1

Volatile Organic Compounds over Eastern Himalaya, India: 1

Temporal Variation and Source Characterization using Positive 2

Matrix Factorization 3

4 5

C. Sarkar1, A. Chatterjee1,2,4, D. Majumdar3, S. K. Ghosh4, A. Srivastava3 and S. 6 Raha2,4 7

8 1Environmental Science Section, Bose Institute, P 1/12 CIT Scheme VII-M, Kolkata-700054, 9 India 10 2National Facility on Astroparticle Physics and Space Science, Bose institute, 16, A.J.C. Bose 11 Road, Darjeeling-734101, India 12 3National Environmental Engineering Research Institute, I-8 Sector-C, EKDP, Kolkata-13 700107, India 14 4Center for Astroparticle Physics and Space Science, Block-EN, Sector-V, Salt Lake, 15 Kolkata-700091, India 16 5National Environmental Engineering Research Institute, Delhi, India 17 18 Correspondence to: A. Chatterjee (abhijit.boseinst@gmail.com) 19 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

2

Abstract 49

A first ever study on the characterization of Volatile Organic Compounds (VOCs) has 50

been made over a Himalayan high altitude station in India. A total of 18 VOCs consisting of 51

mono aromatics-BTEX (Benzene, Toluene, Ethylbenzene, Xylene), non-BTEX substituted 52

aromatics and halocarbon have been measured over Darjeeling (27.01 ºN, 88.15 ºE, 2200 m 53

asl) in the eastern Himalaya in India during the period of July, 2011 – June, 2012. The annual 54

average concentration of the sum of 18 target VOCs (TVOC) was 376.3 ± 857.2 µg m-3. 55

Monoaromatics had the highest contribution (72 %) followed by other substituted aromatics 56

(22 %) and halocarbon (6 %) compounds. Toluene was the most abundant VOC in the 57

atmosphere of Darjeeling with the contribution of ~ 37 % to TVOC followed by benzene (~ 58

21 %), ethylbenzene (~ 9 %) and xylenes (~ 6 %). TVOC concentrations were highest during 59

the postmonsoon season with minimum solar radiation and lowest during the premonsoon 60

season with maximum solar radiation. Anthropogenic activities related mainly to tourists like 61

diesel and gasoline emissions, biomass and coal burning and solid waste emissions were 62

almost equal in both the seasons. Seasonal variation in TVOCs over Darjeeling was mainly 63

governed by the incoming solar radiation rather than the emission sources. Source 64

apportionment study using Positive Matrix Factorization (PMF) model indicated that major 65

fraction of (~60 %) TVOC were contributed by diesel and gasoline exhausts followed by 66

solvent evaporation (18 %) and other sources. Of the measured compounds, diesel exhaust 67

was also found to have the maximum potential in tropospheric ozone formation. The 68

atmospheric loading of BTEX over Darjeeling was found to be comparable with several 69

Indian metro cities and much higher than other cities around the world. 70

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

3

1 Introduction 87

The studies on Volatile Organic Compounds (VOCs) have gained much attention because of 88

their ability in modifying oxidizing capacity of the atmosphere as well as health implications 89

to humans. VOCs play an important role in the formation of photochemical smog and 90

tropospheric ozone by reacting with hydroxyl radicals (OH) in the presence of NOx 91

(Atkinson, 2000). Some of the VOCs also have the potential towards stratospheric ozone 92

depletion and enhancement of the global greenhouse effect (Prather and Watson, 1990). 93

VOCs comprise a wide range of compounds including aliphatic and aromatic hydrocarbons, 94

alcohols, aldehydes, ketones, esters, and halogenated compounds. Many VOCs react with 95

hydroxyl radicals (OH) and/or nitrate (NO3) radicals to form secondary organic aerosol 96

(SOA) by nucleation and condensation with a significant aerosol yield and thus they 97

influence gas phase pollutants directly and particle-phase pollutants indirectly (Brocco et al., 98

1997; Odum et al., 1997). 99

There is as such no general source for VOCs as there are numerous compounds in this group, 100

which can be emitted from very different sources (Yurdakul et al., 2013). In addition to the 101

biogenic sources of VOCs (Williams and Koppmann., 2007), some well documented 102

anthropogenic sources are gasoline powered and diesel powered motor vehicles (Demir et al., 103

2011), fuel storage (Lanz et al., 2008), biomass burning (Yokelson et al., 2008), natural gas 104

(Latella et al., 2005), LPG (Lai et al., 2005), industrial processes and solvents (Lanz et al., 105

2008) etc. 106

High levels of VOCs have been observed in Asian countries where significant fractions of 107

VOCs have been considered to be originating from vehicular emissions (Srivastava et al., 108

2005). Among the Asian countries, India is the second largest contributor to the emission of 109

non-methane VOCs (Kurokawa et al., 2013). In spite of growing population and associated 110

increase in vehicular and industrial activities, the studies on VOCs in India are limited. Some 111

of those important studies have been conducted in the recent past mostly in metro cities such 112

as in Delhi, the capital city of India (Hoque et al., 2008; Khillare et al., 2008; Srivastava., 113

2005; Srivastava et al., 2005a, 2005b, 2005c; Gurjar et al., 2004; Padhay and Varshney, 114

2000), in Mumbai, a metro city and financial capital of India situated in western India 115

(Srivastava and Som, 2007; Srivastava et al., 2006a and 2006b; Srivastava., 2004; Srivastava 116

et al., 2004), in Kolkata, a metro city in eastern India (Dutta et al., 2009; Mujumdar et al., 117

2008; Som et al., 2007; Mukherjee et al., 2003), in Hyderabad, a metro city in southern India 118

4

(Rekhadevi et al., 2010), in Agra in northern India, (Singla et al., 2012), in Firozabad in 119

northern India (Chaudhury and Kumar, 2012), in Mohali, a suburban site in north-western 120

India (Sinha et al., 2014; Sarkar et al., 2013) etc. In India there is no legislation of VOC as a 121

whole except national ambient air quality standard for Benzene by Central Pollution Control 122

Board of India. Globally US Occupational Safety and Health Administration (OSHA) and 123

World Health Organization (WHO) have proposed some guidelines and recommendations for 124

VOCs and not compulsory for governments to follow (Han and Naeher, 2006). 125

Where almost all the studies were conducted over several cities in India, no such study on 126

VOCs have been ever made over high altitude stations over Indian Himalaya with both 127

ecological and climatic importance. Mayewski et al., (1979) studied the fluctuations of 128

Himalayan and Trans-Himalayan glaciers including Kanchenjunga at eastern Himalaya and 129

reported that the volume and extent of these glaciers have been decreasing since 1850 which 130

could be due to the increase in the loading of atmospheric carbonaceous pollutants. Air 131

quality over Himalaya is deteriorating due to increased man-made activities to meet the 132

demands related to urbanization and economic development (Momin et al., 1999; Meena et 133

al., 2012). High altitude Himalayan hill stations especially over eastern part in India which 134

earlier were considered as the pollution-free regions have now become the source of huge 135

amount of hazardous air pollutants due to the increase in various tourism-related 136

anthropogenic activities like fossil fuel and biomass burning etc (Adak et al., 2014). 137

The present study on the characterization of VOCs has been made over a high altitude (2200 138

m asl) hill station, Darjeeling (27.010N, 88.150E) at eastern Himalaya and the first ever study 139

conducted over Indian Himalaya to the best of our knowledge. Our earlier studies (Chatterjee 140

et al, 2010, 2012; Adak et al, 2014; Sarkar et al 2014) over the same region showed high 141

aerosol loading during premonsoon (March-May) due to vehicular emissions related to tourist 142

activities and during winter (December-February) due to massive biomass burning. In 143

addition to the local sources, pollutants were also found to be accumulated over this region 144

transported from long distant regions like Indo-Gangetic Plain (IGP) and other Asian sub 145

continents. Sarkar et al., (2014) found enhancement of Black Carbon aerosols over Darjeeling 146

during postmonsoon (October-November) due to transported plumes of biomass burning 147

from northern India. The seasonal variation of aerosols associated to the variation in emission 148

sources (local and transported) as observed from earlier studies have prompted us to make a 149

yearlong study on VOCs over Darjeeling as major aerosol sources over this region are 150

generally the major sources of VOCs too. 151

5

The present study is thus mainly focused on 1) the identification of the major factors 152

governing seasonal variation of VOCs, 2) contribution of long distant source regions, 3) 153

source apportionment of VOCs using Positive Matrix Factorization (PMF) and their potential 154

in tropospheric ozone formation 155

156

2 Study site and synoptic meteorology 157

The study has been carried out at a high altitude hill station Darjeeling at eastern Himalaya in 158

India. A map showing the geographical position of Darjeeling and its adjacent regions is 159

given in Figure 1. The sampling site (27º 01’N, 88º15’E) in Darjeeling is situated at the 160

elevation of 2200 from mean sea level at eastern Himalaya. The site is located at an elevation 161

of 200 m from the main town centre which is populated mainly by several hotels and resorts, 162

offices, shopping malls, bus and car stands, schools and colleges. The National Highway 163

(NH55) with high vehicular density connects Darjeeling with the city called Siliguri at the 164

foothill of Himalaya. Darjeeling Himalayan Railway (also known as “Toy Train”), is a 165

narrow gauge railway which runs by coal and diesel for the tourists and a world heritage site, 166

too. The site in Darjeeling represents a typical urban atmosphere with major anthropogenic 167

sources like vehicular emissions, biomass burning, agricultural activities etc. The description 168

of the study site has been given in detail in our earlier studies (Chatterjee et al., 2010, 2012, 169

Adak et al., 2014). 170

The seasonal average along with minimum and maximum of surface meteorological 171

parameters; temperature (T) in ºC, wind speed (WS) in ms-1, relative humidity (RH) in % and 172

rainfall (mm) are given in Figure 2. The entire study period is divided into four seasons; 173

winter (December – February), premonsoon (March – May), monsoon (June – September) 174

and postmonsoon (October – November). Figure 2 shows that the temperature was highest 175

during monsoon and lowest in winter whereas relative humidity shows monsoon maximum 176

and premonsoon minimum. Wind speed was found to be maximum in premonsoon which 177

was ~2 times than that in other seasons. We did not observe much variation between daytime 178

and nighttime wind speed except in premonsoon when daytime wind speed was much higher 179

(~1.8 times) than night-time wind speed. The surface reaching solar radiation was maximum 180

during premonsoon and was much higher than postmonsoon, monsoon and winter. The total 181

amount of rainfall over the entire sampling days was 421 mm where ~ 95 % rain occurred 182

during monsoon (397 mm) only. 183

6

3 Methodology 184

3.1 Sampling and analysis of VOCs 185

The study was carried out in the campus of National Facility on Astroparticle Physics and 186

Space Science, Bose Institute, Darjeeling. Samples were collected on a roof top of the 187

building of Bose Institute at a height of about 20 meters from the ground level at day (7 a.m. 188

to 7 p.m.) and night (7 p.m. to 7 a.m.) basis with the sample integration time of 11 hours for a 189

year long period from 7th July 2011 to 25th June 2012. The samples were collected once a 190

week. A total of 90 samples were collected during the study period, using a custom made 191

glass sampling tube containing charcoal and chromosorb. The tubes were pre-conditioned by 192

heating over night at 200 °C temperature. The tubes were connected with a low flow air 193

suction pump (SKC, USA). The flow rate was maintained at ~100 ml/min. The flow was 194

measured before and after each sampling event using a flow meter. After sampling the ends 195

of the tubes were sealed well with the Teflon tape and cap and kept at 4°C for analysis. 196

The analysis was done by thermal desorption followed by detection on GC-MS in accordance 197

with USEPA TO-17 compendium method for the determination of target VOCs and 198

described in details in the authors’ previous publications (CPCB 2007, CPCB 2010, 199

Srivastava & Som, 2007, Majumdar et al., 2014). In short the thermal desorption of sorbent 200

tube was done by heating at 180°C for 25 min. 100 μl of desorbed gas was injected into 201

Varian Make GC-MS [Now Agilent; GC–MS model: (Model 450GC-240MS)] operated 202

under SCAN mode. Target VOCs were separated using DB 624 capillary column of 30 m 203

length and 0.32mm internal diameter. Helium gas with flow rate of 1ml min-1 was used as 204

carrier gas with split ratio 1:20, GC oven was programmed for 35oC hold for 4 min and 205

ramped to 210oC. For estimation of the target compounds external five point calibration 206

curve was prepared in triplicate using VOC mix 20 by Dr. Ehrenstorfer GmbH, Germany. 207

208 3.2 Quality assurance and quality control 209 210 Breakthrough for air sampling procedure value was estimated by connecting two tubes in 211

series and it was considered that breakthrough occurred when the backup tube had 212

concentration more than 10% of the total concentration. Breakthrough has been observed 213

after passing 70 Lit of air sample in ambient condition. As soon as the air pump was turned 214

off, the sorbent tubes were removed, capped tightly and sealed in plastic bags and stored at 4 215

°C. Blank cartridges were also stored under identical conditions. 216

7

Percentage accuracy for observation has been determined as a relative difference of measured 217

concentration and spiked concentration for each component. Also mean response factor and 218

percent relative standard deviation for all target compounds has been calculated. For 219

determination of Relative Standard Deviation (RSD) we performed five runs of the same 220

concentration of mixed standard. For the lowest concentration of 0.005 µg of each target 221

compound the RSD varied between 3-10%. Method detection limit (MDL) has been 222

established by making seven replicated measurements of 0.005 µg of each target compound. 223

The standard deviation for these replicated concentrations multiplied by student’s t value for 224

99% confidence for seven values gives the MDL. The MDL varied from 0.01 µg m-3 for 225

1,2,4-Trimethylbenzene to 0.08 µg m-3 for 1,1-Dichloroethane considering 70 Lit of air 226

sample. The MDL for individual component has been given in Table 1. 227

4 Results and discussion 228

4.1 General characteristics of VOCs over Darjeeling 229

All the 18 VOCs measured in this study denoted as TVOC (Total VOCs) have been classified 230

in to three groups; mono-aromatics BTEX, non-BTEX substituted aromatics (iso-231

propylbenzene, n-propylbenzene, 1,3,5 trimethylbenzene, 1,2,4 trimethylbenzene, sec-232

butylbenzene, 4-isopropyltoluene, 2-chlorotoluene, 1,4 dichlorobenzene, n-butylbenzene, 233

naphthalene), and halocarbons (1,1 dichloroethane, 1,2 dichloroethane, chloroform and 234

carbon tetra chloride). The annual average concentrations of each VOC for each group along 235

with their minimum and maximum concentrations over the entire period of study have been 236

given in Table 1. The concentration of TVOC over Darjeeling was found to vary widely from 237

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 – 238

June, 2012). The annual average concentration of TVOC was 376.3 ± 857.2 µg m-3. BTEX 239

was found to have the highest contribution (72 %) followed by non-BTEX substituted 240

aromatics (22 %) and halocarbon (6 %) compounds. BTEX varied over a wide range between 241

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 242

be the most abundant VOC in the atmosphere of Darjeeling with the contribution of ~ 37 % 243

to TVOC followed by benzene (~ 21 %), ethylbenzene (~ 9 %) and xylenes (~ 6 %). The 244

concentration of non-BTEX aromatics, too, varied widely from a very low (0.3 µg m-3) to a 245

very high (912.1 µg m-3) value with an average of 88.6 ± 220.1 µg m-3. On the other hand, 246

halocarbon compounds, unlike other VOCs, did not show such large variability during the 247

study period. The concentration of halocarbons varied from 1.5 µg m-3 to 73.3 µg m-3 with an 248

8

average of 21.5 ± 15.4 µg m-3. An important observation is that the concentration of carbon 249

tetrachloride (CTC) over Darjeeling was found to be much lower (0.18 µg m-3 which is 250

equivalent to 30 pptv) than the global mean concentration (~ 85 pptv during 2011-2012; 251

http://cdiac.ornl.gov/oceans/new_atmCFC.html). Studies on global distribution of CTC were 252

made using model-based simulation studies (Liang et al., 2014), occultation measurement 253

studies (Allen et al., 2009) etc. But, very few ground-based observations on CTC were made 254

in India where no such studies exist over eastern part of this country. However, the 255

concentration of CTC was found to have wide spatial variation. Srivastava et al., (2006) 256

observed CTC concentration of 55 pptv over an industrial region of Mumbai whereas very 257

high CTC concentration of 560-800 pptv was observed over an industrial region of Delhi 258

(Srivastava et al., 2005). Glavas and Moschonas (2002) observed very low CTC 259

concentration of 40 pptv over Athens, Greece during summer in 2000. There is no industries 260

exist in and around Darjeeling which could result to such low CTC concentration. TVOC and 261

most of its components showed their minimum concentrations during premonsoon (May 15, 262

2012) and maximum concentrations during postmonsoon (November 21, 2012). 263

4.2 Factors affecting seasonal variations of VOCs 264

Figure 3 shows the seasonal variations of VOCs over Darjeeling. The concentration of TVOC 265

was maximum in postmonsoon (1649.9 ± 875.4 µg m-3) followed by monsoon (117.1 ± 88.3 266

µg m-3), winter (60.4 ± 28.2 µg m-3) and minimum during premonsoon (35.9 ± 9.7 µg m-3). 267

BTEX and non-BTEX substituted aromatics showed similar seasonal patterns. The high 268

postmonsoon concentrations were found to be 1228.2 ± 534.1 µg m-3 and 404.0 ± 336.1 µg m-269

3 and the low premonsoon concentrations were found to be 12.9 ± 3.3 µg m-3 and 3.5 ± 1.5 µg 270

m-3 for BTEX and non-BTEX substituted aromatics respectively. Unlike BTEX and non-271

BTEX, halocarbons showed highest abundance in winter (33.5 ± 10.4 µg m-3) with small 272

variabilities between premonsoon (19.1 ± 4.2 µg m-3), postmonsoon (17.6 ± 4.5 µg m-3) and 273

monsoon (14.5 ± 5.2 µg m-3). 274

Postmonsoon and premonsoon are the tourist seasons over Darjeeling. Darjeeling experiences 275

huge emissions of fossil fuel burning from large numbers of tourist vehicles during these two 276

seasons compared to other seasons. We had made rough measurements on vehicle counts and 277

consumption of fossil fuel over Darjeeling earlier in the year of 2005 (Adak et al., 2010). We 278

observed that the number of light and medium duty vehicles was 6000-6700 per day during 279

premonsoon and postmonsoon whereas 3000-3600 per day during winter and monsoon. The 280

9

total consumption of fossil fuel (petrol and diesel) was 6500-7500 lit per day during 281

premonsoon and postmonsoon whereas it was 3500-4500 lit per day during winter and 282

monsoon. In addition to the vehicular emissions, various other anthropogenic activities are 283

also increased in premonsoon and postmonsoon seasons. The tourist activities remained the 284

same in these two seasons but VOCs showed significant variations with high level in 285

postmonsoon and low level in premonsoon. The other factors related to the sinks of VOCs 286

played major roles dominating the emission sources of VOCs, leading to the significant 287

variation between postmonsoon and premonsoon. Observed seasonal trends can thus be 288

addressed by the characteristics of the prevailing meteorology, and most importantly, the 289

availability of solar insolation in these two seasons. Darjeeling recorded maximum solar 290

insolation in premonsoon (360 ± 140 watt m-2; Fig 2) which could help in the photolysis of 291

ozone, carbonyls, water vapour etc leading to the formation of OH radicals in the atmosphere 292

(Ho et al, 2004). Another important meteorological factor is wind speed which was observed 293

to be maximum during premonsoon months (1.4 ± 0.5 m s-1; Fig 2). This could favour the 294

ventilation and dispersion of VOCs from the study site. On the other hand, the solar 295

insolation (220 ± 100 watt m-2) and wind speed (0.65 ± 0.2 m s-1) during postmonsoon were 296

much lower than premonsoon. Thus, although the VOC emissions remained comparable, 297

VOC degradation was maximum in premonsoon than postmonsoon leading to premonsoon 298

low and postmonsoon high VOC concentrations. In addition to the local emissions, 299

transported carbonaceous compounds could also contribute significantly in enhancing 300

carbonaceous compounds over eastern part of Himalaya during postmonsoon. Bonasoni et al., 301

(2010), Marinoni et al., (2010), Dumka et al., (2010) and Kaskaoutis et al., (2014) have 302

shown the influence of carbonaceous compounds (mainly Black Carbon) over Himalayas due 303

to transported plumes associated to crop residue burning over Punjab and adjacent Indo 304

Gangetic Plain regions during postmonsoon seasons. Sinha et al., (2014) and Sarkar et al., 305

(2013) showed significant increase in VOC concentrations due to agricultural residue burning 306

over Mohali, a suburban site in north-western India. Our recent study (Sarkar et al., 2014) 307

showed the impact of this transported biomass burning plumes on Black Carbon aerosols 308

over Darjeeling in the same study period. These biomass burning plumes could also bring 309

significant amount of VOCs over Darjeeling enhancing their concentrations during 310

postmonsoon. 311

The tourist activities remained low both during monsoon and winter months over Darjeeling. 312

The solar insolation during monsoon and winter was comparable in magnitude (180 ± 80 watt 313

10

m-2). Darjeeling recorded maximum temperature during monsoon (15.8 ± 0.9 0C) which may 314

lead to increased evaporative emissions for certain VOC species with higher vapour pressure 315

from vehicular service stations, and also from waste decomposition in the hotter months 316

(Talapatra and Srivastava, 2011). VOC emission from solvent evaporation is less significant 317

at very low temperature (5.3 ± 2.1 0C) during winter. 318

In order to better establish the fact of the dependence of TVOC with meteorological 319

parameters, we have investigated the day-to-day variations of TVOC with temperature and 320

solar radiation during the entire study period and shown in Figure 4. It shows well correlation 321

between TVOC and solar radiation during premonsoon. TVOC was found to decrease with 322

the increase in radiation. Thus, we can say that solar radiation played key role in atmospheric 323

clean-up and degradation of VOCs during premonsoon. On the other hand, correlation was 324

also observed between TVOC and temperature during monsoon. We observed increase in 325

TVOC with the increase in temperature favouring evaporative emissions during monsoon. 326

In general, variation in VOC concentrations between hotter and colder months over plain land 327

cities is addressed with the help of vertical advection through boundary layer dynamics in 328

addition to other meteorological factors. The low VOC concentration during summer is 329

generally associated to favourable vertical mixing due to high boundary layer/mixing height 330

whereas comparatively higher VOC concentration during winter is associated to calm and 331

stable atmospheric condition with low boundary layer/mixing height restricting vertical 332

dissipation. The boundary layer dynamics has been used for addressing seasonal variation of 333

VOCs for most of the studies conducted over several Indian cities (Talapatra and Srivastava, 334

2011 and several references therein). But the case of Darjeeling is unique, unlike plain land 335

cities, the seasonal variation in VOC concentration could not be addressed through boundary 336

layer dynamics as the station itself is situated at a height of 2.2 km, well above the boundary 337

layer. But there is a probability that boundary layer could reach the altitude of Darjeeling 338

during premonsoon under high convective activities. Thus, VOCs emitted from plain land 339

regions could reach Darjeeling after their vertical advection and could contribute and enhance 340

VOC concentrations over Darjeeling. But, photochemical degradation under high solar 341

insolation over Darjeeling could have hindered the development in VOC concentrations 342

during premonsoon. 343

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

11

VOC concentrations over Darjeeling were compared between day and night time for different 345

seasons in order to investigate the potential impact of the variability in emission sources 346

and/or meteorological factors between day and night time. The night to day ratio was greater 347

than 1.0 in each season. We infer that although the emissions were high, the photochemical 348

degradation could decrease the day-time VOC concentrations. Thus, night-time VOCs could 349

be attributed to the VOCs generated during night (which could not degrade by photolysis) 350

plus residual VOCs generated during day-time. Another important factor is higher wind 351

speed during daytime which could favour the dispersion of VOCs more than night. The ratio 352

for TVOC was highest during premonsoon (1.9) followed by postmonsoon (1.4), monsoon 353

(1.2) and minimum during winter (1.1). The highest ratio in premonsoon could be due to the 354

removal of VOCs by efficient and faster photo-degradation by very high solar insolation and 355

higher wind speed favouring dispersion of pollutants during day time leading to lower day-356

time VOC concentrations compared to the other seasons. However, the minimum value of the 357

ratio in winter could be due to minimum night-time VOC emissions due to subdued 358

anthropogenic activities (except biomass burning) in colder nights. Another possibility is that 359

the boundary layer could remain well below the observational site (2200 m asl) during winter 360

nights and hence pollutants could be accumulated below Darjeeling leading to lower night-361

time VOC concentration. 362

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

The transport of air masses from distant sources could affect the pollutant concentrations at 364

the study site in conjunction with the local sources. In order to investigate the transport of 365

VOCs from long distances, we have computed 36 h air-mass back trajectories, arriving at an 366

altitude of 500 m above ground level over Darjeeling for all the days on which VOCs were 367

measured, using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model 368

(http://www.arl.noaa.gov/ready/hysplit4.html). Over the entire period of study, we have 369

identified three major source regions for long range transport as shown in Figure 5. The 370

frequency of transport from each of the source regions has also been shown in the figure. 371

Region 1 corresponds to the transport from SE directions and the air masses originated from 372

southern part of West Bengal, India and Bangladesh with the frequency of 32 %. The average 373

TVOC concentration associated to Region 1 was found to be 117.2 ± 86.1 µg m-3. Region 2 374

corresponds to the transport from W/NW directions and the air masses originated from 375

eastern and central part of Nepal with the frequency of 42 %. The associated TVOC 376

12

concentration was found to be 831.5 ± 955.2 µg m-3. Region 3 corresponds to local/regional 377

sources and the air masses originated mainly from the E/SE directions with the frequency of 378

26 %. The major regions were northern part of West Bengal and the average TVOC 379

concentration was found to be 620.1 ± 535.4 µg m-3. Thus, the contribution from Nepal 380

(Region 2) was found to be 5.5-8 and 1.3-1.8 times higher than West Bengal/Bangladesh 381

(Region 1) and local/regional sources (Region 3) respectively. The contributions from each 382

source regions were also investigated for different seasons. The average TVOC 383

concentrations associated to respective source regions along with their frequencies have been 384

given in Table 2 season-wise. It was observed that during monsoon, all the air masses 385

originated from Region 1 with 100 % frequency with the average TVOC concentration of 386

117.2 ± 86.1 µg m-3. Similarly, during winter, 100 % air masses originated from Nepal with 387

the TVOC concentration of 60.9 ± 28.0 µg m-3. During postmonsoon, 60 % air masses 388

originated from local/regional sources (Region 3) and 40 % originated from Nepal (Region 2) 389

with the average TVOC concentrations of 1206.8 ± 628.3 µg m-3 and 2978.1 ± 1538.1 µg m-3 390

respectively. It was observed that 50 % air masses originated from local/regional and 50 % 391

originated from Nepal during premonsoon and the TVOC concentration associated to Nepal 392

was found to be slightly higher (46.2 ± 12.5 µg m-3) than local/regional sources (34.2 ± 11.3 393

µg m-3). This result indicates that the air masses coming from Nepal carried more VOCs and 394

thus more polluted compared to other source regions. It is important to mention over here that 395

the altitudes of the air masses were below 1000 m asl throughout their trajectories/pathways 396

originating from their source regions. Thus the air masses could pick up the boundary layer 397

pollutants of the regions they passed over before reaching our observational site. 398

As Nepal was found to be most polluted source regions, an attempt was made to roughly 399

estimate the contribution of TVOC from Nepal in postmonsoon and premonsoon seasons. 400

During these two seasons, air masses originated both from Nepal and local/regional source 401

regions and thus contribution from Nepal was estimated in terms of the relative 402

concentrations associated to these two regions. The estimation has been made by the 403

following equation: 404

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

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

additional amount of TVOC coming from Nepal. MCTotal is the measured concentration of 407

TVOC on respective days when air masses generated from Nepal i.e. with the total 408

13

contribution of both Nepal and local air masses. MCLocal is the average measured 409

concentration of TVOC on all the days when air masses originated from local sources i.e. 410

contribution from local sources only. It was observed that VOCs from Nepal contributed to 411

the TVOC concentration over Darjeeling by 38-54 % with the average of ~53 % during 412

postmonsoon and 32-65 % with the average of ~50 % during premonsoon. 413

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

VOCs 415

The relationship between the variability in concentrations and the life time of VOCs can be 416

used to estimate the distance of their source regions regardless the influence of the regional 417

transport. The following empirical equation was first proposed by Jobson et al.(1998). 418

Slnx = Aτ-b 419

Where Slnx is the standard deviation of the natural logarithm of the concentration X of VOC, τ 420

is the atmospheric lifetime of VOC, A and b are the fit parameters. 421

The value of exponent b lies between 0 and 1 and describes the influence of the source 422

contribution. The value of b will approach zero when sampling site is closed to a source and 423

the variability-lifetime relation will be ‘weaker’. In the extreme case, when b = 0, the 424

variability will not depend on the atmospheric lifetime but will depend on the variability of 425

the emission sources. In remote areas b will approach 1 (Jobson et al., 1998, Ehhalt et al., 426

1998, Wang et al., 2005) where the distance of sampling site is longer from the potential 427

sources. The variability concept is based on the assumption that the chosen compounds have 428

more or less the same source distribution. The compounds reported in this paper, are mostly 429

of anthropogenic origin (aromatic hydrocarbons and halocarbons). We have used their 430

concentrations for premonsoon and postmonsoon as the sources of VOCs are same in both 431

the seasons. We have calculated the back trajectory analysis for each sampling day and based 432

on the trajectories we have separated the transported air masses from the local emissions (as 433

discussed above). Figure 6 shows the relationship of variability with lifetime for different 434

VOC species separately for long range transport and local emissions. It can be seen that the 435

value of b was higher for long range transport (b = 0.19, R2 = 0.79) than for local emission (b 436

= 0.09, R2 = 0.64). The value of b for long distant sources (0.19) as obtained in the present 437

study was found to be slightly lower than 0.22 as observed over Mount Tai, China (Ting et 438

al., 2009) and 0.23 as obtained in the Mediterranean Intensive Oxidant Study (MINOS) in 439

14

August 2001 on Crete (Gros et al., 2003). But, it was much lower than 0.44 as observed over 440

the remote NARE locations (Jobson et al., 1999) and 0.41 on a cruise through the western 441

Indian Ocean during the INDOEX field study (Karl et al., 2001). Thus, Darjeeling does not 442

represent a remote site where the variability is strongly dependent on the lifetime of VOC but 443

represent a typical urban site in the vicinity of sources where the sources dictate the 444

variability and not the chemistry. The longest source regions (Central part of Nepal or 445

southern part of West Bengal/Bangladesh) for VOCs over Darjeeling as estimated from 446

HYSPLIT trajectory models were within 200 km from Darjeeling. 447

4.6 Characterization of sources of VOCs by Positive Matrix Factorization receptor 448

model 449

In recent years, an advanced receptor model, Positive Matrix Factorization (PMF), has been 450

applied extensively in identifying VOC contributing sources at different locations in the 451

world (e.g., Jorquera and Rappengluck, 2004; Latella et al., 2005; Xie and Berkowitz, 2006; 452

Brown et al., 2007; Song et al., 2007; Yuan et al., 2009). PMF does not require any priori 453

knowledge on the exact VOC emission profiles, and it can be used to apportion source 454

contributions solely based on observations at the receptor site, thus avoiding VOC decay 455

adjustment problem i.e. quick loss of some reactive VOC species in the path between source 456

and receptor. However, very high reactive species could be excluded from the model run 457

provided they are not marker or tracer of any VOC source. In this study, we have measured 458

18 VOC species which are major and abundant in the atmosphere and can not be considered 459

as highly reactive and have been included in PMF model run by earlier studies, too. More 460

details about the PMF method were described by several studies (Paatero and Tapper, 1994; 461

Paatero, 1997; Reff et al., 2007). Q is a typical parameter in PMF which is the measure of 462

goodness of fit parameters. Q (true) is calculated including all data points where Q (robust) is 463

calculated excluding data points not fit by the model. The difference between Q (true) and Q 464

(robust) is the measure of the impact of data points with high scaled residuals whereas high 465

uncertainties result in similar Q values. Q (robust) is used to choose the optimal run from the 466

multiple runs. If the number of sources is estimated properly, Q (robust) will have little 467

variability between the runs and thus we get stable Q. The variability of Q (robust) is the 468

indication of the variability of initial base run results. 469

In this study, the PMF method has been applied to identify the possible sources of VOCs over 470

Darjeeling. Table 3 shows the source profiles derived by the PMF model. Eight factors were 471

15

selected according to the resulted stable Q values. Figure 7 shows the percentage contribution 472

of each VOC associated to each of eight sources. 473

Table 3 shows that Factor 1 is dominated by high values of BTEX with much higher 474

concentrations of benzene and toluene followed by ethylbenzene and xylene. Toluene to 475

benzene ratio was found to be 0.9 in this factor. Thus factor 1 could be associated to the 476

gasoline-related emissions. Several earlier studies showed the ratio values near 1.0 (1.0 over 477

Kolkata, India by Som et al., 2007; ~ 1.0 over Mumbai, India by Srivastava et al., 2004; ~ 1.0 478

over Ganga Mountain, China by Zhang et al., 2013; 0.8 over Hongkong, China by Lau et al., 479

2007; 0.7 over Sanghai, China by Cai et al., 2010) attributing to gasoline related emissions. 480

VOC emissions from gasoline may occur along many pathways like, evaporative emission 481

from gas stations and bulk terminals and exhaust released from the gasoline-powered vehicles 482

during gasoline combustion (Watson et al., 2001; Choi and Ehrman, 2004). BTEX are the 483

major components of vehicular exhaust, as shown by many studies (Watson et al., 2001; Guo 484

et al., 2006, 2007, Som et al., 2007). High VOC emissions from tourist vehicles during 485

premonsoon and postmonsoon seasons and the gasoline vapours from the frequent use of the 486

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

Factor 2 is also dominated by BTEX. Toluene was found to have the maximum contribution 488

followed by benzene, ethylbenzene and xylene. The toluene to benzene ratio is 2.5 in this 489

factor. Previous study of the authors (Som et al., 2007) reported the same ratio in a study 490

made over Kolkata, India for the VOCs emitted from diesel-driven vehicles. This factor is 491

associated to diesel exhaust. It is interesting to observe that the number of petrol and diesel 492

driven vehicles are nearly same over Darjeeling and PMF result indicates the percentage 493

contribution of TVOCs from Diesel and gasoline sources are also comparable (discussed later 494

in details). 495

Factor 3 is characterized by the high values of TEX. TEX being the primary constituents of 496

solvents (Guo et al., 2004a; Choi et al., 2011), often used as a solvent in paints, coatings, 497

synthetic fragrances, adhesives, inks and cleaning agents, in addition to its use in fossil fuel 498

(Borbon et al., 2002; Chan et al., 2006). This factor can therefore be assigned to the solvent 499

usage and related emission. The rapid growth in tourism related infrastructure like hotels, 500

resorts, restaurants etc over Darjeeling could be the reason for high VOC emission from 501

solvent usage. 502

16

Factor 4 is characterized by the high values of n-propyl benzene, 2-chloro toluene and BTEX 503

and could be assigned to solid waste disposal. Majumdar et al., (2014) reported the high 504

values of these compounds in municipal waste dumping stations in Kolkata, India. With the 505

dramatic increase in tourists and changing consumption patterns, Darjeeling is facing 506

immense problems of waste management. The existing systems of waste management are 507

technically unscientific and the infrastructure is insufficient to manage the waste. 508

Factor 5 is dominated mainly by chloroform and carbon tetrachloride and thus the factor 509

could be associated to chlorine bleach containing house hold products. Odabasi et al., (2008) 510

showed that house hold cleaning agents and fresheners produce these two VOCs 511

significantly. Chloroform and carbon tetrachloride are the major compounds along with 512

several halogenated compounds in chlorinated bleach products. 513

Factor 6 is dominated by m,p-xylene and ethylbenzene followed by n-butylbenzene and 514

toluene and could be assigned to industrial sources (Yuan et al., 2010). Although there is no 515

industry in Darjeeling, but the VOCs could be transported from low land townships and 516

cities. The m,p-xylene to ethylbenzene ratio in this factor was found to be 1.8. The ratio of 517

m,p-xylene to ethylbenzene (X/E ratio) is used as indicator for the age of the VOCs in the 518

atmosphere (Elbir et al., 2007; Guo et al., 2004a, 2004b). The ratio becomes smaller as the 519

VOCs get older in the atmosphere, because m,p-xylene is more reactive than ethylbenzene. 520

Kuntasal (2005) found X/E ratio to be varied between 3.8- 4.4 in fresh emissions at gasoline 521

station, underground garage and a tunnel. The low ratio in this study (1.8) suggests that the 522

species were not emitted in situ but aged/transported. 523

Factor 7 is dominated by chloroalkanes, benzene and toluene and could be associated to coal 524

and biomass burning (Fernandez-Martinez et al., 2001, Barletta et al., 2009). Coal burning is 525

a significant anthropogenic source in Darjeeling as it is used for the domestic cooking 526

purpose and it is also used in a large scale for coal engines in the toy trains. In addition to 527

that, massive biomass burning during winter to get warmth against cold and probable 528

transportation of biomass burning species from northern Indian states (as discussed earlier) 529

during postmonsoon could enhance those VOCs in the atmosphere of Darjeeling. 530

Factor 8 is characterized by high values of aromatics with high molecular weight like 1,2,3-tri 531

methyl benzene, 1,2,4-trimethyl benzene, o-xylene. Liu et al., (2005) reported high emissions 532

of these VOCs from asphalt related road construction works. Road construction works were 533

17

in progress in and around Darjeeling during few sampling events. Thus the factor 8 could be 534

assigned to the asphalt related emission. 535

Figure 8 shows the percentage contributions of each source to the total VOC loading over 536

Darjeeling during the entire study period. It can be seen that the major sources are diesel 537

exhaust (32 %) and gasoline exhaust (29 %) followed by solvent evaporation (18 %). 538

Chlorine bleach containing house hold products and solid wastes contributed equally (6 %) 539

whereas industrial sources situated at the regions far from Darjeeling, coal/biomass burning 540

and asphalt related constructional works contributed nominally by 4 %, 3 % and 2 % 541

respectively. Thus it can be concluded that the major source of 18 VOCs (BTEX, non-BTEX 542

aromatics and halocarbons) measured over Darjeeling is gasoline and diesel driven vehicular 543

activities which contributed by more than 60 %. 544

4.7 Ozone formation potential of VOC sources 545

Although it is well known that tropospheric NOx plays important role in tropospheric ozone 546

formation, the measurement of NOx was beyond our scope during the study period. Hence, in 547

this study, the investigation of ozone formation potential (OFP) was limited to VOCs only. 548

To find out the potential of various VOC sources (as derived from PMF model) to the 549

tropospheric ozone formation over Darjeeling, we have computed OFP of each source using 550

the Maximum Incremental Reactivity (MIR) values derived by Carter (2008). To do this, we 551

have used the equation derived by Na and Kim, (2007). 552

n

jjjiii MIRSOFP

1

)( 553

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

contribution of the source i, αji is the mass fraction of species j in source i and MIRj is MIR 555

value of species j. Figure 9 shows the relative contribution of each source to OFP. It can be 556

seen from the figure that diesel exhaust has the maximum potential (45%) followed by 557

solvent (24 %) and gasoline exhaust (18 %). Although, Gasoline exhaust contributes more 558

(29%) towards TVOC concentration than solvent usage (18%), the later source is 559

contributing more towards tropospheric ozone generation. The MIR values of the individual 560

species are also responsible for the total OFP of a source along with the corresponding source 561

strength. Thus, it can be said that amongst the 18 VOCs measured over Darjeeling, those 562

emitted from vehicular emissions and solvents played the key role in the formation of 563

tropospheric ozone and modifying tropospheric ozone budget. 564

18

4.8 Comparison with other studies 565

The concentration of BTEX over Darjeeling (present study) has been compared with that 566

over several metro cities in India and also with other cities in Asian, European, African, 567

Arabian and American countries (Table 4). We have taken the sum of BTEX (not TVOC) for 568

comparison as the data of BTEX is more available in the literature. 569

Table 4 shows that BTEX over Darjeeling is lower than the commercial, industrial and the 570

areas with high traffic density (traffic intersection) over Delhi, the capital city of India; traffic 571

intersection and petrol pumps over Mumbai, a metro city in western India and Hyderabad, a 572

metro city in south-eastern India. This is quite expected as the vehicular and industrial 573

activities over those metro cities are much higher than Darjeeling. But the most interesting 574

fact is that BTEX over Darjeeling shows ~3, ~2 and ~5 times higher concentrations than 575

residential areas over Kolkata (a mega city in eastern India), Delhi and Mumbai respectively. 576

Even, Darjeeling shows higher BTEX concentrations than commercial areas of Mumbai and 577

much higher than roadside (~10 times) and petrol pump (~7 times) areas in Agra, a city in 578

northern India with much less vehicular activities compared to other Indian metro cities. 579

BTEX over Darjeeling was found to be much higher (10-25 times) than the 580

residential/industrial/commercial areas of Turkey, Houston, Rome and Paris; 2-6 times higher 581

than residential/commercial areas of Bangkok, Yokohama, Kuwait and Hongkong; 1.5-2 582

times higher than roadside/industrial/commercial areas of Kaohsiung, Sanghai and Beijing. 583

Darjeeling shows much higher (~8 times) BTEX concentration than Gongga Mountain, a 584

high altitude (1640 m asl) remote station in southwestern China. However, BTEX over a 585

commercial area with heavy traffic density in Cairo, Egypt shows 1.7 times higher 586

concentration than that over Darjeeling. 587

In our earlier study (Sarkar et al., 2014), we also reported much higher concentration of black 588

carbon aerosols over Darjeeling compared to other high altitude Himalayan stations in India 589

and Nepal and some of the metro cities in India like Ahmedabad, Bangalore, Trivandrum and 590

Chandigarh. The present study corroborate with that findings. The major source for black 591

carbon aerosol and VOCs over Darjeeling is same, vehicular emissions. Thus, Darjeeling 592

represents a typical urban atmosphere at eastern Himalaya with high loading of carbonaceous 593

pollutants. This could be due to high anthropogenic emissions related to tourist activities, 594

high population density and moreover it’s unique orography and land use pattern with narrow 595

19

roads, unplanned township, poor administrative control on solid waste disposal and burning 596

of these wastes, unplanned constructions of buildings/hotels/resorts which reducing open 597

space/area which in turn prevents ventilation and dispersion of pollutants. 598

5 Conclusion 599

The major findings of the study on VOCs conducted over Darjeeling, a high altitude hill 600

station over eastern Himalaya in India are as follows: 601

1. The annual average concentrations of TVOC, BTEX, non-BTEX aromatics and 602

halocarbons were 376.3 ± 857.2, 275.1 ± 685.7, 88.6 ± 220.1 and 21.5 ± 15.4 µg m-3 603

respectively with the maximum contribution from BTEX (72 %), non-BTEX 604

aromatics (22 %) and halocarbons (6 %). Toluene was found to be the most abundant 605

VOC over Darjeeling which contributed 37 % to the TVOC. 606

2. Concentration of TVOC showed well defined seasonal variations with maximum in 607

postmonsoon (1650 ± 875.4 µg m-3) followed by monsoon (117.1 ± 88.3 µg m-3), 608

winter (60.4 ± 28.2 µg m-3) and minimum during premonsoon (36 ± 9.7 µg m-3). The 609

seasonal variation in VOC concentration was mainly governed by the photochemical 610

degradation process rather than the emission source strength. Although, the 611

anthropogenic activities related to massive tourist influxes during premonsoon and 612

postmonsoon were comparable, the solar radiation made the difference between 613

premonsoon and postmonsoon VOC concentrations. 614

3. Other than local sources, two major regions were identified for VOCs over 615

Darjeeling; Nepal and southern part of West Bengal, India/Bangladesh. It was 616

observed that VOC concentration over Darjeeling was higher when air masses arrived 617

from Nepal than West Bengal, India/Bangladesh and local/regional source regions. 618

The relationship between variability and lifetime of VOC was discussed and it was 619

observed that Darjeeling represents the site in the vicinity of sources as compared 620

with other studies. 621

4. Positive matrix facorization receptor model was used to characterize the sources of 622

VOCs over Darjeeling. It was observed that the major source of VOC over Darjeeling 623

was emission from petrol and diesel driven vehicles which contributed by more than 624

60 % followed by solvent evaporation (18 %) and other sources. 625

20

5. Diesel exhaust was found to have the maximum potential (45 %) in the formation of 626

tropospheric ozone followed by solvent evaporation (24 %) and gasoline exhaust (18 627

%). 628

6. The atmospheric loading of BTEX over Darjeeling was comparable with Indian metro 629

cities and much higher than other Asian, American, African, Arabian and European 630

countries. 631

Thus we found that Darjeeling represents a typical urban atmosphere over eastern Himalaya 632

in India from the point of view of VOC pollution. The high VOC pollution over Darjeeling 633

draws a serious attention as it could significantly affect human health as well as the sensitive 634

ecosystem over this part of Indian Himalaya. Study result emphasis the need for better 635

pollution control system for the vehicles plying on the road of Darjeeling. Imposing 636

regulations on uncontrolled solvent usage is also necessary. Better Solid waste management 637

system is also called for. This year long data set of VOC can be used to make further studies 638

on the modification of the budget of tropospheric ozone, NOx and other gaseous and 639

particulate pollutants. This would, in turn, help us to make studies on the implications of 640

VOCs for regional atmospheric chemistry over eastern Himalaya. 641

642 Authors’contribution 643 644 C.S., A.C., D.M., S.K.G., A.S. and S.R. conceived and designed the experiment. C.S. and 645

A.C. performed the experiment. C.S. and D.M. analyzed the samples. D.M. and A.S. supplied 646

the materials/chemicals and instruments for chemical analysis. C.S., A.C. and D.M. analyzed 647

the data. A.C., C.S. and D.M prepared the manuscript with the contribution of rest of authors. 648

Acknowledgement 649

Authors would like to thank Science and Engineering Council, Department of Science and 650

Technology, Government of India for supporting the study under IRHPA (Intensification of 651

Research in High Priority Areas) scheme. Authors also like to thank University Grant 652

Commision, Govt of India for providing fellowship to Mr C. Sarkar (first author). The study 653

was undertaken by Bose Institute in collaboration with CSIR-NEERI. Field study conducted 654

by Bose Institute and sample analysis performed by CSIR-NEERI, Kolkata Zonal 655

Laboratory. Thanks are due to Mr. Sabyasachi Majee, Bose Institute for his consistent 656

support in sampling and Mr. Debasish Sengupta and Ms Pamela Chowdhury, NEERI for their 657

21

assistance during sample analysis. Authors would also like to thank Mr D. K. Roy, Bose 658

Institute for his overall logistic support. 659

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986

987

988

989

990

991

992

993

31

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

997

998

999

1000

1001

1002

1003

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

32

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

1006

1007

1008

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

Period Regions Direction Source region Frequency [TVOC] µg m-3

Region 1 S/SE Bangladesh and West

Bengal 32 117.2±86.1

Region 2 W/NW Nepal 42 831.5±955.2 Annual

Region 3 E/SE Local/Regional 26 620.1±535.4

Monsoon Region 1 S/SE Bangladesh and West

Bengal 100 117.3±86.5

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

33

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

1020

1021

1022

1023

1024

1025

1026

1027

1028 1029 1030 1031 1032 1033 1034

Gasoline Exhaust

Diesel exhaust

Solvent, Paint

Solid Waste

Disposal

Chlorine Bleach

Products Industrial

Source Coal

Burning

Asphalt Related

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

34

Table 4. Comparison of BTEX concentration over Darjeeling with other cities in India 1035 and other countries 1036

1037

Location Nature of Site Sum of BTEX

(µg/m3) Study Period Reference

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

Indian Metro Cities

Delhi

Residential area Commercial area

Industrial area Traffic intersection

186.0 421.0 411.0 456.0

Oct, 2001–Sep, 2002 Hoquea et al., 2008

Kolkata Commercial-cum-residential

area 132.5 Dec, 2003 – Feb,2005 Majumdar et al., 2011

Mumbai

Residential Commercial

Industrial Traffic intersection

Petrol pump

75.8 256.6 281.7 655.6 587.6

May, 2001 – April, 2002 Srivastava et al., 2006

Hyderabad Road side

petrol pump 370.2

2978.8 NA Rekhadevi et al., 2010

Agra Roadside

Petrol pump 30.0 47.1

April, 2010 – March, 2011 Singla et al., 2011

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

Yokohama, Japan Residential-cum-commercial-

cum-industrial 115.9 June, 2007-Nov, 2008 Tiwari et al., 2010

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

35

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

36

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

37

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

38

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

39

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

1079 1080 1081 1082

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

41

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

42

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

1095

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

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

1102 1103