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Aerosol and Air Quality Research, 13: 977–991, 2013 Copyright ©
Taiwan Association for Aerosol Research ISSN: 1680-8584 print /
2071-1409 online doi: 10.4209/aaqr.2012.10.0263 Characteristics and
Sources of Carbonaceous Aerosols in PM2.5 during Wintertime in
Agra, India Tripti Pachauri, Aparna Satsangi, Vyoma Singla, Anita
Lakhani, K. Maharaj Kumari* Department of Chemistry, Faculty of
Science, Dayalbagh Educational Institute, Dayalbagh, Agra 282110,
India ABSTRACT
PM2.5 samples were collected at traffic, rural and campus sites
in Agra during Nov 2010 to Feb 2011 and characterized for
carbonaceous aerosols. The average mass concentrations of PM2.5
were 308.3 ± 51.8 μg/m3, 91.2 ± 17.3 μg/m3 and 140.8 ± 22.3 μg/m3
at the traffic, rural and campus sites, respectively. The 24-h mass
concentrations of PM2.5 were significantly higher than the limit of
60 μg/m3 prescribed in the National Ambient Air Quality Standards
(Indian NAAQS) and 25 μg/m3 of those of the WHO (World Health
Organization). The average concentrations of OC (organic carbon)
and EC (elemental carbon) were 86.1 ± 5.2 and 19.4 ± 2.4 at the
traffic site, 30.3 ± 12.9 and 4.0 ± 1.5 at the rural site and 44.5
± 18.5 μg/m3 5.0 ± 1.4 μg/m3 at the campus one. The contributions
of TCA (Total Carbonaceous Aerosol) at the traffic, campus and
rural sites were found to be 52, 54 and 58% of PM2.5 mass,
respectively. A significant correlation was observed between water
soluble K+ and OC at the rural (R2 = 0.63) and campus (R2 = 0.53)
sites compared to the traffic one (R2 = 0.35). This may be
attributed to increased biomass burning emissions at the rural and
campus sites. The concentrations of SOC (Secondary Organic Carbon)
were estimated based on the minimum OC/EC ratio, and were found to
be 15.3 ± 6.3, 8.2 ± 5.8 and 28.8 ± 15.8 μg/m3, accounting for 18,
24.7 and 60.7% of total OC at the traffic, rural and campus sites,
respectively. The surface morphology of the particles was analyzed
by scanning electron microscopy and energy- dispersive X-ray
spectroscopy (SEM/EDX). The results indicated branched chain-like
aggregates of carbon bearing spheres at the traffic and rural
sites, while at the campus site carbon-rich and minerogenic
(mineral dust) particles were the dominant ones. Keywords: Organic
carbon; Elemental carbon; PM2.5; SEM/EDX; SOC. INTRODUCTION
Atmospheric aerosols play an important role in regional air
quality, public health, atmospheric chemistry and climate change
especially over East Asia (Jacobson, 2001). They are produced from
various natural and anthropogenic sources and have significant
direct radiative impact through absorption and scattering of
incoming radiation (Haywood and Boucher, 2000; Tare et al., 2006).
It comprises a mixture of sulfates, nitrates, carbonaceous
particles, sea salt, and mineral dust (Hansen et al., 2000).
Atmospheric carbonaceous particles have recently gained importance
because of their influence on climate and adverse health effects
(Frazer, 2002). Carbonaceous matter is usually classified into
organic carbon (OC) and elemental carbon (EC). Elemental carbon is
a primary pollutant emitted from anthropogenic combustion sources
and does not undergo chemical transformations, while OC can be
either released directly into the atmosphere * Corresponding
author. Tel.: 91-562-2801545;
Fax: 91-562-2801226 E-mail address:
[email protected]
from anthropogenic and biogenic sources (primary OC, POC) or
formed within the atmosphere through gas-to-particle conversion of
volatile organic compounds through photochemical reactions
(secondary OC, SOC) (Turpin and Huntzicker, 1995; Cao et al., 2003;
Seinfeld and Pandis, 2006). EC possesses a strong capability of
absorbing solar radiation and is considered to play an important
role in global climate change as it causes positive radiative
forcing. It is the second most important component of global
warming, after CO2 (Hansen et al., 2000; Jacobson, 2001) while OC
is mainly a scattering medium and exerts a negative climate forcing
influence (Houghton et al., 2001; Li and Bai, 2009). Despite the
evident significance of carbonaceous aerosols in the process of air
chemistry and physics information concerning their exact impacts on
climatic and environmental processes is still limited because of
our poor understanding of its concentrations, chemical composition
and formation mechanisms (Jacobson et al., 2000; Li and Bai, 2009;
Zhang et al., 2011).
With increasing industrial development and urbanization, the
contribution from anthropogenic sources to aerosol loading has
significantly increased, especially over the urban/industrial
locations of South Asia (Praveen, 2009; Safai et al., 2010; Zhang
et al., 2011; Deshmukh et al.,
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Pachauri et al., Aerosol and Air Quality Research, 13: 977–991,
2013 978
2012b, c, d). This significant increase in air pollution
resulted from natural as well as anthropogenic aerosols consisting
of black carbon (BC), organic carbon, dust, sulfates, nitrates, and
fly ash often referred as Atmospheric brown clouds (ABCs)
(Ramanathan et al., 2005). Considering the key role of ABCs in
atmospheric radiative as well as chemical properties, several
experiments have been carried out including Indian Ocean Experiment
(INDOEX) to develop emissions inventory for PM2.5 (Reddy and
Venkataraman, 2000) as well as Black carbon (BC) and organic matter
(OM) (Reddy and Venkataraman, 2002a, b). In addition to these
studies, there are several studies focusing on the field
measurements of carbonaceous aerosols in TSP (Rengarajan et al.,
2007; Sudheer and Sarin, 2008; Ram et al., 2008; Ram and Sarin,
2010a; Satsangi et al., 2010; Kumar et al., 2012; Satsangi et al.,
2012), PM10 (Venkataraman et al., 2002; Ram and Sarin, 2010b) and
PM2.5 (Ram and Sarin, 2011; Rengarajan et al., 2011) but the
detailed information regarding the concentration levels and site to
site variation of carbonaceous aerosols in fine particles (PM2.5)
is still limited in India.
Therefore, the present work has been carried out to compare the
relative contribution of carbonaceous species (OC and EC) to PM2.5
mass during Nov. 2010 to Feb. 2011 at traffic, rural and campus
sites of Agra, India and to identify the possible sources and
factors affecting carbonaceous species. An attempt has also been
made to characterize the elemental composition and morphology of
individual atmospheric particles using SEM-EDX method. During
winter season, extensive use of biomass combustion results in
significant increase of atmospheric particles and gaseous
pollutants which are known to have major impact on local
atmospheric chemistry and human health. Thus, in order to evaluate
the enhanced pollution the present study mainly emphasizes on
characterization of carbonaceous
aerosols during wintertime. MATERIALS AND METHODS Description of
Sampling Sites
Agra (27°10′N, 78°05′E, and 169 m.s.l.) is located in the north
central part of India. It is the home of world famous heritage
monument Taj Mahal. It is bounded by the Thar Desert of Rajasthan
on its South East, West and North West peripheries and is
therefore, a semiarid area with a marked monsoon season.
In the present study, three sites were selected for the
collection of PM2.5 samples in Agra (Fig. 1): traffic (National
Highway II), rural (Lal Gadi) and campus site (Dayalbagh
Educational Institute). The site descriptions are as follows.
National Highway II: It is the busiest highway of Agra. This
site is influenced by heavy traffic flow with more than 105 average
traffic loads of vehicles per day (Satsangi et al., 2012). It lies
about 3 km away from the industrial area (Nunhai). The sampler was
mounted on the rooftop of a roadside house (8 m away from road)
about 15 m height above the ground.
Lal Gadi: This is small village situated at the northern
outskirts of Agra city. This site is surrounded by agricultural
fields with minimal traffic/industrial activity. Coal, wood, crop
residues and cowdung cakes are mainly used as fuel for cooking
purpose. Agricultural activities predominate throughout the year
(Kulshrestha et al., 2009). The sampler was installed on the
rooftop of a one story small house about 12 m above the ground
levels.
Dayalbagh Educational Institute: This campus site is about 10 km
away from the industrial sector of the city. The Institute campus
lies by the side of the road that carries mixed vehicular traffic
on the order of 103 vehicles in a day (Satsangi et al., 2012). This
site is surrounded by
Fig. 1. Location of Agra indicating traffic, rural and campus
sites.
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2013 979
small residential community. Therefore, it represents mixed area
of traffic and residential environments. The sampler was placed on
the roof of Science Faculty building about 12 m above the ground
level. Sample Collection and Quality Control
PM2.5 samples (n = 48; 16 samples per site) were collected using
Fine Particulate Sampler (Envirotech APM 550) operated at a
constant flow rate of 16.6 L/min on pre-weighed 47 mm quartz fibre
filters (Pallflex, Tissuquartz). Simultaneously sampling was done
for 24 h with frequency of once a week at all the three sites
during winter season from Nov 2010 to Feb 2011. Before exposure,
the quartz fiber filters were pre-heated in a muffle furnace at
800°C for 3 h to remove organic impurities. Filters were weighed
thrice before and after sampling using four digit balance (Mettler,
Toledo, reading precision 10 μg). Before weighing the samples were
equilibrated in desiccators at 20–30°C and relative humidity of
20–35% in humidity controlled room for 24 h. The conditioned and
weighed PM2.5 filters were placed in cassettes and placed inside
polyethylene zip-lock bags and taken to the field for sampling to
avoid contamination of the filters on the way. Laboratory blank
filters (n = 4) were also collected to reduce gravimetric bias due
to filter handling during and after sampling. Filters were handled
only with tweezers coated with Teflon tape to reduce the
possibility of contamination. After weighing the samples were
wrapped in aluminum foil and sealed in polyethylene zip-lock bags
and stored in deep freezer at –4°C until the time of analysis to
prevent the degradation of organic compounds due to
photo-oxidation. It was assumed that the collected particulate
matter was uniformly distributed over the entire area of
filters.
The fine particulate sampler is designed to work at a constant
flow rate of 16.67 ± 0.83 L/min. The flow rate of the sampler was
calibrated before every sample through Gas Flow Meter for “Leak
Test” in order to avoid any air leakage and to check accurate flow
of air to the sample. Daily flow rate calculations (gas meter
reading/timer reading) were made to make sure that the fluctuations
in flow rate are within the range. Glass fibre filter in the wins
impactor was changed after 48 h of sampling or when the filter gets
clogged. The filter in the wins impactor was rinsed with 3–4 drops
of silicon oil at regular intervals as per the need. Periodic
cleaning of the sampler was done to make the sampler dust free so
that the dust on the sampler may not be counted with the mass
concentration of the sample.
Blank test was also monitored by using operational blanks
(unexposed filters), which were processed with field samples. The
blank filters were taken once a month. They were exposed in the
field when the field-sampling box was opened to remove and replace
field samples. Field blank values were very low (0.2 ± 0.1 μg),
typically below or around the method detection limits (0.28 ± 0.1
μg/m3, using 3σ values of total procedural blank concentrations of
the filter). Carbonaceous Species Analysis and Quality Control
A portion of filter samples (1.5 cm2) was cut and analyzed for
OC and EC by a thermal/optical Carbon Aerosol Analyzer
(Sunset Laboratory, Forest Grove, OR) using NIOSH 5040 (National
Institute of Occupational Safety and Health) protocol based on
Thermal Optical Transmittance (TOT). A detailed procedure for the
analysis of OC-EC has been described in Satsangi et al. (2012).
Standardization of the Instrument was carried out by sucrose
solution (3.2 μg/μL). A solution of 10 μL gives 32.0 ± 1.8 μg OC.
For quality control, the analyzer was calibrated using a blank
punch of pre-heated Quartz Fiber Filter and standard sucrose
solutions every day. Sampled quartz filters were also analyzed
similarly for blank corrections. The overall blank concentrations
from the quartz filters for OC and EC were 0.5 ± 0.2 and 0.0 ± 0.02
μg/cm2, respectively. These were subtracted from the measured OC
and EC concentrations in the aerosol samples. The detection limit,
precision and accuracy of OC and EC is given in Table 1.
Water-Soluble K+ Analysis and Quality Control
Water soluble K+ was analyzed by using Dionex ICS 1100 Ion
Chromatograph system (Dionex Corp, Sunnyvale, CA) equipped with
guard column (CG12A), analytical column (CS12A), and cation
self-regenerating suppressor (CSRS 300). To extract, half of each
filter was sonicated for 45 min in 1% HNO3 (Parmar et al., 2001)
and K+ was eluted using 20 mM Methane Sulfonic Acid as an eluent
(mobile phase). A series of K+ standards were used in order to
quantify the resulting peak from the ion chromatography. Detection
limit, precision and accuracy of K+ are 0.06 ppm, 3.7% and 1.5%,
respectively (Satsangi et al., 2012). For quality control, unloaded
filter was extracted as described above and analyzed for blank
corrections and subtracted from the measured K+ concentrations in
the aerosol samples. SEM-EDX Analysis
PM2.5 samples collected from different sites were analyzed by
SEM-EDX at National Institute of Oceanography, Goa. The SEM-EDX
analysis was carried out with the help of computer controlled field
emission scanning electron microscope SEM (JSM-5800 LV) equipped
with an energy dispersive X-ray system (Oxford 6841). The dry and
loaded quartz fiber filters were punched in 1 mm2 from the centre
of each sample. All the samples were mounted on plastic stubs for
gold coating. A very thin film of gold (Au) was deposited on the
surface of each sample using vacuum coating unit called Gold
Sputter Coater (SPI-MODULE) which can prepare 6 samples at a time.
The fine coating of gold makes the samples electrically conductive.
The samples were placed in the corner of SEM-EDX chamber. The
working conditions were set at an accelerating voltage of 20 kV, a
beam current of 40–50 μA and a Si (Li) detector 10 mm away from the
samples to be analyzed. X-Ray detection limit is ~0.1%. The Oxford
ISIS EDS system with 133 eV resolutions is capable of collecting
spectrum from multiple points, lines across the interface and
elemental mapping.
EDX analysis was carried out at each analysis point and the
elements present were both qualitatively and quantitatively
measured. Approximately 100 particles were analyzed on each filter.
The EDX spectra of blank Quartz fiber filter was also obtained and
their composition was manually subtracted
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2013 980
during the evaluation of the EDX spectra of individual aerosol
particles Meteorological Data Analysis
The climate of Agra is divisible into three distinct seasons;
summer (March–June), monsoon (July–September) and winter
(October–February) (IMD report, 2009). The winter season is
associated with cold and stable conditions with low ambient
temperature which sometimes drops below 2°C. Relative humidity in
the winter ranges between 41.8 and 91.4%. Meteorological data viz.
ambient temperature, rainfall, relative humidity, wind speed and
wind direction were recorded through an automatic weather
monitoring system (Envirotech’s Wind Monitor WM271) mounted on the
roof of Science Faculty building, 12 m above the ground level. It
was programmed to collect data at 1 min. interval and store them in
memory to be downloaded to a computer. The weather conditions were
very cold and calm during winter season (46.8% calm, Fig. 2). The
wind speed varied in a range of 0.4 to 7 m/s. The temperature
varied from 3 to 31°C whereas relative humidity varied between 41
and
91%. Table 2 shows the mean and ranges for meteorological
parameters during the sampling period.
Back Trajectory Analysis
In order to identify the source and transport pathways of the
airborne particles arriving at the sampling site, the air mass
backward trajectory analysis was carried out. These air mass
back-trajectories were obtained from the final run data archive of
Global Data Assimilation System model using NOAA (National Oceanic
and Atmospheric Administration) Air Resource Laboratory (ARL)
Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)
Model (http://www.arl.noaa.gov/ready/hysplit4.html (accessed via
NOAA ARL Realtime Environmental Applications and Display sYstem
(READY) website http://ready.arl.noaa.gov). The five-day back
trajectory analysis of winter months was simulated at 12:00 hrs
(local time) at 500, 1000 and 1500 m above the ground level and has
been represented in Figs. 3(a) and 3(b). The results indicate short
trajectories originating from local areas around Agra indicate the
dominance of anthropogenic emission sources.
Table 1. Method Detection Limit, precision, accuracy and number
of field blanks below detection limits of different species.
Species Method Detection Limit Precision
(%) Accuracy
(%) No. of Field Blanks Below
Detection Limit (n = 8) PM2.5 (µg/m3) 0.28 2.1 1.3 6 OC (µg/cm2)
0.2 1.7 1 6 EC (µg/cm2) 0.01 1.5 1 8
K+ (ppm) 0.06 3.7 1.5 5
Fig. 2. A wind rose plot for winter season showing 46.8% calm
wind at Agra.
Table 2. Mean and ranges for meteorological parameters at
sampling site Agra during Nov 2010–Feb 2011.
Month Temperature (°C) Rainfall
(mm) Relative Humidity
(%) Vapor Pressure
(kPa) Wind Speed
(m/s) Wind
DirectionNovember 2010 21 (10–31) 0.0 (0–0.1) 57 (41–61) 1.6
(1–1.8) 2.8 (1.2–3.6) NE December 2010 19 (12–29) 0.1 (0–0.2) 61
(48–77) 1.3 (0.9–2.4) 3.1 (2.1–7) NE January 2011 11 (3–18) 0.4
(0–0.6) 78 (56–91) 1 (0.7–1) 3.8 (0.4–4.6) N
February 2011 23 (8–28) 0.1 (0–1) 68 (51–82) 1.2 (0.4–2) 5.2
(0.9–6.8) NW
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2013 981
(a) (b) Fig. 3. (a,b) Five-day air-mass back trajectories during
Dec 2010 and Jan 2011 calculated for 500, 1000 and 1500 m AGL
indicating short trajectories from localized sources. RESULTS AND
DISCUSSION Concentration Levels of PM2.5
The mass concentrations of PM2.5 ranged from 210.8 to 381.7
μg/m3 at traffic site, 72.9 to 118.2 μg/m3 at rural site and 101.3
to 163.9 μg/m3 at campus site, respectively. At the three sites,
the average mass concentrations ranked in the order of traffic
(308.3 ± 51.8 μg/m3) > campus (140.8 ± 22.3 μg/m3) > rural
(91.2 ± 17.3 μg/m3) sites as shown in Table 3. The average daily
concentration of PM during the measurement period exceeded the 24
hour NAAQS of India (60 μg/m3;
http://www.cpcb.nic.in/National_Ambient _Air_Quality_Standard.php)
and WHO (25 μg/m3; whqlib
doc.who.int/hq2006/WHO_SDE_PHE_OEH_06.02_eng.pdf) 24–h guidelines
100% of the time, respectively.
At traffic site, the concentrations levels were 2.2 and 3.3
times higher than campus and rural site which may be attributed to
higher vehicular emissions and resuspended road dust. On the other
hand, various local sources such as vehicular exhaust, waste
incineration and residential heating around campus site and coal as
well as biomass combustion at rural site were the dominant sources
contributing significantly to PM2.5 mass. In addition to these
anthropogenic emissions, the stable meteorological conditions
during winter season (high RH and low wind speed) favor the
accumulation of pollutants. A wind rose (WR) plot of winter months
with 46.8% calm conditions showed low and steady wind speed (mainly
confined to 0–1 m/s; Fig. 2). These climatic conditions i.e. less
dispersion and low mixing heights or lower boundary layer height,
typically 500–1000 m (Nair et al., 2007) during winter months help
the ambient particles to
remain for longer time in the atmosphere. These stagnant
meteorological conditions is also supported by the results of back
trajectory analysis that shows that the site is under the influence
of different local emissions and account for increased levels of
particulate mass (Fig. 3(a) and 3(b)).
Table 3 summarizes comparison of PM2.5 mass levels in India and
other cities of the world during winter period. On comparison with
other traffic site, the mean concentrations were much higher than
the values reported at other traffic dominated sites like
Kathmandu, Nepal, Polytechnic University, China, Beijing Normal
University, China and Milan, Italy, Durg city, Chhattisgarh (215
μg/m3; Deshmukh et al., 2011) and Raipur (268 μg/m3; Deshmukh et
al., 2013b) but lower than the Industrial site of China, Xian. At
rural site, the mean concentrations were much lower than the values
reported at other rural sites like Wusumu, Jinan and Miyun but
comparable with Linan. However, the mean concentration was much
higher than reported at Rachma, Jordan. The levels of PM2.5 at
Dayalbagh University campus was found to be comparable with Jilin
University, China but higher than National University campus,
Singapore, Gwangju University campus, Korea, Jimei University,
China, Institute of Geochemistry, Chinese Academy of Sciences,
Wushan, China. However, the mass concentration was found to be
lower than Zhongshan Uinversity, Guangzhou, China, Tsinghua
University, Beijing, China and University of Engineering &
Technology campus, Lahore, Pakistan and Pandit Ravi Shankar Shukla
University, Raipur (225 μg/m3; Deshmukh et al., 2013a). A clear
distinction observed between the concentration levels at Agra and
other sites is largely due to variation of site and source
emissions.
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Concentration of Carbonaceous Species in PM2.5 The average
concentrations of OC, EC, OC/EC ratio and
TCA (total carbonaceous aerosol) show strong site variation with
the highest concentration at traffic site followed by rural and
campus sites, respectively. The average concentration of OC was
86.1 ± 5.2, 30.3 ± 12.9 and 44.5 ± 18.5 μg/m3 at traffic, rural and
campus sites, respectively. The OC mass concentration at traffic
site was about 2.8 and 1.9 times higher than rural and campus
sites, respectively. This may be attributed to increased emissions
from vehicular exhaust. The emissions from automobile exhaust are
the main sources of VOCs (Volatile organic compounds) emitted
anthropogenically. Kerbachi et al. (2006) reported that near heavy
road traffic, mobile sources account for 75–85% of the aromatic
VOCs emissions of which 70% is from automobile exhaust. During
winter season dispersion and dilution of pollutants is restricted
due to temperature inversion phenomenon and low mixing heights
which causes accumulation of these organic compounds and hence,
high levels of OC was observed. However, the EC concentrations were
found to be 19.4 ± 2.4, 4.0 ± 1.5 and 5.0 ± 1.4 μg/m3 at traffic,
rural and campus sites, respectively. Furthermore, the percentage
contribution of OC to PM2.5 mass was found to be 28%, 33% and 31.6%
while EC contribute 6.2%, 4.3% and 3.5% to PM2.5 mass
concentrations at traffic, rural and campus sites, respectively.
The results show that OC contribute nearly one third of fine
particulate mass at rural and campus site while found to be lower
at traffic site. On applying t-test, the variations in OC and EC
concentrations at different sites was observed to be statistically
significant. The critical value of t (P = 0.05) for 30 degrees of
freedom is 2.04; since the experimental value of t is greater than
this value (18.2, 16.4 and 14.9 between traffic, campus and rural
sites) the difference between the results are significant at the 5%
level. These results indicated that carbonaceous aerosols at Agra
were influenced more or less by local factors at various types of
sampling sites. In Table 3 levels have been compared with only
those
sites where carbonaceous aerosols was analyzed by NIOSH method
using thermal/optical carbon analyzer and data was for winter
period and sites have similar characteristics. As shown in Table 3,
OC and EC concentrations at traffic site were much higher than the
values reported at other traffic dominated sites like Kathmandu,
Nepal, Polytechnic University, China, but lower than the Industrial
site of China, Xian. It is interesting to note that the levels of
OC was found to be lower than the present study but EC levels were
drastically higher at Beijing Normal University, China and Milan,
Italy. On comparison with other rural sites of China the levels of
OC and EC were found to be comparable with the concentrations at
Wusumu but lower than reported at Miyun and Jinan. However, the
mean concentration was much higher than reported at Rachma, Jordan
and Hok Tsui. At Dayalbagh University campus, the OC concentration
levels was found to be comparable with Zhongshan Uinversity,
Guangzhou, China but higher than National University campus,
Singapore, Gwangju University campus, Korea, Jimei University,
China and Institute of Geochemistry, Chinese Academy of
Sciences,
Wushan, China. However, EC mass concentration was found to be
lower than Zhongshan Uinversity, Guangzhou, China, Tsinghua
University, Beijing, China and University of Engineering &
Technology campus, Lahore, Pakistan. On comparison with Dayalbagh
University campus, the levels of OC and EC reported at two urban
sites of India were found to be higher at Kanpur while lower at
Ahmedabad. This observed variation may be due to the different
source emissions at different sites. Contribution of Carbonaceous
Species to PM2.5
Total carbonaceous aerosol was calculated by the sum of EC and
organic matter (OM) which was estimated by multiplying the amount
of OC by 1.6 (for urban) and 2.1 (for non-urban) (TCA = 1.6 × OC +
EC) (Turpin and Lim 2001; Cao et al., 2003; Rengarajan et al.,
2007). The average concentration of TCA was 157.3 ± 10.6, 61.3 ±
22.1 and 76.3 ± 30.5 μg/m3 at traffic, rural and campus sites,
respectively. TCA contribute 52, 54 and 58% to total PM2.5 mass at
traffic, campus and rural sites indicating that the fine particles
at all the three sampling sites are enriched with carbonaceous
species. Higher contribution of TCA may be attributed to higher
emission sources of carbonaceous aerosols as well as unfavorable
meteorological conditions like low wind speed, low mixing height,
frequent inversion etc. that results in stagnation of the
pollutants. Various studies conducted in India during winter season
also report high TCA contribution (as high as 60%) to PM10 mass at
Kanpur (Ram et al., 2010b) and almost 58% to PM2.5 mass at
Ahmedabad (Rengarajan et al., 2011). Increased levels of TCA at
traffic site may be attributed to high vehicular emissions mixed
with some industrial emissions from industrial area (nearly 3 km
away). On the other hand, increased emissions from extensive
biomass burning (coal, fuel wood, cow dung cakes etc) were the
dominant source contributing to carbonaceous aerosols at rural site
as well as campus site. According to the emission inventory models,
biofuel/biomass burning account for 50 to 90% of carbonaceous
aerosol emission from south Asia and for India, it is ~70%
(Gustafsson et al., 2009; Rengarajan et al., 2011). In India, for
the year 1990, burning of biomass and fossil fuels contributed
about 64% and 36% to the total fuel consumption respectively
(Venkataraman et al., 1999). The largest contributions were from
the burning of wood (33%), coal (30%), animal waste (14%), and
agricultural residues (13%) (Deshmukh et al., 2010b). Most of the
biomass burned in India is used as fuel for cooking and heating on
the other hand the major fossil fuels burnt are coal and diesel
(Venkataraman et al., 1999). Mayol-Bracero et al. (2002) during
INDOEX (Indian Ocean Experiment) reported that biomass burning
accounts for ~20% and fossil fuel ~80% of the total carbonaceous
aerosol emission, whereas the observation from the Bay of Bengal
during winter time suggested more than 80% from biomass burning.
This observed biomass burning was mainly in the form of biofuel
combustion, which is widely used in India, mainly for household
purposes (Olivier et al., 1994; Reddy and Venkataraman, 2002a).
A significant correlation between water soluble K+ and
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2013 984
OC abundances further supports that the biomass burning
emissions are the main source of carbonaceous species at rural (R2
= 0.63) and campus (R2 = 0.53) sites, respectively (Fig. 4). Xu et
al. (2012) has also reported a good correlation between K+ and OC
(0.77) during winter season in Fuzhou, China. Due to release of K+
during combustion processes, it is generally used as an index of
biomass burning (Deshmukh et al., 2010a; Deshmukh et al., 2012a).
The K+/OC and K+/EC ratios can be used to characterize the emission
sources biomass burning in comparison with fossil fuel. Relatively
high K+/EC ratios have been reported for biomass burning (range:
0.21–0.46) and low ratios for fossil fuel emissions (range:
0.025–0.09) (Andreae 1983; Ram and Sarin, 2010a). A ratio of K+/EC
reported at various sites of India varied from 0.30–0.69 at
Allahabad, 0.08–0.19 at Jaduguda and 0.15–0.98 at Kanpur (Ram and
Sarin, 2010a), 0.28–1.21 at Hisar (Rengarajan et al., 2007). Table
4 shows the K+/OC and K+/EC ratios from selected sites of India. In
the present study, K+/EC ratios at rural site ranges from 0.3 to
0.69 with an average value of 0.53 while at campus site it varies
from 0.2 to 0.59 with an average value of 0.48. The relatively
higher value of K+/EC ratios indicates biomass burning emissions.
However, at traffic site, the average value of K+/EC ratio (0.08 ±
0.03) was found to be indicative of fossil fuel emissions.
Relationship between OC and EC
The origin of OC and EC can be evaluated by the relationship
between OC and EC (Turpin and Huntzicker, 1995; Chow et al., 1996;
Li and Bai, 2009). A good OC-
EC correlation with correlation coefficient (R) of 0.87 (± 0.3
and ± 11.2 error in slope and intercept), 0.94 (± 2.3 and ± 0.9
error in slope and intercept) and 0.79 (± 3.8 and ± 1.2 error in
slope and intercept) were obtained for traffic, rural and campus
site respectively (Fig. 5). These results indicated the presence of
common dominant sources for OC and EC (biomass burning, coal
combustion and motor vehicular exhaust) because the relative rates
of OC and EC would be proportional to each other. The variation of
regression slope (8–1.8) might have been resulted from the site
variability (different source emissions), meteorological factors
and SOC (secondary organic carbon) formation.
The mass ratios of OC to EC (OC/EC) are used to interpret the
emission and transformation characteristics of carbonaceous
aerosol. They are influenced by emission sources of OC and EC,
secondary organic aerosol (SOA) formation and different removal
rates by deposition of OC and EC (Cachier et al., 1996). If the
OC/EC ratios exceed 2.0, it suggests secondary organic aerosol
formation in addition to primary emission sources. Thus, OC/EC
ratios have been used to indicate the presence of primary as well
as secondary organic aerosols (Chow et al., 1996). Several studies
have reported OC/EC ratio for various emission sources which
includes vehicular exhaust (OC/EC: 2.5–5.0, Schauer et al., 2002),
coal smoke (OC/EC: 2.5–10.5, Chen et al., 2006), kitchen emissions
(OC/EC: 4.3–7.7, See and Balasubramanium, 2008) and biomass burning
(OC/EC: 3.8–13.2, Zhang et al., 2007). It should be noted that the
OC/EC ratios presented above were measured by TOT method, which
were comparatively higher than those by
Fig. 4. Correlation between OC and K+ mass concentration
collected at traffic, rural and campus sites of Agra.
Table 4. Comparison of the average K+/OC and K+/EC ratios in
India.
Sampling Site Time period K+/OC K+/EC Emission sources
References Dayalbagh University campus, Agra
Nov 2010–Feb 2011 0.05 ± 0.11 0.48 ± 0.2 Biomass burning Present
study
Rural, Agra Nov 2010–Feb 2011 0.13 ± 0.1 0.53 ± 0.27 Biomass
burning Present study Traffic, Agra Nov 2010–Feb 2011 0.3 ± 0.1
0.08 ± 0.03 Fossil fuel emissions Present study Kanpur Oct–2008
0.06 ± 0.02 0.28 ± 0.10 Biomass burning Ram and Sarin (2011) Kanpur
Jan–Feb 2007 0.04 ± 0.01 0.42 ± 0.18 Biomass burning Ram and Sarin
(2010a)Allahabad Dec 2004 0.05 ± 0.01 0.44 ± 0.11 Biomass burning
Ram and Sarin (2010a)Hisar Dec 2004 0.08 ± 0.02
0.04–0.130.64 ± 0.19 Biomass burning
Agricultural waste burning Rengarajan et al. (2007)
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Pachauri et al., Aerosol and Air Quality Research, 13: 977–991,
2013 985
Fig. 5. Correlation between OC and EC mass concentration
collected at traffic, rural and campus sites of Agra.
TOR method (Watson et al., 2005; Zhang et al., 2011).
In the present study, the ratio of OC/EC were in the range of
3.6–5.2, 5.5–8.7 and 4.5–11.9, with the averages of 4.4 ± 0.3, 7.5
± 1.1 and 8.1 ± 2.4 at traffic, rural and campus sites,
respectively. This variation might be due to different surroundings
at all the three sites. The ratios were similar to that reported in
the literature for vehicular exhaust at traffic site, coal smoke,
kitchen emissions and biomass burning at rural site while all the
above sources contribute to OC/EC ratio at campus site. The OC/EC
ratio is mostly greater than 2 for most of the samples indicating
the dominance of OC derived from various burning sources (coal,
biomass and biofuel) as well as the presence of secondary organic
particles. However, high ratios could also be attributed to winter
season. During winter months, several reasons are responsible for
high OC/EC ratios namely: increased residential combustion of coal
and wood contribute more to OC than EC, resulting in increased
emission of volatile organic precursors, the stagnant
meteorological conditions (low mixing layer height) resulted in
more SOA formation in wintertime, more semi-volatile organic
compounds condensed into aerosol in lower temperature. The high
wintertime OC/EC ratio have also been observed in many studies of
China like Beijing (Dan et al., 2004), Guangzhou and Hong Kong
(Duan et al., 2007), Taiyuan (Meng et al., 2007), Tianjin (Li and
Bai, 2009) and Shanghai (Feng et al., 2009). Estimation of
Secondary Organic Carbon (SOC)
The direct separation and quantification of primary organic
carbon (POC) and secondary organic carbon (SOC) are difficult.
Therefore, minimum OC/EC ratio method has received widespread
application for the estimation of secondary organic carbon (Turpin
and Huntzicker, 1995; Castro et al., 1999). This approach suggests
samples having low OC/EC ratio contain almost exclusively primary
carbonaceous compounds. So concentration of SOC can be estimated
from primary carbonaceous compounds and TOC (total organic carbon)
using following equation: SOC = TOC – POC (1)
POC = EC × (OC/EC)min (2) where, (OC/EC)min is the value of the
lowest OC/EC ratio. Since the ratios of OC/EC is usually affected
by many factors such as types of emission sources, temporal and
spatial variation, ambient temperature etc. Therefore, the
measurements of POC and SOC are semi-quantitative (Castro et al.,
1999). The observed values of (OC/EC)min in this study were 3.6,
5.5 and 5.1 at traffic, rural and campus sites, respectively. These
values are close to that reported at some sites of China, Shanghai
(2.2–5.3, Feng et al., 2009), Guangzhou (2.3–4.5, Duan et al.,
2007) and India, Ahmedabad (4.7, Rengarajan et al., 2011) Allahabad
(5.7, Ram and Sarin, 2010a), Mt Abu (3.4–4.8, Ram and Sarin 2008)
and Manora Peak (4.8–6.5, Ram and Sarin, 2008).
The calculated average concentrations of SOC at three sampling
sites using the minimum OC/EC ratio are given in Table 5. The SOC
concentration in PM2.5 samples ranged from 2.2–25.3 μg/m3 at
traffic site, 2.7–17.3 μg/m3 at rural site and 5.4 to 50.1 μg/m3 at
campus site. The average concentration of SOC were 15.3 ± 6.3, 8.2
± 5.8 and 28.8 ± 15.8 μg/m3 accounting for 18, 24.7 and 60.7% of
total OC, at traffic, rural and campus sites, respectively. Among
the three sites, highest contribution of SOC was observed at campus
site followed by rural and traffic sites. During winter season,
stable atmospheric conditions strengthen atmospheric oxidation and
enhance the condensation of volatile secondary organic compounds
(VSOCs) on pre-existing aerosol (Strauder et al., 1999; Duan et
al., 2007). The formation of SOA occurs both with the oxidation of
naturally emitted terpenes and with anthropogenic volatile organic
compounds (VOCs) (Ho et al., 2006).
At campus site, high SOC concentration (55% of TC = OC + EC) may
be attributed to combined effect of anthropogenically emitted VOCs
(namely benzene, toluene and xylene, BTX) from combustion sources
as well as naturally emitted terpenes (as the site is surrounded by
deciduous trees which emit terpenes). Singla et al. (2012) have
also monitored and reported high concentrations of VOCs during the
same study period. These emitted VOCs
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2013 986
Table 5. Levels of SOC estimated from minimum OC/EC ratios at
three sites. Sites (OC/EC)min POC (µg/m3) SOC (µg/m3) SOC/OC %
Traffic 3.6 70.8 ± 9.1 15.3 ± 6.3 18.0 ± 8.1 Rural 5.5 22.0 ±
8.3 8.2 ± 5.8 24.7 ± 12.7 Campus 5.1 15.7 ± 4.3 28.8 ± 15.8 60.8 ±
12.8
were assumed to act as source for secondary aerosol formation.
At rural site, the carbonaceous particles were mainly of primary
origin (66% of TC) because of increased biofuel and biomass burning
emissions while some percentage of SOC (22%) may be contributed by
oxidation of naturally emitted terpenes from deciduous trees
surrounding the rural site. However, at traffic site POC
contribution to TC is highest (67%) probably due to increased
primary emissions from Diesel and Gasoline vehicles. At this site,
high EC/TC percentage (18%) is observed which also indicates the
dominance of fossil fuel combustion. SOC contributes only a small
fraction of TC (15%) at this traffic dominated site as a result of
oxidation of anthropogenically emitted VOCs from vehicles (> 105
vehicles/day).
These results were in agreement with other studies of China that
reported high contribution of SOC to total OC especially during
winter season at Beijing (64%, Dan et al., 2004; Tianjin (46.9%, Li
and Bai, 2009), Xiamen (60.1%, Zhang et al., 2011). In India, the
estimated concentrations of SOC contribute (~45% during day) and
(~35% during night) (Ram and Sarin, 2011) and (12–42%, Rengarajan
et al., 2011) of OC in PM2.5 samples during winter season. SEM/EDX
Characterization of PM2.5 Samples
Morphological characteristics (texture, edges and size) of
ambient atmospheric particles collected from different sites were
compared in order to determine their origin. The surface morphology
of the particles collected from the traffic site indicated to have
branched aggregates of carbonaceous matter. SEM images of the BC
particles collected from the exhaust pipe of a petroleum car show
similar co-aggregates of nanometer-sized BC particles (50–100 nm)
(Fu et al., 2006). Bang et al. (2004) also observed aggregates of
crystalline or nano crystalline particulates, ranging from as few
as 2 to more than 1000 nanocrystals and amorphous branched clusters
of carbonaceous spherules originated from fuel combustion processes
including diesel exhaust (DPM; diesel particulate matter). Various
other studies have also reported the presence of carbon chain
agglomerates derived from fossil fuel combustion (Murr and Bang,
2003; Sachdeva and Attri, 2008; Edgerton et al., 2009). The results
of single particle analysis indicated the presence of carbon as
well as Pb bearing particles. The carbon particles were dominated
by C, O (> 90% relative contribution by weight) and traces of
chloride. These carbon rich particles have nearly spherical
morphology and porous surface configuration which facilitates the
surface deposition of OC functional groups (Hansen, 2005) (Fig.
6(a)). Pb bearing particles as shown in Fig. 6(b) also have high
percentage of C (> 85% relative contribution by weight) and
traces of chloride. Sitzmann et al. (1999) has also reported the
presence of Pb particles in road traffic samples in London.
At rural site, burning of coal, wood and other combustibles
substances produced clustered and branched carbonaceous particles
as illustrated in Figs. 6(c) and 6(d). These soot particles were
dominated by chain like aggregates of carbon bearing spheres.
Various studies have reported that the morphology of carbonaceous
particle originated from combustion processes varied from soot
chains to complex structures, which depend on fuels, burning
conditions and atmospheric processes (Cong et al., 2009; Pósfai and
Buseck, 2010; Tumolva et al., 2010). These branched clusters
results from the interconnection of often hundred of carbonaceous
spherules which stick together through a combination of adhesive
surface forces and partial coalescence which occur at high
temperature common during combustion (Murr and Bang, 2003). In the
present study, some individual carbonaceous spherules with traces
of chloride were also found. Similar type of carbon-rich particles
associated with Cl and Si traces originated from burning has also
been reported by various studies (Shi et al., 2003, 2005) while
Sachdeva and Attri (2008) have reported biomass soot aggregates
with elongated flattened fiber like structure. Li et al. (2003) and
Pósfai et al. (2003) has also reported biomass burning influenced
aerosol during single particle analysis.
Analysis of individual particles collected from campus site was
differentiated into two types of particles: carbon rich particles
(Fig. 6(e)) and minerogenic (mineral dust) particles (Fig. 6(f)).
Carbon rich particles were clearly characterized by chain like and
“fluffy” appearance while minerogenic particles were of irregular
shape. These particles were strongly enriched with carbon (> 98%
relative contribution by weight) while minerogenic particles were a
complex mixture of carbon rich particle containing varying amount
of soil related components like Na, K, Mg, Ca and Al. The weight
percentage of different elements present in mineral dust particles
were in order of Ca (29) > Na (22) > C (16) > Al (14) >
K (16) > Mg (3). These particles indicated that carbon rich
particles were internally mixed with the mineral dust particles of
local origin. Earlier study by Li et al. (2010) has also reported
such type of particles during individual particle analysis in the
coastal city of South China. The SEM/EDS characterization of
airborne particles at Agra by Pipal et al. (2011) also show the
dominance of particles rich in carbon and soil related elements.
Edgerton et al. (2009) have reported that soot particles containing
trace amounts of potassium are indicative of wood burning. In the
present study, few carbon rich particles with traces of K and Cl
(15–20% relative contribution by weight) indicating the
contribution of wood burning emissions. CONCLUSIONS
Organic carbon (OC) and elemental carbon (EC) were
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Pachauri et al., Aerosol and Air Quality Research, 13: 977–991,
2013 987
(a)
(b)
(c) (d)
(e) Fig. 6. *Scanning electron images and energy – dispersive
X-ray spectra: (a) A single carbon particle with nearly spherical
morphology dominated by C and O (> 90%) along with traces of
chloride (b) A PbCl2 particle (c) Scanning electron images of
branched clusters of soot particles embedded in the filter paper.
(d) The texture of branched cluster of soot particles (> 98%
relative contribution by weight) with a high magnification (e) A
single soot particle with characterized by chain like and “fluffy”
appearance (f) A irregular shaped minerogenic particle
characterized by complex mixture of carbon rich particle containing
varying amount of soil related components like Na, K, Mg, Ca and
Al. *Images a and b collected at rural site, Images c and d
collected at traffic site, Images e and f collected at campus
site.
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Pachauri et al., Aerosol and Air Quality Research, 13: 977–991,
2013 988
(f) Fig. 6. (continued).
measured in PM2.5 at traffic, rural and campus sites of Agra,
India. Weekly samples were collected during winter season from
November 2010 to February 2011. During the sampling period, 24 h
average mass concentrations ranged from 210–381 μg/m3, 101–163
μg/m3 and 72–118 μg/m3 at traffic, campus and rural sites,
respectively. Relatively high mass concentrations at traffic site
may be attributed to higher vehicular emissions and resuspended
road dust. The average concentration of OC ranged from 77–95, 16–51
and 28–68 μg/m3 while EC ranged from 12–18, 2–6 and 3–7 μg/m3 at
traffic, rural and campus sites, respectively. TCA contributed
about 52 to 58% of PM2.5 mass at different which is probably due to
increased emission sources and less dispersion during winter
period. High K+/EC ratios at rural and campus sites indicated
biomass burning emissions while low ratios at traffic site show
that fossil fuel emissions were dominant. A good OC-EC correlation
with correlation coefficient (R) of 0.87, 0.94 and 0.79 at traffic,
rural and campus sites suggested the presence of common dominant
sources for OC and EC. Low SOC concentrations at rural and traffic
sites revealed primary origin of carbonaceous particles. SEM/EDX
analysis of particles shows the dominance of clusters or aggregates
of carbon particles at traffic and rural sites while at campus site
carbon rich particles with “fluffy” appearance and minerogenic
particles with irregular shape were found. ACKNOWLEDGEMENTS
The authors are grateful to the Director, Dayalbagh Educational
Institute Agra; Head, Department of Chemistry; for facilities
provided; Dr. Shyam Prasad and Mr. Vijay Khedekar, National
Institute of Oceanography, Goa for SEM-EDX analysis of aerosol
samples and Department of Science and Technology, DST project No.:
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Received for review, October 3, 2012 Accepted, January 15,
2013