Biomass Combustion a Dominant Source of Carbonaceous Aerosols in the Ambient Environment of Western Himalayas

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.


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., 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 CO 2 (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 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; Char-EC (EC from low temperature combustion of biomass), Soot-EC (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 (PM 10 and PM 2.5 ) 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.

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 PM 2.5 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 .PM 10 samples were collected every 10 th 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 m 3 min -1 .The samples (PM 2.5 and PM 10 ) 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 cm 2 area was punched from the filters containing the PM 10 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% O 2 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 PM 2.5 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 PM 10 samples was measured using ion chromatography (Metrohm,Compact IC model 882).The details of this procedure are given elsewhere (Yadav and Kumar, 2014).

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.

Temporal Profile of PM 2.5 , BC and LAOC
The average concentration of PM 2.5 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 PM 2.5 load was 4.7% and 4.2%; both BC and LAOC displayed strong associations with PM 2.5 load.The obtained average values for BC in PM 2.5 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 PM 2.5 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 PM 2.5 , 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 PM 2.5 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 PM 2.5 in all seasons.During summer, BC contents showed a relatively higher correlation with PM 2.5 (R = 0.93) than with LAOC (R = 0.84).During monsoon, both carbon forms followed similar correlations with PM 2.5 : R= 0.87 (BC) and R = 0.86 (LAOC).Interestingly, higher correlations for BC and LAOC with PM 2.5 were observed in winter: R = 0.92 (BC) and R = 0.93 (LAOC).Ambient concentrations of PM 2.5 , 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 PM 2.5 , BC and LAOC was 11%, 41% and 24%; whereas, in winter the increase was substantial: 110% (PM 2.5 ), 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 Table S1).It can be inferred from the results that BC and LAOC contents predominantly contribute to PM 2.5 size fraction.Their steep increase during winter further suggest that their major contributions were from local combustion sources.

Temporal Profiles of PM 10 and Associated TC, EC and OC
The average concentrations of PM 10 and associated OC, EC in the samples obtained in different seasons are given in Table 1.The average, over the study period of PM 10 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 PM 10 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 Indo-Gangetic 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 PM 10 load.On average, 68% of the PM 10 fraction's load was explained by the PM 2.5 load, which agreed with the values reported from other investigation (Cao et al., 2005).
During summer months, 44% of PM 10 concentration (90.7 ± 60.1 µg m -3 ) was from PM 2.5 load; in winter PM 2.5 fraction's proportion increased to 89% whereas during monsoon PM 10 load was moderate but the proportion of PM 2.5 fraction contribution to PM 10 load was 62%.Similar temporal profiles were observed for PM 10 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 PM 10 , PM 2.5 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 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 = Q3 median -Q1 median ) was 2.7, 0.9 and 0. 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  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 Char-EC 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 .Char-EC/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 higher.Implication of this is evident in the plotted Char-EC/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 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 PM 10 , PM 2.5 and associated carbon forms were obtained from multiple correlation analysis (Table 2).Strong correlation of PM 10 was seen with OC (R = 0.82) and EC (R = 0.55); correlation of PM 2.5 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 (PM 2.5 ).The correlation of OC contents with both PM 10 and PM 2.5 , and that of EC correlating strongly only with PM 2.5 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 PM 2.5 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 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/PM 10 , EC/PM 10 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/PM 10 and EC/PM 10 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/PM 10 and EC/PM 10 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 PM 10 .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 (R 2 ) 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 (R 2 = 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).
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   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 0.01-0.12and 0.06-0.64respectively.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.

CONCLUSION
A fourteen month long analysis of ambient PM collected in the Western Himalayan region provides quantitative estimates and characterization of CA fraction: TC, OC, EC, BC, LAOC.Significant variability in the collected aerosol samples (PM 10 , PM 2.5 ) and PM associated carbon forms with change in season was observed.On average, 68% of the PM 10 load was accounted by PM 2.5 size fraction, whereas average proportion of CA to PM 10 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.

Fig. 1 .
Fig. 1.Plotted month-wise profiles for the concentrations of PM 2.5 , BC and LAOC obtained in summer [A1], monsoon [B1] and winter [C1].Regression fit for BC and LAOC concentrations as a function of PM 2.5 concentrations for summer, monsoon and winter are shown in panels [A2], [B2] and [C2] respectively; fitted values for R 2 and correlation coefficient (R) are given in each panel.
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.

Fig. 2 .
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 R 2 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.

Fig. 3 .
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.

Fig. 4 .
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, R 2 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, R 2 and correlation coefficient (R).

Fig 5 .
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 R 2 and correlation coefficient (R).

Table 1 .
Average concentrations of PM 10 and associated carbon forms TC, OC, EC (µg m -3 ) and calculated different ratios obtained in different seasons.

Table 2 .
Calculated Pearson correlation between aerosol load, associated carbon forms and meteorological factors.Correlation significant at 0.01 confidence level (2-tailed test), * Correlation is significant at the 0.05 level (2-tailed test).

Table 3 .
Calculated season-wise average nssK + /OC and nssK + /EC ratios and the spread in the ratios in respective seasons.