1 Volatile Organic Compounds over Eastern Himalaya, India: 2 Temporal Variation and Source Characterization using Positive 3 Matrix Factorization 4 5

6 C. Sarkar1, A. Chatterjee1,2,4, D. Majumdar3, S. K. Ghosh4, A. Srivastava3 and S. 7 Raha2,4 8 9 1Environmental Science Section, Bose Institute, P 1/12 CIT Scheme VII-M, Kolkata-700054, 10 India 11 2National Facility on Astroparticle Physics and Space Science, Bose institute, 16, A.J.C. Bose 12 Road, Darjeeling-734101, India 13 3National Environmental Engineering Research Institute, I-8 Sector-C, EKDP, Kolkata- 14 700107, India 15 4Center for Astroparticle Physics and Space Science, Block-EN, Sector-V, Salt Lake, 16 Kolkata-700091, India 17 5National Environmental Engineering Research Institute, Delhi, India 18 19 Correspondence to: A. Chatterjee ([email protected]) 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

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49 Abstract

50 A first ever study on the characterization of Volatile Organic Compounds (VOCs) has 51 been made over a Himalayan high altitude station in India. A total of 18 VOCs consisting of 52 mono aromatics-BTEX (Benzene, Toluene, Ethylbenzene, Xylene), non-BTEX substituted 53 aromatics and halocarbon have been measured over Darjeeling (27.01 ºN, 88.15 ºE, 2200 m 54 asl) in the eastern Himalaya in India during the period of July, 2011 – June, 2012. The annual 55 average concentration of the sum of 18 target VOCs (TVOC) was 376.3 ± 857.2 µg m-3. 56 Monoaromatics had the highest contribution (72 %) followed by other substituted aromatics 57 (22 %) and halocarbon (6 %) compounds. Toluene was the most abundant VOC in the 58 atmosphere of Darjeeling with the contribution of ~ 37 % to TVOC followed by benzene (~ 59 21 %), ethylbenzene (~ 9 %) and xylenes (~ 6 %). TVOC concentrations were highest during 60 the postmonsoon season with minimum solar radiation and lowest during the premonsoon 61 season with maximum solar radiation. Anthropogenic activities related mainly to tourists like 62 diesel and gasoline emissions, biomass and coal burning and solid waste emissions were 63 almost equal in both the seasons. Seasonal variation in TVOCs over Darjeeling was mainly 64 governed by the incoming solar radiation rather than the emission sources. Source 65 apportionment study using Positive Matrix Factorization (PMF) model indicated that major 66 fraction of (~60 %) TVOC were contributed by diesel and gasoline exhausts followed by 67 solvent evaporation (18 %) and other sources. Of the measured compounds, diesel exhaust 68 was also found to have the maximum potential in tropospheric ozone formation. The 69 atmospheric loading of BTEX over Darjeeling was found to be comparable with several 70 Indian metro cities and much higher than other cities around the world. 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

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87 1 Introduction

88 The studies on Volatile Organic Compounds (VOCs) have gained much attention because of 89 their ability in modifying oxidizing capacity of the atmosphere as well as health implications 90 to humans. VOCs play an important role in the formation of photochemical smog and 91 tropospheric ozone by reacting with hydroxyl radicals (OH) in the presence of NOx 92 (Atkinson, 2000). Some of the VOCs also have the potential towards stratospheric ozone 93 depletion and enhancement of the global greenhouse effect (Prather and Watson, 1990). 94 VOCs comprise a wide range of compounds including aliphatic and aromatic hydrocarbons, 95 alcohols, aldehydes, ketones, esters, and halogenated compounds. Many VOCs react with

96 hydroxyl radicals (OH) and/or nitrate (NO3) radicals to form secondary organic aerosol 97 (SOA) by nucleation and condensation with a significant aerosol yield and thus they 98 influence gas phase pollutants directly and particle-phase pollutants indirectly (Brocco et al., 99 1997; Odum et al., 1997).

100 There is as such no general source for VOCs as there are numerous compounds in this group, 101 which can be emitted from very different sources (Yurdakul et al., 2013). In addition to the 102 biogenic sources of VOCs (Williams and Koppmann., 2007), some well documented 103 anthropogenic sources are gasoline powered and diesel powered motor vehicles (Demir et al., 104 2011), fuel storage (Lanz et al., 2008), biomass burning (Yokelson et al., 2008), natural gas 105 (Latella et al., 2005), LPG (Lai et al., 2005), industrial processes and solvents (Lanz et al., 106 2008) etc.

107 High levels of VOCs have been observed in Asian countries where significant fractions of 108 VOCs have been considered to be originating from vehicular emissions (Srivastava et al., 109 2005). Among the Asian countries, India is the second largest contributor to the emission of 110 non-methane VOCs (Kurokawa et al., 2013). In spite of growing population and associated 111 increase in vehicular and industrial activities, the studies on VOCs in India are limited. Some 112 of those important studies have been conducted in the recent past mostly in metro cities such 113 as in Delhi, the capital city of India (Hoque et al., 2008; Khillare et al., 2008; Srivastava., 114 2005; Srivastava et al., 2005a, 2005b, 2005c; Gurjar et al., 2004; Padhay and Varshney, 115 2000), in Mumbai, a metro city and financial capital of India situated in western India 116 (Srivastava and Som, 2007; Srivastava et al., 2006a and 2006b; Srivastava., 2004; Srivastava 117 et al., 2004), in Kolkata, a metro city in eastern India (Dutta et al., 2009; Mujumdar et al., 118 2008; Som et al., 2007; Mukherjee et al., 2003), in Hyderabad, a metro city in southern India

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119 (Rekhadevi et al., 2010), in Agra in northern India, (Singla et al., 2012), in Firozabad in 120 northern India (Chaudhury and Kumar, 2012), in Mohali, a suburban site in north-western 121 India (Sinha et al., 2014; Sarkar et al., 2013) etc. In India there is no legislation of VOC as a 122 whole except national ambient air quality standard for Benzene by Central Pollution Control 123 Board of India. Globally US Occupational Safety and Health Administration (OSHA) and 124 World Health Organization (WHO) have proposed some guidelines and recommendations for 125 VOCs and not compulsory for governments to follow (Han and Naeher, 2006).

126 Where almost all the studies were conducted over several cities in India, no such study on 127 VOCs have been ever made over high altitude stations over Indian Himalaya with both 128 ecological and climatic importance. Mayewski et al., (1979) studied the fluctuations of 129 Himalayan and Trans-Himalayan glaciers including Kanchenjunga at eastern Himalaya and 130 reported that the volume and extent of these glaciers have been decreasing since 1850 which 131 could be due to the increase in the loading of atmospheric carbonaceous pollutants. Air 132 quality over Himalaya is deteriorating due to increased man-made activities to meet the 133 demands related to urbanization and economic development (Momin et al., 1999; Meena et 134 al., 2012). High altitude Himalayan hill stations especially over eastern part in India which 135 earlier were considered as the pollution-free regions have now become the source of huge 136 amount of hazardous air pollutants due to the increase in various tourism-related 137 anthropogenic activities like fossil fuel and biomass burning etc (Adak et al., 2014).

138 The present study on the characterization of VOCs has been made over a high altitude (2200 139 m asl) hill station, Darjeeling (27.010N, 88.150E) at eastern Himalaya and the first ever study 140 conducted over Indian Himalaya to the best of our knowledge. Our earlier studies (Chatterjee 141 et al, 2010, 2012; Adak et al, 2014; Sarkar et al 2014) over the same region showed high 142 aerosol loading during premonsoon (March-May) due to vehicular emissions related to tourist 143 activities and during winter (December-February) due to massive biomass burning. In 144 addition to the local sources, pollutants were also found to be accumulated over this region 145 transported from long distant regions like Indo-Gangetic Plain (IGP) and other Asian sub 146 continents. Sarkar et al., (2014) found enhancement of Black Carbon aerosols over Darjeeling 147 during postmonsoon (October-November) due to transported plumes of biomass burning 148 from northern India. The seasonal variation of aerosols associated to the variation in emission 149 sources (local and transported) as observed from earlier studies have prompted us to make a 150 yearlong study on VOCs over Darjeeling as major aerosol sources over this region are 151 generally the major sources of VOCs too.

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152 The present study is thus mainly focused on 1) the identification of the major factors 153 governing seasonal variation of VOCs, 2) contribution of long distant source regions, 3) 154 source apportionment of VOCs using Positive Matrix Factorization (PMF) and their potential 155 in tropospheric ozone formation

156

157 2 Study site and synoptic meteorology

158 The study has been carried out at a high altitude hill station Darjeeling at eastern Himalaya in 159 India. A map showing the geographical position of Darjeeling and its adjacent regions is 160 given in Figure 1. The sampling site (27º 01’N, 88º15’E) in Darjeeling is situated at the 161 elevation of 2200 from mean sea level at eastern Himalaya. The site is located at an elevation 162 of 200 m from the main town centre which is populated mainly by several hotels and resorts, 163 offices, shopping malls, bus and car stands, schools and colleges. The National Highway 164 (NH55) with high vehicular density connects Darjeeling with the city called Siliguri at the 165 foothill of Himalaya. Darjeeling Himalayan Railway (also known as “Toy Train”), is a 166 narrow gauge railway which runs by coal and diesel for the tourists and a world heritage site, 167 too. The site in Darjeeling represents a typical urban atmosphere with major anthropogenic 168 sources like vehicular emissions, biomass burning, agricultural activities etc. The description 169 of the study site has been given in detail in our earlier studies (Chatterjee et al., 2010, 2012, 170 Adak et al., 2014).

171 The seasonal average along with minimum and maximum of surface meteorological 172 parameters; temperature (T) in ºC, wind speed (WS) in ms-1, relative humidity (RH) in % and 173 rainfall (mm) are given in Figure 2. The entire study period is divided into four seasons; 174 winter (December – February), premonsoon (March – May), monsoon (June – September) 175 and postmonsoon (October – November). Figure 2 shows that the temperature was highest 176 during monsoon and lowest in winter whereas relative humidity shows monsoon maximum 177 and premonsoon minimum. Wind speed was found to be maximum in premonsoon which 178 was ~2 times than that in other seasons. We did not observe much variation between daytime 179 and nighttime wind speed except in premonsoon when daytime wind speed was much higher 180 (~1.8 times) than night-time wind speed. The surface reaching solar radiation was maximum 181 during premonsoon and was much higher than postmonsoon, monsoon and winter. The total 182 amount of rainfall over the entire sampling days was 421 mm where ~ 95 % rain occurred 183 during monsoon (397 mm) only.

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184 3 Methodology

185 3.1 Sampling and analysis of VOCs

186 The study was carried out in the campus of National Facility on Astroparticle Physics and 187 Space Science, Bose Institute, Darjeeling. Samples were collected on a roof top of the 188 building of Bose Institute at a height of about 20 meters from the ground level at day (7 a.m. 189 to 7 p.m.) and night (7 p.m. to 7 a.m.) basis with the sample integration time of 11 hours for a 190 year long period from 7th July 2011 to 25th June 2012. The samples were collected once a 191 week. A total of 90 samples were collected during the study period, using a custom made 192 glass sampling tube containing charcoal and chromosorb. The tubes were pre-conditioned by 193 heating over night at 200 °C temperature. The tubes were connected with a low flow air 194 suction pump (SKC, USA). The flow rate was maintained at ~100 ml/min. The flow was 195 measured before and after each sampling event using a flow meter. After sampling the ends 196 of the tubes were sealed well with the Teflon tape and cap and kept at 4°C for analysis.

197 The analysis was done by thermal desorption followed by detection on GC-MS in accordance 198 with USEPA TO-17 compendium method for the determination of target VOCs and 199 described in details in the authors’ previous publications (CPCB 2007, CPCB 2010, 200 Srivastava & Som, 2007, Majumdar et al., 2014). In short the thermal desorption of sorbent 201 tube was done by heating at 180°C for 25 min. 100 μl of desorbed gas was injected into 202 Varian Make GC-MS [Now Agilent; GC–MS model: (Model 450GC-240MS)] operated 203 under SCAN mode. Target VOCs were separated using DB 624 capillary column of 30 m 204 length and 0.32mm internal diameter. Helium gas with flow rate of 1ml min-1 was used as 205 carrier gas with split ratio 1:20, GC oven was programmed for 35oC hold for 4 min and 206 ramped to 210oC. For estimation of the target compounds external five point calibration 207 curve was prepared in triplicate using VOC mix 20 by Dr. Ehrenstorfer GmbH, Germany. 208 209 3.2 Quality assurance and quality control 210 211 Breakthrough for air sampling procedure value was estimated by connecting two tubes in 212 series and it was considered that breakthrough occurred when the backup tube had 213 concentration more than 10% of the total concentration. Breakthrough has been observed 214 after passing 70 Lit of air sample in ambient condition. As soon as the air pump was turned 215 off, the sorbent tubes were removed, capped tightly and sealed in plastic bags and stored at 4 216 °C. Blank cartridges were also stored under identical conditions.

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217 Percentage accuracy for observation has been determined as a relative difference of measured 218 concentration and spiked concentration for each component. Also mean response factor and 219 percent relative standard deviation for all target compounds has been calculated. For 220 determination of Relative Standard Deviation (RSD) we performed five runs of the same 221 concentration of mixed standard. For the lowest concentration of 0.005 µg of each target 222 compound the RSD varied between 3-10%. Method detection limit (MDL) has been 223 established by making seven replicated measurements of 0.005 µg of each target compound. 224 The standard deviation for these replicated concentrations multiplied by student’s t value for 225 99% confidence for seven values gives the MDL. The MDL varied from 0.01 µg m-3 for 226 1,2,4-Trimethylbenzene to 0.08 µg m-3 for 1,1-Dichloroethane considering 70 Lit of air 227 sample. The MDL for individual component has been given in Table 1.

228 4 Results and discussion

229 4.1 General characteristics of VOCs over Darjeeling

230 All the 18 VOCs measured in this study denoted as TVOC (Total VOCs) have been classified 231 in to three groups; mono-aromatics BTEX, non-BTEX substituted aromatics (iso- 232 propylbenzene, n-propylbenzene, 1,3,5 trimethylbenzene, 1,2,4 trimethylbenzene, sec- 233 butylbenzene, 4-isopropyltoluene, 2-chlorotoluene, 1,4 dichlorobenzene, n-butylbenzene, 234 naphthalene), and halocarbons (1,1 dichloroethane, 1,2 dichloroethane, chloroform and 235 carbon tetra chloride). The annual average concentrations of each VOC for each group along 236 with their minimum and maximum concentrations over the entire period of study have been 237 given in Table 1. The concentration of TVOC over Darjeeling was found to vary widely from 238 as low as 6.6 µg m-3 to as high as 4707.5 µg m-3 over the entire period of study (July, 2011 – 239 June, 2012). The annual average concentration of TVOC was 376.3 ± 857.2 µg m-3. BTEX 240 was found to have the highest contribution (72 %) followed by non-BTEX substituted 241 aromatics (22 %) and halocarbon (6 %) compounds. BTEX varied over a wide range between 242 1.5 µg m-3 and 3975.6 µg m-3 with an average of 275.1 ± 685.7 µg m-3. Toluene was found to 243 be the most abundant VOC in the atmosphere of Darjeeling with the contribution of ~ 37 % 244 to TVOC followed by benzene (~ 21 %), ethylbenzene (~ 9 %) and xylenes (~ 6 %). The 245 concentration of non-BTEX aromatics, too, varied widely from a very low (0.3 µg m-3) to a 246 very high (912.1 µg m-3) value with an average of 88.6 ± 220.1 µg m-3. On the other hand, 247 halocarbon compounds, unlike other VOCs, did not show such large variability during the 248 study period. The concentration of halocarbons varied from 1.5 µg m-3 to 73.3 µg m-3 with an 7

249 average of 21.5 ± 15.4 µg m-3. An important observation is that the concentration of carbon 250 tetrachloride (CTC) over Darjeeling was found to be much lower (0.18 µg m-3 which is 251 equivalent to 30 pptv) than the global mean concentration (~ 85 pptv during 2011-2012; 252 http://cdiac.ornl.gov/oceans/new_atmCFC.html). Studies on global distribution of CTC were 253 made using model-based simulation studies (Liang et al., 2014), occultation measurement 254 studies (Allen et al., 2009) etc. But, very few ground-based observations on CTC were made 255 in India where no such studies exist over eastern part of this country. However, the 256 concentration of CTC was found to have wide spatial variation. Srivastava et al., (2006) 257 observed CTC concentration of 55 pptv over an industrial region of Mumbai whereas very 258 high CTC concentration of 560-800 pptv was observed over an industrial region of Delhi 259 (Srivastava et al., 2005). Glavas and Moschonas (2002) observed very low CTC 260 concentration of 40 pptv over Athens, Greece during summer in 2000. There is no industries 261 exist in and around Darjeeling which could result to such low CTC concentration. TVOC and 262 most of its components showed their minimum concentrations during premonsoon (May 15, 263 2012) and maximum concentrations during postmonsoon (November 21, 2012).

264 4.2 Factors affecting seasonal variations of VOCs

265 Figure 3 shows the seasonal variations of VOCs over Darjeeling. The concentration of TVOC 266 was maximum in postmonsoon (1649.9 ± 875.4 µg m-3) followed by monsoon (117.1 ± 88.3 267 µg m-3), winter (60.4 ± 28.2 µg m-3) and minimum during premonsoon (35.9 ± 9.7 µg m-3). 268 BTEX and non-BTEX substituted aromatics showed similar seasonal patterns. The high 269 postmonsoon concentrations were found to be 1228.2 ± 534.1 µg m-3 and 404.0 ± 336.1 µg m- 270 3 and the low premonsoon concentrations were found to be 12.9 ± 3.3 µg m-3 and 3.5 ± 1.5 µg 271 m-3 for BTEX and non-BTEX substituted aromatics respectively. Unlike BTEX and non- 272 BTEX, halocarbons showed highest abundance in winter (33.5 ± 10.4 µg m-3) with small 273 variabilities between premonsoon (19.1 ± 4.2 µg m-3), postmonsoon (17.6 ± 4.5 µg m-3) and 274 monsoon (14.5 ± 5.2 µg m-3).

275 Postmonsoon and premonsoon are the tourist seasons over Darjeeling. Darjeeling experiences 276 huge emissions of fossil fuel burning from large numbers of tourist vehicles during these two 277 seasons compared to other seasons. We had made rough measurements on vehicle counts and 278 consumption of fossil fuel over Darjeeling earlier in the year of 2005 (Adak et al., 2010). We 279 observed that the number of light and medium duty vehicles was 6000-6700 per day during 280 premonsoon and postmonsoon whereas 3000-3600 per day during winter and monsoon. The 8

281 total consumption of fossil fuel (petrol and diesel) was 6500-7500 lit per day during 282 premonsoon and postmonsoon whereas it was 3500-4500 lit per day during winter and 283 monsoon. In addition to the vehicular emissions, various other anthropogenic activities are 284 also increased in premonsoon and postmonsoon seasons. The tourist activities remained the 285 same in these two seasons but VOCs showed significant variations with high level in 286 postmonsoon and low level in premonsoon. The other factors related to the sinks of VOCs 287 played major roles dominating the emission sources of VOCs, leading to the significant 288 variation between postmonsoon and premonsoon. Observed seasonal trends can thus be 289 addressed by the characteristics of the prevailing meteorology, and most importantly, the 290 availability of solar insolation in these two seasons. Darjeeling recorded maximum solar 291 insolation in premonsoon (360 ± 140 watt m-2; Fig 2) which could help in the photolysis of 292 ozone, carbonyls, water vapour etc leading to the formation of OH radicals in the atmosphere 293 (Ho et al, 2004). Another important meteorological factor is wind speed which was observed 294 to be maximum during premonsoon months (1.4 ± 0.5 m s-1; Fig 2). This could favour the 295 ventilation and dispersion of VOCs from the study site. On the other hand, the solar 296 insolation (220 ± 100 watt m-2) and wind speed (0.65 ± 0.2 m s-1) during postmonsoon were 297 much lower than premonsoon. Thus, although the VOC emissions remained comparable, 298 VOC degradation was maximum in premonsoon than postmonsoon leading to premonsoon 299 low and postmonsoon high VOC concentrations. In addition to the local emissions, 300 transported carbonaceous compounds could also contribute significantly in enhancing 301 carbonaceous compounds over eastern part of Himalaya during postmonsoon. Bonasoni et al., 302 (2010), Marinoni et al., (2010), Dumka et al., (2010) and Kaskaoutis et al., (2014) have 303 shown the influence of carbonaceous compounds (mainly Black Carbon) over Himalayas due 304 to transported plumes associated to crop residue burning over Punjab and adjacent Indo 305 Gangetic Plain regions during postmonsoon seasons. Sinha et al., (2014) and Sarkar et al., 306 (2013) showed significant increase in VOC concentrations due to agricultural residue burning 307 over Mohali, a suburban site in north-western India. Our recent study (Sarkar et al., 2014) 308 showed the impact of this transported biomass burning plumes on Black Carbon aerosols 309 over Darjeeling in the same study period. These biomass burning plumes could also bring 310 significant amount of VOCs over Darjeeling enhancing their concentrations during 311 postmonsoon.

312 The tourist activities remained low both during monsoon and winter months over Darjeeling. 313 The solar insolation during monsoon and winter was comparable in magnitude (180 ± 80 watt

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314 m-2). Darjeeling recorded maximum temperature during monsoon (15.8 ± 0.9 0C) which may 315 lead to increased evaporative emissions for certain VOC species with higher vapour pressure 316 from vehicular service stations, and also from waste decomposition in the hotter months 317 (Talapatra and Srivastava, 2011). VOC emission from solvent evaporation is less significant 318 at very low temperature (5.3 ± 2.1 0C) during winter.

319 In order to better establish the fact of the dependence of TVOC with meteorological 320 parameters, we have investigated the day-to-day variations of TVOC with temperature and 321 solar radiation during the entire study period and shown in Figure 4. It shows well correlation 322 between TVOC and solar radiation during premonsoon. TVOC was found to decrease with 323 the increase in radiation. Thus, we can say that solar radiation played key role in atmospheric 324 clean-up and degradation of VOCs during premonsoon. On the other hand, correlation was 325 also observed between TVOC and temperature during monsoon. We observed increase in 326 TVOC with the increase in temperature favouring evaporative emissions during monsoon.

327 In general, variation in VOC concentrations between hotter and colder months over plain land 328 cities is addressed with the help of vertical advection through boundary layer dynamics in 329 addition to other meteorological factors. The low VOC concentration during summer is 330 generally associated to favourable vertical mixing due to high boundary layer/mixing height 331 whereas comparatively higher VOC concentration during winter is associated to calm and 332 stable atmospheric condition with low boundary layer/mixing height restricting vertical 333 dissipation. The boundary layer dynamics has been used for addressing seasonal variation of 334 VOCs for most of the studies conducted over several Indian cities (Talapatra and Srivastava, 335 2011 and several references therein). But the case of Darjeeling is unique, unlike plain land 336 cities, the seasonal variation in VOC concentration could not be addressed through boundary 337 layer dynamics as the station itself is situated at a height of 2.2 km, well above the boundary 338 layer. But there is a probability that boundary layer could reach the altitude of Darjeeling 339 during premonsoon under high convective activities. Thus, VOCs emitted from plain land 340 regions could reach Darjeeling after their vertical advection and could contribute and enhance 341 VOC concentrations over Darjeeling. But, photochemical degradation under high solar 342 insolation over Darjeeling could have hindered the development in VOC concentrations 343 during premonsoon.

344 4.3 Day and night time VOCs: Role of anthropogenic and meteorological factors

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345 VOC concentrations over Darjeeling were compared between day and night time for different 346 seasons in order to investigate the potential impact of the variability in emission sources 347 and/or meteorological factors between day and night time. The night to day ratio was greater 348 than 1.0 in each season. We infer that although the emissions were high, the photochemical 349 degradation could decrease the day-time VOC concentrations. Thus, night-time VOCs could 350 be attributed to the VOCs generated during night (which could not degrade by photolysis) 351 plus residual VOCs generated during day-time. Another important factor is higher wind 352 speed during daytime which could favour the dispersion of VOCs more than night. The ratio 353 for TVOC was highest during premonsoon (1.9) followed by postmonsoon (1.4), monsoon 354 (1.2) and minimum during winter (1.1). The highest ratio in premonsoon could be due to the 355 removal of VOCs by efficient and faster photo-degradation by very high solar insolation and 356 higher wind speed favouring dispersion of pollutants during day time leading to lower day- 357 time VOC concentrations compared to the other seasons. However, the minimum value of the 358 ratio in winter could be due to minimum night-time VOC emissions due to subdued 359 anthropogenic activities (except biomass burning) in colder nights. Another possibility is that 360 the boundary layer could remain well below the observational site (2200 m asl) during winter 361 nights and hence pollutants could be accumulated below Darjeeling leading to lower night- 362 time VOC concentration.

363 4.4 Contribution of long distant source regions to VOCs over Darjeeling

364 The transport of air masses from distant sources could affect the pollutant concentrations at 365 the study site in conjunction with the local sources. In order to investigate the transport of 366 VOCs from long distances, we have computed 36 h air-mass back trajectories, arriving at an 367 altitude of 500 m above ground level over Darjeeling for all the days on which VOCs were 368 measured, using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model 369 (http://www.arl.noaa.gov/ready/hysplit4.html). Over the entire period of study, we have 370 identified three major source regions for long range transport as shown in Figure 5. The 371 frequency of transport from each of the source regions has also been shown in the figure. 372 Region 1 corresponds to the transport from SE directions and the air masses originated from 373 southern part of West Bengal, India and Bangladesh with the frequency of 32 %. The average 374 TVOC concentration associated to Region 1 was found to be 117.2 ± 86.1 µg m-3. Region 2 375 corresponds to the transport from W/NW directions and the air masses originated from 376 eastern and central part of Nepal with the frequency of 42 %. The associated TVOC

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377 concentration was found to be 831.5 ± 955.2 µg m-3. Region 3 corresponds to local/regional 378 sources and the air masses originated mainly from the E/SE directions with the frequency of 379 26 %. The major regions were northern part of West Bengal and the average TVOC 380 concentration was found to be 620.1 ± 535.4 µg m-3. Thus, the contribution from Nepal 381 (Region 2) was found to be 5.5-8 and 1.3-1.8 times higher than West Bengal/Bangladesh 382 (Region 1) and local/regional sources (Region 3) respectively. The contributions from each 383 source regions were also investigated for different seasons. The average TVOC 384 concentrations associated to respective source regions along with their frequencies have been 385 given in Table 2 season-wise. It was observed that during monsoon, all the air masses 386 originated from Region 1 with 100 % frequency with the average TVOC concentration of 387 117.2 ± 86.1 µg m-3. Similarly, during winter, 100 % air masses originated from Nepal with 388 the TVOC concentration of 60.9 ± 28.0 µg m-3. During postmonsoon, 60 % air masses 389 originated from local/regional sources (Region 3) and 40 % originated from Nepal (Region 2) 390 with the average TVOC concentrations of 1206.8 ± 628.3 µg m-3 and 2978.1 ± 1538.1 µg m-3 391 respectively. It was observed that 50 % air masses originated from local/regional and 50 % 392 originated from Nepal during premonsoon and the TVOC concentration associated to Nepal 393 was found to be slightly higher (46.2 ± 12.5 µg m-3) than local/regional sources (34.2 ± 11.3 394 µg m-3). This result indicates that the air masses coming from Nepal carried more VOCs and 395 thus more polluted compared to other source regions. It is important to mention over here that 396 the altitudes of the air masses were below 1000 m asl throughout their trajectories/pathways 397 originating from their source regions. Thus the air masses could pick up the boundary layer 398 pollutants of the regions they passed over before reaching our observational site.

399 As Nepal was found to be most polluted source regions, an attempt was made to roughly 400 estimate the contribution of TVOC from Nepal in postmonsoon and premonsoon seasons. 401 During these two seasons, air masses originated both from Nepal and local/regional source 402 regions and thus contribution from Nepal was estimated in terms of the relative 403 concentrations associated to these two regions. The estimation has been made by the 404 following equation:

405 % contribution from Nepal = (ECNepal/ MCTotal)*100 = ((MCTotal – MCLocal)/ MCTotal)*100,

406 where ECNepal is the estimated concentrations of TVOC coming only from Nepal i.e.

407 additional amount of TVOC coming from Nepal. MCTotal is the measured concentration of 408 TVOC on respective days when air masses generated from Nepal i.e. with the total

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409 contribution of both Nepal and local air masses. MCLocal is the average measured 410 concentration of TVOC on all the days when air masses originated from local sources i.e. 411 contribution from local sources only. It was observed that VOCs from Nepal contributed to 412 the TVOC concentration over Darjeeling by 38-54 % with the average of ~53 % during 413 postmonsoon and 32-65 % with the average of ~50 % during premonsoon.

414 4.5 Effect of local and long distant sources on the variability-lifetime relationship for 415 VOCs

416 The relationship between the variability in concentrations and the life time of VOCs can be 417 used to estimate the distance of their source regions regardless the influence of the regional 418 transport. The following empirical equation was first proposed by Jobson et al.(1998).

-b 419 Slnx = Aτ

420 Where Slnx is the standard deviation of the natural logarithm of the concentration X of VOC, τ 421 is the atmospheric lifetime of VOC, A and b are the fit parameters.

422 The value of exponent b lies between 0 and 1 and describes the influence of the source 423 contribution. The value of b will approach zero when sampling site is closed to a source and 424 the variability-lifetime relation will be ‘weaker’. In the extreme case, when b = 0, the 425 variability will not depend on the atmospheric lifetime but will depend on the variability of 426 the emission sources. In remote areas b will approach 1 (Jobson et al., 1998, Ehhalt et al., 427 1998, Wang et al., 2005) where the distance of sampling site is longer from the potential 428 sources. The variability concept is based on the assumption that the chosen compounds have 429 more or less the same source distribution. The compounds reported in this paper, are mostly 430 of anthropogenic origin (aromatic hydrocarbons and halocarbons). We have used their 431 concentrations for premonsoon and postmonsoon as the sources of VOCs are same in both 432 the seasons. We have calculated the back trajectory analysis for each sampling day and based 433 on the trajectories we have separated the transported air masses from the local emissions (as 434 discussed above). Figure 6 shows the relationship of variability with lifetime for different 435 VOC species separately for long range transport and local emissions. It can be seen that the 436 value of b was higher for long range transport (b = 0.19, R2 = 0.79) than for local emission (b 437 = 0.09, R2 = 0.64). The value of b for long distant sources (0.19) as obtained in the present 438 study was found to be slightly lower than 0.22 as observed over Mount Tai, China (Ting et 439 al., 2009) and 0.23 as obtained in the Mediterranean Intensive Oxidant Study (MINOS) in 13

440 August 2001 on Crete (Gros et al., 2003). But, it was much lower than 0.44 as observed over 441 the remote NARE locations (Jobson et al., 1999) and 0.41 on a cruise through the western 442 Indian Ocean during the INDOEX field study (Karl et al., 2001). Thus, Darjeeling does not 443 represent a remote site where the variability is strongly dependent on the lifetime of VOC but 444 represent a typical urban site in the vicinity of sources where the sources dictate the 445 variability and not the chemistry. The longest source regions (Central part of Nepal or 446 southern part of West Bengal/Bangladesh) for VOCs over Darjeeling as estimated from 447 HYSPLIT trajectory models were within 200 km from Darjeeling.

448 4.6 Characterization of sources of VOCs by Positive Matrix Factorization receptor 449 model 450 In recent years, an advanced receptor model, Positive Matrix Factorization (PMF), has been 451 applied extensively in identifying VOC contributing sources at different locations in the 452 world (e.g., Jorquera and Rappengluck, 2004; Latella et al., 2005; Xie and Berkowitz, 2006; 453 Brown et al., 2007; Song et al., 2007; Yuan et al., 2009). PMF does not require any priori 454 knowledge on the exact VOC emission profiles, and it can be used to apportion source 455 contributions solely based on observations at the receptor site, thus avoiding VOC decay 456 adjustment problem i.e. quick loss of some reactive VOC species in the path between source 457 and receptor. However, very high reactive species could be excluded from the model run 458 provided they are not marker or tracer of any VOC source. In this study, we have measured 459 18 VOC species which are major and abundant in the atmosphere and can not be considered 460 as highly reactive and have been included in PMF model run by earlier studies, too. More 461 details about the PMF method were described by several studies (Paatero and Tapper, 1994; 462 Paatero, 1997; Reff et al., 2007). Q is a typical parameter in PMF which is the measure of 463 goodness of fit parameters. Q (true) is calculated including all data points where Q (robust) is 464 calculated excluding data points not fit by the model. The difference between Q (true) and Q 465 (robust) is the measure of the impact of data points with high scaled residuals whereas high 466 uncertainties result in similar Q values. Q (robust) is used to choose the optimal run from the 467 multiple runs. If the number of sources is estimated properly, Q (robust) will have little 468 variability between the runs and thus we get stable Q. The variability of Q (robust) is the 469 indication of the variability of initial base run results.

470 In this study, the PMF method has been applied to identify the possible sources of VOCs over 471 Darjeeling. Table 3 shows the source profiles derived by the PMF model. Eight factors were

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472 selected according to the resulted stable Q values. Figure 7 shows the percentage contribution 473 of each VOC associated to each of eight sources.

474 Table 3 shows that Factor 1 is dominated by high values of BTEX with much higher 475 concentrations of benzene and toluene followed by ethylbenzene and xylene. Toluene to 476 benzene ratio was found to be 0.9 in this factor. Thus factor 1 could be associated to the 477 gasoline-related emissions. Several earlier studies showed the ratio values near 1.0 (1.0 over 478 Kolkata, India by Som et al., 2007; ~ 1.0 over Mumbai, India by Srivastava et al., 2004; ~ 1.0 479 over Ganga Mountain, China by Zhang et al., 2013; 0.8 over Hongkong, China by Lau et al., 480 2007; 0.7 over Sanghai, China by Cai et al., 2010) attributing to gasoline related emissions. 481 VOC emissions from gasoline may occur along many pathways like, evaporative emission 482 from gas stations and bulk terminals and exhaust released from the gasoline-powered vehicles 483 during gasoline combustion (Watson et al., 2001; Choi and Ehrman, 2004). BTEX are the 484 major components of vehicular exhaust, as shown by many studies (Watson et al., 2001; Guo

485 et al., 2006, 2007, Som et al., 2007). High VOC emissions from tourist vehicles during 486 premonsoon and postmonsoon seasons and the gasoline vapours from the frequent use of the

487 gas stations are the most important contributors to this source over Darjeeling.

488 Factor 2 is also dominated by BTEX. Toluene was found to have the maximum contribution 489 followed by benzene, ethylbenzene and xylene. The toluene to benzene ratio is 2.5 in this 490 factor. Previous study of the authors (Som et al., 2007) reported the same ratio in a study 491 made over Kolkata, India for the VOCs emitted from diesel-driven vehicles. This factor is 492 associated to diesel exhaust. It is interesting to observe that the number of petrol and diesel 493 driven vehicles are nearly same over Darjeeling and PMF result indicates the percentage 494 contribution of TVOCs from Diesel and gasoline sources are also comparable (discussed later 495 in details).

496 Factor 3 is characterized by the high values of TEX. TEX being the primary constituents of 497 solvents (Guo et al., 2004a; Choi et al., 2011), often used as a solvent in paints, coatings, 498 synthetic fragrances, adhesives, inks and cleaning agents, in addition to its use in fossil fuel 499 (Borbon et al., 2002; Chan et al., 2006). This factor can therefore be assigned to the solvent 500 usage and related emission. The rapid growth in tourism related infrastructure like hotels, 501 resorts, restaurants etc over Darjeeling could be the reason for high VOC emission from 502 solvent usage.

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503 Factor 4 is characterized by the high values of n-propyl benzene, 2-chloro toluene and BTEX 504 and could be assigned to solid waste disposal. Majumdar et al., (2014) reported the high 505 values of these compounds in municipal waste dumping stations in Kolkata, India. With the 506 dramatic increase in tourists and changing consumption patterns, Darjeeling is facing 507 immense problems of waste management. The existing systems of waste management are 508 technically unscientific and the infrastructure is insufficient to manage the waste.

509 Factor 5 is dominated mainly by chloroform and carbon tetrachloride and thus the factor 510 could be associated to chlorine bleach containing house hold products. Odabasi et al., (2008) 511 showed that house hold cleaning agents and fresheners produce these two VOCs 512 significantly. Chloroform and carbon tetrachloride are the major compounds along with 513 several halogenated compounds in chlorinated bleach products.

514 Factor 6 is dominated by m,p-xylene and ethylbenzene followed by n-butylbenzene and 515 toluene and could be assigned to industrial sources (Yuan et al., 2010). Although there is no 516 industry in Darjeeling, but the VOCs could be transported from low land townships and 517 cities. The m,p-xylene to ethylbenzene ratio in this factor was found to be 1.8. The ratio of 518 m,p-xylene to ethylbenzene (X/E ratio) is used as indicator for the age of the VOCs in the 519 atmosphere (Elbir et al., 2007; Guo et al., 2004a, 2004b). The ratio becomes smaller as the 520 VOCs get older in the atmosphere, because m,p-xylene is more reactive than ethylbenzene. 521 Kuntasal (2005) found X/E ratio to be varied between 3.8- 4.4 in fresh emissions at gasoline 522 station, underground garage and a tunnel. The low ratio in this study (1.8) suggests that the 523 species were not emitted in situ but aged/transported.

524 Factor 7 is dominated by chloroalkanes, benzene and toluene and could be associated to coal 525 and biomass burning (Fernandez-Martinez et al., 2001, Barletta et al., 2009). Coal burning is 526 a significant anthropogenic source in Darjeeling as it is used for the domestic cooking 527 purpose and it is also used in a large scale for coal engines in the toy trains. In addition to 528 that, massive biomass burning during winter to get warmth against cold and probable 529 transportation of biomass burning species from northern Indian states (as discussed earlier) 530 during postmonsoon could enhance those VOCs in the atmosphere of Darjeeling.

531 Factor 8 is characterized by high values of aromatics with high molecular weight like 1,2,3-tri 532 methyl benzene, 1,2,4-trimethyl benzene, o-xylene. Liu et al., (2005) reported high emissions 533 of these VOCs from asphalt related road construction works. Road construction works were

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534 in progress in and around Darjeeling during few sampling events. Thus the factor 8 could be 535 assigned to the asphalt related emission.

536 Figure 8 shows the percentage contributions of each source to the total VOC loading over 537 Darjeeling during the entire study period. It can be seen that the major sources are diesel 538 exhaust (32 %) and gasoline exhaust (29 %) followed by solvent evaporation (18 %). 539 Chlorine bleach containing house hold products and solid wastes contributed equally (6 %) 540 whereas industrial sources situated at the regions far from Darjeeling, coal/biomass burning 541 and asphalt related constructional works contributed nominally by 4 %, 3 % and 2 % 542 respectively. Thus it can be concluded that the major source of 18 VOCs (BTEX, non-BTEX 543 aromatics and halocarbons) measured over Darjeeling is gasoline and diesel driven vehicular 544 activities which contributed by more than 60 %.

545 4.7 Ozone formation potential of VOC sources

546 Although it is well known that tropospheric NOx plays important role in tropospheric ozone 547 formation, the measurement of NOx was beyond our scope during the study period. Hence, in 548 this study, the investigation of ozone formation potential (OFP) was limited to VOCs only. 549 To find out the potential of various VOC sources (as derived from PMF model) to the 550 tropospheric ozone formation over Darjeeling, we have computed OFP of each source using 551 the Maximum Incremental Reactivity (MIR) values derived by Carter (2008). To do this, we 552 have used the equation derived by Na and Kim, (2007).

n 553 OFPi  Si  ( ji  MIR j ) j1

th 554 Where, OFPi is the estimated contribution of i source to OFP, Si is the total mass

555 contribution of the source i, αji is the mass fraction of species j in source i and MIRj is MIR 556 value of species j. Figure 9 shows the relative contribution of each source to OFP. It can be 557 seen from the figure that diesel exhaust has the maximum potential (45%) followed by 558 solvent (24 %) and gasoline exhaust (18 %). Although, Gasoline exhaust contributes more 559 (29%) towards TVOC concentration than solvent usage (18%), the later source is 560 contributing more towards tropospheric ozone generation. The MIR values of the individual 561 species are also responsible for the total OFP of a source along with the corresponding source 562 strength. Thus, it can be said that amongst the 18 VOCs measured over Darjeeling, those 563 emitted from vehicular emissions and solvents played the key role in the formation of 564 tropospheric ozone and modifying tropospheric ozone budget.

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565 4.8 Comparison with other studies

566 The concentration of BTEX over Darjeeling (present study) has been compared with that 567 over several metro cities in India and also with other cities in Asian, European, African, 568 Arabian and American countries (Table 4). We have taken the sum of BTEX (not TVOC) for 569 comparison as the data of BTEX is more available in the literature.

570 Table 4 shows that BTEX over Darjeeling is lower than the commercial, industrial and the 571 areas with high traffic density (traffic intersection) over Delhi, the capital city of India; traffic 572 intersection and petrol pumps over Mumbai, a metro city in western India and Hyderabad, a 573 metro city in south-eastern India. This is quite expected as the vehicular and industrial 574 activities over those metro cities are much higher than Darjeeling. But the most interesting 575 fact is that BTEX over Darjeeling shows ~3, ~2 and ~5 times higher concentrations than 576 residential areas over Kolkata (a mega city in eastern India), Delhi and Mumbai respectively. 577 Even, Darjeeling shows higher BTEX concentrations than commercial areas of Mumbai and 578 much higher than roadside (~10 times) and petrol pump (~7 times) areas in Agra, a city in 579 northern India with much less vehicular activities compared to other Indian metro cities.

580 BTEX over Darjeeling was found to be much higher (10-25 times) than the 581 residential/industrial/commercial areas of Turkey, Houston, Rome and Paris; 2-6 times higher 582 than residential/commercial areas of Bangkok, Yokohama, Kuwait and Hongkong; 1.5-2 583 times higher than roadside/industrial/commercial areas of Kaohsiung, Sanghai and Beijing. 584 Darjeeling shows much higher (~8 times) BTEX concentration than Gongga Mountain, a 585 high altitude (1640 m asl) remote station in southwestern China. However, BTEX over a 586 commercial area with heavy traffic density in Cairo, Egypt shows 1.7 times higher 587 concentration than that over Darjeeling.

588 In our earlier study (Sarkar et al., 2014), we also reported much higher concentration of black 589 carbon aerosols over Darjeeling compared to other high altitude Himalayan stations in India 590 and Nepal and some of the metro cities in India like Ahmedabad, Bangalore, Trivandrum and 591 Chandigarh. The present study corroborate with that findings. The major source for black 592 carbon aerosol and VOCs over Darjeeling is same, vehicular emissions. Thus, Darjeeling 593 represents a typical urban atmosphere at eastern Himalaya with high loading of carbonaceous 594 pollutants. This could be due to high anthropogenic emissions related to tourist activities, 595 high population density and moreover it’s unique orography and land use pattern with narrow

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596 roads, unplanned township, poor administrative control on solid waste disposal and burning 597 of these wastes, unplanned constructions of buildings/hotels/resorts which reducing open 598 space/area which in turn prevents ventilation and dispersion of pollutants.

599 5 Conclusion

600 The major findings of the study on VOCs conducted over Darjeeling, a high altitude hill 601 station over eastern Himalaya in India are as follows:

602 1. The annual average concentrations of TVOC, BTEX, non-BTEX aromatics and 603 halocarbons were 376.3 ± 857.2, 275.1 ± 685.7, 88.6 ± 220.1 and 21.5 ± 15.4 µg m-3 604 respectively with the maximum contribution from BTEX (72 %), non-BTEX 605 aromatics (22 %) and halocarbons (6 %). Toluene was found to be the most abundant 606 VOC over Darjeeling which contributed 37 % to the TVOC.

607 2. Concentration of TVOC showed well defined seasonal variations with maximum in 608 postmonsoon (1650 ± 875.4 µg m-3) followed by monsoon (117.1 ± 88.3 µg m-3), 609 winter (60.4 ± 28.2 µg m-3) and minimum during premonsoon (36 ± 9.7 µg m-3). The 610 seasonal variation in VOC concentration was mainly governed by the photochemical 611 degradation process rather than the emission source strength. Although, the 612 anthropogenic activities related to massive tourist influxes during premonsoon and 613 postmonsoon were comparable, the solar radiation made the difference between 614 premonsoon and postmonsoon VOC concentrations.

615 3. Other than local sources, two major regions were identified for VOCs over 616 Darjeeling; Nepal and southern part of West Bengal, India/Bangladesh. It was 617 observed that VOC concentration over Darjeeling was higher when air masses arrived 618 from Nepal than West Bengal, India/Bangladesh and local/regional source regions. 619 The relationship between variability and lifetime of VOC was discussed and it was 620 observed that Darjeeling represents the site in the vicinity of sources as compared 621 with other studies.

622 4. Positive matrix facorization receptor model was used to characterize the sources of 623 VOCs over Darjeeling. It was observed that the major source of VOC over Darjeeling 624 was emission from petrol and diesel driven vehicles which contributed by more than 625 60 % followed by solvent evaporation (18 %) and other sources.

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626 5. Diesel exhaust was found to have the maximum potential (45 %) in the formation of 627 tropospheric ozone followed by solvent evaporation (24 %) and gasoline exhaust (18 628 %).

629 6. The atmospheric loading of BTEX over Darjeeling was comparable with Indian metro 630 cities and much higher than other Asian, American, African, Arabian and European 631 countries.

632 Thus we found that Darjeeling represents a typical urban atmosphere over eastern Himalaya 633 in India from the point of view of VOC pollution. The high VOC pollution over Darjeeling 634 draws a serious attention as it could significantly affect human health as well as the sensitive 635 ecosystem over this part of Indian Himalaya. Study result emphasis the need for better 636 pollution control system for the vehicles plying on the road of Darjeeling. Imposing 637 regulations on uncontrolled solvent usage is also necessary. Better Solid waste management 638 system is also called for. This year long data set of VOC can be used to make further studies 639 on the modification of the budget of tropospheric ozone, NOx and other gaseous and 640 particulate pollutants. This would, in turn, help us to make studies on the implications of 641 VOCs for regional atmospheric chemistry over eastern Himalaya. 642 643 Authors’contribution 644 645 C.S., A.C., D.M., S.K.G., A.S. and S.R. conceived and designed the experiment. C.S. and 646 A.C. performed the experiment. C.S. and D.M. analyzed the samples. D.M. and A.S. supplied 647 the materials/chemicals and instruments for chemical analysis. C.S., A.C. and D.M. analyzed 648 the data. A.C., C.S. and D.M prepared the manuscript with the contribution of rest of authors.

649 Acknowledgement

650 Authors would like to thank Science and Engineering Council, Department of Science and 651 Technology, Government of India for supporting the study under IRHPA (Intensification of 652 Research in High Priority Areas) scheme. Authors also like to thank University Grant 653 Commision, Govt of India for providing fellowship to Mr C. Sarkar (first author). The study 654 was undertaken by Bose Institute in collaboration with CSIR-NEERI. Field study conducted 655 by Bose Institute and sample analysis performed by CSIR-NEERI, Kolkata Zonal 656 Laboratory. Thanks are due to Mr. Sabyasachi Majee, Bose Institute for his consistent 657 support in sampling and Mr. Debasish Sengupta and Ms Pamela Chowdhury, NEERI for their

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658 assistance during sample analysis. Authors would also like to thank Mr D. K. Roy, Bose 659 Institute for his overall logistic support.

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986

987

988

989

990

991

992

993

30

994 Table 1. Statistical summary of the concentration of each VOC component over the 995 entire period of study (all the concentrations are in µgm-3) 996 MDL: Method Detection Limit 997

Species Mean±SD Max Min MDL 1,1-Dichloroethane 2.8±4.6 19.8 BDL 0.08 1,2-Dichloroethane 0.4±0.8 6.8 BDL 0.06 Chloroform 17.9±17.0 86.8 1.6 0.03 Carbon Tetrachloride 0.2±0.3 1.7 BDL 0.06 Benzene 81.2±212.2 1166.2 2.0 0.04 Toluene 140.8±430 2304.4 2.7 0.01 Ethylbenzene 32.7±93.4 563.5 1.1 0.02 m,p-Xylene 19.9±38.6 216.7 0.9 0.01 o-Xylene 0.9±1.5 7.6 BDL 0.01 Isopropylbenzene 12.1±45 267.1 0.7 0.02 n-Propylbenzene 4.8±9.7 48.5 BDL 0.01 2-Chlorotoluene 5.8±13.3 75.4 BDL 0.04 1,3,5-Trimethylbenzene 24.6±99.4 647.1 0.9 0.02 1,2,4-Trimethylbenzene 3.2±6.1 45.2 BDL 0.01 sec-Butylbenzene 3.7±12.2 104.6 0.1 0.04 4-Isopropyltoluene 28.5±125.1 752.6 0.7 0.03 1,4-Dichlorobenzene 0.4±1.0 6.9 BDL 0.03 n-Butylbenzene 3.9±7.9 58.2 0.1 0.03 Naphthalene 1.1±3.8 36.5 BDL 0.02

998

999

1000

1001

1002

1003

31

1004 Table 2. TVOC concentrations for various source regions over different seasons and 1005 entire study period.

[TVOC] Period Regions Direction Source region Frequency µg m-3 Bangladesh and West Region 1 S/SE 32 117.2±86.1 Bengal Annual Region 2 W/NW Nepal 42 831.5±955.2 Region 3 E/SE Local/Regional 26 620.1±535.4

Bangladesh and West Monsoon Region 1 S/SE 100 117.3±86.5 Bengal

Region 3 E/SE Local/Regional 60 1206.8±628.3 Postmonsoon Region 2 W/NW Nepal 40 2978.1±1538.1

Winter Region 2 W/NW Nepal 100 60.9±28.0

Region 2 W/NW Nepal 50 34.1±11.3 Premonsoon Region 3 E/SE Local/Regional 50 46.2±12.5 1006

1007

1008

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

32

1019 Table 3. Source profiles of several factors estimated from PMF model. 1020

1021

Solid Chlorine Asphalt Gasoline Diesel Solvent, Waste Bleach Industrial Coal Related Exhaust exhaust Paint Disposal Products Source Burning Emission Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 1,1-Dichloroethane 0.00 0.01 0.00 0.03 0.16 0.00 2.14 0.00 1,2-Dichloroethane 0.01 0.04 0.01 0.00 0.01 0.01 0.16 0.03 Chloroform 0.69 0.21 0.00 0.45 13.03 0.55 2.62 0.26 Carbon Tetrachloride 0.00 0.00 0.00 0.00 0.08 0.00 0.06 0.00 Benzene 43.35 22.49 0.00 2.43 1.90 0.00 2.49 0.00 Toluene 37.82 55.35 18.36 2.70 1.23 0.99 1.59 1.25 Ethylbenzene 3.15 15.09 0.36 3.09 0.00 2.97 0.19 0.24 m,p-Xylene 2.29 5.15 1.50 3.36 0.13 5.43 0.00 0.00 o-Xylene 0.00 0.00 0.00 0.00 0.02 0.14 0.00 0.48 Isopropylbenzene 0.42 0.50 8.71 0.37 0.34 0.10 0.00 0.18 n-Propylbenzene 0.00 0.41 0.09 3.09 0.06 0.25 0.07 0.26 2-Chlorotoluene 0.41 0.00 0.00 3.89 0.07 0.09 0.11 0.46 1,3,5-Trimethylbenzene 0.00 0.11 0.14 0.01 0.00 0.00 0.09 0.34 1,2,4-Trimethylbenzene 0.00 1.15 0.00 0.00 0.03 0.02 0.21 1.41 sec-Butylbenzene 0.00 0.00 1.17 0.03 0.00 0.06 0.36 0.00 4-Isopropyltoluene 0.00 0.00 26.18 0.75 1.01 0.32 0.00 0.53 1,4-Dichlorobenzene 0.13 0.01 0.02 0.00 0.01 0.00 0.06 0.01 n-Butylbenzene 0.95 0.00 0.00 0.00 0.00 1.99 0.00 0.00 Naphthalene 0.14 0.00 0.03 0.00 0.01 0.00 0.06 0.17 1022

1023

1024

1025

1026

1027 1028 1029 1030 1031 1032 1033 1034

33

1035 Table 4. Comparison of BTEX concentration over Darjeeling with other cities in India 1036 and other countries 1037 Sum of Location Nature of Site BTEX Study Period Reference (µg/m3)

Darjeeling High altitude tourist station 331.0 June, 2011 – July, 2012 Present Study

Indian Metro Cities Residential area 186.0 Commercial area 421.0 Delhi Oct, 2001–Sep, 2002 Hoquea et al., 2008 Industrial area 411.0 Traffic intersection 456.0 Commercial-cum-residential Kolkata 132.5 Dec, 2003 – Feb,2005 Majumdar et al., 2011 area Residential 75.8 Commercial 256.6 Mumbai Industrial 281.7 May, 2001 – April, 2002 Srivastava et al., 2006 Traffic intersection 655.6 Petrol pump 587.6 Road side 370.2 Hyderabad NA Rekhadevi et al., 2010 petrol pump 2978.8 Roadside 30.0 Agra April, 2010 – March, 2011 Singla et al., 2011 Petrol pump 47.1 Other cities in Asian, European, African, Arabian and American countries

Beijing, China Road Side, High traffic density 173.7 Aug-2005 Song et al., 2007

Gongga Mountain, China High altitude remote station 40.3 Jan, 2008-Dec, 2011 Zhang et al., 2013

Hong Kong, China Residential area 91.7 Sep-Nov, 2010 Lam et al., 2013

Sanghai, China Commercial 191.7 Jan, 2007- Mar, 2010 Cai et al., 2010 Residential-cum-commercial- Yokohama, Japan 115.9 June, 2007-Nov, 2008 Tiwari et al., 2010 cum-industrial Ulsan, Korea Residential 23.8 Mar, 2010-feb, 2011 Lee et al., 2012

Kaohsiung, Taiwan High traffic density 202.8 July and Oct, 2003 Liu et al., 2008

Bangkok, Thailand Commercial 61.6 Jan-Dec, 2009 Ongwandee et al., 2011

Paris, France Residential-cum-Industrial- 17.9 Jan-Feb, 2010 Ait-Helal et al., 2014

Rome High traffic density 15.9 Dec, 2010-Dec, 2011 Fanizza et al., 2014

Cairo, Egypt Commercial 558.9 June-Aug, 2004 Khoder et al., 2007

Ankara, Turkey Residential area 13.5 Jan-June, 2008 Yurdakul et al., 2013

Kuwait, UAE Residential-cum-commercial 127 Aug, 2010-Nov, 2011 Al Khulaifi et al., 2014

Houston, USA Highly industrialized 14.7 Aug-Sep, 2006 Leuchner et al., 2010

1038

34

1039 Figure Caption 1040 1041 Figure 1. The geographical location and aerial view of the sampling area and the hill station 1042 Darjeeling 1043 Figure 2. Seasonal variation of micro-meteorological parameters over Darjeeling during the 1044 study period. 1045 Figure 3. Seasonal variation of a)TVOC, b) mono aromatics-BTEX, c) non-BTEX substituted 1046 aromatics and d) halocarbons shown in box-whisker plot. The lower boundary of the box, the 1047 horizontal line inside the box and upper boundary of the box represent 25th percentile, median 1048 and 75th percentile respectively. The whiskers below and above represent minimum and 1049 maximum respectively. 1050 Figure 4. Day to day variation of TVOC with temperature and solar radiation. 1051 Figure 5. Source regions of VOCs obtained from air mass trajectories from HYSPLIT model. 1052 Figure 6. Variability-Lifetime relationship of different VOC’s for local/regional and long 1053 distant source regions. 1054 Figure 7. VOC Source profiles estimated from PMF model. 1055 Figure 8. Percentage contribution of various sources for VOC’s over Darjeeling. 1056 Figure 9. Ozone formation potential of each source of VOC’s. 1057 1058

35

1059 1060 1061 Figure 1. The geographical location and aerial view of the sampling area and the hill station 1062 Darjeeling 1063 1064

36

1065 1066 Figure 2. Seasonal variation of micro-meteorological parameters over Darjeeling during the 1067 study period.

37

1068 1069 1070 Figure 3. Seasonal variation of a)TVOC, b) mono aromatics-BTEX, c) non-BTEX substituted 1071 aromatics and d) halocarbons shown in box-whisker plot. The lower boundary of the box, the 1072 horizontal line inside the box and upper boundary of the box represent 25th percentile, median 1073 and 75th percentile respectively. The whiskers below and above represent minimum and 1074 maximum respectively. 1075 1076

38

1077 1078 Figure 4. Day to day variation of TVOC with temperature and solar radiation. 1079 1080 1081 1082

39

1083 1084 1085 Figure 5. Source regions of VOCs obtained from air mass trajectories from HYSPLIT model. 1086 1087

40

1088 1089 Figure 6. Variability-Lifetime relationship of different VOC’s for local/regional and long 1090 distant source regions. 1091 1092

41

1093 1094 Figure 7. VOC Source profiles estimated from PMF model. 1095

42

1096 1097 Figure 8. Percentage contribution of various sources for VOC’s over Darjeeling. 1098 1099

43

1100 1101 Figure 9. Ozone formation potential of each source of VOC’s. 1102 1103

44