Black carbon aerosol quantification over north-west Himalayas: Seasonal heterogeneity, source apportionment and radiative forcing

Journal Pre-proof Black carbon aerosol quantification over north-west himalayas: Seasonal heterogeneity, source apportionment and radiative forcing Yogesh Kant, Darga Saheb Shaik, Debashis Mitra, H.C. Chandola, S. Suresh Babu, Prakash Chauhan PII:

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DOI:

https://doi.org/10.1016/j.envpol.2019.113446

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ENPO 113446

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Environmental Pollution

Received Date: 20 June 2019 Revised Date:

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Accepted Date: 20 October 2019

Please cite this article as: Kant, Y., Shaik, D.S., Mitra, D., Chandola, H.C., Babu, S.S., Chauhan, P., Black carbon aerosol quantification over north-west himalayas: Seasonal heterogeneity, source apportionment and radiative forcing, Environmental Pollution (2019), doi: https://doi.org/10.1016/ j.envpol.2019.113446. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Black carbon aerosol quantification over North-west Himalayas: Seasonal heterogeneity, Source apportionment and Radiative forcing

1 2 3 4 5 6 7 8 9 10

Yogesh Kant1, Darga Saheb Shaik,1,2* Debashis Mitra1, H. C Chandola2, S. Suresh Babu3 and Prakash Chauhan1 1

Marine & Atmospheric Sciences Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India 2 Department of Physics, Kumaun University, DSB Campus, Nainital, India 3 Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, Thiruvananthapuram, India

11 12

Abstract

13

Continuous measurements of Black Carbon (BC) aerosol mass concentrations were carried at

14

Dehradun (30.33°N, 78.04°E, 700m amsl), a semi-urban site in the foothills of western

15

Himalayas, India during January 2011–December 2017. We reported both the BC seasonal

16

variations as well as mass concentrations from fossil fuel combustion (BCff) and biomass

17

burning (BCbb). Annual mean BC exhibited a strong seasonal variability with maxima during

18

winter (4.86±0.78 µg m-3) followed by autumn (4.18±0.54 µg m-3), spring (3.93±0.75 µg m-3)

19

and minima during summer (2.41±0.66 µg m-3). Annual averaged BC mass concentrations were

20

3.85 ± 1.16 µg m−3 varying from 3.29-4.37 µg m−3 whereas BCff and BCbb ranged from 0.11 to

21

7.12 µg m−3 and 0.13 to 3.6 µg m−3. The percentage contributions from BCff and BCbb to total BC

22

are 66% and 34% respectively, indicating relatively higher contribution from biomass burning as

23

compared to other locations in India. This is explained using potential source contribution

24

function (PSCF) and concentration weighted trajectories (CWT) analysis which reveals the

25

potential sources of BC originating from the north-west and eastern parts of IGP and the western

26

part of the Himalayas that are mostly crop residue burning and forest fire regions in India. The

27

annual mean ARF at top‐of‐atmosphere (TOA), at surface (SUR), and within the atmosphere

28

(ATM) were found to be -14.84 Wm−2, -43.41 Wm−2, and +28.57 Wm−2 respectively. To

29

understand the impact of columnar aerosol burden on ARF, the radiative forcing efficiency

30

(ARFE) was estimated and averaged values were −31.81, −91.63 and 59.82 Wm−2 τ−1 for TOA,

31

SUR and ATM respectively. The high ARFE within the atmosphere indicates the dominance of

32

absorbing aerosol (BC and dust) over Northwest Himalayas.

33

Key words: Black Carbon, Source apportionment, Biomass burning, Radiative forcing

34

1

35

1. Introduction

36

Black Carbon (BC) is a major constituent of carbonaceous aerosols. Important sources of BC

37

include both anthropogenic as well as natural sources, including incomplete combustion of fossil

38

fuel (FF) and biomass burning (BB) (Resquin et al. 2018). BC aerosols are a strong absorber of

39

electromagnetic radiation over a wide range of wavelengths (UV-NIR) due to its complex

40

chemical composition, optical and physical characterization and long atmospheric lifetime

41

(Drinovec et al. 2015). The absorption properties of BC initiate many atmospheric feedbacks

42

such as alteration of the atmospheric stability, large scale circulations, rainfall duration, rain size

43

distributions and hydro-climatic variations (Koch and Del Genio 2010). BC is the second-largest

44

contributor to global warming after carbon dioxide (CO2), and it has higher direct radiative

45

forcing than methane (CH4) (Jacobson 2001). According to the IPCC 2013, the global direct

46

radiative forcing of BC is +1.1 (+0.20 to +2.10) Wm−2 whereas +1.68 (1.33 to 2.03) Wm−2 for

47

CO2 and +0.97 (0.74 to 1.20) Wm−2 for CH4. However, the quantification of BC climate forcing

48

is still under debate. In addition to climate effects, BC also significantly impact air quality and

49

human health (respiratory, cardiopulmonary, and vascular diseases through direct inhalation)

50

(Heal et al. 2012). Advancement in industrial development, population growth, energy demand,

51

and land use activities (crop, pasture, wood harvest, etc) are the main sources of BC

52

emissions/concentrations at a regional to global scale (Singh et al. 2017). The total global BC

53

emission rate is estimated to be 7500 Gg yr-1 for the year 2000, in which South Asian countries

54

contribute more than 2000-3000 Gg yr-1 (Bond et al. 2013). China and India are the most

55

significant contributor of BC and the emissions increased by ~40% during from 1996-2010 (Lu

56

et al, 2011). The rate of BC emission over India is estimated to 0.41 Tg per year and the

57

percentage of contributions from fossil fuel, biofuel and open burning combustion are 25%, 42%

58

and 33% respectively (Venkataraman et al. 2005). A recent study over the Indian region

59

concludes that the annual BC emission rate is 388–1344 Gg yr−1 based on a bottom-up approach

60

(Verma et al. 2017). The estimated BC over Indian region using regional and global models is

61

found to be lower than the observed BC by a factor of 2 to 5 (Nair et al. 2012). To reduce the

62

uncertainties in the BC predictions, a robust regional network of BC measurements are needed.

63

In this aspect, Indian Space Research Organization’s Geosphere – Biosphere Programme (ISRO-

64

GBP) established a regional network of aerosol observatories named as Aerosol Radiative

65

Forcing over India network (ARFINET). The main objective of this network is to provide a

66

comprehensive scenario of aerosol characteristics and its radiative forcing over Indian region

67

including mainland and terrain regions (Babu et al. 2013). As part of this network, a continuous 2

68

aerosol measurements (Aerosol optical depth, BC, Aerosol number concentration, Particulate

69

Matter etc.) are being carried out over Dehradun (30.33 °N, 7804 °E, ∼700 m amsl), as

70

representation of Northwest Himalayas.

71

In recent years, the effect of BC on the Himalayan cryosphere have attracted significant interest

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because Himalayan glaciers are a source of fresh water to more than 1 billion population in

73

neighboring countries (Kulkarni et al. 2007; Schmale et al. 2017). Major portion of the

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population living at the foothills or at upper ranges of the Himalayas depend on biomass burning

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for cooking and warming purposes (Bhatt et al. 2016). Deposition of BC aerosols on highly

76

reflecting surfaces (like snow or ice) would reduce the surface albedo significantly and

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accelerating the melting of snow and ice packs. BC deposition on Himalayas are closely related

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to the transport processes, lifetime and radiative forcing. Long-range transport of BC from

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highly polluted regions of Indo-Gangetic Plans (IGP) impacting the surface warming (about 0.4

80

to 2.4 ºC) can contribute to accelerating the retreat of Himalayan glacier (Ramanathan &

81

Carmichael, 2008; Li et al., 2016). A few earlier researchers had reported the BC concentration

82

and aerosol properties at different locations in the Northwest Himalayas. For example, at

83

Srinagar, the annual average BC concentration was observed to be 6.0 µg m-3 and its radiative

84

forcing was maximum during autumn season (58.2 Wm-2) (Bhat et al. 2017). Nair et al. (2013)

85

examined the optical and physical properties of composite aerosols over Hanle and estimated

86

direct aerosol radiative forcing at the top of the atmosphere is 1.69 Wm-2 over snow surface and

87

1.54 Wm-2 over sandy surface during spring season. Over Himachal Pradesh, the average

88

concentration of BC was recorded as 1.95 ± 1.12, 2.05 ± 0.80, 1.58 ± 0.87, 2.40 ± 0.72 and 2.83

89

± 0.98 µg m-3 at Palampur, Kullu, Shimla, Solan and Nahan respectively in a campaign mode

90

during 12–22 March 2013 (Sharma et al. 2014). All these studies were confined to short

91

days/time periods and none of them mentioned the source apportionment of BC nor their impact.

92

There is lack of information about the long-term BC characteristics, source contribution and

93

aerosol radiative forcing.

94 95

In view of the above, a long-term BC measurements have been conducted at Dehradun as to

96

assess the variations and to infer the sources. In this study, we report both diurnal and seasonal

97

variability of BC and investigate the contribution of fossil fuels (FF) and biomass burning (BB)

98

to total BC over a period of 5 years (2011-2017; exception of 2014 & 2015). Also investigated

99

impact of biomass burning and potential source regions of BC using receptor trajectory models.

100

We also estimated the composite aerosol radiative forcing (ARF) over Dehradun by using a

101

SBDART Radiative Transfer model. 3

102 103 104 105 106 107 108

Fig. 1. (a) Topography map and geographical location of the Dehradun (30.33°N, 78.04°E, 700m amsl), the top right panel is the close up image of observational site (Courtesy: Cartosat-1 DEM ISRO Bhuvan). (b) Ground based surface wind rose patterns during different seasons, (c) Monthly mean variation of temperature, rainfall and relative humidity (RH) at the observational site for the period of 2011 – 2017.

2. Sampling site and meteorological condition

109 110

BC measurements were done at the Indian Institute of Remote Sensing (IIRS) campus, located in

111

the semi-urbanized central part of Dehradun (30.33oN, 78.04oE) (Fig. 1a), state capital city of

112

Uttarakhand, India. It nestles between the river of Ganga on the east and the river Yamuna on

113

the west with a mean altitude of 700m amsl. Dehradun is a “Valley region” surrounded by

114

Shivalik and Jaunsar-Bawar hills in north/northwest, and Pauri Garhwal ranges in the south

115

while it opens to the southeast side. Also, it is rapidly increasing the industrialized urban area.

116

Large scale near-by industrialized highly polluted cities includes Delhi at 250 km, Chandigarh-

117

200 km, Meerut-170 km and Roorkee at 70 km. Furthermore, Dehradun has high vehicular

118

pollution throughout the year as the site is en route to the famous pilgrim and tourist spots, such

119

as Mussoorie, Rishikesh, Haridwar, Gangotri, Yamunotri, Badrinath, Nainital, etc.

120 121

Based on the Indian Meteorological Department climatological reports, the region experiences

122

four dominant seasons, viz. Winter – (December, January, and February), Spring (March, April,

123

and May), Summer (June, July, and August), and Autumn (September, October, and November). 4

124

It experiences hot summer with the maximum temperature reaching 39ºC in May and cold

125

winter with temperature going as low as 5ºC during December/January. The meteorological

126

parameters in our study were collected from the fully automated weather station (Model

127

HOBOU30; Onset) installed at IIRS campus, Dehradun. Seasonal wind patterns suggested that

128

the site is mostly influenced by southwesterly winds (Fig. 1b) with low speed throughout the

129

year. However, strong winds are experienced during spring season (5 – 12 ms−1), moderately

130

during summer (4-10 ms−1), followed by winter (1-3 ms−1) and lowest in during autumn (0.33-

131

2.67 ms−1). Monthly variations in temperature, rainfall, and relative humidity are shown in Fig.

132

1c. Results revealed that the relative humidity is lowest during May (47.84±5.7%) and highest in

133

August (86.56±1.33%). The average rainfall is 354mm with 73% of the total during the months

134

of June to September, highest in August with an average of 538 mm. Winter rainfall occurs

135

during the months of January (~37.03 mm) and February (~43.64 mm), whereas driest month is

136

during November with ~3.7 mm rainfall. The winter rainfall occurs due to western disturbance a

137

well-known synoptic phenomenon with widespread rains in the plain areas and snowfall over the

138

hilly regions.

3. Instrumentation and data processing

139 140 141

Near real-time measurements of light-absorbing carbonaceous aerosol mass concentration was

142

carried out using a portable seven-wavelength (370, 470, 520, 590, 660, 880 and 950 nm)

143

aethalometer (Model: AE 42, Magee Scientific, USA) during January 2011-December 2017. The

144

880nm wavelength is considered as the standard wavelength for BC measurements since BC or

145

soot strongly absorbs light at this wavelength (Rajeevan et al. 2018) and FF & BB contributions

146

were estimated using measurements at 370 nm and 950 nm wavelengths respectively (details

147

mentioned in next section). Aethalometer works under the principle of “optical transmission”

148

through a quartz filter tape where the aerosol particles are deposited (Hansen et al., 1984). The

149

mass concentrations at seven wavelengths were determined at successive 5-min intervals by

150

measuring optical attenuation (ATN) quartz filter tape. The aethalometer quartz filter tape

151

automatically advances to provide a fresh filtration spot when the carbonaceous aerosol loading

152

reaches a

153

manufacturer's recommendation and the site characteristics. Ambient air is passed into an

154

aethalometer at a flow rate of 4.0 LPM (Liter per minute) through PM10 cutoff impactor inlet to

155

avoid dust and other coarse particles. Moisture is removed and the air is dried using two

156

scrubbers, one fitted inside (air conditioned) and another outside with silica gel. The minimum

pre-set maximum ATN value; this value was set at 75 units based on the

5

157

detection limit (MDL) for AE-42 Aethalometer is below 10 ng m-3. Aethalometer data were

158

subsequently averaged to a time resolution of 10 min and is used for further analysis.

159 160 161 162

The spectral BC mass concentration of the sampled aerosol particles can be calculated using the following relation. ∆ATN A (1) ∆t σV ∆ATN Where is change in light attenuation as a function of time, V is volumetric flow rate, A is ∆t BCλ =

163

area of the filter spot and σ is wavelength dependent specific attenuation cross section (m2g−1).

164

The σ values at each wavelengths are 39.5, 31.1, 28.1, 24.8, 22.2, 16.6 and 15.4 m2g-1 for 370,

165

470, 520, 590, 660, 880 and 950 nm respectively (as suggested by the manufacturer). The

166

uncertainties in BC mass concentration estimate arises due to (i) multiple scattering effect (C),

167

when the filter tape is relatively unloaded with carbonaceous aerosols, (ii) shadowing effect (R)

168

due to increased BC mass loading and (iii) experimental error when converting absorption to BC

169

mass concentration and these should be corrected by several methods listed in earlier studies

170

(Arnott et al. 2005; Collaud Coen et al. 2010; Weingartner et al. 2003; Virkkula et al. 2007).

171

Aerosol spectral absorption coefficient (ßabs) has been estimated by following equation.

172

βabs =

173 174

Where C is the multiple scattering uncertainty factor and which strongly depends on the filter

175

tape materials. Here C=2.14 (for quartz filter) was adopted (Weingartner et al. 2003).

176

The R(ATN) factor to correct the shadowing effect is expressed as,

177

 1  ln( ATN ) − ln(10%) R( ATN ) =  − 1 +1  f  ln(50%) − ln(10%)

BCλ ×σ C × R( ATN )

(2)

(3)

178 179

In Eq. (3) R(ATN) is a linear function of ln(ATN) and f is a parameter used to compensate the

180

instrumental error. The f values are adopted from (Sandradewi et al., 2008). After applying all

181

the above corrections, the uncertainty in BC measurement & absorption coefficient (ßabs) using

182

aethalometer (AE-42) is found to be in the range of 12-15% which is in line with the results cited

183

(Srivastava et al. 2011).

184

4. Source apportionment methodology

185

4.1 BC concentrations from Fossil Fuels (FF) and Biomass Burning (BB) sources

186

Ultraviolet, Infrared and visible light absorption measurements of aethalometer data have been 6

187

used in order to quantify the concentration of potential sources of BC at a given location. Based

188

on the principle of the wavelength dependence of aerosol absorption, two wavelength

189

measurements (370 and 950 nm) were utilized to determine the absorption angstrom exponent

190

(AAE; α), an important parameter for aerosol characterization and source apportionment studies

191

(Liu et al. 2018). The selection of wavelengths for AAE was done based on the assumption that

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aerosols originated from biomass burning have relatively high light absorption at ultraviolet

193

(~370 nm) than near infrared (~970 nm) compared to aerosols from fossil fuel combustion

194

(Kirchstetter et al, 2004). In the present study, quantification of relative share of BB and FF

195

(traffic) aerosol is estimated based on the source apportionment of BC component reported by

196

Sandradewi et al. (2008a; 2008b) which aims to determine the contribution of biomass from

197

wood burning and fossil fuel from traffic to the total BC (two-component assumption).

198

Characterization between BB and FF carbonaceous compounds depends on AAE value derived

199

from the spectral dependence of light absorption (Kirchstetter et al, 2004). The two-component

200

assumption (Ångström exponent model) implies that total aerosol absorption coefficient ßabs(λ)

201

at a particular wavelength can be expressed as the sum of the light absorption of aerosols emitted

202

by FF(traffic) and BB sources and negligible interference from other sources (Drinovec et al.

203

2015; Fuller et al. 2014; Tiwari et al. 2015):

204

βabs ( λ ) = βabs ff ( λ ) + βabs bb ( λ )

(4)

205 206 207

The AAE can be calculated for two observed absorption coefficients at two different wavelength

208

(λ1, λ2), based on the absorption dependency of different particles at UV range and infrared

209

range (Resquin et al. 2018),

210

β abs (λ1 )  λ1  =  β abs (λ2 )  λ2 

211

Similarly, it is used for AAE of both BCff and BCbb, as

−α abs

(5)

212

β abs ( 370 nm , ff )  370 nm  − α = β abs ( 950 nm , ff )  950 nm 

213

β abs ( 370 nm , bb )  370 nm  − α = β abs ( 950 nm , bb )  950 nm 

abs ff

(6)

abs bb

(7)

214

Where αff and αbb represent absorption exponent for fossil fuel and biomass burning emissions

215

respectively. In the present study, we have assumed αff~1.0 for FF (traffic) and αbb~ 2.0 for BB

216

emissions. The quantification of BC from the BB sources is calculated by solving above eqs. (5),

7

BCff = BC − BCbb

217

(6) and (7) and BCff were calculated as

218

apportionment of BC is based on the selection of source-specific absorption angstrom exponent

219

(AAEs). Earlier studies have reported different values of AAEs for different field experiments

220

and were ranged from αff~1.4-2.6 and αbb~ 0.8 – 1.1 for fossil fuel and biomass burning aerosols

221

(Sandradewi et al. 2008a; Zotter et al. 2017; Moosmüller et al. 2011; Kirchstetter et al. 2004).

222

Recent study, by Zotter et al. (2017) report that the uncertainty in BC source apportionment is

223

due to choose of different α values and wavelength pairs and the recommended α values are αff =

224

0.9 and αbb = 1.68 for FF and BB aerosols respectively. Further, Martinsson et al. (2017) have

225

estimated mean α values for fossil fuel (αff = 1.0 ± 0.1) and biomass burning (αbb = 1.81 ± 0.52)

226

based on available literatures. However, The α values (αff and αbb) are highly variable which

227

depends on combustion conditions, fuel type and aerosol aging (Martinsson et al., 2015). Few

228

Indian researchers used the αff = 1.0-1.1 for fossil fuel and αbb = 1.8-2.0 for biomass burning in

229

BC source apportionment studies over Indian region (Vaishya et al. 2017; Rajesh and

230

Ramachandran 2017; Prasad et al. 2018). The performance of this approach is usually checked

231

against C-14 measurements or tracers characteristic analysis (Martinsson et al., 2017; Garg et al.

232

2016; Zotter et al. 2017).

233

4.2 Spatial and temporal variation of potential sourcesmm

234

Variation in meteorological parameters and measured BC concentrations in relation to time has

235

been studied to interpret the temporal and spatial variations of potential sources at a given

236

location. Specifically, potential source contribution function (PSCF) can be a widely adopted

237

tool to identify the source contribution of BC at a receptor site with the help of hybrid single

238

particle lagrangian integrated trajectory (HYSPLIT) model (Resquin et al. 2018; Zhang et al.

239

2017). Five-day backward air mass trajectories were calculated for every 24 hours with arrival

240

height at 500m using NCAR/NCEP Reanalysis meteorological data. The study region covered

241

by the air mass back trajectories is divided into an array of grids defined by the cell indices ‘i’

242

and ‘j’. The PSCF analysis was done by calculating the ratio of BC trajectories to a total number

243

of trajectories that terminates within the grid cell (0.5°×0.5°). PSCF value is a normalized value

244

for each grid cell that can be derived as:

245

PSCFij =

mij

. The accurate estimation of source

(8)

nij

8

246

Here, nij is the number of back trajectory end points that terminates across a grid and mij is the

247

number of endpoints for the same cell having BC value higher than a criterion limit which is taken

248

as the 75th percentile of mean of BC concentration (Zhou et al. 2018). In order to overcome the

249

uncertinity in the low values of nij, an empirical weight function (Wij) is applied on each grid as

250

(Zeng and Hopke 1989),

251

WPSCFij =

mij nij

(9)

Wij

254

Where Wij is an arbitary weighted function and values are, 0.7, nij > 2n  (10) Wij = 0.42, n < nij < 2 n  nij ≤ 0.42n 0.05, Where, n is average number of end points across the cell. The grids with higher WPSCF values

255

represent the areas of high potential contributions to the BC concentration at the receptor

256

location. In PSCF method, it is difficult to separate the intensity gradients of polluted trajectories

257

reaching a grid cell. To overcome this limitation, concentration weighted trajectory (CWT)

258

analysis method was performed. In CWT method, differentiation of the source strength is done

259

by assigning the BC values to their corresponding trajectories arriving at a receptor location

260

(Hsu et al., 2003) as:

261

CWT =

252

253

1 ΣlM=1τ ijl

M

∑cτ l =1

(11)

l ijl

262

where, CWT is the weighted concentration of BC in trajectory in ijth cell, l and M are the index

263

of the trajectory and total number of trajectories respectively. Cl is the concentration of BC

264

observed at receptor site on arrival of trajectory l and τijl is the residential time (time spent)

265

of trajectory l in the ijth cell. Higher the value of CWT represents the magnitude of source

266

strength associated with observed BC at receptor site.

9

267 268 269 270 271

Fig 2. Diurnal variation of mean BC mass concentration (a) for different seasons and (b) for different months observed at Dehradun during January 2011 – December 2017.

5. Results and discussion

272 273 274

5.1 Diurnal and annual variation of BC

275

Dehradun is shown in Fig. 2 (data gaps for 2014 & 2015 is due to instrument malfunctioning).

276

Fig. 2(a), depicts the typical diurnal and seasonal variations of BC with pronounced two peaks in

277

all seasons, i.e., the first peak during the morning and second in the late evening hours. BC

278

concentration starts increasing about an hour before sunrise, attains peak during morning hours

279

between 8:30-10:30 IST and drastic decrease at noon (12:00-17:00 IST). The concentrations

280

again start increasing around sunset time and attain a second peak during evening hours (19:30-

281

22:00 IST) and then decreases later in the night. The evening peak is larger than the morning

282

peak in all the seasons except spring which is due to the variation in local meteorology

283

paricularly in boundary layer dynamics associated with local traffic and domestic cooking

284

activities (i.e., Traffic and cooking activities are more in evening hours as compared to morning)

Seasonal and diurnal variation of surface black carbon aerosol during 2011-2017 observed at

10

285

(Kant et al. 2012). On the other hand, the local air temperatures enhances the

286

turbulence/dispersion of air pollutants during evening hours of spring season which leads to

287

relatively low BC concentration. Interestingly, the occurrences of morning and evening BCmax

288

peaks during winter season were advanced by ~1–2 h than those observed during other seasons.

289

This is consistent with a delay of ~1.5 h in the sunrise time and local fumigation effect (fog

290

formation associated with local anthropogenic emissions) leading to extended accumulation of

291

primary pollutants (BC) during morning and evening hours of the winter season (Yadav et al.

292

2016). The morning BCmax peak occurs due to radiative cooling at the surface, low planetary

293

boundary layer height, and vehicular traffic. During noon hours, air expands with surface heating

294

thereby boundary layer height increases (allowing proper mixing of pollutants corresponding to

295

lower fossil fuel burning and other human activities) resulting in low BC concentrations. The

296

peak during the evening hours occurs due to boundary layer dynamics, vehicular rush, and

297

increased anthropogenic activities. Shifting of seasonal BCmax peak (leftwards shift in peak

298

during spring and summer seasons) and differences in amplitudes (maximum in winter and

299

minimum in summer) depends on the duration of the day (long days in spring & summer), ABL

300

height, solar radiation intensity, seasonal BB emissions, variation in human activities and

301

transport mechanisms (Joshi et al. 2016).

302 303 304 305

Table 1. Average BC mass concentration reported by earlier researchers at different locations over India. Region

Himalayan Range

Indo-Gangetic Plain

Cities Dehradun (30.33oN, 78.04oE) Darjeeling (27.01°N, 88.15°E) Nainital (29.4°N, 79.5°E) Kullu (31.90°N, 77.10°E) Hanle (32.5°N, 78.5°E) Mukteshwar (29.260 N, 79.370 E) Kolkata (22.340′N, 88.220′E) Patiala (30.330′N, 76.460′E) Kharagpur (22.31°N, 87.31°E) Gorakhpur (26.75°N, 83.38°E) Kanpur (26.46o N, 80.32o E)

Average BC (µg m-3)

Measurements Duration

References

3.85±1.16

Jan 2011- Dec 2017

Present study

3.4 ± 1.9

Jan 2010– Dec 2011

(Sarkar et al., 2015)

0.99±0.02

Nov 2004 – Dec 2007

(Dumka et al. 2010)

2.8

Aug 2009 – Mar 2012

(Nair et al. 2013)

0.66 ± 0.05

Aug 2009 – Dec 2014

(Kompalli et al., 2016)

0.81± 0.05

Sep 2005 – Sep 2007

(Hyvärinen et al. 2009)

5.0-27

Jun 2012 – May 2013

(Talukdar et al., 2015)

5.67

Oct 2013 – Sep 2014

(Bansal et al., 2019)

8.0-28

Jan 2006 – May 2006

(Nair et al. 2007)

13 ± 10.25

Aug 2013 – Jul 2015

(Vaishya et al. 2017)

7.96

Sep 2007 - Jul 2011

(Kanawade et al., 2014)

11

Pantnagar (29.0oN, 79.5oE)

Southern peninsular

Western India

Delhi (28.38◦N, 77.12◦E) Vishakapatnam (17.7°N, 83.8°E) Trivandrum (8.55° N, 76.9° E) Vijayawada (16.44°N, 80.62°E) Nagpur (21.15 °N, 79.15 °E) Hyderabad (17.28°N, 78.26°E) Anantapur (14°62′ N; 77°65′ E) Gadanki (13.5° N, 79.2° E) Kadapa (14.47°N, 78.82°E) Ahmedabad (23.03°N, 72.55°E) Ahmednagar (19.09°N, 74.74°E) Pune (18.53°N, 73.80°E)

2.8-7.9

May 2009 – Dec 2012

(Joshi et al. 2016)

14.75

Jan 2006 –Jan 2007

(Bano et al. 2011)

0.4-8

Dec 2005 – Sep 2006

(Sreekanth et al. 2007)

3.51

Sep 2014 – Apr 2017

(Rajeevan et al. 2018)

3.44 ± 2.07

Jan 2016 – Dec 2016

(Prasad et al., 2018)

3.09 ± 1.28

Jan 2011–July 2012

(Kompalli et al. 2014)

4.5 ±0.12

Jan 2009 – Dec 2010

(Dumka et al. 2013)

2.3 ± 0.6

Jan 2013 - Dec 2014

(Kalluri et al. 2016)

2.20

2008 – 2017

(Ravi Kiran et al, 2018)

2.20 ± 0.78

Sep 2011 – Nov 2012

(Begam et al. 2016)

1.9-8.3

Jan 2014 – Dec 2015

(Rajesh and Ramachandran 2017)

13.8 ± 10.4

Dec 2015–Dec 2016

(Kolhe at al., 2018)

3.58 ± 1.55

2005 – 2010

(Safai et al., 2013)

306 307

BC mass concentration exhibited a significant intra-annual variation (Fig. 2b). Highest BC

308

concentration was observed during December (5.5 µg m-3) and lowest was during August (1.9 µg

309

m-3). A noticeable increase in BC concentration has been observed during the evening hours

310

(19:30-22:00 IST) in all seasons especially in the month of December with 11.45 µg m-3 which

311

is significantly high for the study area. The high BC values in December is attributed to the fire

312

wood cooking and burning activities around Dehradun and surrounding villages in the region.

313

The BC mass concentration measured at various locations over India is listed in Table 1. The

314

five year averaged mass concentration of BC of 3.85  ± 1.16 µg m−3 over Dehradun is quite high

315

compared to the other Himalayan locations in India such as Darjeeling (3.4 ± 1.9 µg m-3), Kullu

316

(2.8 µg m-3), Nainital (0.99±0.02), Mukteshwar (0.81± 0.05) and Hanle (0.66 ± 0.05). The

317

observed BC concentration over Dehradun could be due to high vehicular concentration and

318

emissions transport from polluted IGP region (Fig. 6). The annual BC at Dehardun is

319

comparatively lower than the BC observed at Delhi (14.75 µg m-3), Kanpur (7.96 µg m-3),

320

Hyderabad (4.5 ±0.12 µg m-3), Ahmedabad (1.9-8.3 µg m-3), Hisar (1.5-7.2 µg m-3), but is in

321

proximity to Pune (3.58 ± 1.55 µg m-3). BC in urban cites are more pronounced due to the

322

increase in vehicular and industrial emissions and other anthropogenic activities (Beegum et al.

323

2009).

12

324 325 326

Fig. 3. (a) Daily and (b) monthly mean variation of BC, BCff and BCbb mass concentration observed at Dehradun and surrounding region during January 2011 – December 2017.

327 328 329

5.2 Daily and monthly variation of BC, Fossil fuel and Biomass burning components

330

contributions during 1st January 2011 to 31st December 2017, is shown in Fig 3(a). The annual

331

averaged BC mass concentration was 3.85 ± 1.16 µg m−3. Nearly 44% of the daily BC (678 days)

332

concentrations were higher than the annual average value. Daily BCff (BC from traffic) and BCbb

333

(BC from biomass burning) concentrations range from 0.11 to 7.12 µg m−3 and 0.13 to 3.6

334

µg m−3 with corresponding percentages of 35-93% and 7-65% respectively. Five- year annual

335

averages suggested the BCff accounting for 66% (2.54 µg m−3) of the total BC whereas the 34%

336

corresponding to BCbb (1.31 µg m−3). The wide range of the daily BC, BCff and BCbb

The continuous measurement of BC mass concentration and estimated BCff and BCbb

13

337

concentrations indicate the variation in daily aerosol loading produced by anthropogenic

338

activities (vehicular transport, industries, crop residue burning, forest fires, human settlements,

339

etc.) compounded with atmospheric boundary layer dynamics.

340

Monthly variability in BC derived from daily observations during 2011-2017 is shown in Fig. 3a

341

(bottom) and contour plot of BC is shown in Fig. 1S of the supplementary material. An identical

342

pattern was observed in monthly BC, BCff and BCbb for all these years with minima during

343

summer months and maxima during winter months. Interestingly, high BC mass concentration

344

(5.7 µg m-3) was observed during March 2012 due to the high biomass burning events over India

345

(Shaik et al. 2019). Fig. 3(b) shows the climatological monthly mean BC, BCff and BCbb mass

346

concentrations in the form of box and whisker plots. From the figures, it is evident that the mean

347

BC values are high in all seasons except summer, indicating episodic high BC values during

348

these months. The mean and median BC values during the summer are almost same and standard

349

deviation show less variability. The monthly averaged BC, BCff and BCbb values showed a peak

350

during winter followed by autumn. The minimum monthly mean of BC, BCff and BCbb

351

concentration of 1.90 ± 0.56, 1.53 ± 0.53 and 0.36 ± 0.12 µg m-3 were observed during the

352

August and the maxima of 5.50 ± 1.21, 3.45 ± 1.14 and 1.96 ± 0.53 µg m-3 in December

353

respectively over the 5 year period. Relatively, the monthly variations in BC with respect to the

354

annual mean for BCff and BCbb was observed to be 9%, 5%, and 12% respectively. High

355

variability in BC, as well as BCbb reflects the emissions from biofuel cooking combined with

356

agriculture crop residue burning and forest fires emissions in North and North-west Himalayan

357

region (Shaik et al. 2019; Nair et al. 2013).

14

358 359 360

Fig. 4. (a) Seasonal and (b) Inter-annual variation of annual mean BC, BCff and BCbb mass concentration over Dehradun during January 2011 – December 2017.

361 362 363 364

5.3 Seasonal distribution of BC, Fossil fuel and Biomass burning components

365

five year period are shown in Fig. 4(a). Carbonaceous components showed a distinct seasonal

366

pattern with maxima during the winter and minima during summer. Average values of BC, BCff,

367

and BCwb during summer are low due to precipitation wash-out by the seasonal rainfall whereas

368

high concentrations during winter are attributed to the boundary layer height and the local

369

meteorological conditions that control the surface level BC (Rajeevan et al. 2018). Seasonal

370

mean BC mass concentration was highest during the winter (4.86±0.78 µg m-3) followed by

371

autumn (4.18±0.54 µg m-3), spring (3.93±0.75 µg m-3) and lowest during the summer (2.41±0.66

372

µg m-3). Seasonal behavior of BCff is almost unchanged and constant (range 2.96-2.47 µg m-3)

373

except during the summer (1.92 µg m-3), which suggests that the emissions of fossil fuels

374

(vehicular transport and industrial discharge) dominate during all seasons. The BCbb during

375

winter is comparatively higher (1.90±0.48 µg m-3) than the other seasons (1.12±0.24 µg m-3,

376

0.49±0.25 µg m-3 and 1.71±0.35 µg m-3 for spring, summer, and autumn respectively) due to

Average concentrations of BC, BCff, and BCbb mass concentrations for different seasons over a

15

377

burning of dry leaves, shrubs, grass, agricultural residues, cow dung and wood burning for house

378

warming and other activities during this season. Interestingly, the share of BCbb to total BC is

379

higher during autumn which is due to long range transport of smoke aerosols from crop residue

380

burning areas (Punjab and Haryana) to the study area (Shaik et al. 2019). Seasonal contribution

381

of BCff and BCbb to the total BC ranges from 59%-80% and 20-41% respectively observed

382

during the five year period. Seasonal variations in BC, BCff and BCbb with corresponding

383

percentage contribution is reported in table. 1S. The mean annual BC, BCff and BCbb showed

384

gradual increasing trends from 2011 to 2017 (i.e., annual BC is 3.32, 3.67, 3.48, 3.86 and 4.17

385

µg m-3 during 2011, 2012, 2013, 2016 and 2017, respectively); however, inter-annual variations

386

of BC, BCff and BCbb varied from 3.32-4.17, 2.32-2.60, and 0.93-1.96 µgm-3 with relative

387

changes as 23%, 9%, and 49% respectively (Fig. 4b). Annual mean (2011-2017) contribution of

388

BCff and BCbb to total BC is estimated to be 66% and 34% respectively over Dehradun. The

389

results are contrasting to the other locations in India i.e., Dehradun has highest BB contribution

390

(34%) as compared to other locations (table. 2). Dehradun is surrounded by a lower range of

391

Himalayan forests and forest fires frequently occur in these forests during spring season. In

392

contrast, the site is also surrounded by significant crop residue burning hot spot regions, i.e.,

393

western IGP (Punjab and Haryana) and central IGP (some parts of Uttar Pradesh). In addition to

394

point sources, synoptic meteorology also plays a vital role in the dispersion and transport of

395

smoke aerosols from the source regions. The large spread of BB over North-west parts of IGP is

396

a significant contributor of elevated BC and particulate matter over the entire IGP as well as

397

Himalayan ranges through long-range transport (Kaskaoutis et al., 2014; Solanki et al. 2013; Li

398

et al., 2016).

399 400

Table 2. Proportioned contribution of BCff and BCbb over different locations in India using ground data. Station (Latitude, longitude) Dehradun (30.330N, 78.040E) Gorakhpur (26.75°N, 83.38°E) Ahmedabad (23.03° N, 72.55° E) Vijayawada (16.44°N, 80.62°E) Delhi (28.31° N, 76.91° E) Delhi (28.31° N, 76.91° E) Gadanki

Study Period

BCff (%)

BCbb (%)

Reference

Jan 2011– Dec 2017

66%

34%

Present study

Aug 2013 – Jul 2015

74%

26%

Jan 2014 – Dec 2015

80%

20%

Jan 2016 – Dec 2016

79%

21%

(Prasad et al., 2018)

Apr 2015-Mar 2016

81%

19%

(Dumka et al. 2019)

Dec 2011 – Mar 2012

94%

6%

(Tiwari et al., 2015)

Apr 2008 – Nov 2008

80%

20%

(Gadhavi and

16

(Vaishya et al. 2017) (Rajesh and Ramachandran 2017)

(13.46° N, 79.18° E)

Jayaraman 2010)

401

Seasonal frequency distribuition of BC, BCff and BCbb mass concentration is shown in Fig. S2.

402

The frequency distribution of BC shows within a range of 0–10 µg m−3 (winter), 0–

403

9 µg m−3 (spring), 0–6 µg m−3 (summer) and 1–10 µg m−3 (autumn) respectively. The wide spread

404

of BC, BCff and BCbb were observed during winter while narrow bands in summer. During

405

winter about 90% of BC was below 4 µg m−3 while 87% of BCff lies in range 2-4 µg m-3 and

406

98% BCbb lies in range of 1-3µg m-3. During spring and autumn the highest BC occurrences

407

observed within the range of 2-6 µg m-3, about 80% and 86% respectively which is due to the

408

sampling site experienced by frequent forest fire and crop residue burning emissions during

409

those respective seasons (Shaik et al. 2019). During autumn, the BCff and BCbb concentration

410

lies between 1.5-3.5µg m-3 (~87%) and 0-2 µg m-3 (~90%) while during spring, 95% of BCbb in

411

range of 0-2 µg m-3 & 92% of BCff in 0.5-3.5 µg m-3. Minimum frequency were observed during

412

summer (BCff falls within range of 0.5-3.5 µg m-3 & BCbb in range of 0-1.5 µg m-3) indicates the

413

lesser occurrences of BB events over the measurement location and/or less contribution of BB &

414

FF sources through long-range transport.

415

5.4 Regional assessment of biomass burning

416

Biomass burning (BB) is a major contributor of BC aerosol emissions which can be due to

417

natural or man-made forest fires, savanna fires, agriculture residue burning, use of biomass or

418

cow dung for cooking purposes and other activities. Frequency and causative factors of BB vary

419

by region (Vadrevu et al. 2012). Satellite remote sensing data was used for detecting and

420

analyzing the spatiotemporal behavior of BB from the last several decades (Kaufman et al. 1998;

421

Krishna Prasad et al. 2002; Streets et al. 2003). MODIS active fire product (MCD14ML) (Justice

422

et al. 2002), both Terra and Aqua were used in the present study for assessing the fire events

423

over the Indian region (data dowloaded from https://firms.modaps.eosdis.nasa.gov/). Fig. 5 (a)

424

shows the climatological spatial distribution of fire counts density (no. of fire occurrences per

425

0.25°×0.25°grid) based on the MODIS active fire data during January 2003–December 2017. BB

426

over India exhibits a large spatial variability with a minimum of 906 average fires during the

427

summer to a maximum of 42426 fires during spring. A moderate number of fire counts were

428

observed during the autumn (18338) and winter (12871) respectively. There are two dominant

429

BB periods over India, (i) Spring season (March to June) combination of forest fires and crop

430

residue burning accounting for more than 45% of total annual fire counts and (ii) Autumn season

431

(October-November) mostly agriculture residue burning which accounts for 24% of total annual

432

fires (Vadrevu et al. 2013). High fire occurrences (density >1000 per grid) were observed over 17

433

the Northwestern part of the IGP (Punjab and Haryana) along the foothills of the Himalayas,

434

northeast and central India. Moderate fires (density ~200-500 per grid) were noticed over

435

southern peninsular India, whereas low fire occurrences (density<10 per grid) were observed

436

over the western India where desert and scrublands dominate. During the winter, BB is active

437

over IGP and southern India which elevates the BC mass concentrations as compared to other

438

seasons (Prasad et al. 2018).

439

440 441 442 443 444

Fig. 5. (a) Climatological (2003-2017) seasonal average fire density (no. fires per grid) with spatial resolution of 0.25°×0.25° grid based on the MODIS active fire counts over Indian region and (b) Monthly variations of the average fire occurrences over 10x10 area around the observational site during January 2003 – December 2017. Symbols in Fig (a) represents the geographical locations of selected sites.

445

Fig. 5(b) shows month wise fire occurrences (averaged for 2003-2017) recorded over 10x10

446

gridded region around a few selected sites over India. Selection of the above sites was made

447

based on the BC source apportionment study done by earlier researchers (table. 2). Specific to

448

Dehradun, local fire events occur only during spring season which are predominantly forest fires

449

around the region while over Patiala show two fire seasons (spring and autumn) with high

450

maximum fire occurrences (>200 annual average fires) which are being mainly crop residue

451

burning. Dual seasonal fires were also observed in Gorakhpur and spring season fires observed

452

over Gadanki and Vijayawada. However, there were no considerable fires over Ahmedabad and

453

Delhi. The temporal variation of anthropogenic fire depends on environmental factors like

454

monsoon rainfall, hydrological conditions, land use practices, and regional social activities

455

(Vadrevu and Lasko 2015; Bhardwaj et al. 2016).

18

456 457 458 459 460

Fig. 6. (a) Potential source contribution function (PSCF) and (b) Concentration weighted trajectories (CWT) analysis of BC mass concentration derived at Dehradun for different seasons during January 2011 - December 2017. The location of the observation site is marked as star symbol.

461 462 463

5.5 Long range transport

464

BB activities. BC particles are smaller which ranges from Aitken (i.e., diameter ≤ 100 nm) to

465

accumulation mode (i.e., diameters between about 100 nm and one µm). BC can travel long

466

distances from their sources because it is smaller in size and lighter nature. To identify the

467

potential sources of the observed BC at Dehradun, we used the HYSPLIT trajectory model to

468

retrieve 5-days air-mass back trajectories at 500m above ground level with NCEP/NCAR global

469

reanalysis meteorological data as input. The height of air mass trajectory was fixed at 500m in

470

the model as the BC aerosols are more abundantly below 1 km, and are transported within the

471

lower troposphere (Shaik et al. 2019). A potential source contribution function (PSCF) was

472

performed to recognize the possible source regions and their influence in terms of magnitude,

473

which was determined by the concentration weighted trajectories (CWT) analysis. The results of

474

PSCF and CWT analysis for observed BC mass concentration over Dehradun is shown in Fig 6.

475

The grids with high PSCF values were considered as the maximum probability potential source

476

areas, that contributes to high BC mass concentrations at the receptor location (Dehradun)

477

whereas the grids with high CWT values were considered to be high strength sources. Our

478

analysis indicated that the strong potential source probabilities are located in North-west and

479

Western regions of receptor location during all seasons (except summer) and small contributions

480

from the south and south-east regions. The air parcels arriving from these directions have several

The BC aerosols are known to originate mainly through anthropogenic processes such as FF and

19

481

possible BC emission sources like highly polluted urban cities (Lahore, New Delhi, Kanpur,

482

etc.,), thermoelectric power plants, crop residue burning regions (Punjab and Haryana) and

483

frequent forest fire areas (Northwest and the central Himalayas). In winter, high PSCF and CWT

484

values are observed over urban and continental areas which indicate strong regional BC

485

emissions associated with calm wind conditions. During spring season, the air parcels are spread

486

mostly the over north and north-westerly regions and maximum PSCF and CWT values are over

487

frequent biomass burning regions (Punjab and Haryana). During summer, air parcels shifts from

488

the continental to the oceanic region (Arabian Sea) and other few air parcels from eastern IGP

489

region. Relatively low BC was observed during summer due to air mass back trajectories

490

arriving from the oceanic regions and continental wash off by rainfall. However, the high

491

potential source originates from the northwest region during summer. In autumn, the air mass

492

back trajectories again shifts to continental region, and High PSCF and CWT values are

493

observed over the crop residue burning regions i.e., Punjab and Haryana and some parts of the

494

western Uttar Pradesh. Finally, the PSCF and CWT analysis corroborate the major potential

495

sources of BC originating from the North and Northwest regions. These results are consistent

496

with those presented in fig. 5(a) associated with BB areas.

497 498

Several studies have reported the long-range transport of aerosols at various Indian locations.

499

For example, BC variability (7.15–8.54 µgm−3) over Nainital (a high altitude station in central

500

Himalayas (1500 asl)) is mostly influenced by the northwest (50%) and westerly (32%) winds

501

(Joshi et al. 2016). Kumar et al. (2018) reported that the aerosol loading over upper IGP regions

502

(i.e., Karachi, Multan, Lahore) and central IGP regions (i.e., Delhi, Kanpur, Varanasi, and Patna)

503

are predominately influenced by the western dry regions such as Pakistan, Afghanistan, western

504

Indian desert etc., whereas over lower IGP regions (i.e., Kolkata and Dhaka) are influenced both

505

by continental aerosol emissions from northern India and by marine aerosols from adjoining

506

oceanic regions of Bay of Bengal. In another study, Gogoi et al. (2008) in Dibrugarh, northwest

507

India, reported high AOD loads and inferred that the maximum contribution to aerosol extinction

508

could be due to transport of carbonaceous pollutants from the industrialized and urban regions of

509

India and large amounts of desert and mineral aerosols from west Asia and Thar desert. Further,

510

Prasad et al. (2018) and Ravi Kiran et al. (2018) in sites at Vijayawada (semi-urban) and

511

Gadanki (rural), south-east India reported that during winter, ~72% of air-masses arrive at the

512

receptor location (Vijayawada) originating from the northern/central part of India extending up

513

to the east peninsular regions. In contrast, ~46% of air trajectories are found passing across the

514

inland areas of southern peninsular India and they bring the polluted aerosols to the receptor 20

515

location (Gadanki) during the autumn. These diverse studies suggest varying aerosol transport

516

pathways across regions and seasons based on the regional wind patterns and meteorological

517

conditions in India.

518

5.6 Estimation of Aerosol Radiative Forcing and Efficiency

519

Aerosol radiative forcing (ARF) is defined as the perturbation of the radiative flux caused by

520

atmospheric aerosols. The ARF either at the top of the atmosphere (TOA; ~100km) or at the

521

surface (SUR; ~1Km) is defined as the change in the net flux due to the presence of aerosols at

522

that level. It is calculated as the difference in net flux with aerosol and without aerosol

523

conditions.

524

ARFTOA / SUR = ( NetFlux ) with aerosol - ( NetFlux ) without aerosol 

525

The difference between ARF at TOA and SUR is defined as the atmospheric forcing (ARFATM).

526

The value of ARFATM represents the quantity of energy trapped within the atmosphere. If the

527

value of ARF is positive, aerosol leads to a net gain in the radiative flux which leads to heating

528

effect, whereas a negative value indicates a net loss of radiation leading to cooling effect. In

529

order to compute the ARF, a well-established radiative transfer model i.e., Santa Barbara

530

Discrete ordinate Atmospheric Radiative Transfer (SBDART) model has been used in the

531

present study. SBDART is a simple plane-parallel radiative transfer model developed by the

532

University of California (Ricchiazzi et al. 1998) and is a well-calibrated code for radiative

533

transfer calculations worldwide (Kang et al. 2016; Boiyo et al. 2019) and over Indian region

534

(Shaik et al. 2017; Aruna et al. 2016; Kumar et al. 2011). Aerosol optical properties such as

535

AOD at 500nm, angstrom exponent (AE), single scattering albedo (SSA), asymmetry parameter

536

(ASY), water vapour content (WVC), ozone concentration and surface reflectance along with

537

astronomical parameters like solar zenith angle are inputs for estimation of ARF. The spectral

538

AOD and angstrom exponent were obtained from the Multi Wavelength Radiometer (MWR)

539

measurements in the wavelength range 380 to 1025 nm, while SSA and ASY were inferred from

540

the semi-empirical model simulations i.e., OPAC (Optical Properties of Aerosols and Clouds)

541

model. OPAC model has been widely adopted for deriving unmeasured aerosol characteristics in

542

different atmospheric compositions (Dumka et al. 2013; Shaik et al. 2017; Babu et al. 2007).

543

Keeping in view of the composition of atmospheric aerosols over the observational site, five

544

different aerosol types viz., soot (mostly anthropogenic origin i.e. BC), water soluble (mainly

545

sulfate and nitrate aerosols), insoluble (soil particles), mineral accumulation, and mineral

546

transport (generally coming from the arid surfaces by wind) are externally mixed in order to 21

TOA / SUR

(12)

547

attain the best fit between the model simulated properties and ground measurements. Measured

548

BC was used to represent soot in OPAC model and the number densities of all other components

549

(constrained by observed BC mass fraction) are iteratively adjusted until (i) the OPAC derived

550

AOD spectra agree with the mean values of the measured AOD spectra with the RMS deviation

551

less than 5% and (ii) the OPAC estimated Angstrom exponent (α) value match with the observed

552

value within RMSE 3% (Satheesh 2002). In addition to aerosol properties, atmospheric profiles

553

& surface albedo characteristics were also needed inputs for SBDART model. Based upon the

554

measured aerosol parameters and the prevailing weather conditions over Northwest Himalayan

555

region, the mid-latitude summer atmospheric profile chosen in SBDART with an average

556

integrated columnar water vapor (2.92 gcm-2) and ozone (0.324 atm-cm) concentrations. The

557

surface albedo was obtained from MODIS Albedo Product (MODIS/Terra+Aqua Albedo 16-

558

Day, Level 3 Global 500 m SIN Grid) over the measuring site (downloaded from

559

https://ladsweb.modaps.eosdis.nasa.gov/). For solar zenith angle calculation, a SBDART built‐in

560

program code was used by specifying Julian day, time, latitude, and longitude of measurement

561

location. Using these inputs, we estimated the ARF at the surface (SUR), at the top of the

562

atmosphere (TOA) and at the atmosphere (ATM). The model was operated at the one-hour

563

intervals for a 24-h period in all ground data available days and average forcing was estimated

564

for each day of observation and further seasonal averaged values were calculated. The

565

uncertainties in SBDART ARF calculations arise mainly from various assumptions such as

566

model atmospheric profile and OPAC simulation as well as uncertainties in surface albedo and

567

measurements in aerosol properties i.e. AOD, BC etc. The overall uncertainty in SBDART

568

estimation is within the range of 20% (Kumar et al. 2011). Further details about the ARF

569

methodology and associated uncertainties are reported in a series of papers (Babu et al. 2007;

570

Srivastava and Ramachandran 2013; Tiwari et al. 2015).

22

571 572

Fig 7. Seasonal variation mean ARF at SUR, TOA, and ATM observd over Dehradun during 2011-2017.

573 574

Fig. 7 shows the seasonal variation of aerosol radiative forcing (ARF) values obtained over

575

Dehradun region during 20011-2017. Seasonal averaged ARF values observed at TOA, SUR and

576

ATM ranged from -22.44 to -15.50 Wm−2 (mean ~ -19.87 ± 2.6 Wm−2), -48.24 to -33.26 Wm−2

577

(mean ~ -43.59 ± 5.9 Wm−2) and +17.75 to 25.79 Wm−2 (mean ~ 23.72 ± 3.3 Wm−2), during the

578

winter; -17.01 to -12.86 Wm−2 (mean ~ -15.26 ± 2.0 Wm−2), -55.66 to -50.31 Wm−2 (mean ~ -

579

53.57 ± 2.3 Wm−2) and +35.93 to 39.78 Wm−2 (mean ~ 38.30 ± 1.2 Wm−2) during spring; -14.40

580

to -12.29 Wm−2 (mean ~ -12.83 ± 0.9 Wm−2), -39.24 to -33.62 Wm−2 (mean ~ -35.06 ± 2.4

581

Wm−2) and +21.33 to 24.84 Wm−2 (mean ~ 22.23 ± 1.5 Wm−2) during summer; and -16.52 to -

582

5.85 Wm−2 (mean ~ -11.39 ± 3.9 Wm−2), -48.15 to -35.32 Wm−2 (mean ~ -41.44 ± 6.3 Wm−2)

583

and +25.62 to 34.87 Wm−2 (mean ~ 30.04 ± 3.4 Wm−2) during autumn, respectively. The

584

average ARF for the whole period of observation (2011-2017) at the surface was -43.41 Wm−2,

585

at the top of the atmosphere was -14.84 Wm−2 and atmospheric forcing was about +28.57 Wm−2

586

respectively. Table 3 shown the ARF estimated at different locations in India reported by earlier

587

researchers. It can be seen that ARF values (over Dehradun) are higher as compared with those

588

reported for Visakhapatnam, a coastal station (on the coast of Bay of Bengal) situated in south-

589

east India (ARF values observed at TOA~3.86, at SUR~ -16.32 and ATM ~20.19 Wm−2

590

respectively) while lower than observed over Kanpur, an urban site in Northern India (ARF

591

observed at TOA, SUR and ATM are -15.5, -48.9 and 33.42 Wm−2 respectively). The negative

592

forcing at SUR is due to decrease in solar radiation reaching the earth surface resulting in

593

cooling of surface. This phenomenon has been contributing to the solar dimming effect over

23

594

India (Kambezidis et al. 2012). Similarly, the negative values of TOA forcing suggest

595

enhancement of the solar radiation backscattered to space by scattering type of aerosols leading

596

to cooling of the Earth-atmosphere system (Dumka et al. 2014). On the other hand, the

597

absorption of solar radiation by the heavy dust plumes mixed with soot aerosols are leads to

598

large positive ATM forcing over IGP region (Srivastava and Ramachandran 2013). The ARF

599

values at TOA, SUR and ATM exhibit significant variability mostly attributed to the variation in

600

columnar AOD, aerosol types (like BC and dust) and absorbing properties.

601 602

Table 3. Annual ARF reported from various locations in India. Location Dehradun (30.30 °N, 78.04 °E, ~700 m) Trivandrum (8.55 °N, 76.97 °E, ∼3 m) Chennai (12.81 °N, 80.03 °E, ∼45 m) Anantapur (14.46 °N, 77.67 °E, ~25 m) Visakhapatnam (17.7 °N, 83.3 °E, ∼20 m) Pune (18.32 °N, 73.51 °E, ∼559 m) Jaipur (26.9 °N, 75.8 °E, ~450 m) Kanpur (26.47 °N, 80.33 °E, ∼142 m) Dibrugarh (27.3 °N, 94.6 °E, ~111 m) Delhi (28.63 °N, 77.17 °E, ∼235 m) Patiala (30.33 °N, 76.40 °E, ∼250 m) Mohal (31.9 °N, 77.12 °E,∼1154 m)

Period

Aerosol Radiative Forcing (W/m2) at TOA Surface Atmosphere

References

2011-2017

-14.84

-43.42

28.57

Present Study

2013

-2.12

-34.325

33.86

(Babu et al. 2007)

2013

-0.39

-34.63

34.83

(Aruna et al. 2016)

2013-2014

-6.75

-31.65

24.92

(Kalluri et al. 2016)

Dec 2005 to Sep 2006

3.86

-16.32

20.19

2004-2009

-6.37

-37.66

20.37

2011-2015

-12.93

-22.60

18.27

2001-2010

-15.5

-48.9

33.42

June 2008 to May 2009

-0.95

-29.37

28.42

(Pathak et al. 2010)

2010

4.0

-67.0

71.0

(Singh et al. 2010)

Oct 2008 to Sep 2010

-5.7

-49.44

48.82

(Sharma et al. 2012)

2007

0.6±3.7

−18.5±1.7

19.1±3.1

(Guleria et al. 2014)

(Sreekanth et al. 2007) (Kumar and Devara 2012) (Sunita Verma et al. 2017) (Kaskaoutis et al. 2013)

603 604 605

The magnitude of the ARF is highly dependent not only on the aerosol types, but also on

606

columnar aerosol burden (i.e., AOD). The rate at which the atmosphere is forced per unit AOD

607

(τ) is known as the aerosol radiative forcing efficiency (ARFE). It is obtained by dividing ARF

608

by AOD at 500 nm, and is a better indicator of the forcing potential at a given composite

609

aerosols (Babu et al. 2007). The ARFE estimated at TOA, SUR and ATM for different seasons

610

are reported in table. S2 of the supplementary material. It is evident that the variation in ARFE is

611

similar to that of ARF but with relatively higher values. Maximum ATM ARFE (68.40 ± 8.2

612

Wm−2 τ−1) was observed during spring while minimum (52.70 ± 11.7 Wm−2 τ−1) during winter.

613

Seasonal variation of ARFE followed the seasonal variation of AOD500nm (see table S2 in

614

supplementary material). The differences in seasonal pattern of ARFE may arise due to the 24

615

seasonal variation in aerosol types (dust, BC, fog and mixed aerosols etc.) present in the

616

atmosphere. Over the entire study period, average values of TOA, SUR and ATM forcing

617

efficiency over Dehradun were observed to be -31.81 ± 8.9, -91.63 ± 5.4, and 59.82 ± 7.0 Wm−2

618

τ

619

observed to be −46 Wm−2 τ−1 , −17 Wm−2 τ−1 at the surface and TOA during spring while during

620

summer over Arabian Sea were −61 Wm−2 τ−1 for the SUR, −27 Wm−2 τ−1 for the TOA and +34

621

Wm−2 τ−1 for the ATM respectively (Moorthy et al. 2005). Sharma et al (2012) observed over

622

Patiala (spring of 2010) that dust events significantly enhanced the atmospheric heating over the

623

Northern India and also the atmosphere forcing efficiency were found to be −14.66, and −66.64

624

and 51.97 Wm−2 τ−1 for TOA, SUR and ATM, respectively. The estimated ARFE within the

625

atmosphere is positive during all the seasons over Dehradun (Northwest himalayas) indicating

626

atmospheric heating over the region.

−1

respectively. Prasad et al. (2007) have reported the average forcing efficiency over Kanpur

627 628 629

6. Conclusions

630 631

Five year (2011-2017; exception of 2014 & 2015) measurments of BC have been carried out

632

over a Dehradun (30.33°N, 78.04°E, 700m amsl), a semi-urban site in the Northwest Himalayas,

633

India with the aim to examine seasonal and temporal variability of BC and its contribution from

634

the fossil fuels and biomass burning. The potential source regions of BC were identified using

635

the PSCF and CWT trajectory analysis and aerosol radiative forcing (ARF) was estimated. Our

636

results suggests highly polluted areas of IGP region along with seasonal BB events having

637

substantial influence on the BC concentration and radiative impacts over Himalayan ranges. The

638

important conclusions of our study are as follows:

639

i.

BC mass concentration show a well-defined diurnal variation with two maxima peaks,

640

one in the morning and another in late evening hours which attributed to change in

641

boundary layer dynamics, anthropogenic sources of BC and meteorological conditions.

642

ii.

BC exhibits a strong seasonal variability with a maximum in winter (4.86±0.78 µg m-3)

643

followed by autumn (4.18±0.54 µg m-3), spring (3.93±0.75 µg m-3) and minimum during

644

summer (2.41±0.66 µg m-3). The highest BC during winter is attributed to both the local

645

FF combustion and BB emissions combined with atmospheric boundary layer dynamics.

646

In contrast, scavenging of BC aerosols due to seasonal rainfall resulted in minimum

647

values during the summer.

25

648

iii.

Annual BC mass concentration is 3.85 ± 1.16 µg m−3 whereas BCff (traffic) and BCbb

649

(biomass burning) concentrations are 2.54 ± 0.34 µg m−3 and 1.31 ± 0.26 µg m−3 with

650

corresponding contributions to total BC are 66% and 34% respectively.

651

iv.

PSCF and CWT trajectory analysis reveal that the potential source regions of north-west

652

and eastern parts of India is contributing to the seasonal variation of BC over Dehradun.

653

The regional biomass burning sources are forest fires over the western Himalayas and

654

crop residue burning over western IGP regions which is supported by MODIS fire data.

655

v.

Seasonal variation of aerosol is significantly altering the radiative forcing and its

656

efficiency over northwest Himalayan region. The annual mean ARF at TOA, SUR, and

657

within the ATM were found in the range from −19.87 to – 11.39, −53.57 to – 35.06, and

658

22.22 to 38.30 Wm-2, respectively and associated average forcing efficiency is −31.81,

659

−91.63 and 59.82 Wm−2 τ−1 respectively indicating dominance of absorbing

660

anthropogenic aerosols over the region.

661

Acknowledgements

662

This work was carried out as part of the ARFI project of ISRO-GBP. Mr. Shaik Darga Saheb

663

acknowledges ISRO-GBP for providing fellowship to carry out the study. The authors

664

acknowledge the NOAA ARL for the HYSPLIT model simulations and the data. We duly

665

acknowledge the NASA Fire Information for Resource Management System (FIRMS), NASA

666

for the data. The authors are thankful to the three anonymous reviewers for their suggestions and

667

critical comments.

668 669

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Highlights: •

5-year BC measurement and source apportionment analysis is done at Dehradun, a semi-urban site in western Himalayas, India.

•

BC show a well-defined diurnal variation with two maxima peaks, one in the morning and another in late evening hours and also shows a strong seasonal variability with maxima in winter and minima in summer.

•

Annual percentage of contribution from BCff and BCbb to total BC is 66% and 34% respectively at Dehradun.

•

PSCF and CWT results reveals that the major potential sources of BC are originating from the North-west and eastern parts of IGP.

•

ARF ATM is observed to be positive (warming effect) indicating the dominance of absorbing aerosols over Northwest Himalayas.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

All authors have seen and approved the final version of the submitted manuscript.