Atmospheric and Climate Sciences, 2011, 1, 214-224
doi:10.4236/acs.2011.14024 Published Online October 2011 (
Copyright © 2011 SciRes. ACS
An Analysis of Ambient Air Quality Conditions
over Delhi, India from 2004 to 2009
Jhumoor Biswas1, Era Upadhyay2*, Mugdha Nayak2, Anil Kumar Yadav3
1Institute of Social Welfare and Business Management, Kolka ta, India
2Ansal Institute o f Technology & Management, Lucknow, India
3ITM University, Gurgaon, India
E-mail: *
Received July 21, 2011; revised August 28, 2011; accepted September 20, 2011
We analyzed 1-hour, 8-hour and 24-hour averaged criteria pollutants (NO2, SO2, CO, PM2.5 and PM10) during
2004-2009 at three observational sites i.e. Income Tax Office (ITO), Sirifort and Delhi College of Engi-
neering (DCE) in Delhi, India. The analysis reveals increased pollutant concentrations at the urban ITO site
as compared to the other two sites, suggesting the need to better locate hot spots in designing the monitoring
network. There is also significant year to year variation in the design value trends of criteria pollutants at
these three sites, which may be attributed to meteorological variations and local-level emission fluctuations.
Correlations among criteria pollutants vary annually and spatially from site to site, indicating the heteroge-
neous nature of air mix. The annual ratios of CO/NOx are considerably higher than SO2/NOx confirming that
vehicular source emissions are the primary contributors to air pollution in Delhi. The seasonal analysis of
criteria pollutants reveals relatively higher concentrations in winter because of limited pollutant dispersion
and lower concentrations during the monsoon period (rainy season). The diurnal averages of criteria pol-
lutants reveal that vehicular emissions strongly influence temporal variations of these pollutants. Weekdays
and weekend diurnal averages do not show noticeable differences.
Keywords: Ambient Air Quality Status, Criteria Pollutants, Data Analysis
1. Introduction
Delhi, the capital of India (latitude 28˚4' N and longi-
tude 77˚2' E), is located in central India, covering 1483
km3. It is the third most populated city in India with a
population of more than 16 million. Naturally, this has
caused environmental stress and atmospheric concentra-
tion levels of criteria pollutants particulate matter, sulfur
dioxide and nitrogen oxides continue to pose serious
public health risks for sensitive population in Delhi [1].
The pollution levels in Delhi have been rising due to
continuous increase in number of motor vehicles [2],
counteracting the benefits of control programmes that
were implemented. Other sources of pollutants include
coal-based thermal power plants, small-scale industries
and non-road sources such as construction activities. Me-
teorological variables, particularly the prevailing winds
blowing from northwest in winter and from southwest in
summer [3] play a significant role in inducting industrial
pollutants and pollutants from roadways into residential
areas [4] causing widespread air pollution. Several emis-
sion reduction measures such as the use of heavy-duty
Compressed Natural Gas engines (CNG) replacing die-
sel-fueled engines, strict vehicular inspection and main-
tenance procedures, establishment of alternative mode of
transport such as the metro and stricter controls on in-
dustrial pollution have been implemented to improve
local air quality. Scientists have evaluated the effective-
ness of these controls on air pollution and discovered a
decrease in air pollutants due to a switch from diesel to
CNG in Delhi’s transport system [5]. However, an in-
crease in NOx concentrations after the switch was ob-
served [6,7]. Further, there was no discernible impact on
ambient PM10 and CO concentrations noted, stemming
from CNG implementation [8]. SO2 and NOx (NO + NO2)
are important primary precursors emitted by fossil fuel
combustion from industrial point sources and coal-fired
thermal power plants. Heavy-duty vehicles burning die-
sel fuel are important sources of NO2 as well. NO2 and
SO2 are important contributors towards secondary nitrate
and sulfate formation through a series of complex reac-
tions, which are major components of fine particulate
matter (PM2.5). Sources of fine particles include all types
of combustion including motor vehicles, power plants,
residential wood burning, forest fires, agriculture burning
and some industrial processes. The study and subsequent
control of secondary pollutants are further complicated
by the nonlinear nature of their formation processes and
the impact of meteorological variability on their concen-
trations [9,10].
Evaluation of ambient air quality is a method to verify
the effectiveness of the control measures implemented,
and for early detection of potentially harmful changes in
atmospheric composition. According to a detailed analy-
sis of most of the criteria pollutants in Delhi, except for
SO2, all criteria pollutants exceeded the National Ambi-
ent Air Quality Standards (NAAQS) applicable in USA
[11,12]. In this study, the period of interest is from 2004
to 2009. However, observed data available varies from
site to site and from pollutant to pollutant. The pollutants
used in this study are NO2, SO2, CO, PM2.5 and PM10.
Obviously, more observational sites at hot spot locations
and residential areas are needed to adequately investigate
the spatial variability and to provide a more comprehend-
sive status of air quality in Delhi.
2. Prototype Data Collection
The observations of pollutants used in this study are ob-
tained from Central Pollution Control Board [13] (per-
sonal communication) and website (
Currently, CPCB has three fixed continuous air quality
monitoring sites in Delhi. These are Income Tax Office
(ITO), Delhi College of Engineering (DCE) and Sirifort
(Figure 1). The operations and maintenance of monitor-
ing sites in Delhi are undertaken by CPCB under the
nation-wide National Ambient Air Quality Monitoring
The data availability at these sites is as follows:
1-hour and 24-hour averaged data for NO2, SO2 at all
the three sites (ITO, Sirifort and DCE 2004-2009);
1-hour, 8-hour consecutive averaged values and
24-hour averaged data for CO all three sites (ITO,
Sirifort and DCE 2007-2009) since CO concentra-
tions are being monitored since 2007 at these sites;
1-hour and 24-hour averaged data for PM2.5 at one
site (ITO since 2007) since PM2.5 data is being con-
tinuously monitored at this site only;
1-hour and 24-hour averaged data for PM10 at one site
(DCE since 2007) since PM10 data is being monitored
at this site only.
In this analysis, design value trends, persistence of
exceedances of pollutants, monthly averages, pollutant
ratios and correlation coefficients between various pol-
lutants have been used to estimate status of ambient air
quality in Delhi. In addition, hourly averages were used
for diurnal plots of NO2, SO2, CO, PM2.5 and PM10 for
the year 2009.
Figure 1. Monitoring sites (ITO, Sirifort and DCE), major roadways and pow e r plants.
Copyright © 2011 SciRes. ACS
Site Description
The ITO monitoring station is located along a major
transport corridor connecting the east side of the river
“Yamuna”. This site not only captures the signals from
the transport sector, but also the industrial emissions
from the East, primarily from the Ghaziabad industrial
sectors [14]. The Pragati power plant with a capacity of
282 MW is within 2 km from this site and substantially
influences the emissions at this site. Delhi’s outer ring
road, a major roadway with a total length of 47 km is
within 0.5 km of the ITO site. Sirifort in South Delhi is
a semi-urban site enclosed by greenery. However, ma-
jor roads including the outer ring road are within a
kilometre from this site. Though it is far away from
industries, the prevailing winds can bring industrial
pollutants into this area from the east (The Badarpur
coal-based power plant is about 7 km from this site).
DCE is a residential location within a University cam-
pus, away from traffic junctions. Although vehicular
activities are restricted within the campus, it is still in-
fluenced by roadside dust.
3. Data Analysis
3.1. Trends of Criteria Pollutants Utilizing
Design Values
A pollutant determined to be hazardous to human health
and regulated under United States Environmental Prote-
ction Agency’s (US EPA’s) National Ambient Air Qual-
ity Standards is termed as criteria pollutant. All the ubiq-
uitous air pollutants considered in this study meet the
above definition of criteria pollutant. A design value is a
statistic that describes the air quality status of a given
area relative to national ambient air quality standards.
Since the CPCB standards of air pollutants are excee-
dance-based (24-hour NO2, 24-hour SO2, 8-hour CO, 24-
hour PM2.5 and 24-hour PM10), design value calculations
expressed as a concentration instead of an exceedance count,
allow a direct comparison to the level of the standard.
Trends in the design value of criteria pollutants reveal
the efficacy of emission controls. The design values for
the 8-hour and 24-hour averaged concentrations of pol-
lutants have been computed as per CPCB guidelines
( which stipulates that the standards for
the pollutants are allowed to be exceeded only 2 percent
of time annually in each year but not for two consecutive
days. According to the above guidelines for an area to be
in attainment, 98 percent of time the pollutant levels
have to meet the standards. This means that the 98th per-
centile of the 24 hr-average values (array size is 365
since there is one 24-averaged value for each day of year)
has to meet the standard, which would be the 8th highest
value of the pollutant concentration in the sorted array
size of 365 values. Similarly for three 8-hour consecu-
tive averages considered in a day as in the case of pol-
lutant CO, the array size would be 1095 values for each
year. In this case the 98th percentile is the 22nd highest
value in the sorted array, which has to meet the standard.
The design values computed on the second aspect of cri-
teria (pollutant levels not exceeding two consecutive
days), the lower of two consecutive-days value was
stored in the pollutant array and for a given year the
maximum if these values was specified as design value
criteria which was compared against the NAQQS stan-
dard. The results exhibit a similar trend line (Figures not
The time series of design values of criteria pollutants
represents 8th highest concentration in a year for the
24-hour averaged concentrations (NO2, SO2, PM2.5 and
PM10) and 22nd highest concentration for the 8-hour av-
eraged values (CO). The longer-term averaging period
provides a methodology for reducing the dependence of
design values on variable short-term meteorological ef-
fects [15], which can mask the impact of regulatory pro-
grams on air quality. The trends for 24-hour averaged
NO2 and SO2 design values have been assessed from
observational data collected since 2004 at all the three
monitoring sites. Design values of 24-hour averaged
PM2.5 and PM10 concentrations have been analyzed for
three years only (2007-2009) at ITO and DCE respec-
tively. The 8-hour design value trends have been ana-
lyzed for CO from 2007-2009 for all sites. Table 1 pre-
sents the CPCB standards along with preliminary meth-
ods of measurement of each criteria pollutant. The
QA/QC procedures are presented in more details at
3.2. Persistence of Exceedance
Persistence of exceedance was analyzed for criteria pol-
lutants for each year by grouping number of exceedance
days. This type of analysis would indicate the frequency
of exceedances occurring on two consecutive days as
mandated in the standard.
Data analysis-correlations, ratios of criteria pollutants,
time series analysis, monthly and diurnal averages have
been used to identify the sources of the pollutants and
represent temporal variations of the pollutants on annual,
seasonal and diurnal basis.
4. Results and Discussion
4.1. Design Value Trends of Criteria Pollutants
The design value trends of daily 24-hour NO2 and SO2
Copyright © 2011 SciRes. ACS
Table 1. National ambient air quality standards (revised since 2009).
Concentration in Ambient Air
S. No Pollutant Time Weighted
Average Industrial, Residential,
Rural and Other Area
Ecological Sensitive Area
(Notified by Central
Methods of Measurement
Annual 50 20
1 Sulphur Dioxide (SO2) µg/m3 24 Hours 80 80
Improved West and Geake
Ultraviolet Fluorescence
Annual 40 30
2 Nitrogen Dioxide (NO2) µg/m3
24 Hours 80 80
Modified Jacob & Hochheiser
(NA-Arsenic Method)
PM10 Annual 60 60
3 µg/m3 24 Hours 100 100
Gravimetric TOEM Beta
PM2.5 Annual 40 40
4 µg/m3 24 Hours 60 60
Gravimetric TOEM Beta
8 Hours 02 02
5 Carbon Monoxide mg/m3 1 Hour 04 04
Non Dispersive Infra Red
(NDIR) Spectroscopy
values have been illustrated in Figures 2(a)-(b) respec-
tively. The initiative to convert public transport from die-
sel fuel to Compressed Natural Gas (CNG) was launched
in April 2001. This led to a considerable decrease of air
pollutants [16]. However, as seen in Figure 2(a) that at
two of the sites (ITO and Sirifort), design concentrations
of NO2 exceed the National Ambient Air Quality Stan-
dards of 80 µg/m3, based on the new standards released
by CPCB in November 2009. There is a sharp increase
on NO2 concentration at ITO in 2005 and at Sirifort in
2007 with a decrease thereafter. Emissions at an urban
site such as ITO and semi-urban site such as Sirifort are
highly variable and affected by traffic flow patterns [17]
that can result in fluctuations of concentrations. Sirifort
may also be influenced by NO2 emissions from thermal
power plant at Badarpur depending on wind direction.
Note, there is again a sharp rise of NO2 levels at ITO in
2009. ITO recorded unusually high ozone concentrations
in 2009 [18,19]. NO is emitted from traffic which is con-
verted to NO2 by non-linear photochemical reactions
governed by ozone concentrations particularly during the
early morning rush hours when the sunlight catalyzes the
interactions between NOx and VOC to enhance ground
level ozone [14]. It is thus difficult to devise appropriate
emission strategies for NO2 concentrations for a traffic
junction like ITO. DCE being a relatively clean site away
from major roadways does not feel the vehicular impact
and shows a steady decreasing trend.
Design values of SO2 reveal that concentrations at all
sites are mostly well below the standard (Figure 2(b)).
Industrial emissions are the primary contributors to SO2
in Delhi [20]. The diesel vehicles in Delhi do not make
much impact on SO2 concentrations at sites such as ITO
which is impacted by vehicular emissions [21] such as
ITO. ITO and DCE have relatively higher SO2 concen-
trations (40 - 60) µg/m3 as compared to Sirifort. The SO2
concentrations at ITO can be affected by the Pragati
power plant whereas DCE is influenced by wind-borne
pollution from surrounding industries.
Design value trends of 8-hour CO concentrations based
on 22nd highest values (Figure 2(c)) reveal decrease in
CO concentrations at observational sites, though concen-
trations are above the current CPCB standard of 2 mg/m3.
The decreasing trend may be due to lowering of CO con-
centrations from vehicular sources because of newer im-
proved engines, advanced emission reduction technology
and cheaper fuel like diesel and CNG replacing gasoline.
DCE maintained constant values slightly above 2 mg/m3
indicating near compliance with the CO standards. The
reason might be that the location of DCE observational
site is far away from major roads.
The design values of 24-hour concentrations of PM2.5
at ITO (Figure 2(d)) disclose that PM2.5 concentrations
are far above the US and CPCB NAAQS. The ex-
ceedance of PM2.5 indicates that the stringent measures
imposed on vehicular emissions are inadequate in control-
ling PM2.5. Vehicle exhaust, construction activity and
road side dust are important sources for fine particulate
matter. However, PM2.5 concentrations do display de-
creasing trend. In case of PM10 concentrations at DCE
(Figure 2(e)) which exceeds national and international
standards (US NAAQS) the most important contributors in
Delhi are industrial sources [20] and roadside dust.
4.2. Persistence of Exceedances
Figure 3 reveals the persistence of exceedances of NO2
at ITO site, although number of 1-day exceedances are
highest in each year, there are episodes of exceedances in
each year for other two sites also, which persist for sev-
eral consecutive days. ITO has the highest number of
Copyright © 2011 SciRes. ACS
(a) (b)
(c) (d)
Figure 2. (a) Design value trends of NO2 for ITO, Sirifort and DCE, New Delhi; (b) Design values of SO2 at ITO, Sirifort and
DCE, New Delhi; (c) Design values of CO at ITO, Sirifort and DCE, New Delhi; (d) Design values of PM2.5 at ITO, New Delhi;
(e) Design values of PM10 at DCE, New Delhi.
Figure 3. Persistence and count of exceedance.
Copyright © 2011 SciRes. ACS
persistent exceedances amongst the three sites with
highest number of consecutive days ( > 21) exceeding
the threshold values in some years. For urban Sirifort and
residential DCE although the number of successive days
exceeding threshold values is lower than ITO site, there
are consecutive days where the standard is exceeded The
persistence analysis indicates high concentrations of NO2
at traffic junction at ITO due to dominance of diesel ve-
hicles. NO2 shows noticeable day-today autocorrelation
lasting for up to two days exhibiting some temporal de-
pendence on the previous NO2 levels and atmospheric
The persistence of CO exceedances at ITO and Sirifort
(Table 2) indicate that unlike NO2 although there is less
tendency for CO exceedances to last for more than two
consecutive days, CO does not exhibit significant day-to-
day autocorrelations, indicating the ambient concentra-
tions are more random and not influenced as much by
previous day’s concentrations.
The persistence of PM2.5 and PM10 exceedances (Ta-
ble 3) every year reveal episodes of 2 consecutive days
and above exceedances every year since 2007. Particu-
late matters also do not divulge autocorrelations. There-
fore, persistence of exceedances is influenced by high
intermittent emissions of particulate matter.
The persistence of exceedance of pollutants questions
the validity of standards based on exceedance of two
consecutive days. This persistence needs to be taken into
account in developing future policy.
4.3. Monthly Averages of Criteria Pollutants
Monthly averages of NO2, CO, PM2.5 and PM10 have been
studied. For Delhi, CO is considered to be mostly from
gasoline-fuelled vehicles, NO2 and PM10 are influenced by
vehicular and industrial emissions; PM2.5 by vehicular,
industrial and fuelwood emissions in winter. CO does not
exhibit noticeable inter-annual variability at any of the
three sites, signifying that there is not much variation in
emission sources from one year to the next (Figure not
shown). However, there is significant inter-annual and
seasonal variability in the concentration of other criteria
pollutants. PM2.5 is characterized by high concentration in
winter and low concentrations in the monsoon due to re-
moval by precipitation and wet deposition. High PM10
concentrations in summer can be accounted to the effects
of winds from WNW direction [5], which brings dust from
the Thar Desert into Delhi. High winter averages for PM2.5
and PM10 occur due to limited pollutant dispersion be-
cause of formation of high pressure system over Delhi [11]
which results in lower mixing heights with stable bound-
ary layers. The high monthly averages of PM2.5 at ITO
(Table 4) in January have decreased from 219 g/m3 in
2007 to 150 g/m3 in 2009 exhibiting the impact of con-
trol measures on vehicular emission. However, there are
also varying sources of emission for PM in the winter
months, due to an increase in the bio-mass burning for
heating purposes [22] which explains higher peaks of
PM10 at residential site such as DCE in winter (Table 4).
Table 2. Persistence of NO2 exceedance at Sirifort and DCE (2004-2009).
Year 1 day
2 day
3 consecutive
4 consecutive
5 consecutive
6 consecutive
7 day
10 consecutive
Site Sirifort DCE Sirifort DCE Sirifort DCE Sirifort DCE Sirifort DCE Sirifort DCE Sirifort DCE Sirifort DCE
2004 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
2005 0 10 0 2 0 0 0 0 0 2 0 0 0 0 0 0
2006 1 1 1 1 2 0 1 0 0 0 0 0 1 0 0 0
2007 8 3 7 0 3 0 3 0 3 0 0 0 0 0 0 0
2008 4 4 2 0 1 0 1 0 0 0 0 0 0 0 1 0
2009 7 4 3 0 1 0 0 0 0 0 1 0 0 0 0 0
Copyright © 2011 SciRes. ACS
Table 3. Count of exceedances for particulate matter.
PM2.5 (ITO) PM10 (DCE)
Count of exceedances Frequency Count of exceedances Frequency
1-day 2 1-day 8
2-day 4 2-day 2
3-day 3 3-day 1
4-day 3 4-day 2
5-day 2 5-day 6
9-day 1 6-day 2
16-day 1 7-day 1
22-day 1 8-day 1
33-day 1 9-day 1
13-day 1
14-day 1
16-day 1
PM2.5 (ITO) PM10(DCE)
Count of exceedances Frequency Count of exceedances Frequency
1-day 3 1-day 5
2-day 2 2-day 2
3-day 2 3-day 2
4-day 1 4-day 2
5-day 2 5-day 2
9-day 1 6-day 1
11-day 1 7-day 1
23-day 1 14-day 1
28-day 1 33 -day 2
30-day 1 39-day 1
39-day 1
PM2.5 (ITO) PM10 (DCE)
Count of exceedances Frequency Count of exceedances Frequency
1-day 7 1-day 7
2-day 8 2-day 3
3-day 1 3-day 2
4-day 1 4-day 1
5-day 1 5-day 1
6-day 2 6-day 4
7-day 1 7-day 2
18-day 1 10-day 1
12-day 1
15-day 1
17-day 1
18-day 1
Copyright © 2011 SciRes. ACS
Copyright © 2011 SciRes. ACS
4.4. Ratios of Pollutants
As stated before vehicles are primary sources of carbon
monoxide and nitrogen oxides (NOx) in urban regions.
NO is reactive, is emitted from anthropogenic sources
and converted to NO2. Impacts of mobile source emis-
sions are associated with high CO/NO2 ratios and low
SO2/NO2 ratios whereas impacts of point source is seen
with lower CO/NO2 ratios and higher SO2/NO2 ratios
[13]. Data from three monitoring sites were analyzed for
CO/NO2 ratios and SO2/NO2 ratios from 2007 to 2009
(Table 5). The annual ratios of CO/NO2 are considerably
higher than SO2/NO2 at all the three sites, confirming
mobile emissions to be the primary source of air pollut-
ants at the three sites. The uncharacteristic low value of
CO/NO2 ratio at Sirifort in 2007 can be accounted for by
the spike in NO2 emissions (Figure 2(a)). ITO has lower
CO/NO2 ratios as compared to Sirifort and DCE. At ITO,
the NO2 emissions are correspondingly higher because of
the effect of industrial NO2 emissions from Pragati ther-
mal power plant and the proximity of outer ring road
where diesel-powered trucks run during night hours
bringing in excess NO2 emissions into the site. At ITO
the ratio shows a sharp increase in 2008 and this effect
might be due to variable impact of NO2 emissions from
Pragati thermal power plant and traffic at crossroads in
the vicinity. These results support the findings of analy-
sis with 1998 and 1999 ambient data, which demon-
strated higher CO/NO2 ratios at ITO site in comparison
to SO2/NO x ratios [11,12]. The seasonal ratios of these
pollutants have also been computed (Table not shown).
The SO2/NO2 ratios do not exhibit significant seasonal
variations. However, CO/NO2 ratios do show an increase
during the period (April-September) that includes the
rainy season as NO2 is washed out by precipitation and
CO is not as much affected since it is an inert compound.
Winter months are characterized by increasing concen-
tration levels of both the pollutants and, thus, the sea-
sonal ratios are not much affected.
4.5. Correlations among Pollutants
The preliminary correlation analysis considered 24-hour
averaged values of pollutants since 2007 to maintain
consistency amongst pollutants. Linear regression of the
statistical data reveals variable correlations between all
pollutants for all three sites. Although more NOx is emit-
ted from diesel vehicles and more CO from gasoline ve-
hicles, higher correlations in 2007 and 2008 at ITO site,
indicate the emission mix is more homogeneous at ITO
[16] than at Sirifort and DCE. The correlations between
NOx and PM2.5 are higher in 2007 and 2008 at ITO since
both originate primarily from vehicular exhaust emissions
Table 4. Monthly averages of PM2.5 at ITO and PM10 at
PM10 at DCE (mg/m3) PM2.5 at ITO
200720082009 2007 2008 2009
January 397475254.52 219.67 151.64149.58
February 232337228.2 137.73 137.73122.57
March 202271215.49 79.49 79.4987.26
April 355185264.62 96.84 96.8455.5
May 225185198.25 67.66 67.6654
June 17296 150.36 63.33 63.3369.1
July 102106116.13 45.08 45.0851.65
August 99 89 NA 33.78 33.7839.19
September102115 NA 47.35 47.3533.85
October 291307268 159.89 159.89138.74
November370356NA NA NA NA
December373325NA 189.32 178.67 NA
Table 5. CO/NOx and SO2/NOx Annual Ratios at ITO, Siri-
fort and DCE, New Delhi.
Annual Ratios at ITO
Year CO/NO2 SO2/NO2
2007 6.40 0.06
2008 15.95 0.10
2009 24.04 0.05
Annual Ratios at Sirifort
Year CO/NO2 SO2/NO2
2007 18.39 0.14
2008 32.07 0.22
2009 62.80 0.5
Annual Ratios at DCE
Year CO/NO2 SO2/NO2
2007 45.92 0.52
2008 43.08 0.44
2009 77.96 1.1
and poor correlation between NO2 and PM10 suggests
different sources of origin. In the Delhi region, signify-
cant proportions of PM10 concentrations are initiated by
roadside dust, industrial emissions and long-distance
transport from the Thar Desert during summer time.
There is no significant correlation between SO2 and any
of the pollutants for any year (not shown in Table). The
significance of correlation test shows no significant cor-
relations between pollutants in 2009. The spatial and
annual variability of correlations (poor correlations in
2009 in comparison to 2007 and 2009) between the pol-
lutants (Table 6) also depict the uncertainty of emission
sources and influence of meteorological parameters such
as temperature, wind speed and wind direction on emis-
sion mix.
4.6. Diurnal Averages of Criteria Pollutants
Study of spatio-temporal characteristics of criteria pollut-
ants is important to devise appropriate control strategies
for them. The diurnal averages of pollutants reveal varia-
tions that occur because complex physical and chemical
processes, which determine pollutant concentrations, are
impacted by factors such as spatio-temporal variations of
emission sources, daytime and nighttime chemistry of
atmosphere, pollutant transport, and precipitation.
PM2.5 averaged diurnal profile at ITO (Figure 4(a)),
revealing early morning peak at 6 am followed by office
rush hour traffic around 10 am. The PM2.5 peak is at eve-
ning rush hour at 6 pm and rising concentration till mid
night because of diesel fueled trucks travelling on outer
ring road close to the ITO site and stable boundary layer,
which cause nighttime increase in PM2.5.
The NO2 averaged diurnal profile (Figure 4(b)) indi-
cates that NO2 concentrations are much higher at ITO in
comparison to Sirifort and DCE. The trend of NO2 val-
ues at ITO is dictated by diesel vehicles and is strongly
correlated with traffic rush hours. There is a peak in
morning office rush hours during 7 am to 9 am. There is
again a rise from 6 pm due to evening rush hour traffic.
NO2 concentrations at Sirifort and DCE are not much
affected by traffic as compared to ITO. Weekends do not
have any significant impact on NO2 concentrations at all
the three sites.
The CO diurnal (Figure 4(c)) profile is traffic related
at all the three sites with rush hour traffic signal during
morning and evening hours manifested at all the three
sites. ITO has the highest CO concentration. Both ITO
and Sirifort are characterized by higher nighttime con-
centrations. The elevated levels of nighttime concentra-
tions of CO persist in the presence of stable boundary
layer. CO is not affected by nighttime chemistry in the
atmosphere. It is also seen that at the urban ITO site
there are no significant differences between weekday and
weekend effects. However, at DCE and the Sirifort sites
there is a slight drop in CO concentrations over weekend
indicating lesser traffic influence during this period.
(a) (b)
Figure 4. (a) Diurnal average of PM2.5 at ITO, New Delhi. (b) Diurnal average of NO2 at ITO, DCE and Sirifort, New Delhi. (c)
Diurnal average of CO at ITO, DCE and Sirifor t , New De lhi.
Copyright © 2011 SciRes. ACS
Table 6. Correlations of pollutants.
Correlation for NO2 vs. CO concentration
Year ITO Sirifort DCE
2007 0.643 0.701 0.085
2008 0.392 0.088 0.207
2009 0.069 0.26 0.121
Correlation for NO2 vs. PM2.5 at ITO, New Delhi Correlation for NO2 vs. PM10 at DCE, New Delhi
2007 0.545 2007 0.171
2008 0.456 2008 0.054
2009 0.20 2009 0.26
5. Summary
A study was conducted with analysis of 1-hour, 8-hour
and 24-hour averaged values of criteria pollutants at
three monitoring sites over the Delhi region, to examine
the spatio-temporal variability of the pollutants at these
sites and examine the status of ambient air quality in
Delhi. The results derived from the most recent observa-
tions for the years 2004-2009 reaffirmed the conclu-
sions drawn by earlier studies that mobile sources con-
tribute mostly to emissions loading. It is as well revealed
that although ambient concentrations of SO2 are still un-
der control, the design values for NO2, CO and PM2.5
exhibit exceedance. There is considerable variability in
the NO2 design value at ITO, signifying that meteoro-
logical uncertainties and emission fluctuations have to be
considered in designing emission controls. The persis-
tence of exceedance needs to be taken into account in
designing policy that two consecutive days’ exceedance
can be treated as violation of ambient air quality stan-
dards; more realistic procedures should be set as criteria
to meet the standards. The variability in monthly aver-
ages of pollutants denotes the impact of seasonal vari-
ability on concentrations. The high CO/NOx ratios indi-
cate that gasoline-powered vehicles are significant con-
tributors of air pollution at all the three sites. Low values
of SO2/NOx indicate that point sources contribute mainly
to SO2 concentrations [11]. Despite exceedance, decrease
of annual monthly concentrations of PM2.5 suggests the
benefits of vehicular emissions reduction measures. An-
nual ratios indicate traffic emissions as primary source
for NOx, CO and PM2.5. The diurnal averages of criteria
pollutants disclose that vehicular emissions are the main
contributor towards temporal variation of these pollut-
ants. Weekday and weekend diurnal averages do not
show noticeable differences. Detailed emissions invent-
tory and advanced modeling approaches are required to
present more realistic future scenarios for developing
emissions control programs. A more comprehensive and
holistic picture of air quality assessment in Delhi can
occur only when better temporal and spatial coverage of
emissions, meteorological and air quality data are inte-
grated with appropriate modeling approaches.
6. Acknowledgements
The authors are thankful to Central Pollution Control
Board (CPCB) for providing the data and are grateful to
the anonymous reviewers for the comments which helped
improve quality of the paper.
7. References
[1] J. S. Pandey, K. Rakesh and S. Devotta, “Health Risks of
NO2, SPM and SO2 in Delhi (India),” Atmospheric Envi-
ronment, Vol. 39, No. 36, 2005, pp. 6868-6874.
[2] MOEF, “White Paper on Air Pollution on Delhi with an
Action Plan,” Ministry of Environment and Forests, Delhi,
[3] K. Varshney and P. K. Padhy, “Total Volatile Organic
Compounds in Urban Environment of Delhi,” Journal of
Air and Waste Management Association, Vol. 48, 1998,
pp. 448-453.
[4] K. Srivastava and V. K. Jain, “A Study to Characterize
the Influence of Outdoor SPM and Metals on Indoor En-
vironments in Delhi,” Journal of Environmental Science
and Engineering, Vol. 47, No. 3, 2005, pp. 222-231.
[5] P. Goyal and Sidhartha, “Present Scenario of Air Quality
in Delhi: A Case Study of CNG Implementation,” At-
mospheric Environment, Vol. 37, 2003, pp. 5423-5431.
[6] R. Khaiwal, E. Wauters, S. Tyagi, M. Suman and R. V.
Grieken, “Assessment of Air Quality after the Implemen-
tation of Compressed Natural Gas (CNG) as Fuel in Pub-
lic Transport in Delhi, India,” Environmental Monitoring
and Assessment, Vol. 115, No. 1-3, 2006, pp. 405-417.
Copyright © 2011 SciRes. ACS
[7] V. Kathuria, “Impact of CNG on Vehicular Pollution in
Delhi: A Note. Transportation Research Part D,” Trans-
port and Environment, Vol. 9, No. 5, 2004, pp. 409-417.
[8] S. Mukherjee and V. Kathuria, “Is Economic Growth
Sustainable? Environmental Quality of Indian States after
1991,” International Journal of Sustainable Development,
Vol. 57, No. 3, 2006, pp. 477-493.
[9] A. Chaloulakou, P. Kassomenos, N. Spyrellis and P.
Koutrakis, “Measurements of PM10
and PM2.5 Particle
Concentrations in Athens, Greece,” Atmospheric Envi-
ronment, Vol. 37, No. 5, 2003, pp. 649-660.
[10] S. T. Rao, I. G. Zurbenko, R. Neagu, P. S. Porter, J. Y.
Ku and R. F. Henry, “Space and Time Scales in Ambient
Ozone Data,” Bulletin of the American Meteorological
Society, Vol. 78, No. 10, 1997, pp. 2153-2166.
[11] V. P. Aneja, A. Agarwal, P. A. Roelle, S. B. Phillips, Q.
Tong, N. Watkins and R. Yablonsky, “Measurements and
Analysis of Criteria Pollutants in New Delhi, India,” En-
vironment International, Vol. 27, No. 1, 2001, pp. 35-42.
[12] V. P. Aneja, B. E. Hartsell, D. S. Kim and D. Grosjean,
“Peroxyacetyl Nitrate in Atlanta, GA: Comparison and
Analysis of Ambient Data for Suburban and Downtown
Locations,” Journal of the Air and Waste Management
Association, Vol. 49, 1999, pp. 174-185.
[13] Central Pollution Control Board (CPCB), Data.
[14] S. Guttikunda, “Photochemistry of Air Pollution in Delhi,
India: A Monitoring Based Analysis,” SIM-Air Working
Paper Series, Vol. 25, 2009a, pp. 1-12.
[15] J. M. Jones, C. Hogrefe, R. F. Henry, J. Ku and G. Sistla,
“An Assessment of the Sensitivity and Reliability of the
Relative Reduction Factor Approach in the Development
of 8-hr Ozone Attainment Plans,” Journal of Air and
Waste Management Association, Vol. 55, 2005, pp.
[16] D. Parrish, M. Trainer, M. P. Buhr, B. A. Watkins and F.
C. Fehsenfeld, “Carbon Monoxide Concentrations and
Their Relation to Concentrations of Total Reactive Oxi-
dized Nitrogen at Two Rural U. S. Sites,” Journal of
Geophysical Research, Vol. 96, No. D5, 1991, pp. 9309-
9320. doi:10.1029/91JD00047
[17] S. Gokhale and M. Khare, “Vehicle Wake Factor for
Heterogeneous Traffic in Urban Environments,” Interna-
tional Journal of Environment and Pollution, Vol. 30, No.
1, 2007, pp. 97-105. doi:10.1504/IJEP.2007.014505
[18] CSE, “Delhi Is Reeling under High Levels of Deadly
Ozone, Says Latest CSE Analysis,” Centre for Science
and Environment, New Delhi, 2009.
[19] CSE, “The Smokescreen of Lies: Myths and Facts about
CNG, Right to Clean Air Campaign Team,” Centre for
Science and Environment, New Delhi, 2001.
[20] R. Gurjar, J. A. Van Aardenne, J. Lelieveld and M.
Mohan, “Emissions Estimates and Trends (1990-2000)
for Megacity Delhi and Implications,” Atmospheric En-
vironment, Vol. 38, No. 33, 2004, pp. 5663-5681.
[21] R. Gurjar, T. M. Butler, M. G. Lawrence and J. Lelieveld,
“Evaluation of Emissions and Air Quality in Megacities,”
Atmospheric Environment, Vol. 42, No. 7, 2002, pp.
1593-1606. doi:10.1016/j.atmosenv.2007.10.048
[22] S. Guttikunda, “Air Quality Management in Delhi, India:
Then, Now and Next. Air Quality Management in Delhi,
India,” SIM-Air Working Paper Series, Vol. 22, 2009b,
pp. 1-8.
Copyright © 2011 SciRes. ACS