and DVI, higher UHI intensity indexes are associated
with greater NDVI value, greater DVI values, and lower atmospheric
particulate concentrations. However, in March, 2009, the correlation was negative in all annular zones except for the expanding
urban functional zone, meaning that higher UHI intensity indexes
were associated with lower NDVI values, lower DVI values, and
greater atmospheric particulate concentrations. Therefore, the degree of correlation is the same for the same month in 2007 and
2008, while the correlation is different between different months in
2009.
Notes: (*) indicates the leading equation.
Bold values indicates the leading regression equations or the main correlation types.
(3) UHI intensity index, NDWI, and DVI
2007, May 30, 2008, March 14 and September 22, 2009. The results
show the remarkable correlation between the UHI intensity index
and DVI (Table S1).
On May 28, 2007, May 30, 2008, and September, 2009, the
correlation between UHI intensity index and DVI is positive in all
annular zones, meaning that higher UHI intensity indexes are
correlated with greater DVI values and lower atmospheric particulate concentrations. However, in March, 2009, the correlation is
After deleting obviously incorrect sampling points, a correlation
analysis was conducted among UHI intensity index, NDWI and DVI
based on the relevant data extracted from the TM images on May
28, 2007, May 30, 2008, March 14 and September 22, 2009. The
results show that the UHI intensity index and NDWI exhibit a
remarkable degree of negative correlation, and that NDWI and DVI
also have a remarkable degree of correlation, except in March, 2009
(Table S4 and 5).
Table 3
Analysis of UHI intensity and main correlation types in May 2007, May 2008, September 2009, and March 2009.
Time
Zones
Main correlation types
Correlation
UHI intensity levels
May 2007
Core functional zone
Expanding urban functional zone
New urban development zone
Ecological conservation development
Core functional zone
Expanding urban functional zone
New urban development zone
Ecological conservation development
Core functional zone
Expanding urban functional zone
New urban development zone
Ecological conservation development
Core functional zone
Expanding urban functional zone
New urban development zone
Ecological conservation development
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
DVI:
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Negative
Positive
Positive
Positive
Positive
Negative
Positive
Negative
Negative
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Mostly below weak heat island (<0.1)
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Mostly above weak heat island (<0.1)
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Below weak heat island (<0.1)
Mostly below weak heat island (<0.1)
Above weak heat island (>0.1)
Below weak heat island (<0.1)
Above weak heat island (>0.1)
Mostly above weak heat island (>0.1)
May 2008
September 2009
March 2009
zone
zone
zone
zone
UHI
NDVI:UHI
NDVI: UHI
UHI
UHI
NDVI: UHI
NDVI: UHI
UHI
UHI
NDVI: UHI
UHI
UHI
UHI
NDVI: UHI
NDVI: UHI
NDVI: UHI
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
111
The correlations between the UHI intensity index and NDVI and
between NDWI and DVI are most often both negative, meaning that
higher UHI intensity indexes are associated with lower NDWI
values, greater DVI values, and lower atmospheric particulate
concentrations. This proves that the urban heat island effect has a
more or less negative effect on atmospheric particulate concentration in the downtown area by affecting the humidity. In addition,
for the same month in 2007 and 2008, the correlation is the same,
while it is different between different months in 2009.
Fig. 8. The general correlations between the UHI intensity index and DVI.
3.3. Correlation model of UHI intensity index and DVI
Based on the above correlation analyses, the following regression equations were generated: (1) UHI intensity index and DVI, (2)
UHI intensity index, NDVI and DVI, (3) UHI intensity index, NDWI
Fig. 9. Variation of the distribution of DVI when the UHI intensity index is decreased by 0.1 on (a1, a2) March 14, 2009; (b1, b2) September 22, 2009; (c1, c2) May 30, 2008; (d1, d2)
May 28, 2007; in which the left are the original and the right are the altered distributions.
112
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
Fig. 9. (continued).
and DVI. Then, the evaluation model is constructed in terms of the
four annular zones (Table 2).
For example, for the core functional zone in September, the
regression equation between the UHI intensity index and DVI is
y ¼ 82.04x þ 9.189 (1), and then the regression equation between
UHI intensity index and NDVI is y ¼ 0.986x þ 0.112; the regression
equation between NDVI and NDVI is y ¼ 77.63x þ 0.187, so the
regression equation between the UHI intensity index and DVI becomes y ¼ 76.54x þ 8.882 (2). Finally, the regression equation between the UHI intensity index and NDWI is y ¼ 9.918x þ 1.382
and the regression equation between NDWI and NDVI is
y ¼ 8.140x þ 20.38, making the evaluation equation between the
UHI intensity index and DVI y ¼ 80.73x þ 9.131 (3). There are no
evaluation equations for pairs of variables without significant correlations. Finally, considering the correlation between the UHI
intensity index and DVI, this first regression equation is the most
significant and is the leading equation.
3.4. Discussion
According to the equation model relating the UHI intensity index and DVI (Table 2), the three correlation types are proven to
exist in different annular zones, but the leading equations in
different annular zones are different. Specifically, (1) for the core
functional zone, the leading equations are generally the first correlation type (DVI: UHI), and these correlations are positive in May
and September but negative in March; (2) for the expanding urban
functional zone, the leading equations are generally the second
correlation type (DVI: NDVI: UHI), and these correlation are always
positive in March, May and September; (3) for the new urban
L.Y. Xu et al. / Environmental Pollution 178 (2013) 102e114
development zone, the leading equations are the second correlation type (DVI: NDVI: UHI) except in September (DVI: UHI), and
these correlations are positive except in March; (4) for the new
urban development zone, the leading equations are the first correlation type (DVI: UHI) except in March (DVI: NDVI: UHI), and
these correlation are positive except in March, 2009 and May, 2008
(Table 3). This proves that the correlation between UHI and the
atmospheric particulate concentration has a regional dependence,
which can be explained by the regional features of UHI and atmospheric particulates (Dettwiller, 1970; Unger et al., 2001).
And for the four annular zones, the correlation between the UHI
intensity index and DVI is always positive in May and September
but is generally negative in March, except for the core functional
zone. This proves that the correlation between UHI and the atmospheric particulate concentration has a seasonal dependence,
which can be explained by the seasonal features of UHI (Gallo and
Owen, 1999) and atmospheric particulates (Ellis et al., 2005).
Generally, the analyses of UHI intensity levels and the main
correlation types in different zones (Table 3) indicate that the
correlation between UHI intensity index and DVI is negative in
March 2009 and positive in May 2007, 2008 and September 2009,
and that relative UHI intensity levels are mostly above the weak
heat island in March 2009 and below the weak heat island in May
2007, 2008 and September 2009. This means that if UHI intensity is
in the weak heat island or green island range (below 0.1), the DVI
value increases and the atmospheric particulate concentration
decreases as the UHI intensity index increases. If the UHI intensity
is largely in the range of a medium heat island, strong heat island,
or extremely strong heat island (greater than 0.1), then increases in
the UHI intensity index lead the DVI value to decrease and the atmospheric particulate concentration to increase (Fig. 8).
This phenomenon can be supported by the previous studies in
some extent. Zhou et al. (2008) found that UHI intensity was
significantly negative with the atmospheric particulate concentration in developed areas of Guangzhou from 1960 to 2005, with a
negative correlation coefficient of 0.676, when the UHI intensity
index was 0.0374 (0.82/21.9 C). Also, another study in urban area of
Beijing came to the conclusion that the correlation between the
temperature and the atmospheric particulate concentration was
positive in the heating season (in the December) from 2007 to
2009, with a positive correlation coefficient of 0.238 (Zhao et al.,
2010), when the UHI intensity index was mostly greater than 0.1.
In order to further study the effect of this phenomenon in
different four urban zones, it made sensitivity analysis. Fig. 9 shows
changes in the spatial distribution of DVI in Beijing in response to a
0.1 decrease in the UHI intensity index. Compared with the primary
spatial distribution of DVI, the spatial distribution of DVI in the
expanding urban functional zone and the new urban development
zone change more than the core functional zone and the ecological
conservation development zone. This illustrates that the spatial
distributions of DVI in the expanding urban functional zone and the
new urban development zone are influenced more by the urban
heat island than other zones. Hence, the presence of atmospheric
particulates and the urban heat island effect can be controlled by
green space planning in these two zones.
4. Conclusions
The above analysis enables the following conclusions.
(1) This study carried out a correlation analysis to establish a
correlation model, and the results prove the existence of three
correlation types with regional and seasonal differences.
(2) The results also represent an interesting phenomenon
regarding the correlation between the UHI intensity index and
113
DVI. If the UHI intensity index is below 0.1, DVI increases with
increases in the UHI intensity index, while for UHI intensity
indexes greater than 0.1, DVI decreases with increases in the
UHI intensity index.
(3) Compared with the primary spatial distribution of DVI, the
spatial distribution of DVI in the expanding urban functional
zone and the new urban development zone changes more
than in the core functional zone and the ecological conservation development zone. Considering that the main correlation type in these zones is the second correlation type (DVI:
NDVI: UHI), the presence of atmospheric particulates and the
urban heat island effect can be controlled by green space
planning in these two zones, a topic that requires further
research.
Acknowledgments
This work was financially supported by the National Ministry of
Science and Technology (No. 2012BAC05B02) and the National
Natural Science Foundation of China (No. 41271105).
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.envpol.2013.03.006.
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