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Seasonal heterogeneity in aerosol types over Dibrugarh-North-Eastern India
Atmospheric Environment 47 (2012) 307e315
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Seasonal heterogeneity in aerosol types over Dibrugarh-North-Eastern India Binita Pathak a, Pradip Kumar Bhuyan a, *, Mukunda Gogoi b, Kalyan Bhuyan a a b
Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh 786 004, India Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram 695022, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 July 2011 Received in revised form 20 October 2011 Accepted 29 October 2011
Columnar aerosol properties retrieved from Multi-Wavelength solar Radiometer (MWR) measurements during the period 2001e2010 over Dibrugarh (27.3 N, 94.6 E, 111 m amsl), North-Eastern India are analyzed to identify the types of aerosols in the atmospheric column. Highest Aerosol optical depth (AOD) characterizes the pre-monsoon (MarcheMay), while lowest AOD has been observed during the post-monsoon (OcteNov) season. The Ångström exponent (a) indicates predominance of fine aerosols during post-monsoon and winter (DeceFeb) and dominance of coarse mode in pre-monsoon and monsoon (JuneeSept). NOAA HYSPLIT back trajectory analysis suggests that the seasonal heterogeneity in aerosol characteristics can be attributed to the varying contribution from different source regions. Using the relationship between AOD500 and a, the aerosols can be classified into five main types viz. continental average (CA), marine continental average (MCA), urban/industrial and biomass burning (UB) and desert dust (DD) while the remaining cases are considered as unidentified or mixed type (MT). These aerosol types exhibit seasonal heterogeneity in their contribution depending upon variability in sources. In winter, local production contributes to observed appreciable CA aerosol type, while highest percentage of UB type is attributed to both local and transported aerosols. On the other hand, transported UB and DD types play a significant role in the pre-monsoon season. Post-monsoon season is indicative of background continental average aerosol condition with a significant contribution from CA and MCA aerosols. Monsoon aerosols couldn’t be distinguished properly due to different particle growth processes like humidification, hygroscopic growth etc. and hence MT aerosol type is predominant in this season. This is the first ever attempt to classify aerosols over this environment. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Aerosol type Multi-wavelength solar Radiometer AOD Ångström exponent HYSPLIT
1. Introduction The atmospheric aerosols along with green house gases are one of the principal internal agents of climate change (Kaufman et al., 2002). However, the regional and global impact of atmospheric aerosols on climate is still uncertain owing to the large heterogeneity in their spatial and temporal distribution throughout the globe. This heterogeneity results from the variability of sources or origin of different aerosols as well as their short residence time in the atmosphere (Textor et al., 2006; Kinne et al., 2006). Consequently aerosols present the largest source of uncertainties in the model simulations of climate change (Ferrare et al., 2005). This is because describing the aerosol characteristics as input to these models is vital for determining the uncertainties in simulations of aerosol radiative forcing and hence climate change. The heterogeneity in spatial and * Corresponding author. E-mail addresses: [email protected] (B. Pathak), [email protected] (P.K. Bhuyan), [email protected] (M. Gogoi), [email protected] (K. Bhuyan). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.10.061
temporal distribution also leads to presence of different aerosol types in both spatial and temporal domain. Based on various measurements over the globe, aerosols can generally be classified into four main types namely biomass-burning aerosols produced by forest and grassland fires, urban/industrial aerosols from fossil fuel combustion in populated urban/industrial regions, desert dust blown into the atmosphere by wind and aerosol of maritime origin (Kaskaoutis et al., 2007a). Each of these types can be further divided into others depending on aerosol absorbing or scattering capabilities, sphericity, chemical composition, mineralogy, etc. Due to strong dependence of both AOD and Ångström exponent on wavelength, a realistic characterization of aerosol properties can be attempted using these two parameters (Holben et al., 2001) and therefore, are widely used to identify different aerosol types over the globe (Kaskaoutis et al., 2007b and references therein). The geographical diversity and meteorological pattern driven primarily by regional monsoon over the Indian subcontinent implicitly ensures a very diverse and complex aerosol environment, which needs to be investigated for the aerosol types present in each zone and their impact on both regional and global climate.
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Moreover, because of the rapid growth of anthropogenic activities and the possible regional and global climatic impacts from aerosol emissions the characterization of aerosol properties over both land and ocean is of great importance (Satheesh et al., 2006). Earlier studies over Dibrugarh, have revealed that the topography of the location and surrounding areas make the site prone to heavy external influences almost throughout the year. For example, west Asian locations and the Indo-Gangetic Plains (IGP) are the potential source regions, which contribute to total aerosol abundance over Dibrugarh particularly in pre-monsoon and winter as obtained from trajectory clustering and concentration weighted trajectory analysis (Gogoi et al., 2011). Gogoi et al. (2011) have found moderate to strong (33%e72%) contribution of near surface aerosols to columnar abundance during post-monsoon and winter seasons, while heterogeneity between surface and column aerosol properties exist in the other two seasons. This is also supported by the existence of elevated aerosol layer over the north-east region during pre-monsoon season detected by spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization observations (Sharma et al., 2009). Further, Gogoi et al. (2009) have affirmed that the columnar aerosol properties closely resemble that of a typical coastal or arid environment during pre-monsoon and monsoon seasons while in winter it behaves almost like a polluted and anthropogenically active site. Therefore, the post-monsoon season having low external influence can be treated as the season representing background aerosol environment. It is thus expected that the relative concentration of each aerosol type shall vary with season. In this report, an attempt has been made to identify the aerosol types and examine the heterogeneity in their seasonal distribution over Dibrugarh. So far only Kaskaoutis et al. (2009) have identified and characterized the aerosol types over Hyderabad, a location within the Indian subcontinent. A clear understanding of the seasonal aerosol types will also help in retrieval of more accurate physico-optical parameters of aerosols by satellite remote sensing over Dibrugarh as the satellite-retrieval algorithms rely on assumptions about the optical properties of different aerosol types (Kaufman et al., 1997; King et al., 1999).
Fig. 1. Map of North-East India and the adjoining region showing the study location Dibrugarh (27.3 N, 94.6 E, 111 m amsl). The study region is also shown within a map of India (inset).
maximum and minimum temperatures respectively in the panels below. Climatologically winter is the driest season of the year at Dibrugarh (along with entire North-East India) with least rainfall (w4% of the annual), while monsoon receives maximum (63%) of annual rainfall with monthly rainy days varying between w15e25 days. The RH is always >70% with maximum during monsoon (80%). The minimum temperature ranges from w8 C in winter to w20 C in monsoon, while the range of maximum temperature is w21 C to 36 C in monsoon.
2. Site description and meteorology The present study location (Dibrugarh: 27.3 N, 94.6 E, 111 m amsl), is situated on the southern bank of river Brahmaputra in eastern Assam, close to the North-Eastern boundary of the Indian subcontinent (Fig. 1). It is a rural, continental site and slightly affected by anthropogenic activities. The environment of Dibrugarh which is surrounded by large number of tea plantations, rivers and rivulets is not conducive for local dust production except during dry seasons and is quite far away from the active dust source regions of west Asia and western/central India. During dry periods some rare dust events which occur locally are capable of uplifting soil dust from the Brahmaputra basin (Sharma et al., 2009). However, transportation through the natural gateway in western Assam is capable of dumping aerosols over the site. Besides, the transportation from the oil wells situated mostly in North-East direction together with the vehicular emissions from the national highway running through the University campus and the seasonal biomass burning activities in the nearby hills are the other possible sources of aerosols (Pathak et al., 2010). On the basis of the distribution of long-term meteorological characteristics, a year is classified into four seasons, namely winter (December to February), pre-monsoon (March to May), monsoon (June to September) and post-monsoon (October and November). The annual variation of number of rainy days and monthly total rainfall at Dibrugarh is shown in the top two panels of Fig. 2 followed by monthly mean values of relative humidity (RH) as well as
Fig. 2. Annual variations of monthly total rainy days and rainfall (top two panels) followed by monthly mean values of relative humidity (RH) as well as maximum (TMax) and minimum (TMin) temperatures.
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3. Data and measurement uncertainty Spectral AOD in the wavelength range 380 nme1025 nm has been estimated employing a ground based Multi-Wavelength solar Radiometer (MWR) for the period October, 2001 to November, 2010, following Langley plot technique (e.g. Gogoi et al., 2009; Pathak et al., 2010). The variance of the Langley intercept (typically 5%) along with the other uncertainties due to water vapor, ozone or NO2 absorption etc. puts the overall uncertainty in AOD measurement in the range of 0.02e0.03 at different wavelengths (Gogoi et al., 2009). The details of the instrument, method of analysis and error budget are discussed earlier in detail by several investigators (Moorthy et al., 1997, 2007; Gogoi et al., 2009 and references therein). In this study, 974 data sets measured on 719 days for nine years from October, 2001 to November, 2010 have been utilized. The number of data sets varies seasonally due to variability in sky conditions. The largest number of data set is for winter (42%) due to the stable and sunny weather condition followed by post-monsoon (27%). Relatively few measurements could be made during the pre-monsoon (18%) and particularly in monsoon (13%) due to cloud cover and prevailing rainy weather. The data collected during a day is also separated into forenoon and afternoon whenever the Langley plot showed a change in slope from forenoon to afternoon, which is considered as independent data in calculating daily average of AOD. Data collected is considered as a single set if it spans only for 3e4 h and the AOD is considered as mean for the day. 4. Results and discussion 4.1. Spectral and temporal variations of aerosol properties The climatological mean AODs are estimated by grouping together the individual months of different years and are presented in Fig. 3, at four representative wavelengths (380, 500, 750
Fig. 3. Annual variation of Aerosol optical depth at 380 nm, 500 nm, 750 nm and 1025 nm averaged over the period October, 2001eNovember, 2010.
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and 1025 nm) spanning over the spectral range covered by the MWR. Seasonally, AOD attains its peak level in pre-monsoon, which decreasing through monsoon attains the lowest value in post-monsoon. AOD then starts to buildup in winter. Comparison of this seasonal cycle observed from the present analysis with that reported by Gogoi et al. (2009) shows that it is consistent for all the MWR wavelengths and for all the years of observation. AOD decays exponentially from shorter to longer wavelengths though in some cases higher AOD values at longer wavelengths 935 nm and 1025 nm than that at 750 nm have also been observed. This may be attributed to the effect of water vapor at 935 nm. However, the extreme MWR wavelength 1025 nm has very weak absorption cross-section of water vapor and this small contribution of water vapor absorption to the total optical depth are removed using the columnar water vapor estimated from the 935 nm channel of the MWR following Nair and Moorthy (1998). Hence, the higher AOD values at 1025 nm may be due to abundance of coarse mode aerosols in the particular months (Pathak et al., 2010). Similar observation of higher AOD at longer wavelength has been reported earlier from Maitri, Antarctica (Gadhavi and Jayaraman, 2004). Higher AOD in pre-monsoon may be attributed to long-range transportation of mineral dust from the arid regions of west Asia and North-West India across the IGP (Gogoi et al., 2009). Many recent studies (Gautam et al., 2010 and references therein) have revealed that IGP is influenced by significant dust transported from the northwestern arid regions that constitutes the bulk of the regional aerosol loading. The maximum pre-monsoon AOD over Dibrugarh may also be attributed to the highest biomass burning associated with shifting cultivation practices taking place during MarcheApril in North-East India every year (Badarinath et al., 2004). The lowering of aerosol loading in monsoon is associated with wet removal processes. The combined effect of local
Fig. 4. Annual variation of Ångström parameters (a) a and (b) b for the MWR wavelength range 380e1025 nm.
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meteorological conditions and topography of the region results in observed lowest AOD in post-monsoon (Gogoi et al., 2011). Aerosol loading starts to build up by end of post-monsoon leading to continued high AODs in winter under favorable meteorological
conditions. This is also associated with the enhanced black carbon emissions and concentration over the location (Pathak et al., 2010). Ångström exponent, a is an indicator of aerosol particle size, which is obtained from the Ångström’s formula (1961) as
Fig. 5. Air mass back trajectories, grouped according to the regions covered and arriving at (a) 500 m AGL and (b) 1800 m AGL over Dibrugarh. The vertical bars over the mean line show the spatial spread of each group. (c) Air mass back trajectories, grouped according to the regions covered and arriving at 3600 m AGL over Dibrugarh. The vertical bars over the mean line show the spatial spread of each group.
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Fig. 5. (continued).
s ¼ bla
(1)
where s is AOD at wavelength l (in mm) and b is the optical depth at l ¼ 1 mm, which is related to aerosol loading in the column and is known as the turbidity coefficient (Ångström, 1964). The value of a is higher when the columnar aerosol size spectrum has larger relative dominance of accumulation mode aerosols (size < 1 mm). In the present study a estimated from the regression analysis of lns versus lnl is found to be 1 for the pre-monsoon and monsoon seasons (Fig. 4). This is associated with desert dust and sea salt particles according to Eck et al. (1999) and Westphal and Toon (1991), but may also be due to hygroscopic growth of aerosols under high ambient relative humidity (>75%) particularly in monsoon season. Earlier, Remer and Kaufman (1998) have reported that particle growth of urban/industrial aerosols at high RH condition along with cloud contamination results in great variability in aerosol size. The value of a w 1.5 indicates clear dominance of fine-mode aerosols whereas a w 1 show a bimodal distribution where the coarse mode may also have a significant fraction (Eck et al., 2005). The turbidity coefficient b remains low (<0.2) for most part of the year (July to January). The highest aerosol loading occurs during March (b w 0.32 0.04) (Fig. 4). 4.2. Contribution of transported aerosols: identification of source regions With a view to determine the possible flow paths and source regions of air flow and in order to establish a link between the synoptic air masses with the seasonal variation of aerosol loading over Dibrugarh, five day isentropic back trajectories were computed using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model of National Atmospheric and Oceanic
Administration (NOAA). To delineate the distinct mean pathways of the trajectories, Ward’s hierarchical method (Ward, 1963) has been used to form clusters by combining the nearest trajectories. The seasonal mean airmass back trajectories grouped according to regions covered and arriving over Dibrugarh at different altitudes viz. 500 m (Fig. 5 (a)), 1800 m (Fig. 5 (b)) and 3600 m (Fig. 5 (c)) following considerations by Moorthy et al. (2003) are being discussed below. During pre-monsoon the majority of air masses are westerly coming mainly from IGP at lower two altitudes and almost all from West Asia, traversing IGP at 3600 m. The 3600 m trajectories may transport dust aerosols from Sahara and other African regions to Dibrugarh, as they generally interact with the boundary layer over the arid and semiarid locations (Kaskaoutis et al., 2009). The low level air masses are mainly from arid regions in north-western India and Pakistan. Advection from above mentioned regions carries coarse mode aerosols, resulting in lower value of a. Also significant number (62%) of trajectories carries aerosols from eastern India coast at 500 m, where densely populated areas and industries are situated. Local fine dusts from the Brahmaputra lifted by comparatively strong surface winds and convection activities due to surface heating and fine-mode particles from the nearby oil wells may also lead to increased aerosol loading within the boundary layer (500 m AGL) during pre-monsoon. In the monsoon season large number of trajectories arising from Bay of Bengal (BoB) at both the lower altitudes enters the North-East India carrying marine aerosols. This is supplemented by fine aerosols transported from East Asia at higher altitude (3600 m). Moorthy et al. (2003) have shown that increased abundance of fine aerosols in monsoon is associated with advection from East Asia. The mixing of aerosols by coagulation and hygroscopic growth of the watersoluble aerosols under high RH condition results in enlargement of particle size, thereby leading to a < 1.
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Table 1 Threshold values of AOD500 and a in various wavelength ranges for different aerosol types. Location
AOD (500 nm)
Alpha
Type of aerosol
Reference
Concepcion Mongu GSFC Dalanzadgad Bahrain Lampedusa
0.23e2.09 1.29 0.17e1.02 0.44 0.41 AOD495.7 0.15 AOD495.7 0.1 remaining <0.06 >1
a340e1020 ¼ 1.75e1.98 a340e1020 ¼ 1.65 a340e1020 ¼ 1.57e1.79 a340e1020 ¼ 0.19 a340e1020 ¼ 1 a415.6-868.7 0.5 a415.6-868.7 1.5
Eck Eck Eck Eck Eck
>0.15 0 to w1.5 <0.15 >0.2 >0.25 remaining <0.3 >0.5 >0.6 remaining <0.2 <0.2 >0.35
a440e870 < 0.5 a440e870 0.5w1.5 a340e1020 < 1.3 a340e1020 > 1 a340e1020 < 0.7
>0.45 remaining
a380e1025 < 0.7
biomass burning biomass burning Urban/industrial desert dust desert dust desert dust biomass burning/urban mixed clean maritime biomass burning and urban/industrial desert dust mixed clean maritime Urban/industrial desert dust mixed clean maritime urban/industrial desert dust mixed continental average Marine continental average urban/industrial and biomass burning desert dust mixed
Nauru Alta Floresta and Ispra Solar Village
Arabian Sea/BoB
Hyderabad
Dibrugarh
remaining a440e870 < 1.3 a440e870 > 1.5
remaining
a380e870 < 0.9 a380e870 < 1 a380e870 < 0.7 remaining
a380e1025 < 1.4 a380e1025 < 0.9 a380e1025 > 1 remaining
Long-range transportation to Dibrugarh during post-monsoon does not possess a clear pattern and air masses from different origins are present in this season. However, the local sources dominate as revealed by the confinement of more than 50% of the trajectories locally. At 1800 m altitude, transportation from BoB is appreciable (54%). Trans Himalayan advection from the foothills in Nepal is noticeable during this season at all the altitudes. Such advection to Tibet has also been reported by Hindman and Upadhyay (2002) using a sequence of surface meteorological and condensation nuclei measurements. In winter the trajectories at higher levels are mostly westerly, originating from IGP, Pakistan or West Asian locations. Trajectories originating from industrialized IGP, where intense fog and pollution haze conditions often occur during winter (Badarinath et al., 2007) are capable of carrying fine-mode and black carbon aerosols. The trajectories below boundary layer are mostly confined locally. Being the post-harvesting season, burning of residual agricultural crop in the nearby places might contribute to the production of fine aerosols and precursor gases (Pathak et al., 2010). Moreover, as the Indian subcontinent experiences severe forest fires (SFR, 2001) and crop residue burning during winter and early spring, fine aerosols carried by the westerlies dominate over Dibrugarh and hence a > 1. 4.3. Aerosol types and their seasonal heterogeneity 4.3.1. Identification of aerosol types The discrimination of the aerosol types can be achieved by means of the widely used method of relating aerosol load (i.e. AOD500) and particle size (i.e. a) used by several earlier investigators (Eck et al., 1999; Pace et al., 2006; Kaskaoutis et al., 2007b, 2009, 2011; Kalapureddy et al., 2009 etc.) as illustrated in Table 1.Eck et al. (1999) have identified three distinct aerosol types: Biomass burning, urban/industrial and desert dust by assuming a variety of AOD at 500 nm and a in the spectral range 340e1020 nm over different AERONET locations Concepcion (Bolivia), Mongu (Zambia) Goddard Space Flight Center (Maryland, USA), Dalanzadgad (Mongolia) and Bahrain during 1997 and 1998.
et et et et et
al., al., al., al., al.,
1999 1999 1999 1999 1999
Pace et al., 2006
Kaskaoutis et al., 2007a
Kalapureddy et al., 2009/ Kaskautis et al., 2011
Kaskaoutis et al., 2009
Present Study
Pace et al. (2006) made a distinction between desert dust and biomass burning/urban aerosols over Lampedusa at central Mediterranean Sea and treated the remaining as mixed aerosols. Kaskaoutis et al. (2007b) made similar study over four AERONET sites representing four aerosol regimes (a) Alta Floresta (Brazil), biomass burning, (b) Ispra (Italy), urban industrial, (c) Nauru (Pacific Ocean), maritime and (d) Solar Village (Saudi Arabia). They treated clean maritime as background aerosols and distinguished other aerosols types such as desert dust, biomass burning and urban/industrial and mixed aerosols. In the Indian subcontinent Kaskaoutis et al. (2009) have made the first attempt to distinguish different aerosol types originating from variety of sources over Hyderabad. Furthermore, Kalapureddy et al. (2009) over Arabian Sea and Kaskaoutis et al. (2011) over BoB have discriminated different aerosol types over Oceanic regions surrounding the Indian landmass. The present study location represents a rural continental clean environment influenced by human activities. As such the continental average (CA) type can be considered as the background aerosol as suggested by d’Almeida et al. (1991). For defining this background aerosol type the continental average model given by Hess et al. (1998) is assumed. According to this aerosol model, AOD at 550 nm is 0.151 for background aerosols and a in the spectral range 350e500 nm is 1.11 while in the range 500e800 nm is 1.42 at relative humidity of 80%. The average AOD500 over the present study location is w0.36 while a is found to be 1.03 and 1.44 for 350e500 nm and 500e850 nm respectively. Further, the location is affected by advection from BoB as seen from back trajectory analysis discussed above, marine continental average (MCA) aerosol type is assumed to be present as additional background aerosols. The threshold values for these types are taken as AOD500 < 0.2; a < 1.4 for CA and AOD500 < 0.2; a < 0.9 for MCA (Table 1). The aerosols from locally generated or transported biomass burning and the aerosols of anthropogenic origin are termed as urban/industrial and biomass burning (UB) type. The dust or the minerals originated by the action of wind, particularly in western deserts including the coarse mode aerosols under high RH conditions are termed as
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Table 2 Percentage contribution of each aerosol type Continental average, Marine continental average, Urban/industrial and Biomass burning, Desert dust and Mixed type in each season. Aerosol type
Percentage contribution Pre-monsoon
Continental average Marine continental average Urban/industrial and Biomass burning Desert dust Mixed type
Fig. 6. The scatter plot diagram of AOD500 versus a for the five aerosol types over Dibrugarh: (a) continental average (CA), (b) marine continental average (MCA), (c) urban/industrial and biomass burning (UB), (d) desert dust (DD) and (e) mixed type (MT).
Desert Dust (DD). These two types are discriminated following considerations by Kalapureddy et al. (2009) over Arabian Sea, Kaskaoutis et al. (2009) over Hyderabad and Kaskaoutis et al. (2011) over BoB as the source regions for long-range transported aerosols over these locations have been found to be nearly same as those for Dibrugarh (Moorthy et al., 2003; Kaskaoutis et al., 2009; Kedia et al., 2010 etc.). The threshold values for UB and DD are respectively AOD500 > 0.35; a > 1 and AOD500 > 0.45; a < 0.7. We have used lower AOD500 for UB and DD than that considered over Hyderabad, but greater than that over Arabian Sea and BoB because AOD500 over Dibrugarh is lower compared to that over Hyderabad and higher compared to that over Arabian Sea and BoB, keeping the value of a unchanged. The remaining aerosols which do not fall in any of the above categories are considered as undetermined or mixed type (MT) (Pace et al., 2006).
Monsoon
Post-monsoon
Winter
2 5
7 10
26 21
12 9
41
18
13
41
15 37
4 61
1 46
2 36
The scatter plot diagram of AOD500 versus a (Fig. 6) form physically interpretable individual cluster regions separated by the solid lines, each corresponding to different aerosol types (Table 1). Within each cluster, however, large dispersion has been observed in terms of AOD500 and a. For example, a varies over a wide range at low AOD500 (<0.2) for CA as well as for MCA types. The increase in a with increasing AOD500 indicates presence of fine aerosols in the atmospheric column. Significant amount of points are accumulated in the cluster region AOD500 > 0.35, a > 1, corresponding to the particles of UB. A very less dense area with higher AOD500 (>0.45) and a < 0.7 indicates presence of DD. The rest of the points are scattered corresponding to a variety of AOD500 values ranging from w0 to w1.7 and a wide range of a (w0.2 to w2.7). These points together with the highly dense area 0.2 < AOD500 < 0.35, a > 1, are difficult to be included in a specific cluster region like other aerosol types and hence are categorized as the MT aerosols. This mixed or undetermined aerosol type is formed by the external or internal mixture of anthropogenic and natural aerosols. Additionally, RH plays a significant role in the modification of the aerosol types (Kaskaoutis et al., 2011). 4.3.2. Seasonal heterogeneity in aerosol types The seasonal distribution of the percentage contributions of the different aerosol types is presented in Fig. 7. During pre-monsoon season UB is the dominant aerosol type followed by MT and DD,
Fig. 7. Fraction pies of each aerosol type continental average (CA), marine continental average (MCA), Urban/industrial and biomass burning (UB), desert dust (DD) and mixed type (MT) over Dibrugarh contributing to the total in each season: Pre-monsoon, Monsoon, Post-monsoon and Winter.
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while the background aerosols are insignificant (Table 2). The lower air masses mainly from the arid regions in north-western India and Pakistan are capable of carrying DD aerosols. The DD aerosols in this period may be also produced from the Brahmaputra valley (Sharma et al., 2009). The biomass burning in pre-monsoon is mainly associated with shifting cultivation practices taking place in North-East India every year (Badarinath et al., 2004). Moreover, the transportation from densely populated and industrialized eastern Indian coast at 500 m significantly contributes to UB type. MT (61%) aerosols predominantly override the other two significant contributors (UB and MCA) during monsoon. It is possibly due to the particle growth mechanism under high RH condition, which presents a difficulty in distinguishing them in different categories. Advection from East Asia and that from BoB at upper two altitudes carries respectively the UB and MCA type. Contribution of CA and DD aerosols are less prominent in this season. The locally confined trajectories results in highest percentage of background CA (26%) aerosol type during post-monsoon. The transportation from BoB (54%) again results in significant percentage of MCA (21%) type. The appreciable percentage (46%) of mixed type aerosols may be due to the lack of a clear pattern of airmass from different origins, which presents a difficulty in distinguishing the aerosol types. From the observations of the low AOD and dominance of background aerosol types, post-monsoon season can be treated as the season representing background continental average aerosol environment. UB and DD aerosol contribution is least during this season. The dominance of UB type in the winter season may be attributed to both local and transported aerosols. The former is revealed by confinement of w50% of trajectories below boundary layer locally, while the later is evident from the trajectories at higher levels, which are mostly westerly. It has also been reported by Gogoi et al. (2011) from Concentration Weighed Trajectory (CWT) analysis that w70% of accumulation-mode aerosols (mostly UB type) are being carried out from western locations towards Dibrugarh. Even though the majority of the air masses originate from western locations during winter, DD type is less prominent in this season as the dust activity is at its lowest level. The local trajectories are capable of contributing more to the UB type as discussed in the preceding section. 12% of background CA type is the result of aerosols generated locally under favorable meteorological conditions (clear sky and scanty rainfall). Under such meteorological conditions it is probable that particles may float for longer time in the atmosphere due to reduction in loss process and may undergo transformation of size. The comparison of the present results with those reported over Hyderabad (Kaskaoutis et al., 2009) reveals that the background MCA (Maritime influenced or MI over Hyderabad) aerosols exhibit similar seasonal variation over both sites with highest contribution in post-monsoon and least in pre-monsoon seasons. This may be attributed to the fact that both the places are influenced by same marine air masses originating over BoB. On the other hand the other aerosol types exhibit different seasonal variations over the two locations. The UB (high AOD urban/industrial or HUI over Hyderabad) aerosol type is the highest contributor during pre-monsoon and second highest contributor in winter over both the places; however, it’s contribution is least in post-monsoon over Dibrugarh and in monsoon over Hyderabad. The DD (high AOD desert dust over Hyderabad) aerosols are predominant in pre-monsoon over Dibrugarh, while they are also present during monsoon season over Hyderabad. The contribution of MT aerosols is maximum in monsoon over Dibrugarh and in post-monsoon over Hyderabad. The difference in the observed seasonal contribution of aerosol types over Dibrugarh and Hyderabad may be attributed to the location, meteorology, topography, influences of air masses and mixing of aerosols.
5. Conclusions The columnar aerosol properties have been studied using nine years of AOD data retrieved from the measurements made by MWR over Dibrugarh. These together with identified source regions by HYSPLIT back trajectory analysis were further used to discriminate the aerosol types and their seasonal heterogeneity. The key findings of the study include: 1. The climatology of aerosol properties possesses clear seasonal variabilities with maximum aerosol loading associated (maximum value of b) with mostly coarse particles (a < 1) during pre-monsoon season with occurrence of the highest AOD value compared to all other seasons. Post-monsoon is the season with lowest aerosol abundance over the location. 2. The HYSPLIT back trajectory analysis identifies the IGP and west Asia as the dominant potential source regions in pre-monsoon carrying primarily the coarse mode aerosols. Advection from BoB and East Asia is prominent in monsoon. On the other hand, during the post-monsoon and winter seasons the main contribution to different aerosol types is from local sources in addition to other sources, which is entirely westerly in winter carrying fine aerosols mainly from IGP. 3. Five different aerosol types have been classified from the relation between AOD500 and a, applying appropriate threshold values. The percentage contribution of each type varies seasonally. In winter, local production contribute to observed appreciable continental average (CA) aerosol type, while highest percentage of urban/industrial and biomass burning (UB) type is attributed to both local and transported aerosols. On the other hand, pre-monsoon season is highly influenced by transported aerosols with significant contribution from UB and desert dust (DD) type. Post-monsoon season is indicative of background continental average aerosol condition with a significant contribution from CA and marine continental average (MCA) aerosols. Monsoon aerosols due to different particle growth processes like humidification and hygroscopic growth couldn’t be distinguished properly and hence the unidentified or Mixed (MT) aerosol type is predominant in that season.
Acknowledgments This work was carried out as part of the Aerosol Radiative Forcing over India (ARFI) project under ISRO Geosphere Biosphere program. Binita Pathak is indebted to the ISRO for providing her fellowship under the ARFI project. The authors are grateful to the Project Director K. Krishna Moorthy and S. Suresh Babu for their constant encouragement and support. Authors are thankful to the working team (http://ready.arl.noaa.gov) for global hysplit data set. The authors are grateful to the anonymous referee for his critical comments and suggestions towards improvement of the manuscript.
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Seasonal heterogeneity in aerosol types over Dibrugarh-North-Eastern India Binita Pathak a, Pradip Kumar Bhuyan a, *, Mukunda Gogoi b, Kalyan Bhuyan a a b
Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh 786 004, India Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram 695022, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 July 2011 Received in revised form 20 October 2011 Accepted 29 October 2011
Columnar aerosol properties retrieved from Multi-Wavelength solar Radiometer (MWR) measurements during the period 2001e2010 over Dibrugarh (27.3 N, 94.6 E, 111 m amsl), North-Eastern India are analyzed to identify the types of aerosols in the atmospheric column. Highest Aerosol optical depth (AOD) characterizes the pre-monsoon (MarcheMay), while lowest AOD has been observed during the post-monsoon (OcteNov) season. The Ångström exponent (a) indicates predominance of fine aerosols during post-monsoon and winter (DeceFeb) and dominance of coarse mode in pre-monsoon and monsoon (JuneeSept). NOAA HYSPLIT back trajectory analysis suggests that the seasonal heterogeneity in aerosol characteristics can be attributed to the varying contribution from different source regions. Using the relationship between AOD500 and a, the aerosols can be classified into five main types viz. continental average (CA), marine continental average (MCA), urban/industrial and biomass burning (UB) and desert dust (DD) while the remaining cases are considered as unidentified or mixed type (MT). These aerosol types exhibit seasonal heterogeneity in their contribution depending upon variability in sources. In winter, local production contributes to observed appreciable CA aerosol type, while highest percentage of UB type is attributed to both local and transported aerosols. On the other hand, transported UB and DD types play a significant role in the pre-monsoon season. Post-monsoon season is indicative of background continental average aerosol condition with a significant contribution from CA and MCA aerosols. Monsoon aerosols couldn’t be distinguished properly due to different particle growth processes like humidification, hygroscopic growth etc. and hence MT aerosol type is predominant in this season. This is the first ever attempt to classify aerosols over this environment. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Aerosol type Multi-wavelength solar Radiometer AOD Ångström exponent HYSPLIT
1. Introduction The atmospheric aerosols along with green house gases are one of the principal internal agents of climate change (Kaufman et al., 2002). However, the regional and global impact of atmospheric aerosols on climate is still uncertain owing to the large heterogeneity in their spatial and temporal distribution throughout the globe. This heterogeneity results from the variability of sources or origin of different aerosols as well as their short residence time in the atmosphere (Textor et al., 2006; Kinne et al., 2006). Consequently aerosols present the largest source of uncertainties in the model simulations of climate change (Ferrare et al., 2005). This is because describing the aerosol characteristics as input to these models is vital for determining the uncertainties in simulations of aerosol radiative forcing and hence climate change. The heterogeneity in spatial and * Corresponding author. E-mail addresses: [email protected] (B. Pathak), [email protected] (P.K. Bhuyan), [email protected] (M. Gogoi), [email protected] (K. Bhuyan). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.10.061
temporal distribution also leads to presence of different aerosol types in both spatial and temporal domain. Based on various measurements over the globe, aerosols can generally be classified into four main types namely biomass-burning aerosols produced by forest and grassland fires, urban/industrial aerosols from fossil fuel combustion in populated urban/industrial regions, desert dust blown into the atmosphere by wind and aerosol of maritime origin (Kaskaoutis et al., 2007a). Each of these types can be further divided into others depending on aerosol absorbing or scattering capabilities, sphericity, chemical composition, mineralogy, etc. Due to strong dependence of both AOD and Ångström exponent on wavelength, a realistic characterization of aerosol properties can be attempted using these two parameters (Holben et al., 2001) and therefore, are widely used to identify different aerosol types over the globe (Kaskaoutis et al., 2007b and references therein). The geographical diversity and meteorological pattern driven primarily by regional monsoon over the Indian subcontinent implicitly ensures a very diverse and complex aerosol environment, which needs to be investigated for the aerosol types present in each zone and their impact on both regional and global climate.
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Moreover, because of the rapid growth of anthropogenic activities and the possible regional and global climatic impacts from aerosol emissions the characterization of aerosol properties over both land and ocean is of great importance (Satheesh et al., 2006). Earlier studies over Dibrugarh, have revealed that the topography of the location and surrounding areas make the site prone to heavy external influences almost throughout the year. For example, west Asian locations and the Indo-Gangetic Plains (IGP) are the potential source regions, which contribute to total aerosol abundance over Dibrugarh particularly in pre-monsoon and winter as obtained from trajectory clustering and concentration weighted trajectory analysis (Gogoi et al., 2011). Gogoi et al. (2011) have found moderate to strong (33%e72%) contribution of near surface aerosols to columnar abundance during post-monsoon and winter seasons, while heterogeneity between surface and column aerosol properties exist in the other two seasons. This is also supported by the existence of elevated aerosol layer over the north-east region during pre-monsoon season detected by spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization observations (Sharma et al., 2009). Further, Gogoi et al. (2009) have affirmed that the columnar aerosol properties closely resemble that of a typical coastal or arid environment during pre-monsoon and monsoon seasons while in winter it behaves almost like a polluted and anthropogenically active site. Therefore, the post-monsoon season having low external influence can be treated as the season representing background aerosol environment. It is thus expected that the relative concentration of each aerosol type shall vary with season. In this report, an attempt has been made to identify the aerosol types and examine the heterogeneity in their seasonal distribution over Dibrugarh. So far only Kaskaoutis et al. (2009) have identified and characterized the aerosol types over Hyderabad, a location within the Indian subcontinent. A clear understanding of the seasonal aerosol types will also help in retrieval of more accurate physico-optical parameters of aerosols by satellite remote sensing over Dibrugarh as the satellite-retrieval algorithms rely on assumptions about the optical properties of different aerosol types (Kaufman et al., 1997; King et al., 1999).
Fig. 1. Map of North-East India and the adjoining region showing the study location Dibrugarh (27.3 N, 94.6 E, 111 m amsl). The study region is also shown within a map of India (inset).
maximum and minimum temperatures respectively in the panels below. Climatologically winter is the driest season of the year at Dibrugarh (along with entire North-East India) with least rainfall (w4% of the annual), while monsoon receives maximum (63%) of annual rainfall with monthly rainy days varying between w15e25 days. The RH is always >70% with maximum during monsoon (80%). The minimum temperature ranges from w8 C in winter to w20 C in monsoon, while the range of maximum temperature is w21 C to 36 C in monsoon.
2. Site description and meteorology The present study location (Dibrugarh: 27.3 N, 94.6 E, 111 m amsl), is situated on the southern bank of river Brahmaputra in eastern Assam, close to the North-Eastern boundary of the Indian subcontinent (Fig. 1). It is a rural, continental site and slightly affected by anthropogenic activities. The environment of Dibrugarh which is surrounded by large number of tea plantations, rivers and rivulets is not conducive for local dust production except during dry seasons and is quite far away from the active dust source regions of west Asia and western/central India. During dry periods some rare dust events which occur locally are capable of uplifting soil dust from the Brahmaputra basin (Sharma et al., 2009). However, transportation through the natural gateway in western Assam is capable of dumping aerosols over the site. Besides, the transportation from the oil wells situated mostly in North-East direction together with the vehicular emissions from the national highway running through the University campus and the seasonal biomass burning activities in the nearby hills are the other possible sources of aerosols (Pathak et al., 2010). On the basis of the distribution of long-term meteorological characteristics, a year is classified into four seasons, namely winter (December to February), pre-monsoon (March to May), monsoon (June to September) and post-monsoon (October and November). The annual variation of number of rainy days and monthly total rainfall at Dibrugarh is shown in the top two panels of Fig. 2 followed by monthly mean values of relative humidity (RH) as well as
Fig. 2. Annual variations of monthly total rainy days and rainfall (top two panels) followed by monthly mean values of relative humidity (RH) as well as maximum (TMax) and minimum (TMin) temperatures.
B. Pathak et al. / Atmospheric Environment 47 (2012) 307e315
3. Data and measurement uncertainty Spectral AOD in the wavelength range 380 nme1025 nm has been estimated employing a ground based Multi-Wavelength solar Radiometer (MWR) for the period October, 2001 to November, 2010, following Langley plot technique (e.g. Gogoi et al., 2009; Pathak et al., 2010). The variance of the Langley intercept (typically 5%) along with the other uncertainties due to water vapor, ozone or NO2 absorption etc. puts the overall uncertainty in AOD measurement in the range of 0.02e0.03 at different wavelengths (Gogoi et al., 2009). The details of the instrument, method of analysis and error budget are discussed earlier in detail by several investigators (Moorthy et al., 1997, 2007; Gogoi et al., 2009 and references therein). In this study, 974 data sets measured on 719 days for nine years from October, 2001 to November, 2010 have been utilized. The number of data sets varies seasonally due to variability in sky conditions. The largest number of data set is for winter (42%) due to the stable and sunny weather condition followed by post-monsoon (27%). Relatively few measurements could be made during the pre-monsoon (18%) and particularly in monsoon (13%) due to cloud cover and prevailing rainy weather. The data collected during a day is also separated into forenoon and afternoon whenever the Langley plot showed a change in slope from forenoon to afternoon, which is considered as independent data in calculating daily average of AOD. Data collected is considered as a single set if it spans only for 3e4 h and the AOD is considered as mean for the day. 4. Results and discussion 4.1. Spectral and temporal variations of aerosol properties The climatological mean AODs are estimated by grouping together the individual months of different years and are presented in Fig. 3, at four representative wavelengths (380, 500, 750
Fig. 3. Annual variation of Aerosol optical depth at 380 nm, 500 nm, 750 nm and 1025 nm averaged over the period October, 2001eNovember, 2010.
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and 1025 nm) spanning over the spectral range covered by the MWR. Seasonally, AOD attains its peak level in pre-monsoon, which decreasing through monsoon attains the lowest value in post-monsoon. AOD then starts to buildup in winter. Comparison of this seasonal cycle observed from the present analysis with that reported by Gogoi et al. (2009) shows that it is consistent for all the MWR wavelengths and for all the years of observation. AOD decays exponentially from shorter to longer wavelengths though in some cases higher AOD values at longer wavelengths 935 nm and 1025 nm than that at 750 nm have also been observed. This may be attributed to the effect of water vapor at 935 nm. However, the extreme MWR wavelength 1025 nm has very weak absorption cross-section of water vapor and this small contribution of water vapor absorption to the total optical depth are removed using the columnar water vapor estimated from the 935 nm channel of the MWR following Nair and Moorthy (1998). Hence, the higher AOD values at 1025 nm may be due to abundance of coarse mode aerosols in the particular months (Pathak et al., 2010). Similar observation of higher AOD at longer wavelength has been reported earlier from Maitri, Antarctica (Gadhavi and Jayaraman, 2004). Higher AOD in pre-monsoon may be attributed to long-range transportation of mineral dust from the arid regions of west Asia and North-West India across the IGP (Gogoi et al., 2009). Many recent studies (Gautam et al., 2010 and references therein) have revealed that IGP is influenced by significant dust transported from the northwestern arid regions that constitutes the bulk of the regional aerosol loading. The maximum pre-monsoon AOD over Dibrugarh may also be attributed to the highest biomass burning associated with shifting cultivation practices taking place during MarcheApril in North-East India every year (Badarinath et al., 2004). The lowering of aerosol loading in monsoon is associated with wet removal processes. The combined effect of local
Fig. 4. Annual variation of Ångström parameters (a) a and (b) b for the MWR wavelength range 380e1025 nm.
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meteorological conditions and topography of the region results in observed lowest AOD in post-monsoon (Gogoi et al., 2011). Aerosol loading starts to build up by end of post-monsoon leading to continued high AODs in winter under favorable meteorological
conditions. This is also associated with the enhanced black carbon emissions and concentration over the location (Pathak et al., 2010). Ångström exponent, a is an indicator of aerosol particle size, which is obtained from the Ångström’s formula (1961) as
Fig. 5. Air mass back trajectories, grouped according to the regions covered and arriving at (a) 500 m AGL and (b) 1800 m AGL over Dibrugarh. The vertical bars over the mean line show the spatial spread of each group. (c) Air mass back trajectories, grouped according to the regions covered and arriving at 3600 m AGL over Dibrugarh. The vertical bars over the mean line show the spatial spread of each group.
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Fig. 5. (continued).
s ¼ bla
(1)
where s is AOD at wavelength l (in mm) and b is the optical depth at l ¼ 1 mm, which is related to aerosol loading in the column and is known as the turbidity coefficient (Ångström, 1964). The value of a is higher when the columnar aerosol size spectrum has larger relative dominance of accumulation mode aerosols (size < 1 mm). In the present study a estimated from the regression analysis of lns versus lnl is found to be 1 for the pre-monsoon and monsoon seasons (Fig. 4). This is associated with desert dust and sea salt particles according to Eck et al. (1999) and Westphal and Toon (1991), but may also be due to hygroscopic growth of aerosols under high ambient relative humidity (>75%) particularly in monsoon season. Earlier, Remer and Kaufman (1998) have reported that particle growth of urban/industrial aerosols at high RH condition along with cloud contamination results in great variability in aerosol size. The value of a w 1.5 indicates clear dominance of fine-mode aerosols whereas a w 1 show a bimodal distribution where the coarse mode may also have a significant fraction (Eck et al., 2005). The turbidity coefficient b remains low (<0.2) for most part of the year (July to January). The highest aerosol loading occurs during March (b w 0.32 0.04) (Fig. 4). 4.2. Contribution of transported aerosols: identification of source regions With a view to determine the possible flow paths and source regions of air flow and in order to establish a link between the synoptic air masses with the seasonal variation of aerosol loading over Dibrugarh, five day isentropic back trajectories were computed using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model of National Atmospheric and Oceanic
Administration (NOAA). To delineate the distinct mean pathways of the trajectories, Ward’s hierarchical method (Ward, 1963) has been used to form clusters by combining the nearest trajectories. The seasonal mean airmass back trajectories grouped according to regions covered and arriving over Dibrugarh at different altitudes viz. 500 m (Fig. 5 (a)), 1800 m (Fig. 5 (b)) and 3600 m (Fig. 5 (c)) following considerations by Moorthy et al. (2003) are being discussed below. During pre-monsoon the majority of air masses are westerly coming mainly from IGP at lower two altitudes and almost all from West Asia, traversing IGP at 3600 m. The 3600 m trajectories may transport dust aerosols from Sahara and other African regions to Dibrugarh, as they generally interact with the boundary layer over the arid and semiarid locations (Kaskaoutis et al., 2009). The low level air masses are mainly from arid regions in north-western India and Pakistan. Advection from above mentioned regions carries coarse mode aerosols, resulting in lower value of a. Also significant number (62%) of trajectories carries aerosols from eastern India coast at 500 m, where densely populated areas and industries are situated. Local fine dusts from the Brahmaputra lifted by comparatively strong surface winds and convection activities due to surface heating and fine-mode particles from the nearby oil wells may also lead to increased aerosol loading within the boundary layer (500 m AGL) during pre-monsoon. In the monsoon season large number of trajectories arising from Bay of Bengal (BoB) at both the lower altitudes enters the North-East India carrying marine aerosols. This is supplemented by fine aerosols transported from East Asia at higher altitude (3600 m). Moorthy et al. (2003) have shown that increased abundance of fine aerosols in monsoon is associated with advection from East Asia. The mixing of aerosols by coagulation and hygroscopic growth of the watersoluble aerosols under high RH condition results in enlargement of particle size, thereby leading to a < 1.
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Table 1 Threshold values of AOD500 and a in various wavelength ranges for different aerosol types. Location
AOD (500 nm)
Alpha
Type of aerosol
Reference
Concepcion Mongu GSFC Dalanzadgad Bahrain Lampedusa
0.23e2.09 1.29 0.17e1.02 0.44 0.41 AOD495.7 0.15 AOD495.7 0.1 remaining <0.06 >1
a340e1020 ¼ 1.75e1.98 a340e1020 ¼ 1.65 a340e1020 ¼ 1.57e1.79 a340e1020 ¼ 0.19 a340e1020 ¼ 1 a415.6-868.7 0.5 a415.6-868.7 1.5
Eck Eck Eck Eck Eck
>0.15 0 to w1.5 <0.15 >0.2 >0.25 remaining <0.3 >0.5 >0.6 remaining <0.2 <0.2 >0.35
a440e870 < 0.5 a440e870 0.5w1.5 a340e1020 < 1.3 a340e1020 > 1 a340e1020 < 0.7
>0.45 remaining
a380e1025 < 0.7
biomass burning biomass burning Urban/industrial desert dust desert dust desert dust biomass burning/urban mixed clean maritime biomass burning and urban/industrial desert dust mixed clean maritime Urban/industrial desert dust mixed clean maritime urban/industrial desert dust mixed continental average Marine continental average urban/industrial and biomass burning desert dust mixed
Nauru Alta Floresta and Ispra Solar Village
Arabian Sea/BoB
Hyderabad
Dibrugarh
remaining a440e870 < 1.3 a440e870 > 1.5
remaining
a380e870 < 0.9 a380e870 < 1 a380e870 < 0.7 remaining
a380e1025 < 1.4 a380e1025 < 0.9 a380e1025 > 1 remaining
Long-range transportation to Dibrugarh during post-monsoon does not possess a clear pattern and air masses from different origins are present in this season. However, the local sources dominate as revealed by the confinement of more than 50% of the trajectories locally. At 1800 m altitude, transportation from BoB is appreciable (54%). Trans Himalayan advection from the foothills in Nepal is noticeable during this season at all the altitudes. Such advection to Tibet has also been reported by Hindman and Upadhyay (2002) using a sequence of surface meteorological and condensation nuclei measurements. In winter the trajectories at higher levels are mostly westerly, originating from IGP, Pakistan or West Asian locations. Trajectories originating from industrialized IGP, where intense fog and pollution haze conditions often occur during winter (Badarinath et al., 2007) are capable of carrying fine-mode and black carbon aerosols. The trajectories below boundary layer are mostly confined locally. Being the post-harvesting season, burning of residual agricultural crop in the nearby places might contribute to the production of fine aerosols and precursor gases (Pathak et al., 2010). Moreover, as the Indian subcontinent experiences severe forest fires (SFR, 2001) and crop residue burning during winter and early spring, fine aerosols carried by the westerlies dominate over Dibrugarh and hence a > 1. 4.3. Aerosol types and their seasonal heterogeneity 4.3.1. Identification of aerosol types The discrimination of the aerosol types can be achieved by means of the widely used method of relating aerosol load (i.e. AOD500) and particle size (i.e. a) used by several earlier investigators (Eck et al., 1999; Pace et al., 2006; Kaskaoutis et al., 2007b, 2009, 2011; Kalapureddy et al., 2009 etc.) as illustrated in Table 1.Eck et al. (1999) have identified three distinct aerosol types: Biomass burning, urban/industrial and desert dust by assuming a variety of AOD at 500 nm and a in the spectral range 340e1020 nm over different AERONET locations Concepcion (Bolivia), Mongu (Zambia) Goddard Space Flight Center (Maryland, USA), Dalanzadgad (Mongolia) and Bahrain during 1997 and 1998.
et et et et et
al., al., al., al., al.,
1999 1999 1999 1999 1999
Pace et al., 2006
Kaskaoutis et al., 2007a
Kalapureddy et al., 2009/ Kaskautis et al., 2011
Kaskaoutis et al., 2009
Present Study
Pace et al. (2006) made a distinction between desert dust and biomass burning/urban aerosols over Lampedusa at central Mediterranean Sea and treated the remaining as mixed aerosols. Kaskaoutis et al. (2007b) made similar study over four AERONET sites representing four aerosol regimes (a) Alta Floresta (Brazil), biomass burning, (b) Ispra (Italy), urban industrial, (c) Nauru (Pacific Ocean), maritime and (d) Solar Village (Saudi Arabia). They treated clean maritime as background aerosols and distinguished other aerosols types such as desert dust, biomass burning and urban/industrial and mixed aerosols. In the Indian subcontinent Kaskaoutis et al. (2009) have made the first attempt to distinguish different aerosol types originating from variety of sources over Hyderabad. Furthermore, Kalapureddy et al. (2009) over Arabian Sea and Kaskaoutis et al. (2011) over BoB have discriminated different aerosol types over Oceanic regions surrounding the Indian landmass. The present study location represents a rural continental clean environment influenced by human activities. As such the continental average (CA) type can be considered as the background aerosol as suggested by d’Almeida et al. (1991). For defining this background aerosol type the continental average model given by Hess et al. (1998) is assumed. According to this aerosol model, AOD at 550 nm is 0.151 for background aerosols and a in the spectral range 350e500 nm is 1.11 while in the range 500e800 nm is 1.42 at relative humidity of 80%. The average AOD500 over the present study location is w0.36 while a is found to be 1.03 and 1.44 for 350e500 nm and 500e850 nm respectively. Further, the location is affected by advection from BoB as seen from back trajectory analysis discussed above, marine continental average (MCA) aerosol type is assumed to be present as additional background aerosols. The threshold values for these types are taken as AOD500 < 0.2; a < 1.4 for CA and AOD500 < 0.2; a < 0.9 for MCA (Table 1). The aerosols from locally generated or transported biomass burning and the aerosols of anthropogenic origin are termed as urban/industrial and biomass burning (UB) type. The dust or the minerals originated by the action of wind, particularly in western deserts including the coarse mode aerosols under high RH conditions are termed as
B. Pathak et al. / Atmospheric Environment 47 (2012) 307e315
313
Table 2 Percentage contribution of each aerosol type Continental average, Marine continental average, Urban/industrial and Biomass burning, Desert dust and Mixed type in each season. Aerosol type
Percentage contribution Pre-monsoon
Continental average Marine continental average Urban/industrial and Biomass burning Desert dust Mixed type
Fig. 6. The scatter plot diagram of AOD500 versus a for the five aerosol types over Dibrugarh: (a) continental average (CA), (b) marine continental average (MCA), (c) urban/industrial and biomass burning (UB), (d) desert dust (DD) and (e) mixed type (MT).
Desert Dust (DD). These two types are discriminated following considerations by Kalapureddy et al. (2009) over Arabian Sea, Kaskaoutis et al. (2009) over Hyderabad and Kaskaoutis et al. (2011) over BoB as the source regions for long-range transported aerosols over these locations have been found to be nearly same as those for Dibrugarh (Moorthy et al., 2003; Kaskaoutis et al., 2009; Kedia et al., 2010 etc.). The threshold values for UB and DD are respectively AOD500 > 0.35; a > 1 and AOD500 > 0.45; a < 0.7. We have used lower AOD500 for UB and DD than that considered over Hyderabad, but greater than that over Arabian Sea and BoB because AOD500 over Dibrugarh is lower compared to that over Hyderabad and higher compared to that over Arabian Sea and BoB, keeping the value of a unchanged. The remaining aerosols which do not fall in any of the above categories are considered as undetermined or mixed type (MT) (Pace et al., 2006).
Monsoon
Post-monsoon
Winter
2 5
7 10
26 21
12 9
41
18
13
41
15 37
4 61
1 46
2 36
The scatter plot diagram of AOD500 versus a (Fig. 6) form physically interpretable individual cluster regions separated by the solid lines, each corresponding to different aerosol types (Table 1). Within each cluster, however, large dispersion has been observed in terms of AOD500 and a. For example, a varies over a wide range at low AOD500 (<0.2) for CA as well as for MCA types. The increase in a with increasing AOD500 indicates presence of fine aerosols in the atmospheric column. Significant amount of points are accumulated in the cluster region AOD500 > 0.35, a > 1, corresponding to the particles of UB. A very less dense area with higher AOD500 (>0.45) and a < 0.7 indicates presence of DD. The rest of the points are scattered corresponding to a variety of AOD500 values ranging from w0 to w1.7 and a wide range of a (w0.2 to w2.7). These points together with the highly dense area 0.2 < AOD500 < 0.35, a > 1, are difficult to be included in a specific cluster region like other aerosol types and hence are categorized as the MT aerosols. This mixed or undetermined aerosol type is formed by the external or internal mixture of anthropogenic and natural aerosols. Additionally, RH plays a significant role in the modification of the aerosol types (Kaskaoutis et al., 2011). 4.3.2. Seasonal heterogeneity in aerosol types The seasonal distribution of the percentage contributions of the different aerosol types is presented in Fig. 7. During pre-monsoon season UB is the dominant aerosol type followed by MT and DD,
Fig. 7. Fraction pies of each aerosol type continental average (CA), marine continental average (MCA), Urban/industrial and biomass burning (UB), desert dust (DD) and mixed type (MT) over Dibrugarh contributing to the total in each season: Pre-monsoon, Monsoon, Post-monsoon and Winter.
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while the background aerosols are insignificant (Table 2). The lower air masses mainly from the arid regions in north-western India and Pakistan are capable of carrying DD aerosols. The DD aerosols in this period may be also produced from the Brahmaputra valley (Sharma et al., 2009). The biomass burning in pre-monsoon is mainly associated with shifting cultivation practices taking place in North-East India every year (Badarinath et al., 2004). Moreover, the transportation from densely populated and industrialized eastern Indian coast at 500 m significantly contributes to UB type. MT (61%) aerosols predominantly override the other two significant contributors (UB and MCA) during monsoon. It is possibly due to the particle growth mechanism under high RH condition, which presents a difficulty in distinguishing them in different categories. Advection from East Asia and that from BoB at upper two altitudes carries respectively the UB and MCA type. Contribution of CA and DD aerosols are less prominent in this season. The locally confined trajectories results in highest percentage of background CA (26%) aerosol type during post-monsoon. The transportation from BoB (54%) again results in significant percentage of MCA (21%) type. The appreciable percentage (46%) of mixed type aerosols may be due to the lack of a clear pattern of airmass from different origins, which presents a difficulty in distinguishing the aerosol types. From the observations of the low AOD and dominance of background aerosol types, post-monsoon season can be treated as the season representing background continental average aerosol environment. UB and DD aerosol contribution is least during this season. The dominance of UB type in the winter season may be attributed to both local and transported aerosols. The former is revealed by confinement of w50% of trajectories below boundary layer locally, while the later is evident from the trajectories at higher levels, which are mostly westerly. It has also been reported by Gogoi et al. (2011) from Concentration Weighed Trajectory (CWT) analysis that w70% of accumulation-mode aerosols (mostly UB type) are being carried out from western locations towards Dibrugarh. Even though the majority of the air masses originate from western locations during winter, DD type is less prominent in this season as the dust activity is at its lowest level. The local trajectories are capable of contributing more to the UB type as discussed in the preceding section. 12% of background CA type is the result of aerosols generated locally under favorable meteorological conditions (clear sky and scanty rainfall). Under such meteorological conditions it is probable that particles may float for longer time in the atmosphere due to reduction in loss process and may undergo transformation of size. The comparison of the present results with those reported over Hyderabad (Kaskaoutis et al., 2009) reveals that the background MCA (Maritime influenced or MI over Hyderabad) aerosols exhibit similar seasonal variation over both sites with highest contribution in post-monsoon and least in pre-monsoon seasons. This may be attributed to the fact that both the places are influenced by same marine air masses originating over BoB. On the other hand the other aerosol types exhibit different seasonal variations over the two locations. The UB (high AOD urban/industrial or HUI over Hyderabad) aerosol type is the highest contributor during pre-monsoon and second highest contributor in winter over both the places; however, it’s contribution is least in post-monsoon over Dibrugarh and in monsoon over Hyderabad. The DD (high AOD desert dust over Hyderabad) aerosols are predominant in pre-monsoon over Dibrugarh, while they are also present during monsoon season over Hyderabad. The contribution of MT aerosols is maximum in monsoon over Dibrugarh and in post-monsoon over Hyderabad. The difference in the observed seasonal contribution of aerosol types over Dibrugarh and Hyderabad may be attributed to the location, meteorology, topography, influences of air masses and mixing of aerosols.
5. Conclusions The columnar aerosol properties have been studied using nine years of AOD data retrieved from the measurements made by MWR over Dibrugarh. These together with identified source regions by HYSPLIT back trajectory analysis were further used to discriminate the aerosol types and their seasonal heterogeneity. The key findings of the study include: 1. The climatology of aerosol properties possesses clear seasonal variabilities with maximum aerosol loading associated (maximum value of b) with mostly coarse particles (a < 1) during pre-monsoon season with occurrence of the highest AOD value compared to all other seasons. Post-monsoon is the season with lowest aerosol abundance over the location. 2. The HYSPLIT back trajectory analysis identifies the IGP and west Asia as the dominant potential source regions in pre-monsoon carrying primarily the coarse mode aerosols. Advection from BoB and East Asia is prominent in monsoon. On the other hand, during the post-monsoon and winter seasons the main contribution to different aerosol types is from local sources in addition to other sources, which is entirely westerly in winter carrying fine aerosols mainly from IGP. 3. Five different aerosol types have been classified from the relation between AOD500 and a, applying appropriate threshold values. The percentage contribution of each type varies seasonally. In winter, local production contribute to observed appreciable continental average (CA) aerosol type, while highest percentage of urban/industrial and biomass burning (UB) type is attributed to both local and transported aerosols. On the other hand, pre-monsoon season is highly influenced by transported aerosols with significant contribution from UB and desert dust (DD) type. Post-monsoon season is indicative of background continental average aerosol condition with a significant contribution from CA and marine continental average (MCA) aerosols. Monsoon aerosols due to different particle growth processes like humidification and hygroscopic growth couldn’t be distinguished properly and hence the unidentified or Mixed (MT) aerosol type is predominant in that season.
Acknowledgments This work was carried out as part of the Aerosol Radiative Forcing over India (ARFI) project under ISRO Geosphere Biosphere program. Binita Pathak is indebted to the ISRO for providing her fellowship under the ARFI project. The authors are grateful to the Project Director K. Krishna Moorthy and S. Suresh Babu for their constant encouragement and support. Authors are thankful to the working team (http://ready.arl.noaa.gov) for global hysplit data set. The authors are grateful to the anonymous referee for his critical comments and suggestions towards improvement of the manuscript.
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