Changing aerosol loadings over Central Himalayan region (2007–2016) – A satellite perspective

Accepted Manuscript Changing aerosol loadings over Central Himalayan region (2007–2016) – A satellite perspective Manu Mehta, Narendra Singh, Raman Solanki PII:

S1352-2310(19)30186-4

DOI:

https://doi.org/10.1016/j.atmosenv.2019.03.024

Reference:

AEA 16616

To appear in:

Atmospheric Environment

Received Date: 25 September 2018 Revised Date:

18 March 2019

Accepted Date: 20 March 2019

Please cite this article as: Mehta, M., Singh, N., Solanki, R., Changing aerosol loadings over Central Himalayan region (2007–2016) – A satellite perspective, Atmospheric Environment (2019), doi: https:// doi.org/10.1016/j.atmosenv.2019.03.024. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

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CHANGING AEROSOL LOADINGS OVER CENTRAL HIMALAYAN REGION (2007-

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2016) – A SATELLITE PERSPECTIVE

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Manu Mehta1, Narendra Singh2 and Raman Solanki3 Aryabhatta Institute of Observational Sciences, Nainital, India

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Indian Institute of Remote Sensing, Dehradun, India

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National Astronomical Research Institute of Thailand, Chiang Mai, Thailand

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Abstract

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The paper presents the variation of aerosol columnar variation and vertical profiles over the

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study area encompassing a portion of Central Himalayan region and the surrounding planar

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areas. The analysis is carried out using decadal (2007-2016) Level-2 CALIOP (onboard

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CALIPSO), Level-3 MODIS (onboard Aqua/Terra) and MISR (onboard Terra) datasets. Four

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seasons have been considered, namely, DJF (December-January-February), MAM (March-April-

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may), JJA (June-July-August) and SON (September-October-November). The CALIOP data has

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been screened using quality flags, i.e., CAD score, aerosol layer fraction and extinction quality

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checks. There are also signatures of elevated aerosol layers, especially during winter (DJF) and

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post-monsoon (SON) seasons. Latitudinal and longitudinal variations of the vertical structure

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have also been presented. The HYSPLIT back-trajectory analysis has been carried out in order to

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explore the changes in the sources of the aerosols during different seasons. It is seen that as we

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go higher up in the altitudes, the long range transport becomes more dominant. Compared to the

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other seasons, the air masses are more localized in JJA season. The changing aerosol amounts at

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different heights of the atmosphere have also been discussed in terms of decadal trends for the

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four seasons. These trends have been supplemented by the AOD trends from the passive satellite

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sensors. We find that aerosol amounts have increased in the last decade during DJF and SON

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seasons, when the atmospheric conditions are more stable. On the contrary, aerosol amounts

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have significantly decreased in JJA season throughout the vertical column. The effect of

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changing amounts of aerosols may have impacts on the snow cover variability as well. In this

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connection, the seasonal trends in snow cover have also been explored during the considered

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decadal period. Especially during the DJF and SON seasons, decreasing trends in snow cover are

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observed when the aerosol amounts have been found to be increased.

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Keywords: Aerosols, columnar, vertical, CALIPSO, trends

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1. Introduction An accurate estimation of Earth’s radiation budget requires precise information of columnar as

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well as vertical distribution of aerosols which is one of the largest uncertainties in radiative

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forcing computations (Forster et al., 2007; Gadhavi and Jayaraman, 2006; Heitzenberg et al.,

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1997; Samset et al., 2013; Satheesh et al., 2008; Zarzycki and Bond, 2010). Hence, a spatially

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continuous information of aerosols is required both at regional and global scales. Although,

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efforts have been made time and again to observe the total columnar distribution and the vertical

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profiles of aerosols using air-borne and ground-based instruments, but they lack the continuous

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spatial heterogeneous representativeness of aerosol variability. For this purpose, several space

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borne instruments like MODIS, MISR, OMI, AVHRR and SEAWIFS have been operationally

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providing estimates of aerosol columnar properties since quite long (Diner et al., 1998; Levy et

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al., 2010; Martonchik et al., 2002; Remer et al., 2005, 2009; Stowe et al., 2002; Torres et al.,

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2002; von Hoyningen-Huene et al., 2003). On the other hand, CALIPSO, a space borne dual

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polarized lidar, operational since June 2006, is unique in its purpose and is the only satellite that

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has been monitoring the global aerosol vertical structure for over a decade (Winker et al., 2007).

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The dynamics of the atmospheric boundary layer (ABL) over mountainous regions is quite

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different from that over the plains (Coulter and Holdridge, 1998). Owing to the geography of the

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Central Himalayas, with the polluted Indo-Gangatic Plains (IGP) to its South, its pristine

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atmosphere is bound to be influenced by the transport of pollution from the low lying level plains

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(Di Girolamo et al., 2004; Jethva et al., 2005; Kopacz et al., 2011; Lee et al., 2008; Lu et al.,

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2011; Ojha et al., 2012; Sarangi et al., 2014). With specific reference to aerosols, Bonasoni et al.

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(2010) have reported high concentration of black carbon over Himalayan region especially

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during the pre-monsoon season. Later, Srivastava et al. (2012) found that during the pre-

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monsoon season, aerosols over IGP get elevated up to 4-5 km due to coupling of strong

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convection and capping inversion mechanisms. A recent study by Singh et al. (2016) has shown

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that for complex topographical terrains of the Himalayas, the ABL could be variable in space,

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wherein the topographical interactions with mixing processes can transport aerosols to higher

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elevations. In fact, there are evidences of the impacts of aerosols on the snow and glacier cover

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of the Himalayan region which is an important water resource especially for the adjacent planar

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areas (Gautam et al., 2013; Gautam and Nainwal, 2017; Lau et al., 2010; Nair et al., 2013; Xu et

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al.; 2009 and references therein). In this context, as the aerosol concentrations are increasing

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over the Indian subcontinent, particularly over the IGP as reported in several studies (Babu et al.,

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2013; Hsu et al., 2012; Kaskaoutis et al., 2011; Mehta, 2015; Mehta et al., 2016; Pozzer et al.,

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2015, Yoon et al., 2014); there may be serious implications on the neighboring Himalayan

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region. Henceforth, a comprehensive study focusing on the distribution and trends of aerosols in

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the Himalayan region surrounded by the adjacent planar areas altogether becomes important. To

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this end, there have been several studies focusing on columnar aerosol properties over the

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Himalayan region (Guleria et al., 2012; Hyvärinen et al., 2009; Ram et al., 2010; Ramana et al.,

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2004; Sagar et al., 2004). However, only few studies have been reported on the vertical aerosol

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distribution over the Himalayas. For instance, Hegde et al. (2009) analyzed lidar observations in

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conjugation with optical aerosol properties over the Central Himalayan location. Reddy et al.

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(2013) in a study over Himalayan region, found that the aerosol vertical profiles affect their

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radiative forcing uncertainties. Solanki and Singh (2014) compared year-long ground based lidar

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observations with those from CALIPSO and found aloft aerosol layers over Central Himalayan

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region. Bucci et al. (2014) studied the aerosol variability and transport in the Himalayas using 4-

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year CALIPSO datasets. A recent study by Mehta et al. (2018) has utilized the 3-D structure of

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aerosol types from CALIOP Level 3 datasets and found evidences of increasing aerosol amounts

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over Indian region at different altitude regimes of the atmosphere. However, the study was done

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at a coarse spatial resolution of 2º X 5º and does not provide quantitative estimates of the

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changing amounts of aerosols over a complex terrain like Central Himalayan region. Moreover,

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the topographical and seasonal aspects of the aerosol distribution could not be addressed in that

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study. A detailed study of aerosol columnar and vertical properties over this fragile mountain

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ecosystem is, henceforth, required to be conducted.

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The present paper aims at providing a seasonal spatial and vertical profile structure of aerosols

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over the Central Himalayan region (which is mainly a data void region) in conjugation with the

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adjoining planar region using ten-year long datasets (2007-2016) from space borne instruments.

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The vertical distribution has been retrieved from CALIOP Level 2 datasets, both at a spatially

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averaged level as well as at a spatial grid. In order to illustrate the aloft aerosol layers, latitudinal

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and longitudinal variation of aerosol vertical profiles have also been presented. The columnar

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aerosol optical depth variation is also presented using data from MODIS/Terra, MODIS/Aqua

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and MISR/Terra sensors. In order to explore the sources of aerosols over the study region during

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different seasons, HYSPLIT back-trajectory frequency analysis has been carried out. Further,

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with an aim to study the changing amounts of aerosols, the decadal backscatter trends have also

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been presented in conjugation with the changing AOD levels.

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2. Study area

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The area of study ranges from 29ºN to 33ºN and 78ºE to 82ºE as shown in Figure 1. This region

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mainly consists of Uttarakhand and Eastern Himachal Pradesh states of India; Western Nepal

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and South-West China. Uttarakhand state has majority of hilly terrains, with Himalayan peaks

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and glaciers to its North and around 65% of forest covered land. The variety in climate and

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vegetation is a manifestation of changes in elevation, with glaciers at high altitudes to tropical

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forests at lower heights. The state of Himachal Pradesh receives a cold, alpine and glacial

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climate in the East. Most of the terrain is mountainous with varied elevation levels. This state has

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been successful in the implementation of Clean Development Mechanism (CDM) (Ranganathan

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and Goyal, 2015). The portion of Western Nepal considered in the study includes Southern low

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lying plains (known as Terai) and high-rise mountains which extend to Himalayan range towards

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North. The deforestation activities carried out in this region has led to increased soil erosion in

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the past years (Chaudhary et al., 2015). South-West China primarily comprises of the Himalayan

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Mountains.

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In order to study the seasonal aspects of the aerosol properties over the study area, the climate is

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divided into four seasons, i.e., December-January-February (DJF), March-April-May (MAM),

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June-July-August

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September-October-November

(SON).

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Figure 1 (a) The area under study shown by a red box. The subsurface map has been adapted from

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http://www.iitk.ac.in/gangetic/intro_gallerie/subsurface_indo-gang.htm. (For details, the readers may pl. refer to the original image.)

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(b) The elevation map of the study area created using the SRTM 90m DEM, brighter values correspond to higher elevations

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3. Data used

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3.1 CALIPSO Level 2 datasets

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Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is a satellite

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mission by NASA-CNES that carries Cloud-Aerosol Lidar with Orthogonal Polarization

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(CALIOP) onboard. The orbit of the satellite is sun synchronous at an altitude of 705 km above

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the Earth’s surface (Hunt et al., 2009; Winker et al., 2009). The sensor has been observing the

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vertical structure of aerosols and clouds since 2006, with 15 orbits per day. CALIOP Level 2

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datasets from version 3.01 (December 2006-October 2011), version 3.02 (November 2011-

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February 2013) and version 3.03 (March 2013-November 2016) have been used in the study. The

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spatial resolution of the datasets is 5 km, and the vertical resolution is 60 m (up to 20.2 km

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height). The data is captured at two wavelengths i.e. 532 nm and 1024 nm respectively. CALIOP

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provides attenuated backscattered profiles which are then converted to Level 2 data product

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using a hybrid collection of procedures, known as Hybrid Extinction Retrieval Algorithm

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(HERA). Details on the aerosol retrieval algorithm can be found in works by Hu et al., 2007,

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2009; Hunt et al., 2009; Liu et al., 2009; Omar et al., 2009; Vaughan et al., 2009; Young and

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Vaughan, 2009. An improved cloud-aerosol discrimination (CAD) algorithm, quality flags and

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implementation of improved calibration algorithm enable better screening of clouds (Winker et

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al., 2013). The data quality is a function of uncertainties arising due to the detector sensitivity,

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lidar ratios and feature misclassifications. The day time CALIOP data has low signal-to-noise

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ratio due to background solar light and hence, only night time profiles were considered in this

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study. We have utilized aerosol backscatter coefficients at 532 nm. The CALIPSO data can be

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downloaded from NASA’s Langley Research Centre’s Atmospheric Sciences Data Centre

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(ASDC) (http://eosweb.larc.nasa.gov).

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3.2 AOD datasets

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Apart from using vertical profile data of aerosols, columnar AOD Level 3 datasets from MISR,

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MODIS (Terra and Aqua) have also been utilized in the study. Both Terra and Aqua are NASA’s

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platforms at an altitude of 705 km above the surface of Earth. MODIS has 36 spectral bands

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spanning over a spectral range of 0.4 µm to 14.4 µm with variable spatial resolutions, while

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MISR has 4 wavelength channels with 9 cameras (Diner et al., 1998; Remer et al., 2005, 2008).

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Sayer et al. (2014) in their study recommended that the choice of MODIS aerosol retrieval

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algorithm should be regionally dependent. We have used Deep Blue (DB) retrievals over land

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surfaces at 550 nm from Collection 6 in this study. Choice of DB over Dark Target (DT)

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retrievals is made based on the analysis by Kumar et al. (2018) where they found DB retrievals

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are better suited over the study region. For more details on MODIS datasets, reader may refer to

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works by Hsu et al., 2013; Ichoku et al., 2002; Levy et al., 2010; Remer et al., 2005, 2009. The

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datasets can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/. While the spatial

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resolution of MODIS Level 3 AOD datasets is 1 degree, the corresponding MISR datasets are

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available at 0.5 degree spatial grid. For more details on the MISR datasets, one can refer to

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studies by Diner et al., 1998; Kahn et al., 2005, 2010; Martonchik, et al., 2009. The MISR AOD

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datasets can be downloaded from ftp://l5eil01.larc.nasa.gov/pub/misrl2l3/MISR.

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3.3 Ancillary datasets

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To investigate the possible local sources and long range transport of aerosols to the study area

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across different seasons, we have carried out seasonal 5-day Back-Trajectory frequency analysis

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using NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model

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(Stein et al., 2012). The wind vectors were obtained from the National Centers for

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Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis

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data products. In addition, the elevation data provided by SRTM DEM at 90 m spatial resolution

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was utilized in the study (Jarvis et al., 2008). Further, with a concern on the possible links to the

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snow cover change, we have analyzed the monthly Level 3 0.05 degree Global snow cover

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datasets available from MODIS (Terra), i.e., MOD10CM (Hall and Riggs, 2015). For details on

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the validation and accuracy of these snow cover products, reader may refer to the works by Klein

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and Bernett (2003), Parajka and Bloesch (2006), Simic et al. (2004), Hall and Riggs (2007).

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4. Results and discussion

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4.1 Seasonal distribution of aerosol loadings

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The Level 3 monthly AOD data (from MODIS and MISR) were seasonally averaged and

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extracted corresponding to the study domain. We observed a seasonality in the AOD variation as

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seen by all the three sensors (Figure 2). Figure 2(A) shows the spatially averaged seasonal AOD

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variability over the study area over each of the 10 years considered in the study. Figure 2(B)

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shows the decadal seasonal variability (2007-16) over the 1 degree study grid. The AOD levels

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remained low during DJF and SON seasons and high during MAM and JJA seasons respectively.

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Similar seasonality was also captured using in-situ measurements in the works by Jethva et al.,

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2005; Ram et al., 2010; Sagar et al., 2004 and Srivastava et al., 2015. In their studies, they have

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found that during summer, with increased solar radiation and enhanced wind activities,

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upwelling of particles and transported dust particles result in higher AOD levels, while

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anthropogenic particles (due to local emissions) dominate during the winter time periods. MISR

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reported

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lower

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Figure 2 (A) Seasonal variability of AOD for each year during 2007-16 (vertical bars represent the standard deviation in AOD

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values) and (B) Seasonal variation of AOD over the study grid using data from MISR (Terra) and MODIS (Terra/Aqua) sensors

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for (a) DJF (b) MAM (c) JJA (d) SON seasons during 2007-16

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In order to study the seasonal aerosol vertical profile distribution over the study area, the

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screening of the Level 2 night time aerosol backscatter at 532 nm was done. The screening

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process includes removing the invalid data, and the profiles severely affected by the cloud cover.

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Further, during processing of the vertical profiles, the data is screened out using quality flags like

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CAD score, aerosol layer fraction and extinction quality checks. The screened aerosol

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backscatter profiles so obtained were averaged for the considered extent of the study region.

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These zonally averaged profiles were then further averaged seasonally. Such a procedure yielded

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a spatially averaged information on aerosol vertical distribution. However, in order to understand

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the spatial distribution of aerosol vertical properties over the study region, we further divided the

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study area into 1º X 1º spatial grid and carried out the computations over the four different

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seasons. This procedure required computation of the spatially averaged profiles over each 1

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degree grid cell individually, followed by averaging at a seasonal scale. In the supplementary

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material, we have provided the number of valid vertical profiles (cloud free observations) of

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aerosols over the grid above the surface level. As the surface elevation levels within each grid

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cell at 1 degree resolution were different owing to the topographical variations in the study area,

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we have extracted the profiles above ground level (agl) for individual cells using the SRTM

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DEM. The DEM was available at 90 m spatial resolution, which was resampled at a spatial

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resolution of 1 degree. It was observed during all the seasons that aerosol loadings reached upto

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6 km over the surrounding polluted IGP planar areas (refer to the supplementary material)

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However, being on a conservative side, we restrained our computations up to 5 km agl. Hence, in

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order to visualize the aerosol loadings within different altitude levels of the vertical atmospheric

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column, we have considered five altitude bins, i.e., 0-1 km, 1-2 km, 2-3 km, 3-4 km and 4-5 km

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agl.

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Spatially averaged seasonal variability of aerosol backscatter coefficients at different altitude

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levels is reported in Figure 3(A) for the four seasons i.e., DJF, MAM, JJA and SON. The signals

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have been averaged over the entire study area, considering ±2 degrees around the central point of

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the study area. The observations were averaged over the period 2007-2016. The horizontal bars

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correspond to the standard deviation in the datasets. However, care should be taken to interpret

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Figure 3(A) as the altitudes in this case are reported above mean sea level (amsl). All through

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the year, higher values of mean aerosol backscatter coefficient (MABC) close to the ground

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(mainly below 2 km amsl) were observed which could be mainly due to the aerosol load from the

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low lying planar region included in the study area. It is observed that during DJF season

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accompanied by lower temperatures and weak solar radiation, the MABC values decreased as we

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move high up in the altitude till, there were increased signals within 3.5-4 km and then again

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increased around 5 km with MABC ranging between 2 (mm.Sr)-1 to 4 (mm.Sr)-1. In general, if

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we exclude the long range horizontal transport at higher altitude regimes, the low surface

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temperatures and low level capping mechanisms during winter season could result in low vertical

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spread of aerosols (Prijith et al., 2016). During the MAM season, as the solar radiation increased

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accompanied by high wind speeds, the MABC values increased owing to the expansion of ABL

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and the transport mechanisms compared to that in winter season. The higher standard deviations

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are indicative of larger variability in the datasets during the pre-monsoon season when compared

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with the winter. At the outset of monsoons, the MABC increased substantially at higher altitude

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levels (4-6 km) compared to that in the MAM season, though accompanied with larger standard

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deviations. The vertical mixing of the aerosols was maximum during the pre-monsoon and

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summer periods as evident from the Figure 3(A). Similar findings were also observed in the

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work by Solanki and Singh (2014). During SON season, the MABC values (especially within

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higher altitudes) fell down due to monsoonal washouts of particles. However, higher MABC

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values were still found within 4.5-5 km but with higher variability, compared to those in DJF

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seasons which were associated with lower standard deviations. Figure 3(B) presents the seasonal

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variability of MABC over the study area grid for five different altitude bins. It can be seen that

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the MABC values gradually fell from lower to higher altitude bins in all the seasons. The highest

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amount of aerosol backscatter came from the North-West portion of the study grid owing to the

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aerosol activities in the planar region. On a seasonal scale, largest backscatter was captured in

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the JJA season followed by MAM. However, the MABC values in DJF and SON seasons

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remained low. The presence of elevated aerosol layers might not be evident in this Figure

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because the extent of the elevated aerosol layer could only partially fill the 1 km altitude bin and

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hence, the effect may be averaged out. However, their presence can be observed in the latitudinal

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and longitudinal variation of vertically resolved MABC which has been illustrated in Figure 4.

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Considering the latitudinal variation, it can be seen from Figure 4(A), that maximum aerosol

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loadings were concentrated towards lower part of the study region. Over 29.5ºN, elevated aerosol

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layers could be observed at ~ 4.5 km agl during DJF, while aerosol loadings increased near the

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surface in MAM. During JJA, elevated aerosol layers were again found at around 3.5 km and 5.5

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km agl, while during SON, broad elevated layers could be found within 2.7-3.5 km agl.

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Considering the aerosol distribution at 30.5ºN, elevated aerosol layers were found at around 3.5

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km and 5.5-6.0 km agl during DJF, in contrast to very low values of MABC near the surface.

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Elevated aerosol layers were also observed during MAM (2.5-3.0 km agl), JJA (2.5-3.5 km agl)

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and SON (~ 3.0 km and 4.0 km agl) seasons. The presence of elevated aerosol layers over a

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particular location in Central Himalayas has been confirmed in an earlier study by Reddy et al.,

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2011 and later by Solanki and Singh, 2014. Further, higher up in latitudes, the aerosol loading

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decreased both near the surface and at higher atmospheric layers. Similar observations could be

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recorded in Figure 4(B) where the vertically distributed properties of aerosols are presented

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longitudinally.

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Figure 3 Seasonal variation of MABC (A) spatially averaged over study area and (B) over study

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grid within five different altitude bins during 2007-16

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Figure 4 (A) Latitudinal and (B) Longitudinal variation of MABC ((mm.Sr)-1) during 2007-16

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4.2 Back-Trajectory analysis : Possible sources of aerosols across different seasons

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The back-trajectory calculations were performed in order to study the possible sources of

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aerosols at different levels of the vertical column over the study grid. The seasonal 5-day back-

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trajectory frequency plots so obtained provided the most frequent transport pathways of the air

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masses reaching the study region. With an aim to observe the changes in the pathways of air

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masses during the two halves of the decadal period, the frequency maps were generated for the

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first five (2007-11) and the last five (2012-16) years using the NOAA HYSPLIT model. This

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analysis was carried out at the central point of the study grid under consideration, i.e., 31ºN and

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80ºE. The aerosol sources were investigated at five altitude levels i.e., 0.5 km agl, 1.5 km agl, 2.5

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km agl, 3.5 km agl and 4.5 km agl, corresponding to the mid points of the five altitude bins (i.e.

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0-1 km, 1-2 km, 2-3 km, 3-4 km and 4-5 km). Figure 5 provides the back-trajectory frequency

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maps for the four seasons during the the last five years (2012-16) of the decade. The frequency

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maps for the first half of the considered study period, i.e., 2007-11, are provided in the

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supplementary material. There were small changes in the trajectory frequency maps but the

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major sources of aerosols remained the same during the first and the last halves of the decade.

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This was found to be true for all the seasons and all the altitude levels under consideration.

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Hence, there were no major changes in the aerosol source region domains during the last ten

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years. At lower altitudes, the local aerosol sources were more dominant but higher up in the

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vertical column, the influence of long range transport increased. Upto 1.5 km agl, apart from the

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Central IGP, the major transport of aerosols occurred from North-West i.e., Afghanistan,

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Pakistan, Tajikistan; and Nepal during DJF when wind speeds are low. However, at higher

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altitudes, air masses are also transported from Middle-East countries. Interestingly, at higher

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altitudes, there were no air masses from Nepal or China. During the MAM season, when most of

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the dust activities were prevalent, at lower altitude regimes, the aerosol sources were from North-

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West as well as from China. At higher altitudes, however, the contribution from the West and the

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North became prominent. During the JJA season, when monsoonal effects dominated, the

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aerosol sources were more confined compared to the other three seasons. The major aerosol

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sources at lower altitudes were diagonal, i.e., from North-West, North-East, South-East and

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South-West of the study region. The spread of these sources increased at higher altitudes. Also,

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at higher altitudes, the air masses were affected by transport from Bay of Bengal and hence,

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carried lot of moisture. SON, also considered as the post-monsoon season captured the effects of

312

aerosols coming from the North-West and South-East in particular, at lower altitudes. With

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increase in the height of the atmospheric column, we see that the air masses mainly came from

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West and South-East regions.

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Figure 5 HYSPLIT back-trajectory frequency maps* during different seasons presented for 2012-16

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4.3 Decadal AOD and MABC Trends

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As we observed seasonality in the aerosol distribution over the study region, decadal (2007-16)

324

seasonal trends were computed for AOD over the study region using least square regression. In

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order to compute spatially averaged seasonal MABC trends, the seasonal averages for each year

326

were computed at each vertical level of the atmosphere and then linear trends were fitted.

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Further, to compute the MABC trends over each grid point location, similar procedure was

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carried out for each of the cells within each altitude bin. Student’s t-test was performed to test the

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statistical significance of the trends. Significant trends are reported at ≥90% confidence interval.

330

Table 1 shows the columnar AOD trends computed using the data from MODIS and MISR for

331

the four seasons, + values indicate positive trends, while – values indicate negative trends. The

332

red and green colors represent statistically significant trends at ≥90% confidence interval while

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yellow and cyan colors represent insignificant trends. We observed from both MODIS Terra and

334

Aqua that the AOD has significantly increased during the SON season over the last decade.

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Results from MISR were also in agreement, though statistically insignificant. During the DJF

336

seasons also, all the three sensors unanimously agreed that AOD levels have been increased over

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the study region. During MAM season, however, the columnar AOD has declined as reported by

338

the three sensors. These findings are strengthened by a recent study employing measurements

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from ground, satellite and model retrievals which has revealed that pre-monsoonal dust loadings

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over South Asia have decreased during 2000-2015 (Pandey et al., 2017). The JJA season, which

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is dominated by the monsoonal effects, witnessed decreasing trends by MODIS (Terra and Aqua

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both) but increasing trends as observed by MISR sensor. However, these trends were statistically

343

insignificant.

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Table 1 Seasonal AOD Trends (per decade) using data from MISR (Terra) and MODIS

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(Terra/Aqua) sensors during 2007-16; + values indicate positive trends, while – values

347

indicate negative trends; red and green colors represent statistically significant trends

348

while yellow and cyan colors represent insignificant trends MODIS TERRA

MODIS AQUA

AOD

AOD

AOD

DJF

+0.07

+0.09

MAM

-0.04

-0.02

-0.02

JJA

+0.05

~0.00

~0.00

SON

+0.03

+0.06

+0.05

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+0.08

Comparison of AOD trends over the spatial grid revealed that in the South-West of the study

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region, i.e., the planar areas, the AOD levels have increased significantly during the DJF and

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SON periods; while decreased over the North-Eastern portion of the study area (Figure 6).

353

During MAM season, we found statistically decreasing AOD trends especially over the North-

354

East of the study region. During JJA season, MODIS didn’t show any statistically significant

355

trend. However, MISR reported mostly increasing trends in AOD. The results mostly agree from

356

all the three sensors during DJF, MAM and SON seasons but the same was not true for JJA

357

season.

358

Figure 7 (A) reports the spatially averaged seasonal trends in MABC over the study region. The

359

dots are indicative of significant trends at ≥90% confidence interval. It was observed that during

360

the DJF season, increasing and decreasing trends of MABC were found at different altitude

361

levels of the atmosphere. However, the overall trend was increasing. During SON season, the

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aerosol amounts have increased in almost all levels of the vertical column. However, during JJA

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season, the aerosol levels have decreased above 2 km amsl, but increasing trends were found

364

below 1.5 km amsl. The overall trends in JJA were negative. During MAM season, we found that

365

trends were increasing as well as decreasing at varying heights of the atmosphere. However,

366

overall statistically significant decreasing trends were observed in MAM season. The spatial

367

variation of seasonal MABC trends over the study area is provided in Figure 7 (B).

368

The observed increasing trends of columnar and vertically distributed aerosol amounts during

369

winter and pre-monsoon seasons are in agreement with some of the recent studies. It has been

370

found using different datasets that aerosol levels have increased over the surroundings of the

371

study area, which are the potential source regions (Section 4.2), in particular over the Indian

372

region (Babu et al., 2013; Dey & Di Girolamo, 2011; Hsu et al., 2012; Kishcha et al., 2011; Lu,

373

et al., 2013; Mehta, 2015; 2016; 2018 and Pozzer et al., 2015). A recent study by Kang et al.,

374

2017, (using OMI datasets spanning over the period 2005-16) has revealed that aerosol loadings,

375

especially due to absorbing aerosol types, have increased during DJF and SON seasons over

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some parts and the vicinity of the study area.

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Figure 6 Seasonal Trends over the study grid using data from MISR (Terra) and MODIS

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(Terra/Aqua) sensors for (a) DJF (b) MAM (c) JJA and (d) SON seasons during 2007-16

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Figure 7 Decadal seasonal trends of MABC (A) spatially averaged over study area; and

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(B) over study grid within five different altitude bins

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4.4 Possible connections with changing snow cover

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In order to investigate whether there exists any association of aerosol loading to the overall snow

388

cover in the study region, a correlation analysis between decadal AOD values and snow cover

389

was performed for each of the seasons over the last ten years (refer to supplementary material).

390

We observed an overall negative correlation between the aerosol loadings and snow cover all 23

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through the year. The decadal seasonal variability of snow cover as revealed by MOD10CM

392

products shows that peak snow cover area was found during DJF while the amount of snow

393

cover in the individual pixels reached its maximum during MAM season (Figure 8(A)). The

394

minimum snow cover area was found during the JJA season. The details on the seasonal

395

variability of snow cover over the considered study domain can be found in works by Gurung et

396

al. (2011), Maskey et al. (2011), Pu et al. (2008), Singh et al. (2014) and references therein.

397

However, we are more concerned with the trends in seasonal snow cover and we found overall

398

decreasing trends during DJF (insignificant) and SON (significant) seasons. One of the peculiar

399

things to note is that during the winter season, the snow cover decreased over the regions

400

receiving more than 60% snow cover (Figure 8(B)). Over the regions with lesser amounts of

401

averaged snow cover, the trends were found to be increasing. Several studies have indicated that

402

the snow cover (fraction/depth/albedo) might decrease due to deposition of aerosol particles of

403

different types and origins (Menon et al., 2010; Qian et al., 2011; Ming et al., 2008; Xu et al.,

404

2015). Since we observed increasing trends of aerosols loadings during the winter season, there

405

might be possible connections with the overall decreasing trends in snow cover (Figure 6-7).

406

During SON season, however, the trends in snow cover were decreasing everywhere. In this

407

context, it is again worth to note the significantly increasing trends in columnar and vertically

408

distributed aerosol loadings within all the altitude regimes during the SON season. Our findings

409

are strengthened by a recent study which has indicated increasing absorbing type aerosol load

410

over the area surrounding the Central Himalayan region (Kang et al., 2017). Interestingly, we

411

observed increasing trends in snow cover during the spring (MAM) season, especially over the

412

regions with lower amounts of snow cover. To this end, it is worth to recall Figure 6-7 where we

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have also found negative trends in columnar aerosol loadings, along with significantly

414

decreasing vertical trends especially at higher altitude regimes; during the spring season.

415

The reader should be cautious that these observations are just indicative of the possible

416

connections and implications of changing amounts of aerosols over the snow cover of the Central

417

Himalayan region surrounded by the polluted IGP. In fact, testing a causality relationship should

418

require a combined evaluation of space-borne and in-situ measurements accompanied by a

419

detailed analysis using radiative transfer models that involve atmosphere-land surface

420

interactions taking into account the chemical transport and cloud microphysics (Lee et al., 2017);

421

which is beyond the scope of present study. Further, for a detailed qualitative and quantitative

422

assessment, one needs to consider the impact of the seasonal changes in the meteorological

423

parameters e.g., humidity, wind speed, wind direction; along with the changes in the land surface

424

temperature, snow depth, snow melt and snow albedo during the considered time frame.

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Figure 8 (A) Seasonal variability and (B) Decadal trends of snow cover during 2007-2016 over the study grid for (a) DJF (b) MAM

428

(c) JJA and (d) SON seasons

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5. Conclusions This study brings out the seasonal columnar and vertical distribution of aerosols over the Central

431

Himalayan region surrounded by the neighboring plains during the last ten years (2007-16)

432

utilizing data from MODIS, MISR and CALIOP sensors. We found that there is a strong

433

seasonality in the aerosol distribution with higher loadings during summer compared to the

434

winter. The possible sources of the aerosols have also been explored using the HYSPLIT back-

435

trajectory frequency analysis. It was found that the study area is affected by both the local as well

436

as long range transport origins. At higher altitude regimes, however, the effect of long range

437

transport became more prominent. A trend analysis was also carried out at seasonal level to

438

investigate the changing amounts of aerosols both within total column as well as at different

439

levels of altitudes. We observed that the aerosol levels have increased significantly over the

440

study region during the post-monsoon period. Previous studies have indicated more dominance

441

of anthropogenic sources during this period and hence, such increasing amounts of particles

442

within all the levels of the atmosphere is of major concern. Similar increase in aerosol amounts

443

are also observed during the winter season, though statistically non-significant. Further, we could

444

also find decreasing snow cover during winter and post-monsoon seasons which needs attention

445

in light of increasing aerosol loadings over the study region.

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6. Acknowledgements

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Authors are thankful to the entire CALIOP, MODIS, MISR and SRTM Teams for providing

449

access to the data which could lead to the present analysis. Thanks are also due to NOAA Team

450

for providing access to the HYSPLIT back-trajectory model, NCEP/NCAR Team for providing

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451

access to Reanalysis datasets. Thanks are also due to Director, IIRS and Director, ARIES for

452

their encouragement and support.

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Increase in the aerosol loading during winter and post-monsoon

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Agreement between columnar and vertically distributed aerosol trends

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Effect of long range transport of aerosols more prominent at higher altitudes

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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.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: