Study of aerosol types and seasonal sources using wavelength dependent Ångström exponent over North-East India: Ground-based measurement and satellite remote sensing

Accepted Manuscript Study of aerosol types and seasonal sources using wavelength dependent Ångström exponent over North-East India: ground-based measurement and satellite remote sensing Pranab Dhar, Trisanu Banik, Barin Kumar De, Mukunda M. Gogoi, S. Suresh Babu, Anirban Guha PII: DOI: Reference:

S0273-1177(18)30483-6 https://doi.org/10.1016/j.asr.2018.06.017 JASR 13799

To appear in:

Advances in Space Research

Received Date: Revised Date: Accepted Date:

17 June 2017 8 June 2018 11 June 2018

Please cite this article as: Dhar, P., Banik, T., Kumar De, B., Gogoi, M.M., Suresh Babu, S., Guha, A., Study of aerosol types and seasonal sources using wavelength dependent Ångström exponent over North-East India: groundbased measurement and satellite remote sensing, Advances in Space Research (2018), doi: https://doi.org/10.1016/ j.asr.2018.06.017

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Study of aerosol types and seasonal sources using wavelength dependent Ångström exponent over North-East India: ground-based measurement and satellite remote sensing Pranab Dhar1, Trisanu Banik1, Barin Kumar De1, Mukunda M. Gogoi2, S. Suresh Babu2 and Anirban Guha1* 1

Department of Physics, Tripura University, Suryamaninagar-799 022, India Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram-695022, India 2

Abstract: The spectral estimates of Aerosol Optical Depth (AOD, τ) were made by operating a Microtops-II sun photometer in the spectral range 0.380-0.870 μm over Tripura in northeastern India and analyzed to infer the aerosol types and source characteristics in different seasons. The Ångström exponent (α) derived from spectral AOD in different wavelength (λ) range and subsequent second order derivative of Ångström exponent (i.e., α´) and their curvature analysis in the lnτ versus lnλ relationship has revealed the crucial information related to the dominance of different aerosol types and their characteristics in different seasons. The average AOD (mean ± standard deviation) at 0.5 μm in winter, pre-monsoon, monsoon and post-monsoon seasons are observed to be 0.70 ± 0.28, 0.74 ± 0.18, 0.55 ± 0.20, 0.44 ± 0.19 respectively; while the corresponding seasonal mean values of α (over 0.380-0.870 μm spectral range) are found to be 1.09 ± 0.17, 0.92 ± 0.24, 0.51 ± 0.27, 0.89 ± 0.38 respectively. The estimation of the values of α at different spectral ranges indicate that winter season is mainly influenced by the fine-mode aerosols having fine-mode fraction (FMF) ~ 0.7; whereas coarse-mode aerosols dominate in the monsoon season having FMF ~ 0.3. The pre-monsoon and post-monsoon seasons exhibit the presence of mixed type of aerosols, with slightly greater fraction of fine-mode aerosol in pre-monsoon. Curvature analyses of Ångström exponent put insight in to the consistency of observed features of seasonal aerosol types. Examination of the Ångström exponents derived from satellite retrieved AOD by Moderate Resolution Imaging Spectroradiometer (MODIS) instrument operating on board Terra satellite, along with MODIS fine mode aerosol fraction and aerosol types indicates broad seasonal features of aerosol size spectrum over the study region similar to those observed from ground based measurements. Keywords: Sun photometer, Aerosol Optical Depth (AOD), Ångström Exponents, Fine and coarse-mode particles, MODIS

*Corresponding author: Tel: +91 381 237 9120; Fax: +91 381 237 4802 E-mail address:[email protected]

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1. Introduction The accurate estimation of aerosol radiative forcing at the surface and at the top of the atmosphere depends mainly on the accurate estimates of aerosol parameters, viz., aerosol optical depth (AOD), single scattering albedo (SSA), phase function, aerosol size distribution etc. (Pathak et al., 2010; Niranjan et al., 2011; Guleria and Kuniyal, 2013, 2015; Moorthy et al., 2007 and references therein). All of these properties possess extreme spatial and temporal variability influenced by several different factors (Singh et al., 2004; Kassianov et al., 2007; Ram et al., 2016). Large variability in the size distribution of atmospheric aerosols is one of the most influencing factors due to varying source and mixing processes in the atmosphere (Kedia and Ramachandran, 2009). Typically, aerosols exhibit bimodal size distribution having fine (particle diameter < 1.0 μm and coarse (particle diameter > 1μm) mode regimes (Whitby and Cantrell, 1976; Schuster et al., 2006). This classification of aerosol in fine-mode and coarse-mode is performed on the basis of particle size. Diameter or radius both are used to characterize the size of the particle (Whitby and Cantrell, 1976; Seinfeld and Pandis, 2016). The fine-mode aerosols are mainly produced by the condensation of vapors, accumulation, and coagulation, whereas mechanical weathering of surface materials produces coarse particles (Pöschl, 2005). Combustion of coal, oil, diesel, gasoline, wood, biomass etc., or the gas-toparticle conversion of NO x, SO2 and volatile organic carbon (VOC) are the prime sources of fine-mode aerosols (Junge, 1963; Pöschl, 2005; Pósfai and Buseck, 2010). The sources of coarse particles are suspension of industrial dust and soil (farming mining, unpaved roads), Oceanic sprays and biological sources etc. Thus, aerosol size distribution in a region is mainly determined by the local production and long-range transport of aerosol particles from distant locations (Chin et al., 2007; Badarinath et al., 2009; Guleria et al., 2011a, 2011b; Qin et al., 2016). The knowledge of the spectrally varying aerosol optical depth (AOD), single scattering albedo (SSA) and phase function is very essential for understanding the radiative effect due to aerosol–radiation interactions (Srivastava et al., 2006; Guleria et al., 2014). All these parameters, in turn, depend on aerosol chemical composition (refractive index) and mixing state (IPCC, 2013). In this context, the spectral analysis of AOD provides useful knowledge regarding the types of aerosols, their source characteristics and climatic consequences (Ghan and Schwartz, 2007). Ångström exponent (denoted by α) derived from the spectral distribution of AOD provide the basic information on the aerosol size distribution, while the turbidity coefficient (denoted by β) indicates a measure of total aerosol loading in a vertical column (Ångström, 1961). Being strongly dependent on wavelength, Ångström exponent is used for designating the spectral curvature of AOD, (Kedia and Ramachandran, 2009; Guleria et al., 2012a) where the vital information pertaining to the aerosol size distribution is concealed in the wavelength dependence of Ångström exponent (Eck et al., 1999, 2001, 2003; O’Neill et al., 2001; Schuster et al., 2006). The curvature analysis of α can be used to differentiate aerosol types and also to obtain the information regarding relative impact of fine-mode particle with respect to coarse-mode particle in the aerosol size distribution (Schuster et al., 2006) over a particular geographic region and season.

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Over the northeastern part of India, several recent investigations have reported the peculiar aerosol properties over the region, based on both short and long term measurements (Gogoi et al., 2009a, 2009b, 2011; Pathak et al., 2010, 2012; Pathak and Bhuyan, 2014; Guha et al., 2015; Dhar et al., 2017). Through these studies, the regional characteristics of aerosols over the northeastern part of India and their impact on radiation and climate have been highlighted. Among these studies, Pathak et al. (2012) have revealed the discrimination of aerosol types and their seasonal heterogeneity over the extreme easternmost part of the region (i.e., at Dibrugarh), identifying the synoptic source regions based on HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) back trajectory analysis. Over the same study region, the relative dominance of fine/coarse-mode aerosols in different seasons has been reported by Gogoi et al. (2009a, b) indicating the possible sources of such aerosols in different seasons. The present study delineates the measurements carried out at a westernmost location (i.e., ‘Agartala’ in the state of Tripura) of northeastern India for examining the seasonal variability of AOD, Ångström exponent, and curvature in spectral AOD. Tripura, a small state in the northeastern part of India, has its strategic importance owing to its geographical position, surrounding topographical structure and strong influence of aerosol outflow from the Indo-Gangetic Plains (IGP) of India. Moreover, the region is in close proximity to the mouth of Bay-of-Bengal (BoB). Thus, the aerosols over the study location possess seasonal variability in their types and depict distinct optical properties. Hence these variability needs to be investigated in a regional scale too for better understanding the regional climatic impacts of aerosol. Previous studies over the present observational site have indicated the broad features of near surface Black Carbon properties (Guha et al., 2015), in addition to inferring the radiative properties due columnar aerosol load (Dhar et al., 2017). The present study elaborate, for the first time over the region, the seasonal pattern of aerosol properties and types and associate them with local emissions, regional sources and long-range transport. In addition, a comparison of satellite derived data with the ground-based measurement is made to understand the efficiency and accuracy of satellite remote sensing at different seasons over the region. The AOD database used in this study is obtained from continuous sun photometer measurements at Agartala during the period from September 2010 to September 2012. We have organized the paper as follows: Section-2 provides brief information about the geographical position and meteorological conditions of the observational site and its importance in the context of the present study. The observational details and the data used in present study are discussed in section 3.Section 4 includes the theoretical background of the present study and the methodology adopted for the analysis of different data sets. Section-5 highlights the results and discussion, while the concluding remarks are presented in section 6.

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2. Brief description of the observational site and meteorology

Fig. 1: Geographical location of observational site Tripura University (Agartala) (23.76˚ N, 91.26˚ E, 43 m a.s.l.) shown in Indian subcontinent. (Source: https://www.google.com/maps/@24.4666104,81.9267674,4.5z)

The study location, Agartala (23.76°N and 91.26°E) (shown in Fig.1) is a continental site in the northeastern part of India, in the state of Tripura. The emission from a large number of automobiles that include buses, cars, two‐wheelers (motor bikes and scooters), and three‐wheelers (auto rickshaws), running through national highway 44A near the University campus at Agartala contribute significantly to the local production of aerosols, including black carbon (Guha et al., 2015). Transportation from the brick kiln situated around the study location also contributes to aerosol loading in addition to the transportation of aerosol from other parts of the country as well as from BoB. In addition, seasonal biomass burning activities in nearby villages and agricultural fields, both in India and Bangladesh, are other possible sources of aerosol loading in the region.

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Fig. 2:Annual variation of (a) monthly mean wind speed (m/s) and temperature (°C) and (b) monthly total rainfall (mm) and monthly mean relative humidity (RH %)

Climatically, the observational site and the neighboring locations of northeastern India experiences tropical monsoon climate with mild winter, warm, and humid summer. During winter months (December–February), minimum average temperature vary between 5 to 6°C with calm winds, while during pre-monsoon (March–May), the weather is hot and humid with the maximum temperature going up to around 36°C. The average annual temperature is 24°C and the average annual rainfall is ~ 2000 mm. The peak rainfall occurs in the month of June–July with total rainfall of 450 mm. The relative humidity, in general, is high across the whole year with an average of 81%. However, average relative humidity in dry season (~74%) is moderately lower compared to the values (~ 87%) during wet monsoon season (June–September) Figs. 2(a) and 2(b) show the variation of monthly mean wind speed, temperature, relative humidity and monthly total rainfall during the observational period (September 2010–September 2012). The surface wind (monthly mean) is noticed as high as 1.1 m/s during pre-monsoon and monsoon seasons while minimum monthly mean wind speed of 0.2 m/s is observed in winter. In general, the monthly mean wind speed remains low (<1.0 m/s) with less significant monthly/seasonal variations. The standard deviation of wind speed from the respective monthly mean is large in pre-monsoon and monsoon months. Sometimes, the instantaneous wind speed during these months reaches

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as high as 5-6 m/s, though the occurrences of such high wind speed events are not consistent throughout the whole month. Such high wind speed is observed to continue for maximum 2-3 hours in few days during pre-monsoon and monsoon months. The average monthly temperature is found to vary between 17°C in winter and 28°C during monsoon and pre-monsoon. The average relative humidity during the observational period is higher (~75%), having values of 87%–90% during monsoon and lowest (69%–72%) during February–March in the boundary of winter and pre-monsoon. Seasonal mean rainfall shows maximum value in monsoon and minimum in winter. During the study period, maximum monthly total rainfall occurred in June-2012 (446 mm). In the subsequent sections, the dominance of types of aerosol over the study location is described with respect to four distinct seasons, the classification of which is based on the long-term distribution of meteorological and climatic characteristics, such as temperature, rainfall, rainy days, humidity, occurrence of fogs and thunderstorms (Gogoi et al., 2009a, Pathak et al., 2010, Pathak and Bhuyan, 2014). This include: winter (December–February), pre-monsoon/ summer (March–May), monsoon (June–September), and retreating monsoon/post-monsoon (October–November). Though the naming of the seasons is according to the criteria of India Meteorological Department (IMD), the grouping of months into such seasons is not done strictly following the IMD criteria, rather taking the characteristics of meteorological parameters over this region under consideration. As already mentioned, the winter season is associated with clear skies, fine weather, light northerly winds, low humidity and temperatures, and large daytime variations of temperature. This is the driest season of the year with least rainfall and low relative humidity. The occasional prevailing of fog and haze impede the fair weather and clear skies during winter. Towards the end of winter, the temperatures start to increase and the skies become clear to partly cloudy during the pre-monsoon season. The season is characterized by cyclonic storms and thunderstorms associated with rain and sometimes hail. Local severe storms or violent thunderstorms associated with strong winds and rain lasting for short durations occurs. Though the actual period at a particular place varies by the onset and withdrawal dates, the monsoon season over this location is spread over four months with intense rainfall having rainfall of about 70% of annual rainfall. Very uncomfortable weather due to high humidity and temperatures is another feature associated with monsoon. The monsoon withdraws by the end of September or beginning of October. Post-monsoon season is transition season associated with the establishment of the north-easterly wind regime. The rainfall decreases abruptly and the day temperatures start falling sharply. The decrease in humidity levels and clear skies after mid-October are characteristics features of this season.

3. Observational details and data 3.1 Ground-based observations The spectral estimates of columnar AOD were made by continuous operation of Microtops sun-photometer (Solar Light Co, USA) during clear and partly clear sky conditions. Microtops-II is a 5-channel hand held sun-photometer used to measure the instantaneous AOD from individual measurements of direct solar flux, using a set of internal calibration constants. A Global Positioning System (GPS) receiver attached to the photometer provides information about the location, altitude and time. A sun target and

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pointing assembly are permanently attached to the optical block of sun-photometer to ensure accurate alignment with the optical channels. The pointing accuracy of the instrument is better than 0.1º. The wavelengths are centred on 0.380, 0.440, 0.500, 0.675 and 0.870 μm, with a full width half-maximum bandwidth of 2 to 10 nm and a field of view of 2.5º. The typical error in AOD is of the order of ± 0.03 in the ultra violet (UV) wavelength and ± 0.02 in the visible wavelengths. More details about the instrument and the accuracy of measurements for precision and consistency are discussed in detail by Morys et al. (2001). The AOD values at the respective wavelengths are recorded at an interval of half an hour from 09:00 to 17:00 IST (Indian Standard Time) during each day of observation. The AOD measurements are performed mainly during clear-sky conditions but sometimes measurement were also performed in partly scattered cloud hazy condition, especially when the sun was visible. In the present analysis AOD data from September 2010 to September 2012 are used. The AOD value at each of the MICROTOPS channels are estimated based on the instantaneous channel’s signal and atmospheric pressure (for Rayleigh scattering) at the time of measurement over a particular location. Solar distance correction is automatically applied. Following Bouguer-Lambert-Beer law, the values of AOD are estimated as follows:

 

ln V0   ln V * SDCORR  P   R * m P0

(1)

Where the index “λ” references the channel’s wavelength, ln(V 0λ) is the calibration constant, Vλ is the signal intensity in [mV], SDCORR is the mean Earth-Sun distance correction, m is the optical airmass, τRλ is the Rayleigh optical thickness, and P and P 0 are station pressure and standard sea-level pressure (1013.25 mb) respectively.

3.2 Satellite observations Satellite derived aerosol parameters, viz., AOD, Fine-Mode Fraction (FMF) and aerosol types from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument operating on-board Terra (originally known as EOS AM-1) satellite are considered and analyzed in the present study. Distributed over 36 spectral bands between 0.405 and 14.385 µm, MODIS acquires data at varying spatial resolutions: 2 bands at 250m, 5 bands at 500m, and 29 bands at 1,000m to feature dynamical processes on the land, oceans and atmosphere on local to global scales (Yu et al., 2004; Ichoku et al., 2004; Remer et al., 2005; Ichoku et al., 2006). The Higher-level MODIS atmosphere products produced by the MODIS Adaptive Processing System (MODAPS) are available through the LAADS web (http://ladsweb.nascom.nasa.gov/). In the present analysis, we have utilized two different types of MODIS atmosphere products, namely Level-3 MODIS Atmosphere Daily Global Product (MOD08_D3) and Level-2 MODIS Atmosphere Products (MOD04_L2). The MOD08_D3 product is a level-3 gridded (1 x 1 degree = 360 x 180 pixels) atmosphere daily global product; which includes the parameters related to the properties of atmospheric aerosols, along with various other

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parameters related to atmospheric stability, water vapor, cloud, and ozone. The standard deviations, quality assurance, assurance weighted means and other statistically derived quantities for each parameter are also available in this product. Among the aerosol parameters, we have considered “Aerosol_Optical_Depth_Land_QA_Mean” from MOD08_D3 which consists of AOD at three wavelengths (0.47, 0.55, 0.66 μm). Based on Dark Target (DT) and Deep Blue (DB) algorithms, aerosol properties over the globe are presented in MODIS level-2 atmospheric aerosol product (MOD04_L2). The DT algorithm is utilized to determine the aerosol properties over ocean and dark land (e.g., vegetation), while the aerosol properties over the entire land areas including both dark and bright surfaces are derived using the DB algorithm. The data of five-minute time interval is available in each MOD04_L2 product file. The output grid is 135 pixels in width by 203 pixels in length for 10 km resolution. Every tenth file has an output grid size of 135 by 204 pixels. In the present study, we have considered “Aerosol_Type_Land” and “Optical_Depth_Ratio_Small_Land” from MOD04_L2 products to understand aerosol type and the measure of aerosol fine-mode fraction. The fine-mode fraction provides qualitative information regarding the relative dominance of fine-mode aerosol in the aerosol distributions. On the other hand, the integer values in Aerosol_Type_Land data products indicate the information of various aerosol types: such as 1 = Continental, 2 = Moderate Absorption Fine, 3 = Strong Absorption Fine, 4 = Weak Absorption Fine, 5 = Dust (Coarse).

4. Theoretical background and methodology The Ångström exponent, ‘α’, is a commonly used parameter to characterize the wavelength dependence of AOD, which provides basic information on the aerosol size distribution. The spectral variations of AOD and α, both are observed corresponding to the aerosol physical and chemical characteristics (Eck et al., 1999; Reid et al., 1999). The Ångström parameters α and β can be computed using an empirical formula, known as Ångström Power Law (Ångström, 1961),

    

(2)

Where, τλ is the estimated AOD at the wavelength λ (in micrometers), while α and β are the Ångström exponent and turbidity coefficient, respectively. The logarithmic scale of Eq. (2) yields a straight line as:

ln    ln    ln 

(3)

The plots of lnτλ versus lnλ, yield a straight line of slope –α and intercept lnβ. The validity of the Ångström Power Law is stated after Junge Power Law (Junge 1955) for a limited size range of particles (particle size range of 0.05µm to 10 µm). In this particle range, the extinction is significant and the spectral variation of the refractive index does not vary the Mie extinction factor significantly (Kaskaoutis et al., 2006). Following the Volz method, Ångström exponent () in a particular wavelength range can also be calculated using any pair of wavelengths 1 and 2 as

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(4)

Where, τ1 and τ2 are the AODs at wavelengths λ1 and λ2.Thus, the value of α depend upon the wavelength region used for its determination. Various authors have been reported different values of α calculated in various spectral range (Eck et al., 1999; Reid et al., 1999), while the wavelength dependence of Ångström exponent was investigated by O’Neill et al. (2002). The second order derivative of lnτ versus lnλ, which is related to the derivative of Ångström exponent, α with respect to lnλ can be utilized as a parameter for assessing the curvature of the lnτ versus lnλ curves (Eck et al., 1999). This second order derivative (denoted by αˊ) is a measure of the rate of change of the slope with respect to wavelength and therefore, can acts as a supplement to the Ångström exponent (Eck et al., 2001). The expression for αˊ can be written as:



 ln  i 1  ln  i ln  i  ln  i 1  2    ln   ln  ln   ln  ln i  ln i 1  i 1 i 1  i 1 i 

  

(5)

This curvature of AOD can be an indicator of the aerosol size. The negative curvature (positive αˊ values) indicates the dominance of fine-mode aerosols while the positive curvature (negative αˊ values) suggests that aerosol size distributions are bimodal with significant contribution from the coarse-mode aerosols (Eck et al., 1999; Reid et al., 1999; Schuster et al., 2006).When the value of αˊ is zero, the slope of the AOD spectra is constant; on the other hand the slope of the AOD spectra changes rapidly when the αˊ value is higher. The sources of aerosols, their mixing processes and formation mechanisms in the atmosphere are different and variable and as a result the atmosphere exhibits hardly a unimodal aerosol size distribution. When the aerosol size distribution is multimodal, the wavelength dependence of AOD does not exactly follow Ångström Power Law (Eq. 2)). Consequently, a departure from the linear behavior of lnτ versus lnλ is expected and has been reported by several studies (Eck et al., 1999; O’Neill et al., 2001). To study the curvature in the AOD spectra, the second order polynomial fit is stated as:

ln    2  ln    1  ln     0 2

(6) Where, α0, α1, and α2 are constants. The coefficient α2 accounts for the curvature often observed in the spectral distribution of AODs. From Ångström power law (Eq. 3) and the second-order polynomial fit (Eq. 6), αˊ can also be obtained as

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 

d  2 2 d ln 

(7)

The curvature in the lnτ versus lnλ data is found to be concave if α2< 0, i.e., αˊ > 0 which suggests the dominance of fine-mode particles (such as biomass burning, urban or industrial aerosols) in the aerosol size distribution; the curvature is convex if α2> 0, i.e., αˊ < 0 which indicates that the aerosol size distribution in the atmosphere is dominated by coarse-mode aerosols (such as dust, sea salt) or the distribution is bimodal with significant contribution from coarse-mode particles (Eck et al., 1999, 2001).

5. Result and discussion 5.1 Monthly Variation of AOD and Ångström Parameters

Fig. 3: Temporal variation of monthly mean values of AOD (0.5 μm),  and . The vertical bars through mean indicate the standard deviations.

Fig. 3 shows the temporal variations of the monthly mean values of AOD at 0.5 μm and Ångström parameters α and β for the entire observational period. The data gap in August, 2011 owes to lack of AOD data as the sun photometer was not in operational during that time due to some technical issue. The average AOD (at 0.5 μm) is observed to

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be 0.70 ± 0.28, 0.74 ± 0.18, 0.55 ± 0.20, 0.44 ± 0.19 during winter, pre-monsoon, monsoon, and post-monsoon seasons respectively, during which the average values of β also followed nearly the same seasonal patterns having mean values of 0.31 ± 0.13, 0.37 ± 0.12, 0.34 ± 0.13, 0.22 ± 0.10. The local meteorology at Agartala along with the synoptic conditions mainly influences the high AOD during winter and pre-monsoon seasons. The lower boundary layer and relatively low wind speed in winter causes the ventilation coefficient low that accumulates pollutants and aerosol particles. Moreover, the black carbon concentrations in winter become the highest throughout the year due to several factors as explained by Guha et al. (2015) from the study carried out at the same experimental site. The burning of agricultural field residue, dry leaves, shrubs etc. in nearby location and wood combustion and biomass burning for cooking and warming in the nearby villages and residential houses in winter season are among the potential local sources of anthropogenic aerosol accountable for increase in near surface aerosol loading in winter. In addition, very less rainfall in winter as is obvious from Fig. 2b; also facilitate the aerosol particle to remain in the atmosphere for long time due to absence of effective wet removal mechanism. As the season advances from winter to monsoon, the particles are dispersed higher up in the atmosphere; however their contribution to columnar abundance remains consistent through the pre-monsoon season. The lower values of AOD in monsoon and post-monsoon are owing to the reduction of aerosols by wet depositions, mostly influencing the coarse mode particles. Seasonal mean values of Ångström exponent α clearly exhibit the seasonal change in the aerosol types over the study location. The seasonal mean values of α are found to be 1.09 ± 0.17, 0.92 ± 0.24, 0.51 ± 0.27, 0.89 ± 0.38 for winter, premonsoon, monsoon, and post-monsoon seasons, respectively. The higher value of α observed during winter and pre-monsoon is related to abundance fine mode aerosols, whereas the lower values of α observed during monsoon season is indicative of the sharp reduction of fine mode aerosols. In order to examine the impact of long-range transport from different source regions and also to establish the links between the synoptic air masses and aerosol loading over Agartala, the 7-day back trajectories are analyzed for four different seasons using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The trajectories are considered at the height level of 500 m above ground and mean trajectories with corresponding spread are computed for each season which is presented in Fig. 4. It is noticed from Fig. 4 that the mean trajectories traverse distinct geographic locations in different seasons. During monsoon, the air mass trajectories mainly stretch across the oceanic regions bringing coarse-mode sea-salt aerosols to the study location. In winter, high value of AOD along with high α value indicate abundance of fine-mode aerosol, during which the air mass trajectory (Fig, 4) originate at the highly polluted IGP and industrialized northern part of India. During monsoon, the higher value of β, associated with low α value represents abundance of coarse-mode aerosols, associated with the air mass flow from oceanic regions (Fig. 4).

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Fig. 4: Mean seven-day back trajectories with their spread arriving at Agartala at 500 m height levels above ground during four different seasons: winter, pre-monsoon, monsoon and post-monsoon.

5.2 Variation of Ångström exponent in different wavelength ranges

Fig. 5: Scatter plot of  obtained in different wavelength intervals (0.38-0.675 μm, 0.675-0.87 μm and 0.380.87 μm) versus AOD at 0.5 μm for different seasons. (Number data points (N) in winter, summer, monsoon and post monsoon are 175, 98, 49 and 74 respectively for each α value)

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Ångström wavelength exponent (α) are obtained for three different wavelength regions, namely, 0.38–0.5 μm, 0.675–0.870 μm and 0.38–0.870 μm following Eq. (4). Daily mean values of α in each of the wavelength intervals are plotted against respective daily mean values of AOD (at 0.5 μm) in Fig. 5 for four different seasons. The relationships between α (at different wavelength intervals) and AOD show variability when aerosols in a region exhibit bimodal size distributions. In monsoon and post-monsoon seasons, the values of α, both at lower and higher wavelength intervals show an increasing trend with AOD; where as in summer (premonsoon) the values at higher wavelength intervals shows decreasing trend while remaining steady at lower wavelengths intervals. In winter, the values of α, both at shorter and longer wavelength intervals show a decreasing trend with AOD. On the other hand, values of α in the broad wavelength range (0.380 μm – 0.870 μm) shows a decreasing trend with AOD in winter and summer and an increasing trend in monsoon and post-monsoon.

Fig. 6: Variations of differences in  values (380-500 - 675-870) with AOD at 0.5 μm for different seasons

The differences in the values of  (i.e, 380-500 - 675-870) obtained at distinct wavelength regimes against the corresponding daily mean values of AOD at 0.5 μm are presented in Fig.6. In winter and summer, the negative values of 380-500 - 675-870 indicate the presence of fine-mode aerosols. The positive and negative differences imply the curvatures in spectral distribution of AOD, while nearly zero differences suggest the absence of spectral variation in the Ångström exponent. In monsoon, the percentages of

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positive and negative value of differences in  values (i.e., 380-500 - 675-870) are nearly same with some values nearly equal to zero. This implies that the relative contribution of fine-mode and coarse-mode to aerosol abundance in the atmosphere is significant during monsoon. 5.3 Curvatures in Aerosol Optical Depth Spectra

Fig. 7: Variation of second order Ångström exponent () with AOD at 0.5 μm for different seasons.

In order to quantify the curvature effects in spectral distribution of AODs, second-order Ångström exponent (α´) are derived and its variation as a function of daily mean AOD at 0.5 μm for four different seasons are plotted in Fig. 7. Near-zero or negative α´ values are characteristic of aerosol size distribution with dominant coarse-mode or a bimodal distribution with significant contribution of coarse-mode aerosols. On the other hand, finemode dominated aerosol size distributions, originated from biomass burning or urban/industrial activities result in positive α´ (Eck et al., 1999; O’Neill et al., 2001). In winter, and post-monsoon seasons, there are large positive values of α´ as to the strong optical effect of fine-mode particles contribute to higher optical depths. In contrast, the mostly negative or close to zero value of α´ observed in monsoon season indicates the presence of coarse-mode particle in addition to the background fine mode aerosols. During post-monsoon season, α´ have a wide range of value, in both positive and negative. This indicates the existence of bimodal aerosol size distributions.

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Fig.8: Correlation between 675-870 and 380-500 for different seasons (α380-500, α675-870 are Ångström wavelength exponent (α) at wavelength intervals 0.38-0.675 μm, 0.675-0.87 μm respectively).

To further recognize the mode of aerosols that dominates the aerosol size distributions in different seasons, the correlations between 675-870 and 380-500 for different seasons are examined in Fig. 8. The straight line having slope of unity denotes the line where the two α values (α380–500 and α675–870) would be equal. It also represent the line with zero curvature (α2 = 0), as indicated by Eq. (6). When the atmosphere contains higher finemode aerosol, 2< 0, otherwise 2> 0 for an atmosphere having higher concentration of coarse-mode aerosols or bimodal aerosol distribution with large contribution from coarsemode aerosols (Eck et al., 1999; Schuster et al., 2006). In most of the cases during winter, pre-monsoon and post-monsoon, 2 < 0 which indicates that the aerosols size distribution have a relatively higher concentration of fine-mode aerosols. There are cases when 2> 0, which indicates the presence of coarse-mode aerosols during these seasons. According to Schuster et al. (2006), different aerosol size distribution with same α value can give rise to distinct curvatures. Therefore, only curvature analysis is not sufficient to get information about aerosol size distribution (Reid et al., 1999). So, the correlation between the coefficients α1 and α2 can be utilized to get significant information regarding aerosol types and size distribution. The situation corresponding to α2=0 represents a special case without any curvature, where α = -α1 (following Eqs. (2, 5)). According to Schuster et al. (2006), the AOD spectra for which α2 - α1> 2 represents fine-mode aerosols and for α2 -

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α1 < 1 the dominant aerosols are coarse-mode. When α2 - α1lie between 1 and 2, the atmosphere have fine-mode aerosol with wide range of size or a mixture of modes.

Fig.9: Correlation between the coefficients 1 and 2 computed in the spectral range of 0.380 μm to 0.870 μm following equation (2) for four different seasons. Straight lines correspond to 2-1=1 and 2-1=2.

In Fig. 9, 2 and 1 obtained following Eq. (6) for all the AOD spectra measured during different seasons are plotted. The two straight lines represent 2 - 1 = 2 and 2 1=1. In winter, the difference between 2 and 1 is > 1 and < 2 for most of the spectra, which suggest that fine-mode aerosols are dominant in winter. In contrast, the difference between 2 and 1 is less than 1 during monsoon for most of the AOD spectra indicating the presence of coarse-mode aerosols. To further examine the issue, the differences between 2 and 1 are plotted with respect to the AOD at 0.5 μm for different seasons in Fig. 10. It is clear that for most of the AOD spectra in winter, 2 - 1 > 1; where as in monsoon 2 - 1 < 1 for most of the AOD spectra. During summer and post-monsoon, all the observations are almost equally distributed on both sides of the line at 2 - 1 = 1, which indicates the presence of aerosols made up of particles from a mixture of mode.

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Fig.10: Differences between coefficients 1 and 2 plotted with AOD at 0.5 μm for different seasons. The straight lines represent the segments where 2 - 1= 0.5, 2 - 1= 1.0 and 2 - 1= 1.5.

The information on the atmospheric condition under which α is independent of wavelengths can be obtained from the correlation between 2 and AOD at 0.5 μm (Sharma et al., 2010). Fig.11 shows the scatter plot between polynomial coefficient 2 and AOD at 0.5 μm. The data points lying on or around 2 = 0 line in Fig. 11 indicates an aerosol distribution of bimodal type having no curvature (Schuster et al., 2006; Kaskaoutis et al., 2007). This shows that in maximum number of days, 2 is negative during winter and post-monsoon implying the presence of large fraction of fine-mode aerosol in this season. During monsoon and summer, though significant number of observation lies near or on 2 = 0 line, there are some observations which are far from the line 2 = 0 on both side.

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Fig.11: Scatter plots of coefficients 2 against AOD at 0.5 μm.

5.4 Comparison with satellite observations With an aim to understand the efficiency and accuracy of satellite remote sensing, aerosol parameters derived from satellite based observations are analyzed and compared with the ground-based measurements. The main intention of using the satellite data is to fill the gaps when the in-situ measurements are not available, in addition to using it for understanding the spatial distribution of aerosols based on comparison of the satellite data with the ground based point measurements. In this context, we have presented the comparison of results from two independent platforms and analysis. Using the MODIS AOD data over land, which is available in three wavelengths, we have derived Ångström exponent (α) in different wavelength ranges using Eq. (3) and (4) and are presented for highlighting aerosol size distribution in different seasons. 5.4.1 Ångström exponent derived from MODIS AOD Monthly mean Ångström exponents derived from MODIS AOD in two different wavelength ranges (0.47-0.55 μm) and ( 0.55-0.66 μm) using Eq.(4) and in full wavelength range (0.47-0.66 μm) using Eq. (3) are shown in Fig.12. A large seasonal variation of the values of α representing different wavelength ranges is observed. The values of α470-550 are higher than the values of α550-660 during the observational period. The value of α470-660 in winter months is nearly equal to 1 whereas in monsoon months, its value is nearly equal to 0.7. This suggests the abundance of fine-mode aerosols in winter and the presence of relatively more abundant larger size aerosol in monsoon, resembling to the ground based sun-photometer observations.

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Fig. 12: Monthly mean Ångström exponent (α) obtained from MODIS AOD in three spectral ranges of 0.470–0.550 μm, 0.550–0.660 μm, and 0.470–0.660 μm (full) over the observational site. ( α(470-550), α (550-660) and α(470- 660) are Ångström wavelength exponent (α) at wavelength intervals 0.470-0.550μm, 0.550-0.660μmand 0.470-0.660μm respectively).

5.4.2. Aerosol type and fine-mode fraction The fine-mode fraction obtained from MODIS data is plotted in box & whisker plots in Fig.13. From this monthly variation of fine-mode fraction, the highest mean and median value of FMF is observed in January (winter) and lowest in July and August months (monsoon). The FMF variation shows a seasonal pattern over the observational site with gradually decreasing trend from winter to monsoon and again increasing through postmonsoon. In winter, the mean FMF value of 0.7 with median value 0.8 indicates the large share of fine-mode aerosol. The FMF values are lowest during June–July–August with mean FMF values between 0.2 and 0.4 indicating prevalence of coarse-mode aerosol in monsoon.

Fig. 13: Box and whisker plots of monthly fine-mode fraction over the observational site from MODIS data

Fig.14 shows the type of aerosol in different months over the observational site. In winter months (mainly during January, February, March), the aerosol type (value ~ 3) represent strongly absorbing fine-mode aerosol. In all other months, the aerosol type (value ~ 2) represents moderately absorbing fine-mode aerosols. From this figure, it is also

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observed that fine-mode aerosols (moderately absorbing) exist in three other seasons too as indicated by MODIS aerosol types (value ~ 2). It is obvious from Fig.14 that contribution of moderately absorbing fine-mode aerosol in monsoon is not so significant (percentage count of data with value 2 in monsoon season is less compared to other seasons).

Fig. 14: Month wise representation of aerosol type over the observational site from MODIS data. Here % count implies the percentage of days (having a particular value of aerosol type) in that month.

6. Conclusion The present work addresses the seasonal discrimination of dominant aerosol types in terms of contribution from fine-mode and coarse-mode aerosols by applying the concept of wavelength dependence of spectral AOD. The monthly and seasonal variations of AOD and Ångström exponents (α, β) during the two years period from September 2010 to September 2012 at Agartala (Tripura) are discussed in relation to the seasonal dominance of aerosol types along with its sources of origin. The winter and pre-monsoon seasons are characterized by higher aerosol loading with mean AOD value of 0.70 ± 0.28 and 0.74 ± 0.18 respectively, mainly due to dominance of fine-mode aerosol (α ~ 1.09 ± 0.17 in winter). The comparatively low AOD value of 0.55 ± 0.20 is noticed in monsoon with lowest value of α ~ 0.51 ± 0.27 indicating the abundance of coarse-mode aerosol during this season. This is further supported by the negative curvature appeared in the AOD in winter and pre-monsoon, while the curvatures are mostly positive in the monsoon season. Similarly, the positive values of α´ also supported the abundance of fine-mode particles in winter and pre-monsoon at Agartala .In contrast, a wide range of α´ values, both negative and positive are observed in monsoon and post-monsoon, specifying the bimodal distribution of aerosols with large coarse-mode fractions. Analysis of MODIS fine-mode fraction and aerosol type indicate the broad seasonal features of size spectrum of dominant aerosol similar to those derived from ground based observation.

Acknowledgements This work was supported by the Aerosol Radiative Forcing over India (ARFI) project of Indian Space Research Organization’s Geosphere Biosphere program (ISROGBP). The authors wish to acknowledge that the Terra/MODIS Aerosol (Daily L3 Global 1 Deg. CMG) datasets were acquired from Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight

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Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/).The authors humbly acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.arl.noaa.gov/ready.html) used in this paper. The work is also supported by DST FIST with reference no Ref.SR/FST/PSI-191/2014.

References Ångström, A. (1961). Techniques of determining the turbidity of the atmosphere.Tellus. 13: 214–223. doi:10.1111/j.2153-3490.1961.tb00078.x Badarinath, K.V.S., Kharol, S.K., Sharma, A.R. (2009). Long-range transport of aerosols from agriculture crop residue burning in Indo-Gangetic Plains—a study using LIDAR, ground measurements and satellite data. J. Atmos. Solar-Terr. Phys. 71: 112–120. https://doi.org/10.1016/j.jastp.2008.09.035. Chin, M., Diehl, T., Ginoux, P., Malm, W. (2007). Intercontinental transport of pollution and dust aerosols: implications for regional air quality. Atmos. Chem. Phys. 7: 5501–5517. Dhar, P., De, B.K., Banik, T., Gogoi, M.M., Babu, S.S., Guha, A. (2017). Atmospheric aerosol radiative forcing over a semi-continental location Tripura in North-East India: Model results and ground observations. Sci Total Environ. 580: 499-508.doi: 10.1016/j.scitotenv.2016.11.200 Eck, T.F., Holben, B.N., Reid, J.S., Dubovik,O., Smirnov, A., O’Neill, N.T., Slutsker, I., Kinne, S. (1999). Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. 104(D24): 31333–31349. doi:10.1029/1999JD900923. Eck, T.F., Holben, B.N., Ward, D.E., Dubovik, O., Reid, J.S., Smirnov, A., Mukelavi, M.M., Hsu, N.C., O’Neill, N.T., Slutsker, I. (2001). Characterization of the Optical Properties of Biomass Burning Aerosols in Zambia during the 1997 ZIBBEE Field Campaign. J. Geophys. Res. 106(D4): 3425–3448. doi: 10.1029/2000JD900555. Eck, T.F., Holben, B.N., Ward, O., Reid, J.S., O’Neill, N.T., Schafer, D.E., Dubovik, J.S., Smirnov, A., Yamasoe, M.A., Artaxo, P. (2003). High Aerosol Optical Depth Biomass Burning Events: A Comparison of Optical Properties for Different Source Regions. Geophys. Res. Lett. 30: 2035.doi:10.1029/2003GL017861. Ghan, S.J., Schwartz, S.E. (2007). Aerosol Properties and Processes: A Path from Field and Laboratory Measurements to Global Climate Models. Bull. Am. Meteorol. Soc. 88(7): 1059–1083. doi:10.1175/BAMS-88-7-1059.

22

Gogoi, M.M., Bhuyan, P.K., Moorthy, K.K.(2009a). Climatology of columnar aerosol properties and the influence of synoptic conditions: first-time results from the north-eastern region of India. J. Geophys. Res. 114: D08202. http://dx.doi.org/10.1029/2008JD010765. Gogoi, M.M., Moorthy, K.K., Babu, S.S., Bhuyan, P.K. (2009b). An investigation of aerosol size distribution properties at Dibrugarh: north-eastern India. Terr. Atmos. Ocean.Sci. 20(3): 521-533. http://dx.doi.org/10.3319/TAO.2008.06.11.01 (A). Gogoi, M.M., Pathak, B., Moorthy, K.K., Bhuyan, P.K., Babu, S.S., Bhuyan, K., Kalita, G.(2011). Multi-year investigations of near surface and columnar aerosols over Dibrugarh, North-Eastern location of India: heterogeneity in source impacts. Atmos. Environ. 45: 1714-1724.http://dx.doi.org/10.1016/j.atmos env.2010.12.056. Guha, A., De, B.K., Dhar, P., Banik, T., Chakraborty, M., Roy, R., Choudhury, A., Gogoi, M.M., Babu, S.S., Moorthy, K.K. (2015). Seasonal characteristics of aerosol black carbon in relation to long range transport over Tripura in northeast India. Aerosol Air Qual. Res. 15: 786–798. doi:10.4209/aaqr.2014.02.0029. Guleria, R.P., Kuniyal, J.C., Rawat, P.S., Thakur, H.K., Sharma, M., Sharma, N.L., Singh, M., Chand, K., Sharma, P., Thakur, A.K., Dhyani, P.P., Bhuyan, P.K. (2011a). Aerosols optical properties in dynamic atmosphere in the northwestern part of the Indian Himalaya: a comparative study from ground and satellite based observations. Atmos. Res. 101: 726– 738. doi:10.1016/j.atmosres.2011.04.018. Guleria, R.P., Kuniyal, J.C., Rawat, P.S., Sharma, N.L., Thakur, H.K., Dhyani, P.P., Singh, M. (2011b). The assessment of aerosol optical properties over Mohal in the northwestern Indian Himalaya using satellite and ground based measurements and an influence of aerosol transport on aerosol radiative forcing. Meteorol. Atmos. Phys. 113: 153–169. doi: 10.1007/s00703-011-0149-5. Guleria, R.P., Kuniyal, J.C., Sharma, N.L., Dhayani, P.P. (2012a). Seasonal variability in aerosol optical and physical characteristics estimated using the application of the Ångström formula over Mohal in the northwestern Himalaya, India. J. Earth Sys. Sci. 121(3): 697710. doi:10.1007/s12040-012-0194-6. Guleria, R.P., Kuniyal J.C. (2013). Aerosol climatology in the northwestern Indian Himalaya: a study based on the radiative properties of aerosol. Air Qual. Atmos. Health 6(4): 717-724. doi:10.1007/s11869-013-0206-y. Guleria, R.P., Kuniyal, J.C., Joshi, N., Dhyani, P.P., Sharma, N.L. (2014). Impact of aerosol on surface reaching solar irradiance over Mohal in the northwestern Himalaya, India. J. Atmos. Solar-Terr. Phys. 108: 41-49. http://dx.doi.org/10.1016/j.jastp.2013.12.002. Guleria, R.P., Kuniyal, J.C. (2015). Characteristics of atmospheric aerosol particles and their role in aerosol radiative forcing over northwestern Indian Himalaya in particular and over India in general. Air Qual. Atmos. Health 1-14. doi: 10.1007/s11869-015-0381-0.

23

Ichoku, C., Kaufman, Y.J., Remer, L.A., Levy, R. (2004). Global aerosol remote sensing from MODIS. Adv. Space Res. 34: 820–827. https://doi.org/10.1016/j.asr.2003.07.071. Ichoku, C., Chu, D.A., Mattoo, S., Kaufman, Y.J., Remer, L.A., Tanre’, D., Slutsker, I., Holben, B. (2006). A spatio-temporal approach for global validation and analysis of MODIS aerosol products.Geophys. Res. Lett. 29(12): 8006. doi:10.1029/2001GL013206. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535. doi:10.1017/CBO9781107415324. Junge, C.E. (1955). The size distribution and ageing of natural aerosols as determined from electrical and optical measurements in the atmosphere. J. Meteorol. 12: 13–25. Junge, C.E. (1963). Air chemistry and radioactivity. Academic Press, New York. Kaskaoutis, D.G., Kambezidis, H.D., Adamopoulos, A.D., Kassomenos, P.A. (2006). On the Characterization of Aerosols using the Ångström Exponent in the Athens Area. J. Atmos. Sol-Terr. Phys. 68: 2147–2163. doi: 10.1016/j.jastp.2006.07.008. Kaskaoutis, D.G., Kambezidis, H.D., Hatzianastassiou, N., Kosmopoulos, P.,Badarinath, K.V.S. (2007). Aerosol Climatology: On the Discrimination of the Aerosol Types Over Four AERONET Sites. Atmos. Chem. Phys. Discuss. 7: 6357–6411. doi: 10.5194/acpd-76357-2007. Kassianov, E.I., Flynn, C.J., Ackerman, T.P., Barnard, J.C. (2007). Aerosol singlescattering albedo and asymmetry parameter from MFRSR observations during the ARM Aerosol IOP 2003. Atmos. Chem. Phys. 7: 3341–3351. doi: 10.5194/acp-7-3341-2007. Kedia, S., Ramachandran, S. (2009). Variability in Aerosol Optical and Physical Characteristics Over the Bay of Bengal and the Arabian Sea Deduced from Ångström Exponents. J. Geophys. Res.114: D14207. doi: 10.1029/2009JD011950. Moorthy, K.K., Babu,S.S., Satheesh, S.K. (2007).Temporal heterogeneityin aerosol characteristics and the resulting radiative impact at atropical coastal station. Part 1: Microphysical and optical properties. Ann. Geophys. 25: 2293–2308. Morys, M., MimsIII, F. M.,Hagerup, S., Anderson, S. E., Baker, A., Kia, J.,Walkup, T. (2001). Design, Calibration, and Performance of MICROTOPSII Handheld Ozone Monitor and Sun Photometer. J. Geophys. Res.106(14):573–582. doi:10.1029/2001JD900103. Niranjan, K., Spandana, B., Devi, T.A., Sreekanth, V., Madhavan, B.L. (2011). Measurements of aerosol intensive properties over Visakhapatnam, India for 2007. Ann. Geophys. 29: 973–985.

24

O’Neill, N.T., Eck, T.F., Holben, B.N., Smirnov, A., Dubovik, O. (2001). Bimodal size distribution influences on the variation of Ångström derivatives in spectral and optical depth space. J. Geophys. Res. 106(D9): 9787– 9806. doi: 10.1029/2000JD900245. O’Neill, N.T., Eck, T.F., Holben, B.N., Smirnov, A., Royer, A., Li, Z. (2002). Optical properties of boreal forest fire smoke derived from Sun photometry. J Geophys Res 107: D11. doi:10.1029/2001JD000877. Pathak, B., Kalita, G., Bhuyan, K., Bhuyan, P.K., Moorthy, K.K. (2010). Aerosol temporal characteristics and its impact on shortwave radiative forcing at a location in the northeast of India. J. Geophys. Res. 115: D19204. doi: 10.1029/2009JD013462. Pathak, B., Bhuyan, P.K., Gogoi, M.M.,Bhuyan, K. (2012). Seasonal heterogeneity in aerosol types over Dibrugarh, North–Eastern India. Atmos. Environ. 47: 307–315, doi: 10.1016/j.atmosenv.2011.10.061. Pathak, B., Bhuyan, P.K.(2014). Absorbing and scattering properties of boundary layer aerosols over Dibrugarh, Northeast India. Int. J. Remote. Sens. 35: 5527–5543. Pöschl, U. (2005). Atmospheric aerosols: composition, transformation, climate and health effects. Angew. Chem. Int. Ed., 44: 7520–7540. doi:10.1002/anie.200501122. Pósfai, M., Buseck, P.R. (2010).Nature and climate effects of individual tropospheric aerosol particles.Annu. Rev. Earth Planet Sci., 38: 17–43. doi:10.1146/annurev.earth.031208.100032. Qin, K., Wu, L., et al. 2016. Trans-boundary aerosol transport during a winter haze episode in China revealed by ground-based Lidar and CALIPSO satellite. Atmos. Environ. 141: 20– 29. http://doi.org/10.1016/j.atmosenv.2016.06.042. Ram, K., Singh, S., Sarin, M.M., Srivastava, A.K., Tripathi, S.N. (2016). Variability in aerosol optical properties over an urban site, Kanpur, in the Indo-Gangetic Plain: A case study of haze and dust events. Atmos. Res. 52–61. https://doi.org/10.1016/j.atmosres.2016.01.014. Reid, J.S., Eck, T.F., Christopher, S.A., Hobbs, P.V.,Holben, B. (1999). Use of the Ångström exponent to estimate the variability of optical and physical properties of aging smoke particles in Brazil. J. Geophys. Res. 104(D22): 27473–27489. doi: 10.1029/1999JD900833. Remer, L., Kaufman, Y.J., Tanre´, D., et al. (2005).The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci. 62(4): 947–973. https://doi.org/10.1175/JAS3385.1. Schmid, B., Spyak, P.R.,Biggar, S.F.,Wehrli,C.,Sekler,J.,Ingold,T.,Matzler,C.,Kampfer, N. (1998). Evaluation of the applicability of solarand lamp radiometric calibrations of a precision Sun photometer operating between 300 and 1025 nm. Appl. Opt. 37: 3923 – 3941.

25

Schuster, G.L., Dubovik, O.,Holben B.N. (2006). Ångström exponent and bimodal aerosol size distributions. J. Geophys. Res. 111: D07207. doi:10.1029/2005JD006328. Seinfeld, J.H., Pandis, S.N. (2016). Atmospheric chemistry and physics: from air pollution to climate change. John Wiley & Sons, New Jersey. https://lccn.loc.gov/2015043236. Sharma, A.R., Kharol, S.K., Badarinath, K.V.S., Singh, D. (2010). Impact of agriculture crop residue burning on atmospheric aerosol loading – a study over Punjab State, India. Ann. Geophys. 28: 367–379.doi:10.5194/angeo-28-367-2010 Singh, R.P., Dey, S., Tripathi, S.N., Tare, V., Holben, B. (2004). Variability of aerosol parameters over Kanpur, northernIndia, J. Geophys. Res. 109: D23206. doi: 10.1029/2004JD004966. Srivastava, M.K., Singh, S., Saha, A., Dumka, U.C., Hegde, P., Singh, R., Pant, P. (2006). Direct solar ultraviolet irradiance over Nainital, India, in the central Himalayas for clearsky day conditions during December 2004. J. Geophys. Res. 111: D08201. doi:10.1029/2005JD006141. Whitby, K.T., Cantrell, B.K. (1976). Atmospheric aerosols characteristics and measurements.Presented at the Int. conf. environmental sensing and assessment, Las Vegas. NV. Yu, H., Dickinson, R.E., Chin, M., Kaufman, Y.J., Zhou, L., Tian, Y., Dubovik, O., Holben, B.N. (2004). Direct radiative effect of aerosols as determined from a combination of MODIS retrievals and GOCART simulations. J. Geophys. Res. 109: D03206. doi:10.1029/2003JD003914.