ozone 2009_clean - soil, air, water
DESCRIPTION
Ozone at surface levelTRANSCRIPT
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Venkata Swamy Yerramsetti1
Nikhil Gauravarapu Navlur1
Venkanna Rapolu1
N. S. K. Chitanya Dhulipala1
Puna Ram Sinha2
Shailaja Srinavasan1
Gangagni Rao Anupoju1
1Bioengineering and Environmental
Centre, Indian Institute of Chemical
Technology, Hyderabad, India2National Balloon Facility, Tata Institute
of Fundamental Research,
Hyderabad, India
Research Article
Role of Nitrogen Oxides, Black Carbon, andMeteorological Parameters on the Variation ofSurface Ozone Levels at a Tropical Urban Site Hyderabad, India
In this study, temporal variations of surface ozone (O3) were investigated at tropical
urban site of Hyderabad during the year 2009. O3, oxides of nitrogen (NOxNONO2),black carbon (BC), and meteorological parameters were continuously monitored at the
established air monitoring station. Results revealed the production of surface O3 from
NO2 through photochemical oxidation. Averaged datasets illustrated the variations in
ground-level concentrations of these air pollutants along different time scales.
Maximum mean concentrations of O3 (56.75 ppbv) and NOx (8.9 ppbv) were observed
in summer. Diurnal-seasonal changes in surface O3 and NOx concentrations were
explicated with complex atmospheric chemistry, boundary layer dynamics, and local
meteorology. In addition, nocturnal chemistry of NOx played a decisive role in the
formation of O3 during day time. Mean BCmass concentration in winter (10.92mgm3)
was high during morning hours. Heterogeneous chemistry of BC on O3 destruction and
NOx formation was elucidated. Apart from these local observations, long-range trans-
port of trace gases and BC aerosols were evidenced from air mass back trajectories.
Further, statistical modeling was performed to predict O3 using multi-linear regression
method, which resulted in 91% of the overall variance.
Keywords: Anthropogenic source; Back trajectory; Diurnal change; Statistics; Trace gas
Received: November 25, 2011; revised: July 4, 2012; accepted: July 6, 2012
DOI: 10.1002/clen.201100635
1 Introduction
Atmospheric trace gases namely NOx, carbon monoxide (CO), and
sulfur dioxide (SO2) derived from the combustion of fossil fuels are
not only pollutants themselves but also react with many other
compounds such as volatile organic compounds (VOC) leading to
changes in atmospheric compositions [1]. Surface O3 is a secondary
pollutant, that is, not emitted directly by any natural source but is
formed by various complex reactions with atmospheric trace gases
[2]. Abdul-Wahab et al. [3] reported that anthropogenic sources are
responsible for more than 95% of the O3 in the lower atmosphere.
Increase of these air pollutant concentrations decrease the ambient
air quality of the surroundings. High levels of O3 are of serious
concern to human health and environment [4, 5].
Besides the formation of O3, destruction might as well take place
through a number of pathways, mainly by surface deposition.
Scavenging processes dominate the removal of O3 with nitrogen
oxide (NO) by titration process [6]. O3 destruction can occur also
by air-borne particulates namely black carbon (BC) aerosols, sea salt
aerosols, dust, etc. [7, 8]. BC is directly emitted as a primary aerosol
species into the atmosphere through a variety of incomplete com-
bustion of fossil fuels (www.iasta.org.in). The role of BC in O3reduction and as a major contributor of global warming was well
studied by Latha and Badrinath [9].
The mechanisms of surface O3 formation are not identical every-
where and usually depends on relationships between the geographic
location, emission sources, and meteorological factors over a wide
range of temporal and spatial scales [10]. Such relationships have
been examined with several statistical studies using a combination
of regression, graphical analysis, fuzzy logic based method, and
cluster analysis. Multi-linear regression (MLR) analysis is one of
the most widely used methodologies for expressing the dependence
of a response variable on three or more independent variables [6].
Prediction models can help in the identification of O3 episodes,
which is a key issue for protecting the population against the
harmful effects on human health upon exposure, is being widely
investigated [11, 12].
In recent times, there is increased availability of satellite based
observations for atmospheric trace gases and aerosol monitoring
over the globe. Nevertheless, ground based monitoring is important
to validate and to complement space-based measurements and to
clarify local/regional specific sources and sinks of these green house
gases. Such ground based data can assist in deriving the dynamic
Correspondence: Dr. Y. V. Swamy, Bioengineering and EnvironmentalCentre, Indian Institute of Chemical Technology, Discovery Building,Tarnaka, Hyderabad 500067, IndiaE-mail: [email protected]
Abbreviations: ABL, atmospheric boundary layer; AGL, above groundlevel; BC, black carbon; MLR, Multi-linear regression; RH, relativehumidity; SR, solar radiation; VOC, volatile organic compounds; WD,wind direction; WS, wind speed
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behavior of pollutants and to check compliance of statistical models.
These models will help in the development of an environmental
policy, in particular to green house gases, on a local and regional
scale [2].
The urban region of Hyderabad is prone to significant anthropo-
genic impacts due to increase in population and related factors. In
this study, season wise diurnal changes in surface O3 concentration
of NOx and BC were carried out at Hyderabad during 2009. And also,
an attempt was made to understand the chemistry of O3 precursors
and meteorological parameters on O3 formation. Three-dimensional
air back trajectory analysis was carried out to establish the role of
long-range air parcel transport of O3, NOx, and BC aerosols. In
addition to the observational survey, statistical prediction was made
to evaluate the real time changes attributing to the possible causes
for surface O3 fluxes.
2 Materials and methods
2.1 Geographical details and climate
The air monitoring station is situated in the urban area of
Hyderabad, Andhra Pradesh, India. Gas analyzers were installed at
Tata Institute of Fundamental Research National Balloon Facility
(TIFR-NBF; 17.478N and 78.588E), at a site altitude of 536m abovemean sea level. Hyderabad is the capital of Andhra Pradesh state and
is the fifth largest metropolitan city in India, with a population
above 8 million (www.censusindia.gov.in). Hyderabad has a unique
combination of wet and dry climate.
Geographical view of the air monitoring station is shown in
Fig. 1. The site encompasses many industrial development areas
in which several small-scale chemical/pharmaceutical industries
and industrial complexes are established in the south (S), south-
east (SE), and south-west (SW) directions. The vehicular traffic in
Hyderabad is a major contributor to the urban pollution load and
the total vehicular pollution load in the city is 1500 t day1 of whichNOx contribution is 3.85% (www.aptransport.org/html/pollution-
control.htm).
2.2 Methodology
The air samples were continuously analyzed for surface O3 and NOx.
Accuracy of the instruments is sustained by calibrating every fort-
night. Both the analyzers were zero calibrated with dry air. Span
calibration of O3 analyzer was carried out using multi-point inter-
nally assembled O3 generator. Span calibration for NOx was done
using NIST traceable standard NOx gas through multi-point calibra-
tor cum dynamic dilutor (Model 146i, Thermo Scientific, USA). The
Aethalometer used is a self-contained, automatic instrument. It
requires no calibration other than periodic checks of the air flow
meter response. The details of the analyzers used and their working
principles are given in Tab. 1.
Air pollutants namely O3, NOx, BC, and meteorological variables
namely temperature (T), relative humidity (RH), wind speed (WS),
wind direction (WD), and solar radiation (SR) were analyzed during
JanuaryDecember, 2009, while summer is from March to June,
monsoon is from July to October, and winter is from November
to February. Further, statistical analysis was carried out using
ambient air pollutants data and meteorological parameters, which
are the major factors (predictors) that influence O3 concentration.
The correlation matrix obtained for each data set was assessed to
measure the pair wise association among the various variables with
observed O3. Finally, MLR was carried out using the independent
variables and a model equation was derived.
3 Results and discussion
3.1 Temporal variations of trace gases
3.1.1 Ozone
Diurnal O3 episodes at different seasons (Fig. 2) elucidates that mean
concentrations increased from the early hours of the day, then
attained a peak value in the late noon and thereafter dropped at
night. Vertical bars in the figure show the standard deviation (1s)
from the mean. Day time increase in O3 concentration is a pro-
nounced feature of an urban polluted site, because of the photo-
chemical oxidation of the precursors such as CO, CH4, and VOC in
presence of sufficient NOx concentration. Hyderabad being an urban
site, NOx concentrations are commonly observed above the
threshold level (10pptv) which is conducive for O3 production[13]. Following are the conventional photochemical reactions occur-
ring in the lower troposphere.
CO OH! H CO2 (1)
H O2 M! HO2 M (2)
HO2 NO! NO2 OH (3)
NO2 hv! NO O3P (4)
O3P O2 M! O3 M (5)
where M is either O2 or N2.
In the diurnal O3 profile, a sudden drop is observed in themorning
(08:0009:00), which is attributed to O3 titration with NO. After
09:00 am, a raise in O3 concentration is observed till late afternoon
due to the combined effect of NO2 photolysis and increase in atmo-
spheric boundary layer (ABL) height [13]. MaximumO3 concentration
was observed in the afternoon hours (12.00 pm 04.00pm), due to
mixing up of different trace gases in the mixed layer which is
relatively rich in O3 [14, 15]. At night, low O3 levels are observed
due to absence of photochemical oxidation and also, O3 titration
withNO occurs in the residual boundary layer. The variation in night
time O3 values are probably due to difference in reactivity of O3 with
anthropogenic components such as NO, VOC in different environ-
mental, meteorological, and dispersion conditions.
Themonthly O3 episodes are illustrated usingmean values in a 3D
surface contour plot shown in Fig. 3. It revealed an apparent and
systematic seasonal profile for a typical geographical location.
High O3 concentrations were observed in March, April, October,
and December. The increase or decrease in O3 concentration could
be the seasonal variations and related chemical transformations [16].
Seasonal O3 episodes clearly showed maximum O3 in summer
(56.74 ppbv) which is attributed to regional photochemistry
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Figure 1. Geographical location of air monitoring station situated at TIFRNBF (dark green), Hyderabad and its surroundings. major airpollutant emission sources, busy roads (light green), 2012 GeoEye.
Table 1. Equipment details used in air monitoring laboratory
Species Working principle Instrument make/model Range andflow rate
Lower detectionlimit
Responsetime (s)
Zero drift(per day)
O3 Non-dispersive UV photometer Thermo Scientific, USA 01000ppb 1.0 ppb 20
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conditioned by O3 transport. In monsoon, the peak time O3 level is
observed to be low (29 ppbv) due to wet surface deposition of air
pollutants by monsoonal rains and scarce availability of solar inso-
lation. In winter, the peak time O3 concentration is considerably
higher than monsoon (40.29 ppbv), attributing to localization of
precursor trace gases in shallow boundary layer under humid con-
ditions. Observed mean of O3 over Hyderabad are in concurrence
with other sites in India (Tab. 2).
It is apparent from Fig. 4 that O3 concentrations with larger
amplitude are observed almost throughout the year, except during
cloudy and rainy days, due to non-availability of sufficient SR and
washout of air pollutants, respectively. Sharp increase in O3 ampli-
tude during October is attributed to the change in the wind pattern
from southwesterly to southeasterly, which brings the trace gases
from the surrounding industrial development areas to the observa-
tional site. The wider amplitude of the diurnal cycles during winter
and summer is attributed to thermal inversion [13].
These observed changes in O3 concentration is influenced by
several factors in complex process of atmospheric chemistry, dynam-
ics, and transport of air pollutants. ABL is one such factor and it is a
part of troposphere, where vertical mixing of atmospheric pollu-
tants occurs. Generally, ABL height varies both in time and space.
During day time, vertically mixed layer of air mass is convectively
driven and reaches its maximum height by the afternoon due to
solar insolation. The convective energy transfer between the surface
Table 2. Spatial comparison of O3 (meanSD) at different sites in India
S. No. Location Lat./Long. O3 (ppbv) Reference
1 Chennai 13.048N/80.238E 15 4 Pulikesi et al. [17]2 Ahmedabad 23.048N/72.628E 24 14 Lal et al. [13]3 Agra 27.108N/78.028E 53 12 Singla et al. [18]4 Pune 18.548N/73.818E 32 13 Beig et al. [19]5 Kannur 12.268N/75.398E 29 5 Nishanth et al. [20]6 Delhi 28.658N/77.278E 24 4 Ghude et al. [21]7 Kolkatta 22.368N/88.248E 36 26 Purkait et al. [22]8 Hyderabad 17.478N/78.588E 42 14 Present study
Figure 2. Diurnal variation of ozone during different seasons.
Figure 3. Surface contour plot of ozone (ppbv) during different months.
Figure 4. Monthly amplitude distribution profile of trace gases and blackcarbon.
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and air is partly accomplished by turbulent eddies which are pro-
duced primarily by wind shear and buoyancy. After sunset, turbu-
lence decays in the mixed layer and a residual layer is formed.
During night, temperature decreases throughout the depth of the
residual layer causing neutral stratification. Mahalakshmi et al. [23]
observed that the ABL height at Hyderabad varied between 1 and
3.7 km during January to December. The ABL was high duringsummer (MarchMay) with maximum height in April (3.7 km),whereas the ABL was shallow in winter (DecemberFebruary), with
minimum height in December (1.3 km).
3.1.2 Nitrogen oxides
The diurnal NOx plot (Fig. 5) exhibited a double-peak pattern with
two distinct peaks, which are observed in the morning (08:00
09:00 am) and the other at night (9:0010:00 pm). After 09:00 am,
increase in downward solar flux initiates a series of photochemical
reactions between several precursors resulting in formation of O3 by
the conversion of NO2 to NO. Hence, low concentrations of NOx are
noted during afternoon hours. After 5:00 pm, NOx concentration
gradually increases and reaches to a peak value at night. Peak time
NOx concentrations during dawn and dusk are attributed to emis-
sions from heavy vehicular traffic and weak vertical diffusion in the
boundary layer. Low WS and high RH might as well contribute to
weak diffusion of gases. Consequently, the trace gases localization
resulted in high NOx values.
After mid-night, availability of NOx is reduced due to decrease in
traffic emissions, and formation of dinitrogen pentoxide (N2O5)
(reactions (6)(9)). The N2O5 formed exists in equilibrium with NO3and NO2 causing the decrease in NOx concentration. The removal of
N2O5 is expected by its heterogeneous chemistry with moisture and
carbon aerosol particles (reactions (10), (13)(15)). These reactions
increase the availability of NO and NO2 in the atmosphere for
O3 titration and photochemical oxidation, respectively, during
day time (reactions (11) and (12)). Accordingly, day-time and night-
time chemistry of NOx varies significantly with season and environ-
mental conditions [24].
NO O3 ! NO2 O2 (6)
NO2 O3 ! NO3 O2 (7)
NO3 NO! 2NO2 (8)
NO3 NO2 M$ N2O5 M (9)
N2O5 H2O! 2HNO3 (10)
NO3 hv! NO O2 (11)
NO3 hv! NO2 O (12)
The monthly mean of NOx episodes, in the form of 3D surface
contour plot are illustrated in Fig. 6. High concentrations are
observed during March, April, and October. The amplitude pattern
of NOx is shown in Fig. 4 with peak values observed in February, June,
August, and October. The peak hour-averaged NOx values are highest
in summer (8.9 ppbv) followed by monsoon (8.0 ppbv) and winter
(5.0 ppbv). Common NOx profile was observed for all three seasons,
but concentrations varied along the time scale. These changes are
attributed to influence of climate, vehicular and industrial emission
fluxes, long-range transport, and O3 sensitivity to VOC/NOx ratio [25].
3.2 Meteorological parameters
3.2.1 Solar radiation
Diurnal profile of SR (Fig. 7) showed peak value atmid-day (12:00 pm).
Summer showed the highest mean SR of 1048Wm2, monsoonshowed 649Wm2, and winter showed 770Wm2. Pearson corre-lation (r) between O3 and SR (Tab. 3) was positive, suggesting that
surface O3 increases proportionately with SR. Significant correlation
was observedwithO3 for all three seasons indicating O3 formation by
Figure 5. Diurnal variation of NO2, NO, and NOx during different seasons. Figure 6. Surface contour plot of NOx (ppbv) during different months.
Role of Nitrogen Oxides, Black Carbon and Meteorological Parameters on O3 219
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photochemical oxidation. During day-time, trace gases are trans-
ported from surface to upper troposphere due to strong updrafts
by wind. However, downward convective fluxes as well occur, which
transports upper tropospheric air into the lower height ranges [26,
27]. These exchange processes are frequent in summer and may
influence tropospheric O3 concentrations.
3.2.2 Temperature and relative humidity
Diurnal profile of temperature and RH for three seasons is shown
in Fig. 8. The air temperature decreased from evening (04.00 pm)
till early morning (06:00 am), and thereafter, increased gradually
and reached to a maximum value in the afternoon (2:003:00pm).
The increase in air temperature is due to downward solar insolation.
Maximum mean temperature (368C) was recorded in summer.Temperature showed positive correlationwithO3, since temperature
determines rates of chemical reactions important for O3 formation
[28, 29].
Relative humidity was high at midnight and in the early morning,
and it gradually dropped after sunrise. Maximum mean RH is
observed in monsoon (73%), followed by winter (71%) and summer
(58%) at 07:00 am. RH showed negative correlation with O3 for all
three seasons, which signifies that the decline in O3 concentration in
atmosphere occurs by wet deposition and/or by contribution of BC
and NOx at night. The diurnal trends of temperature and RH have
inverse relationships, but the variation is much higher during the
daytime.
3.2.3 Wind velocity
O3 concentration may vary with WS and WD. These two parameters
characterize mechanical turbulence causing dilution/concentration
and transport of air pollutants [30, 31]. Seasonal variations inWS and
WD are illustrated by wind roses (Fig. 9). WS is classified into four
different classes calm (WS< 1.4ms1), soft (1.4WS 2.3ms1),moderate (2.4WS 4ms1), and strong (> 4ms1). A fair estimateof the dispersion of pollutants in the atmosphere is possible based on
the frequency distribution of WD and WS [32]. Accordingly, it was
observed that in summer 91.7% of soft winds advent from the S and
SW directions. In monsoon, 66.7% of strong winds arrive from the SW
direction. But, inwinter 100%of softwinds approach fromSE direction.
Sector between 90 and 1808 in the site layout shows many indus-trial plants and busy roads; which are the potent emission sources
for different trace gases like CO, NOx, etc. Perhaps, the calmwinds in
summer and winter could have localized the air pollutants from
surroundings, causing high O3 levels. Duenas et al. [30] reported that
weak diurnal winds assist in thermal convection in the day which
destabilizes boundary layer and favors O3 mixing from stratosphere
to troposphere, contributing to high O3 concentrations. Rohling
et al. [33] reported that WD causes advection of air masses associated
with high O3 levels. However, it varies by location because of the
unique local topographic factors and mixing height.
3.3 Black carbon aerosols
Black carbon is a ubiquitous atmospheric air mass which is formed
by incomplete combustion fossil fuels. BC affects the radiative
Figure 7. Diurnal variation of SR during different seasons.
Table 3. Pearson correlation matrix of O3 with different variables
Variable Summer Monsoon Winter
NO 0.169 0.294 0.210NO2 0.336 0.213 0.247Solar 0.587 0.464 0.553Temp 0.618 0.454 0.487RH 0.676 0.726 0.768WS 0.332 0.413 0.312WD 0.2785 0.533 0.095BC 0.165 0.104 0.201
Figure 8. Diurnal variation of temperature and RH during differentseasons.
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balance of the earths atmosphere either directly or indirectly. In
addition, heterogeneous reactions on BC aerosols might be of
importance for the transformation of atmospheric pollutants,
caused by the fractal structure of BC to act as a reducing agent
[9]. Average BC concentrations during the entire study period ranged
between 2 and 11mgm3.Black carbon prominently exhibits a pronounced diurnal vari-
ation (Fig. 10). Two peaks are observed, the first sharp is in the
morning (07:0009:00 am) and the second broad peak at night
(7:009:00pm). The morning and night peaks are attributed to vehic-
ular traffic and other anthropogenic activities. Minimum BC values
are observed in the daytime (10:00 am4:00pm) due to increased
mixing within the turbulent boundary layer, temperature gradient,
and WS variations. This turbulence increases the fast dispersion of
BC, reducing its near surface concentration. Gradual increase in BC
concentrations from the evening (5:00 pm) is due to increase in BC
emissions from domestic activities [34] and decrease in the
ABL height by surface-based inversion [35]. As a result, BC settles
down to the lower troposphere and attains maximum value around
7:009:00pm. The existing BC concentration near the surface would
be partly lost due to decreased human activity and by wet deposition
(02:0005:00 am). Stull [36] and Tripathi et al. [37] reported, fumi-
gation effect brings down aerosols from the nocturnal residual layer
just before sunrise. The observed diurnal variation of BC mass
concentration is mainly attributed to dynamics of ABL, local vehic-
ular emissions and burning of fossil fuels [38]. Comparison of mean
occurrences of BC at different sites in India is given in Tab. 4.
Seasonal profile of BC (Fig. 10) showed highest in winter
(10.92mgm3), followed by summer (7.97mgm3), and lowest inmonsoon (4.8mgm3). The monthly BC distribution profile(Fig. 11) showed high values in January, February, and October.
Minimum BC values are observed in July and August due to wet
surface deposition by rain. BC amplitude in Fig. 4 showedmaximum
value in winter due to entrapment in the shallow ABL. Low tempera-
ture and soft winds as well contributed in localization of BC aerosol
concentration [34]. In monsoon, minimum amplitude is observed
due to scavenging effect of rainfall [41]. Furthermore, mechanical
turbulence of wind might have aided in dispersion and dilution of
BC concentration [35]. October showed sudden raise in amplitude
due to change in WD and seasonal transition [13]. In addition to
regional transport, the air particulate transport from long distances
might have contributed to the observed BC variations.
Recent studies indicated that BC aerosols may interact heteroge-
neously with O3 and its precursors to influence O3 variability, NOx/
HNO3 ratio andHOx balance in the atmosphere [4244]. Olaguer et al.
[45] proposed that heterogeneous reactions between NO and HNO3adsorbed on BC surface may contribute to renoxification through
the production of HONO (reactions (13)(15)). Photolysis of HONO
after sunrise produces highly oxidizing OH free radical, which
strongly influences atmospheric chemistry. The NO formed by this
pathway might reduce O3 (08:00 am and 9:00pm). Hence, the obser-
vations between BC and O3 (Fig. 10) showed strong anti-correlation,
which confirms the chemistry between them.
HNO3 BCs ! NO NO2 (13)
Figure 9. Seasonal wind roses for the year 2009.
Figure 10. Diurnal variation of BC at different seasons and its correlationwith ozone.
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HNO3 NO BCs ! HONO NO2 (14)
NO2 BCs ! NO (15)
3.4 Air mass back trajectory analysis
These air pollutants are being transported both horizontally and
vertically long distances by prevailing winds and can be found in
substantial amounts even in the regions situated far away from
potential sources [46]. The possible transport pathways of these
air pollutants from their potential sources of origin are often
examined by tracing the trajectory of a hypothetical air parcel into
the location of interest [47]. In this aspect, back trajectories are
calculated using the Air Resources Laboratorys Hybrid Single-
Particle Lagrangian Integrated Trajectory (HYSPLIT) model (v.4.8)
(www.arl.noaa.gov/ready/hysplit4.html, www.arl.noaa.gov/data/web/
models/hysplit4/win95/arl-224.pdf) [48]. Seven day isentropic back
trajectories were computed such that the trajectory terminated at
TIFR-NBF, Hyderabad at 12:30pm (to commensurate with the O3observations) at different elevations: 300m (red-triangle), 800m
(blue-square), and 1500m (green-circle) above ground level (AGL)
on a typical experimental day. The 7-day period was considered in
view of the atmospheric lifetime of trace gases (O3 and NOx) and BC
[49]. The data points of O3, NOx, and BC (Fig. 12) representing fre-
quent values in each season are considered for trajectory simulation.
It is quite discernible from Fig. 13 that air mass trajectories
(indicated with arrow) have shown seasonal change along with
variation in air mass origin and path. During summer (referring
to 29thMarch, 2009), the transport of trace gases at heights1500m AGL (green) showed inter-continental transport.
Badarinath et al. [38] reported that during pre-monsoon period
(MarchMay) forest fire activity over the southern peninsular region
could bring additional loadings of trace gases as well as BC aerosols.
During monsoon (referring to 15th July, 2009), clean and fresh air is
transported from south and south-west directions. Air originating
from Arabian Sea and Indian Ocean implied mixed type transport,
i.e., continental and marine. Since, the trajectory traversed vastly
from oceanic regions scavenging effect of sea salt aerosols on
air pollutants might occur. Also, washout of air pollutants by
monsoonal rains results in low concentration of air pollutants.
During winter (referring to 29th November, 2009), the approaching
air mass trajectories have a very long continental overpass from
north/north west part of India through the central India before
arriving to the observatory site. These trajectories would thus be
conducive for significant advection of continental trace gases
and aerosols to the measurement region. The high value of trace
gases and BC at the observed site is the consequence of the above
attributions. Many have reported high emissions of BC aerosols
from Indo-Gangetic plain, central, east coastal and south Indian
regions due to extensive use of bio fuels, particularly wood and
large industry installations [38, 50].
3.5 Statistical analysis
O3 being a secondary pollutant (dependent/response variable), its
ambient air concentration is influenced by two major factors (inde-
pendent variables or predictors) namely air pollutant concentrations
(NO, NO2, BC) and meteorological conditions (SR, T, RH, WS, WD).
Since, the predictor variables are often correlated with other pre-
dictor variables, a stepwise regression process was used to select
the most significant predictor variables to be used in the model.
In step wise regression, the variables were added iteratively to the
model, until no additional variables contributed significantly to
Table 4. Spatial comparison of mean BC concentrations at different sites in India
S. No. Location Lat/Long BC (mgm3) Reference
1 Kanpur 26.468N/80.318E 6.020.0 Tripathi et al. [37]2 Bangalore 12.108N/77.608E 0.410.2 Babu et al. [35]3 Trivandrum 08.508N/76.918E 4.08.0 Babu and Moorthy [39]4 Mumbai 18.968N/72.828E 7.517.5 Venkataraman et al. [40]5 Hyderabad 17.478N/78.588E 2.011.0 Present study
Figure 11. Surface contour plot of BC (mgm3) during different months. Figure 12. Day-wise annual variation of BC in the year 2009.
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the explained variance of the observed O3. The regression process
resulted in model equation (16) and its statistics inferred that
variables showed 91% of the overall variance in the observed O3.
O3 118:08 1:13RH 0:18 SR 0:16NO2 0:15NO 0:42WS 0:52 T (16)
4 Conclusions
Typical temporal variations of O3, NOx, and BC were observed at
Hyderabad site for the year 2009. Complex atmospheric chemistry,
boundary layer dynamics, local meteorology were the key players
responsible for the observed changes. The ground-level O3 concen-
trations followed a specific pattern which matches with the daily
solar cycle. NOx and BC peak concentrations correlated well with
busy traffic hours. Contour plots showed relative gradient of O3, NOx,
and BC during different months at different time intervals. Summer
season exhibited the highest O3 concentrations mainly attributed
to the regional NO2 photochemistry. While, monsoon recorded
lowest O3 value due to wet deposition. Winter showed high O3 levels
due to localization of trace gases. BC along the diurnal scale caused
reduction in O3 concentration and participated in heterogeneous
chemistry with NOx. Nocturnal chemistry of NOx played an impor-
tant role in its potential sequestration and in the formation of
radicals that may fuel O3 photochemistry after sunrise. Apart
from local and regional emission sources, long-range transport
of air pollutants was evidenced from air mass back trajectories.
Statistical modeling using MLR was also carried out to forecast
surface O3 concentration using the observed variables as predictors.
The regression model resulted in 91% of overall variance.
Acknowledgments
The authors wish to thank Director, Indian Institute of Chemical
Technology for his encouragement and support. Fruitful discussions
and constant support extended by Prof. Shyam Lal, Dr. C. B. S. Dutt,
and Dr. P. P. N. Rao Programme Director during the course of
the project is highly acknowledged. We also acknowledge ATCTM
under ISRO-GBP trace gas programme for financial support and
Tata Institute of Fundamental Research (TIFR Balloon Facility) at
Hyderabad for providing lab space.
The authors have declared no conflict of interest.
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