Journal of Environmental Protection, 2010, 1, 264-277
doi:10.4236/jep.2010.13032 Published Online September 2010 (
Copyright © 2010 SciRes. JEP
Spatial and Temporal Variation of Urban Air
Quality: A GIS Approach
Subrata Chattopadhyay1, Srimanta Gupta1, Raj Narayan Saha2
1Department of Environmental Science, The University of Burdwan, Burdwan, India; 2Department of Chemistry, National Institute
of Technology, Durgapur, India.
Received May 19th, 2010; revised June 5th, 2010; accepted June 19th, 2010.
This study investigated th e seasonal variation of amb ient air quality status of Bu rdwan town using GIS approach. Con-
centration of SO2 (sulphur dioxide), NO2 (nitrogen dioxide) and RSPM (respiratory suspended particulate matter) were
measured once a week for 24 hour in both premonsoon and postmonsoon season. The seasonal average concentration
of the RSPM, SO2 and NO2 in premonsoon season was observed to be 188.56 ± 88.63, 5.12 ± 6.27 and 92.51 ± 64.78
g/m3 respectively whereas in postmonsoon it was 53.03 ± 38.27, 8.51 ± 7.11 and 162.85 ± 184.80
g/m3 respectively.
Statistical analysis showed the significant monsoonal effect on mean difference of RSPM, SO2 and NO2 concentration.
Postmonsoon concentration of ambient SO2 and NO2 were observed to be higher than premonsoon, suggesting longer
residence times of these pollutants in the atmosphere due to stagnant conditions and low mixing height. Spatial distri-
bution of polluta nts throughout the town in both th e season was represented by digital elevation model (DEM). On the
basis of Air Quality Index (AQI) a GIS based air pollution surface models were generated in both the seasons by means
of Inverse Distance Interpolation (IDINT) technique. From the output surface model it was found that in comparison to
premonsoon there was a significant increase of clean and fairly clean area and decrease of moderately polluted area of
the town during postmonsoon .
Keywords: Ambient Air Quality, Seasonal Variation, Air Quality Index (AQI), Geographi c Inf orm at i o n System (GIS)
1. Introduction
Throughout the world, air pollution is a matter of con-
cern at all levels. The worldwide epidemiological study
on the effect of air pollution had revealed that gaseous
pollutants and particulate matter had enough potential to
cause severe health effect like respiratory, cardiovascular
diseases and cardio pulmonary mortality [1,2]. Being a
serious matter of concern now-a-day, a systematic moni-
toring programme all over the world especially in urban
cities are urgently needed as the level of air pollution is
increasing rapidly in many areas of mega cities of the
developing world [3]. It was found that the moderniza-
tion and industrialization of developing countries had led
to the increase use of fossil fuels and their derivatives. As
such, developing countries were confronted with the
great challenge of controlling the atmospheric pollution
especially in the rapidly growing mega cities. Concern
about air pollution in urban regions is receiving increas-
ing importance world-wide, especially pollution by gase-
ous and particulate trace metals [4-7]. The urban centers
might be viewed as dense sources of enormous anthro-
pogenic emissions of pollutants, which could alter the
atmospheric composition, chemistry and life cycles in its
down wind regimes, extending over several hundred kil-
ometers [8]. It had been found that world motor vehicle
population growth had reached 700 million in the year
2000 [9]. Petrol and diesels engines of motor vehicles
were found to emit a wide variety pollutants, principally,
oxide of nitrogen (NOx) which had an increasing impact
on urban air quality [10]. Various monitoring programme
had already been done in developing countries like Bang-
ladesh and Pakistan [11,12].
In India, air pollution had also become a topic of in-
tense debate at all levels mainly because of the enhanced
anthropogenic activities [13]. Today India is one of the
first ten industrial countries of the world [14]. Urban air
pollution in India had increased rapidly with the popula-
tion growth, numbers of motor vehicles, use of fuels with
poor environmental performance, badly mentioned tran-
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
sportation systems, poor land use pattern, and above all,
ineffective environmental regulations [8,9]. Among the
worst air city the name of the capital of India is enlisted
followed by Beijing, China, Xian, Kathmandu, Dhaka in
Asia. So far various research work had been done on spa-
tial and temporal variation of urban air pollution in various
cities of India like Kolkata, Delhi, Lucknow, Haryana,
Chennai, Mumbai, Dhanbad-Jharia and on Raniganj-
Asansol [8,13,15-20].
GIS is used as a platform for spatio-temporal analysis
or for building relationships between the GIS database
and stand-alone modeling tools. Air data are generally
very complex to model due to the underlying correlation
among several pollutants. The significant differences
among the results obtained from the techniques, indi-
cated that proper air quality management requires sensi-
tive air quality evaluation [21]. Various research work
had also been done on the GIS aspect of spatio-temporal
analysis of urban air quality [22-25].
Burdwan being a city of West Bengal state in eastern
India and headquarters of Burdwan district now a day
draws attention with respect to ambient air quality status.
Not only being a busy town (populated by 2,85,871 peo-
ple as per 2001 census) but also being nearest to Dur-
gapur, this place was given importance keeping in mind
that Durgapur is the 7th polluted city in India and air pol-
lutants had the capacity to travel a long distance. Apart
from several residential projects a major public private
project “the largest heath city of Asia” was also proposed
here. Medical report (Table 1), collected from the Govt.
Hospital of this town, reflected that health problem due
to air pollution is increasing day by day. So far no sys-
tematic air quality-monitoring programme with GIS ap-
proach was reported from this town. Hence the quality of
ambient air deserved a systematic as well as scientific
investigations so that proper strategies could be taken to
mitigate in case of any pollution was found. The objec-
tive of this study was to evaluate the premonsoon and
postmonsoon distribution of selected gaseous pollutants
i.e. sulphur dioxide (SO2), nitrogen dioxide (NO2) and
respiratory suspended particulate matter (RSPM) and its
interaction with meteorological parameters. This study
also performed to develop a GIS based air pollution sur-
face model on the basis of air quality index (AQI) by
using continuous surface generation technique.
2. Methodology
2.1. Study Area
Burdwan town is located at 23.25° N latitude and 87.85°
E longitude. It has an average elevation of 40 meters (131
feet). The city is situated a little less than 100 km north-
west of Kolkata on the Grand Trunk Road (NH-2) and
Table 1. Medical record (2008) of respiratory disease in
Burdwan municipality.
Sl. No Month Case Death
1. January 17 03
2. February 12 01
3. March 34 00
4. April 09 01
5. May 22 00
6. June 19 05
7. July 18 01
8. August 04 01
9. September 09 00
10. October 16 01
11. November 39 03
12. December 30 00
*Data Obtained from Medical Record Department Burdwan Medical Col-
lege & Hospital Burdwan, West Bengal.
eastern railway (Figure 1). It is a city with an increasing
number people opting for better residential spaces and
higher living standards. The number of registered motor
vehicle in the town (according to 2007 statistics) was 3,
97,5509. On basis of land use/land cover classification
map (Figure 2) the respective locations of sampling en-
compassing sensitive, residential and industrial areas
were selected. Altogether 25 locations encompassing all
the three areas were selected randomly for air quality
monitoring (Figure 2). Details of the sampling locations
are represented in Table 2. Mainly the schools, colleges,
university and children parks are enlisted as sensitive
zones whereas the places beside road and others residen-
tial areas are considered as residential zones and others
as per Central Pollution Control Board (CPCB). The
place where the industries (mainly rice mills in the study
area) are aggregated is considered here as industrial zone.
2.2. Sampling (6:00 A.M. to 6:00 A.M.) and
Analysis of Gaseous Pollutants and
Particulate Matter
In both the seasons the sampling was done for twenty-
four (24) hours at each site. The seasonal classification
was followed as per specification laid by Indian mete-
orological department [26]. March, April and May months
were considered as premonsoon season and June, July,
August, and September were considered as postmonsoon
season. Air quality parameter such as repairable suspended
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
Figure 1. Study area location.
Figure 2. Land use/land cover map showing various sam-
pling locations.
particulate matter (RSPM) which was also known as PM10
Table 2. Details of sampling sites.
Sampling Sites Description
R11, R12, R13,
R15, R6, R7 Residential area with high traffic density
R3, R4, R5, R8,
R14 Residential area with moderate traffic density
R1, R2, R10 Residential area with low traffic density
R9 Residential area influenced by industrial
I1 and I2 Industrial area with high traffic density
I4 Industrial area with moderate traffic density
I3 Industrial area with low traffic density
S2, S5, S6 Sensitive area with high traffic density
S1 Sensitive area with moderate traffic density
S3 Sensitive area with moderate traffic density
and highly influenced by industrial emission
S4 Sensitive area with high traffic and highly
influenced by industrial emission
was monitored by using High Volume Sampler (MODEL
NPM HVS) following standard procedure by IS: 5182
(Part iv). Glass fiber filter paper, popularly known as
GF/A filter paper was used and the flow rate was kept at
1-1.5 m3/min. The model NPM HVS had a cyclone
separator, which separated the coarser particulate matter
larger than 10 μm from air stream (drawn into the HVS)
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
before filtering on GF/A filter paper. Air was also al-
lowed to pass through two impingers having specific
absorbing reagent for SO2 and NO2. SO2 and NO2 were
collected by bubbling the sample in specific absorbing
reagents of 25 ml put in two impingers. The average flow
rate through the impingers was 0.5 l/min. After the sam-
pling the impinger samples were kept in iceboxes and
transferred to a freeze until the analysis was done. So-
dium tetrachloromercurate and Sodium hydroxide were
used as absorbing reagents for SO2 and NO2 respectively
to arrest SO2 in the form of dichlorosulfitomercurate
complex measured spectrophotometrically at 560 nm and
NO2 as sodium nitrite measured at 540 nm. For analysis
of SO2 and NO2 by spectrophotometeric method, de-
scribed in IS: 5182 (Part ii) and IS: 5182 (Part vi) were
followed [27,28]. National ambient air quality standard
(NAAQS) is represented in Table 3.
2.3. Meteorology
In each sampling location meteorological parameters
such as humidity, temperature, wind speed, wind direc-
tion and rainfall were recorded both in premonsoon and
postmonsoon seasons. Humidity and temperature were
measured by a portable hygrometer (Model-HTC-1), rain-
fall is measured by a digital rain gauge (Model-RGR 126;
Make-Oregon) meter whereas wind speed and direction
is measured by a digital anemometer along with wind
vane (Model-Lutron-AM-4201). For both the seasons
two windrose diagrams (Figures 3(a) and (b)) were pre-
pared by using windrose pro software. Apart from wind
velocity and direction other meteorological data of the
study area during monitoring period is represented in
Table 4.
2.4. Air Quality Index (AQI)
An AQI could be defined as a scheme that transforms the
(weighted) values of individual air pollution related pa-
rameters into single number. Air quality index [29] was
also measured here for each place in each zone. At first
air quality rating of each parameter used for monitoring
is calculated in each zone by the formula as;
1) q = 100xV/Vs; where q = quality rating; V = ob-
served value of parameter; Vs = value recommended for
that parameter.
If total ‘n’ no of parameters were considered for air
monitoring, then geometric mean of these ‘n’ number of
quality ratings was calculated in the following way:
2) g = anti log {(log a + log b + ……………. log x)/n};
where g = geometric mean; a, b, c, d, x = different values
of air quality rating; and n = number of values of air
quality rating, log = logarithm.
Air quality status [30] on the basis of AQI is repre-
sented in Table 5.
Figure 3. Windrose diagram. (a) Premonsoon; (b) Post-
2.5. Statistical Analysis
2.5.1. Pearson Correlation Coefficient
The Pearson correlation among SO2, NO2 and RSPM was
calculated by using the following formula
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
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Table 3. National ambient air quality standards.
Concentration in ambient air in average
Pollutants (g/m3) Time weighted Sensitive Industrial
Residential and
Respirable Suspended Particulate Matter
(RSPM) 24h 75 150 100
Sulphur dioxide (SO2) 24h 30 120 80
Oxides of nitrogen (NO2) 24h 30 120 80
Source: Central pollution control board, Delhi, 1994
Table 4. Meteorological condition during premonsoon and postmonsoon season.
Premonsoon Postmonsoon
parameters Maximum Minimum Average Maximum Minimum Average
Rainfall (mm) 118.7 42.7 78.8 433.6 228.2 324.2
Humidity (%) 75 42.5 58.22 85 53 66.13
Temperature () 35.5 16.43 24.54 33.5 16.2 23.81
Table 5. Air quality index table (Mudri, 1999).
Category AQI of
ambient air Description of ambient air
I Below 10 Very clean
II Between 10-25 Clean
III Between 25-50 Fairly clean
IV Between 50-75 Moderately Polluted
V Between 75-100Polluted
VI Between 100-125Heavily polluted
VII Above 125 Severely polluted
ii i
 
where X and Y are two variables, with means
Y respectively with standard deviations SX and SY. Sta-
tistical significance of r value is calculated by t-test.
2.5.2 Student’s t-Test for Difference of Means
A student’s (t) test [31] was carried out for testing sig-
nificant difference between means of factors for pre- and
postmonsoon periods against left sided alternative hy-
pothesis, i.e., the mean of premonsoon is less than that of
the other. The test statistic, which follows t-distribution
with (n1 + n2 – 2) degrees of freedom, is given by t = (X1
– X2)/
P1 P2
S/n 1S/n1 ; where SP
2 = (n1S1
2 +
2)/(n1 + n2).
X1 is the mean variable of premonsoon, X2 is the mean
variable of post-monsoon, SP
2 is the variance of com-
bined sample (Standard Error of difference between
means of pre- and postmonsoon parameters), n1 is the
number of observations on variable of premonsoon and
n2 is the number of observations on variable of post-
monsoon. If computed value is greater than critical value
there is significant difference between means.
2.6. RS and GIS Methodology
The following RS and GIS methodologies were adopted
for carrying out the research work.
2.6.1. Su pe rvised Classification of Study Area
Supervised classification of the Burdwan town was per-
formed with the help of Resourcesat-1 satellite image
and Geomatica V.10.2 software. Map collected from
Burdwan municipality was considered as base map (Fig-
ure 1). Base map was georeferenced at latitude/longitude
projection system with a datum level of India-Nepal
(D076) with an output pixel spacing of 0d00’00.1900”.
For georeferencing ground control points (GCPs) were
collected from study area by using Germin 12 GPS re-
ceiver. Burdwan municipality area was clipped from the
satellite imagery and image to image georeferencing was
done by using already georeferenced base map. Then
supervised classification was run by using maximum
likelihood classifier with null class. Thereafter both lan-
duse/landcover and base maps were reprojected to Uni-
versal Transverse Marcater Projection (UTM) system.
Twenty five (25) air sampling locations were then down-
loaded to the classified image from GPS through Map-
source software. Locational details along with different
air quality parameters and their concentrations were at-
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
tached to this 25 spatial data as an attribute data.
2.6.2. Di gital Ele vation Model (DEM)
DEM is generated on the basis of sampling points, stored
as a point layer along with attributes such as RSPM, SO2
and NO2 etc. DEM is generated by using VEDIMINT
algorithm in the Geomatica V.10.1 software. The output
DEM is represented as a zonation map of the said pa-
rameters (Figures 5, 6 and 7). The algorithm consists of
three major steps plus an optional step for processing 2D
features. In the first step, input vector points (RSPM, SO2
and NO2 concentration with respect to different locations)
are reprojected to the raster coordinates and burned into
the raster buffer, with the elevations generated due to
different concentration of the said parameters interpo-
lated linearly between vector nodes. 2D layers are ig-
nored in this stage. If multiple elevation values are
scanned into a single pixel, the maximum value is as-
signed the pixel, and the pixel is marked as a cliff. In the
second step, the elevation at each DEM pixel is interpo-
lated from the source elevation data. The interpolation
process is based on an algorithm called Distance Trans-
form. Interpolation is made between the source eleva-
tions and elevations at equal-distance points from source
locations. If 2D vector layers are present, they are scan
converted into a flag buffer during the optional step. The
2D features are also initialized to prepare for use in the
smoothing stage. In step 3, a finite difference method is
used to iteratively smooth the DEM grid. The algorithm
uses over relaxation technique to accelerate the conver-
gence. During the iterations, the source elevation values
are never changed, while the interpolated values are up-
dated based on the neighborhood values.
2.6.3. Inverse Distance Interpolation (IDINT)
Inverse distance interpolation is used to read the gray
level values for an arbitrary number of pixel locations in
order to generate a raster image based upon interpolation
between the specified gray levels. This method of inter-
polation combines the idea of Thiessen polygon with the
gradual change of trend surface. It considers weighted
moving average. Weights are computed from a linear
function of distance between sets of points and the points
to be predicted. In this method the size of the starting
radius is specified, which defines the starting search area
for interpolation points around grid point.
3. Results and Discussion
Results of premonsoon and postmonsoon ambient air
quality status of different monitoring sites of study area
encompassing industrial, residential and sensitive areas
are represented in Tab l e s 6, 7 and 8 respectively whereas
the average seasonal values of RSPM, SO2 and NO2 are
represented in Table 9.
3.1. RSPM Scenario
During premonsoon all the industrial sites had high level
of RSPM than the standard prescribed by NAAQS. This
might be due to resuspension of road dust, soil dust, and
vehicular traffic and nearby industrial emission [8]. But
during postmonsoon RSPM level in these sites lied well
below the prescribed limit. This implied that the mon-
soon in these sites had a major role in washing out of
RSPM [20].
In residential sites RSPM level exceeded its standard
in every monitoring site except R7, R13, and R15 where
the level of RSPM lied very near to the standard. But in
postmonsoon opposite phenomenon was observed. Only
12% of residential sites i.e. R11, R12, and R15 have
higher level of RSPM than the permissible standard. In
general it is found that most of the postmonsoon RSPM
concentration was significantly less than the premonsoon
concentration except site R15. This phenomenon might
be corroborated to monsoonal wash out of the particles
[8]. The site R15 was situated just beside National
Highway. So, at that particular time of monitoring high
density of traffic, road dust etc might cause it to be more
negating the effect of rain which was supported by simi-
lar observation of a research work [32].
Regarding sensitive sites except site S5 and S6 most of
the RSPM value lied above the limit of NAAQS standard
in premonsoon while in postmonsoon the level exceeds
Table 6. Premonsoon and postmonsoon ambient air quality status in various industrial locations of Burdwan municipality
(Except AQI, all values are expressed in g/m3).
Sites RSPM SO2 NO2 AQI Status
Pre Post Pre Post Pre Post Pre Post Pre Post
I1 173.9 74 1.59 20.34 26.62 125.78 15.05 44.33 Clean Fairly clean
I2 326.2 53.7 26.69 23.11 66.06 60.29 64.33 32.59
polluted Fairly clean
I3 154.6 55.2 8.6 0.38 20.19 98.6 23.16 9.80 Clean Very clean
I4 231.1 60.41 19.46 7.51 56.09 23.91 48.88 17.12 Fairly clean Clean
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
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Table 7. Premonsoon and postmonsoon ambient air quality status in various residential locations of Burdwan municipality
(Except AQI, all values are expressed in g/m3).
Sites RSPM SO2 NO2 AQI Status
Pre Post Pre Post Pre Post Pre Post Pre Post
R1 203.30 22.80 4.37 7.06 238.19 44.05 69.15 22.29
polluted Clean
R2 174.70 15.46 0.88 1.47 161.79 90.73 33.87 14.76 Fairly clean Clean
R3 230.00 34.26 2.13 BDL 109.74 230.5143.79 16.60 Fairly clean Clean
R4 303.30 38.92 0.34 10.60 29.19 163.6916.75 47.25 Clean Fairly clean
R5 267.30 71.80 2.21 6.95 63.15 191.2138.77 71.37 Fairly clean
R6 285.80 16.30 3.18 12.53 87.16 168.7049.83 37.76 Fairly clean Fairly clean
R7 99.40 40.97 0.6 9.11 36.77 129.6615.04 42.29 Clean Fairly clean
R8 135.17 32.63 0.34 3.94 88.36 193.9018.51 33.90 Clean Fairly clean
R9 119.16 10.00 3.17 20.34 63.04 207.1233.38 40.38 Fairly clean Fairly clean
R10 137.77 69.62 4.37 16.95 220.87 426.2859.22 92.29
polluted Polluted
R 11 323.10 112.80 6.93 5.45 140.43 79.05 78.90 42.35 Polluted Fairly clean
R12 264.40 102.46 3.22 5.60 3.79 166.7017.15 53.07 Clean Moderately
R13 69.61 47.16 2.69 1.32 16.10 30.34 16.76 14.34 Clean Clean
R14 168.90 7.11 9.75 4.55 54.71 45.97 52.02 16.62
polluted Clean
R15 96.54 141.00 2.72 6.00 158.82 97.99 40.24 50.60 Fairly clean
Table 8. Premonsoon and postmonsoon ambient air quality status in various sensitive locations of Burdwan municipality
(Except AQI, all values are expressed in g/m3).
Sites RSPM SO2 NO2 AQI Status
Pre Post Pre Post Pre Post Pre Post Pre Post
S1 123.60 10.95 2.68 BDL 77.49 39.94 72.45 13.49 Moderately polluted Clean
S2 191.70 45.60 2.64 7.30 158.58207.11105.94100.79 Heavily polluted
S3 156.40 130.4512.71 12.94 62.62 48.98 122.63106.99 Heavily polluted
S4 363.60 87.15 2.72 21.96 101.46209.28114.13181.04 Heavily polluted
S5 60.00 40.62 0.57 3.80 79.57 937.2534.29 128.93 Fairly clean Severely
S6 54.45 4.48 3.50 3.42 191.8754.33 81.52 23.10 Polluted Clean
only at site S3 and S4. This might be due to their loca-
tional disadvantages as because both these two places
were located in the region where majority rice mills fac-
tories of the town are situated. So, in spite of monsoonal
wash out of dust particle they reflected a high level of
RSPM than the standard. Maximum RSPM level during
premonsoon was found in S4 site, which was not only
beside rice mills but also beside a main road. So, such a
high level of RSPM might be attributed to resuspension
of road dust, soil dust and vehicular traffic and nearby
industrial emission.
Digital elevation model (DEM) with respect spatio-
temporal distribution of RSPM in the study area were
presented in Figures 4(a) and (b).
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
Table 9. Statistical summary of ambient air quality status of Burdwan municipality during pre and postmonsoon season.
Statistics RSPM (g/m3) SO2 (g/m3) NO2 (g/m3)
Premonsoon Postmonsoon Premonsoon Postmonsoon Premonsoon Postmonsoon
Average 188.56 53.03 5.12 8.51 92.51 162.85
Maximum 363.6 141 26.69 23.11 238.19 937.25
Minimum 54.45 4.48 0.34 BDL* 3.79 23.91
Standard deviation 88.63 38.27 6.27 7.11 64.78 184.80
*BDL indicated below detection limits
Figure 4. Digital Elevation Model (DEM) on spatio-temporal distribution of RSPM over the study area in (a) Premonsoon (b)
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
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Figure 5. Digital Elevation Model (DEM) on spatio-temporal distribution of SO2 over the study area in (a) Premonsoon (b)
3.2. SO2 Scenario
The concentration of SO2 was comparatively lower in
both the seasons than the prescribed standard of NAAQS
in all the monitoring sites. Similar kind of SO2 status was
also highlighted by other research workers such as Reddy
and Ruj 2003 [21] and Gupta et al. 2008 [8]. Among
industrial, residential and sensitive sites maximum SO2
level was observed in industrial sites i.e. 26.69 μg/m3
during premonsoon and 23.11 μg/m3 during postmon-
soon. This might possibly be due to emission from in-
dustrial boiler, heating and cooking sources. Within in-
dustrial sites except I1, rest of the three sites had low SO2
level during postmonsoon. While in case of residential
sites most of the sites has higher level of SO2 concentra-
tion during postmonsoon except R3, R11, R13, R14 sites.
Burning of coal by local people might influence it. Simi-
lar trend also followed by most of the sensitive sites ex-
cept S1 and S6.
Spatio-temporal distribution of SO2 concentration are
represented in Figures 5(a) and (b).
3.3. NO2 Scenario
Through out the study area NO2 level was very high. Ma-
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
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Figure 6. Digital Elevation Model (DEM) on spatio-temporal distribution of NO2 over the study area in (a) Pre-
monsoon (b) Postmonsoon .
ximum concentration was observed in S5 site in the tune
of 937 μg/m3. This elevated level might be attributed to
the high traffic density of the town. This was also sup-
ported by a published work of [33]. Among industrial
sites, I2 and I4 show the low level of NO2 concentration
in postmonsoon while in the same season high concen-
tration was observed in site I1, I13. To explain the later it
could be said NO2 was not only dependent on rainfall but
also dependent on vehicle density and the distance of the
monitoring site from road [18]. Among all industrial sites
only site I1 had the higher-level of NO2 than the standard
in postmonsoon season. Regarding residential sites, R1,
R2, R3, R6, R8, R10, R11, R15 had higher level of NO2
concentration in premonsoon season while in postmon-
soon except R1, R11, R13, R14 all have shown higher
level of NO2 than the prescribed standard. In the sensitive
sites both the pre and postmonsoon value of NO2 were
exceeded its standard.
Digital elevation model with respect to spatio-tempo-
ral distribution of NO2 were represented in Figures 6(a)
and (b).
3.4. Overall Scenario of RSPM, NO2 and SO2
Average concentration level of all the pollutants in both
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
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Figure 7. Continuous surfaces from point data (AQI) by using Inverse Distance Interpolation (IDINT) technique in (a) Pre-
monsoon (b) Postmonsoon.
the season was represented in Table 9. In general RSPM
level varied from 363 to 54 g/m3 with a mean of 188
g/m3 in premonsoon and from 141 to 4.48 g/m3 with a
mean of 53.03 g/m3 during postmonsoon season. NO2
level varied from 3 to 238 g/m3 with a mean of 92
g/m3 and 23 to 937 g/m3 with a mean of 162 g/m3
respectively during premonsoon and postmonsoon season.
Whereas SO2 level varied from 0.34 to 26 g/m3 with a
mean of 5 g/m3 and BDL to 23 g/m3 with a mean of 8
g/m3 respectively during premonsoon and postmonsoon
season. Finally, to compare the pre and the postmon-
soonal value of SO2 and NO2 in this town it was found
that in most places the level of SO2 as well as NO2 was
increased in postmonsoon season in spite of lowering
down by rain. This might be explained by over crowded
condition in the town of Burdwan, which was also the
center of commercial activities in the district. The build-
ing structures were constructed literally wall to wall with
very narrow streets separating one block from the other.
Even the vehicular traffic was at most times bumper to
bumper and sometimes at a stand still every time it
rained. Hence, the increased amount of exhaust gases in
the air negated the effect of the monsoon rains [32].
Statistical significance (student t-test) of seasonal vari-
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
ation of air quality parameters were carried out for the
combined data of pre- and postmonsoon. Result of t-test
for the combined data was given in Table 10 . The Table
value (critical value) at 48 degree of freedom was 1.68
for left-sided alternative hypothesis. Since the computed
values of t were greater than the critical value of 1.68 for
all the parameters the difference of means between pre
and postmonsoon was significant at 5% level. Hence, the
results clearly indicated that there was significant mon-
soonal effect on mean values of RSPM, SO2 and NO2
4. Influence of Meteorological Parameters
In the study area premonsoon temperature ranged from
16 to 35 while in postmonsoon it varied from 16 to
33. Humidity ranged from 42 to 75% and 53 to 85%
during premonsoon and postmonsoon season respectively
(Table 4). Regarding rainfall study area received an av-
erage rainfall of 78 mm in premonsoon where as in post-
monsoon it receives 324 mm. Windrose which was gra-
phical representation of wind data giving the % frequen-
cies of wind speed, wind direction for a given location
was represented in Figures 3 and 4 for premonsoon and
postmonsoon season respectively. From windrose dia-
grams it was found that the percentage of calm condition
is higher (11%) in postmonsoon than premonsoon season
(3%). As a result, higher concentration of gaseous pol-
lutants was observed in postmonsoon than the premon-
soon in the study area of this town. The wind speed
ranged between 0 to 18 km/hr. Wind blew almost from
all direction. But during premonsoon the predominant
direction was from North-East, South-East and South-
West direction while in postmonsoon season it was main-
ly from South-East, South-Southwest and North direction
also. The major significant changes in the spatial and
temporal variation of the ambient air quality of the town
were due to variation of rainfall in the two seasons which
was also supported by the Table 10.
5. Season-Wise Classified Image on the Basis
of Air Quality Index (AQI)
Air Quality Index of all the three categories of monitor-
ing sites was represented in Tables 6, 7 and 8. From AQI
status of the all monitoring sites in both pre- and post-
monsoon season it was found that 40% of the total
monitoring sites (I4, I2, I3, R1, R2, R3, R11, R14, S6, S1)
became less polluted in postmonsoon. Whereas 40% of
the total monitoring sites (I1, S4, S5, R4, R5, R7, R8,
R10, R12, R15) became more polluted in postmonsoon
season and 20% of the total monitoring sites (R6, R9,
R13, S2, S3) remained same in status i.e. they are indif-
ferent of rainy season and by using IDINT technique,
season wise continuous surfaces of AQI have been gen-
Table 10. Student t-test of mean difference between air
quality parameters.
Parameter Calculated
t value Tabulated value of t
at 0.05 level Significant/
RSPM 6.88 1.677 Significant
SO2 –1.7519 1.677 Significant
NO2 –1.7597 1.677 Significant
Table 11. Season-wise percentage area of different AQI statu s.
Seasonal areal coverage (%)
AQI status Premonsoon Postmonsoon
Clean 2.19 9.43
Fairly clean 41.09 52.02
polluted 53.37 30.18
Polluted 3.30 7.52
Heavily polluted0.05 0.82
Severely pollutedNil 0.03
erated. The output surfaces generated by IDINT were
unclassified grey scale images. Though output surfaces
were smooth but it is difficult to compare these surfaces
on the basis of seasonal trend. On the basis of AQI rating,
and using classification technique, these seasonal images
had been classified (Figures 7(a) and (b)). Premonsoon
classified image revealed that the western part of the
town mainly covered the moderately polluted area while
the eastern part of the town covers the fairly polluted
region. But after the offset of monsoon just opposite
scenario was observed. The moderately polluted region
was seemed to be shifted to eastern part while the fairly
clean part was shifted to western part. This phenome-
non was also influenced by meteorological phenomenon
which might be supported by wind rose diagram. It was
observed that in premonsoon season wind mainly blew
from North-East, South-East and South-West direction. It
looked that the pollutants were seemed to be dispersed
more in these direction from the polluted region. In
postmonsoon just opposite picture was found. The pre-
dominant wind direction was South, South-West and
North direction. So, the just opposite dispersion of pol-
lutants had been occurred from the heavily polluted re-
gion. According to IDINT surface classification on the
basis of AQI clean and fairly clean area had increased
upto 7% and 2% respectively in comparison to premon-
soon, whereas moderately polluted classified area de-
creased to 23% (Table 11).
Spatial and Temporal Variation of Urban Air Quality: A GIS Approach
Copyright © 2010 SciRes. JEP
6. Acknowledgements
The authors gratefully acknowledge Dr A. R. Ghosh,
Reader, Department of Environmental Science, Burdwan
University for his critical evaluation and suggestion, and
suggestions, which greatly helped to improve the manu-
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