In-depth discrimination of aerosol types using multiple clustering ...

Atmospheric Research 181 (2016) 106–114

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In-depth discrimination of aerosol types using multiple clustering techniques over four locations in Indo-Gangetic plains Humera Bibi, Khan Alam ⁎, Samina Bibi Department of Physics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan

a r t i c l e

i n f o

Article history: Received 26 March 2016 Received in revised form 9 June 2016 Accepted 19 June 2016 Available online 20 June 2016 Keywords: Aerosol Optical Depth Angstrom Exponent Single Scattering Albedo Refractive Index Dust Biomass burning Urban industrial

a b s t r a c t Discrimination of aerosol types is essential over the Indo-Gangetic plain (IGP) because several aerosol types originate from different sources having different atmospheric impacts. In this paper, we analyzed a seasonal discrimination of aerosol types by multiple clustering techniques using AERosol RObotic NETwork (AERONET) datasets for the period 2007–2013 over Karachi, Lahore, Jaipur and Kanpur. We discriminated the aerosols into three major types; dust, biomass burning and urban/industrial. The discrimination was carried out by analyzing different aerosol optical properties such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Extinction Angstrom Exponent (EAE), Abortion Angstrom Exponent (AAE), Single Scattering Albedo (SSA) and Real Refractive Index (RRI) and their interrelationship to investigate the dominant aerosol types and to examine the variation in their seasonal distribution. The results revealed that during summer and pre-monsoon, dust aerosols were dominant while during winter and post-monsoon prevailing aerosols were biomass burning and urban industrial, and the mixed type of aerosols were present in all seasons. These types of aerosol discriminated from AERONET were in good agreement with CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) measurement. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Indo Gangetic plain (IGP) is influenced by various natural and anthropogenic aerosols due to its densely population and high pollution emission resulting in spatio-temporal variation (Tiwari et al., 2015). Therefore, it is challenging to categorize atmospheric aerosols into different types (dust, biomass burning, urban/industrial and mixture of these) because of differences in regional climate, topography, nature and lifetime duration (Giles et al., 2012). Each type particle has different climatic effect due to their size and absorptive nature, such as dust particles having large size can absorb shortwave radiation and carbonaceous aerosols with small size having strongly absorbing nature, whereas, sulphate particles having small size which reflect solar radiation back to space (Higurashi and Nakajima, 2002). The diversity in spatio-temporal distribution also leads to the existence of aerosol and their impact on global and regional climate. It is further predicted that relative loading of each aerosol type changes with the season (Pathak et al., 2012). Atmospheric aerosols and their properties are responsible for the uncertainty in estimating the global climate change. Long term measurements of aerosol properties over large scale are required to reduce the uncertainty in the observation (Verma et al., 2015). Classification is necessary to understand the aerosol climatic effect, sources, ⁎ Corresponding author. E-mail address: [email protected] (K. Alam).

http://dx.doi.org/10.1016/j.atmosres.2016.06.017 0169-8095/© 2016 Elsevier B.V. All rights reserved.

transformation as well as to improve the accuracy of satellite measurements (Russell et al., 2014). Numerous techniques can be carried out by using different aerosol optical properties to discriminate aerosol types and to evaluate the assessment of forcing (Higurashi and Nakajima, 2002; Alam et al., 2012; Tiwari et al., 2015). Particularly, aerosol radiative forcing may be varying subjecting to the absorptive nature of aerosol and specific surface condition (Lee et al., 2010). Since the aerosol climatic effects significantly differ from one type to another therefore, to attain better knowledge of aerosol impact, different authors carried out the classification of aerosol types using ground based measurements (Dubovik et al., 2002; Kim et al., 2004; Kaskaoutis et al., 2007a, 2007b; Mielonen et al., 2009; Kumar et al., 2014; Kumar et al., 2015) and satellite based measurements (Higurashi and Nakajima, 2002; Barnaba and Gobbi, 2004; Kaskaoutis et al., 2007a, 2007b). The understanding of the heterogeneity in the seasonal aerosol types supports the more precise optical properties by both satellite and ground based sensors (Higurashi and Nakajima, 2002; Pathak et al., 2012; Sharma et al., 2014). Therefore, it is essential to discriminate the prominent aerosol types in different seasons to improve the understanding about the impact of these aerosols on climate and monsoon circulation. Previously, different clustering methods were attempted for categorizing and quantification of aerosol types over different location (Omar et al., 2005; Russell et al., 2010; Toledano et al., 2009). Most studies focused on Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) clustering technique in limited sites of IGP, such as Jaipur (Verma et al., 2015),

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Lahore (Tariq et al., 2016) and Dibrugarh (Pathak et al., 2012). On the other hand, few studies were conducted on Extinction Angstrom Exponent (EAE) and Absorption Angstrom Exponent (AAE) clustering technique over Kanpur (Mishra and Shibata, 2012), IGP (Giles et al., 2011), Karachi and Lahore (Alam et al., 2012), Kanpur and Gandhi College (Kedia et al., 2014). Previously, Russell et al. (2010) discriminated the aerosol types using EAE and AAE clustering technique. Recently, Tiwari et al. (2016) also seasonally categorized the aerosols dominant types using AOD and AE clustering technique over IGP. However, the characterization of aerosols into different types by using ground based aerosol optical properties over IGP are still remains fractional and limited. In the present study, for the first time, we examined a detailed seasonal classification of aerosol types by multiple clustering techniques using AERONET datasets for the period 2007–2013 over Karachi, Lahore, Jaipur and Kanpur. Such classification is carried out by analyzing different aerosol properties such as AOD vs AE, EAE vs AAE, EAE vs SSA (Single Scattering Albedo) and EAE vs RRI (Real Refractive Index) in order to investigate the dominant aerosol types and to examine the variation in their seasonal distribution. Furthermore, the confirmation of AERONET derived aerosol types analyzed in the studied sites were compared with CALIPSO retrieved subtypes. 2. Instrument and method 2.1. Site description IGP is supposed to be one heavily polluted and densely populated region in the world (Singh et al., 2015) having huge area of 23.0°N, 68.0°E to 30.0°N, 93.50°E. It is surrounded from east to Bay of Bengal, from west to Thar Desert and Arabian Sea, from the north to the Himalaya and from south to the Vindyana Satpura range. Since last decade, it is one of the popular regions for high aerosol loading due to vehicular emissions, coal burning, and long range transport of dust coupled with abrupt variation in meteorological conditions (Verma et al., 2015;

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Tiwari et al., 2016). These variations occur from natural and anthropogenic sources (Lodhi et al., 2013). This region experiences four distinct seasons every year. In this study the long term data were seasonally arranged into summer (June–August), winter (December–February), premonsoon (March–May) and post-monsoon (September–November). In IGP, the monsoon starts from July and continue till the month of September with heavy rain followed by lower wind. During summer and pre-monsoon due to high temperature and low pressure, dust particles (coarse) are lifted upward from the arid and semi-arid areas along with strong surface winds, resulting storms which can affect the IGP (Singh et al., 2004; Pandithurai et al., 2007; Prasad and Singh, 2007). While during winter, low temperature and high pressure resulting in low surface convection which causes haze and dense fogs within the lower atmospheric layer (Tariq et al., 2016; Gautam et al., 2007). Furthermore, during this season biomass burning aerosols (fine) are also dominant due to fossil fuel burning. In the post-monsoon, harvesting generates high concentrations of fine particles (Bibi et al., 2015). This study focused on four sites in IGP (see Fig. 1) to seasonally categorize the aerosol into three types. These sites are situated in the monsoon region of Pakistan and India playing crucial role not only in monsoon circulation over IGP but also affect the global climate (Tiwari et al., 2015). Karachi (24.8°N and 67.0°E) having altitude 8 m Above Mean Sea level (AMSL) located on the Arabian Sea, one of the largest and most populous metropolitan cities in Pakistan having arid and semi-arid climate with warm and dry winter, while hot and humid summer that dominates the warm Pre-monsoon. Lahore (31.5°N, 74.3°E) with high altitude 210 m AMSL situated on the eastern bank of the Ravi River with semi-arid climate having hot summer and cold winter with dense fog in the month of January. Jaipur (26.9°N, 75.8°E) at an altitude of 431 m AMSL located in the western part of IGP near the western edge of the Thar Desert with semi-arid climate having a very hot summer while pleasant and mild winter. During monsoon, heavy rains as well as thunderstorms are also frequent. Kanpur (26.5°N, 80.3°E) positioned at an altitude of 142 m AMSL. It is consider as one of the densely polluted city, located

Fig. 1. Map of study area.

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in the central part of IGP, experiences very hot and long summer while relatively short winter season. Dust storms are frequent in premonsoon (Dey et al., 2004; Singh et al., 2004) and severe fog is common in winter. This region is suffering with high aerosol loading originating from the complex combination of natural and anthropogenic sources showing strong seasonal variation (Srivastava et al., 2012). 2.2. Instrumentation The analysis of seasonal variations in aerosol properties plays an important role on in-depth classification of atmospheric aerosols. For this purpose, we used ground and satellite sensors to distinguish different dominant types of aerosols. The current study includes CIMEL sun/sky radiometer observations established by NASA over the study sites in IGP. The AERONET is a surface based instruments which computes direct solar irradiance at eight wavelengths (340, 380, 440, 500, 670, 870, 940, and 1020 nm) with triplet observations are made per wavelength at every 15 min and the sky radiance measurements at four spectral channels (440, 675, 870, and 1020 nm) (Holben et al., 1998). The uncertainty in a retrieval under clear sky condition for AOD is usually less than ±0.01 for the wavelength N 440 nm and less than ±0.02 for 440 nm whereas, ± 0.05 for sky radiance measurements (Dubovik et al., 2002). The AERONET provides data in three different levels: level 1.0 (cloud contaminated), level 1.5 (cloud screened) and level 2.0 (quality assured) (Smirnov et al., 2000). In this study, AERONET all point level 2.0 data of direct product (AOD500 nm, AE440–870 nm) and inversion product (SSA440 nm, RRI440 nm, EAE440–870 nm and AAE440–

870 nm)

for the period 2007–2013 were used. The aerosol distribution patterns in each season were quantitatively identified according to scattered plots of aerosol optical properties. Therefore, the variations in aerosol optical properties were seasonally investigated to determine the aerosol types over studied sites. The data were retrieved from the AERONET website (http://aeronet.gsfc.nasa.gov/). The CALIPSO satellite gives the distribution of aerosols and clouds in vertical atmospheric profiles on the global/regional scale (Winker et al., 2003). The main aim of the CALIPSO satellite is to provide a global, multi-year data sets of aerosol and cloud spatial as well as optical properties from which, the uncertainties of aerosol direct and indirect effects on climate forcing and cloud climate feedback are evaluated (Ma and Yu, 2014). Uncertainties in the CALIPSO calibration are reported by Powell et al. (2009) and the CALIOP calibration is probable to be b6%. The high uncertainty is due to the assumption of negligible aerosol scattering in the calibration region. Measurements from the CALIPSO satellite provide the significant improvement in our knowledge of discrimination of different aerosol types including clean marine, dust, polluted continental, clean continental, polluted dust and smoke. CALIPSO retrieved data at two wavelengths (532 and 1064 nm) provides the continuous measurements with attenuated back scattered, during day and night covering the entire globe. In the present study level 2 version 3.01 attenuated backscatter data were utilized to classify the aerosols for the selected days corresponding to different seasons in order to explain the seasonal variation of aerosol types in total atmospheric column over IGP. The data downloaded from the website (http://www.calipso.larc.nasa.gov/).

Fig. 2. Scattered plot for AOD500 nm vs AE440–870 nm, shows the clusters of aerosol types during summer, winter, pre-monsoon and post-monsoon over a) Karachi, b) Lahore, c) Jaipur and d) Kanpur.

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Table 1 Threshold values of aerosol properties for different types of aerosol over each site. Aerosol types

EAE vs AAE

Dust Biomass burning Urban/Industrial

0.4 b EAE N 0.01 1.7 b EAE N 0.8 1.6 b EAE N 0.8

EAE vs SSA 3.0 b AAE N 1.0 2.3 b AAE N 1.1 1.3 b AAE N 0.6

EAE vs RRI

0.4 b EAE N 0.1 1.7 b EAE N 0.9 1.7 b EAE N 0.9

2.3. Techniques used for aerosol classification Identification of aerosol types is essential because different aerosol types are originated from several sources having diverse physical, chemical and optical properties also showing different atmospheric impacts (Dubovik et al., 2002). Seasonal variation of aerosol types and their optical characteristics over four different locations having different meteorological conditions in IGP can be helpful to evaluate the aerosol radiative forcing as well as to improve the climate models. Several methods can be adopted to distinguish aerosol types such as dust, biomass burning, urban/industrial and mixed type aerosol, which include polluted dust, polluted continental, clean continental etc. originating from the mixture of natural and anthropogenic pollutants. The most used clustering technique for discrimination of aerosols into different types is to correlate AOD with AE as both are wavelengths dependent which were adopted recently by numerous researches for selected region (Sharma et al., 2014; Kumar et al., 2015; Verma et al., 2015; Tiwari et al., 2016; Tariq et al., 2016). Moreover, variation in AE indicates the change in particle size. This AOD-AE clustering method sorts the aerosol types into dust, anthropogenic and marine aerosols but cannot have the potential to further classify anthropogenic into absorbing and non-absorbing (Lee et al., 2010). Similarly the classification of aerosol can be carried out by other clustering technique by correlating the different optical properties. In all these techniques, for better assessment of aerosol types, some particular threshold values are selected to categorize the aerosols depending on the composition of aerosols in different seasons (Giles et al., 2011; Mishra and Shibata, 2012; Kumar et al., 2015). Dominant aerosol types were computed by correlation between absorption and size relationship (Giles et al., 2011) which may be distinguished from one another depending on seasons. AAE of aerosols is a function of their composition (Mishra and Shibata, 2012) and EAE is an indicator of particle size (Russell et al., 2010). Hence AAE is a key to distinguish aerosol types when accompanied by EAE, however, it cannot have the potential to discriminate biomass burning from urban/industrial alone (Mishra and Shibata, 2012). Furthermore, to verify identification of aerosol types, it is useful to correlate EAE with sensible parameter like SSA and RRI, which can better separate biomass burning from urban/industrial (Russell et al., 2014). As SSA can be helpful to discriminate the aerosols according to absorbing and non-absorbing nature in different size range due to spectral absorption feature of different types of aerosol (Lee et al., 2010; Kedia et al., 2014) and RRI is also used to differentiate the scattering behavior of aerosol (Sinyuk et al., 2003). These types of aerosol retrieved from AERONET can further validate with the aerosol types retrieved from the CALIPSO satellite.

3. Results and discussion 3.1. Multiple clustering techniques Cluster analysis is an important technique used for classification of aerosol. Such analysis is used for classification of huge datasets into numerous groups using predefined aerosol parameters. The AERONET dataset based on several optical and physical characteristics of the aerosols can be categorized into several groups for the discrimination of aerosol types (Omar et al., 2005). The discrimination of aerosol types was carried out by analyzing the scattered graph between AOD and AE (Sharma et al., 2014; Kumar et al., 2015), EAE and AAE (Mishra

0.96 b SSA N 0.88 0.91 b SSA N 0.82 0.96 b SSA N 0.89

0.41 b EAE N 0.01 1.50 b EAE N 1.00 1.74 b EAE N 0.70

1.59 b RRI N 1.44 1.57 b RRI N 1.43 1.43 b RRI N 1.35

and Shibata, 2012), EAE and SSA (Giles et al., 2012) and RRI and AAE (Russell et al., 2014). Some other clustering techniques were also used by earlier researchers (Omar et al., 2005; Giles et al., 2011; Kedia et al., 2014). 3.1.1. Aerosol optical depth versus angstrom exponent The distribution of aerosol types in different seasons depends on production mechanism and the lifetime of aerosols in the atmosphere as well as on geographical locations, creating a different seasonal circulation of AOD and Angstrom Exponent (Pace et al., 2006). Variances in the relationship between the AOD and AE provide a potential approach to classify and asses the effects of different sources on the seasonal aerosol concentration and size of aerosol particles (Wang et al., 2014). For the classification of aerosol types the selected seasonal threshold values of AOD and AE were compiled as, dust; 2.9 b AOD N 0.5 and 0.4 b AE N 0.01 and Biomass burning & urban/industrial; 1.7 b AOD N 0.01 and 1.7 b AE N 0.7 for each site. To date, numerous researchers categorized different aerosol types using the AOD-AE clustering technique over different regions (Kaskaoutis et al., 2011; Kumar et al., 2014; Yu et al., 2016). The classification of aerosol types can be obtained by the seasonal scattered plot of AOD500 against Angstrom Exponent (α440–870) as shown in the Fig. 2(a–d), in which specific cluster indicates the different types of aerosol. It was noted that dust particle concentrations were low in Lahore as compared to other sites throughout the studied period. The cluster points associated with high AOD and a low AE represent dust aerosols (coarse particles) mainly coming from the arid regions of IGP during summer and pre-monsoon. While during winter and post-monsoon, the presence of biomass burning and urban/ industrial were observed, which corresponds to the absorbing aerosols (fine particles) over all sites. During winter (dry) seasons, the biomass burning aerosols increases due to the combustion processes which causes high AOD than that in the summer (wet) season (Ma et al., 2016). It should be mentioned here that clean marine aerosols (not circled) were also present during winter season in Karachi (see Fig. 2a) as it is a large coastal site located near Arabian Sea which is in agreement with the observation made by CALIPSO. The leftover scattered points (not circled) corresponding to a wide range of AOD and AE over all seasons were classified as mixed type of aerosol resulting from the mixture of natural and anthropogenic aerosol (Yu et al., 2016). The mixed type aerosol concentrations were high over Karachi and Kanpur as compared to Lahore and Jaipur. Characterization of aerosol into different types (Saharan dust, urban/industrial and biomass burning) was carried out based on AOD-AE relationship over three oceanic sites using POLEDER/ADEOS measurements (Goloub et al., 1999). AOD-AE scattered plots were analyzed in order to discriminate aerosol into different types through the cluster region at several locations (Masmoudi et al., 2003; Pace et al., 2006; Kaskaoutis et al., 2007a, 2007b). By using same AOD-AE clustering technique, Kumar et al. (2015) categorized four types of aerosol such as clean marine, continental clean, biomass burning/urban industrial and desert dust over Durban, South Africa. In similar way, Sharma et al. (2014) was reported the seasonal distribution of aerosol and discriminated them into five groups (clean marine, anthropogenic, biomass burning, mostly dust and mixed aerosol) over Greater Noida using Ground Sunphotometer. Tan et al. (2015) identified different types of aerosol like biomass burning, urban/industrial, marine and dust by analyzing AOD and AE over Penang and Kuching, Malaysia. Four prevailing aerosol clusters (biomass burning, anthropogenic, mostly dust and mixed aerosol) based on

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AOD-AE were recognized in New Delhi via Sun./Sky radiometer POM-02 (Tiwari et al., 2016). Further, using the similar clustering technique, Pathak et al. (2012) classified the aerosol into five categories (continental average, marine continental average, urban/industrial and biomass burning, desert dust and mixed type) over Dibrugarh using MultiWavelength solar Radiometer (MWR) measurements and the seasonal variation of aerosol type showing the contribution of urban/industrial and biomass burning during the winter and pre-monsoon and mixed type during monsoon and post-monsoon. Moreover, Toledano et al. (2009) characterized the aerosols into five types (desert, mixed, biomass, marine and continental) over El Arenosillo using AERONET data. Five dominant aerosols (desert dust, maritime, biomass burning, mixed and arid background aerosols) were found using AOD versus AE clustering method based on AERONET in Jaipur (Verma et al., 2015). Tariq et al. (2016) found that the pronounced aerosol type during haze events was biomass burning over Lahore using AERONET data. 3.1.2. Extinction Angstrom Exponent versus Absorption Angstrom Exponent Several techniques can be adopted to distinguish aerosol types by assigning some threshold values. Table 1 summarizes the threshold values of aerosol properties for different types of aerosol over each site. Fig. 3(a–d) depicts seasonal scatter plots of AAE440–870 nm against EAE440–870 nm to characterize different types of aerosol over Karachi, Lahore, Jaipur and Kanpur. The cluster analysis represents three significant types of aerosol which were classified as: dust (high AAE and low EAE), biomass burning (high AAE and high EAE), and urban/industrial aerosol (low AAE and high EAE). The theoretical values of AAE are close to 1 for black carbon (Russell et al., 2010). The urban/industrial and biomass

burning types aerosol tend to overlap each other as documented by previous authors (Giles et al., 2012; Mishra and Shibata, 2012). The intermediate values of AAE and EAE were observed overall sites, which indicate the existence of mixed type aerosol. However, the scatter graph shows clear cluster separation over Karachi and Jaipur as compared to Lahore and Kanpur, where mixed type aerosols concentrations were high, resulting from the mixture of different size particles especially in the summer and pre-monsoon. It is clear from the figure that dust aerosols were dominant during summer and pre-monsoon, originating from the desert or from the long-range transport activities, while during winter and post-monsoon biomass burning and urban industrial aerosols were dominant which highlight the significant anthropogenic activities. Mishra and Shibata (2012) carried out similar seasonal classification of aerosol by analyzing the scattered plot of EAE against AAE over Kanpur and grouped the aerosol in dust, biomass burning and urban/industrial. Some clustering analysis techniques were adopted by numerous authors to categorize the different aerosol types (Dubovik and King, 2000; Cattrall et al., 2005; Mielonen et al., 2009; Qin and Mitchell, 2009). On the basis of above mentioned discrimination technique, during summer and pre-monsoon, coarse particles were enhanced which specify the presence of dust over Karachi, Jaipur and Kanpur, while in post-monsoon and winter experiences fine particles due to biomass burning accompanied by urban industrial. Whereas, over Lahore during summer and pre-monsoon mixed type and fine aerosol were also observed. The distributions and types of anthropogenic aerosols are rather complex because of the variety of their sources with respect to location and season (Lee et al., 2010). Giles et al. (2011) categorized three types of aerosol (dust, black carbon

Fig. 3. Same as Fig. 2, but for EAE440–870 vs AAE440–870 nm.

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and mixed) by using a similar cluster technique of EAE and AAE over IGP. Che et al. (2015) adopted AAE with respect to EAE clustering technique to sort the aerosol types into mixed, urban/industrial and biomass burning during the heavy haze period in Beijing. Russell et al. (2010) grouped the aerosol in three types (desert dust, biomass burning and urban industrial) using the cluster analysis of EAE and AAE over worldwide location. Alam et al. (2012) classified the aerosol into two categories (mineral dust and urban/industrial) using the same technique over Karachi and Lahore. Kedia et al. (2014) seasonally categorized the absorbing aerosol into mostly dust, mostly black carbon and mixed dust and black carbon over IGP using scatter plots of EAE versus AAE via AERONET dataset. Russell et al. (2010) classified desert dust, urban industrial and biomass burning over different AERONET locations. 3.1.3. Extinction Angstrom Exponent versus Single Scattering Albedo To categorize the aerosols into different types, SSA as a function of EAE can be plotted. The SSA values may vary from 0 (completely absorbing) to 1 (completely scattering). Basically, the SSA is an important parameter to classify the aerosols into different types including desert dust, urban/industrial, biomass burning due to their spectral absorption nature of aerosol mixture (Dubovik et al., 2002) and EAE is an indicator of particle size (Russell et al., 2010). The analyses of both parameters (EAE and SSA) provide the better categorization of aerosol types (Giles et al., 2012; Russell et al., 2014). Lee et al. (2010) discriminated the aerosols into absorbing and non-absorbing by using SSA at 440 nm. The fine particle can also be categorized into non-absorbing, moderately absorbing and strongly absorbing depending on their SSA values (Levy et al., 2007). Fig. 4(a–d) reveals the seasonal scattered plot between EAE440–870 nm and SSA440 over all sites, pursuing the

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cluster technique as suggested by Russell et al. (2014). SSA versus EAE, categorized the aerosol into dust having high SSA and low EAE, biomass burning with moderate SSA and high EAE, and urban/industrial were associated with high SSA and high EAE. The remaining scattered points were discriminated as a mixed type of aerosol. It was evident from the figure that dust type aerosol were predominant during summer and pre-monsoon, while, biomass burning and urban/industrial aerosol were in abundant during winter and post-monsoon over all sites except Lahore, where some biomass burning and urban/industrial aerosols were also noted in summer which matched with the above result as discussed in Section 3.2. Similar partitioning of aerosols were carried out by Russell et al. (2014) into seven specified classes such as i) pure dust, ii) polluted dust, iii) biomass burning, dark smoke, iv) biomass burning white, v) urban-industrial, developed economy, vi) urban-industrial, developing economy, vii) pure marine using Mahalanobis SSA-EAE clustering technique over the island of Crete, Greece. Similarly, Giles et al. (2012) established the relationship between SSA and EAE to categorize between dust, mixed, urban/industrial and biomass burning using similar techniques at different AERONET sites. 3.1.4. Extinction Angstrom Exponent versus Real Refractive Index The aerosol complex refractive index is not independent of SSA and size of aerosol, but its values may vary from type to type, due to the chemical composition of aerosol (Dubovik et al., 2002). The complex refractive index is an optical property of great importance to achieve reliable results for identification of aerosol. The classification of aerosols into different types over IGP was carried out by correlating the EAE with RRI, following the approach provided by Russell et al. (2014). Fig.

Fig. 4. Same as Fig. 2, but for EAE440–870 nm vs SSA440 nm.

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5(a–d) reveals the seasonal scattered plot of EAE440–870 nm against RRI440 nm and observed that during summer and pre-monsoon the dust particles were prominent while during winter and post-monsoon the biomass burning and urban/industrial particles were prominent over all sites. The three significant types of aerosol which were identified as dust having low EAE and high RRI, biomass burning having high EAE and high RRI, and urban/industrial aerosol having high EAE and low EAE over all sites. The remaining scattered points were not included in any of the above mentioned aerosol types can be identified as a mixed type aerosol. The overlapping between the clusters (Biomass burning and Urban/Industrial) can be reduced by using different properties against EAE such as by replacing SSA with RRI versus EAE. Such type of clustering techniques are implemented by Russell et al. (2014), they categorized aerosols into different types (e.g. pure dust, polluted dust, light biomass smoke, dark biomass smoke, urban-industrial, and pure marine) over the Island of Crete, Greece. Our values for biomass burning are comparable with the findings of Raut and Chazette (2007) for soot aerosols over Paris. 3.2. Vertical profile of aerosol from CALIPSO The measurement of aerosol subtype profiles from CALIPSO close to the studied sites during selected days representing summer, winter, pre-monsoon and post-monsoon seasons were shown in Fig. 6. It is clearly evident from the Fig. 6(a and c) that the presence of dust and polluted dust layers reached up to a height of 7 km during summer and pre-monsoon over all sites, while these layers reached up to 11 km during pre-monsoon over Lahore. These dust aerosols are

prominent due the long range transport and dust storms while polluted dust are attributed to anthropogenic activities in IGP (Yu et al., 2016). Kumar et al. (2012) observed the dominancy of dust and polluted dust during monsoon and pre-monsoon over central India. Che et al. (2015) found the contribution of dust or polluted dust during haze events over Beijing. Fig. 6(b) reveals that during winter aerosol layer reached up to 5 km were frequently consist of polluted continental, smoke, and polluted dust while seldom distribution of dust were extending to an altitude of 10 km from the surface for all sites, along with a minor contribution of clean marine below 1 km over Karachi. These aerosol types may be due the biomass burning, anthropogenic activities and from long range transportation (Kumar et al., 2012). Yu et al. (2016) recorded the major contribution of clean continental with some contribution of smoke and polluted dust during the non-haze episode, whereas, smoke, dust and polluted dust were prominent during a haze episode over Beijing. Fig. 6(d) shows a mixed aerosol layer consisting of dust, polluted dust, polluted continental and smoke, lies below an altitude of 5 km during post-monsoon. It was observed that dust and polluted dust were prominent relative to smoke and polluted continental. Similar subtypes of aerosol were noted by Tariq et al. (2016) during a haze episode in October over Lahore. 4. Conclusions Aerosol optical properties retrieved from AERONET were utilized to categorize aerosols into different dominant groups over four sites in IGP. These dominant aerosol groups were identified by correlating the aerosol properties associated to the dominant size and radiation absorptivity

Fig. 5. Same as Fig. 2, but for EAE440–870 nm vs RRI440 nm.

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Fig. 6. Classification of aerosol subtypes derived from CALIPSO data, for the selected days representing a) summer, b) winter, c) pre-monsoon and d) post-monsoon over the studied sites.

and can be distinguished from one another by specific physical interpretable cluster region. To investigate seasonal variation of aerosol types, it is essential to classify the complex mixture of aerosols into different categories. First of all, the most common AOD-AE clustering technique was carried out to distinguished dust from biomass burning and urban industrial. To further differentiate between biomass burning and urban/industrial EAE versus AAE cluster techniques were adopted because AAE is a key to distinguish aerosol types when accompanied by EAE. Furthermore, to verify identification of aerosol types it is useful to correlate EAE with other sensible parameter like SSA and RRI, which can better separate biomass burning from urban/industrial. From all these clustering techniques it was concluded that during summer and pre-monsoon, dust particles were dominant while during winter and post-monsoon prevailing aerosols were biomass burning, urban industrial and the mixed type of aerosols were present in all seasons. Additionally, AERONET classified aerosol types were further compared with the CALIPSO retrieved aerosol subtypes. From the seasonal classification of aerosol subtypes derived from CALIPSO data, it was observed that, during summer and pre-monsoon, dust and polluted dust layers reached up to a height of 10 km. While during winter aerosol loading up to 5 km were mostly consist of polluted continental, smoke, and polluted dust, however, seldom distribution of dust particles was extended to an altitude of 10 km from the surface along with a minor contribution of clean marine below 1 km. Whereas, during post-monsoon a mixed aerosol layer consisting of dust, polluted dust, polluted continental and smoke, lies below an altitude of 5 km was noted. From both satellite and ground based observations, it was concluded that during the summer and pre-monsoon dust particles were dominant, while during post-monsoon, biomass burning particles (smoke) were the dominant type of aerosols.

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