Investigation of seasonal variation of compensation parameter and absorption Ångström Exponent of aerosol after loading correction over a remote station in north-east India

Atmospheric Environment 212 (2019) 106–115

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Investigation of seasonal variation of compensation parameter and absorption Ångström Exponent of aerosol after loading correction over a remote station in north-east India

T

Nilamoni Barmana,b, Rakesh Royc, Biswajit Sahaa, S.S. Kundub, Arup Borgohainb, Barin Kumar Ded, Anirban Guhad,∗ a

Department of Physics, National Institute of Technology Agartala, Tripura, India North-Eastern Space Application Centre, Umiam, Meghalaya, India Maharaja Bir Bikram University, Agartala, Tripura, India d Department of Physics, Tripura University, Tripura, India b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Compensation parameter Black carbon Wind vector Bay of Bengal Backward trajectory

One of the extensively used methods for aerosol mass concentration measurement is the optical filter method used in Aethalometer. Due to the aerosol loading and multi scattering effect, there are uncertainties that can lead to underestimation or overestimation of aerosol concentration and measurement of the absorption coefficient. To overcome such uncertainties, a data correction method has been adopted in the present work. During the winter, pre-monsoon and post-monsoon seasons, Aethalometer underestimated black carbon (BC) concentration by 8 ± 1%, 11 ± 1%, and 10.5 ± 0.2%, due to the higher aerosol deposition. While in the monsoon season, underestimation of 5.5 ± 2% is observed because of the presence of bigger scattering nature aerosol. For 70% of days, found a positive correlation between the compensation parameter (k880) and wavelengths, which indicated the dominance of the smaller aerosol particles. While 30% of days have a negative correlation due to the bigger aerosol particle. In the winter, pre-monsoon and monsoon seasons, the absorption coefficient at 370 nm was 86%, 90%, and 93% higher than that of 880 nm due to the higher biomass burning emissions. The absorption Ångström exponent (α370-880) was 1.02 for fossil fuel burning and the corresponding k880 was 0.0037. In the winter and post-monsoon seasons, BC emissions from fossil fuel combustion dominated over the BC emitted by biomass burning emissions with α370-880 < 1.1, whereas α370-880 is found to be more than 1.2 during the premonsoon and monsoon seasons due to the higher biomass burning.

1. Introduction Atmospheric aerosols are the particles (solid, liquid, and gaseous) that move freely within the atmosphere. The incoming solar radiation can directly interact with the aerosol particles by scattering and absorption processes. However, the absorption property of the aerosol is still uncertain (Virkkula et al., 2007, 2015). Black carbon (BC) is the carbonaceous aerosol particle that absorbs both shortwave solar radiation and long-wave terrestrial radiation. So, Knowing the BC's optical properties (absorption) is important. The filter-based BC concentration measurement method is conventional across the globe. By virtue of BC, the direct radiative forcing is more than the methane, known as the second most greenhouse gas after carbon dioxide in terms of warming potential (Jacobson, 2001; Ramanathan and Carmichael,

∗

2008; Bond et al., 2013; Drinovec et al., 2015). BC can transmute the negative forcing of aerosol to the positive forcing of aerosol (Heintzenberg et al., 1997). So, if we know the genuine BC aerosol absorption magnitude, then the direct radiative forcing can be accurately calculated. Ackerman et al. (2002) demonstrated by analyzing the data collected during the INDOEX experiment that BC aerosol curtails cloud fraction over the Indian Ocean. Therefore, it is necessary to discover the source of emissions, concentration, and dissipation of BC on a regional and global scale. Black carbon is not the only aerosol particle present and many other types of aerosol particles are also available in the atmosphere (Virkkula et al., 2007, 2015). In the optical filter method (in Aethalometer), the air is sucked from the ambient atmosphere and aerosol particles are deposited on the quartz filter tape. In this process, the transmitting light

Corresponding author. E-mail address: [email protected] (A. Guha).

https://doi.org/10.1016/j.atmosenv.2019.05.036 Received 16 December 2018; Received in revised form 16 May 2019; Accepted 18 May 2019 Available online 20 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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vehicular activity close to the measurement site is quite high. The observational site is about 9 km south of the Agartala city where the main sources of air pollution are vehicular emissions and biomass burning emissions. Moreover, within the 50 km radius, there are several brick kilns around the observation site. The complex topography contributes towards the regional diversities, especially in the hill ranges. The region experiencing four distinct seasons which are winter (December–February), pre-monsoon (March and April), monsoon (May–September), and post-monsoon (October and November) (LSC, 2012). In the monsoon season, the southwesterly wind carries moisture and causes massive precipitation, which often leads to floods over the region (AP, 2012). Since 1995, the annual precipitation varies between 1979.6 and 2745.9 mm over the region (GMR, 2011; SHD, 2007). In the winter season, temperature varies from 13 °C to 27 °C, while in the pre-monsoon season, the temperature recorded between 24 °C and 36 °C (MYRT, 2012). The station is approximately 165 km away from the Bay of Bengal (BoB), and wind transports aerosols and water vapor to the observation site.

used by the ocular photometer (Hansen et al., 1982; Bond et al., 1999; Drinovec et al., 2015) or totally reflected and transmitted light to estimate the BC concentration (Petzold et al., 2005; Drinovec et al., 2015). Whenever the aerosol particles deposited on the filter tape in Aethalometer, not only the BC aerosols are dumped, some other aerosol particles are accumulated too in the filter tape and influence the optical properties of the deposited aerosol. In a filter-based method, the optical attenuation (ATN) recorded by the instrument is only due to the absorption of BC deposited on the filter. Ideally, there should be a linear correlation between ATN and BC concentration (Virkkula et al., 2007, 2015). However, due to the unwanted aerosol particles, the correlation between ATN and BC concentration is not so linear. Many researchers have proposed a self-reliant algorithm to reduce the above-mentioned non-linearity in the measurements. Weingartner et al. (2003), Arnott et al. (2005) and Virkkula et al. (2007) have proposed modified algorithms and used different compensation parameters to minimize the uncertainty in the measurements. These algorithms were used before the analysis to correct the Aethalometer data. In Aethalometer, the sensitivity of the measurements decreases with the enhancement of the aerosol deposition on the quartz filter which is known as the filter loading effect (Weingartner et al., 2003; Virkkula et al., 2007; Collaud Coen et al., 2010; Drinovec et al., 2017). Virkkula et al. (2007) demonstrated the correction algorithm for the BC concentration for the loading correction by multiplying the raw BC concentration by the term f = 1 + k.ATN, where k is known as a compensation parameter. Compensation parameter is useful to correct the loading effect, but unstable due to the larger change in absorption coefficients compared to the absorption coefficients induced by the tape advanced (Collaud Coen et al., 2010). Weingartner et al. (2003) reported that uncoated BC aerosol increases the loading effect compared to the coated BC. In addition, parameter relies on the backscatter fraction and single scattering albedo (SSA) (Virkkula et al., 2015). Song et al. (2013) reported that in the winter season the values of the compensation parameter higher than in the summer season and proposed that it was likely because of the darker aerosols. Absorption Ångström Exponent (α) is frequently used to characterize the spectral dependence of light absorption. Aerosols from various sources absorb light with different characteristics of the spectrum. It was reported that α of aerosols emitted from combustion of fossil fuel was approximately 0.8–1.1 (Bergstrom et al., 2007), while the biomass burning aerosols showed strong spectral dependency due to the higher light absorption at the wavelength of 370 nm (ultraviolet) or 450 nm (blue), α between 0.9 and 3.5 (Chen and Bond, 2010; Kirchstetter et al., 2004). Schnaiter et al. (2005) demonstrated α of the diesel vehicle at approximately 1.1. Lewis et al. (2008) utilized a photoacoustic spectrometer's 532 nm and 870 nm wavelengths to estimate aerosol absorption from biomass burning emissions, and the mean α was about 2.5, but occasionally it was as large as 3.5 due to light absorption of organic carbon at short wavelengths. In this study, we have investigated the seasonal variation of compensation parameter and absorption Ångström Exponent of aerosol at a remote location of north-eastern India. One of the main focuses of this study is to investigate the cause of overestimation and underestimation of aerosol deposition by Aethalometer in different seasons and impact of transported aerosol on BC estimation.

2.2. Aethalometer AE-31 A seven-channel Aethalometer (Make: Magee Scientific, USA, Model: AE-31) has been used for the aerosol measurement at wavelengths λ = 370, 470, 520, 590, 660, 880, and 950 nm. This automatic filter based instrument aspirates ambient air with the help of the internal pump and aerosol particles are deposited on the quartz filter tape. After each pre-assigned collection interval, the mass of the atmospheric aerosol is calculated from the change in light transmittance between successive measurements. In this work, the instrument was operated with a time base of 5 min at a flow rate of 3.9 L min−1 continuously for the total observation period. In highly humid condition, the optical properties of BC aerosols used to modify (Levin et al., 2010; Sakamoto et al., 2016). It is due to its hygroscopic nature; in this context, a heating rod has been used in the inlet pipe before the air enters the system, and this process assured that the excess amount of humidity evaporated from the aerosols. The optical attenuation method has been widely used for aerosol measurements as the method shows excellent agreement with other methods (Allen et al., 1999; Im et al., 2001; Babu and Moorthy, 2002; Begum et al., 2009). However, several reports (Weingartner et al., 2003; Arnott et al., 2005; Corrigan et al., 2006) documented on the uncertainties in the estimation of aerosol by Aethalometer. The existing methods for measurement of the aerosol depend on some assumptions that are greatly related to instrument specification, site selection and type of aerosol (Weingartner et al., 2003; Hitzenberger et al., 2006; Moorthy et al., 2007; Nair et al., 2007). 2.3. Long-range transport of air masses Backward trajectory analysis based on the National Oceanic and Atmospheric Administration (NOAA) HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was studied to understand the sources of the air masses over the study region. HYSPLIT model is an accessory that assists to describe how, when, and where aerosol particles were dynamically carried, diffused, and dumped by air. The model computation technique is a combination between the Lagrangian technique, utilizing a running frame of reference for the advection and dispersion computations as the air mass transported from the source. The Eulerian technique, which needs a specified 3-D framework as a frame of reference to gauge pollutant mass concentrations (Stein et al., 2005). We have chosen the Tripura University as an endpoint of the backward trajectory and run the model for a backward trajectory of all weeks for the year 2014, for convenience, this study showed only the plots of 15th date for each month. These trajectories are only included here as a representative of aerosol transportation for the complete year over the region. Northward and eastward winds at 50 m above displacement height

2. Instrumentation and methodology 2.1. Site description The present study is being conducted at Agartala (23.76° N and 91.26° E), which is situated in Tripura state in north-eastern India. It is a semi-urban site and the capital city of Tripura (Fig. 1). The continuous measurement of atmospheric aerosol was carried out in the premises of Tripura University Campus. The university is situated adjacent to the Agartala-Sabroom National Highway (NH-44) and therefore the 107

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Fig. 1. Google topographical map of India and Agartala (red star) in the north-eastern region of India. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

monthly data from “The Modern-Era Retrospective Analysis for Research and Applications, version 2” (MERRA-2) model at 0.5° × 0.667° was used for wind vector plots for different seasons over the region. MERRA-2 is computed using the atmospheric data from the Global Earth Observing System (GEOS) assimilation system. The system's key components are the GEOS atmospheric model (Molod et al., 2015) and the Gridpoint Statistical Interpolation (GSI) analysis scheme (Wu et al., 2002; Kleist et al., 2009). In this study, wind vector plots represented the dominant wind pattern over the region.

and Bergstrom, 2006; Bergstrom et al., 2002; Moosmüller et al., 2009). This dependence of α370-880 on the wavelength is due to the α370-880 value for small particles is usually greater than that of the bulk material. It is believed that a larger absorption Ångström exponent value (α370-880 > 1) is characteristic of biomass and biofuel burning. Wood burning and other types of biomass combustion are considered producing a significant amount of primary organic matter, which is internally mixed with the emitted BC (Chakrabarty et al., 2010; Pósfai et al., 2003).

2.4. Filter loading correction algorithm

3. Results and discussion

In this study, algorithms developed by Virkkula et al. (2007) have been used for data amelioration and analysis. The computation details of the corrected absorption coefficient (σc) and corrected black carbon concentration (BCc) are described in the supplementary material (section S.1.).

3.1. Backward trajectory analysis The HYSPLIT backward trajectory analysis and wind vectors indicate that during January to April (Supplementary Figs. S1a–d, Figs. S2a–b) and November and December (Fig. S1k, l, Fig. S2d) air masses to the measurement site transported from Bangladesh, Indo-Gangetic plain (IGP) of India and north-eastern region (NER) of India. Bangladesh and IGP are extremely polluted and so, an enormous amount of fine aerosol particles loaded into the atmosphere. May to October (Figs. S1e–j, Fig. S2c), air masses transported coarse mode (sea salt and dust aerosol) particles from the BoB to the station. Backward trajectory and wind vectors reveal that the air mass traveled from north-west direction over the station during the winter, premonsoon, and post-monsoon seasons. The source of air mass predominantly from Bangladesh and north-eastern India at 500 m and 2500 m altitudes (Figs. S1a–d, Figs. S2a–b). In northwestern India, agricultural residues are burned every year during the post-monsoon season, which increases the biomass burning aerosol into the atmosphere (Vadrevu et al., 2011, 2013). As shown in earlier studies (Radojevic, 2003; Cristofanelli et al., 2014; Reddington et al., 2014), the biomass burning particles may remain in the atmosphere for weeks to months, affecting ambient air quality, biogeochemical cycles, atmospheric composition, weather, and climate. Bonasoni et al. (2010) demonstrated that pollutant from IGP finds the southern part of the Himalayan plane as a “straight way” to the BoB. This transported aerosols can extend up to 5 km in height at the BoB and highly affect the atmospheric chemistry during pre-monsoon season. Gogoi et al. (2014) exhibited larger AOD and BC mass concentration deposited over the high elevated Himalayan portion. In the north-east India, The aerosol

2.4.1. Computation of compensation parameter (k) The empirical equation for measuring compensation parameter (k) carries great importance in the rectification of Aethalometer raw data. Uncorrected black carbon concentration (BCo) and attenuation measurements (before and after the filter spot change in Aethalometer) has been utilized to compute k values (Virkkula et al., 2007, 2015). The details of k-value estimation provided in the supplementary material (section S.1.1.) 2.4.2. Absorption Ångström exponent Absorption Ångström exponent (α) gives the change in light absorption as a function of wavelength. Absorption Ångström exponent was computed for the wavelength pairs of 370–520 nm (α370-520), 520–880 nm (α520-880) and 370–880 nm (α370-880), using equation as follows σ

α =−

ln( σcλ1 ) cλ2

λ1

ln λ2

, (1)

where σcλ1 and σcλ2 are corrected absorption coefficients at λ1 and λ2 wavelengths. The higher value of α370-880 (> 1.0) implies a higher spectral dependence of light absorption by the sample. BC from fossil fuel burning sources typically has an α370-880 value of 1 ± 0.1 (Bond 108

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originated from various sources such as open agriculture field and human activities, e.g., vegetation burning, combustion release, brick kilns, coal mines, oil wells (Pathak et al., 2015; Kundu et al., 2018; Barman et al., 2018). In north-east India, aerosols are confined because of the mountainous terrain; predominantly in the Brahmaputra valley, where the air convergence phenomena support the favorable atmosphere for the accumulation of both transported and local pollutants (Pathak et al., 2015). During monsoon season, back trajectory and wind vectors reveal that the air mass traveled predominantly from the southward direction over the station (Figs. S1e-i, S2c). For 500 m and 1000 m altitudes, the air mass transported mainly from BoB and Bangladesh's coastal area. Bangladesh is predominately affected by transported particles and desert dust from India combined with sea-salt aerosols from the Arabian Sea (Begum et al., 2013). As illustrated in the earlier studies (Hopke et al., 2008; Begum et al., 2011, 2013), aerosols travel over Iran, Afghanistan and then approach to Pakistan, as soon as the air mass enter India and Bangladesh diverted to Sri Lanka because of meteorological condition. Satheesh et al. (2010) demonstrated that during a full year, the presence of a north-south gradient of extremely scattering nature aerosols (predominantly sea-salt) over the open BoB and drops as proceeding to the northward direction. 3.2. Temporal variation of k880 and ak In the filter-based BC deposition measurements, BC concentration is directly calculated from the attenuation of light. There are uncertainties in the measurements of BC estimation, some factors such as successive loading and multi-scattering effect may cause the deviation in the measurement. These factors are usually overestimated or underestimated the BC concentration and absorption coefficient for a particular location. Virkkula et al. (2007) algorithm was adopted for preprocessing of the dataset to overcome the uncertainties. The value of k880 plays an essential role in data correction. During January and December (winter season), the minimum weekly average value of k880 was 3.5 × 10−3 and the maximum of 16.6 × 10−3 just before monsoon season (Fig. S3). During the monsoon season, the variation of k880 was frequent than the other seasons. Stable k880 value has been observed during winter, pre-monsoon, and postmonsoon seasons. It is seen that the air masses transported from IGP, Bangladesh, and NER of India to the region in the winter, pre-monsoon, and post-monsoon seasons (Figs. S1a, b, c, d, k, l; S2a, b, d). These transported aerosols (fossil fuel and biomass burning) further transported to the study region (Guha et al., 2015). It could be one reason for higher BC and stable k880 values. However, maximum air mass transportation occurred from the BoB and coastal areas of Bangladesh during the monsoon season (Figs. S1e–j; S2c). It is concluded that the maximum transported aerosols during the monsoon season were scattering natural particles due to which the k880 values of the depositions were not stable. Virkkula et al. (2015) demonstrated that the atmospheric aerosol in Nanjing the compensation parameter decreases due to the higher coating factor (a mass of sulfate, ammonium, nitrate and organic mass available for coating). Laborde et al. (2013) reported on the basis of MEGAPOLI campaign measurements that fresh BC of vehicular emission aerosols are non-coated, while long-range transported BC aerosol indicates substantial coating of scattering material. Drinovec et al. (2017) also reported that the larger value of k880 indicates the regional pollution and smaller values signifies the long-range aerosol transportation. In the monsoon season, the average k880 value was 60%, 59%, and 20% higher than that in the winter, pre-monsoon and postmonsoon seasons, which indicates a local aerosol loading over the region. In the winter and pre-monsoon seasons, k880 values were smaller than in the monsoon and post-monsoon seasons (Fig. 2a), which indicates a long-range coated aerosols dominated over the station. While in the monsoon season, local biomass burning aerosols dominated over the station, which indicated by the higher α370-880 (Fig. 2b). In Fig. 3, the measurement of average ak value for 23rd September

Fig. 2. Monthly comparison of k880 (a) and α370-880 (b) with respect to BCc for the year 2014.

Fig. 3. Compensation parameter vs wavelengths correlation for 23rd and 29th September to verify the positive and negative slope (ak) of k.

2014 and 29th September 2014 have been shown as ak > 0 and ak < 0, respectively. The maximum number of days in 2014, the value of k increased as the wavelength increases. Basically ak > 0 and ak < 0 indicates the aerosol particle size distribution at the station. For the smaller aerosol particles, ak is greater than zero, whereas ak is smaller than zero for bigger aerosol particles (Virkkula et al., 2007). Here, on 29th September, the particles were smaller than the particles deposited on 23rd September. In terms of wavelength dependency, smaller particles showed a positive correlation with wavelength, whereas bigger particles showed a negative correlation. The negative correlation manifested for bigger particles, which were transported from BoB and enlarge of the biomass burning BC aerosol due to higher relative humidity (RH) over the station. The maximum number of negative correlation events of k and wavelength manifested in the monsoon season when the aerosol loading was less (washed out due to rain) compared to other seasons. Because of frequent rainfall over the region evaporation/ 109

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Fig. 5. Daily variation of ak and BCc for the year 2014. Fig. 4. Seasonal correlation between compensation parameter to the wavelength at the station.

monsoon season, rainfall over the measuring site is frequent, resulting in a higher rate of evaporation/condensation over the region. These processes also affect the composition and size distribution of the aerosol particles (Levin et al., 2010; Sakamoto et al., 2016). Levin et al. (2010) showed that the fresh smoke consists of fine aerosol particles, while condensation causes the particle diameter to grow rapidly within a few hours (Janhäll et al., 2010). The biomass burning is the major source of numerous absorbing aerosols in the atmosphere every year over the region (Singh et al., 2002; Badrinath et al., 2007). Many satellites observations verified the existence of absorbing aerosols due to the biomass burning over the region (Badrinath et al., 2007). It has been confirmed in laboratory studies as well as in the land campaigns, that the biomass burning aerosols have the ability to create the secondary organic aerosols in the atmosphere (Cubison et al., 2011; Hennigan et al., 2011; Heringa et al., 2011; Ortega et al., 2013). The ak values were almost stable in January and March, but as soon as the monsoon season started large variation occurred, (Fig. 5). For the higher concentration of BCc, the ak value was almost steady. However, for rainy days, BCc was very less and ak fluctuated. In the present case, for higher BCc, ak > 0, and k880 value is stable during winter, premonsoon and post-monsoon seasons, which indicates small aerosol particles from the same origin deposited on the filter paper. The measured concentration is underestimated in those days because of higher loading on the filter paper. For lower loading of BCc and ak < 0 as observed during monsoon days, which occurred due to the presence of bigger light scattering particles on the filter paper. In winter, premonsoon and post-monsoon seasons, 1.5%, 2%, and 1.2% of negative ak values have observed out of the total dataset, while 44% of negative ak values obtained in the monsoon season. In the monsoon season, 44% of negative ak values signifies the presence of bigger aerosol particles over the station. The negative ak values occurred on the highly humid (RH > 90%) days, which enlarge the size of the aerosol particles over the station.

condensation rate is also higher which enlarges the size of the biomass burning aerosol (Levin et al., 2010; Sakamoto et al., 2016) Even on 23rd September the RH was over 90% whereas on 29th September the RH was less than 90%. Liu et al. (2013) demonstrated that the deposited BC aerosol regularly contained a less-hygroscopic mode at a growth factor of around 1.05 at 90% RH (diameter 163 nm). In the winter, pre-monsoon, and post-monsoon seasons, it is seen that the RH (75 ± 10%, 70 ± 5%, and 77 ± 8%) was less than 90%, whereas in monsoon season RH was more than that of 90% at the station. From the backward trajectory, it is concluded that the bigger particles (scattering nature) transported from BoB were one of the reasons for the negative correlation. The value of compensation parameter k is calculated for each of the seven measurement wavelengths, produces a specific spectral fingerprint, which differs substantially among the different seasons. The seasonal correlation between compensation parameter and wavelengths depicted in Fig. 4. A positive correlation is seen in all the seasons with the variation of ak range from 1.97 × 10−6 to 3.68 × 10−6. In the monsoon, the mean ak value is 60%, 32%, and 8% higher than in the winter, pre-monsoon and post-monsoon seasons. The coating enhances the SSA and decreases the backscatter fraction, which ultimately decreases the compensation parameters (Virkkula et al., 2015). Drinovec et al. (2017) observed a strong negative correlation between the compensation parameter and coating factor, which indicates that the coating is responsible for compensation parameter reduction in the filter-loading effect. They also reported that the enhancement in the mobility diameter [diameter of a spherical particle with the same mobility as the particle in question (Kulkarni et al., 2011)] induces an increment in k880 and due to the coating of BC, the increment of BC diameter causes a higher value of k880. In the monsoon season, as the higher biomass burning aerosols dominated in the presence of high RH (< 90%) atmosphere which sped up the growth of BC aerosol leads to higher compensation parameters. The ak value for each day (total 318 days) is shown in Fig. 5. Nearly 70% of days have ak values above zero and 30% of days have values below zero. The backscatter fractions were calculated as the ratio of the backscattering coefficient and the truncation-corrected total scattering coefficients. The days with ak > 0, have larger backscattering fractions, while the days with ak < 0, signifies less backscattering fraction (Virkkula et al., 2015). Higher backscatter fraction indicates higher backscattering coefficient due to smaller sized particles. Backscatter fraction was higher mainly in winter, pre-monsoon, and post-monsoon days. Whereas, backscatter fraction is smaller for bigger particles, which has been observed more during the monsoon season. In the

3.3. Diurnal variation of BC Fig. 6 illustrates the diurnal variation of BC before and after correction. It is seen that the value of BC increases after correction. During the night and early morning hours, the underestimation is maximum because BC deposition was maximum and the quartz filter quickly reached the threshold value. In the diurnal cycle of BCo and BCc, the underestimation of 9–10% occurs during the morning hours (0700–0900 IST, India Standard Time = UTC+5:30 h) and in the evening hours (1800–2000 IST). While in the day hours (0900–1800 IST) and in the night hours (2000–0700 IST) the underestimation was less than 8%. The diurnal cycle of BC was such that the highest values 110

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lower wind speeds in the evening, all these local factors reduced the ventilation effects and consequently, the BC aerosol confined near to surface caused the secondary peak. The diurnal variation of BCc for all seasons have been shown in Fig. S4. Nevertheless, in diurnal variation, human activities in the morning hours can also contribute to the morning peak. Although the station is not highly industrialized and not a populated city, situated 9 km away of Agartala, the influence of the local activities may not significant in morning hours. Local anthropogenic activities i.e., combustion of wood, bamboo, and cow-dung for cooking by the local villagers, contributed to the morning peak of BC concentration (Guha et al., 2015). In addition to the regional activities, air mass transported the aerosol particles from IGP and continental regions over the station and ensured a dominance of fossil fuel burning BC in the winter season (Guha et al., 2015). Furthermore, the biomass burning contributed to the local source of BC concentration in the winter season (Guha et al., 2015). In the winter, pre-monsoon and post-monsoon seasons, it is seen that the maximum transported aerosols transported from Bangladesh and NER of India (Fig. S1). These transported fine particles also have an influence on the characteristics of the aerosols over the study region.

Fig. 6. Diurnal variation of BCc and BCo on 2nd January 2014 at the station.

were reached at the night and morning hours and the lowest at the day hours. BC's diurnal variation is attributed to the boundary layer dynamics, though variations in emission sources may also be partly responsible for this. The sharp peak occurred between 0700 and 0800 IST after sunrise due to the fumigation effect in the boundary layer (Stull, 1988; Babu and Moorthy, 2002; Begum et al., 2009), which brings a large part of the aerosols and pollutants to the surface after sunrise from the residual nocturnal-boundary layer. As the day progresses, the higher surface heating increases the atmospheric boundary layer (ABL) leads to the enhancement of the turbulent effect that thoroughly mixes and redistributes aerosols to higher heights. This results in faster dispersion and hence dilution of aerosol concentration in the afternoon. The diurnal variation in wind speed, which was higher at the daytime (0930–1700 IST) than the morning and night hours, may also affect the observed low concentration of BC (6.18 ± 1.2 μg m−3) during the afternoon period (see Table 1). The minimum boundary layer mixing and shallow stable-boundary layer causes the aerosols to get accumulated near the ground during 1630–2000 IST, which contributed toward the higher concentration of BC aerosol (33.73 ± 4.44 μg m−3). Moreover, the stable-boundary layer is shallower compared to the unstable boundary layer by a factor of about 3 (Kunhikrishnan et al., 1993). Due to calm wind speed at night hours, the smaller ventilation coefficient causes the aerosol trapping leads to a higher concentration during evening hours. As the night progresses, the reduction in local anthropogenic events and automobile emission leads to a reduction in basic production and consequently, BC concentration (14.06 ± 7.57 μg m−3) decreased. The sedimentation loss of the aerosol near ground also helps to reduce the BC concentration at the night-time. In addition, the fuel burning (both fossils and biofuels) for cooking and other household activities in the adjacent residential areas increased in the evening. Therefore, substantial emissions from the local traffic and the burning of fuels could contribute to the increase in BC concentration in the evening. Furthermore, the burning of biomass (wood, dry leaves, and shrubs) also contributes to the evening peak (40.43 μg m−3) and the morning peak (15.46 μg m−3) during the winter season. In addition to the shallower nocturnal-boundary layer and

3.4. Seasonal variation of BC aerosol Fig. 7 shows the seasonal mean variation between BCc and BCo at the station. In the winter, pre-monsoon, monsoon and post-monsoon seasons, the mean BCc was 8.0 ± 1%, 11.0 ± 1%, 5.5 ± 2%, and 10.5 ± 0.2% higher than that of BCo. In winter, pre-monsoon, and post-monsoon, the underestimation of BC deposition may be due to the higher loading on the filter paper, while in the monsoon season, a higher difference between the BCc and BCo due to higher deposition of the scattering nature aerosols (Figs. S5c and d). From the backward trajectory analysis, it is seen that the maximum air mass transported from the Bay of Bengal in the monsoon season (Figs. S1e-j, 3c). A large scattering type of natural aerosols (mostly sea-salt particles) have been detected over the BoB throughout the year (Satheesh et al., 2010). Figure S5 illustrated the frequency distribution of BC mass concentration for different seasons. For a better analysis, BC concentrations are classified into the smaller intervals of 2.5 μg m−3. A maximum number of BC mass concentration within the range 10–12.5 μg m−3 is obtained in the winter, which occupied 21.4% of total data (Fig. S5a). In the pre-monsoon season, 2.5–5 μg m−3 range has the highest frequency with 36.1% of data in this range (Fig. S5b). The aerosol deposition on the polluted days ranged from 25 to 27.5 μg m−3 on the filter tape during the winter and pre-monsoon seasons and claims 4.2% of the total share. During monsoon season maximum BC concentration ranged from 0 to 2.5 μg m−3, which was 75% of total deposition. In the monsoon season, as the ambient atmosphere was less polluted compared to the other seasons the minimum BC deposition observed and rainfall over this region plays a crucial role to clean the atmosphere. In post-monsoon season, most of the aerosols deposition was within the 2.5–5 μg m−3 range and occupied 34.7% of total data. It is seen that the aerosols deposition increased and reached a peak in winter. 3.5. Seasonal variation of σc and α The wavelength dependency of σc for different seasons are shown in

Table 1 Mean BCc in different periods of 24 h at the station. Season

BCc (μg m−3) Morning (0600–0900 IST)

BCc (μg m−3) Day (0930–1600 IST)

BCc (μg m−3) Evening (1630–2000 IST)

BCc (μg m−3) Night (2030–2330 IST, 0000–0530 IST)

Winter Pre-monsoon Monsoon Post-monsoon

13.82 ± 0.87 9.26 ± 2.79 2.23 ± 0.11 8.04 ± 1.46

8.52 4.29 1.86 4.38

25.97 ± 3.99 15.84 ± 6.48 4.89 ± 1.39 17.17 ± 2.14

17.72 ± 5.40 12.69 ± 3.96 2.77 ± 1.21 9.75 ± 3.49

± ± ± ±

1.35 0.28 0.16 1.48

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Fig. 7. Comparison of seasonal mean of corrected and uncorrected black carbon at the station. The vertical bars represent one standard deviation for the mean.

Fig. 9. Seasonal variation of absorption Ångström exponent at the station for different wavelength pairs.

station. Fig. 9 depicted the seasonal variation of average α370-520, α520-880, and α370-880 at different wavelength pairs (see Table 2). In winter, we obtained the same value of α370-880 reported by the theoretical analysis of fossil fuel burning BC aerosol (Bond and Bergstrom, 2006; Bergstrom et al., 2002; Moosmüller et al., 2009). The average value of α370-520, α520-880, and α370-880 increased from winter to monsoon and again decreased in post-monsoon. In addition, the average α370-520 value and α520-880 value were almost the same in winter and post-monsoon, respectively. Sandradewi et al. (2008) measured light absorption from roadside aerosol and found α ∼1.1, while α of aerosol absorption from wood burning was higher in the range of 1.8–1.9 and Kirchstetter et al. (2004) also obtained similar output from the same origins. The investigation showed that vehicular emissions were the main source of BC aerosol at the station in the winter, pre-monsoon and post-monsoon seasons. Biomass burning emissions were significant in the rural environment of Tripura University in all seasons. Hence, it can be concluded that BC from fossil fuel combustion contributed mainly to the light absorption in winter, which is well consistent with an average α370-880 close to 1. Table 2 compares the absorption Ångström exponent for different wavelength pairs as measured by Aethalometer at Tripura University. The wood or biomass burning (cooking and brick Kiln) are among the regular event around the local area. The fossil fuel emission source restricted particularly to the vehicular emission. While biomass burning occurred every day at the neighboring villages which were the constant source of biomass burning BC aerosol in the monsoon season. But in winter the transported and local vehicular emissions contributed towards higher BC concentration into the atmosphere. The light absorption at all wavelengths was within 1–1.5, indicates the dominance of vehicular emission in the winter season whereas in monsoon season biomass burning emission dominated. The biomass burning events were seen frequently during all seasons at the station. To study the influence of BC sources, we investigated the dependency of k880 on the α370-880 for all months (Fig. S6). Fig. S6 confirm a positive correlation (R = 0.69) between k880 and α370-880. The value of k880 increases with α370-880, showing higher values for biomass burning emissions than vehicular emissions. This is in agreement with the laboratory biomass burning experiment where the high k880 values were obtained. Fig. S5 shows a value for k880 close to 0.0037 at α370880 = 1.02, as expected for exhaust fossil fuel burning as reported by the other researchers (Bond and Bergstrom, 2006; Bergstrom et al., 2002; Moosmüller et al., 2009). It can be concluded that the higher k880 values for biomass burning compared to fossil fuel burning are related to the difference in BC agglomerate size.

Fig. 8. Spectral variation of seasonal mean absorption coefficient over the station.

Fig. 8. The data shows the absorption coefficient has an approximate dependency is in agreement with the fine particle limit for BC aerosol for a constant refractive index. Fuller et al. (1999) and Bond and Bergstrom (2006) demonstrated that the coating comprises of secondary organic and inorganic substances, which is transparent in the visible wavelengths and can influence the light absorption by the aerosol. On average, aerosol absorption was higher in the winter and pre-monsoon seasons than in the monsoon and post-monsoon season's at all seven wavelengths, with a larger difference in the shorter than the longer wavelengths (Fig. 8). At the longer wavelengths of 880 nm and 950 nm, where BC was expected to control the entire absorption, σc was higher in winter and pre-monsoon season, similar to the trend of BC mass concentration. During these months, aerosols at the station were originated locally as well as the transported particles from the northwestern region of India. At shorter wavelengths, the absorption coefficient raised abruptly in the dry season, more significantly at 370 nm wavelength where the absorption coefficient was three times higher than that of monsoon and post-monsoon seasons. This indicates a significant contribution of biomass burning aerosols in the winter and premonsoon season. In the winter, pre-monsoon, monsoon, and postmonsoon seasons, σc at 370 nm was 86%, 90%, 93%, and 51% higher compared to σc at 880 nm. The higher σc at 370 nm was because of the local biomass burning emissions of BC aerosols in the winter and premonsoon seasons. Whereas in monsoon, higher σc at 370 nm than 880 nm was due to the local biomass burning emission around the 112

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Table 2 Monthly variation of BCc, σc, k880 and α at the station. Season Winter Pre-monsoon Monsoon Post-monsoon

BC (μg m−3) 15.76 ± 7.17 10.2 ± 5.54 3.46 ± 1.91 9.14 ± 4.98

σc (m−1) (1.45 (1.18 (0.23 (0.69

± ± ± ±

α370-520

k880 −4

1.01) × 10 0.62) × 10−4 0.16) × 10−4 0.39) × 10−4

−3

(4.1 ± 1.9) × 10 (4.15 ± 2.05) × 10−3 (7.68 ± 9.3) × 10−3 (6.25 ± 3.45) × 10−3

1.06 1.16 1.24 1.15

± ± ± ±

α520-880 0.07 0.14 0.09 0.07

1.06 1.12 1.19 1.16

± ± ± ±

α370-880 0.04 0.06 0.07 0.04

1.04 1.14 1.21 1.16

± ± ± ±

0.06 0.09 0.76 0.06

4. Conclusion

References

The present work has importance as the station influenced by the different types of transported particles and local emissions in different seasons. In the winter, pre-monsoon, monsoon, and post-monsoon seasons, the dominance of fossil fuel, biomass burning, industrial emissions, scattering natural aerosol, dust particles and secondary organic aerosols have a significant contribution to the aerosol radiative forcing over the region, which is not within the scope of the present work. Here, we have studied the seasonal variation of compensation parameter, absorption coefficient, and absorption Ångström exponent at the station. In the monsoon season, as the higher biomass burning aerosols prevailed in the presence of a highly moist atmosphere that stimulated the size of BC aerosol and contributed toward the higher unstable values of compensation parameters. The Compensation parameter values were smaller in winter and pre-monsoon than in the other seasons, which showed a dominance of long-range coated aerosols over the region. In the monsoon, the influence of local biomass burning aerosols observed, which signifies by the higher values of absorption Ångström exponent. Because of the smaller aerosol particles, a higher number of days has ak greater than 0 values (winter, pre-monsoon, and postmonsoon) and vice versa for the bigger aerosol particles in the monsoon. A positive correlation is observed in all the seasons. Out of the total dataset, less number of negative ak values are observed in the winter, pre-monsoon, and post-monsoon seasons and vice versa in the monsoon. In the monsoon, a higher percentage of negative ak values indicated the existence of bigger aerosol particles over the station. The negative ak values observed on the highly humid days, which increased the aerosol's diameter over the station. The underestimation was higher in uncorrected BC concentration in the morning and evening peak of BC concentration than in the daytime and nighttime. Uncorrected BC aerosol was lower than the corrected BC. In the pre-monsoon and post-monsoon seasons, underestimation of BC was higher than in the monsoon and winter seasons. For the higher loading of BC aerosol, the underestimation occurred in the winter, premonsoon and post-monsoon seasons, while in the monsoon, the underestimation occurred due to the scattering natural aerosol deposition on the filter tape. Nevertheless, as the biomass burning events escalated from winter to the monsoon, the absorption coefficient at 370 nm is increased than at 880 nm, absorption Ångström exponent also increased from 1 to 1.5 and decreased in the post-monsoon season. Absorption Ångström exponent was approximately the same in the winter and post-monsoon seasons for shorter and longer wavelength pairs. The α370-880 was 1.02 for vehicular emission and its corresponding compensation parameter was 0.0037. The average absorption Ångström exponent and compensation parameter for vehicle emission was lower than the biomass burning emission.

Ackerman, A.S., Toon, O.B., Stevens, D.E., Heymsfield, A.J., Ramanathan, V., Welton, E.J., 2002. Reduction of tropical cloudiness by soot. Science 288, 1042–1047. Allen, J.O., Sarom, A.F., Smith, K.A., 1999. Thermodynamic Properties of Polycyclic Aromatic Hydrocarbons in the Subcooled Liquid State. Polycyclic Aromatic Compounds. AP, 2012. Annual Plan 2011–12. Department of Agriculture, Government of Tripura. Arnott, W.P., Hamasha, K., Moosmuller, H., Sheridan, P.J., Ogren, J.A., 2005. Towards aerosol light absorption measurements with a 7-wavelength Aethalometer: evaluation with a photoacoustic instrument and 3-wavelength Nephelometer. Sci. Technol. 39, 17–29. https://doi.org/10.1080/027868290901972. GMR, 2011. Geology and Mineral Resources of Manipur, Mizoram, Nagaland, and Tripura. Miscellaneous Publication No. 30 Part IV. 1 (Part-2). Geological Survey of India, Government of India. Babu, S.S., Moorthy, K.K., 2002. Aerosol black carbon over a tropical coastal station in India. Geophys. Res. Lett. 29 (23), 2098. https://doi.org/10.1029/2002GL015662. Badrinath, K.V.S., Kharol, S.K., Kaskaoutis, D.G., Kambezidis, H.D., 2007. Influence of atmospheric aerosols on solar spectral irradiance in an urban area. J. Atmos. Sol. Terr. Phys. 69, 589–599. Barman, N., Saha, B., Borgohain, A., Kundu, S.S., Roy, R., Raju, P.L.N., 2018. Investigation of curvature effect of Angstrom exponent to classify the aerosol types over the region of interest (88°-98° E and 20°-30° N). Atmos. Pollut. Res. https://doi. org/10.1016/j.apr.2018.09.002. Begum, B.A., Paul, S.K., Hossain, M.D., Biswas, S.K., Hopke, P.K., 2009. Indoor air pollution from particulate matter emissions in different households in rural areas of Bangladesh. Build. Environ. 44, 898–903. Begum, B.A., Biswas, S.K., Pandit, G.G., Saradhi, I.V., Waheed, S., Siddique, N., Seneviratne, M.C.S., Cohen, D.D., Markwitz, A., Hopke, P.K., 2011. Long-range transport of soil dust and smoke pollution in the South Asian region. Atmos. Pollut. Res. 2, 151–157. Begum, B.A., Hopke, P.K., Markwitz, A., 2013. Air pollution by fine particulate matter in Bangladesh. Atmos. Pollut. Res. 4 (75), 86. https://doi.org/10.5094/APR.2013.008. Bergstrom, R.W., Russell, P.B., Hignett, P., 2002. On the wavelength dependence of the absorption of black carbon particles: predictions and results from the TARFOX experiment and implications for the aerosol single scattering albedo. J. Atmos. Sci. 59 (3), 567–577. Bergstrom, R.W., Pilewskie, P., Russell, P.B., Redemann, J., Bond, T.C., Quinn, P.K., Sierau, B., 2007. Spectral absorption properties of atmospheric aerosols. Atmos. Chem. Phys. 7, 5937e5943. Bonasoni, P., Laj, P., Marinoni, A., Sprenger, M., Angelini, F., Arduini, J., Bonafè, U., Calzolari, F., Colombo, T., Decesari, S., Di Biagio, C., di Sarra, A.G., Evangelisti, F., Duchi, R., Facchini, M.C., Fuzzi, S., Gobbi, G.P., Maione, M., Panday, A., Roccato, F., Sellegri, K., Venzac, H., Verza, G.P., Villani, P., Vuillermoz, E., Cristofanelli, P., 2010. Atmospheric Brown Clouds in the Himalayas: first two years of continuous observations at the Nepal Climate Observatory-Pyramid (5079 m). Atmos. Chem. Phys. 10, 7515–7531. Bond, T.C., Bergstrom, R.W., 2006. Light absorption by carbonaceous particles: an investigative review. Aero. Sci. Technol. 40 (1), 27–67. Bond, T.C., Anderson, T.L., Campbell, D., 1999. Calibration and intercomparison of filterbased measurements of visible light absorption by aerosols. Aerosol Sci. Technol. 30, 582–600. https://doi.org/10.1080/027868299304435. Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., DeAngelo, B.J., Flanner, M.G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P.K., Sarofim, M.C., Schultz, M.G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S.K., Hopke, P.K., Jacobson, M.Z., Kaiser, J.W., Klimont, Z., Lohmann, U., Schwarz, J.P., Shindell, D., Storelvmo, T., Warren, S.G., Zender, C.S., 2013. Bounding the role of black carbon in the climate system: a scientific assessment. J. Geophys. Res. 118, 5380–5552. https://doi.org/10.1002/jgrd.50171. Chakrabarty, R.K., Moosmüller, H., Chen, L. W. a., Lewis, K., Arnott, W.P., Mazzoleni, C., Dubey, M.K., Wold, C.E., Hao, W.M., Kreidenweis, S.M., 2010. Brown carbon in tar balls from smoldering biomass combustion. Atmos. Chem. Phys. 10 (13), 6363–6370. Chen, Y., Bond, T.C., 2010. Light absorption by organic carbon from wood combustion. Atmos. Chem. Phys. 10, 1773e1787. Collaud Coen, M., Weingartner, E., Apituley, A., Ceburnis, D., Fierz-Schmidhauser, R., Flentje, H., Henzing, J.S., Jennings, S.G., Moerman, M., Petzold, A., Schmid, O., Baltensperger, U., 2010. Minimizing light absorption measurement artifacts of the Aethalometer: evaluation of five correction algorithms. Atmos. Meas. Tech. 3, 457–474. https://doi.org/10.5194/amt-3-457-2010. Corrigan, C.E., Ramanathan, V., Schauer, J.J., 2006. Impact of monsoon transitions on the physical and optical properties of aerosols. J. Geophys. Res. 111, D18208. https:// doi.org/10.1029/2005JD006370. Cristofanelli, P., Putero, D., Adhikary, B., Landi, T.C., Marinoni, A., Duchi, R., Calzolari,

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.036.

113

Atmospheric Environment 212 (2019) 106–115

N. Barman, et al.

burning smoke aerosol properties measured during Fire Laboratory at Missoula Experiments (FLAME). J. Geophys. Res. 115, D18210. https://doi.org/10.1029/ 2009JD013601. Lewis, K., Arnott, W.P., Moosmuller, H., Wold, C.E., 2008. Strong spectral variation of biomass smoke light absorption and single scattering albedo observed with a novel dual-wavelength photoacoustic instrument. J. Geophys. Res. Atmos. 113. https://doi. org/10.1029/2007jd009699. Liu, D., Allan, J., Whitehead, J., Young, D., Flynn, D., Coe1, H., McFiggans, G., Fleming, Z.L., Bandy, B., 2013. Ambient black carbon particle hygroscopic properties controlled by mixing state and composition. Atmos. Chem. Phys. 13, 2015–2029. https:// doi.org/10.5194/acp-13. LSC, 2012. Land, Soil, and Climate. Department of Agriculture, Government of Tripura. Molod, A., Takacs, L., Suárez, M., Bacmeister, J., 2015. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA 2. Geosci. Model Dev. (GMD) 8, 1339–1356. https://doi.org/10.5194/gmd-8-1339-2015. Moorthy, K.K., Babu, S.S., Satheesh, S.K., 2007. Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station. Part 1: microphysical and optical properties. Ann. Geophys. 25, 2293–2308. Moosmüller, H., Chakrabarty, R.K., Arnott, W.P., 2009. Aerosol light absorption and its measurement: a review. J. Quant. Spectrosc. Radiat. Transfer 110 (11), 844–878. MYRT, 2012. Monthly and Yearly Quinquennial Average Rainfall in Tripura. Statistical Abstract of Tripura – 2007. Directorate of Economics & Statistics, Planning. Statistics) Department, Government of Tripura, pp. 13. Nair, V.S., Moorthy, K.K., Alappattu, D.P., Kunhikrishnan, P.K., George, S., Nair, P.R., Babu, S.S., Abish, B., Satheesh, S.K., Tripathi, S.N., Niranjan, K., Madhavan, B.L., Srikant, V., Dutt, C.B.S., 2007. Winter time aerosol characteristics over the IndoGangetic plain (IGP): impacts of the local boundary layer processes and long range transport. J. Geophys. Res. 112. Ortega, A.M., Day, D.A., Cubison, M.J., Brune, W.H., Bon, D., de Gouw, J.A., Jimenez, J.L., 2013. Secondary organic aerosol formation and primary organic aerosol oxidation from biomass-burning smoke in a flow reactor during FLAME-3. Atmos. Chem. Phys. 13, 11551–11571. https://doi.org/10.5194/acp-13- 11551. Pathak, B., Subba, T., Dahutia, P., Bhuyan, P.K., Moorthy, K.K., Gogoi, M.M., Babu, S.S., Chutia, L., Ajay, P., Biswas, J., Bharali, C., Borgohain, A., Dhar, P., Guha, A., De, B.K., Banik, T., Chakraborty, M., Kundu, S.S., Sudhakar, S., Singh, S.B., 2015. Aerosol characteristics in North-East India using ARFINET spectral optical depth Measurements. Atmos. Environ. https://doi.org/10.1016/j.atmosenv.2015.07.038. Petzold, A., Schloesser, M., Sheridan, P.J., Arnott, W.P., Ogren, J.A., Virkkula, A., 2005. Evaluation of multi-angle absorption photometry for measuring aerosol light absorption. Aerosol Sci. Technol. 39, 40–51. https://doi.org/10.1080/ 027868290901945. Pósfai, M., Simonics, R., Li, J., Hobbs, P.V., Buseck, P.R., 2003. Individual aerosol particles from biomass burning in southern Africa: 1. Compositions and size distributions of carbonaceous particles. J. Geophys. Res. 108. https://doi.org/10.1029/ 2002JD002291. Radojevic, M., 2003. Chemistry of forest fires and regional haze with emphasis on Southeast Asia. Pure Appl. Geophys. 160, 157–187. Ramanathan, V., Carmichael, G., 2008. Global and regional climate changes due to black carbon. Nat. Geosci. 1, 221–227. https://doi.org/10.1038/ngeo156. Reddington, C.L., Yoshioka, M., Balasubramanian, R., Ridley, D., Toh, Y.Y., Arnold, S.R., Spracklen, D.V., 2014. Contribution of vegetation and peat fires to particulate air pollution in Southeast Asia. Environ. Res. Lett. 9, 094006. Sakamoto, K.M., Laing, J.R., Stevens, R.G., Jaffe, D.A., Jeffrey, R., Pierce, J.R., 2016. The evolution of biomass-burning aerosol size distributions due to coagulation: dependence on fire and meteorological details and parameterization. Atmos. Chem. Phys. 16, 7709–7724. https://doi.org/10.5194/acp-16-7709. Sandradewi, J., Prevot, A.S.H., Weingartner, E., Schmidhauser, R., Gysel, M., Baltensperger, U., 2008. A study of wood burning and traffic aerosols in an Alpine valley using a multi-wavelength Aethalometer. Atmos. Environ. 42, 101–111. Satheesh, S.K., Vinoj, V., Moorthy, K.K., 2010. Assessment of aerosol radiative impact over oceanic regions adjacent to Indian subcontinent using multi-satellite analysis. Hindawi Publ. Corp. Adv. Meteorol. https://doi.org/10.1155/2010/139186. 2010, Article ID 139186, 13 pages. Schnaiter, M., Linke, C., Möhler, O., Naumann, K.-H., Saathoff, H., Wagner, R., Schurath, U., 2005. Absorption amplification of black carbon internally mixed with secondary organic aerosols. J. Geophys. Res. 110, D19204. https://doi.org/10.1029/ 2005JD006046. SHD, 2007. The State of Human Development. Tripura Human Development Report 2007. Government of Tripura. Singh, T.P., Singh, S., Roy, P.S., Rao, B.S.P., 2002. Vegetation mapping and characterization in West Siang District of Arunachal Pradesh, India—a satellite remote sensingbased approach. Curr. Sci. 83 (10), 1221–1230. Song, S., Wu, Y., Xu, J., Ohara, T., Hasegawa, S., Li, J., Yang, L., Hao, J., 2013. Black carbon at a roadside site in Beijing: temporal variations and relationships with carbon monoxide and particle number size distribution. Atmos. Environ. 77, 213–221. Stein, A.F., Draxler, R.R., Rolph, G.D., Stunder, B.J.B., Cohen, M.D., Ngan, F., 2005. NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-14-00110.1. Stull, R.B., 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, Dordrecht. Vadrevu, K.P., Ellicott, E., Badarinath, K.V.S., Vermot, E., 2011. MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season north India. Environ. Pollut. 159, 1560–1569. Vadrevu, K.P., Csiszar, I., Ellicott, E., Giglio, L., Badarinath, K.V.S., Vermote, E., Justice, C., 2013. Hotspot analysis of vegetation fires and intensity in the Indian region. IEEE Appl. Earth Obs. Remote Sens. 6 (1).

F., Laj, P., Stocchi, P., Verza, G., Vuillermoz, E., Kang, S., Ming, G., Bonasoni, P., 2014. Transport of short-lived climate forcers/pollutants (SLCF/P) to the Himalayas during the South Asian summer monsoon onset. Environ. Res. Lett. 9, 084005. Cubison, M.J., Ortega, A.M., Hayes, P.L., Farmer, D.K., Day, D., Lechner, M.J., Brune, W.H., Apel, E., Diskin, G.S., Fisher, J.A., Fuelberg, H.E., Hecobian, A., Knapp, D.J., Mikoviny, T., Riemer, D., Sachse, G.W., Sessions, W., Weber, R.J., Weinheimer, A.J., Wisthaler, A., Jimenez, J.L., 2011. Effects of aging on organic aerosol from open biomass burning smoke in aircraft and laboratory studies. Atmos. Chem. Phys. 11, 12049–12064. https://doi.org/10.5194/acp-11-12049. Drinovec, L., Mocnik, G., Zotter, P., Prévôt, A.S.H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., Hansen, A.D.A., 2015. The “dual-spot” Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation. Atmos. Meas. Tech. 8, 1965. https://doi.org/10.5194/amt-81965. Drinovec, L., Gregorič, A., Zotter, P., Wolf, R., Bruns, E.A., Prévôt, A.S.H., Petit, J.-E., Favez, O., Sciare, J., Arnold, I.J., Chakrabarty, R.K., Moosmüller, H., Filep, A., Močnik, G., 2017. The filter-loading effect by ambient aerosols in filter absorption photometers depends on the coating of the sampled particles. Atmos. Meas. Tech. 10, 1043–1059. https://doi.org/10.5194/amt-10-1043-2017. Fuller, K.A., Malm, W.C., Kreidenweis, S.M., 1999. Effects of mixing on extinction by carbonaceous particles. J. Geophys. Res. 104https://doi.org/10.1029/ 1998JD100069. 941–915. Gogoi, M.M., Moorthy, K.K., Kumar, K.S., Chaubey, J.P., Babu, S.S., Manoj, M.R., Nair, V.S., Prabhu, T.P., 2014. Physical and optical properties of aerosols in a free Tropospheric environment: results from long-term observations over western transHimalayas. Atmos. Environ. 84, 262–274. 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. https://doi.org/10.4209/aaqr.2014.02.0029. Hansen, A.D.A., Rosen, H., Novakov, T., 1982. Real-time measurement of the aerosol absorption coefficient of aerosol particles. Appl. Optic. 21, 3060–3062. https://doi. org/10.1364/AO.21.003060. Heintzenberg, J., Charlson, R.J., Clarke, A.D., Liousse, C., Ramaswamy, V., Shine, K.P., Wendish, M., Helas, G., 1997. Measurements and modeling of aerosol single scattering albedo: progress, Problems, and Prospects. Beitr. Phys. Atmos. 70, 249–263. Hennigan, C.J., Miracolo, M.A., Engelhart, G.J., May, A.A., Presto, A.A., Lee, T., Sullivan, A.P., McMeeking, G.R., Coe, H., Wold, C.E., Hao, W.M., Gilman, J.B., Kuster, W.C., de Gouw, J., Schichtel, B.A., Collett, J.L., Kreidenweis, S.M., Robinson, A.L., 2011. Chemical and physical transformations of organic aerosol from the photo-oxidation of open biomass burning emissions in an environmental chamber. Atmos. Chem. Phys. 11, 7669–7686. https://doi.org/10.5194/acp-11-7669. Heringa, M.F., DeCarlo, P.F., Chirico, R., Tritscher, T., Dommen, J., Weingartner, E., Richter, R., Wehrle, G., Prévôt, A.S.H., Baltensperger, U., 2011. Investigations of primary and secondary particulate matter of different wood combustion appliances with a high-resolution time-of-flight aerosol mass spectrometer. Atmos. Chem. Phys. 11, 5945–5957. https://doi.org/10.5194/acp-11-5945. Hitzenberger, R., Petzold, A., Bauer, H., Ctyroky, P., Pouresmaeil, P., Laskus, L., Puxbaum, H., 2006. Inter-comparison of thermal and optical measurement methods for elemental carbon and black carbon at an urban location. Environ. Sci. Technol. 40, 6377–6383. Hopke, P.K., Cohen, D.D., Begum, B.A., Biswas, S.K., Ni, B., Pandit, G.G., Santoso, M., Chung, Y.S., Davy, P., Markwitz, A., Waheed, S., Siddique, N., Santos, F.L., Pabroa, P.C.B., Seneviratne, M.C.S., Wimolwattanapun, W., Bunprapob, S., Vuong, T.B., Duy Hien, P., Markowicz, A., 2008. Urban air quality in the Asian region. Sci. Total Environ. 404 (103), 112. Im, J., Saxena, V.K., Wenny, B.N., 2001. An assessment of hygroscopic growth factors or aerosols in the surface boundary layer for computing direct radiative forcing. J. Geophys. Res. 10, 20213–20224. Jacobson, M.Z., 2001. Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature 409, 695–697. Janhäll, S., Andreae, M.O., Pöschl, U., 2010. Biomass burning aerosol emissions from vegetation fires: particle number and mass emission factors and size distributions. Atmos. Chem. Phys. 10, 1427–1439. https://doi.org/10.5194/acp-10-1427. Kirchstetter, T.W., Novakov, T., Hobbs, P.V., 2004. Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. J. Geophys. Res. Atmos. 109. https://doi.org/10.1029/2004jd004999. Kleist, D.T., Parrish, D.F., Derber, J.C., Treadon, R., Wu, W.S., Lord, S., 2009. Introduction of the GSI into the NCEPs global data assimilation system. Weather Forecast. 24, 1691–1705. https://doi.org/10.1175/2009WAF2222201.1. Kulkarni, P., Baron, P.A., Willeke, K., 2011. Aerosol Measurement: Principles, Techniques, and Applications, third ed. Wiley, Hoboken NJ. Kundu, S.S., Borgohain, A., Barman, N., Devi, M., Raju, P.L.N., 2018. Spatial variability and radiative impact of aerosol along the Brahmaputra river valley in India: results from A campaign. J. Environ. Prot. 9, 405–430. https://doi.org/10.4236/Jep.2018. 94026. Kunhikrishnan, P.K., Gupta, K.S., Ramachandran, R., Prakash, W.J., Nair, K.N., 1993. Study on thermal internal boundary layer structure over thumba, India. Ann. Geophys. 11, 52–60. Laborde, M., Crippa, M., Tritscher, T., Jurányi, Z., Decarlo, P.F., Temime-Roussel, B., Marchand, N., Eckhardt, S., Stohl, A., Baltensperger, U., Prévôt, A.S.H., Weingartner, E., Gysel, M., 2013. Black carbon physical properties and mixing state in the European megacity Paris. Atmos. Chem. Phys. 13, 5831–5856. https://doi.org/10. 5194/acp-13-5831-2013. Levin, E.J.T., McMeeking, G.R., Carrico, C.M., Mack, L.E., Kreidenweis, S.M., Wold, C.E., Moosmüller, H., Arnott, W.P., Hao, W.M., Collett, J.L., Malm, W.C., 2010. Biomass

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Atmospheric Environment 212 (2019) 106–115

N. Barman, et al.

Weingartner, E., Saatho, H., Schnaiterb, H., Streita, N., Bitnarc, B., Baltenspergera, U., 2003. Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometer. Aerosol Sci. 34, 1445–1463. Wu, W.S., Purser, R.J., Parrish, D.F., 2002. Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Weather Rev. 130, 2905–2916. https:// doi.org/10.1175/1520-0493(2002)130, 2905:TDVAWS.2.0.CO; 2.

Virkkula, A., Mäkelä, T., Yli-Tuomi, T., Hirsikko, A., Koponen, I.K., Hämeri, K., Hillamo, R., 2007. A simple procedure for correcting loading effects of Aethalometer data. J. Air Waste Manag. 57, 1214–1222. https://doi.org/10.3155/1047-3289.57.10.1214. Virkkula, A., Chi, X., Ding, A., Shen, Y., Nie, W., Qi, X., Zheng, L., Huang, X., Xie, Y., Wang, J., Petäjä, T., Kulmala, M., 2015. On the interpretation of the loading correction of the Aethalometer. Atmos. Meas. Tech. 8, 4415–4427.

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