Ozone 2009_CLEAN - Soil, Air, Water

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

215

2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean Soil, Air, Water 2013, 41 (3), 215225

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

216 Y. V. Swamy et al.


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

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.



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


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.



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.



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.



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.

References

[1] J. Mioduszewski, Y. Xiao-Ying, V. Morris, C. Berkowitz, J. Flaherty,In Situ Monitoring of Trace Gases in a Non-Urban Environment,Atmos. Pollut. Res. 2011, 2, 89.

[2] A. V. Shavrina, Y. V. Pavlenko, A. A. Veles, V. A. Sheminova, I. I.Synyavski, M. G. Sosonkin, Y. O. Romanyuk, et al., TroposphericOzone Columns and Ozone Profiles for Kiev in 2007, KosmichnaNauka Tekhnol. 2008, 14 (5), 85.

[3] S. A. Abdul-Wahab, W. S. Bouhamra, H. Ettouney, B. Sowerby, B. D.Crittenden, A Statistical Model for Predicting Ozone Levels in theShuaiba Industrial Area in Kuwait, Environ. Sci. Pollut. Res. 1996, 3,195.

[4] R. Ooka, M. Khiem, H. Hayami, H. Yoshikado, H. Huang, Y.Kawamoto, Influence of Meteorological Conditions on SummerOzone Levels in the Central Kanto Area of Japan, Proc. Environ. Sci.2011, 4, 138.

[5] W. Chang, H. Liao, H. Wang, Climate Responses to Direct RadiativeForcing of Anthropogenic Aerosols, Tropospheric Ozone, and Long-Lived Greenhouse Gases in Eastern China over 19512000, Adv.Atmos. Sci. 2009, 26, 748.

[6] S. A. Abdul-Wahab, C. S. Bakheitb, S. M. Al-Alawia, PrincipalComponent and Multiple Regression Analysis in Modelling ofGround-Level Ozone and Factors Affecting Its Concentrations,Environ. Model. Softw. 2005, 20, 1263.

Figure 13. NOAAHysplit derived air mass back trajectories at 300m (red),800m (blue), and 1500m (green) AGL during (a) summer, (b) monsoon,and (c) winter.



[7] S. Menon, J. Hansen, L. Nazarenko, Y. Luo, Climate Effectsof Black Carbon Aerosols in China and India, Science 2002, 297,2250.

[8] H. Akimoto, Global Air Quality and Pollution, Science 2003, 302, 1716.

[9] K. M. Latha, K. V. S. Badarinath, Correlation between Black CarbonAerosols, Carbon Monoxide, and Tropospheric Ozone over aTropical Urban Site, Atmos. Res. 2004, 71, 265.

[10] C. Y. Lin, Z. Wang, C. C. K. Chou, C. C. Chang, S. C. Liu, A NumericalStudy of an Autumn High Ozone Episode over SouthwesternTaiwan, Atmos. Environ. 2007, 41, 3684.

[11] S. A. Abdul-Wahab, S. M. Al-Alawi, Assessment and Prediction ofTropospheric Ozone Concentration Levels Using Artificial NeuralNetworks, Environ. Model. Softw. 2002, 17, 219.

[12] C. Mara, A. Franco, D. Cartelle, M. Febrero, E. Roca, Identification ofNOx and Ozone Episodes and Estimation of Ozone by StatisticalAnalysis, Water Air Soil Pollut. 2009, 198, 95.

[13] S. Lal, M. Naja, B. H. Subbaraya, Seasonal Variation in the SurfaceOzone and Its Precursors over an Urban Site in India, Atmos. Environ.2000, 34, 2713.

[14] M. Coyle, R. I. Smith, J. R. Stedman, K. J. Weston, K. J. Fowler,Quantifying the Spatial Distribution of Surface OzoneConcentration in the UK, Atmos. Environ. 2002, 36, 1013.

[15] Y. N. Ahammed, R. R. Reddy, K. Rama Gopal, K. Narasimhulu, D. BabaBasha, L. Siva Sankara Reddy, T. V. R. Rao, Seasonal Variation of theSurface Ozone and Its Precursor Gases during 20012003, Measuredat Anantapur (14.620N), a Semiarid Site in India, Atmos. Res. 2006, 80,151.

[16] S. A. Abdul-Wahab, W. S. Bouhamra, Diurnal Variations of AirPollution from Motor Vehicles in Khaldiya Residential AreaKuwait, Int. J. Environ. Stud. 2004, 61, 73.

[17] M. Pulikesi, P. Bhaskaralingam, V. N. Rayudu, D. Elango, V.Ramamurthi, S. Sivanesan, Surface Ozone Measurements atUrban Coastal Site Chennai, in India, J. Hazard. Mater. 2006, 137,1554.

[18] V. Singla, T. Pachauri, A. Satsangi, K. M. Kumari, A. Lakhani, SurfaceOzone Concentrations in Agra: Links with the PrevailingMeteorological Parameters, Theor. Appl. Climatol. 2012, publishedonline. DOI: 10.1007/s00704-012-0632-z

[19] G. Beig, S. Gunthe, D. B. Jadhav, Simultaneous Measurement ofOzone and Its Precursors on a Diurnal Scale at a Semi Urban Sitein India, J. Atmos. Chem. 2007, 57, 239.

[20] T. Nishanth, M. K. Satheesh Kumar, Diurnal Variation of SurfaceOzone with Meteorological Parameters at Kannur, India, Adv. Appl.Sci. Res. 2011, 2, 407.

[21] S. D. Ghude, S. L. Jain, B. C. Arya, G. Beig, Y. N. Ahammed, A. Kumar, B.Tyagi, Ozone in Ambient Air at a Tropical Megacity, Delhi:Characteristics, Trends and Cumulative Ozone Exposure Indices,J. Atmos. Chem. 2008, 60, 237.

[22] N. N. Purkait, S. De, S. Sen, D. K. Chakrabarty, Surface Ozone and ItsPrecursors at Two Sites in the Northeast of India, Indian J. Radio SpacePhys. 2009, 38, 86.

[23] D. V. Mahalakshmi, K. V. S. Badarinath, C. V. Naidu, Influence ofBoundary Layer Dynamics on Pollutant Concentrations over UrbanRegion a Study Using Ground Based Measurements, Indian J. RadioSpace Phys. 2011, 40, 147.

[24] J. L. Ambrose, H. Mao, H. R. Mayne, J. Stutz, R. Talbot, B. C. Sive,Nighttime Nitrate Radical Chemistry at Appledore Island, Maine,during the 2004 International Consortium for AtmosphericResearch on Transport and Transformation, J. Geophys. Res. 2007,112, D21302.

[25] U. Kumar, A. Prakash, V. K. Jain, A Photochemical ModelingApproach to Investigate O3 Sensitivity to NOx and VOCs in theUrban Atmosphere of Delhi, Aerosol Air Qual. Res. 2008, 8, 147.

[26] P. Tuleta, K. Suhrec, C. Maria, F. Solmona, R. Rosseta, Mixing ofBoundary Layer and Upper Tropospheric Ozone during a DeepConvective Event over Western Europe, Atmos. Environ. 2002, 36,4491.

[27] M. L. Sanchez, M. A. Garca, I. A. Perez, B. de Torre, Evaluation ofSurface Ozone Measurements during 20002005 at a Rural Area inthe Upper Spanish Plateau, J. Atmos. Chem. 2008, 60, 137.

[28] S. Sillman, J. Logan, S. Wofsy, The Sensitivity of Ozone to NitrogenOxides andHydrocarbons in Regional Ozone Episodes, J. Geophys. Res.1990, 95, 1837.

[29] C. J. Walcek, H.-H. Yuan, Calculated Influence of Temperature-Related Factors on Ozone Formation Rates in the LowerTroposphere, J. Appl. Meteorol. 1995, 34, 1056.

[30] C. Duenas, M. C. Fenandez, S. Canete, J. Carretero, E. Liger,Assessment of Ozone Variations and Meteorological Effects in anUrban Area in the Mediterranean Coast, Sci. Total Environ. 2002,299, 97.

[31] S. Rodriguez, J. C. Guerra, Monitoring of Ozone in a MarineEnvironment in Tenerife (Canary Islands), Atmos. Environ. 2001,135, 1829.

[32] N. Manju, R. Balakrishnan, N. Mani, Assimilative Capacity andPollutant Dispersion Studies for the Industrial Zone of Manali,Atmos. Environ. 2002, 36, 3461.

[33] E. J. Rohling, M. Sprovieri, T. Cane, J. S. L. Casford, S. Cooke,I. Bouloubassi, K. C. Emeis, et al., Reconstructing PastPlanktic Foraminiferal Habitats Using Stable Isotope Data: A CaseHistory for Mediterranean Sapropel S5, Mar. Micropaleontol. 2004,50, 89.

[34] K. M. Latha, K. V. S. Badarinath, Seasonal Variations of Black CarbonAerosols and Total Aerosol Mass Concentrations over UrbanEnvironment in India, Atmos. Environ. 2005, 39, 4129.

[35] S. S. Babu, S. K. Satheesh, K. K. Moorthy, Aerosol Radiative Forcingdue to Enhanced Black Carbon at an Urban Site in India, Geophys. Res.Lett. 2002, 29, 1880.

[36] R. B. Stull, An Introduction to Boundary Layer Meteorology, Kluwer,Dordrecht 1988.

[37] S. N. Tripathi, S. Dey, V. Tare, S. K. Satheesh, Aerosol Black CarbonRadiative Forcing at an Industrial City in Northern India, Geophys.Res. Lett. 2005, 32, L08802.

[38] K. V. S. Badarinath, A. R. Sharma, S. K. Kharol, V. Krishna Prasad,Variations in CO, O3 and Black Carbon Aerosol Mass ConcentrationsAssociated with Planetary Boundary Layer (PBL) over Tropical UrbanEnvironment in India, J. Atmos. Chem. 2009, 62, 73.

[39] S. S. Babu, K. K. Moorthy, Aerosol Black Carbon over a TropicalStation in India, Geophys. Res. Lett. 2002, 29, 2098.

[40] C. Venkataraman, K. Reddy, S. Josson, M. S. Reddy, Aerosol Size andChemical Characteristics at Mumbai, India, during the INDOEX-IFP(1999), Atmos. Environ. 2002, 36, 1979.

[41] K. R. Kumar, K. Narasimhulu, G. Balakrishnaiah, B. S. K. Reddy, K.Rama Gopal, R. R. Reddy, S. K. Satheesh, et al., Characterization ofAerosol Black Carbon over a Tropical Semi-Arid Region ofAnantapur, India, Atmos. Res. 2011, 100, 12.

[42] J. Kleffmann, P. Wiesen, Heterogeneous Conversion of NO2 and NOon HNO3 Treated Soot Surfaces: Atmospheric Implications, Atmos.Chem. Phys. 2005, 5, 77.

[43] D. J. Lary, A. M. Lee, R. Toumi, M. J. Newchurch, M. Pirre, J. B. Renard,Carbon Aerosols and Atmospheric Photochemistry, J. Geophys. Res.2007, 102, 3671.

[44] C. Bhugwant, H. Cachier, P. Bremaud, S. Roumeau, J. Leveau,Chemical Effect of Carbonaceous Aerosols on the Diurnal Cycleof MBL Ozone at a Tropical Site: Measurements and Simulations,Tellus Ser. B 2000, 52, 1232.

[45] E. P. Olaguer, B. Rappengluck, B. Lefer, J. Stutz, J. Dibb, R. Griffin,W. H. Brune, et al., Deciphering the Role of Radical Precursorsduring the Second Texas Air Quality Study, J. Air Waste Manage.Assoc. 2009, 59, 1258.

[46] B. Suresh Kumar Reddy, K. Raghavendra Kumar, G. Balakrishnaiah,K. Rama Gopal, R. R. Reddy, L. S. S. Reddy, Y. Nazeer Ahammed, et al.,Potential Source Regions Contributing to Seasonal Variations ofBlack Carbon Aerosols over Anantapur in Southeast India, Aerosol AirQual. Res. 2012, 12, 344.



[47] A. W. Delcloo, H. De Backer, Five-Day 3D Back TrajectoryClusters and Trends Analysis of the Uccle Ozone Sounding TimeSeries in the Lower Troposphere (19692001), Atmos. Environ. 2008,42, 4419.

[48] R. R. Draxler, G. D. Hess, Description of the HYSPLIT 4 Modeling System,(NOAA Technical Memorandum ERL ARL-224), NOAA Air ResourcesLaboratory, Silver Spring, MD 2004.

[49] M. S. Reddy, C. Venkataraman, Direct Radiative Forcing fromAnthropogenic Carbonaceous Aerosols over India, Curr. Sci. 1999,76, 1005.

[50] S. Tiwari, K. Atul Srivastava, S. Deewan Bisht, T. Bano, S. Singh, S.Behura, K. Manoj Srivastava, et al., Black Carbon and ChemicalCharacteristics of PM10 and PM2.5 at an Urban Site of NorthIndia, J. Atmos. Chem. 2009, 62, 193.



Ozone 2009_CLEAN - Soil, Air, Water

Documents

Transcript of Ozone 2009_CLEAN - Soil, Air, Water