Absorption characteristics of aerosols over the central Himalayas and its adjacent foothills

Journal Pre-proof Absorption characteristics of aerosols over the central Himalayas and its adjacent foothills

Hema Joshi, Manish Naja, Liji M. David, Tarun Gupta, Mukunda M. Gogoi, S. Suresh Babu PII:

S0169-8095(19)30838-5

DOI:

https://doi.org/10.1016/j.atmosres.2019.104718

Reference:

ATMOS 104718

To appear in:

Atmospheric Research

Received date:

30 June 2019

Revised date:

30 September 2019

Accepted date:

21 October 2019

Please cite this article as: H. Joshi, M. Naja, L.M. David, et al., Absorption characteristics of aerosols over the central Himalayas and its adjacent foothills, Atmospheric Research(2018), https://doi.org/10.1016/j.atmosres.2019.104718

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© 2018 Published by Elsevier.

Journal Pre-proof Absorption Characteristics of Aerosols over the Central Himalayas and its Adjacent Foothills

Hema Joshi1 , Manish Naja2 , Liji M. David3 , Tarun Gupta1 , Mukunda M Gogoi4 , S. Suresh Babu4 1

Department of Civil Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh,

India 2

Aryabhatta Research Institute of Observational Sciences, Nainital, Uttarakhand, India Department of Chemistry and Atmospheric Science, Colorado State University, Colorado,

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3

United States

Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India

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Keywords: Black Carbon, Aerosol Optical Depth, Carbonaceous Aerosols, Himalayas,

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GEOS-Chem.

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Abstract

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Corresponding Author: [email protected] and [email protected]

The absorption characteristics and source processes of aerosols are investigated at two nearby distinct altitude sites: Nainital, located over the central Himalayas (~1958 m amsl) and Pantnagar, in the adjacent foothill region (~ 231 m amsl) in the Indo-Gangetic Plain region (IGP); based on in-situ measurements and model (GEOS-Chem) simulations. The study reveals the significant influence of biomass burning sources over both the locations during spring, indicating the efficiency of the vertical transport of biomass burning aerosols during the peak of the fire activity period over the northern Indian region. On the other hand, the dominance of fossil fuel emission sources is seen during most part of the year at the mountain site, while biomass/biofuel sources are prevalent at the foothill site. Simulations of different

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Journal Pre-proof aerosol components in the GEOS-Chem model have revealed that dust aerosols, in addition to carbonaceous aerosols from fossil fuel and biomass burning sources, significantly influence aerosol burden over this broad region covering both high-altitude site Nainital and adjacent foothill site Pantnagar in IGP. Examination of dominant aerosol types and their contribution to the columnar abundance of aerosols is performed. During spring, the contribution of dust aerosols is as high as 22%, even though inorganic aerosols (42%), organic carbon (29%) play a dominant role in modulating aerosol absorption characteristics in the column over this

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This study highlights the importance of absorbing aerosol, their types and

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region.

quantification for better estimates of radiative forcing of aerosols over this region. This might

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also provide valuable information for the regional impact assessment of aerosols over the

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Himalayan region.

Introduction

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The south Asian region, particularly the Indo-Gangetic Plain (IGP), is a significant

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contributor to the deteriorating air quality. High anthropogenic aerosols (consisting of

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sulphates and soot) along with seasonally transported dust lead to a significant pollution load over this region (Dey et al., 2004; Singh et al., 2004; Nair et al., 2007; Gautam et al., 2010, 2011; Giles et al., 2011; Babu et al., 2011a; Joshi et al., 2016a), adjoining oceanic regions, and the nearby Himalayan region (Pant et al., 2006; Gobbi et al., 2010; Bonasoni et al., 2010). In the pre-monsoon season, aerosol abundance is found to be maximum over the IGP region as well as over the nearby Himalayan region (Gobbi et al., 2010; Joshi et al., 2016a). In this season, this region experience aerosols from biomass burning activities, seasonally transported dust along with other anthropogenic emissions. The pollution load from the IGP is found to be associated with the Himalayan climate that affects the dynamics and radiation

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Journal Pre-proof budget of this region, thus affecting the regional climate significantly (Menon et al., 2002; Lau et al., 2006, 2010; Gautam et al., 2013). The absorbing aerosol, black carbon (BC), is mainly identified as one of the influencing parameters for changing the hydrological cycle, accelerated warming and melting of the snow-covered regions (Jacobson, 2004; Hansen and Nazarenko, 2004; Flanner et al., 2007). The amount of absorbing aerosols, their type and the altitude at which they are present are of

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crucial importance for an accurate estimation of their radiative impacts (Satheesh, 2002;

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Babu et al., 2011a, 2011b). It is reported that the spectral dependence of BC light absorption

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is relatively less as compared to other aerosol components (i.e., brown carbon, dust, organic compounds, hematite) present in the aerosol sample, which exhibits a much higher spectral

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dependence (Kirchstetter et al., 2004; Andreae and Gelencser, 2006). The spectral

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dependence of aerosols are thus important to study and distinguish different aerosol types

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(Collaud-Coen et. al, 2004; Sandradewi et al., 2008).

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Recent studies have highlighted that certain organic aerosols also possess the absorbing characteristics (in near UV region), contrary to their well know scattering nature (reference).

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The carbonaceous aerosols whose optical properties lie in between the strongly absorbing aerosol soot or BC and non-absorbing aerosol, organic aerosol, are termed as “BrC”, which have attained a significant scientific interest recently (Andreae and Gelencser, 2006; Cheng et al., 2011). The absorption due to BC in the wavelength region from 370 nm to 950 nm is found to be inversely proportional to the wavelength, with strong and nearly constant absorption from UV to visible spectrum (Andreae and Gelencser, 2006; Bergstrom et al., 2007) while BrC absorption is found to be stronger in the UV region (Moosmuller et al., 2009). BC is a major contributor (72%) to light absorption leading to a positive radiative forcing of the atmosphere. However, the light absorption by BrC becomes important over the

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Journal Pre-proof regions dominated by biomass burning and biofuel combustion contributing ~ 20%-50% to radiative forcing (Feng et al., 2013). The main sources of BrC include primary sources of origin via biomass burning aerosols (Washenfelder et al., 2015), fossil fuel (Bond et al., 2001), biogenic aerosols, as well as secondary sources of origin from the anthropogenic and biogenic precursors (Zhang et al., 2011). The dominant source of BrC has been reported to be biomass burning aerosols (Chung et al., 2012; Laskin et al., 2015). These aerosols are ubiquitous in nature, and are found in

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rural, urban and marine regions (Liu et al., 2014). Despite their significant impact on global and regional climate forcing by direct and semi-direct effect (Bahadur et al., 2012; Jacobson,

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2012), there exist large uncertainty in accessing their climate impacts due to lack of ground-

region is dominated

by combustion sources mainly originating from

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The IGP

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based measurements and their poor characterization.

biofuel/biomass burning and fossil fuel emissions. The enhancement in the carbonaceous

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aerosols, particularly BC, is found to be closely associated with the atmospheric brown

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clouds (Ramanathan et al., 2001; Liepert et al., 2004). In spring, the northern Indian region

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receives a significant amount of carbonaceous aerosols from biomass burning (Kumar et al., 2011) as well as transported dust (Gautam et al., 2011). The possibility of aerosol transport from the IGP region to the nearby Himalayan region is reported in earlier studies (Gautam et al., 2011; Joshi et al., 2016a).

The aerosol sources and their variations in the foothills regions in the IGP can differ from the aerosol sources and their variation in the high altitude regions of the Himalayas in the absence of direct transport of aerosols, particularly in winter. On the other hand, aerosol sources of similar origin are expected to influence both of the regions in spring when aerosols can be easily transported from the foothills regions to the high altitude regions of the Himalayas due to the boundary layer dynamics. The present work highlights the comparative 4

Journal Pre-proof characterization of aerosol from two distinct locations: one at the top of the mountain (far away from any major anthropogenic activities) in the central Himalayan region and another at its adjacent foothill site (near to the anthropogenic source regions) in the IGP. In this study, we have discussed the aerosol sources, their absorption characteristic, and seasonality utilizing ground-based measurements along with the model (GEOS-Chem) simulations. The variation in absorbing aerosols and the possibility of transport from the foothill region to the Himalayan region is discussed. In addition, aerosol sources in the vertical column are studied

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and quantified over this region utilizing the model simulations. These investigations are carried out in order to provide key information about the role of absorbing aerosols, as well

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as major aerosols types. This might improve the understanding of emission estimates of

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aerosols over this region and provide a better insight of their role in regional radiation budget

2.1.

Site Description and Measurement Database

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and impact assessment.

Observational Sites

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The ground-based observations of aerosol at Nainital (29.4o N, 79.5o E, 1958 amsl) in the central Himalayas are unique.

The observation site is located on the mountain top, away

from any major anthropogenic activities, and therefore, the site is a good regional representative as shown by other trace gases observations also (Kumar et al., 2010; Sarangi et al., 2014; Naja et al., 2014). The north and northeast of the site are encompassed by more elevated Himalayan mountains, while the south and southwest have low elevated plain regions gradually merging with the Indo-Gangetic Basin (Fig. 1a). The main town of Nainital is located at an aerial distance of ~2 km (~9 km by road) due north from the observational site. The other site, Pantnagar (29.0° N, 79.5° E, 231 m amsl) in the foothills region, is a semi-urban site in the IGP and it is away from any major local anthropogenic activities. 5

Journal Pre-proof However, there are few small-scale industries in the adjoining towns, e.g., Rudrapur (16 km, southwest of Pantnagar) and Haldwani (25 km, northeast of Pantnagar) (Joshi et al., 2016a). The population density around the Pantnagar site is reported to be ~250-1000 persons per km2 and it is less than ~25-250 persons per km2 due north (towards Nainital site) (Ojha et al., 2012). Thus, both of these observational sites Nainital and Pantnagar are different from each other in terms of the surrounding topography, meteorological conditions, forest/vegetation types and anthropogenic impacts.

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The maximum temperature at the Himalayan site, Nainital typically touches 30o C (Tmax ) in May/June month and the minimum temperature is observed up to -1o C (Tmin ) during

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December/January with an annual mean temperature of ~15o C. The other site, Pantnagar, is

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characterized by high temperatures in May/June (Tmax ~42o C) and low temperatures in

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January (Tmin ≥3o C) (Joshi et al., 2016a). Nainital site experiences clear blue sky with no fog/haze formation during winter (December-February) but receives some episodes of

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snowfall in winter, which vary from year-to-year. On the other hand, Pantnagar site

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experiences fog/haze formation during each winter. The seasons are classified as winter

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(December-February), spring (March-May), summer-monsoon (June-August), and autumn (September-November).

Nainital site experiences heavy rainfall and

high-density fog

formation in July-August months, while Pantnagar site experiences less rainfall compared to the mountainous site. The comparison of daily rainfall data for the year 2011 at the two sites shows that the association is moderate at the two sites (R2 =0.58). The annual rainfall estimate shows that the Nainital site receives more rainfall (~31%) compared to Pantnagar site. More details regarding the observational site and prevailing meteorological conditions at Nainital (Pant et al., 2006; Kumar et al., 2011; Naja et al., 2016) and Pantnagar (Joshi et al., 2016a) are well documented.

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Journal Pre-proof 2.2.

Ground Based Measurements

2.2.1 Ground Based Measurements of Black Carbon Mass Concentrations Measurement of spectral absorption properties of aerosols are carried out using sevenchannel (370-950 nm) Aethalometer (Magee Scientific, USA) at both the sites. The instrument aspirates the ambient air, which is then passed to the measurement chamber where the aerosol sample is collected onto a quartz filter paper. The intensity of light through the blank and the

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particle-laden quartz filter paper is measured using the photodiode placed directly underneath the filter to calculate the optical attenuation (Hansen et al., 1984). The values of absorption

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coefficients are converted to BC mass concentrations by utilizing the optical absorption

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cross-section called the “mass-specific absorption cross-section (MAC)”, which is equal

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to14625/λ for the model of Aethalometer used in the present study, where λ represents the wavelength. Thus, the specific attenuation values are 39.5, 31.1, 28.1, 24.8, 22.2, 16.6 and

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15.4 m2 g−1 corresponding to the 370, 470, 520, 590, 660, 880, and 950 nm wavelengths

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(Hansen, 1996). Here, BC measurements were taken continuously at a time resolution of 5 minutes, at a flow rate of 5 litres per minute, on all the days and round the clock. As per the

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manufacturer, the only calibration suggested for this instrument after installation is a periodic check of air flow-meter response. The flow rate was maintained and calibrated periodically by checking with an external flow-meter as well as by adjusting the same via computer program provided by the manufacturer (Hansen et al., 2005). The instrument was also intercompared with other Aethalometers from time to time, and the performance was found to be satisfactory. The datasets used here were converted to the hourly average values in order to reduce the uncertainties associated with the sampling conditions and instrument noise (Dumka et al., 2010; Kumar et al., 2011). The absorption measurements at 880 nm wavelength are used for

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Journal Pre-proof the estimation of BC due to its relatively strong absorption at this wavelength compared to other species (Bodhaine, 1995) and has been widely used for BC measurements (Babu et al., 2004; Moorthy et al., 2004, 2011; Pant et al., 2006; Cheng et al., 2010; Dumka et al., 2010; Gogoi et al., 2017), since the contributions from other absorbing aerosols are insignificant at this wavelength. More details on the instrument are given elsewhere (Pant et al., 2006; Dumka et al., 2010; Joshi et al., 2016a).

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The filter-based absorption techniques are known to suffer from various systematic

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errors that need to be corrected. Several studies were made to study the performance and

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uncertainties involved in the Aethalometer data and its comparison with other methods (Weingartner et al., 2003; Arnott et al., 2005; Hitzenberger et al., 2006). It is reported that

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there exists no generally accepted method for BC measurement, since all the existing

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methods are reported to be instrument and method dependent. The existing methods also suffer from cross-sensitivity to light scattering particles and other potential measurement

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artifacts (Petzold et al., 2013), resulting in a wide variation in BC measurements (Schmid et

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al., 2001). It is reported by Petzold et al., (2013) that BC estimate from the filter-based

factor,

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optical instruments might not represent ~100% of BC due to variation in the conversion which introduces additional biases while comparing the simulated BC with

measurements. However, Aethalometer measurements of BC are inter-compared from timeto-time with other measurements, i.e., those estimated from thermo-optical and chemical analysis. These inter-comparison results have shown good agreement (Hitzenberger et al., 2006). Several studies have pointed out that Aethalometer, in general, overestimates BC compared to those measured by single-particle soot photometer by a factor ranging from 1-5 (Wang et al., 2014; Raatikainen et al., 2015). The measurements from Aethalometer need to be corrected for light scattering effects due to the multiple scattering by the filter matrix (also called as the C-factor) and due to the 8

Journal Pre-proof deposition of scattering material on to the filter causing the ‘shadowing effect’ (also called as the R-factor) following Weingartner et al., (2003). Here we used a constant (C=1.9) for both of the sites based upon Bodhaine et al., 1992; Bodhaine, 1995, while R is assumed to be unity (Pant et al., 2006; Dumka et al., 2010; Kumar et al., 2011; Joshi et al., 2016a). Also, the data from the high-altitude site are also corrected for the ambient temperature and pressure as mentioned in Moorthy et al., (2004). As reported by the manufacturer, the sensitivity and accuracy of Aethalometer are <100 ng m−3 and 5% respectively (Hansen, 2005). The

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uncertainty in the BC measurement has been reported up to 20% (Moorthy et al., 2007),

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which can reach up to 30% (Müller et al., 2011).

The multi-wavelength data from the Aethalometer are utilized to infer the absorption

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characteristics, and sources identification of aerosols by utilizing the spectral dependence of

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aerosol absorption. The mass concentration at near UV wavelength (370 nm) has been reported to represent organic aerosols (i.e., from tobacco smoke, wood fire smoke) (Hansen,

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2005; Wang et al., 2011a). The percentage contribution of BrC is also studied at both the

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observational sites utilizing the difference of mass concentration at 370 nm and 880 nm

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wavelength. Considering the complexity in the direct observations of BrC, indirect methods based on calculating the difference between total absorption and that of BC have been used extensively. It has been suggested that the mass concentration at 370 nm have both BrC and BC, while mass concentration at 880 nm consists of pure BC (Kanawade et al., 2014; Wang et al., 2016). The percentage difference in the mass concentration at two different wavelengths (370 nm and 880 nm) given by (BrC370nm − BC880nm)/BC880nm is utilized to determine the local sources (wood-burning/fossil fuel) of BrC. The wood-burning aerosols has been reported to cause enhanced absorption at near UV wavelength (370 nm), while aerosol originating from the fossil fuels has been reported to cause higher enhancement at the near-infrared wavelength (880 nm) (Herich et al., 2011; Wang et al., 2011). 9

Journal Pre-proof In order to understand the source characterization of aerosols, the absorption characteristics of aerosols are studied by investigating the absorption coefficients (σ abs) and absorption angstrom exponent (αabs). The estimation of σabs at operational wavelengths is made using the attenuation data from the instrument, which is then corrected for light scattering, as suggested by Bodhaine (1995) and Weingartner et al., (2003) as follows:

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where, ΔATN represents the change in the attenuation in the quartz filter tape before and after the deposition of aerosol particles onto the quartz filter tape and is given by ΔATN

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=−100[ln (I2 / I1 )], where, I1 and I2 represent the ratio of intensities measured by the detector for the

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before (reference beam) and after (sensing beam) each sample in the time

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respective wavelengths. Q is the volume of the air being sampled and A is the spot area where aerosols are being collected. The parameter C accounts for the multiple scattering of

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the light beam by the filter matrix and R value accounts for the deposition of scattering

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material on to the filter as mentioned previously.

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Further, the wavelength dependence of BC absorption is then used to infer the aerosol sources. The absorption coefficients estimated from equation (1) are then used to estimate absorption angstrom exponent (αabs) by utilizing the power law relation (Kirchstetter et al., 2004) expressed as: (2) where β is the particle loading and coefficient values

is the wavelength exponent. Here, the absorption

at the seven wavelengths (370, 470, 520, 590, 660, 880, and 950 nm)

are used to estimate αabs values from the slope of a linear regression between σ abs and λ in log-log scale for each set of measurements following equation (2).

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Journal Pre-proof 2.2.2. Ground Based Measurement of Aerosol Optical Depth In addition to the surface variation of aerosols, the columnar abundance of aerosol in the vertical column is also studied. The columnar abundance of aerosols at Nainital site are measured

using

a

hand

held,

portable,

multiband

(380-870

nm)

Sunphotometer

MICROTOPS-II (Solar Light Company, USA). The instrument utilizes the BouguerLambert-Beer law for the aerosol optical depth (AOD) estimations. The 380 nm channel has the wavelength precision of ±0.4 nm, and a full width at half maximum (FWHM) band pass

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of 4 nm. The filters used in all other channels have a peak wavelength precision of ±1.5 nm, and a full width at half maximum (FWHM) band pass of 10 nm. The Sunphotometer

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observations were taken when the region of sky (~10°) around the Sun was free from clouds

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in order to avoid the cloud contaminations. The data has been taken at 30 min interval during

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daytime with a minimum of three consecutive observations at a time (within a short span <20 s). The minimum AOD out of the three consecutive measurement was used for further

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analysis, as minimum AOD corresponds to maximum pointing accuracy (Morys et al., 2001;

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Porter et al., 2001; Ichoku et al., 2002). The details of the instrument and its performance are

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well documented (Morys et al., 2001). The details regarding the instrument setup and its operation are similar to previous studies (Pant et al., 2006; Kumar et al., 2011).

2.3.

GEOS-Chem Model

In the present study, a global 3-D chemical-transport model, Goddard Earth Observing System (GEOS)-Chem (version 10-01) (Bey et al., 2001) has been used to simulate the AOD and its various components (i.e., black carbon, organic carbon (OC), inorganic aerosol (sulphate, nitrate, and ammonium), dust and sea-salt) and PM2.5 mass concentrations. The model is driven by GEOS-5 meteorology. The temporal resolution for the wind data (3-D fields such as u and v wind components) and the temperature is 6 hours, while for the mixing

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Journal Pre-proof layer depth and surface parameters, the resolution of 3 hours is used. The horizontal resolution of the model is set to 0.5°×0.667° with 47 eta vertical levels from the surface to ~80 km. More details on the model, emission inventories used, and evaluation of AOD and PM2.5 over India are described in David et al., (2018, 2019).

Results and Discussion

3.1.

Absorption Characteristic of Aerosols

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The seasonal variation in absorption coefficients (σabs) and absorption angstrom exponent

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(αabs) at Nainital and Pantnagar sites are shown in Fig. 2a and Fig. 2b, respectively. The monthly variation in σabs (at 520 nm) at Nainital (red triangles) and Pantnagar (black circles)

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sites are shown in Fig. 2a. The σabs at Nainital and Pantnagar shows a significant difference in

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terms of the magnitude. Pantnagar being a semi-urban site showed high values of absorption coefficient in all the months compared to Nainital. Seasonally, if we examine the σabs at these

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two sites, reduction in the σabs is observed at the Pantnagar site from March-May, while

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enhancement is seen at the Nainital site during this time. The maximum value of the σabs is

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observed in winter at Pantnagar, while it is maximum in spring at Nainital. The build-up in σabs is again seen at both sites after the summer-monsoon season from October to February. The seasonal mean σ abs is calculated using the daily mean σ abs values at Nainital and Pantnagar sites. Seasonally, σ abs shows a maximum (~25.61±5.6 Mm-1 ) in the spring season at Nainital, which reduced to a minimum (8.65±5.46 Mm-1 ) in the summer-monsoon. The seasonal mean σ abs values for winter and autumn are 18.14±7.75 Mm-1 and 17.68±11.61 Mm1

, respectively. At Pantnagar, σabs showed a maximum (108.13±41.82Mm-1 ) in winter and a

minimum (33.73±22.38 Mm-1 ) in the summer-monsoon. The seasonal mean σ abs observed in spring and autumn seasons are 59.34±27.41 Mm-1 and 81.78± 45.65 Mm-1 , respectively. The values of σ abs observed at the Pantnagar site during the autumn (seasonal mean during SON ~ 12

Journal Pre-proof 81.78± 45.65 Mm-1 ) and winter months (seasonal mean during DJF ~108.13±41.82 Mm-1 ) are significantly higher than the values seen at the Nainital site during the autumn (seasonal mean ~ 17.68±11.61Mm-1 ) and winter month December (~18.14±7.75 Mm-1 ). The observed enhancement in σabs in winter at the Pantnagar site is due to increased anthropogenic activities, and shallow boundary layer, which leads to the confinement of anthropogenic aerosols near the surface. The mixing layer depth values estimated for the

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Pantnagar site for winter and spring were reported to be ~1078 m AGL and ~2746 m AGL

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respectively (Joshi et al., 2016a). The mountainous site, Nainital, is located above the well-

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mixed region or the atmospheric boundary layer in winter, which leads to lower BC values observed at this site. In spring, the Nainital site is well within the mixed region and is

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influenced by the pollution load from the foothill regions of the IGP. The presence of

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elevated aerosol layers in spring has been reported over the Indian region (Satheesh et al., 2008; Babu et al., 2011b). The high abundance of aerosols in the form of elevated aerosol

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layers within 2 km to 3 km has been studied and reported by Moorthy et al., (2004) and Babu

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et al., (2008). Interestingly, BC aerosols can also be found at much higher altitudes in the

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form of multiple elevated aerosol layers as reported by Babu et al., 2011b (over the central Indian region), where these BC layers were reported to cause the additional warming and affect the atmospheric stability parameters. The daily and monthly variation in αabs is studied at both sites in order to investigate the local sources of aerosols. The aerosols originating from fossil fuel burning are known to show weak dependence (αabs ~ 1), while the wavelength dependence deviation of αabs from 1.0 (αabs >1.0) is indicative of the presence of absorbing aerosols originating from biomass burning, brown carbon and dust (Kirchstetter et al., 2004). The αabs values are not only used to study the source of aerosols from fossil fuel and biomass/biofuel burning but also used to separate the BrC absorption from the BC absorption (Kirchstetter and Thatcher, 2012). 13

Journal Pre-proof Figure 2b shows the daily and monthly variation in αabs at the observation sites, Nainital and Pantnagar. The monthly variation in αabs shows lower values at Nainital site compared to Pantnagar site. The monthly mean αabs values at Nainital site in general are close to 1.0 indicating the dominance of aerosols originating from fossil fuel. The αabs values are maximum in the spring season at both the sites, indicating the dominance of biomass/biofuel burning in this season. The αabs values are found to be minimum in the summer-monsoon season with fossil fuel origin at Nainital while from biofuel/biomass at Pantnagar. The

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observed variation in αabs at these two sites can be explained in terms of dominant aerosol sources over these regions. The lower αabs values at Nainital are indicative of the background

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BC values, arising out of the local sources (local household usage of fuel and vehicular

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emission due to tourist activity) as well as transported components from the nearby regions.

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On the other hand, the relative dominance of different types of absorbing aerosols get modulated at Pantnagar due to the large influence and variability of local sources, including

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the strong seasonal impact of biomass burning sources. The different environmental

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conditions (described in section 2.1), aerosol sources and heterogeneity in the vertical

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distribution of biomass burning aerosols can be attributed to the observed difference in αabs values at Nainital and Pantnagar sites, in addition to the variations in the magnitude of fossil fuel combustion sources at both the sites. In order to understand the role of boundary layer dynamics and local anthropogenic activities on αabs, we have examined the diurnal variation in αabs for different seasons. Figure 3 shows the diurnal variation in αabs for the four seasons at (a) Nainital and (b) Pantnagar sites. The diurnal variation in αabs shows a morning and evening peak at Nainital site, while the reported diurnal variation in BC at this site showed an afternoon peak (Joshi et al., 2016b). The morning and evening peak in αabs at Nainital might be indicative of the variations in local absorbing aerosols present over the site, and the daytime (1200-1600 hrs) 14

Journal Pre-proof minimum in αabs might indicate aerosol transported from the valley region. The αabs values during daytime in winter at Nainital are found to be <1.0, indicating the transport of anthropogenic aerosols originating from fossil fuel from nearby valley regions to the mountainous site along with the evolving boundary layer during the daytime. The mixing layer depth reported at Pantnagar site by Joshi et al., (2016a) for the winter season (1078 m AGL) was below the altitude of the Nainital site (1958 m amsl), therefore, the direct transport of aerosols from Pantnagar site to the Nainital site is expected to be less in winter. At the

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Pantnagar site, the diurnal variation in αabs shows peaks in the morning and evening similar to what was reported for BC (Joshi et al., 2016a). It is evident from Fig. 3 that the morning and

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evening peak in αabs are more pronounced at the Pantnagar site, indicating a distinct role of

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atmospheric boundary layer and anthropogenic influence. The enhancement in the daytime

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(1200-1600 hrs) αabs is also seen at this site where the presence of biomass burning (αabs>1) aerosols is evident. In spring, the intense mixing of aerosols within the boundary layer might

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during daytime.

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have transported the absorbing aerosols from the region of biomass burning to both the sites

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The diurnal variation of αabs is indicative of the relative strength of different sources of absorbing aerosols (e.g., fossil fuel or biomass burning). The diurnal variability in α abs is greater at Pantnagar (Fig. 3b) compared to Nainital (Fig. 3a). The dominance of fossil fuel aerosols is seen in summer-monsoon at Nainital when the diurnal variation in αabs is <1 during the day (except minor enhancement in the morning and evening hours). The diurnal variation in αabs in spring showed significant enhancement during daytime at Nainital (Fig. 3a), indicating a strong influence of biomass burning. Diurnal variation in αabs is less at Nainital (Fig. 3a) during winter as compared to spring. The influence of biomass burning aerosols is also evident in the spring season at the Pantnagar site. As the peak in fire activity occurs mostly during afternoon hours, the peak in αabs is seen during the evening time, as 15

Journal Pre-proof during this time the boundary layer also starts collapsing. The daytime lower values of αabs could be attributed to the enhanced vehicular activities. The observed variation in the αabs is further investigated by utilizing the fire data from Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Collection 6, Level 2, 1 km resolution dataset are used (Giglio, 2015). We examined MODIS fire dataset with high confidence (80% and above) to avoid any false detection of fire over the region 27 o N to 31o N

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and 77o E to 81o E for the period 2010-2011. The seasonal variation in fire count is shown in

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Fig. S1 in the supplementary material. The maximum fire counts occurred in April, followed

pr

by May. Seasonally, maximum fire count of ~825 is observed in the spring season. The fire count in winter is observed to be less (~131) followed by autumn (~51). The spatial variation

e-

of fires is examined further to investigate the nature of these fires (agricultural/forest fires).

Pr

The spatial variation of fire nearby and toward the north of the Nainital is observed in MarchMay, mainly contributed by the forest biomass burning (it is a forest region). Further, fires

al

are also observed toward the plain regions of the IGP, dominated by agricultural practices

rn

during this time. The dominance of fire in the spring season is consistent with the observed

Jo u

values of αabs at both sites. The maximum fire activity in spring (Fig. S1) over this region results in maximum diurnal variation in the αabs values. The influence of fires on the αabs variation is more evident at the Nainital site, where the enhancement in the background levels of αabs is less influenced by other major sources of aerosols. The fire activity observed in the October-November month is primarily associated with the burning activities due to the agricultural practices during the post-monsoon harvest period, while in December-January months, the use of biomass/biofuel for household activities is the dominant source of aerosols. We have also examined the relative contribution of fossil fuel or biomass burning sources to BC load in the atmosphere by investigating the variation of BC mass concentration 16

Journal Pre-proof with respect to αabs as shown in Fig. 4. BC mass concentration is observed to be maximum in winter followed by autumn at the Pantnagar site, while maximum BC is observed in spring at the Nainital site. The significant difference in the BC levels at both sites is shown in Fig. 4, which indicates the dominance of strong sources of BC at the Pantnagar site. It is seen that the variation in BC at Pantnagar during all the seasons is associated with αabs values ranging between 1 and 1.3, depicting the influence of biomass burning as well as fossil fuel combustions sources. On the other hand, the variation of BC against α abs at Nainital is mostly

oo

f

associated with αabs values ~1.0, depicting the dominant role of fossil fuel combustion sources. The difference in the impact of biomass burning sources to BC load at these sites is

pr

observed maximum in spring in Fig. 4. The dominance of BC from similar sources (possibly

e-

from biomass/biofuel usage), although in different magnitude, is observed in winter at both of

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the sites. In summer-monsoon, BC from fossil fuel origin is dominant at Nainital, in contrast to biomass/biofuel at Pantnagar.

al

The diverse sources of absorbing aerosols observed at both sites further motivated us to

rn

examine the seasonal variation in percentage contribution of BrC. The estimation of BrC, as

Jo u

discussed in section 2.2 is made at both sites, Nainital and Pantnagar. The seasonal variation in BrC is shown in Fig. 5a, which shows significant different seasonal variation in BrC at these sites. The positive BrC contribution (5-12 %) is seen during April and May over the Himalayan site, while it is negative in other seasons. The significant positive BrC contribution is seen during all the months at the foothill site in the IGP region.

The diurnal

variation in BrC is examined in Fig. 5b, which are observed to be very prominent at the foothill site as compared to what is observed at the mountain site (Fig. 5b). The possible reason for this is the strong anthropogenic influence at the foothill site, as compared to the Nainital site that has quite less anthropogenic influence. The strong evidence of BrC contribution is seen in morning and evening hours at the foothill site, as an indicator of the 17

Journal Pre-proof strong anthropogenic influence during the morning and evening rush hours. In addition, the other common sources of aerosols are vehicular emissions and biomass/biofuel from household

usage

(mainly for cooking usage).

In winter,

the

household

usage of

biomass/biofuel is practiced for heating purpose as well. The BrC contribution is found to be minimum during the daytime at both of the sites. This is also observed for the absorption angstrom exponent (αabs). The BrC might contribute significantly to the radiative forcing

f

estimation of aerosols in spring over this region, in particular over the IGP region.

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The investigation of absorbing aerosol sources from the

ground-based surface

pr

observations of BC at both of the present sites has revealed the diverse seasonal variation of aerosol sources at these sites. Interestingly, aerosols are uplifted and transported from the

e-

foothill site to the Himalayan site in the spring season, and therefore are believed to be of

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similar origin. The aerosol abundance in the vertical column, and the dominant aerosol types contributing to the observed aerosol abundance over this region are important to study. The

rn

GEOS-Chem simulations.

al

columnar abundance of aerosol and its components are thus studied over this region utilizing

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3.2. GEOS-Chem Simulations

3.2.1. Comparison of Observed BC and AOD with GEOS-Chem Simulations The ground-based observations of BC and AOD are compared with the GEOS-Chem simulated values in order to see the model performance over this region. The variations in daily averaged observed and simulated BC mass concentration is shown in Fig. 6. Model is able to show similar seasonal variation to those observed. Considering the model resolution that encompasses both the observation sites in the same model grid (due to the coarse resolution of model simulations), model results are higher than those observed at the mountain site, while model results are lower than what observed at foothill site (Fig. 6a). The 18

Journal Pre-proof model simulated BC is in better agreement with the observed data when we combine (by averaging) the point observations at both of these sites in order to better represent this region by model grid (Fig. 6b). The observed (averaged for both the sites) and simulated mean BC mass concentration comparison is then made for the days when both the datasets (observation and simulation) are available (N=181). The observed and simulated mean BC mass concentration is found to be ~3.4 µg m-3 and 3.6 µg m-3 respectively. The mean bias of the comparison is calculated (as the average of the difference between simulated and observed

oo

f

BC mentioned in supplementary material). The mean bias of BC comparison is found to be ~0.23±1.82 µg m-3 . The slope and correlation coefficient for BC comparison are 0.46±0.04

pr

and 0.63 respectively. The seasonal mean observed and simulated BC mass concentrations

e-

(mentioned inside parenthesis) for winter (DJF), spring (MAM), summer-monsoon (JJA) and

3

Pr

autumn (SON) are found to be ~5.97±1.27 (5.46±1.85) µg m-3 , ~2.36±0.89 (3.06±0.84) µg m, ~1.77±1.10 (2.69±1.23) µg m-3 and ~5.59±2.00 (4.9±1.61) µg m-3 respectively (Table 1).

al

Model estimated BC values are 8-12 % lower in winter and autumn, while they are higher by

rn

29-52% in spring and autumn. The satisfactory performance of the model simulations over

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this region motivated us to investigate the columnar abundance of aerosols, particulate matter concentration, and aerosol sources over this region. Ground-based measurements of AOD (at 500 nm) is compared with the GEOS-Chem simulated AOD for year 2010. David et al. (2018) had evaluated the AOD over India and the simulated AOD at 500 nm compared reasonably to the AERONET observations with a slope and correlation coefficient of 0.87 ± 0.01 and 0.91, respectively. The simulated fine mode AOD was found to be higher compared to the observations with a slope, correlation coefficient, and mean bias of 1.1 ± 0.02, 0.86, and 0.16 ± 0.26, respectively. The reasonable performance of GEOS-Chem model is utilized to compare and study the AOD variation in more detail over the present region. Here, AOD observations at Nainital site are available for 19

Journal Pre-proof comparison. AOD observations are not available from Pantnagar for the year of study. The comparison in temporal and seasonal variation in AOD is shown in Fig. 6c. The observed AOD and simulated AOD values are given in Table 1 and are found to be 0.08±0.04 (0.16±0.05), 0.33±0.17 (0.32±0.15), 0.31±0.14 (0.44±0.20) and 0.10±0.04 (0.24±0.11) for winter (DJF), spring (MAM), summer-monsoon (JJA) and autumn (SON) respectively. The annual mean AOD (500 nm) calculated from the ground-based measurement (N=126) is ~0.23, while the GEOS-Chem model simulated AOD is found to be ~0.28 for the

f

The slope, correlation coefficient and mean bias values of AOD

oo

observational days.

comparison are obtained as 0.55±0.07, 0.59 and 0.06±0.15 respectively. Here the AOD data

pr

at Pantnagar site was not available for comparison, which might have further improved the

e-

comparison results.

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The model simulated AOD values except spring are higher as compared to the observed AOD at Nainital. The model simulated AOD is higher than the observed AOD at Nainital

al

(Fig. 6c), which is quite obvious due to the fact that the model grid here includes both

rn

Nainital and Pantnagar site. The foothills site, Pantnagar in IGP is reported to possess, higher

Jo u

AOD (annual mean ~0.56, year 2008-2009) (Joshi et al., 2016a). Interestingly, model simulated AOD in spring is in better agreement with the observed AOD as both of these sites experiences enhancement in the aerosol abundance characterized by similar sources due to intense mixing/uplifting of aerosols from foothills to the mountainous sites. The satisfactory comparison of the ground-based observations with the model simulated BC and AOD is then utilized to examining the particulate matter mass concentration over the region before investigating the aerosol sources over this region.

The GEOS-Chem

simulations are utilized to simulate PM2.5 mass concentration over the region. The seasonal variation in the daily and monthly variation in PM2.5 mass concentration over the study region are shown in Fig. 7. The maximum PM2.5 mass concentration is observed in the April month

20

Journal Pre-proof (~70.7±17.7 g m-3 ), and minimum in August (25.2±20.2 g m-3 ). The enhancement in PM2.5 concentration is again seen soon after the withdrawal of monsoon, which then reaches higher value in the November month (~57.8±29.0 g m-3 ) and then decreases. The seasonal variation in the particulate matter over this region indicates that the particulate matter concentration is maximum in spring season. The seasonal variation in the fire count data, as shown in Fig. S1 (supplementary material), indicates that the occurrence of

f

fires is maximum in the spring season over this region. These fires originate from both the

oo

agricultural practices as well as due to the forest fires during this season and are the major

pr

contributor in enhancing the PM2.5 concentration over this region. The seasonal variation in surface PM2.5 is similar to the seasonal variation in AOD over this region.

e-

Furthermore, the GEOS-Chem model simulations are also utilized to study the aerosol

Pr

variation with altitude. The observed variation in aerosols at Nainital and Pantnagar sites (as discussed in previous sections) can also be explained from the vertical distribution of

al

aerosols. Here, we have simulated vertical profile of BC (µg m-3 ), PM2.5 (µg m-3 ) and aerosol

rn

extinction coefficient (km-1 ) at 500 nm over the region of study for 2010. Figure 8 depicts

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that PM2.5 and BC show similar variation in the vertical profile with highest concentration near the surface. The vertical profiles of BC/PM2.5 show a gradual decrease with altitude. The contribution of BC to PM2.5 is found to vary from ~6% near the surface to ~3% at 3-4 km altitude. The ratio of BC to PM2.5 increases above 5 km altitude, which can be attributed to either increase in BC at higher altitude or decrease in the aerosol mixture other than BC. Detailed chemical study of aerosol is required in this regard. The vertical profile of annual aerosol extinction coefficient is shown in Fig. 8d, which shows gradually decreasing nature with altitude with higher extinction coefficient values below 2 km. The aerosol extinction coefficient vertical profile is also examined for the spring season and is shown in Fig. 8e. The altitude variation of aerosols is nearly homogeneous up-to 2 km altitude and the built-up of 21

Journal Pre-proof aerosols at higher altitudes (~2-3 km) is evident. The low aerosol extinction coefficient is observed near surface but high vertical gradient in aerosols is observed (Fig. 8e). In spring, aerosols are well mixed and most of the near surface aerosols get uplifted to the higher altitude, resulting in build-up of aerosols at the higher altitude regions. The confinement of aerosols to the low altitude regions in winter was also reported by Joshi et al., (2016a).

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3.2.2. Aerosol Components from GEOS-Chem

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The various aerosol components, and their contribution in the columnar abundance of

pr

aerosols is investigated here for regional representation of aerosol over this region. We investigated the variation in inorganic aerosols, organic carbon, black carbon, dust, and

e-

accumulation mode sea-salt (SSa) and coarse mode sea-salt (SSc) in the total columnar

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abundance of aerosols (AOD 500 nm) over the study region as shown in Fig. 9. The maximum columnar loading (AOD 500 nm) due to inorganic aerosols is observed in the

al

month of June. The maximum AOD due to BC and OC is observed in May. It is evident from

rn

Fig. 10 that the major contributor to total columnar loading is inorganic aerosols followed by

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OC and dust. The highest AOD was observed in spring season and contribution from OC (29%) and dust (22%) is maximum during this period. The dust contribution to total aerosol loading in other seasons is observed to be less (~3-8%). However, the actual contribution of dust to aerosol loading can be more over the region because the model underestimates dust over India as reported by David et al., (2018). The observed aerosol loading and its component over this region can be explained by the local anthropogenic sources as well as the regional sources of aerosols. Here, the observed aerosol abundance and their dominant components in different seasons are explored for their regional sources for which we have identified the regional sources of aerosols and then examined the back-air trajectories passing through the source 22

Journal Pre-proof dominant region. The seven days back-air trajectories are calculated at Nainital site (for regional representation) at 500 m above ground level (AGL) using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT; Draxler and Rolph, 2003) model utilizing NCEP/NCAR global reanalysis data. The air mass trajectories for different seasons are shown in Fig. 11. The major pathways of airmass during different seasons are then calculated using cluster analysis in which the nearest back trajectories are clustered according to the angular distance in order to identify the major pathways of air mass arrival.

oo

f

The two major pathways are identified (shown by red and blue color in Fig. 11) in general except in summer-monsoon when air masses from three pathways are identified. In

pr

winter, air masses from north and northwest directions arrive at the site. The air-masses

e-

generally arrive from long distance, but high-altitude regions in winter season. The clear

Pr

evidence of air masses arriving from the dust dominant regions from west direction (shown by blue color pathway) is evident from Fig. 11b. The dust aerosols are carried towards the

al

site along with the airmasses. The presence of dust aerosols in spring and early summer, has

rn

also been reported by Hegde et al., (2007) and Kumar et al., (2014). Interestingly, dust

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observed over the northern Indian region is reported to be of absorbing nature as compared to the pure dust (which is of scattering nature) originating over the African region (Moorthy et al., 2007). The incorporation of dust aerosols is thus quite essential in calculating the radiative impact assessments of aerosols over this region. Additionally, the air masses particularly arriving from the northwest direction shown by red color in Fig. 11b passes through the regions of biomass burning before reaching the site, which might thus contribute to organic aerosols, as well as BC aerosols towards the site, as significant amount of organic aerosols are also co-emitted along with BC during biomass burning. The enhancement in the aerosol loading due to northern Indian biomass burning is also reported by Kumar et al., (2011) in spring season. The arrival of marine air masses are

23

Journal Pre-proof quite evident in summer-monsoon as shown in Fig. 11c when ~63% of air masses are of marine origin. The sea-salt aerosol optical depth enhances in late spring and summermonsoon season, which is associated with the onset of monsoon winds.

The air-masses of

marine origin thus contribute to sea salt aerosols in the summer-monsoon over this region. The direction of air masses changes as soon as the southwest monsoon withdraws from the continental India. Thus, the significant presence of dust aerosols along with organic aerosols, particularly the absorbing organic aerosol, might be of crucial importance over this region in

oo

f

spring. The presence of absorbing aerosol, BC along with absorbing dust, can have

Conclusions

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4.

e-

pr

significant radiative impact over this region.

The absorption characteristics of aerosols were studied using the ground-based data of

al

carbonaceous aerosols over the central Himalayan site ‘Nainital’ and its adjacent foothills site

rn

‘Pantnagar’ in the Indo-Gangetic Plain. The local sources of aerosols, their seasonal

Jo u

variations and types were highlighted along with the possible source processes. The aerosol sources and their percentage contribution to the aerosol loading in the vertical column over the region covering both the high altitude and foothill sites were studied utilizing GEOSChem model simulations. The important findings of the study are mentioned as follows– 1.

The seasonal variation in absorption coefficient at foothill site Pantnagar showed higher values (highest seasonal mean in winter ~108.13±41.82 Mm-1 ) than what was observed at Nainital site (highest seasonal mean in spring ~ 25.61±5.6 Mm-1 ). This indicates the influence of local sources and dynamical processes in the governing the magnitude and types of absorbing aerosols prevailing at the two distinct altitude sites located in Himalayan region and its adjacent foothill region in IGP.

24

Journal Pre-proof 2.

The dominance of the aerosols from the biomass burning sources was found in spring over the Himalayan site as well as over the foothill region of IGP, indicating the efficiency of the vertical transport of biomass burning aerosols during the peak season of fire activity as well as during the period when mixing layer depth attained maximum height crossing the height of the mountainous site.

3.

The aerosols of fossil fuel origin (except in spring) mostly dominated at Nainital site,

4.

oo

sources, were more prevalent at the Pantnagar site.

f

while aerosols from biomass/biofuel/dust origin, in addition to fossil fuel combustion

On a diurnal scale, the maximum contribution of BrC was observed at Pantnagar, largely

pr

in morning and evening hours during peak of the fire occurring period (i.e., spring). In

e-

addition, significant presence of black carbon and dust aerosols was observed over

Pr

Pantnagar, which might contribute significantly to the radiative forcing estimation of aerosols over this region.

The GEOS-Chem model was found to capture the observed levels of BC. A good

al

5.

rn

correlation between the modeled and measured BC is observed in spring when the extent

Jo u

of aerosol mixing is high. The model simulated PM2.5 mass concentration and AOD showed enhancement in the spring season. 6.

Examination of dominant aerosol types and their contribution to the columnar abundance of aerosols reveals that, during spring, the contribution of dust aerosols is as high as 22%, even though inorganic aerosols (42%) and organic carbon (29%) play dominant role in modulating aerosol absorption characteristics over the study region.

Acknowledgements The

present

study

is

financially

supported

through

the

research

grant

(No.: PDF/2016/003468) provided by the Science and Engineering Research Board (SERB),

25

Journal Pre-proof a Statutory Body of the Department of Science and Technology (DST), Government of India. The same is highly acknowledged. The observations used in this work are made under ISROARFI project. We are grateful to Dr. K. Krishnamoorthy, ISRO, Bangalore for his continuous support to this work. We acknowledge NOAA Air Resources Laboratory for HYSPLIT model usage. The authors are thankful to all the three reviewers for their constructive comments and suggestions.

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Table 1: The GEOS-Chem simulated and observed seasonal mean BC mass concentration and AOD at 500 nm. The percentage difference between the simulated and observed values are also shown.

Season s

BC (µg m-3 ) BC Observed (NTLPNT)

BC Simulate d

PM 2.5 (µg m3 )

AOD % Differenc e

AOD (NTL)

AOD Model

% Differenc e

PM2.5 Simulate d 33

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5.97±1.27

MAM

2.36±0.89

JJA

1.77±1.10

5.46±1.8 5 3.06±0.8 4 2.69±1.2 3

SON

5.59±2.00

4.9±1.61

0.086±0.04 6 0.334±0.17 2 0.31±0.142 * 0.106±0.04 2

8.54 -29.66 -51.98 12.34

0.166±0.054

-100.00

0.322±0.159 0.442±0.206 *

3.03

0.249±0.116

-140.00

-41.94

43.4±19. 7 57.4±19. 7 31.2±20. 1 42.7±26. 0

seasonal mean value is available only for June.

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*

DJF

Table 2: The annual mean observed and GEOS-Chem simulated BC and AOD at 500 nm for

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2010. The slope, correlation coefficient (R), intercept and mean bias of comparison is also

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shown. Simulated

Slope

BC

3.57±1.50

3.39±2.30

0.46±0.04

0.63

2.05 0.23±1.82

AOD

0.29±0.16

0.23±0.17

0.55±0.07

0.59

0.16 0.06±0.15

R

Intercept

Mean Bias

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Observed

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Journal Pre-proof Figure 1: Location of the observation sites (a) Nainital (NTL: 29.4 o N, 79.5o E, 1958 amsl) in the central Himalayas and adjacent foothill location, Pantnagar (PNT: 29.0° N, 79.5° E, 231 m amsl) in the Indo Gangetic Plain (IGP) region, (b) zoomed image of the observation sites and (c) spatial variation of black carbon (BC) mass concentrations from GEOS-Chem simulations. The observation sites, Nainital (red) and Pantnagar (black) are also marked in

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the figure. BC hotspot with values higher than 20 µg m-3 are seen around Delhi (DLH).

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Figure 2: Seasonal variation in (a) monthly absorption coefficient (σ abs) and (b) daily and monthly absorption angstrom exponent (αabs) at Nainital and Pantnagar for 2010-2011. The upper and the lower edges of the box and whisker plot represents the 25th and 75th percentiles, the whiskers represent the 10th and 90th percentiles. The mean and median are shown by the thick (blue line) and the thin line inside the box.

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Figure 3: The mean diurnal variations in the absorption angstrom exponent (αabs) for winter

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(DJF), spring (MAM), summer-monsoon (JJA), and autumn (SON) seasons at (a) Nainital and(b) Pantnagar sites.

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Figure 4: Seasonal variations in black carbon (BC) and absorption angstrom exponent (αabs)

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at Nainital and Pantnagar during 2010-2011.

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Figure 5: The seasonal variation of percentage contribution of BrC at both the observational sites. (a) The monthly mean BrC is shown for year 2010-2011. (b) The diurnal variation in annual BrC is also shown. The time mentioned here is the Indian standard time (IST) in hours.

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Figure 6: Seasonal variation in BC and AOD (a) The comparison of model simulated BC over the region with the observed BC at Nainital (NTL) and Pantnagar (PNT). (b) Comparison of model simulated BC with the ground-based observation averaged over the region. (c) The comparison of model simulated AOD (500 nm) with the ground based AOD at Nainital for year 2010.

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Figure 7: The seasonal variation in the GEOS-Chem simulated daily (scatter plot) and monthly

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(box and whisker plot) PM2.5 mass concentration. The upper and lower edges of the box represent the 25th and 75th percentiles and the whiskers represent the 10th and 90th percentiles. The black line and red circle inside the box plot represent the median and mean, respectively.

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Figure 8: The annual mean vertical profiles of GEOS-Chem simulated (a) BC mass concentration, (b) PM2.5 mass concentration, (c) BC/PM2.5 ratio (d) aerosol extinction coefficient (km-1 ) at 500 nm wavelength and (e) aerosol extinction coefficient in the spring season over the study region for 2010.

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Figure 9: Monthly variation in simulated inorganic aerosols, organic aerosols, black carbon, dust, accumulation mode sea-salt (SSa) and coarse mode sea-salt (SSc) optical depths simulated using GEOS-Chem model for 2010 over the study region. Note the y-axis is in log scale.

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Figure 10: The contribution of various aerosol components to total aerosol loading during winter, spring, summer-monsoon and autumn seasons over the study region. The simulations

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of aerosol components used here are from GEOS-Chem model.

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Figure 11: Seasonal variation of synoptic air-masses at Nainital utilizing the seven days

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HYSPLIT back-air trajectories for 2010. The major airmass pathways are identified and are shown in red and blue colour for winter, spring and autumn and red, blue and green colour for summer-monsoon.

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Journal Pre-proof Conflict of Interest and Authorship Conformation Form

Please check the following as appropriate:

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

o

This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

o

The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

o

The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:

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Affiliation

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Author’s name

Department of Civil Engineering, Indian Institute of Technology, Kanpur, India

Manish Naja

Aryabhatta Research Institute of Observational Sciences, Nainital, India

Tarun Gupta

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Liji M. David

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Hema Joshi

Department of Chemistry and Atmospheric Science, Colorado State University, USA Department of Civil Engineering, Indian Institute of Technology, Kanpur, India

Mukunda M. Gogoi

Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India

S. Suresh Babu

Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India

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Highlights

1. Simultaneous observations of aerosol absorption properties at two nearby distinct altitude sites. 2. Investigations of distinct aerosol sources based on in-situ measurements and GEOSChem model simulations.

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3. The significant influence of biomass burning in spring over the central Himalayas and foothills region.

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4. Model simulated BC agrees with the observations over the region. 5. Quantification of the share of inorganic, organic and dust aerosols to the total

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aerosol load. The inorganic aerosols (42%), organic carbon (29%), dust (22%) and

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BC (6%) are the aerosol components in spring.

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