Content uploaded by Sailesh N. Behera
Author content
All content in this area was uploaded by Sailesh N. Behera on Nov 24, 2020
Content may be subject to copyright.
Passive measurement of NO
2
and application
of GIS to generate spatially-distributed air
monitoring network in urban environment
Sailesh N. Behera
a,
⇑
, Mukesh Sharma
b
, P.K. Mishra
c
, Pranati Nayak
b
,
Bruno Damez-Fontaine
d
, Renaud Tahon
e
a
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
b
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
c
Central Pollution Control Board, Lucknow 226010, India
d
INPG BURGEAP, Paris, France
e
Inter-American Development Bank, 1300 New York Avenue, N.W. Washington, DC 20577, USA
article info
Article history:
Received 26 February 2014
Revised 24 October 2014
Accepted 14 December 2014
Available online xxxx
Keywords:
GIS
Nitrogen oxides
Passive sampling
Vehicular pollution
Monitoring network
Kriging method
abstract
Nitrogen dioxide (NO
2
) plays an importantrole in atmospheric chem-
istry through formation of secondary particulate nitrate that contrib-
utes a major portion to the mass of particulate matter (PM) that has
several impacts on the environment in the scales of local, regional
and global. NO
2
is regulated in most of the countries, because it acts
as a precursor for nitrate formation and it shows significant negative
effects on human health. The present study was conducted for mea-
surement of NO
2
concentration at 204 and 101 sampling locations
in two Indian large cities (population more than 4 million), Delhi
and Kanpur, respectively by using passive samplers. From the
experimental results, an average concentration of 68.6 ± 20.1
l
g/m
3
was observed in Delhi and it was 36.9 ± 12.1
l
g/m
3
in Kanpur. The
observed data from all sampling sites were utilized on the platform
of Geographic Information System (GIS) to generate spatially distrib-
uted pollution maps (using Kriging interpolation method) and maps
of probability of exceedences of air quality standards. Based on the
survey results on emission activities, meteorology and pollution
maps, this study proposed locations of air quality sampling sites for
a long-term monitoring network in Delhi and Kanpur.
Ó2014 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.uclim.2014.12.003
2212-0955/Ó2014 Elsevier B.V. All rights reserved.
⇑
Corresponding author.
E-mail address: saileshnb@gmail.com (S.N. Behera).
Urban Climate xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Urban Climate
journal homepage: www.elsevier.com/locate/uclim
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
1. Introduction
A fast growing population coupled with industrialization, urbanization and high levels of energy
consumptions is worsening the ambient air quality of urban areas, particularly in developing coun-
tries (Bhati, 2000; Lawrence et al., 2007; Sharma et al., 2013). Out of several air pollutants, nitrogen
dioxide (NO
2
) (representative of nitrogen oxides, (NO
x
) = nitric oxide (NO + NO
2
), is originated from
both primary sources and secondary transformation in the atmosphere. NO
2
is regulated in most of
the countries and is used as an indicator to assess the status of ambient air quality in the urban
environments (Baldasano et al., 2003; Ravindra et al., 2008). NO
x
is predominantly present as NO
2
in the ambient air, whereas NO dominates at the emitting sources followed by its oxidation to
NO
2
in the ambient air. The important attribute of NO
2
is to act as a precursor in the atmospheric
chemistry leading to formation of secondary particulate nitrate, which in turn has major impacts on
local, regional and global scales in the environment (Millstein and Harley, 2010; Stock et al., 2013).
In the atmospheric system, various NO
2
-
related complex reactions take place which produce several
secondary pollutants that are sometimes more harmful than NO
2
(Vione et al., 2006; Stock et al.,
2013). In addition, NO
2
causes several adverse effects on human health, causing pulmonary edema
and damages to the central nervous system, tissues etc. (Lal and Patil, 2001; Kampa and Castanas,
2008).
The major sources of NO/NO
2
in urban environments are fuel combustion in motor vehicles, ther-
mal power plants and industrial furnaces (Garg et al., 2006; Klimont et al., 2009; Bootdee et al., 2012).
The motor transport system is increasing at an alarming rate in large cities of the world due to rapid
growth in urbanization and modernization. For example, from 1981 to 2002, the total number of
motorized two-wheelers raised from fewer than 3 million to 42 million in India, with a 14-fold
increase (Pucher et al., 2007; Behera et al., 2014). The fast growing transportation system causes rapid
increase in levels of ambient NO
2
and other criteria pollutants in the large cities (Cohen et al., 2004;
Gokhale and Raokhande, 2008). Therefore, it is pertinent to monitor ambient NO
2
levels on regular
basis in the large cities, as a surrogate of other pollutants and to control NO
2
emissions (Janssen
et al., 2001; Maruo et al., 2003; Jackson, 2005).
The measurement of NO
2
is normally conducted either through bubbling air in a chemical medium
or using real-time online analysers through chemiluminescence technique (Allegrini and Costabile,
2002; Hadad et al., 2005). Both these methods require financial and human resources, however, pas-
sive samplers require almost insignificant resources and can be installed at multiple locations simul-
taneously (Krochmal and Kalina, 1997; Ferm and Svanberg, 1998; Briggs et al., 2000; Varshney and
Singh, 2003). Several studies in the past measured NO
2
levels using passive sampling diffusion tubes
in different urban environments (e.g., Hansen et al., 2001; Lewne et al., 2004; Lozano et al., 2009;
Salem et al., 2009; Ahmad et al., 2011). Most of these studies focused on presentation of results of
NO
2
concentrations and their interpretation with respect to spatial distribution and meteorology.
The site selection for air quality monitoring depends largely on data availability on intensity of pol-
lutant concentrations, distribution of emitting sources, meteorology and possible extent of human
exposure (Noll et al., 1977; Allegrini and Costabile, 2002). To develop a control strategy for effective
reduction of NO
2
pollution, information on spatial distribution of NO
2
levels is necessary. Spatial dis-
tribution of NO
2
concentration can be prepared from the measurements taken at several monitoring
sites more than 50. The measurements at more sites can conveniently be done through installation of
a large number of inexpensive passive samplers (Hadad et al., 2005; Allegrini and Costabile, 2002).
Preparation of pollution maps through the techniques of Geographic Information System (GIS)
requires a spatial interpolation method that includes statistical or other methods to model the pollu-
tion surface. Among relevant interpolation methods (e.g. trend surface analysis, moving window
methods, Kriging method, spline interpolation), Kriging method is a linear interpolation procedure
which is reliable and provides linear unbiased estimation for quantities at unsampled sites that vary
in space (e.g., Oliver and Webster, 1990; Myers, 1994; Briggs et al., 1997). Several studies reported the
applications of Kriging method with various applications; e.g., Haas (1992) on design of continental-
scale monitoring networks; Schaug et al. (1993) on acid precipitation; Campbell et al. (1994) on
national patterns of NO
2
concentrations; Liu et al., 1995 on ozone concentrations. However, it is
2S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
necessary to combine the results of passive air sampling with Kriging method to achieve some signif-
icant applications, such as optimization of air quality networks.
The present study demonstrates a systematic approach on application of GIS through Kriging
interpolation method from the experimental outcomes of passive sampling to generate spatially-
resolved maps of NO
2
followed by identification of monitoring sites required for continuous
measurement in two large cities (population more than 4 million) in the Ganga basin, India; i.e.
Delhi (latitude 28°36
0
N and longitude 77°13
0
E) and Kanpur (latitude 26°28
0
N and longitude
80°24
0
E) (Fig. 1). The Ganga basin is the largest river basin in India, located between latitudes
22°30
0
N and 31°30
0
N and longitudes 73°30
0
Eto89°0
0
E(Fig. 1), supporting more than 40% of India’s
population and accounting for 26% of Indian landmass (Behera and Sharma, 2010; Behera et al.,
2011). The findings of this study can also be implemented in other large cities of developing coun-
tries (e.g., Mumbai, Kolkata, Dhaka, Lahore) and the methodology demonstrated in this study can
also be followed for other pollutants.
2. Materials and methods
2.1. Characteristics of the study areas
Fig. 1 shows the study areas; Delhi and Kanpur. These cities represent typical urban agglomeration
and pollution patterns of the large cities in developing countries. The dotted marks in the maps of
Delhi and Kanpur show the sampling sites in both these cities, where passive sampling of NO
2
was
undertaken.
Fig. 1. Map of India, Ganga basin, study areas (Delhi and Kanpur): dots in maps of Delhi and Kanpur show locations of sampling
sites.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 3
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
2.1.1. Delhi
Delhi, the capital city of India, situated on the banks of River Yamuna has an area of about 1500 km
2
and population of 13,782,976 (2001 Census of India) and is the largest city in the Ganga basin. The
sources of air pollution in Delhi include vehicles, industries, power production, biomass burning, land-
fills, sewage treatment plants etc. (Singh et al., 2011; Sharma et al., 2013). A significant rise in levels of
ambient NO
2
concentration during 1999 to 2004 can be attributed to rapid incremental trends in var-
ious types of vehicles in Delhi (Fig. 2). It can be inferred from the findings of previous studies that the
cause of increase in levels of ambient NO
2
concentrations in urban areas is mainly due to rise in vehi-
cle population (Jackson, 2005; Agarwal et al., 2006; Mallik and Lal, 2014).
2.1.2. Kanpur
Kanpur, a large industrial city in the state of Uttar Pradesh, has a population of over 4.0 million
(2001 Census of India) and an area of about 500 km
2
. The sources of air pollution in Kanpur include
motor vehicles, industries, coal-based power plants, domestic cooking and biomass burning (Behera
and Sharma, 2010; Behera et al., 2011). The number of vehicles in Kanpur increased from 0.37 to
0.47 million during 2001 to 2004, adding to increased air pollution (RTO, 2006; Behera et al., 2014).
2.2. Sampling site classification and site selection criteria
The United States Environmental Protection Agency (USEPA) has adopted a classification of micro,
middle, neighborhood, urban, regional, national/global air quality stations, depending on the scale of
representativeness of measurements (USEPA, 1994). Similar to USEPA, the EuroAirNet classifies mon-
itoring stations based on three criteria including traffic, industrial and background (EEA, 1999). In this
study, a modified station typology was adopted based on convention of the prevailing European clas-
sification that included (i) suburban, (ii) urban, and (iii) traffic.
Maps of Delhi and Kanpur were obtained from respective municipal authorities for preliminary
surveys and identification of the sampling sites. Initially, the whole study area was divided into sev-
eral zones and the basis for generation of zones was population density. Hence, sizes of the zones in
highly populated areas were smaller compared to less populated areas. In the process of site selection,
grids were constructed on the maps and sampling sites were identified considering emission sources,
traffic flow and number of inhabitants in that particular locality. Overall, total area of Delhi was
divided into twenty zones based on the grid sizes of 2 km 2 km and passive samplers were installed
at 204 locations. Kanpur was divided into seven zones based on the grid sizes of 1.5 km 1.5 km and
passive samplers were installed at 101 locations. The purpose for selection of two different grid sizes
of the study areas was on the hypothesis to accommodate equal number of grids surrounding each
sampling location. In other words, the distribution of sampling locations per grid would be uniform
in both the cities. In that way, by adopting 2 km 2 km grid size for Delhi and 1.5 km 1.5 km grid
Fig. 2. Trends of increase in vehicular population and NO
2
concentration in Delhi (Source:Motor Transport Statistics of India,
2005 and Central Pollution Control Board (CPCB), 2006).
4S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
size for Kanpur, Delhi and Kanpur were divided into 408 and 202 grids, respectively. Thus, the number
of grids presenting each sampling location was two grids in both cities.
While deciding the sampling locations, the following precaution measures were considered: (i)
security of the samplers, (ii) the location of sampling site was at least 100 m away from heavy traffic
roads and 40 m away from the minor traffic lanes, (iii) no instantaneous sources of air pollution (espe-
cially responsible for NO
2
pollution) were present close to the sampling location, and (iv) height of the
sampler was maintained between 3–10 m above ground level with unrestricted air movement. The
measurement campaign was conducted at Delhi and Kanpur during February–March, 2004 and the
exposure time for passive sampling at each sampling site was two weeks.
2.3. Passive sampling and analysis of NO
2
The principle of passive sampler is based on Fick’s diffusion law; as a pollutant from the ambient air
gets accumulated in the sampling medium via gaseous diffusion (Shoeib and Harner, 2002; Bartkow
et al., 2005). The driving force is the concentration gradient between the surrounding air and the
absorbing surface, where the pollutant concentration is initially zero. The air pollutant remains in
the absorbing medium after being absorbed by the medium. The advantages of using passive samplers
can be summarized as follows: it needs no field calibration and power supply or technical people at
the sampling sites for operation, and it can also be reused with 100% time coverage (Cruz et al.,
2004; Harner et al., 2004; Hadad et al., 2005).
In this study, plastic Passam diffusion tubes (Passam Ag, Switzerland) of approximately 1 cm in diam-
eter and 6 cm in length were used for NO
2
sample collection. The principle of sampling and analysis were
based on Palmes et al. (1976). Molecules of NO
2
diffused along the tube were trapped within the absor-
bent (triethanolamine (TEA) impregnated mesh) by forming TEA–NO
2
complex. After exposure of tubes
for the time period of two weeks, NO
2
was quantified by chemically extracting nitrite (NO
2
), and analyz-
ing it by colorimetric method. Due to limitation of resources, one tube was installed at each samplingsite
in both the study areas except for duplicates and site repetitions. For quality assurance and quality con-
trol (QA–QC) of the laboratory method for analysis, duplicate tubes were installed simultaneously at 10%
locations of the total sampling sites in Delhi and Kanpur. These duplicates were blank samples and kept
sealed during sampling. The break-up of number of sites in each category was: suburban – 31, urban – 85
and traffic – 88 in Delhi; and suburban – 18, urban – 41 and traffic – 42 in Kanpur. As the sampling lasted
for two months, repetitions of sampling at few sites were done to collect samples. In other words, the
sampling was continued during two months at these sampling sites. Hence, four sets of samples were
collected at each of those sites to assess the variations of NO
2
concentrations with respect to time at a
particular site. For this purpose, the selected numbers of sites were: suburban – 1, urban – 4 and traffic
– 4 in Delhi; and suburban – 1, urban – 2 and traffic – 2 in Kanpur.
The boxes (10 cm diameter and 17 cm length) closed at the top and open at the bottom were used
to protect the sampling tubes from direct sunlight and excessive aeration. The caps of tubes were
removed and mounted vertically with the absorbent at the uppermost part of the tube with the open-
ing pointing downwards to prevent the entry of rain water and dust particles. The time at the start and
end of each exposure period (two weeks) was recorded accurately. At end of the exposure period, the
samples and blanks were collected, and were placed in air tight containers. These sealed samples were
then refrigerated at 4 °C and protected from sunlight until sent out for further analysis by colorimetric
method. After sampling were done at all locations, sampled tubes, duplicate tubes and blank samples
were shipped with air tight package containing dry ice to Passam Ag Laboratory, Switzerland for fur-
ther analysis.
In this study, all chemicals used were of analytical grades from Sigma–Aldrich (USA) or Merck
(Germany) and deionized water was used throughout the analysis. During sample preparation, a pre-
mixed color reagent was prepared by adding sulphanilamide (20 g sulphanilamide reagent +50 ml
concentrated orthophosphoric acid diluted to 1000 ml with distilled water) and N-1-naphthylethylen-
ediamine- dihydrochloride (NEDA; 0.5 g NEDA in 500 ml distilled water) in 1:1 proportion. After
sampling and prior to analysis, 3 ml of the pre-mixed color reagent was added to each sample tube.
The tube was then closed and was stirred with a vortex mixer for 10–15 seconds and left for 30 min.
After formation of purple red azodye, absorbance was measured with an UV spectrophotometer at
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 5
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
540 nm. The intensity of the azodye was proportional to the amount of NO
2
absorbed over the sampling
period. The mass of NO
2
present in the analyte was derived from the reference calibration curve pre-
pared from the standard solutions of sodium nitrite.
From the mass of NO
2
, the average ambient concentration of NO
2
was estimated using Eq. (1):
C
ðNO
2
Þ
¼ðM
a
M
b
Þl
DAtð1Þ
where C
ðNO
2
Þ
was the ambient concentration of NO
2
,M
a
was the mass of NO
2
absorbed in
l
g, M
b
was
NO
2
of blank in
l
g, lwas the diffusion path or length of the tube in cm, Awas the surface area of the
tube in cm
2
,Dwas the diffusion co-efficient of NO
2
through air in cm
2
/s, twas time of exposure in s. A
suitable conversion factor was multiplied with the value of C
ðNO
2
Þ
in Eq. (1) (i.e.
l
g/cm
3
) to get the con-
centration of NO
2
in
l
g/m
3
.
Before commencement of this measurement campaign, some passive sampling tubes were cali-
brated against the active air sampler with calibration factor as 0.9939 with R
2
value as 0.9604. The
detection limit of NO
2
measurement (i.e., 0.3
l
g/m
3
) was determined by 3 times the standard devia-
tion of the concentration of blank samples. The uncertainty and repeatability of laboratory analysis
were <20% and <10%, respectively.
2.4. Pollution mapping with geostatistics tool
In general, an air quality management plan (AQMP) describes the current state of air quality in an
area, the ways for its change over time and steps to be followed for better air quality in that region.
The process of developing AQMP and implementing it effectively involves several steps that include
goal setting, baseline air quality assessment, air quality management system (AQMS), development
and evaluation of policies for strategic reduction of air pollution, initiative for implementation of
action plans, time to time evaluation of action plans and follow up. Among these steps, AQMS is
the central theme of the AQMP. AQMS involves several steps that include air pollution monitoring,
development of emission inventory and air quality modeling. In this study, we focused on monitoring
of NO
2
at several places, acquiring of activity data from on-site surveying. These data were put on the
GIS modeling system to generate spatially distributed pollution maps that helped in identification of
hotspots in Delhi and Kanpur.
For mapping the pollution levels of NO
2
, we used the ArcGIS geostatistical analysis tool due to its
ability to manage a wide range of data formats that are represented by layers of digital maps of various
observations within the framework of spatio-temporal analysis (Briggs et al., 1997; Behera et al., 2011).
Ordinary Kriging method was adopted for interpolation to create a continuous surface from the data-
base of measured concentration (at various locations) due to its capability of quick interpolation that
can be extracted or smoothed depending on the measurement error model (Oliver and Webster,
1990; Myers, 1994; Briggs et al., 1997). The generated surface was then used to predict the NO
2
levels
at unmeasured locations. In addition to above facts, ordinary Kriging method predicted standard error
of indicators and probability of exceedance of certain pollution level.
In this study, the topographical maps of Delhi and Kanpur, issued by the Survey of India (prepared
in 1977) having scale of 1:50,000 were geo-coded as the base map in the form of polygons for geo-ref-
erencing other maps. The base map of each city was then transformed to Universal Transverse Mer-
cator projection with Everest 1956 as the datum. The other two maps, (i) road network and
intersections (from CPCB, Delhi), and (ii) ward (smallest political unit in a city) boundary (from Delhi
and Kanpur Municipal Corporations) were geo-referenced with respect to the base map.
3. Results and discussion
3.1. Passive sampling of NO
2
The statistics of observed results of NO
2
concentration after 2 weeks of exposure through passive
samplers at 204 sampling sites in Delhi were as follows: mean 68.6
l
g/m
3
; standard deviation of
6S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
20.1
l
g/m
3
; minimum 27.9
l
g/m
3
and maximum 126.3
l
g/m
3
(Table 1). From Table 1, it was con-
firmed that the concentrations at traffic sites were higher than urban sites, and the suburban sites
had least concentrations. The concentrations at urban sites located in the industrial areas were as high
as the traffic sites (see Fig. 1 for land-use patterns of the study area). From the trends in levels of NO
2
at various types of sites, it can be inferred that vehicles could be the predominant sources responsible
for ambient NO
2
concentration. Fig. 3a shows the frequency distribution of NO
2
concentration in
10
l
g/m
3
unit intervals which ranged from 0 to 130
l
g/m
3
in Delhi. The values of NO
2
concentration
appeared most frequently in a range from 60 to 70
l
g/m
3
with a frequency of 23%, followed by a range
from 80 to 90
l
g/m
3
with a frequency of 18% and by a range from 50 to 60
l
g/m
3
with a frequency of
16% (Fig. 3a). Overall, the existing levels of NO
2
exceeded the 24-h Indian Standard of 80
l
g/m
3
at 24%
of observed data. In other words, 24% monitoring sites in the city had levels of NO
2
that exceeded the
24-hr Indian standard.
Varshney and Singh (2002) measured ambient levels of NO
2
along the north-west and south-east
transact of Delhi using passive samplers from November 1998 to June 1999 with two weeks of expo-
sure at 9 traffic sites. They observed NO
2
levels varied from 13
l
g/m
3
to 200
l
g/m
3
, and NO
2
levels
exceeded the Indian standard of 80
l
g/m
3
at 45% of the sites. The reason for difference in percentage
of exceedence compared to our study could be due to the site characteristics. Looking into the percent-
age of exceedence of only traffic sites in our study, it was found that 51% sites could not meet the
Indian standard. The average NO
2
concentration at traffic sites (85
l
g/m
3
) was higher than
Varshney and Singh (2002) (i.e. 70
l
g/m
3
) and that could be attributed to increase in vehicle popula-
tion along with time. Our study has the advantage of observations at 204 sampling sites which gave
the overall pollution picture of the entire city, and these results were utilized in developing the pol-
lution map through GIS techniques.
The statistics of NO
2
concentration in Kanpur at 101 sampling sites can be summarized as follows:
mean 36.9
l
g/m
3
; standard deviation of 12.1
l
g/m
3
; minimum 16.5
l
g/m
3
and maximum 68.8
l
g/m
3
(Table 1). Similar to Delhi, higher levels of NO
2
were observed at traffic sites in Kanpur. In contrary to
NO
2
levels in Delhi, NO
2
concentrations in Kanpur were less than the 24-h Indian standard (80
l
g/m
3
)
at all sites. Fig. 3b shows the frequency distributions of NO
2
concentration in 10
l
g/m
3
unit intervals
which ranged from 0 to 60
l
g/m
3
. The values of NO
2
concentration appeared most frequently in a
range from 20 to 30
l
g/m
3
with a frequency of 29%, followed by a range from 30 to 40
l
g/m
3
with
a frequency of 27% and by a range from 40 to 50
l
g/m
3
with a frequency of 22% (Fig. 3b).
As described in a previous section that the repetition of sampling was performed at few sites for the
duration of two months to assess the variations in concentrations of NO
2
with respect to time at a par-
ticular site. From the results at those sites, it was observed that the standard deviation of the results at
Table 1
Statistical summary of the measurement results in Delhi and Kanpur (NO
2
concentration is in
l
g/m
3
).
Study area Statistical parameter Type of sampling sites Overall
SU U T
Delhi N
*
31 85 88 204
Mean 46.7 74.5 84.6 68.6
SD 14.9 22.6 22.9 20.1
Min 27.9 39.4 52.3 27.9
Max 64.3 114.6 126.3 126.3
Kanpur N
*
18 41 42 101
Mean 24.9 34.9 50.7 36.9
SD 7.5 15.2 13.4 12.1
Min 16.5 18.2 33.2 16.5
Max 33.7 53.4 68.8 68.8
SU: suburban site; U: urban site; T: traffic site.
SD: standard deviation.
Min: minium; Max: maximum.
*
N: number of observations and the results are presented from the observations of single samples collected at each of the sites
(i.e., number of sampling sites is equal to number of observations).
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 7
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
all categories of sites were less than 8% of the mean value at the respective sites. Therefore, it could be
inferred that the distinctions in NO
2
concentrations presented in Table 1 at all categories of sites were
more dependent on spatial variations.
Table 2 presents NO
2
levels measured at various parts of the world through passive sampling tech-
niques. It appears that Delhi is more polluted than other cities of the world and it is expected that
there would be more pollution in future, unless any mitigation measure has been undertaken. During
the sampling campaign, meteorological parameters, such as temperature, relative humidity and wind
speed were measured at sampling sites in both cities. Locally available potable wet and dry bulb ther-
mometers were used for measurement of temperature and relative humidity, respectively. Wind
speed was measured by potable anemometer. To assess the influence of meteorology on ambient lev-
els of NO
2
concentration, the results of correlation analysis of NO
2
in relation to independent meteo-
rological parameters including temperature, relative humidity and wind speed are presented in Tables
3 and 4. Correlation analyses of meteorological parameters with NO
2
levels indicated that NO
2
was
significantly associated with temperature, relative humidity and wind speed in Delhi and Kanpur.
3.2. Spatial variable analysis and pollution maps
In this study, the pollution mapping for the entire study area was done at two stages in the Kriging
interpolation method. In the first stage, spatial structure of the data was quantified to produce a vari-
ogram that represented a spatial dependence model of the data. For prediction of concentrations at
unknown locations, Kriging used the fitted model from the variogram, the spatial data configuration
Fig. 3. Frequency distributions of NO
2
concentrations observed from passive sampling in: (a) Delhi, and (b) Kanpur.
8S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
and the values of the measured sample points around the location of interest. We deduced the exper-
imental variogram by averaging the variograms over all intervals that were required for the definition
of a lag value of 1 km. Then a theoretical variogram was fitted to the experimental variogram. The the-
oretical variogram acted as a key tool for weighting the influence of different sampling points for eval-
uating concentration at a node of the target grid.
For spatially-resolved pollution maps, all grids in the digitized maps of Delhi and Kanpur were
assigned conventional codes to locate respective sampling sites. The cells in grids falling over the areas
of Delhi and Kanpur were digitized to convert each cell into an individual polygon. The continuous
surfaces created by interpolation technique were in greyscale. For creating color figures, the images
were processed using the unsupervised classification method available in Geomatica to assign colors
to each pixel based on its data/emission value. To generate the spatial map showing probability of
passing a concentration level, we preferred a reference mark of 40
l
g/m
3
, as this value represented
the annual Indian National Standard of NO
2
. As a result, an overall view of the entire study area with
strategic point of view in long turn aspect could be assessed.
The probability Kriging method was used to create a probability map that exceeds a critical thresh-
old value (40
l
g/m
3
). Probability Kriging method assumes the model as follows:
IðsÞ¼IðZðsÞ>c
t
Þ¼
l
1
þ
e
1
ðsÞð2Þ
Table 2
Comparison of the measurement results with other reported studies.
Study area Year of measurement NO
2
(
l
g/m
3
) Reference
Urban area, Sweden 1989 24.4 Ferm and Svanberg (1998)
Delhi, India 1998-99 70.0 Varshney and Singh (2002)
Germany 1999-2000 28.8 Lewne et al. (2004)
Netherland 1999-2000 28.9 Lewne et al. (2004)
Sweden 1999-2000 18.5 Lewne et al. (2004)
Shiraz, Iran 2003 >100.0 Hadad et al. (2005)
Rawalpindi, Pakistan 2008 55.74 Ahmad et al. (2011)
Malaga, Spain 2000-2001 22.8 Lozano et al. (2009)
Al-Ain, UAE 2005-2006 59.3 Salem et al. (2009)
Delhi, India 2004 68.6 This study
Kanpur, India 2004 36.9 This study
Table 3
Pearson correlation coefficients (r) between NO
2
and meteorological parameters in Delhi.
NO
2
concentration Temperature Relative humidity Wind speed
NO
2
concentration 1.00
Temperature 0.86 1.00
Relative humidity 0.91 0.94 1.00
Wind speed 0.97 0.73 0.87 1.00
Bold marked are statistically significant with pvalue <0.01.
Table 4
Pearson correlation coefficients (r) between NO
2
and meteorological parameters in Kanpur.
NO
2
concentration Temperature Relative humidity Wind speed
NO
2
concentration 1.00
Temperature 0.91 1.00
Relative humidity 0.89 0.92 1.00
Wind speed 0.93 0.82 0.91 1.00
Bold marked are statistically significant with pvalue <0.01.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 9
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
ZðsÞ¼
l
2
þ
e
2
ðsÞ ð3Þ
where
l
1
and
l
2
are unknown constants and I(s) is a binary variable created by using a threshold indi-
cator, I(Z(s)>c
t
). It can be noticed about two types of random errors,
e
1
(s) and
e
2
(s), so there is auto-
correlation for each of them and cross-correlation between them.
Probability Kriging method strives to do the same thing as indicator Kriging method, but it uses
cokriging in an attempt to do a better job (http://resources.arcgis.com/en/home/). For more informa-
tion about the steps to follow, please refer the tutorials explained in the weblink (http://resources.arc-
gis.com/en/home/) and the tutorial documented by Johnston et al. (2001).
Fig. 4a shows the spatial distribution map of NO
2
in Delhi and it can be clearly seen that the con-
centrations were at the highest levels near roadsides indicating that vehicles were largely responsible
for NO
2
emission. Next to roadside levels, industrial areas showed high levels of NO
2
concentrations
also. For checking the consistency of our results, we compared our spatial map with concentrations
at specific location measured at a few locations through active sampling by our team members at
CPCB (Central Pollution Control Board, Delhi) for the study on national ambient air quality status
(CPCB, 2004). From the comparison, it appeared that the trends in spatial variations of NO
2
levels were
quite similar in both the observations with the highest levels were observed in heavy traffic areas of
ITO traffic intersection and Town Hall due to more traffic volumes and traffic congestion in these
areas. Fig. 4b shows the spatial distribution of NO
2
which represents the probability of exceeding
40
l
g/m
3
. It could be inferred that most of the areas in Delhi were having higher chances with prob-
ability of 0.9 to exceed the annual Indian standard.
The spatial distribution of NO
2
concentration in Kanpur is shown in Fig. 5a. In comparison to Delhi,
Kanpur experienced less pronounced levels of pollution. Similar to Delhi, Kanpur had higher pollution
levels near roadsides and industrial areas. Due to large commercial activities and more traffic conges-
tions taking place in the city central area, this area could be considered as one of the hotspots of NO
2
concentrations. The trends of spatial distributions of NO
2
levels in this study were quite similar to the
levels, measured at a few locations through active sampling by our team members at CPCB (CPCB,
2004). Fig. 5b shows the spatial distribution of NO
2
that represented the probability to exceed
40
l
g/m
3
concentrations in a locality. From Fig. 5b, it could be inferred that 50% area in Kanpur had
higher chance with probability of 0.9 to exceed the annual Indian standard.
3.3. Proposing monitoring network for sampling sites
To examine the fact that control of NO
2
can lead to reduce levels of other pollutants, we performed
a linear correlation analysis between particulate matter with aerodynamic diameter 610
l
m (PM
10
)
and NO
2
for 15 days average data during 2004 at national air quality sites that were maintained by
our team members at CPCB in Delhi and Kanpur. As our passive sampling measurement was under-
taken during February-March, 2004, we considered the observational data during these two months
from national air quality sites for correlation analysis. The PM sampling was carried out using high
volume respirable dust sampler (APM-460 RDS, Envirotech, Delhi), which was equipped with a
cyclone to provide separation of PM
10
particles with sharper cutoff (D
50
at 10
l
m) from the coarser
particles by centrifugal forces. When the suspended particles in air were drawn through the sampler,
non-respirable particles (NRP) in air fell into the cyclone’s conical hopper and got collected in the
cyclonic dust cup. The fine dust comprising the respirable fraction (PM
10
) passed through the cyclone
and gets collected on the 20 25 cm Glass Microfibre Filter (GFF; Whatman grade). The sampling flow
rate was maintained between 0.8 and 1.2 m
3
/min. The GFF filters and dust cups were pre-conditioned
and post-conditioned at temperature 22 °C and RH 40% with controlled desiccators in the room
meant for conditioning for 24-hr. PM
10
mass concentration (
l
g/m
3
) was determined gravimetrically
by the difference in mass of GFF after and before sampling (
l
g) divided by the volume of sampled
air (m
3
). In a similar way to gravimetric method of PM
10
, the concentration of NRP was determined
from mass difference of respective dust cup after and before sampling and the volume of sampled
air. The total suspended particle (TSP) concentration was calculated as the sum of PM
10
collected
on the filter and NRP collected in the dust cup. In this study, we used the measurement data of
10 S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
PM
10
for interpretation. NO
2
was measured by sampling through an absorbing solution in midget imp-
inger system (connected in APM-460 RDS) followed by colorimetric method in the laboratory.
The number of national air quality sites being nine and six in Delhi and Kanpur, the pairs for linear
correlation analysis were: n=94 = 36 in Delhi and n=64 = 24 in Kanpur. Fig. 6a and b shows the
Fig. 4. Spatial distribution NO
2
in Delhi: (a) map showing NO
2
concentration in
l
g/m
3
, and (b) map showing possibility to pass
40
l
g/m
3
.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 11
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
scatter plots between PM
10
and NO
2
in Delhi and Kanpur, respectively. The squared correlation coef-
ficients (R
2
) were determined from the linear best fit line. The correlation coefficients were significant
in Delhi and Kanpur; i.e. R
2
= 0.87 and p< 0.01 for Delhi and R
2
= 0.82 and p< 0.01 for Kanpur. Inter-
estingly, lower correlation in Kanpur compared to Delhi could be attributed to PM coming from non-
combustion sources (e.g., road and soil dust). In other words, the contribution of road and soil dust to
PM mass was higher in Kanpur than Delhi and these trends of observations were similar to our pre-
vious studies (Behera and Sharma, 2010; Behera et al., 2011). Overall, significant correlation between
NO
2
and PM
10
in both the cities suggested that the spatial pollution mapping of NO
2
was a better way
to describe the patterns and trends of other pollutants prevailing in the study areas. Moreover, studies
in the past (Boogaard et al., 2011; Minguillón et al., 2012; Wang et al., 2013) also reported strong cor-
relations between concentrations of NO
x
and PM at urban sites, suggesting that NO
x
can be used as a
proxy for PM exposure to emitting sources.
Fig. 5. Spatial distribution NO
2
in Kanpur: (a) map showing NO
2
concentration in
l
g/m
3
, and (b) map showing possibility to
pass 40
l
g/m
3
.
12 S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
In the decision making process to propose the locations and numbers of sampling sites in these two
large cities, maps showing spatial distributions of NO
2
were examined to assess the most vulnerable
areas through identification of hot spots. As described in a previous section, the current situation of
traffic volume, congestion and commercial activities were considered in addition to identifying the
hotspots. In other words, the hotspots having higher probability of exceedance of critical threshold
value of 40
l
g/m
3
on NO
2
concentration and with more traffic congestion were highly sensitive to
human health risk due to more exposure of pollution. This should be noted that the sequence of plans
for making a proposal on location of monitoring sites was within the guidelines of EEA (1999). Hence,
after identifying the critical areas, survey results on long-term meteorology, demographic patterns,
and distribution of emitting sources were taken into account during the exercise in locating the sam-
pling sites. It was observed that most populated districts in Delhi were (in decreasing order) as fol-
lows: Northeast, Central, East, North and West. The site classifications were based on the
convention of three types; i.e. traffic, urban and suburban. The purpose for choosing suburban site
was to get the background concentration and assess the effects of city pollution on the surrounding
areas under downwind conditions.
Fig. 7a shows the locations of existing monitoring sites of national ambient air quality study main-
tained by CPCB team during 2003–04. During decision making process, the existing sites of CPCB were
examined for their suitability on the aspect whether to retain or reject. From this map, it was clear that
the areas labelled with red color were extremely polluted and termed as hotspots for NO
2
pollution.
Fig. 6. Scatter plots between NO
2
and PM and linear correlation from the best-fit line: (a) in Delhi, and (b) in Kanpur.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 13
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
Based on source characteristics and population density, it was assessed that locating two traffic and
three urban monitoring sites would be more appropriate to present the data at such a highly polluted
area of the city. The use of prevailing meteorology became paramount during finalization of sampling
location of suburban sites. From the meteorological data during 2003–04, it was observed that the
directions of prevailing wind in Delhi were North to Northwest and East to Southeast. Therefore, allo-
cating suburban sites at SE and NW would fulfil the purpose to represent the sites of background and
Fig. 7. Sampling network in spatial distribution map of Delhi: (a) existing NAMP sites, and (b) proposed monitoring sites.
14 S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
effects on city pollution flux on suburban region. For example under NW wind, NW suburban site
would give background level characteristic of air in Delhi and SE suburban site would give levels of
pollution coming from city centre after photochemical transformation, where ozone (O
3
) levels are
expected to be the highest. This should be noted that during allocation of sampling sites, the trends
in levels of ambient O
3
must be considered as the role of ambient O
3
is very important in the atmo-
spheric chemistry. From the observations of previous studies (Mavroidis and Ilia, 2012; Banan et al.,
2013), it was believed that higher levels of O
3
prevailing in the suburban regions could undergo trans-
portation to reach the city area and would play significant role in atmospheric chemistry the city area.
Therefore, as a future scope of work, it could be recommended that levels of tropospheric O
3
at sub-
urban regions should be monitored regularly. Fig. 7b shows the possible locations of automated sites
to be included in our proposed monitoring network in Delhi. In the decision making process for pro-
posal of sampling sites, we considered to retain six sampling sites of national ambient air quality sites
and proposed nine more sites (total fifteen). Overall, these sites would present a complete pollution
scenario and the potential for population exposure to pollution over the city.
The prevailing wind direction in Kanpur was northwest to southeast. Approaches similar to Delhi
were adopted for decision making process on proposal of sampling sites (traffic, urban and suburban)
in Kanpur for a monitoring network. Fig. 8 shows the proposed locations of the sampling sites. Two
suburban sites were proposed to represent both upwind and downwind characteristics of pollution
in the city. One sampling site was identified to account for industrial emissions (marked as industrial
site in Fig. 8 and it was urban type in nature). Based on the locations of hotspots, source characteristics
and population density, it was decided to allocate one traffic and two urban sampling sites in the city
central area. Other possible locations were considered for traffic and urban set up at moderate pollu-
tion levels. In addition, one urban sampling site was proposed to account for the second critical area in
the east side of Kanpur. Retaining location of the industrial site, traffic and urban sites in the city cen-
tral area of the national ambient air quality study, we proposed five additional sampling sites. Hence,
it appeared that by allocating eight sampling sites in Kanpur, overall pollution scenario of the city
could be represented.
The future scope of the research on proposal of monitoring network for measurement of air
pollutants in urban environment could be extended through parallel measurement of O
3
,SO
2
and
NH
3
along with NO
2
using passive samplers. With the measurement results of these precursor gases,
the trends of atmospheric chemistry could be studied in distinctions with time and space. The findings
from the role of atmospheric chemistry of these precursor gases on formation of secondary compo-
nents of PM would improve the decision making process on allocation of sampling sites.
Fig. 8. Sampling network in spatial distribution map of Kanpur: proposed monitoring sites.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 15
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
4. Conclusions
The present study demonstrates a systematic approach that describes the application of GIS
through Kriging interpolation method with the experimental outcomes of passive sampling to gener-
ate spatially-resolved maps of NO
2
levels. These results were used in identification of monitoring sites
required for continuous long-term measurement in two megacities of India (Delhi and Kanpur). Pas-
sive sampling of NO
2
was conducted at 204 and 101 sampling locations in Delhi and Kanpur, respec-
tively. It was observed that Delhi and Kanpur experienced an average NO
2
concentration of
68.6 ± 20.1
l
g/m
3
and 36.9 ± 12.1
l
g/m
3
, respectively. A significant correlation between NO
2
and
PM
10
in both cities suggests that the spatial pollution mapping of NO
2
is good enough to describe
the patterns and trends that are also valid for other pollutants. On the basis of survey results on emis-
sion activities, meteorology and guidelines for site selection criteria with its characteristics (urban,
traffic and suburban types), this study proposes that Delhi and Kanpur need fifteen and eight number
of sampling sites to represent the complete pollution scenario. The study emphasizes on location of
suburban or background sampling sites for overall assessment on the pollution levels and their expo-
sure to the existing population.
Acknowledgements
This research study was part of the project ‘‘Air Quality Monitoring in India’’, conducted in collab-
orative efforts among Central Pollution Control Board, Delhi, ETI group, BURGEAP, France, and Indian
Institute of Technology, Kanpur. We also thank two anonymous reviewers for their valuable com-
ments that helped for substantial improvement of this article.
References
Agarwal, R., Jayaraman, G., Anand, S., Marimuthu, P., 2006. Assessing respiratory morbidity through pollution status and
meteorological conditions for Delhi. Environ. Monit. Assess. 114 (1–3), 489–504.
Ahmad, S.S., Biiker, P., Emberson, L., Shabbir, R., 2011. Monitoring nitrogen dioxide levels in urban areas in Rawalpindi, Pakistan.
Water Air Soil Poll. 220, 141–150.
Allegrini, I., Costabile, F., 2002. A new approach for monitoring atmospheric pollution in urban environment, Global Conference
on Building a Sustainable World, San-Paolo, Brasil.
Baldasano, J.M., Valera, E., Jiménez, P., 2003. Air quality data from large cities. Sci. Total Environ. 307 (1), 141–165.
Banan, N., Latif, M.T., Juneng, L., Ahamad, F., 2013. Characteristics of surface ozone concentrations at stations with different
backgrounds in the Malaysian Peninsula. Aerosol Air Qual. Res. 13, 1090–1106.
Bartkow, M.E., Booij, K., Kennedy, K.E., Muller, J.F., Hawker, D.W., 2005. Passive air sampling theory for semi-volatile organic
compounds. Chemosphere 60, 170–176.
Behera, S.N., Sharma, M., 2010. Reconstructing primary and secondary components of PM2.5 composition for an urban
atmosphere. Aerosol Sci. Technol. 44 (11), 983–992.
Behera, S.N., Sharma, M., Dikshit, O., Shukla, S.P., 2011. GIS-based emission inventory, dispersion modeling, and assessment for
source contributions of particulate matter in an urban environment. Water Air Soil Poll. 218, 423–436.
Behera, S.N., Sharma, M., Nayak, P., Shukla, S.P., Gargava, P., 2014. An approach for evaluation of proposed air pollution control
strategy to reduce levels of nitrogen oxides in an urban environment. J. Environ. Plann. Manage. 57, 467–494.
Bhati, K.R., 2000. In: Bose, R.K., Sundar, S., Nesamani, K.S. (Eds.), Performance of On-road Vehicles and Emission Standards in
Clearing the Air – Better Vehicles, Better Fuels. Tata Energy Research Institute, Delhi, pp. 49–54.
Boogaard, H., Kos, G., Weijers, E.P., Janssen, N.A., Fischer, P.H., van der Zee, S.C., de Hartog, J.J., Hoek, G., 2011. Contrast in air
pollution components between major streets and background locations: particulate matter mass, black carbon, elemental
composition, nitrogen oxide and ultrafine particle number. Atmos. Environ. 45 (3), 650–658.
Bootdee, S., Chalemrom, P., Chantara, S., 2012. Validation and field application of tailor-made nitrogen dioxide passive samplers.
Int. J. Environ. Sci. Technol. 9, 515–526.
Briggs, D.J., Collins, S., Elliott, P., Fischer, P., Kingham, S., Lebret, E., Pryl, K., Reeuwijk, H.V., Smallbone, K., Veen, A.V.D., 1997.
Mapping urban air pollution using GIS: a regression based approach. Int. J. Geogr. Inf. Sci. 11, 699–718.
Briggs, D.J., de Hoogh, C., Gulliver, J., Wills, J., Elliott, P., Kinghamc, S., Smallbone, K., 2000. A regression-based method for
mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci. Total Environ.
253, 151–167.
Campbell, G.W., Stedman, J.R., Stevenson, K., 1994. A survey of nitrogen dioxide concentrations in the United Kingdom using
diffusion tubes July–December 1991. Atmos. Environ. 28 (3), 477–487.
Cohen, A.J., Anderson, H.R., Ostro, B., Pandey, K.D., Krzyzanowski, M., Kuenzli, N., Gutschmidt, K., Pope, C.A., Romieu, I., Samet,
J.M., Smith, K., et al, 2004. Mortality impacts of urban air pollution. In: Ezzati, m. (Ed.), Comparative Quantification of Health
Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. World Health Organization,
Geneva, pp. 1353–1434.
CPCB, 2004. National Ambient Air Quality Status 2004, Central Pollution Control Board, Delhi, India.
16 S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
Cruz, L.P.S., Campos, V.P., Silva, A.M.C., Tavares, T.M., 2004. A field evaluation of a SO
2
passive sampler in tropical industrial and
urban air. Atmos. Environ. 38, 6425–6429.
EEA, 1999. Criteria for EUROAIRNET – The EEA Air Quality Monitoring and Information Network. Technical Report No. 12.
European Environment Agency, Copenhagen.
Ferm, M., Svanberg, P.-A., 1998. Cost-efficient techniques for urban- and background measurements of SO
2
and NO
2
. Atmos.
Environ. 32, 1377–1381.
Garg, A., Shukla, P.R., Kapshe, M., 2006. The sectoral trends of multigas emissions inventory of India. Atmos. Environ. 40, 4608–
4620.
Gokhale, S., Raokhande, N., 2008. Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at
urban traffic intersection during winter period. Sci. Total Environ. 394 (1), 9–24.
Haas, T.C., 1992. Redesigning continental-scale monitoring networks. Atmos. Environ. 26A, 3323–3333.
Hadad, K., Safavi, A., Tahon, R., 2005. Air pollution assessment in Shiraz by passive sampling techniques, Iran. J. Sci. Technol. 29
(A3), 471.
Hansen, T.S., Kruse, M., Nissen, H., Glasius, M., Lohse, C., 2001. Measurements of nitrogen dioxide in Greenland using Palmes
diffusion tubes. J. Environ. Monitor. 3, 139–145.
Harner, T., Shoeib, M., Diamond, M., Stern, G., Rosenberg, B., 2004. Using passive air samplers to assess urban–rural trends for
persistent organic pollutants. 1. polychlorinated biphenyls and organochlorine pesticides. Environ. Sci. Technol. 38, 4474–
4483.
Jackson, M.M., 2005. Roadside concentration of gaseous and particulate matter pollutants and risk assessment in Dar-es-Salaam,
Tanzania. Environ. Monit. Assess. 104 (1–3), 385–407.
Janssen, N.A., van Vliet, P.H., Aarts, F., Harssema, H., Brunekreef, B., 2001. Assessment of exposure to traffic related air pollution
of children attending schools near motorways. Atmos. Environ. 35 (22), 3875–3884.
Johnston, K., Ver Hoef, J.M., Krivoruchko, K., Lucas, N., 2001. Using ArcGIS Geostatistical Analyst, vol. 300. Esri, Redlands.
Kampa, M., Castanas, E., 2008. Human health effects of air pollution. Environ. Pollut. 151 (2), 362–367.
Klimont, Z., Cofala, J., Xing, J., Wei, W., Zhang, C., Wang, S., Kejun, J., Bhandari, P., Mathur, R., Purohit, P., Rafaj, P., Chambers, A.,
Amann, M., 2009. Projections of SO
2
,NO
x
and carbonaceous aerosols emissions in Asia. Tellus Ser. B 61, 602–617.
Krochmal, D., Kalina, A., 1997. Measurements of nitrogen dioxide and sulphur dioxide concentrations in urban and rural areas of
Poland using a passive sampling method. Environ. Pollut. 96, 401–140.
Lal, S., Patil, R.S., 2001. Monitoring of atmospheric behaviour of NO
x
from vehicular traffic. Environ. Monit. Assess. 68 (1), 37–50.
Lawrence, M.G., Butler, T.M., Steinkamp, J., Gurjar, B.R., Lelieveld, J., 2007. Regional pollution potentials of megacities and other
major population centers. Atmos. Chem. Phys. 7 (14), 3969–3987.
Lewne, M., Cyrys, J., Meliefste, K., Hoek, G., Brauer, M., Fischer, P., et al, 2004. Spatial variation in nitrogen dioxide in three
European areas. Sci. Total Environ. 332, 217–230.
Liu, L.J.S., Rossini, A., Koutrakis, P., 1995. Development of co-Kriging models to predict 1- and 12-hour ozone concentrations in
Toronto. Epidemiology 6, S69.
Lozano, A., Usero, J., Vanderlinden, E., Raez, J., Contreras, J., Navarrete, B., 2009. Air quality monitoring network design to control
nitrogen dioxide and ozone, applied in Malaga,Spain. Microchem. J. 93, 164–172.
Mallik, C., Lal, S., 2014. Seasonal characteristics of SO
2
,NO
2
, and CO emissions in and around the Indo-Gangetic Plain. Environ.
Monit. Assess. 186 (2), 1295–1310.
Maruo, Y.Y., Ogawa, S., Ichino, T., Murao, N., Uchiyama, M., 2003. Measurement of local variations in atmospheric nitrogen
dioxide levels in Sapporo, Japan, using a new method with high spatial and high temporal resolution. Atmos. Environ. 37 (8),
1065–1074.
Mavroidis, I., Ilia, M., 2012. Trends of NO
x
,NO
2
and O
3
concentrations at three different types of air quality monitoring stations
in Athens,Greece. Atmos. Environ. 63, 135–147.
Millstein, D.E., Harley, R.A., 2010. Effects of retrofitting emission control systems on in-use heavy diesel vehicles. Environ. Sci.
Technol. 44 (13), 5042–5048.
Minguillón, M.C., Rivas, I., Aguilera, I., Alastuey, A., Moreno, T., Amato, F., Sunyer, J., Querol, X., 2012. Within-city contrasts in PM
composition and sources and their relationship with nitrogen oxides. J. Environ. Monitor. 14 (10), 2718–2728.
Myers, D.E., 1994. Spatial interpolation: an overview. Geoderma 62, 17–28.
Noll, K.E., Miller, T.L., Narco, J.E., Raufer, R.K., 1977. An objective air monitoring site selection methodology for large point
sources. Atmos. Environ. 11, 1051–1059.
Oliver, M.A., Webster, R., 1990. Kriging: a method of interpolation for geographical information systems. Int. J. Geogr. Inf. Sci. 4,
313–332.
Palmes, E.D., Gunnison, A.F., Dimatto, J., Tomezyk, C., 1976. Personal sampler for nitrogen dioxide. Am. Ind. Hyg. Assoc. J. 37,
570–577.
Pucher, J., Peng, Z.R., Mittal, N., Zhu, Y., Korattyswaroopam, N., 2007. Urban transport trends and policies in China and India:
impacts of rapid economic growth. Transport rev. 27, 379–410.
Ravindra, K., Sokhi, R., Van Grieken, R., 2008. Atmospheric polycyclic aromatic hydrocarbons: source attribution, emission
factors and regulation. Atmos. Environ. 42 (13), 2895–2921.
RTO, 2006. Annual Report on Vehicles Status in Kanpur, Regional Transport Office, Kanpur, India.
Salem, A.A., Soliman, A.A., El-Haty, A.I., 2009. Determination of nitrogen dioxide, sulfur dioxide, ozone and ammonia in ambient
air using the passive sampling method associated with ion chromatographic and potentiometric analyses. Air Qual. Atmos.
Health 2, 133–145.
Schaug, J., Iversen, T., Pedersen, U., 1993. Comparison of measurements and model results for airborne sulphur and nitrogen
components with Kriging. Atmos. Environ. 27A, 831–844.
Sharma, S.K., Mandal, T.K., Saxena, M., Sharma, A., Gautam, R., 2013. Source apportionment of PM10 by using positive matrix
factorization at an urban site of Delhi, India. Urban Clim.. http://dx.doi.org/10.1016/j.uclim.2013.11.002.
Shoeib, M., Harner, T., 2002. Characterization and comparison of three passive air samplers for persistent organic pollutants.
Environ. Sci. Technol. 36, 4142–4151.
S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx 17
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
Singh, D.P., Gadi, R., Mandal, T.K., 2011. Characterization of particulate-bound polycyclic aromatic hydrocarbons and trace
metals composition of urban air in Delhi, India. Atmos. Environ. 45 (40), 7653–7663.
Stock, Z.S., Russo, M.R., Butler, T.M., Archibald, A.T., Lawrence, M.G., Telford, P.J., Abraham, N.L., Pyle, J.A., 2013. Modelling the
impact of megacities on local, regional and global tropospheric ozone and the deposition of nitrogen species. Atmos. Chem.
Phys. 13 (24), 12215–12231.
U.S. Environmental Protection Agency (US EPA), 1994. Photochemical Assessment Monitoring Stations (PAMS) Implementation
Manual. Office of Air Quality Planning and Standards, Report No. EPA-454/B-93-051.
Varshney, C.K., Singh, A.P., 2002. Measurement of ambient concentration of NO
2
in Delhi using passive diffusion tube sampler.
Curr. Sci. India 83 (6), 731–735.
Varshney, C.K., Singh, A.P., 2003. Passive samplers for NO
x
monitoring. Environmentalist 23, 127–136.
Vione, D., Maurino, V., Minero, C., Pelizzetti, E., Harrison, M.A., Olariu, R.I., Arsene, C., 2006. Photochemical reactions in the
tropospheric aqueous phase and on particulate matter. Chem. Soc. Rev. 35 (5), 441–453.
Wang, M., Beelen, R., Basagana, X., Becker, T., Cesaroni, G., de Hoogh, K., et al, 2013. Evaluation of land use regression models for
NO
2
and particulate matter in 20 European study areas: the ESCAPE project. Environ. Sci. Technol. 47 (9), 4357–4364.
18 S.N. Behera et al. / Urban Climate xxx (2015) xxx–xxx
Please cite this article in press as: Behera, S.N., et al. Passive measurement of NO
2
and application of GIS to gen-
erate spatially-distributed air monitoring network in urban environment. Urban Climate (2015), http://
dx.doi.org/10.1016/j.uclim.2014.12.003
