Regional and seasonal variations in aerosol optical characteristics and their frequency distributions over India during

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi:10.1029/2007jd008560, 2008 Regional and seasonal variations in aerosol optical characteristics and their frequency distributions over India during 2001 2005 S. Ramachandran 1 and Ribu Cherian 1,2 Received 29 May 2007; revised 14 July 2007; accepted 19 December 2007; published 22 April 2008. [1] Regional and temporal variations in aerosol characteristics in 35 locations spread over seven different regions in India are studied during 2001 2005 from the daily mean MODIS Terra aerosol optical depth (AOD) and fine mode fraction (FMF) data. Northeast India has the lowest annual mean AOD of 0.28 while south comes next with 0.35. In the other regions AODs are higher than 0.35. Annual mean variations in AOD and FMFs in different regions do not show any noticeable increase or decrease during the 5-year period. High altitude locations are found to have lower AODs while densely populated, urban and industrialized locations have high AODs. Many locations show a winter low and summer high in AODs. Locations/regions dominated by pollution are found to have high FMF and high AODs, while regions in which natural (biogenic) aerosols are dominant had high FMF and range of AODs. The abundance of mechanically generated aerosols over a region results in low FMF and range of AODs. These features suggest that in addition to AOD variations knowledge on sources over a region are essential in understanding the FMF variations. Frequency distribution histograms of AODs and FMFs are consistent with the fact that aerosol sources exhibit seasonal and spatial variations over India. Dust activity peaks over north and west India during March-May which results in low FMFs as the aerosol distributions are influenced by large size dust aerosols. In Northeast FMFs are found to be higher than 0.8 throughout the year indicating the dominance of fine mode aerosols. Citation: Ramachandran, S., and R. Cherian (2008), Regional and seasonal variations in aerosol optical characteristics and their frequency distributions over India during 2001 2005, J. Geophys. Res., 113,, doi:10.1029/2007jd008560. 1. Introduction [2] Atmospheric aerosols influence the Earth s climate in many important ways. Aerosols interact (both directly and indirectly) with solar radiation and give rise to radiative forcing. The direct interaction of aerosols involves both scattering and absorption of radiation, while the indirect effect of aerosols on climate occurs by modifying the optical properties and lifetimes of clouds. In the troposphere the sources of aerosols are widely varied and differ on a regional basis leading to regional variations on the Earth s radiative budget. The role of aerosols in climate is one of the major sources of uncertainty in the prediction of climate change. The physical and chemical properties of aerosols are strong functions of their sources. Asia accounts for 60% of the world s population and is a major aerosol source region [Ramanathan et al., 2001]. The ratio of aerosol optical depth (AOD) from natural sources to the total (natural + anthropogenic) AOD was low (0.2 0.3) over the south and southeast Asia, indicating the dominance of 1 Space and Atmospheric Sciences Division, Physical Research Laboratory, Ahmedabad, India. 2 Now at Department of Chemical Engineering, Indian Institute of Technology, Bombay, Mumbai, India. Copyright 2008 by the American Geophysical Union. 0148-0227/08/2007JD008560 anthropogenic aerosols in this region [Chung et al., 2005]. It was found that regionally the populated tropical regions contribute most to the global aerosol surface forcing, with Asia being the largest contributor [Chung et al., 2005]. The anthropogenic contribution to the aerosol optical depths over the Indian Ocean region during the winter monsoon when the winds transport pollutants from the Indian subcontinent was estimated to be 80 ± 10% [Ramanathan et al., 2001]. In this context, it is important to determine how aerosols from man-made and natural sources over the Indian subcontinent influence not only the regional, but global air quality and climate. The focus of this study is the Indian subcontinent, the importance of which is discussed below. 2. Motivation and Scope of the Study [3] The Indian population, in excess of 1 billion people, places significant demands on natural resources such as water, land and fossil fuels. The rural and urban mix of population is 74% and 26% respectively. In rural areas biofuels such as fuelwood, dung cake and crop waste predominantly contribute to aerosol formation [Habib et al., 2006]. Over urban areas aerosol emissions from fossil fuels such as coal, petrol and diesel oil dominate. These fossil fuels are used in a variety of processes such as electrical power generation, iron and steel production, oil refining and petrochemical processes, domestic usages and, 1of16

of course, transportation. The Indian subcontinent apart from being a source region for aerosols, is bordered by densely populated and industrialized areas on the east and western sides from where different aerosol species such as mineral dust, soot, nitrates, sulfate particles and organics are produced and transported, thus making it a regional aerosol hot spot. The Indian landmass covers coastal regions, inland plains, semi arid regions, mountains and plateau regions. The Indian subcontinent experiences tropical and subtropical climatic conditions resulting in extreme temperatures, rainfall and relative humidity. These features introduce large variabilities in aerosol characteristics on spatial and temporal scales over India. [4] The impressive economic development witnessed by India in the recent years coupled with growing population and globalization has led to higher demands of energy, transport and communications [Ramanathan et al., 2001]. This has led to increases and changes in fossil and biofuel use patterns across the country resulting in changes in aerosol properties which in turn could change the precipitation patterns in the country and spin down the hydrological cycle [Ramanathan et al., 2001]. This work discusses the changes in aerosol properties (optical depth, fine mode fraction) across the capitals of states and union territories of India during the years 2001 to 2005. The objective is to study the interannual and seasonal variations in aerosol characteristics across India divided into seven regions (Table 1). The division is done on the basis of geography and meteorology, and within a context of urban and rural development patterns. The reason for choosing the capitals of states and union territories is that most of these locations are urban centers, have medium to dense population (based on 2001 Indian census) and are industrialized in some regions. On the basis of the spatial and temporal variations in aerosol characteristics inferences are drawn on aerosol sources over different regions. 2.1. Features of Different Regions in India and Their Climate [5] India comprises 28 states and 7 union territories (Table 1). They are divided into seven regions from top to bottom and in a clockwise direction. (i) The northwest: Jammu and Kashmir, Himachal Pradesh, Punjab, Chandigarh and Haryana; (ii) the north: Uttarakhand, Delhi and Lucknow; (iii) the east: Bihar, Jharkhand, West Bengal and Orissa; (iv) the northeast: Arunachal Pradesh, Sikkim, Nagaland, Assam, Meghalaya, Manipur, Tripura and Mizoram; (v) the central: Madhya Pradesh and Chattisgarh; (vi) the west: Rajasthan, Gujarat, Daman and Diu, Dadra and Nagar Haveli, and Maharashtra; (vii) the south: Andhra Pradesh, Goa, Karnataka, Tamilnadu, Pondicherry (Puducherry), Andaman and Nicobar islands, Kerala, and Lakshadweep islands. The state of Jammu and Kashmir has two capitals: Srinagar in summer and Jammu for winter. Chandigarh is a union territory and serves as capital for Chandigarh, Punjab and Haryana. [6] The landscape of the northwest India varies from snow covered Himalayas, the highest mountains on Earth to the Siwalik range and then to the food plains of Punjab. More than 70% of the population in this region are employed in the agriculture sector. The famous Bakhra Nangal project on the Sutlej river generates hydroelectric power which is used to fuel a variety of industries such as sugar processing, glass, chemicals, textiles, light engineering and automobile parts. This region experiences a semi arid type of climate with high summer and cold winter temperatures. Urbanization and large scale migration have made Chandigarh one of the most densely populated cities (more than 7500 people per sq km). [7] The northern India comprising the union territory of Delhi, Uttarakhand and Uttar Pradesh is one of the most populous regions of the country. Uttar Pradesh alone accounts for roughly one sixth of the total population of India. Delhi, the capital of India, is the largest city in the country (13.8 million). It is also the largest commercial center in the north and largest center for small scale industries. Textiles, sports, leather goods and light engineering products are manufactured in Delhi. The state of Uttar Pradesh has several medium and large scale industrial establishments, while tourism is a major industry in Uttaranchal. This region experiences a typical tropical monsoon type climate. [8] The eastern India is abundant in natural resources. There are huge iron ore, coal and mineral reserves in Orissa, Bihar, Jharkhand and West Bengal. The east accounts for about one fifth of India s population. Kolkata, the capital of West Bengal is the third most populous city. Agriculture continues to be of primary importance in this region despite its recent advances in the industrial sector. The region s growth and the expansion in the heavy engineering, coal mining, power generation and electronics have made it an important industrial center in the south Asia, in addition to its jute (West Bengal) and fabrics. Kolkata has in its fold several automobile, textile, paper, engineering industries apart from tobacco processing units. The summer monsoon in June-September is dominant in this region. [9] India s northeast is linked to the rest of India through a narrow strip of land. The region is sparsely populated because of its remoteness and inaccessability. Arunachal Pradesh, for example, has the lowest density with only about 10 persons per sq km. Natural resources oil and natural gas are found in Assam. There exist only a few modern industries and handloom weaving is a major cottage industry in this region. More than 70% of the population in this region depend on land for their livelihood. The northeast is one of the wettest regions in the world and Cherrapunji in Meghalaya is the wettest place on Earth. [10] Madhya Pradesh and Chattisgarh lie at the center of India. Three quarters of this region s population lives in rural areas making the economy dependent on agriculture. Petrochemical, automobile, telecommunications and electronics industries have only recently been developed in Madhya Pradesh, while Chattisgarh accounts for 20% of India s steel and cement. The Central Indian region goes through a transitional climate; extreme weather in the north, temperate and breezy in the uplands, and hot and humid in the eastern and southern plains. [11] The western India is one of the highly industrialized zones in the country, with Maharashtra alone accounting for about 25% of the nation s industrial output. Mumbai is the commercial capital of India with a large number of engineering, manufacturing, food processing and textile units. Gujarat has in its fold, dairy products, textiles, cement, vegetable oils and petrochemical industries. Rajasthan is 2of16

Table 1. Details of the States and Union Territories (Denoted by *) in India, Their Capital Cities, Location (Latitude, Longitude) and Population of Cities (Based on 2001 Census of India) a Region State/Union Territory Capital Location Lat. ( N) Lon. ( E) Population (in Millions) Elevation (MSL) Dominant Source Type NORTHWEST 1 Jammu and Kashmir Srinagar (Summer) 34.0 74.8 1.2 1666 N Jammu (Winter) 32.7 74.8 1.7 367 A 2 Himachal Pradesh Shimla 31.1 77.2 0.7 2202 N 3 Punjab Chandigarh 30.7 76.9 0.9 347 A 4 Chandigarh* Chandigarh 30.7 76.9 0.9 347 A 5 Haryana Chandigarh 30.7 76.9 0.9 347 A NORTH 6 Uttarakhand Dehradun 30.3 78.0 1.3 695 N 7 Delhi* New Delhi 28.5 77.1 13.8 233 A, N 8 Uttar Pradesh Lucknow 26.8 80.9 3.7 128 A, N EAST 9 Bihar Patna 25.6 85.1 4.7 60 A 10 Jharkhand Ranchi 23.3 85.3 2.8 647 A, N 11 West Bengal Kolkata 22.6 88.4 4.6 6 A 12 Orissa Bhubaneshwar 20.2 85.8 0.66 46 A NORTHEAST 13 Arunachal Pradesh Itanagar 27.1 93.6 0.035 524 N 14 Sikkim Gangtok 27.3 88.6 0.2 1756 N 15 Nagaland Kohima 25.6 94.2 0.3 1406 N 16 Assam Dispur 26.1 91.8 0.8 54 N 17 Meghalaya Shillong 25.6 91.9 0.27 1598 N 18 Manipur Imphal 24.7 93.9 0.8 774 N 19 Tripura Agartala 23.9 91.2 1.5 16 N 20 Mizoram Aizawl 23.7 92.7 0.3 1026 N CENTRAL 21 Madhya Pradesh Bhopal 23.3 77.4 1.8 523 A, N 22 Chattisgarh Raipur 21.2 81.7 3.0 325 A, N WEST 23 Rajasthan Jaipur 26.8 75.8 5.2 390 A, N 24 Gujarat Gandhinagar 23.2 72.7 1.3 61 A, N 25 Daman and Diu* Daman 20.4 72.8 0.1 12 N 26 Dadra and Nagar Haveli* Silvassa 20.3 73.0 0.2 32 N 27 Maharashtra Mumbai 18.9 72.8 11.9 11 A, N SOUTH 28 Andhra Pradesh Hyderabad 17.5 78.6 3.8 545 A, N 29 Goa Panaji 15.3 73.5 0.8 52 A, N 30 Karnataka Bangalore (Bengaluru) 12.9 77.6 4.3 921 A, N 31 Tamilnadu Chennai 13.0 80.2 4.2 16 A, N 32 Pondicherry (Puducherry)* Pondicherry (Puducherry) 11.9 79.8 0.97 6 A, N 33 Andaman and Nicobar Islands* Port Blair 11.6 92.7 0.3 1 N 34 Kerala Trivandrum 8.5 77.0 3.2 7 A, N 35 Lakshadweep Islands* Kavaratti 10.6 72.6 0.06 4 N a The elevation (in MSL) of the locations and dominant aerosol source types (A for anthropogenic and N for Natural) are given. rich in mineral resources such as zinc. The western region exhibits large differences in its landscape and rainfall. The coastal areas experience temperate climate with heavy rainfall during the southwest monsoon (June-September). Gujarat and Rajasthan have a dry, arid climate. The union territories of Daman and Diu, and Dadra and Nagar Haveli were former Portuguese colonies. [12] The southern region forms the peninsular part of India. In this region also about 70% of the people depend on agriculture. Note that Bangalore and Pondicherry are now known as Bengaluru and Puducherry respectively (Table 1). However, in figures, tables and further discussion throughout this article Puducherry and Bengaluru are referred to as Pondicherry and Bangalore respectively. The iron ore deposits in Tamilnadu, Andhra Pradesh and Karnataka are used to make steel. Textiles, sugar and leather are Tamilnadu s major industries and are mostly concentrated in big cities such as Chennai, Coimbatore and Salem. Kerala and Karnataka are famous for their spices, tea, coffee and rubber plantations. Hyderabad and Bangalore, capital cities of Andhra Pradesh and Karnataka, have become the high tech cities with a focus on information technology and are major urban centers. Visakhapatnam in Andhra Pradesh is well known for its shipbuilding and engineering industries. This region gets its rainfall from the southwest monsoon and the winter monsoon which is active during October-December. [13] Synoptic scale wind patterns over India indicate that during the northeast monsoon (January-April) the winds are calm, north/north-easterly and are from the land (polluted northern hemisphere) toward the ocean. During the southwest monsoon season of June-September the winds are stronger, moist and are from the marine and western regions surrounding India. Prior to the southwest monsoon, during March-May, the winds come from the adjacent arid, semiarid regions to south, east and west India. Most of the annual rainfall in the west, east and the north is due to the 3of16

southwest monsoon, though the quantum of rainfall differs in each location. The southern belt gets rainfall during the southwest monsoon as well as during October-December. 2.2. Regional Sources and Emissions Over India [14] Coal combustion in thermal power plants, fossil fuels used in transportation and industrial sector were found to contribute most of aerosol pollutant emissions over India [Reddy and Venkataraman, 2002a]. Aerosol emission inventory over India showed that highest emission fluxes of sulfur dioxide (SO 2 ) (>5000 kg SO 2 km 2 ) from fossil fuels were observed in Tamilnadu, Maharashtra, Uttar Pradesh, Gujarat and West Bengal due to power plants and large point sources in these states [Reddy and Venkataraman, 2002a]. It was found that over the central and northwest India SO 2 fluxes were less because of lower levels of industrial activity. It was also mentioned that the northeast states, Jammu and Kashmir, and western Rajasthan were found to have low SO 2 emissions owing to low industrial and transportation activity. Coal combustion and diesel oil were found to contribute 99% of fine mode particulate matter emissions over India. The four metros (Chennai, Kolkata, Mumbai and Delhi) were found to have the highest black carbon (BC) emissions from diesel combustion in the transport sector [Reddy and Venkataraman, 2002a]. [15] In India, biomass combustion mainly arises from fuel used for domestic cooking, forest fires and burning of crop waste and harvest. Wood, crop waste and dung cake are the biofuels used in rural India and in urban areas wood is used [Reddy and Venkataraman, 2002b]. Biofuels (wood, crop waste and dung cake) accounted for 94% of SO 2 emissions while forest fires contributed the rest 6%. SO 2 emissions from Uttar Pradesh, Madhya Pradesh and Bihar were found to account for 48% of total emissions. SO 2 emission fluxes were moderate to high over the entire east coast of India and Assam, while the SO 2 emissions were low in western Rajasthan, Jammu and Kashmir, Arunachal Pradesh and Mizoram. Particulate matter emissions in fine mode from biofuels were found to be the highest over Madhya Pradesh, Andhra Pradesh and Uttar Pradesh and were about 0.30 Tg a 1 [Reddy and Venkataraman, 2002b]. Fuelwood and crop waste accounted for 72% of BC emissions in India. BC emissions were found to be high over Andhra Pradesh, Uttar Pradesh, Bihar and Assam [Reddy and Venkataraman, 2002b]. It is to be noted that while emissions from fossil fuel combustion over India were localized to large point sources (utilities, refineries and petrochemicals, cement and fertilizers) and major cities, emissions from biomass combustion are area sources spread all over the country [Reddy and Venkataraman, 2002b]. [16] Large fluxes of anthropogenically produced absorbing aerosol emissions (black carbon and inorganic oxidized matter, which is mostly fly ash from coal based power plants and mineral matter from open burning of crop waste and forests) have been seen over the Indo-Gangetic plain, east, east coast plains and south India in January-March [Habib et al., 2006]. Dust emissions were seen over the Thar desert in the west during these months. In April-May the Indo-Gangetic plain is found influenced by emissions from central India from forest fires and open burning of crop waste. Mineral dust emissions were seen in the northwest and west combined with long range transport from Africa and west Asia [Habib et al., 2006]. A good relation was found to exist between the spatial distributions of absorbing aerosols detected by TOMS and the anthropogenic and dust emissions on a subcontinental scale, in the dry season when rainfall and dispersion are insignificant [Habib et al., 2006]. However, atmospheric dispersion on a regional scale, for example, in January-March over north India and rainfall during June-September are found to reduce aerosol loads and modify their spatial distributions [Habib et al., 2006]. [17] Reliable seasonal-regional data on species wise aerosol optical depths which can be used to determine the contributions due to sea salt, dust, biogenic, anthropogenic, mixtures etc. to the composite AOD is not available over India. However, in this work, based on aerosol emissions and wind patterns an attempt is made to classify the dominant aerosol source type in these locations over India (Table 1). For example, in urban cities such as Delhi and Mumbai, anthropogenic sources will dominate the aerosol distribution, which could get modulated by the presence of natural aerosols (sea salt during monsoon and dust from adjacent arid regions). In contrast, for example, in the northeast India because of the large forest cover aerosols from biomass burning and forest fires dominate the distribution, thus making it a natural aerosol source region. A coastal, urban station, such as Trivandrum, will be influenced by the local man-made pollutants and strong presence of sea spray aerosols, thus making it both an anthropogenic and natural source location. In Table 1 the locations have been classified as A (anthropogenic), N (natural) and A, N when these locations are influenced by both the sources. It is clear that over the seven regions of India (Table 1) the geography, meteorology, sources of aerosols and their emissions are different. The population of the locations (Table 1) vary from over 10 million (Delhi, Mumbai) to less than a million (Bhubaneshwar). Most of the locations are urban centers and hence have a large number of automobiles. Thus the gas to particle reaction products of the exhausts from the industry and automobiles can contribute to the fine mode particles in these areas, while during monsoon and favorable wind conditions the larger size sea salt and dust will be present in these locations. At any given location, it is to be noted that both natural and anthropogenic sources contribute to the aerosol concentration. The percentage contribution of natural versus anthropogenic sources to AODs will therefore depend on aerosol emissions, their lifetimes, extinction (scattering + absorption) characteristics, in addition, to the long range transport of pollutants. 3. Data Analysis and Methodology [18] The daily mean MODIS Terra derived Level 3 1 1 grid aerosol optical depths (AOD) are used for analysis [Kaufman et al., 1997]. Data corresponding to the capitals of 28 states and 7 union territories in India divided into seven regions are analyzed and discussed (Table 1). 550 nm AODs which are above 0 and less than 1.0 are only considered. This limit is imposed on the assumption that AOD value greater than 1.0 would have resulted most likely due to cloud contamination [Chung et al., 2005]. Monthly averaged data for 60 months (January 2001 December 4of16

2005) are computed from the daily AOD and fine mode fraction (FMF) products and are used in the study. [19] MODIS derived AOD data and the analysis method are validated against in situ measured AODs by Aerosol Robotic Network (AERONET) Sun photometer at Kanpur (26.4 N, 80.3 E) [Singh et al., 2004] during 2001 2005 [Ramachandran, 2007]. Level 2 Quality assured AOD (total, fine modes) data obtained at 440, 500, and 675 nm were used in the validation study. A good match was found between AODs derived from MODIS and AERONET during January May and August-December. The absolute difference between MODIS and AERONET AODs during January May and August-December of 2001 2005 over Kanpur was found to be 0.08 ± 0.07, while during June July the absolute difference in the 5-year period was estimated to be 0.24 ± 0.10 [Ramachandran, 2007]. During June July when the southwest monsoon is active over most of India MODIS was found to overestimate the AODs. It should be emphasized that during the monsoon months it is cloudy and retrieval of AODs could be affected by the cloud cover. The intense cloud cover limited the number of days of AOD retrievals during this period [Ramachandran, 2007]. Further, it was found that errors in AODs were within the predicted retrieval uncertainty of DAOD = ±0.05 ± 0.2AOD [Kaufman et al., 1997; Chu et al., 2002]. [20] The MODIS derived FMF provides a reasonably good indication of whether the AOD is dominated by fine or coarse mode particles [Kaufman et al., 2005]. The fine mode fraction of aerosols for those days in which AODs are available are used in the study. The definition of MODIS FMF and its utilization is based on the fact that the aerosol mass in the atmosphere is generally made up of two modes, namely, (1) a fine mode produced by combustion and/or gas to particle conversion and (2) a mechanically produced coarse mode [Anderson et al., 2005]. Further, the size distribution of aerosol surface area and light extinction are closely related to mass and hence they also follow the general bimodal pattern. The fine mode aerosols over urban, industrialized and densely populated regions arise mostly from gas to particle conversion and combustion which are mainly anthropogenic, while coarse mode aerosols such as wind blown mineral dust and sea salt particles come from natural sources. By comparing the MODIS and AERONET retrievals of aerosol fine mode fraction over ocean surfaces Kleidman et al. [2005] found that MODIS slightly overestimated fine mode fraction for dust-dominated aerosols by 0.1 0.2, while in smoke and pollution dominated aerosol conditions it underestimated. [21] Aerosol optical depth is directly proportional to aerosol loading and the size distribution of aerosol mass burden in the atmospheric column. Typically in an aerosol size distribution the number of sub micron particles will be orders of magnitude higher than super micron particles. It has been seen that a high burden of aerosol mass in polluted regions can result in high AODs and FMFs [Kaufman et al., 2002]. Aerosols emitted from natural sources (sea salt and desert dust) contain larger particles than aerosols emanating from man-made combustion sources such as urban/industrial pollution [Remer et al., 2005]. Anthropogenic aerosols downwind from vegetation fires and industrial pollution were found to have high concentration of fine particles [Kaufman et al., 2002]. It is expected that AODs dominated by pollution will result in a high FMF and AOD while natural aerosols (biogenic) can produce a high FMF and a range of AODs and if the aerosols are mechanically generated (for example sea salt and desert dust) then that can result in a low FMF and a range of AODs [Kaufman et al., 2002; Remer et al., 2005]. [22] FMFs derived from MODIS over Kanpur were found to be higher by about 0.1 0.2 during most months when compared to AERONET values [Ramachandran, 2007]. FMF values over Kanpur from AERONET were found to have lower values (0.5 0.6) during March-September (premonsoon and monsoon) and higher values (>0.8) during October February (postmonsoon and winter), suggesting the presence of higher amount of fine mode particles during winter. MODIS FMF over Kanpur during January May and August December 2001 2005 was found to be higher on an average by 0.09 when compared to AERONET FMF, while in June July period MODIS overestimates FMF by 0.15 [Ramachandran, 2007], which is in good agreement with the results obtained by Kleidman et al. [2005]. The 5- year annual average (2001 2005) MODIS AODs and FMFs over Kanpur are found to be higher by 0.10 than AERONET AODs and FMFs. Remer et al. [2005] found on analyzing the MODIS and AERONET AOD retrievals over Kanpur that 90% of MODIS AOD retrievals were within the expected uncertainty at 550 nm. The percentage difference between MODIS and AERONET AODs was estimated to be -2%. Remer et al. [2005] in addition pointed out that MODIS aerosol products are sufficiently accurate for a wide variety of studies including estimates of aerosol radiative effects. The validation and comparison discussed by Ramachandran [2007] suggested that MODIS aerosol products (AODs, FMFs) can be used to study the spatial and temporal variations in aerosol characteristics as the MODIS derived AODs and FMFs capture the major part of seasonal variations and the dominant aerosol type. In this work, the monthly mean features in aerosol optical depths and fine mode fractions for select locations from different regions in India are discussed in detail. The frequency distribution histograms of aerosol optical depths and fine mode fraction values are analyzed and inferences are drawn. 4. Results and Discussion 4.1. 5-Year Mean AOD and FMF Variations Over India [23] In Figure 1, the 5-year (2001 2005) average of annual (January-December) AOD and FMF means are plotted for the 35 locations. AODs and FMF amplitudes are displayed using proportionately sized symbols. Note that no location had any FMF value in the 0 0.2 range and hence no symbol for this range appears on the figure. The annual mean AOD is higher than 0.4 in the north, east, central and west India (Table 2). The northwest region has an AOD of 0.36 while the south comes closer at 0.35 and the northeast has the lowest AOD of 0.28. It is interesting to note that the east and northeast which have the highest and lowest AODs have higher FMF values of 0.92 and 0.95 respectively. The high FMF values obtained for the eastern region could well be related to Ramachandran s [2007] observation that the AOD influence of dust transport or sea 5of16

Figure 1. Map showing the capitals of 28 states and 7 union territories of India. The cities are classified into seven regions as marked in the figure. 5-year (2001 2005) annual mean aerosol optical depths (AOD) and fine mode fraction (FMF) values obtained at each location are drawn. 5-year annual mean AODs are classified in 5 ranges (>0.1 0.2, >0.2 0.3, >0.3 0.4, >0.4 0.5 and >0.5 0.6) while FMFs are classified in 4 categories (>0.2 0.4, >0.4 0.6, >0.6 0.8 and >0.8 1.0). 5-year annual mean FMFs for all the locations are found to be >0.2. See text for details. salt aerosols during monsoons is minimal throughout the year. The northeast India has large forest areas. The biomass burning aerosols produced by forest fires in this region [Habib et al., 2006] will contribute dominantly to the AODs. As the region is less densely populated man-made contribution to AODs will be minimal. FMF is found to be the lowest at 0.56 over the northwest followed by the west (0.62). FMFs in the southern region are in the 0.70 0.80 range (Figure 1). 5-year mean AODs and FMFs over India are found to exhibit the following features: (1) both AODs and FMFs are quite similar (within the uncertainty and overestimates) among the locations over a particular region (e.g., Srinagar and Shimla, Jammu and Chandigarh), (2) similar AODs but significantly different FMF values as in Bhopal and Raipur in the central India, (3) different AODs and more or less similar FMF values as seen in locations of the northeast and (4) quite different AODs and FMFs as in the south India. The above differences in the AODs and FMFs for the locations in a particular region and in different regions indicate that sources of aerosols and their contributions to the AODs and FMFs are different. 4.2. Annual Mean Variations in Aerosol Optical Depths Over India [24] Annual mean variations in AODs during 2001 2005 over all city locations in Table 1 are given in Table 3. AOD variations for 20 select locations from the above 35 are plotted in Figure 2. We allowed ourselves to be limited to this subset for plotting purposes since the variations characterizing the other 15 locations were each similar to the variations observed for a neighboring location in the same region. At the outset, the (location to location) spatial variations are more evident and dominant when compared to temporal variations of the annual means during the 5-year period. Annual mean AOD of a particular year is found to lie within ±1s of the other years during the 5-year period and hence no significant increasing or decreasing trends are seen. [25] As mentioned earlier the mean absolute difference between MODIS and AERONET AODs during January- May and August December of 2001 2005 over Kanpur was found to be 0.08, while during June-July the mean 6of16

Table 2. Region Wise 5-Year (2001 2005) Mean Aerosol Optical Depths and Fine Mode Fraction Values in India Aerosol Optical Depth Fine Mode Fraction Region Mean s Mean s Northwest 0.36 0.17 0.56 0.20 North 0.47 0.23 0.75 0.08 East 0.49 0.11 0.92 0.04 Northeast 0.28 0.09 0.95 0.04 Central 0.42 0.01 0.70 0.13 West 0.44 0.03 0.62 0.12 South 0.35 0.06 0.74 0.07 absolute difference increased to 0.24. MODIS FMF over Kanpur during January May and August-December 2001 2005 was higher on an average by 0.09 when compared to AERONET FMF, while in June July period the overestimation by MODIS was 0.15. The 5-year annual average (2001 2005) MODIS AODs and FMFs over Kanpur were higher by 0.10 than AERONET AODs and FMFs. Thus on an annual scale any variation above 0.10 in AOD and FMF between different locations can be considered significant. On the monthly mean scale the variation above 0.10 in AOD and FMF during the year except for June July period is significant, while in June July variation above 0.2 in AOD and FMF is deemed significant. [26] In the northwest, Srinagar (1700 MSL (meters above sea level)) and Shimla (2202 MSL) have lower AODs while Jammu and Chandigarh have higher AODs. The annual mean AODs during 2001 2005 are in the 0.16 0.29 range (Table 3) in Srinagar and Shimla, while in Jammu and Chandigarh the AODs are about 0.5. It should be noted that in a particular region the locations which have more or less similar environment (e.g., Srinagar, Shimla) the variation in the annual mean AODs are within the uncertainty/overestimation associated with MODIS products. Higher AODs over Jammu and Chandigarh in the same region clearly suggests an increase in aerosol loading. Dehradun in the north being a hill station and situated at a height of about 700 MSL is devoid of low altitude anthropogenic pollution, which results in low AOD values. Lucknow, a densely populated location, has AODs higher than 0.55. The annual mean AODs in Lucknow and Delhi are about three times higher than Dehradun during Table 3. Annual Mean 550 nm Aerosol Optical Depths for 5 Years From 2001 to 2005 Over India 2001 2002 2003 2004 2005 Station Mean s Mean s Mean s Mean s Mean s NORTHWEST Srinagar 0.24 0.05 0.27 0.11 0.27 0.11 0.26 0.06 0.29 0.10 Jammu 0.48 0.14 0.48 0.13 0.49 0.11 0.53 0.15 0.50 0.14 Shimla 0.16 0.10 0.16 0.10 0.17 0.09 0.16 0.09 0.18 0.10 Chandigarh 0.50 0.16 0.49 0.17 0.50 0.15 0.55 0.16 0.50 0.13 NORTH Dehradun 0.18 0.08 0.20 0.13 0.18 0.11 0.22 0.10 0.22 0.11 Delhi 0.57 0.13 0.59 0.18 0.61 0.15 0.64 0.15 0.57 0.10 Lucknow 0.57 0.10 0.61 0.19 0.58 0.12 0.67 0.16 0.60 0.12 EAST Patna 0.58 0.08 0.55 0.10 0.58 0.12 0.64 0.11 0.61 0.13 Ranchi 0.35 0.08 0.35 0.11 0.34 0.09 0.46 0.19 0.39 0.14 Kolkata 0.51 0.10 0.57 0.14 0.53 0.12 0.61 0.13 0.59 0.12 Bhubaneshwar 0.34 0.12 0.37 0.08 0.39 0.07 0.48 0.14 0.48 0.15 NORTHEAST Itanagar 0.21 0.14 0.19 0.09 0.21 0.10 0.28 0.15 0.26 0.13 Gangtok 0.18 0.09 0.18 0.13 0.18 0.07 0.26 0.12 0.26 0.10 Kohima 0.16 0.09 0.20 0.12 0.17 0.09 0.24 0.14 0.22 0.12 Dispur 0.39 0.12 0.38 0.11 0.38 0.14 0.49 0.16 0.48 0.16 Shillong 0.29 0.13 0.28 0.11 0.27 0.12 0.36 0.16 0.35 0.15 Imphal 0.22 0.11 0.25 0.13 0.22 0.11 0.29 0.15 0.27 0.14 Agartala 0.36 0.18 0.33 0.10 0.35 0.14 0.47 0.12 0.43 0.17 Aizawl 0.22 0.11 0.22 0.10 0.22 0.12 0.30 0.14 0.26 0.16 CENTRAL Bhopal 0.40 0.12 0.43 0.15 0.43 0.14 0.44 0.19 0.42 0.15 Raipur 0.40 0.08 0.35 0.07 0.41 0.11 0.47 0.18 0.44 0.12 WEST Jaipur 0.49 0.18 0.50 0.18 0.47 0.14 0.51 0.19 0.46 0.15 Gandhinagar 0.36 0.14 0.39 0.11 0.46 0.19 0.45 0.20 0.42 0.17 Daman 0.40 0.09 0.45 0.13 0.46 0.14 0.48 0.18 0.46 0.16 Silvassa 0.42 0.18 0.41 0.15 0.41 0.16 0.42 0.17 0.39 0.16 Mumbai 0.40 0.10 0.43 0.13 0.44 0.13 0.43 0.14 0.41 0.13 SOUTH Hyderabad 0.36 0.06 0.39 0.14 0.42 0.11 0.41 0.14 0.42 0.14 Panaji 0.39 0.15 0.39 0.15 0.42 0.16 0.39 0.18 0.41 0.17 Bangalore 0.26 0.06 0.25 0.06 0.24 0.05 0.34 0.12 0.32 0.11 Chennai 0.38 0.09 0.37 0.09 0.39 0.06 0.40 0.08 0.47 0.12 Pondicherry 0.39 0.05 0.38 0.07 0.41 0.07 0.46 0.09 0.48 0.09 Port Blair 0.25 0.05 0.28 0.11 0.25 0.07 0.29 0.11 0.29 0.10 Trivandrum 0.29 0.05 0.28 0.06 0.29 0.07 0.38 0.10 0.41 0.09 Kavaratti 0.33 0.05 0.33 0.11 0.34 0.07 0.34 0.09 0.32 0.10 7of16

Figure 2. Annual mean variations in aerosol optical depths along with ±1s for 20 sites representing the 7 regions outlined in Table 1. 2001 2005, which is quite significant. Patna, in the east, also has higher AODs varying from 0.55 (2002) to 0.64 (2004). AODs were found to be high over the entire state of Bihar (Patna is its capital) by Di Girolamo et al. [2004], which was attributed to the high population density, transport and topography. [27] In the northeast Dispur and Agartala AODs are more or less the same and are in the 0.4 0.5 range, while Imphal AODs are about 0.2. Bhopal and Raipur in the central India have similar annual mean AODs. The western region represented here by Jaipur, Gandhinagar and Daman have annual mean AODs in the 0.4 0.5 range. The south on an average has lower AODs than the west (Table 2), with Bangalore and Port Blair having lower AODs among the four stations (Table 3). A higher standard deviation on the annual mean AODs indicate that the seasonal variations are more prominent in a year over certain locations in certain regions, for example, as seen in Chandigarh and Jaipur; while Port Blair, Trivandrum and Bangalore have less intraannual variation in AODs as suggested by their low s values. Despite the fact the stations are urban centers and medium to densely populated locations differences in annual mean AOD patterns over different regions suggest that the distribution of aerosol emissions and burdens are different. 4.3. Annual Mean Variations in Aerosol Fine Mode Fraction Over India [28] Annual mean FMF values during 2001 2005 also show clear regional variations (Figure 3 and Table 4). The northwest FMF values show large variations in their annual mean values. FMFs of Srinagar and Shimla, both at higher 8of16

Figure 3. Annual mean variations in fine mode fraction along with ±1s for 20 cities over different regions of India. altitudes, are in the 0.34 0.41 range. These low values indicate that large particles dominate the aerosol size distributions. About 60% or more of the AODs is due to fine mode particles in Jammu and Chandigarh, which also have higher AODs. In urban locations, large number of fine mode particles, mainly from human activities contribute to the AODs at smaller wavelengths. Larger variations in FMF arise due to different sources of aerosols contributing to the AODs and indicate that source strengths of fine mode and coarse mode aerosols can vary on an interannual basis over a region. [29] The east and the northeast have FMF values that are above 0.80 in all the years. This happens despite the fact the aerosol sources are different in these regions. The east is dominated by anthropogenic sources because of the urbanization and industrialization. Annual mean aerosol optical depths of Kolkata and Bhubaneshwar are found to be different while the FMF for these locations are higher than 0.90. Annual mean AODs over Kolkata are quite stable during 2001 2005, while Bhubaneshwar AODs showed a larger variation (Table 3). FMF values for Kolkata decreased only slightly, while in Bhubaneshwar the decrease was larger (Table 4). [30] The northeast FMF values are above 0.9 during the 5-year period in all locations except for Gangtok. Even in Gangtok FMFs were in the 0.81 0.87 range during 2001 2003 and then increased to 0.92 and 0.93 in 2004 and 2005 respectively. In the northeast more than 50% of the land is forests, which provide timber, resins and tanning material. The northeast region is meagerly populated. The sources of aerosols are mainly natural and dominated by aerosols from biomass burning and forest fires over this region [Habib et al., 2006]. Annual mean AODs are low over this region (Tables 2 and 3). 9of16

t4.1 Table 4. Annual Mean Fine Mode Fraction Values for 5 Years From 2001 to 2005 Over India t4.2 2001 2002 2003 2004 2005 t4.3 Station Mean s Mean s Mean s Mean s Mean s t4.4 NORTHWEST t4.5 Srinagar 0.39 0.17 0.36 0.15 0.39 0.13 0.38 0.16 0.37 0.18 t4.6 Jammu 0.77 0.19 0.74 0.26 0.74 0.28 0.62 0.29 0.63 0.32 t4.7 Shimla 0.41 0.30 0.37 0.26 0.40 0.30 0.34 0.28 0.41 0.30 t4.8 Chandigarh 0.82 0.24 0.77 0.28 0.78 0.28 0.74 0.26 0.72 0.33 t4.9 NORTH t4.10 Dehradun 0.65 0.16 0.65 0.19 0.64 0.20 0.69 0.17 0.66 0.20 t4.11 Delhi 0.89 0.20 0.84 0.26 0.85 0.24 0.78 0.24 0.74 0.34 t4.12 Lucknow 0.79 0.33 0.83 0.24 0.80 0.31 0.79 0.29 0.71 0.36 t4.13 EAST t4.14 Patna 0.92 0.12 0.97 0.05 0.89 0.14 0.90 0.12 0.83 0.26 t4.15 Ranchi 0.89 0.08 0.92 0.05 0.88 0.13 0.87 0.11 0.81 0.20 t4.16 Kolkata 0.98 0.02 0.96 0.04 0.96 0.03 0.95 0.05 0.93 0.07 t4.17 Bhubaneshwar 0.98 0.03 0.94 0.07 0.95 0.06 0.92 0.07 0.86 0.18 t4.18 NORTHEAST t4.19 Itanagar 0.97 0.05 0.96 0.07 0.97 0.04 0.97 0.05 0.95 0.07 t4.20 Gangtok 0.86 0.09 0.81 0.10 0.87 0.09 0.93 0.09 0.92 0.12 t4.21 Kohima 0.95 0.07 0.95 0.07 0.94 0.07 0.94 0.06 0.92 0.11 t4.22 Dispur 0.99 0.01 0.99 0.03 0.98 0.04 0.96 0.04 0.97 0.03 t4.23 Shillong 0.96 0.04 0.96 0.05 0.96 0.05 0.94 0.06 0.94 0.07 t4.24 Imphal 0.95 0.07 0.95 0.07 0.95 0.07 0.91 0.10 0.92 0.08 t4.25 Agartala 0.99 0.01 0.99 0.01 0.99 0.01 0.99 0.01 0.98 0.02 t4.26 Aizawl 0.99 0.01 1.00 0.00 1.00 0.01 1.00 0.01 0.99 0.01 t4.27 CENTRAL t4.28 Bhopal 0.64 0.37 0.56 0.37 0.59 0.41 0.69 0.37 0.56 0.37 t4.29 Raipur 0.77 0.19 0.82 0.15 0.84 0.18 0.81 0.18 0.72 0.26 t4.30 WEST t4.31 Jaipur 0.40 0.28 0.39 0.27 0.49 0.31 0.36 0.29 0.42 0.24 t4.32 Gandhinagar 0.68 0.25 0.50 0.34 0.69 0.31 0.59 0.34 0.58 0.38 t4.33 Daman 0.80 0.09 0.79 0.08 0.79 0.09 0.77 0.08 0.70 0.19 t4.34 Silvassa 0.64 0.32 0.59 0.32 0.64 0.32 0.62 0.28 0.57 0.32 t4.35 Mumbai 0.73 0.11 0.70 0.08 0.70 0.11 0.73 0.07 0.72 0.09 t4.36 SOUTH t4.37 Hyderabad 0.71 0.21 0.65 0.22 0.59 0.33 0.66 0.22 0.56 0.30 t4.38 Panaji 0.80 0.10 0.79 0.09 0.79 0.08 0.79 0.08 0.84 0.05 t4.39 Bangalore 0.82 0.06 0.76 0.14 0.76 0.16 0.66 0.13 0.69 0.15 t4.40 Chennai 0.68 0.21 0.73 0.07 0.71 0.09 0.70 0.10 0.68 0.16 t4.41 Pondicherry 0.86 0.11 0.82 0.14 0.80 0.14 0.77 0.12 0.73 0.26 t4.42 Port Blair 0.82 0.08 0.81 0.09 0.80 0.08 0.78 0.11 0.84 0.07 t4.43 Trivandrum 0.90 0.06 0.87 0.07 0.89 0.04 0.66 0.19 0.67 0.24 t4.44 Kavaratti 0.69 0.11 0.66 0.11 0.67 0.11 0.68 0.13 0.69 0.10 [31] Bhopal and Raipur in the central India have more or less similar AODs during the 5-year period while FMFs are lower over Bhopal than in Raipur. FMFs in Bhopal are also found to exhibit a larger deviation from the annual mean which indicates that there exists a larger seasonal variability in aerosol characteristics over Bhopal when compared to Raipur. Annual mean AODs over the western region are in the 0.35 0.50 range in all the locations, while FMFs are found to be the lowest over Jaipur (0.36 0.49) and they are high for Daman (0.70 0.80). Jaipur FMFs also exhibit >50% coefficient of variation (sigma/mean) values underscoring the large seasonal variations in aerosol FMF values over this location (see the following section). Gandhinagar, Mumbai and Silvassa also have low FMFs. [32] AODs over the south India are the second lowest among the seven regions (Table 2) next only to northeast. Hyderabad, Panaji, Pondicherry and Chennai AODs are higher than 0.35. Trivandrum AODs are <0.30 during 2001 2003 and become higher than 0.35 in 2004 and 2005 respectively. AODs over Port Blair and Kavaratti, the island locations are quite stable during the 5-year period. Though the AODs are similar FMFs over Hyderabad, Bangalore, Panaji, Pondicherry and Chennai show variations. Hyderabad and Bangalore FMFs decrease during the 5-year period while they increase in Panaji (Table 4). Chennai and Kavaratti FMF values are around 0.70, while FMF over Port Blair is >0.75 and goes up to 0.84 in 2005. Similar to the decrease seen over Hyderabad and Bangalore FMFs in Trivandrum significantly decrease from 0.90 in 2001 to 0.67 in 2005. Also, the FMF coefficient of variation values for Trivandrum increase from 7% in 2001 to 36% in 2005. [33] It is suggested that higher FMFs indicate the dominance of fine mode aerosols, which arise mainly from anthropogenic sources while lower FMFs indicate the dominance of coarse mode particles, the sources of which are natural [Kaufman et al., 2002]. It is seen that FMFs in the east and the northeast are higher, while their AOD values are high and low respectively. It should be noted that while the east is dominated by anthropogenic sources, natural sources of aerosols dominate the northeast. Bhopal and Raipur in the central India did not have significantly 10 of 16

Figure 4. Monthly mean variation in aerosol optical depths at 20 select locations representing the seven different regions of India. Vertical bars indicate ±1s from the 5-year mean. different AODs while their FMFs are different; Raipur FMFs are higher by a minimum of 0.12 than Bhopal FMFs during 2001 2005. These results provide a valuable contribution to our knowledge on FMF that it is important to consider both FMF and AOD as well as information on regional sources when interpreting the regional variations in FMF. The interannual mean variations in AOD and FMF values and their deviations over different regions indicate there exist differences in source contributions of natural and anthropogenic aerosols and in their seasonal variations. 4.4. Monthly Mean Variations in AOD and FMF in Seven Regions [34] Monthly mean AOD variations during the 5-year (2001 2005) period over 20 select locations of Figure 2 are plotted in Figure 4 alongwith ±1s. Regional and intraregional variations are evident in the monthly mean AOD patterns. Jammu and Chandigarh AODs are higher than Srinagar and Shimla AODs throughout the year. Monthly mean AOD patterns are similar for Jammu and Chandigarh which show summer monsoon high and winter low. AODs are in the range of 0.4 during winter months which increases to about 0.8 during summer. The higher AODs over Jammu and Chandigarh are likely due to the higher levels of anthropogenic activities combined with the larger sea salt particles characteristic of the summer monsoon. The lower AOD values over Srinagar and Shimla occur as they are located at higher than 1500 MSL and are devoid of any lowlevel anthropogenic pollution. Jammu and Chandigarh, are major urban centers and are influenced more by anthropogenic sources. It is to be noted that in most locations where both anthropogenic and natural aerosol sources are dominant during the year, the summer-winter AOD differences 11 of 16

Figure 5. Intraannual variation in fine mode fraction values for 20 stations covering the different regions of the country. ±1s from the mean is represented by vertical bars in the figure. are higher than the uncertainty and overestimation associated with MODIS derived AODs. [35] Lucknow and New Delhi exhibit high AODs while the hill station Dehradun has lower AODs throughout the year. Lucknow and Delhi AODs are higher than 0.4 and reach the maximum values of 0.8 in summer. Patna and Ranchi in the east also exhibit a summer high and winter low. It has been shown that topography, meteorology and aerosol sources favor in developing a concentrated pool of airborne particles over Bihar during winter resulting in higher AODs [Di Girolamo et al., 2004]. The winter (December-January-February) mean AOD during 2001 2005 for Patna in the present study is estimated to be 0.55 ± 0.06, close to the value of 0.6 for Bihar state [Di Girolamo et al., 2004]. In Bihar percentage of rural population is rather high (89.5%) and in Patna it is 58%. Higher population results in large aerosol sources (due to fossil fuel consumption and biofuels used for domestic purposes) [Habib et al., 2006], which produces higher AODs. [36] Dispur, Agartala and Imphal exhibit different monthly mean patterns. Dispur AODs show a winter low and start increasing thereafter reaching a peak in April. Agartala AODs show an increasing trend from January to June, suddenly decrease in July and the values remain the same thereafter. Imphal shows a winter low and summer high AODs. The central and the western India stations display a winter low and summer high AOD variations. In the south, Chennai [Ramachandran, 2007], Hyderabad, Trivandrum and Port Blair exhibit a summer high and a winter low. Hyderabad winter time AODs are around 0.3 which increases to 0.6 during summer. In Bangalore the AODs are less than 0.4. Trivandrum AODs during winter are around 0.3 which increase to 0.4 during June-August. Similar features of a premonsoon/summer peak and a winter low in AODs have 12 of 16

been reported earlier over Trivandrum [Moorthy et al., 2001]. The increase in AODs during the summer and the southwest monsoon were attributed to the dominance of sea spray aerosols by Moorthy et al. [2001]. Port Blair AODs are almost constant (0.2) from September to April while during the summer monsoon the AODs are higher. In Port Blair, a coastal location similar to Trivandrum, a summer high in AODs suggest the influence of large size sea spray aerosols. These results clearly show that the AODs exhibit different seasonal patterns in different spatial domains of India. 4.5. Intraannual FMF Variations Over India [37] 5-year monthly mean FMF values over the 20 locations representing the 7 different regions in India are plotted in Figure 5. FMFs in Srinagar are less than 0.5 in winter months and increases slightly during July August. In Shimla FMFs are around 0.1 during winter and show a maximum of 0.86 in August and then decrease rapidly. In quite contrast Jammu FMFs are higher than 0.9 during January March and August-September. During April July FMFs are in the 0.26 0.54 range. Chandigarh FMFs are low only during April June period while the rest of the year FMFs are higher than 0.80 peaking at 0.99 during August. The seasonal variations in FMFs seen are larger than the uncertainty associated with FMF derivations, suggesting that sources of aerosols in the same region exhibit different seasonal patterns. [38] Dehradun has low FMFs during January June with values in the 0.42 0.67 range and increases suddenly to 0.90 and above during July August. Delhi and Lucknow exhibit similar FMF monthly mean variations with low values during April June. During April June westerly winds blow across the plain causing dust storms, high temperatures and high relative humidity. These rapid changes in FMF would appear to corroborate the observation of Prospero et al. [2002] who noted that dust activity contributes to a sharply defined pattern across the northern India. In contrast during winter the temperatures are colder over much of northern India. During winter the boundary layer is shallow and pollutants get trapped resulting in confinement of aerosols. The high population density combined with the industrial emissions (fossil fuel and biomass combustion) in this region contribute to the fine aerosol particles in winter. The colder temperatures along with the trapping of pollutants give rise to hazy and foggy conditions. These factors lead to higher FMFs during winter over the north India. [39] Patna and Ranchi in the east have FMFs in the range of 0.7 0.8 during April August, while they are close to 1 during the rest of the year. AODs are found to be slightly higher during April August (Figure 4) while the FMFs are slightly lower which indicates the dominance of the larger size aerosols. It is very interesting to note that FMFs of Dispur and Agartala are about 1.0 in spite of the differences in their AODs. Imphal FMFs are about 0.84 in winter while they are higher than 0.90 rest of the year peaking at 0.99 in June. These high FMF values indicate the dominance of fine mode aerosols in aerosol size distribution in these locations and that dust transport is almost insignificant. Bhopal and Ranchi exhibit quite distinct seasonal FMF patterns though their AODs were similar. Low level winds over the central India during March-May are found to come from the surrounding arid regions in west and during monsoon the winds are from the Arabian Sea. These features are likely the cause of the lower FMFs during the March June time frame. [40] Jaipur in the west has lower FMFs (<0.8), while Daman has higher FMFs. Gandhinagar is found to have low FMFs during March June while they show an increase during July (0.80). Intense dust storms have been witnessed over Gandhinagar and Jaipur during March May. This and the summer monsoon results in larger aerosol particles bringing down the FMF values. Hyderabad exhibits quite interesting features in FMF monthly mean pattern. FMFs are quite low in May June with values around 0.25, increases to 0.63 in July and reach a maximum of 0.96 in October. Bangalore FMF values are almost constant throughout the year with values in the 0.65 0.80 range while in October FMF becomes 0.94. In Trivandrum FMFs are higher than 0.80 during October April while they decrease to 0.60 0.70 range in May September timeframe. Similar features in FMF are seen in Port Blair. It is clear that even though the AOD patterns are quite similar over different locations their FMFs need not exhibit the same patterns. [41] The monthly mean variations in AODs and FMFs show that locations/regions dominated by pollution produce a high FMF and AOD (e.g., Patna) while natural aerosols (mainly biogenic) can result in high FMF and a range of AODs (Tables 3 and 4, the northeast for example). On the other hand if at a particular location the aerosols are mechanically generated (for example sea salt and desert dust) that can result in a low FMF and a range of AODs (e.g., Jaipur, Gandhinagar). These results suggest that in different regions in India depending on the dominant source types of aerosols (anthropogenic, natural or combination of both) the aerosol optical depths and fine mode fraction values can show strong seasonal variations. 4.6. Frequency Distribution Histograms of AODs and FMFs Over India [42] In Figure 6 frequency distribution of AODs over the different chosen locations are plotted in five different ranges. Frequency distribution of AODs for Srinagar and Shimla show that more than 80% of the AODs over these locations are less than 0.4. For Jammu and Chandigarh the distribution is spread throughout the >0.0 and <1.0 range, where more than 50% of AODs are above 0.2. In Dehradun AODs <0.2 contribute 60% indicating the low AOD levels obtained over this high altitude hill station. Ranchi in the east has about 50% of AODs in the >0.2 0.4 range, while more than 60% of Lucknow and Patna AODs are greater than 0.4. More than 80% of AODs in the northeast are lower than 0.6. The central and west India locations have about 50% of AODs in the less than 0.6 bin. The south Indian locations also have more than 60% AODs which are below 0.4. [43] Frequency of occurrence of FMF values in five different ranges for the different locations representing different Indian regions are plotted in Figure 7. Srinagar and Shimla FMF frequency distributions are spread in all the five bins; 40% of FMF values are in the 0.0 0.2 range. Srinagar and Shimla in the northwest had lower AODs 13 of 16