Aerosol and Air Quality Research, 16: 519–529, 2016
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online
doi: 10.4209/aaqr.2015.05.0284
Biomass Combustion a Dominant Source of Carbonaceous Aerosols in the
Ambient Environment of Western Himalayas
Ajay Kumar, Arun K. Attri*
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India
ABSTRACT
Use of biomass combustion as primary energy source emit substantial amounts of carbonaceous aerosols (CA) in the
Himalayan environment. Any understanding regarding the impact of CA on human health and climate requires a reliable
estimation of compositional variability of CA associated carbon forms: Elemental carbon (EC), Organic carbon (OC), and
Light absorbing organic carbon (LAOC). This investigation spanning over 14 months was undertaken in the rural part of
the Western Himalayas to estimate temporal variability in the ambient aerosol load (PM10, PM2.5), CA associated carbon
forms. All CA associated carbon forms were part of PM2.5 size fraction, their significantly high concentrations in winter
corresponded with the high biomass combustion. Source apportionment of CA done on the basis of Char-EC/Soot-EC
estimates showed that > 90% of the EC was Char-EC contributed by biomass and coal combustion in winter. Estimates of
K+ (tracer for biomass combustion) showed a strong association with CA associated carbon forms. The estimated values of
CA associated carbon forms during winter matched with the reported values of emission factors for biomass burning. Both
the mass and composition of ambient aerosol were predominantly contributed by biomass combustion in the region.
Keywords: Biomass combustion; Carbonaceous aerosol; Elemental carbon; Organic carbon; Western Himalayas.
INTRODUCTION
Worldwide 2.8 billion people use biomass fuel to meet
their daily needs for cooking and heating (Bonjour et al.,
2013). Almost 66% of the population in India use biomass
(wood, crop residues and dung) as a primary energy source
(Arora and Jain, 2015); however, the percentage of the
population using biomass fuel is far greater in rural, lessurbanized and the Himalayan mountainous regions (Parikh,
2011). In the Western Himalayas, the state of Himachal
Pradesh (HP) having population of 6.8 million (Censes,
2011), > 90% households use 1.46 kg capita–1 day–1 fuelwood
for their daily chores (Table S1, supplementary information).
The fuelwood consumption during festivals increases to
100–200 kg festival–1, and the requirement is even higher
during occasions like marriages and cremations (750–1125
kg occasion–1) ( Rawat et al., 2009; Aggarwal and Chandel,
2010; Parikh, 2011; Chakrabarty et al., 2013). Fuelwood
combustion in traditional stoves and open burning practices
directly emit carbonaceous aerosol (CA), comprising of
elemental carbon (EC) and organic carbon (OC) as major
constituents (Andreae and Gelencser, 2006; Roden et al.,
*
Corresponding author.
Tel.: +91-11-26704309
E-mail addresses: [email protected]; [email protected]
2006; Chakrabarty et al., 2010; Arora and Jain, 2015). These
constituents contribute significantly to indoor and outdoor
air pollution (Pope III, 2000; Balakrishnan et al., 2013;
Bonjour et al., 2013), which has emerged as a leading cause
for millions of deaths every year globally (Lim et al., 2013).
The presence of BC contents, which also include EC, in
ambient environment induce significant climate forcing
next only to CO2 (Jacobson, 2002; Seinfeld and Pandis,
2012; Bond et al., 2013; Jacobson, 2014). In the Himalayan
environment, in recent decades a significant increase in the
BC content has been reported (Lamarque et al., 2010; Menon
et al., 2010; Lu et al., 2012), this is further substantiated from
their reported deposition (10–200 µg kg–1) on glaciers
(Nair et al., 2013), which has raised concerns about their
accelerated melting. In addition, presence of light absorbing
organic compounds (LAOC) in aerosol further add to the
radiative forcing (Andreae and Gelencser, 2006). Climate
forcing related impacts from brown cloud formation over
South Asia region anticipate changes in the monsoon
circulation, an increase in extreme weather events (floods
and droughts), and melting of the Himalayan glaciers
(Menon et al., 2002; Ramanathan et al., 2005; Ramanathan
et al., 2007; Menon et al., 2010; Das and Jayaraman, 2011).
Initiation of mitigation measures to minimize the health
and climate related concerns from ambient CA require a
reliable information on two counts: 1) Time dependent
(monthly, seasonal and annual) variability in CA and
associated carbon forms (EC, BC and LAOC), 2) Source
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Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
apportionment and determination of contributions of the
combustion sources to CA. Lack of information on either
of these two counts would limit the formulation of adequate
mitigation measures to reduce the emissions of CA. Emission
of different carbon forms in CA from combustion; CharEC (EC from low temperature combustion of biomass), SootEC (EC from condensation of gas-phase precursors emitted
from high temperature combustion of fossil fuel), LAOC,
tar balls, BC and OCs depend upon the fuel type and its
combustion conditions (Andreae and Gelencsér, 2006; Han
et al., 2007; Keiluweit et al., 2010). In the absence of a
suitable methodology to distinguish between CA contributing
sources, a reliable source apportionment determination will
be tentative at best. Recently, a temperature-based scientific
rationale of differentiating the estimation of Char-EC and
Soot-EC for determining the source apportionment of
biomass and fossil fuel combustion to CA has been put
forth (Han et al., 2007; Han et al., 2010). The ratios of
estimated Char-EC/Soot-EC is equated as a measure for
the extent of biomass emissions, thereby allowing a reliable
estimation of the contributions arising from combustion
sources. The significance of knowing the segregated estimates
of CA emissions and their temporal variability in terms of
associated carbon forms is crucial information towards an
attempt to evaluate CA’s impact on human health and
climate (Novakov et al., 2000; Mayol-Bracero et al., 2002;
Stone et al., 2007; Bond et al., 2013; Cohen and Wang,
2014).
In this paper we present a comprehensive composition
characterization of the ambient aerosol load (PM10 and
PM2.5) collected in two size fractions from a rural site
located in the Western Himalayas over 14 months. The
analysis of the samples was done to estimate the CA and
associated carbon forms: TC, OC, EC (thermal/optical),
BC (optical) and LAOC. Reliable contributions of CA
arising from biomass and fossil fuel combustion sources
were determined on the basis of the estimated Char-EC and
Soot-EC contents in the aerosol samples. The proportion of
CA arising from the biomass combustion was further
ascertained by measuring the biomass burning tracer K+
ion present in the aerosol samples. The time series profiles
of the aerosol samples and their associated carbon forms
were analyzed with reference to the Himalayan region’s
meteorology.
METHODOLOGY
Study Site Description
The study site was selected in Kangra, the most populated
district (263 km–2) of the state of Himachal Pradesh
(Census, 2011), located in the Western Himalayas (31°2′–
32°5′N and 75°0′–77°45′E). The region is a transition zone
[600 m above mean sea level (msl)] between the planes of
Punjab state and the Himalayan mountains (Fig. S1). The
valley of Kangra is marked with a sub-tropical and subhumid climate conditions and the landscape is covered
with mixed vegetation forests. The practice of using biomass
fuel is widespread for cooking and heating requirements.
About 97% households in the district use wood as a
primary fuel for daily chores (Parikh, 2011). Other possible
sources of CA emission in this region are vehicular traffic
and a few brick kilns located within the radius of 20 km.
Sample Collection
The ambient PM2.5 samples were collected every week
over 24–30 hours, on PTFE (Whatman CN 7592-104) filters
using a low volume sampler (Envirotech–APM 550 MFC)
operated at a flow rate of 16.6 L min–1. PM10 samples were
collected every 10th day over 24–30 hours, on Quartz
microfiber filters (Whatman QMA Cat No. 1851–865) using
a high volume sampler (Envirotech, Model-APM 460 BL)
operated at a flow rate of 1.0–1.1 m3 min–1. The samples
(PM2.5 and PM10) were collected at a height of 20 feet
above the ground along with their respective field blanks
from Jan 2012 to March 2013. The Quartz filters used for
the collection were prebaked at 550°C for six hours and
weighed using a Sartorius electronic microbalance, having an
accuracy of measuring 0.01 mg, in a clean room environment.
The samples were stored at –20°C until their analysis.
Sample Analysis: Estimation of OC and EC
A 0.5 cm2 area was punched from the filters containing
the PM10 sample for the estimation of total carbon (TC), OC
and EC contents by using thermal/optical carbon analyzer
(DRI Model 2001A, Atmos-lytic, Inc., Calabasas, CA, USA);
analysis was done using IMPROVE_A protocol (Interagency
Monitoring of Protected Visual Environments_A): Thermal/
Optical Reflectance and Thermal/Optical Transmittance
based measurements. Sample filters were heated in pure
helium (He) atmosphere in the absence of oxygen to
quantify OC fractions (OC1, OC2, OC3, and OC4) at 140,
280, 480, and 580°C respectively. Subsequent steps estimated
differentiated EC fractions in an oxidizing atmosphere (2%
O2 and 98% He) as EC1, EC2, EC3 at 580, 780, and 880°C
respectively. Continuous laser reflectance signal corrects
the pyrolysis of OC if it occurs. Estimation of Char-EC =
EC1 – Pyrolized-OC and Soot-EC = EC2 + EC3 followed
the established approach (Han et al., 2007). However, the
OC, EC, Char-EC and Soot-EC exhibit a continuum and
their experimental separation is not as clear as their definition.
But, due to lack of accepted protocol of their separation,
measurements using thermal/optical methods have been
considered reliable. The details regarding this method have
been discussed elsewhere (Han et al., 2010). The details of
the underlying physical principle for the measurements of
different carbon forms in the samples are available in the
literature (Chow et al., 2007).
Estimation of BC, LAOC and Water Soluble K+ Ion
The BC and LAOC contents in the aerosol size fraction
PM2.5 were quantified by using a transmissometer (Magee
scientific, USA); their estimation with reference to the
blank was done at 880 nm and 370 nm respectively (Husain
et al., 2007). The values obtained were corrected with
reference to the field blanks. Water soluble K+ in PM10
samples was measured using ion chromatography (Metrohm,
Compact IC model 882). The details of this procedure are
given elsewhere (Yadav and Kumar, 2014).
Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
Meteorological Data
To assess the impact of the meteorological variables on
the temporal profiles of TC, OC, EC BC and LAOC present
in the aerosol samples, the required relevant data (3 hourly
resolution) was obtained from Air Resources Laboratory,
National Oceanic and Atmospheric Administration (http://
www.arl.noaa.gov/) for the time span over which the aerosol
samples were collected. The average values of meteorological
variables– Temperature (T), relative humidity (RH), dew
point (DP), planetary boundary layer (PBL), wind direction
(WD) and wind speed (WS)– over the sample collection
time span (24–30 hours) were calculated from 3-hourly data.
RESULTS AND DISCUSSION
Temporal Profile of PM2.5, BC and LAOC
The average concentration of PM2.5 and associated BC
and LAOC carbon forms for the study period, respectively
were 47.3 µg m–3, 2.2 µg m–3 and 2.0 µg m–3; the average
of BC and LAOC concentration in the PM2.5 load was
4.7% and 4.2%; both BC and LAOC displayed strong
associations with PM2.5 load. The obtained average values
for BC in PM2.5 samples agreed with the reported values
for summer season (Sharma et al., 2014b). To evaluate the
seasonal effects, the study period was classified into three
seasons: summer (Mar–Jun), monsoon (Jul–Oct) and
winter (Nov–Feb) (Goyal, 2004). Significant season wise
change was observed in the average PM2.5 load: 35.8
µg m–3 (summer), 32.0 µg m–3 (monsoon) and 67.4 µg m–3
(winter). Seasonal average concentration of BC and LAOC
corresponded with the changes observed in the aerosol
load; respectively they were (BC and LAOC) 2.2 µg m–3 and
1.5 µg m–3 (summer), 1.6 µg m–3 and 1.2 µg m–3 (monsoon)
and 2.8 µg m–3 and 2.9 µg m–3 (winter). The seasonal profile
of BC was comparable with other investigations from
India (Tiwari et al., 2014; Guha et al., 2015).
The concentration variability of PM2.5, BC and LAOC is
plotted for three seasons in Figs. 1[A1], 1[B1] and 1[C1].
The regression fit for the concentrations of BC and LAOC
are plotted as a function of PM2.5 load in Figs. 1[A2], 1[B2]
and 1[C2]. Strong and statistically significant (p < 0.001)
correlations of BC and LAOC carbon forms were observed
with the PM2.5 in all seasons. During summer, BC contents
showed a relatively higher correlation with PM2.5 (R =
0.93) than with LAOC (R = 0.84). During monsoon, both
carbon forms followed similar correlations with PM2.5: R=
0.87 (BC) and R = 0.86 (LAOC). Interestingly, higher
correlations for BC and LAOC with PM2.5 were observed
in winter: R = 0.92 (BC) and R = 0.93 (LAOC). Ambient
concentrations of PM2.5, BC and LAOC were lowest during
monsoon, taking these concentrations as a reference the %
increase in other seasons was calculated. During summer
the respective increase in PM2.5, BC and LAOC was 11%,
41% and 24%; whereas, in winter the increase was
substantial: 110% (PM2.5), 81% (BC) and 131% (LAOC).
An increase in % change was observed in summer, but this
increase was much larger in winter and it corresponded
with an increase in the trend of biomass combustion activity
in this region (seasonal values of biomass use is given in
521
Table S1). It can be inferred from the results that BC and
LAOC contents predominantly contribute to PM2.5 size
fraction. Their steep increase during winter further suggest
that their major contributions were from local combustion
sources.
Temporal Profiles of PM10 and Associated TC, EC and
OC
The average concentrations of PM10 and associated OC,
EC in the samples obtained in different seasons are given
in Table 1. The average, over the study period of PM10
load was 76.6 ± 43.1 µg m–3 of which 26% was from TC
(18.6 ± 9.8 µg m–3); the average concentration of OC was
13.5 ± 6.9 µg m–3 and of EC was 5.1 ± 3.6 µg m–3; they
accounted for 19% and 7% of the PM10 load respectively.
The concentrations were in agreement with the reported
findings for summer season (Sharma et al., 2014b). Similar
agreement was also observed with the reported emission
factors (EF) obtained for biomass combustion for IndoGangetic Plain (Saud et al., 2012). The Total_CA fraction
present in the samples, calculated by using the relationship
Total_CA = 1.6 × OC + EX (Cao et al., 2005), accounted
35% of the PM10 load. On average, 68% of the PM10
fraction’s load was explained by the PM2.5 load, which
agreed with the values reported from other investigation
(Cao et al., 2005).
During summer months, 44% of PM10 concentration
(90.7 ± 60.1 µg m–3) was from PM2.5 load; in winter PM2.5
fraction’s proportion increased to 89% whereas during
monsoon PM10 load was moderate but the proportion of
PM2.5 fraction contribution to PM10 load was 62%. Similar
temporal profiles were observed for PM10 associated OC
and EC in different seasons: OC concentrations were 11.9
± 4.58 µg m–3 (summer), 7.56 ± 3.49 µg m–3 (monsoon) and
18.26 ± 6.74 µg m–3 (winter); whereas, EC contents were
3.24 ± 2.17 µg m–3 (summer), 2.59 ± 1.17 µg m–3 (monsoon)
and 7.97 ± 3.34 µg m–3 (winter). The calculated percent
change, with reference to the study period’s averages,
observed in different seasons for aerosol fractions and
associated carbon forms is shown in Fig. S2: maximum
concentrations of PM10, PM2.5 and carbon forms (OC, EC,
BC and LAOC) were encountered in winter, minimum
during monsoon and were marginally higher in summer.
The significant increase in the aerosol load and CA
fraction during winter implied higher emissions of CA
from biomass combustion; biomass burning requirements
during this season increase significantly (Rawat et al.,
2009). Further, increase in the ambient aerosol load is also
assisted by the lowering of the planetary boundary layer
(PBL) height during winter (Yadav et al., 2013). A decline
in biomass combustion activity and increase in the PBL
height during summer results in the observed decrease in
the ambient aerosol load; overall, a sharp decline in the
aerosol concentration during monsoon is induced through
rain assisted washout (Yadav et al., 2013).
Variability and Statistical Distribution of OC/EC Ratios
in Different Seasons
The variability of OC/EC ratio over the study period
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Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
Fig. 1. Plotted month-wise profiles for the concentrations of PM2.5, BC and LAOC obtained in summer [A1], monsoon
[B1] and winter [C1]. Regression fit for BC and LAOC concentrations as a function of PM2.5 concentrations for summer,
monsoon and winter are shown in panels [A2], [B2] and [C2] respectively; fitted values for R2 and correlation coefficient
(R) are given in each panel.
ranged from 1.33 to 8.7 and the average was 3.07 ± 1.51.
The obtained ratios were in agreements with those reported
in literature (Aggarwal et al., 2013; Sharma et al., 2014a,
b). Season specific spread in OC/EC ratios was assessed
from the changes in statistical distribution profiles of
OC/EC ratios plotted as box and whisker plots for summer,
monsoon and winter season (Fig. 2[A]). The maximum
spread in the ratios in different season was 7.3 (summer),
5.2 (monsoon) and 2.1 (winter). The estimated extent of
variability in the OC/EC ratios in terms of Inter Quartile
Range (IQR = Q3median – Q1median) was 2.7, 0.9 and 0.6 in
the corresponding respective season. Presence of outliers
(data point having value > 3σ) in summer (open circle point)
and monsoon (asterisk symbol) represents unusually high
values of OC/EC ratio (Fig. 2[A]). This may suggests large
OC contribution from an episodic event. Departure from
statistical normal distribution of OC/EC ratio during summer
(Kurtosis = 1.6 and Skewness = 1.0) and monsoon (Kurtosis
= 6.3 and Skewness = 2.4) implies that CA composition
involves additional confounding factors (meteorological
factors and emission of OCs from vegetation as secondary
organic carbon). During winter season the ratios displayed
near normal distribution (Kurtosis = 0.06 and Skewness =
0.2) indicating CA emissions from homogeneous sources.
A strong correlation between OC and EC (R = 0.8) suggested
their emissions from common sources, where EC arises
from primary combustion sources, which implies that a
major proportion of OC also originated from combustion
Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
523
Table 1. Average concentrations of PM10 and associated carbon forms TC, OC, EC (µg m–3) and calculated different ratios
obtained in different seasons.
Species
PM10
PM2.5
TC
OC
EC
BC
LAOC
Char-EC
Soot-EC
nssK+
PM2.5/PM10
TC/PM10
OC/PM10
EC/PM10
Study
Mean ± σ
76.6 ± 43.1
47.3 ± 28.0
18.60 ± 9.8
13.5 ± 6.9
5.1 ± 3.6
2.2 ± 1.01
2.0 ± 1.12
5.0 ± 3.18
0.19 ± 0.1
1.11 ± 0.6
0.68 ± 0.31
0.26 ± 0.08
0.19 ± 0.05
0.07 ± 0.03
Summer
Mean ± σ
90.7 ± 60.1
35.8 ± 12.1
15.19 ± 5.21
11.9 ± 4.58
3.24 ± 2.17
2.2 ± 0.65
1.5 ± 0.44
2.93 ± 0.77
0.25 ± 0.07
0.96 ± 0.3
0.44 ± 0.13
0.20 ± 0.04
0.16 ± 0.03
0.05 ± 0.02
Monsoon
Mean ± σ
49.45 ± 17.48
32.0 ± 8.8
10.16 ± 4.32
7.56 ± 3.49
2.59 ± 1.17
1.6 ± 0.37
1.2 ± 0.27
2.61 ± 1.13
0.14 ± 0.06
0.49 ± 0.2
0.62 ± 0.18
0.21 ± 0.07
0.15 ± 0.05
0.06 ± 0.02
Winter
Mean ± σ
82.92 ± 32.70
67.4 ± 29.1
26.23 ± 9.16
18.26 ± 6.74
7.97 ± 3.34
2.8 ± 1.02
2.9 ± 1.09
7.72 ± 2.81
0.19 ± 0.12
1.57 ± 0.52
0.89 ± 0.33
0.32 ± 0.05
0.22 ± 0.04
0.10 ± 0.02
Fig. 2. Statistical distribution of OC/EC ratios obtained in summer, monsoon and winter are plotted in panel [A] as box
and whisker plots. Regression fit of EC as a function of OC concentration for the study period is shown in panel [B};
obtained R2 and correlation (R) for the fit are given as an inset. The symbol “1” (summer) and “5” (monsoon) in [A]
represents respectively the outlier sample number in the respective season’s data.
sources. However, additional OC emissions (vegetation and
Secondary OC) may arise from different sources in summer
and monsoon as indicated by an increase in the average
OC/EC ratio (Turpin and Huntzicker, 1995).
Monthly and Seasonal Variation in Char-EC, Soot-EC
and Ratios
Char-EC, emitted during pyrolysis is an important tracer for
biomass combustion; its emission from vehicular combustion
is negligible (Han et al., 2007; Han et al., 2010). In this
investigation a significant variation in the estimates of CharEC and Soot-EC was observed with change of season.
During summer, average Char-EC and Soot-EC respectively
were 2.93 ± 0.77 µg m–3 and 0.25 ± 0.07 µg m–3; in
monsoon 2.61 ± 1.13 µg m–3 and 0.14 ± 0.06 µg m–3; and
in winter 7.72 ± 2.81 µg m–3 and 0.19 ± 0.12 µg m–3. CharEC/Soot-EC ratio during summer, monsoon and winter
were 12.3, 19.0 and 45.6 respectively. Monthly estimates
of Char-EC, Soot-EC and their ratios are plotted in Figs.
3[A] and 3[B]. In winter months (Nov–Feb), high Char-EC
concentrations were detected in the aerosol samples; but
during summer and monsoon months Char-EC concentrations
significantly declined. Season wise changes encountered in
Soot-EC were not significant, during summer their
concentration registered a slight increase, whereas during
the rest of the year they were close to the study period average
concentration. This was also evident from significantly high
Char-EC/Soot-EC ratios during winter, moderate in the
monsoon and minimum in summer months. Starting two
months (January and February) were part of winter season.
The values for Char-EC in aerosol samples in these two
months were comparable to the values obtained in the
following winter months (November–February), however
the Soot-EC contents during initial two winter months were
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Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
Fig. 3. Month-wise variation in the estimated concentrations of Char-EC and Soot-EC contents in aerosol samples shown
in panel [A]: Char-EC represents EC1 and Soot-EC represents sum of EC2 and EC3. The month-wise variation in the
Char-EC/Soot-EC ratios for the study period shown in panel [B]. Dotted vertical lines through [A] and [B] demarcates
seasons (summer, monsoon and winter) and the months in respective season.
higher. Implication of this is evident in the plotted CharEC/Soot-EC ratios (Fig. 3[B]). This implied higher emissions
of Soot-EC and ambient aerosol load, which may arise from
transported particulate matter from the surrounding region.
The variability in the ratios may also be influenced due to
their size and deposition rate (Han et al., 2010). The high
concentrations of Char-EC suggest their emissions from
wood combustion as the extent of this activity surges for
heating requirement during cold winter conditions. In summer
the traditional biomass combustion declines as its usage is
limited only for cooking, consequently its presence in aerosol
load is also low; whereas during monsoon, high humidity
and frequent rain washout could play a role in the decline
of Char-EC and Soot-EC; nevertheless the possibility of
washout of Char-EC is higher due to its bigger size. The
high concentration of Char-EC in winter also is favored by
meteorological conditions; low wind speed and low planetary
boundary layer height reduce the mixing volume and cause
increase in aerosol concentration (Yadav et al., 2013).
Relationship of Aerosol Load and Carbon Forms with
Meteorological Factors
Relationships between meteorological factors with PM10,
PM2.5 and associated carbon forms were obtained from
multiple correlation analysis (Table 2). Strong correlation
of PM10 was seen with OC (R = 0.82) and EC (R = 0.55);
correlation of PM2.5 was even stronger with OC (R = 0.86)
and EC (R = 0.80), which suggested that OC and EC
contributed more to the finer aerosol size fraction (PM2.5).
The correlation of OC contents with both PM10 and PM2.5,
and that of EC correlating strongly only with PM2.5
suggested that additional contributions of OC may come
from non-combustion sources. Higher OC contributions
can arise both from primary and secondary sources, and
EC only from primary combustion sources. Statistically
significant correlations among OC, EC, BC and LAOC
further emphasized that they were predominantly emitted
from common combustion sources.
The aerosol associated carbon forms showed a significant
(p ≤ 0.01) correlation with meteorological parameters DP
> T > PBL. The negative correlations of temperature with
PM2.5 and associated carbon forms indicated an increase in
CA in winter, which was linked with the increase in the
biomass combustion activities; correlations between DP
indicated the removal/scavenging of hygroscopic aerosols
species from ambient environment. The average PBL
height during summer and monsoon varied between 500–
1500 m, whereas it was below 500 m during winter season.
The large variation in PBL height from winter to summer
accompanies a corresponding change in the mixing volume,
which amplifies the ambient aerosol load in winter and
their decrease in summer (Yadav et al., 2013). Calm wind
condition during winter season and the lowering of
ventilation coefficient further ensures the persistence of
aerosol in the ambient environment.
Source Apportionment of CA Using Established
Indicators: The OC/EC, OC/PM and EC/PM Ratios
The OC/EC, OC/PM and EC/PM ratios have been used
in many investigations for determining apportionment of
Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
525
Table 2. Calculated Pearson correlation between aerosol load, associated carbon forms and meteorological factors.
PM10
PM2.5
OC
EC
BC
LAOC
T
DP
PBL
PM10
1
PM2.5
1
0.63**
OC
1
0.82**
0.86**
EC
1
0.55**
0.80**
0.79**
BC
1
0.70**
0.78**
0.82**
0.68**
LAOC
1
0.62**
0.82**
0.86**
0.78**
0.94**
T
1
–0.32*
–0.62**
–0.64**
–0.72**
–0.55**
–0.72**
DP
1
–0.47**
–0.64**
–0.71**
–0.71**
–0.71**
–0.72**
0.76**
PBL
–0.14
1
–0.51**
–0.49**
–0.61**
–0.38*
–0.58**
0.82**
0.41**
** Correlation significant at 0.01 confidence level (2-tailed test), * Correlation is significant at the 0.05 level (2-tailed test).
CA emitting sources. The variability of OC/EC, OC/PM and
EC/PM ratios with change in the season is given in Table S2.
This table also provides ratios obtained in other reported
investigations for comparison. The OC/EC ratio exceeding
2.0 indicates the presence of secondary organic carbon
(Turpin and Huntzicker, 1995). In present investigation,
the OC/EC ratios were 4.14, 3.07 and 2.41 in summer,
monsoon and winter respectively; whereas the OC/PM10,
EC/PM10 ratios were 0.13, 0.04 in summer; 0.15, 0.05 in
monsoon; and during winter 0.22, 0.10 respectively. The
higher OC/EC ratios in summer suggested additional
contributions from secondary organic carbon. Secondary
OC contributions were moderate during monsoon and
minimal in winter. The observed OC/EC, OC/PM10 and
EC/PM10 ratios in this study were comparable to the reported
emissions factors of biomass burning; moreover, the
obtained ratios were in agreement with other reported field
investigations (Table S2) (Aggarwal et al., 2013; Zhang et
al., 2013; Sharma et al., 2014a, b). The OC/PM10 and
EC/PM10 ratios were minimum in summer and monsoon,
but significantly higher in winter; the ratios obtained in
summer corresponded with those reported by Sharma et al.
(2014b). Interestingly, during winter the ratios were closer
to the reported values of emission factors, suggesting that
dominant contributions were from biomass combustion.
Char-EC/Soot-EC
On average, 95% of EC was contributed by Char-EC
and 5% by Soot-EC. Char-EC contributions were 91% in
summer, 94% in monsoon and 97.5% in winter in PM10.
Low Soot-EC in aerosol was expected as the region has no
industrial activity and a very low vehicular density of 17
vehicles km–2 (Vehicle-Transport Department of HP).
Char-EC/Soot EC ratios are considered a better indicator
of biomass burning than OC/EC ratios because OC/EC
ratios can be influenced by their primary emission sources,
different deposition rates, secondary organic carbon and
formation of secondary organic aerosol. A high Char-EC/
Soot-EC ratio indicates the dominance of biomass burning
associated Char-EC contributions to total EC contents;
whereas, ratios < 1 will indicate that Soot-EC from fossil
fuel combustion predominantly contributes to total EC. The
ratios obtained in present investigation indicated that the
ambient aerosol load was arising from biomass combustion,
high ratios in the winter indicated escalation in the biomass
combustion activity. Linear relation between Char-EC (EC1)
and total-EC (EC1 + EC2 + EC3) was obtained from
regression analysis (Fig. 4[A]). Coefficient of determination
(R2) for the fit was 0.99 and correlation (R) 0.99 (significant
at p < 0.001). On the other hand regression fit of Soot-EC
with total-EC was poor (R2 = 0.13, and R = 0.36). This
suggested that dominant contributions of Char-EC originated
from biomass combustion to total-EC contents of the ambient
aerosol. The estimated correlations from present investigation
(total-EC with Char-EC and Soot-EC) (Fig. 4[B]) were in
agreement with findings reported from China (Han et al.,
2009).
Appraisal of Biomass Combustion from Non-Sea Salt K+
(nssK+)
Water soluble K+ is an established tracer for biomass
combustion, but its estimation requires a suitable correction
by accounting for the contributions from sea salt. Therefore,
the estimates of K+ in aerosol samples were suitably
corrected using correction factor for sea salt sodium ions
(Na+) (Rajput et al., 2014).
Regression fits between OC, EC, BC, LAOC, Char-EC,
and Soot-EC with nssK+ (Figs. 5[A]–5[F]) were obtained
to evaluate their emissions from biomass combustion. Except
for Soot-EC, significantly (p = < 0.01) strong relationships
were seen between nssK+ and OC, EC, BC, LAOC and
Char-EC. This was evident from the strong correlation of
nssK+ with OC and EC (R = 0.93 and R = 0.89 respectively),
which validated the assertion that biomass combustion is a
dominant source (Figs. 5[A] and 5[B]); equally strong
correlations were also observed between BC (R = 0.81)
and LAOC (R = 0.85) with nssK+ (Figs. 5[C] and [D]).
Interestingly, while correlation was strong between CharEC and nssK+ (R = 0.89), a comparatively poor correlation
of Soot-EC (R = 0.52) with nssK+ implied that Soot-EC was
arising from sources other than the biomass combustion
(Fig. 5[F]).
The relationships of OC and EC in aerosol samples with
nssK+ were examined for different seasons by subjecting the
data to linear regression. In summer and monsoon, correlation
of nssK+ with OC was strong, whereas correlation with EC
was weak. In winter highly significant correlations of nssK+
with OC and EC underlines the dominance of biomass
burning as a source. During summer wind transported aerosol
from distant regions could contribute significantly. The
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Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
Fig. 4. Regression fit of Char-EC with total EC in aerosol samples for the study period season are shown in panel [A]; the
panel also provides the estimated fit parameters, R2 and correlation coefficient (R). Panel [B] of the Fig. show the
regression fit for Soot-EC as a function of EC along with the fit parameters, R2 and correlation coefficient (R).
Fig 5. Regression fit for aerosol associated carbon forms OC, EC, BC, LAOC, Char-EC and Soot-EC as a function of
nssK+ is shown in Fig.; panels A to F respectively. Each panel also show the fitted parameters values along with R2 and
correlation coefficient (R).
Kumar and Attri, Aerosol and Air Quality Research, 16: 519–529, 2016
527
Table 3. Calculated season-wise average nssK+/OC and nssK+/EC ratios and the spread in the ratios in respective seasons.
nssK+/OC
Summer
Monsoon
Winter
Average
Min—Max
0.06–0.12
0.01–0.10
0.07–0.11
0.01–0.12
nssK+/EC
Mean ± SD
0.09 ± 0.02
0.06 ± 0.03
0.08 ± 0.01
0.08 ± 0.02
regression fit for summer and monsoon with nssK+ is shown
in Fig. S3[A]); OC contents strongly associated with nssK+
(R = 0.8), but moderate correlation of EC with nssK+ (R =
0.55) indicated that during summer and monsoon, significant
EC emissions were arising from non-biomass combustion
activity. However, during winter a strong association of
nssK+ with both OC (R = 0.90) and EC (R = 0.91) does
reflect that their predominant emissions were from increased
biomass combustion.
Characterizing the Type of Biomass Combustion
The composition of the biomass undergoing combustion
can be estimated on the basis of the changes in the nssK+/OC
and nssK+/EC ratios (Mkoma et al., 2013). The calculated
ratios (their spread and average value) for nssK+/OC and
nssK+/EC for winter, summer, and monsoon seasons are
given in Table 3. The table also provides the overall average
spread and mean values obtained for the study period. The
respective ratios (nssK+/OC, nssK+/EC) varied between
0.01–0.12 and 0.06–0.64 respectively. The mean values of
nssK+/OC were 0.08 ± 0.02, and nssK+/EC 0.24 ± 0.11: (1)
the reported values of ratios for tropical forest biomass
combustion respectively are nssK+/OC = 0.06, nssK+/EC =
0.42; (2) for agriculture residues burning nssK+/OC = 0.08,
nssK+/EC = 0.40; and (3) for wood and charcoal combustion
nssK+/OC = 0.08, nssK+/EC = 0.27) (Andreae and Merlet,
2001; Mkoma et al., 2013). Comparison of the ratios obtained
in the present study with those reported in literature suggested
that in the Western Himalayan region wood combustion
was the major source for the emissions of carbonaceous
species in the ambient environment. The major proportion
of CA comprising of OC and EC originated from the
regional biomass burning activities. The observed OC/EC
ratio matches with the biomass combustion emissions factors.
Char-EC overwhelmingly (> 90%) contributed to total EC
content. High correlation of nssK+ with OC, EC, Char-EC,
BC and LAOC suggests their emissions from biomass
combustion. The estimated emission factor of nssK+/OC
and nssK+/EC in the present study emphasized that fuelwood
combustion is a dominant source of CA in this region.
Significant lowering of PBL height and calm wind speed
during winter further ensures that most of the carbon forms
would be from the sources located within the close proximity
of the sampling site.
Min—Max
0.16–0.64
0.06–0.26
0.19–0.26
0.06–0.64
Mean ± SD
0.35 ± 0.14
0.17 ± 0.07
0.24 ± 0.03
0.24 ± 0.11
LAOC. Significant variability in the collected aerosol samples
(PM10, PM2.5) and PM associated carbon forms with change
in season was observed. On average, 68% of the PM10 load
was accounted by PM2.5 size fraction, whereas average
proportion of CA to PM10 load was 35%. The source
apportionment using established indicators suggested the
major proportion of CA originated from the regional biomass
burning activities. The observed OC/EC ratio matches with
the biomass combustion emissions factors. Char-EC
overwhelmingly (> 90%) contributed to total EC content;
high correlation of nssK+ with OC, EC, Char-EC, BC and
LAOC suggests their emissions from biomass combustion.
The estimated emission factor of nssK+/OC and nssK+/EC
in the present study emphasized that fuelwood combustion
is a dominant source of CA in this region. Significant
lowering of PBL height and calm wind speed during winter
further ensured that predominant PM associated carbon
forms were contributed from the sources located within the
close proximity of the sampling site.
ACKNOWLEDGEMENT
Ajay Kumar extends his appreciation to the Council of
Scientific and Industrial Research (CSIR) India for the
award of Research fellowship. Authors also extend their
sincere appreciation towards two anonymous reviewers; their
comments and suggestions have contributed significantly to
improve this manuscript.
SUPPORTING INFORMATION
Table S1 provides details on the daily use of fuelwood
and on specific occasions (festivals/marriages/cremation)
in the different Himalayan regions. Location of the
sampling site, where aerosol samples were collected is
shown in Fig. S1., Fig. S2 show the season-wise % change
in aerosol load (PM10, PM2.5) and of associated carbon
forms. Figs. S3[A] and S3[B] plots the regression fit to
OC, EC concentrations as a function of nssK+ for summer,
monsoon, and winter. For comparison the obtained seasonwise values of aerosol load, TC, OC, EC, OC/EC, TC/PM,
EC/PM in present investigations are given along with the
reported values in literature in Table S2.
Supplementary data associated with this article can be
found in the online version at http://www.aaqr.org.
CONCLUSION
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