Spectral -Temporal variations of AOD in the context of meteorological parameters at Kannur

34 Chapter 3 Spectral -Temporal variations of AOD in the context of meteorological parameters at Kannur Aerosols offer scattering centers to the incoming solar radiation in the atmosphere. Measurements of AOD provide the simplest and effective method to analyze the aerosol properties both qualitatively and quantitatively. The scattered intensities of various wavelengths depend on the size of the particles, and hence the spectral variations of AOD can effectively be used for estimating the size distribution of particles and their seasonal variations (Ranjan et al., 2007). Currently, MICROTOPS II offers a prominent sun photometer for direct retrieval of seasonal and annual variabilities of AOD. In this chapter, the diurnal and seasonal variations of AOD measured by using a MICROTOPS II at five discrete wavelengths over Kannur during a period of three years from November 2009 to May 2012 are discussed. The relative domination of fine mode aerosols over coarse mode aerosols is analyzed using Angstrom power law. The Angstrom parameters (α and β) are the simplest indicators commonly used to classify the relative abundance of fine to coarse mode particles and turbidity of the atmosphere (Angstrom, 1964; Iqbal, 1983). The Angstrom parameters derived from both the liner and polynomial fit are analysed. This investigation reveals that AODs are strongly influenced by seasonal variations at this site. One of the significant features of observation is that AODs are quite higher in April (~0.401 at 440nm) and relatively low during November-December (~0.208 at 440nm). Thus this observation throws light to

35 the mounting concentrations of particulate matter present in the atmosphere during the co-ordination of fireworks associated with Vishu and temple festivals that are usually celebrated in the month of April, which causes a dramatic rise in AOD. Moreover, the AOD values measured at different geographical locations under the field campaign are compared with those observed at Kannur. 3.1 Instrument used for the study Spectral AOD measurements were made using a MICROTOPS II of Solar Light Company, USA, and the details of the instrument are available in research publications (Morys et al., 2001 ; Ichoku et al., 2002). It is a five channel hand held sun photometer to measure the instantaneous aerosol optical depth from individual measurements of direct solar flux, using a set of internal calibration constants. Internal baffles are integrated into the device to eliminate internal reflections. The MICROTOPS II used in this study has optical filters transmitting the radiation centered at wavelengths of about 340, 440, 675, 870 and 1020 nm with a full width at half maximum (FWHM): ±2 10 nm. Therefore, investigations of the spectral variation of aerosol attenuation within the near UV, visible and near infrared regions could be analyzed and are highly informative (Adeyewa and Balogun, 2003; Ranjan et al., 2007). A sun target and pointing assembly is permanently attached to the optical block. As the image of the sun is centered in the bull s-eye of the sun target, all optical channels are oriented directly at the solar disc. A small amount of circumsolar radiation is also captured, but it makes little contribution to the signal. Radiation captured by the collimator and band pass filters, produce electrical current that are proportional to the radiant power. These signals are first amplified and then converted to a digital signal by a high resolution A/D converter and are processed in series with high speed. AOD is

36 retrieved in MICROTOPS by validating the Beer Lambert s law. The optical depth resulting from Rayleigh scattering is always subtracted from the total optical depth to obtain AOD. (Optical depth from other processes, such as O 3 and NO 2 absorption is ignored in MICROTOPS II). AOD at a particular wavelength is computed as AOD λ = [ ln(v 0λ ) ln (V λ *SDCORR) / m] τ Rλ * (P/P 0 ) (1) where lnv 0λ is the AOD calibration constant, V λ is the signal voltage in mv, SDCORR is the mean earth-sun distance correction, m is the air mass and τ Rλ is the correction for the Rayleigh s scattering. P is the atmospheric pressure at the observation site and P 0 that at the ground level. MICROTOPS II stores two sets of calibration constants: the factory calibrations (FC) and user calibrations (UC). The FC is programmed into the instrument during the calibration process and any modifications are restricted for the user. The UC are initially set to equal FC but can be individually modified from the instrument's keypad. Values for AOD and irradiance are not stored in memory at the time of measurement. Instead, the raw data in millivolt (mv) is stored and the AOD and irradiance values are calculated based on the recorded voltage and user calibration constant and the results are displayed. For a reliable performance the instrument must be calibrated periodically; either by Langley technique or by inter-comparison with a newly calibrated sun photometer. 3.2 Theoretical background The chemical composition of aerosols and their size distribution are affected by various transformation processes. But the resultant spectral aerosol extinction coefficient is governed by a simple analytical relation (Angstrom, 1929) k = bλ α ext (2)

37 where α is called the Angstrom parameter and b gives the value of aerosol extinction coefficient at the wavelength 1 µm, and λ the wavelength in micrometers. The aerosol extinction coefficient integrated along a vertical column of atmosphere with unit cross section is the AOD. h ( ) k ( λ, ) τ λ = z dz (3) 0 ext where h is the top of the atmosphere altitude and z the height above the ground level. Subsequently, angstrom extinction law can also be represented as τ = β λ α (4) The wavelength exponent α describes the spectral behaviour of the optical depth and β is a measure of the vertical column burden of aerosols and is equal to τ for λ = 1 µm. Angstrom found that the value of α is close to 1.3 for average continental aerosols (Angstrom, 1961) which was confirmed by other researchers (Junge, 1963) as well. Values of α 1 indicate size distribution dominated by coarse mode aerosols that are typically associated with the dust and sea salt while α 2 indicating size distribution, dominated by fine mode aerosols produced from urban pollution and biomass burning (Schuster et al., 2006; Eck et al., 1999). Different α values determined in various spectral bands were already reported by various authors (Eck et al., 1999; Reid et al., 1999). Even some of the studies revealed negative values of α obtained in the visible and near-infrared region of the solar spectrum (Cachorro et al., 1987; Adeyewa and Balogun, 2003). However, this relationship does not hold good to all types of aerosols. (King and Byrne, 1976; Tomasi et al., 1983). AOD values in the shortwave spectral region (~0.3 4 µm) are necessary to compute the aerosol shortwave radiative forcing. The AOD is usually measured only in discrete

38 spectral intervals using sun photometers because gas and water vapour absorption restrict the measurements at all wavelengths of interest. Equation (4) yields lnτ = ln β αln λ (5) Equation (5) represents a straight line from which α and β can be determined. τ 2 λ 2 α = dlnτ dln λ = ln ln τ1 λ1 (6) in which τ 1 and τ 2 are the magnitudes of AODs measured at two different wavelengths λ 1 and λ 2. Thus, the value of α depends strongly on the wavelength region selected for its determination. It is shown that the size distribution of aerosols does not typically follow the Junge law (Dubovik et al., 2002) but rather exhibit a bimodal distribution. Subsequently, eqn. (5) deviates from the straight line and a second order polynomial fit between ln τ and ln λ data is found to provide better correlation with the measured AOD. Hence apart from a linear fit, we have employed second order polynomial fit of the form ( ) 2 lnτ = a ln λ + a ln λ+ a (7) 2 1 0 where a terms are constants. As a parameter to quantify the curvature in the ln τ versus ln λ graph, the second derivative of ln τ a versus ln λ is utilized as it is related to the derivative of α with respect to ln λ as (Eck et al., 1999). ( lnτ dln λ) d d α = dα dln λ = = 2a2 (8) d ln λ The coefficient a 2 accounts for the curvature often observed in sun photometer measurements and this curvature provides more information regarding aerosol size. Subsequently, a negative curvature indicates aerosol size distribution dominated by the fine mode and positive curvature indicating size distribution with significant contribution by the coarse mode aerosols (Schuster et al., 2006). The situation where a 2 =0

39 corresponds to a special case without curvature, and a 1 = - α. i.e. aerosol size distributions without curvature follows Junge distribution. Further the angstrom exponent (modified) α can be approximated to (a 2 a 1 ) and this (a 2 a 1 ) 2 corresponds to domination of fine mode aerosols with size distribution (radii 0.5µm) that are usually associated with urban pollution and biomass burning, and (a 2 a 1 ) 1 indicates a domination of coarse mode particles (radii 0.5µm) like sea salt and dust (Eck et al., 1999; O Neil et al., 2001 ). In terms of a 2, negative a 2 values indicates the domination of fine mode aerosols, whereas a 2 values, close to zero indicate a bimodal aerosol distribution and a positive a 2 value represents the presence of significant fractions of coarse aerosols (Kaskaoutis and Kambezidis 2006; Kaskaoutis et al., 2007). 3.3 Data collection and analysis Experimental set up used for the present study is shown in the figure 3.1. For taking observations MICROTOPS II was mounted on a tripod, in order to minimize the sun targeting error. Initially the MICROTOPS settings like universal date and time, geographic coordinates, altitudes and atmospheric pressure of the observation site were made with the help of a GPS (Global Positioning System). AOD data were collected daily from 09:00 17:00 hrs of IST at 30 minutes intervals from November 2009 to May 2012. Extreme care has been taken during the collection of AOD data to avoid the strong seasonal effects such as strong wind, cloudy sky and drizzle. Five sets of measurements were collected in quick succession to avoid any possible errors due to sun pointing exactly at the centre of the bull s eye of the instrument. If the two consecutive measurements at an interval of 30 minutes were not found close in magnitude, producing large differences in the AOD s, the data set was rejected. The daily average was calculated only for those days which had at least six clear observations.

40 Figure 3.1: MICROTOPS II used for the present study During the months of June to October, especially in July, data could be collected only for very few days due to cloudy sky conditions. Figure 3.2 shows the frequency distribution of observation days on monthly basis. Monthly averaged AOD values were used for the estimation of Angstrom coefficients α and β. The α and β values were calculated by linear regression of ln λ and ln τ. The second order polynomial fit was also incorporated to the lnτ vs lnλ points. The best fit was controlled by norm of residuals and R 2 values, and the corresponding α and β values were retained. Figure 3.3 shows a model linear fit and polynomial fit between the ln τ versus ln λ points for the month of January 2010. The norm of residuals for the polynomial fit is an order of magnitude less than that of linear fit.

41 16 14 Number of days 12 10 8 6 4 2 0 NDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAM 2009 2010 2011 2012 Month Figure 3.2: Frequency distribution of observation days in monthly basis Figure 3.3: A model linear and polynomial plot between ln λ - ln τ. Blue line and red line represent linear and polynomial fits (January 2010).

42 3.3.1 Calibration of the instrument During the period of observation the instrument was calibrated at regular intervals (Once in six months). The calibration was carried out at Paithalmala hill top which is positioned at a height of 1472 m from the ground level, using standard Langly technique. Readings were continuously recorded from 9.00 to 11:30 hrs at an interval of 15 minutes, to determine the calibration constant for each wavelength. The extent of deviation of the calibration constant from the initially set values was found to be quite marginal. 3.4. Air trajectory analysis Air mass back trajectories ending at the observation site at 1.30 p.m. (IST)for 500, 1000 and 1500 m above the ground level was calculated by the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model (Draxler and Rolph, 2003) and the trajectory analysis is shown in the figure 3.4 The altitude levels were chosen decisively to signify the atmospheric column which contributes the most towards the loading of aerosols. The Air Resources Laboratory s HYSPLIT model is a complete system for computing sample air parcel trajectories, complex dispersion and deposition simulations using particle approaches. The model calculation method is a hybrid between the Lagrangian approaches, which uses a moving frame of reference as the air parcels move from their initial location. The back trajectory analysis provides a three dimensional (latitude, longitude and height) description of the path followed by air masses as a function of time by using National Center for Environmental Prediction (NECP) reanalysis wind, as input to the model. These trajectories help us to identify the

43 source regions. Hence they are immensely helpful in the investigation of aerosol transport. Figure 3.4: Backward air trajectories during January to December 2010 at Kannur using HYSPLIT model. 3.5. Observational site and general meteorology A schematic representation of the location of the sampling site at Kannur University Campus (KUC) (11.9 o N, 75.4 o E 5 m ASL) is shown in figure 3.5 (Praseed et al., 2012b) This site is 15 km north from Kannur town, a location lying along the coastal belt of Arabian sea in the west-coast region of the Indian subcontinent. This site is close to the National Highway (NH 17) and the Arabian Sea, and 5 m above mean sea level. It is a semi urban area with no major industrial activities except a few small scale industries including plywood and mattress manufacturing units. The air distance to the sea shore is

44 4 km and that to the Western Ghats is 50 km. The land area of Kannur is about 3000 km 2 with an average population density of 1000 per square kilometers. KUC is situated in an open land to receive plenty of sunshine throughout the day without any shadows, and the land is surrounded by a good amount of vegetation. Figure 3.5: Observational site and surroundings 3.5.1. Meteorological scenario at the observational site In Kannur, the prominent seasons are winter (December, January and February), summer (March, April and May), Monsoon (June, July and August) and Post monsoon (September, October and November). The meteorological parameters like wind speed, temperature and relative humidity were collected from the local automatic weather station, which is one of the stations of Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) established by the Indian Space Research Organization (ISRO). Figure 3.6 shows the monthly mean variations of meteorological parameters like wind speed, temperature, relative humidity and rainfall in Kannur during the period of observation. The wind speed was high during the period from June to September and low

45 from December to March. The maximum average wind speed ranged from 2.4 to 5.9 km/hr and the minimum from 1.3 to 4 km/hr during the period of observation. Wind speed (m/sec) 12 10 8 6 4 2 Max: temperature Min: temperature Max: wind speed Min: wind speed (a) 40 30 20 Temperature ( o C) Relative humidity (%) 0 100 90 80 70 60 50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Max: RH Min: RH Rain fall (b) 10 700 600 500 400 300 200 100 Rainfall (mm) 40 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month 0 Figure 3.6: Monthly mean variations of maximum and minimum (a) wind speed, temperature (b) relative humidity and total rainfall during the period of study at KUC (2010). During winter the average wind speed ranged from 1.5 to 3.8 m/s. The temperature was high in the months March to May and was low during June through August. The average monthly high temperature ranged from 29.6 to 37.1 o C and low

46 temperature ranged from 22.9 to 25.8 o C. The humidity was maximum during monsoon and minimum in the winter months. The maximum monthly average relative humidity ranged from 55.5% to 88.4% and minimum of 45% to 80% at this location. The maximum rainfall was recorded during monsoon, while minimum was observed in winter season. The most prominent meteorological feature at this location is the monsoon rainfall occurring in two spells every year. The southwest monsoon is quite active during the months of June, July and August. The intensity of summer is masked by the southwest monsoon season over this region because of intense rainfall. About 80% of the total rainfall occurs from June to August which constitutes the main monsoon season. This is followed by the northeast monsoon in the middle of October, which lasts till the middle of November. Hence September, October and early November are earmarked as the post-monsoon season with some scattered showers accompanied by heavy thunder and lightning. Figure 3.7 shows the monthly mean air flow pattern at 1000 hpa in the range 4 N-40 N latitude and 60 E to100 E longitude observed during the study period. The wind pattern was obtained from the National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (http://www.esrl.noaa.gov/psd/data/gridded/reanalysis/). (A) (B)

47 (C) (D) (E) (F) (G) (H)

48 (I) (J) (K) (L) Figure 3.7: Monthly mean air flow patterns at 1000 hpa for one year, from (A) November 2009 (B) December 2009 (C) January 2010 (D) February 2010 (E) March 2010 (F) April 2010 (G) May 2010 (H) June 2010 (I) July 2010 (J) August 2010 (K) September 2010 (L) October 2010 over Indian region using NCEP/NCAR reanalysis data. This region experiences easterly wind during winter months and westerly wind during summer months. During the first phase of the monsoon season (June August), winds are stronger and the circulation is southwesterly-westerly (from ocean to land). The southwesterly-westerly wind gets weakened by September and northeasterly wind starts in November. The wind direction remains northeasterly until February, when the airflow is mostly from the continent. The months, December through February with

49 meager rain and relatively low humidity constitute the winter season at this site, while from March to May high convective movement persists and intense sun scorches the surface. The period from December to March records the maximum sunshine hours of more than 9.1 hours/day due to the clear sky and the minimum from June August due to cloudy sky conditions. 3.6 Results and discussion About eighteen observations of AOD have been made on each clear sky day, in between 09:00 hrs and 05:00 hrs. On most of the occasions, the measured AOD exhibit diurnal variations, day to day variations and monthly variations. 3.6.1 Diurnal variations in AOD The temporal variations in columnar AOD at different wavelengths observed on a clear sky day (February 2010) are shown in figure 3.8 (a). It is found that the AOD at lower wavelength is much higher than that obtained for higher wavelength. This variation features can be attributed to the abundance of fine mode particles of continental origin. It is also evident that the AOD shows a temporal variation with higher values in the morning and late afternoon. It may be due to the high relative humidity as depicted in figure 3.9. The peak observed in AOD during the mid-day hours could be attributed to the local convective activity leading to change in aerosol particle number distributions. Moreover, a sharp enhancement in AOD was found during 1530 hr (IST) at 340 nm, which is due to the horizontal advection of pollution leading to higher aerosol column content. On the other hand, a slight enhancement in AOD observed in other three wavelengths (675, 870 and 1020 nm) found in the afternoon hours reveals the influence of wind over this region. These results are in general agreement with those reported

50 earlier by different investigators at various coastal stations in India (Pinker et al., 1994; Devara et al., 1996; Latha and Badarinath, 2005; Suresh and Elgar, 2005). In order to validate our observation, the diurnal variation of direct solar flux at these wavelengths is shown in figure 3.8(b). Aerosol Optical Depth 0.7 0.6 0.5 0.4 0.3 0.2 340 nm 440 nm 675 nm 870 nm 1020 nm (a) 0.1 18 15 9 10 11 12 13 14 15 16 17 340 nm 440 nm 675 nm 870 nm 1020 nm (b) (b) Solar flux (W/m 2 ) 12 9 6 3 0 9 10 11 12 13 14 15 16 17 Time (IST) Figure 3.8: Diurnal variation of (a) AOD (b) solar flux for different wavelengths Since the observation site is away from National highway and industrial areas, the aerosol variations are mainly influenced by the seasonal long range transport. This is

51 substantiated by air mass trajectory analysis which reveals that the observations were influenced by both land and ocean in the month of February. Relative humidity (%) 60 55 50 45 Maximum RH Minimum RH (a) 40 2 4 6 8 10 12 14 16 18 20 22 24 Temperature ( o C) 36 33 30 27 24 Maximum temperature Minimum temperature (b) 21 2 4 6 8 10 12 14 16 18 20 22 24 Time (IST) 330 0 6 5 30 (C) 300 4 3 60 2 6 270 5 4 3 2 1 1 0 0 0 90 1 2 240 3 4 120 210 5 6 180 150 Figure 3.9: Diurnal variations of (a) maximum and minimum relative humidity (b) maximum and minimum temperature (c) wind speed and wind direction at Kannur in February 2010

52 The diurnal variation follows similar pattern in almost all the days. The variations for a typical day on different months are depicted in figure 3.10. 0.7 0.6 0.5 330 nm 440 nm 675 nm 870 nm 1020 nm January 0.7 0.6 0.5 340 nm 440 nm 675 nm 870 nm 1020 February 0.7 0.6 0.5 340 nm 440 nm 675 nm 870 nm 1020 nm March 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.0 9 10 11 12 13 14 15 16 17 0.0 9 10 11 12 13 14 15 16 17 0.0 9 10 11 12 13 14 15 16 17 Aerosol Optical Depth 0.7 0.6 0.5 0.4 0.3 0.2 0.1 340 nm 440 nm 675 nm 870 nm 1020 nm April 0.7 0.6 0.5 0.4 0.3 0.2 0.1 340 nm 440 nm 675 nm 870 nm 1020 nm May 0.7 0.6 0.5 0.4 0.3 0.2 0.1 340 nm 440 nm 675 nm 870 nm 1020 nm September 0.0 9 10 11 12 13 14 15 16 17 0.0 9 10 11 12 13 14 15 16 17 0.0 9 10 11 12 13 14 15 16 17 18 0.7 0.6 0.5 440 nm 340 nm 675 nm 870 nm 1020 nm October 0.7 0.6 0.5 340 nm 675 nm 440 nm 870 nm 1020 nm November 0.7 0.6 0.5 340 nm 440 nm 675 nm 870 nm 1020 nm December 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.0 9 10 11 12 13 14 15 16 17 0.0 9 10 11 12 13 14 15 16 17 Time (IST) 0.0 9 10 11 12 13 14 15 16 17 Figure 3.10: The diurnal variation in columnar AOD at different wavelengths observed on typical days in 2010 3.6.2. Monthly and seasonal variations in AOD The monthly mean value and standard deviation of AOD at five wave lengths are given in table 3.1. From the table, it is revealed that the mean values of AOD at all wavelengths are high during April-May and low during November-December. It is further observed that AODs were fairly high in summer (March-May), moderate in monsoon (June-November) and low in post monsoon and winter seasons (December- February).

53 Month & Year AOD at different wavelengths (nm) Standard deviation of AOD at different wavelengths 340 440 675 870 1020 340 440 675 870 1020 Nov 09 0.337 0.208 0.131 0.098 0.09 0.022 0.021 0.021 0.021 0.020 Dec 09 0.365 0.205 0.132 0.09 0.081 0.027 0.027 0.050 0.025 0.026 Jan 10 0.420 0.293 0.196 0.159 0.146 0.032 0.025 0.020 0.019 0.018 Feb 10 0.428 0.260 0.195 0.122 0.108 0.038 0.026 0.022 0.018 0.012 Mar 10 0.466 0.274 0.221 0.133 0.120 0.040 0.030 0.025 0.021 0.020 Apr 10 0.512 0.401 0.308 0.162 0.129 0.080 0.055 0.043 0.041 0.032 May10 0.491 0.356 0.256 0.202 0.197 0.060 0.041 0.035 0.031 0.029 Jun 10 0.419 0.328 0.232 0.175 0.178 0.040 0.030 0.029 0.021 0.024 Aug 10 0.395 0.281 0.213 0.162 0.158 0.035 0.026 0.021 0.019 0.018 Sep 10 0.404 0.302 0.214 0.164 0.159 0.040 0.029 0.025 0.020 0.019 Oct 10 0.399 0.282 0.201 0.174 0.169 0.035 0.028 0.023 0.019 0.021 Nov 10 0.358 0.229 0.132 0.180 0.103 0.022 0.021 0.019 0.017 0.013 Dec 10 0.388 0.236 0.148 0.103 0.100 0.020 0.015 0.014 0.013 0.011 Jan 11 0.412 0.218 0.147 0.130 0.110 0.031 0.024 0.021 0.019 0.014 Feb 11 0.439 0.253 0.177 0.139 0.132 0.036 0.026 0.022 0.020 0.017 Mar 11 0.470 0.288 0.203 0.150 0.133 0.039 0.031 0.024 0.021 0.019 Apr 11 0.514 0.398 0.306 0.164 0.127 0.075 0.061 0.052 0.043 0.032 May 11 0.429 0.305 0.214 0.139 0.125 0.070 0.054 0.048 0.032 0.031 Jun 11 0.425 0.333 0.237 0.198 0.175 0.050 0.040 0.030 0.022 0.022 Aug 11 0.402 0.297 0.211 0.158 0.152 0.042 0.037 0.021 0.019 0.018 Sep 11 0.306 0.209 0.176 0.133 0.131 0.039 0.025 0.020 0.016 0.016 Oct 11 0.347 0.205 0.164 0.136 0.131 0.030 0.025 0.021 0.015 0.017 Nov 11 0.366 0.223 0.188 0.150 0.146 0.021 0.019 0.015 0.013 0.011 Dec 11 0.347 0.208 0.133 0.107 0.099 0.021 0.013 0.012 0.010 0.009 Jan 12 0.402 0.248 0.158 0.117 0.107 0.030 0.025 0.018 0.015 0.014 Feb 12 0.449 0.272 0.198 0.128 0.107 0.031 0.024 0.019 0.015 0.012 Mar 12 0.466 0.274 0.224 0.138 0.123 0.040 0.031 0.029 0.021 0.019 Apr 12 0.501 0.402 0.305 0.161 0.128 0.080 0.064 0.042 0.032 0.030 May 12 0.499 0.365 0.264 0.178 0.138 0.050 0.045 0.035 0.029 0.021 Table 3.1: Monthly mean aerosol optical depth with standard deviation Monthly variations of AOD during the observation period and spectral average monthly variation of AOD are depicted in figure 3.11 and 3.12 respectively. The vertical bars in these figures represent one sigma standard deviation. Figure3.11 shows that the minimum AOD is observed in December while the maximum is in April for all wavelengths.

54 Aerosol Optical Depth 0.8 0.6 0.4 0.2 340nm 440 nm 675 nm 870 nm 1020 nm 0.0 2009 2010 2011 2012 NDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAM Month Figure 3.11: Monthly variations in AOD with standard deviation The average AOD at 340 nm is 0.38 ± 0.02 in winter, 0.48 ± 0.03 in summer, and 0.387 ± 0.02 in monsoon. For 675 nm it is 0.148 ± 0.03 in winter, 0.244 ± 0.03 in summer, 0.206 ± 0.02 in monsoon whereas for 1020 nm it is 0.104 ± 0.02 in winter, 0.133 ± 0.04 in summer, 0.156 ± 0.05 in monsoon. Figure 3.13 shows the seasonal variation of aerosol optical depth. The low value of AOD in post monsoon and winter seasons may be due to the clear sky environment resulting in the settling of aerosols due to the rain washout process. Moreover in winter months, the atmospheric boundary layer is shallow which ensures a minimum mixing volume for the suspended particles. Since the humidity is low, the marine aerosols cannot uptake water and grow in size. But in summer months, the boundary layer height is higher and therefore pollutants have additional volume for scattering and absorption of solar radiations passing through them. The influence of air mass movement from the western side of this location indicates a strong marine influence during pre-monsoon and monsoon seasons.

55 0.4 Aerosol Optical Depth 0.3 0.2 0.1 0.0 NDJFMAMJJASONDJFMAMJJASONDJFMAM Month Figure 3.12: Monthly spectral average variation of aerosol optical depth The marine hygroscopic aerosols can uptake water and its subsequent growth in size influences the intensity of solar and terrestrial radiations received on the surface (Moorthy et al., 2005). Aerosol Optical Depth 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Winter Summer Monsoon Post monsoon 0.0 300 400 500 600 700 800 900 1000 1100 Wavelength Figure 3.13: Seasonal variations in aerosol optical depth

56 3.6.3 Monthly variations of Angstrom parameters α and β Variations in the Angstrom parameters associated with light scattering by aerosols are used to classify the abundance of fine and coarse mode particles. The Ångström wavelength exponent α and turbidity coefficient β are derived from the ln τ ln λ plot, in which λ is expressed in µm. Monthly average values of α and β computed from the linear fit and shown in table 3.2. The second order polynomial fit obtained according to eqn (7) between ln τ and ln λ provides better agreement with measured AODs rather than a linear fit. The norm of residuals for the polynomial fit is found to be an order of magnitude less than that of the linear fit during the nine months from April to January. Monthly average values of a 2, a 1 and α computed from the polynomial fit are also shown in table 3.2. The ratios between AOD observed at 1020nm and at the other four wavelengths (340nm, 440nm, 675 nm and 870nm) are shown in figure 3.14. Larger ratios obtained for wavelengths 340, 440, 675 and 870nm during May-October indicate the abundance of coarse mode particles. 1.4 1.2 1020 nm/340 nm 1020 nm/440 nm 1020 nm/675 nm 1020 nm/870 nm Ratio of AOD's 1.0 0.8 0.6 0.4 0.2 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 3.14: Ratio of Aerosol optical depth

57 Month Linear fit Polynomial fit α β R 2 (a1) (a2) α = (a 2 a 1 ) β R 2 Nov 09 1.11 0.08 0.96-0.45 0.67 1.116 0.08 0.99 Dec 09 1.3 0.078 0.97-0.83 0.45 1.276 0.082 0.98 Jan 10 1.26 0.09 0.96-0.55 0.66 1.21 0.10 0.99 Feb 10 1.21 0.11 0.96-1.15 0.05 1.20 0.11 0.94 Mar 10 1.18 0.12 0.94-1.14 0.04 1.18 0.12 0.92 Apr 10 1.25 0.15 0.91-2.23-0.94 1.30 0.13 0.96 May 10 0.74 0.20 0.91-0.29 0.5 0.78 0.20 0.98 Jun 10 0.72 0.18 0.91-0.33 0.44 0.77 0.18 0.97 Aug10 0.67 0.16 0.93-0.39 0.33 0.72 0.16 0.96 Sep 10 0.76 0.17 0.93-0.38 0.43 0.80 0.17 0.98 Oct 10 0.68 0.17 0.87-0.15 0.60 0.75 0.17 0.99 Nov 10 1.14 0.09 0.96-0.34 0.76 1.09 0.10 0.99 Dec 10 1.23 0.09 0.96-0.62 0.58 1.19 0.09 0.99 Jan 11 1.1 0.11 0.87-0.09 0.96 1.04 0.11 0.95 Feb 11 1.05 0.12 0.93-0.24 0.87 1.11 0.12 0.98 Mar 11 1.11 0.13 0.97-0.74 0.34 1.08 0.13 0.98 Apr 11 1.24 0.15 0.91-2.24-0.95 1.29 0.13 0.96 May 11 1.11 0.13 0.09-1.21-0.10 1.106 0.12 0.98 Jun11 0.88 0.17 0.91-0.89 0.001 0.885 0.16 0.86 Aug11 0.90 0.15 0.98-0.65 0.23 0.883 0.15 0.99 Sep11 0.75 0.14 0.93-0.39 0.33 0.72 0.13 0.92 Oct11 0.83 0.12 0.89-0.01 0.77 0.78 0.13 0.94 Nov 11 0.78 0.14 0.88-0.11 0.62 0.78 0.14 0.92 Dec 11 1.11 0.09 0.95-0.35 0.73 1.07 0.11 0.91 Jan 12 1.18 0.13 0.98-0.64 0.52 1.15 0.10 0.99 Feb 12 1.24 0.11 0.97-1.2 0.06 1.24 0.11 0.96 Mar 12 1.19 0.13 0.94-1.05 0.09 1.13 0.13 0.94 Apr 12 1.23 0.14 0.91-2.27-0.97 1.3 0.13 0.96 May12 1.12 0.14 0.98-1.54-0.36 1.144 0.14 0.99 Table 3.2: Monthly means value of wavelength exponent (α) and the turbidity coefficient (β) using linear fit and polynomial fit. The (a 2 -a 1 ) values can be approximated to alpha values as suggested by Eck et al., (1999) which is fruitfully validated by the correlation analysis (Praseed et al., 2012b). It is found that there exists a strong correlation (R 2 = 0.92) between (a 2 -a 1 ) values and α values retrieved from linear fit as shown in figure 3.15.

58 1.6 correlation coefficient (R 2 ) =0.92 1.4 (a2-a1) 1.2 1.0 0.8 0.6 0.6 0.8 1.0 1.2 1.4 1.6 1.8 alpha Figure 3.15: Correlation between (a 2 -a 1 ) and α The monthly variations of α and β are shown in figure 3.16 and this reveals an inverse relationship between α and β values. Angstrom parameter (α) 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 Alpha Beta 0.25 0.20 0.15 0.10 0.05 Turbidity factor (β) 0.0 NDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAM Month 0.00 Figure 3.16: Seasonal variations of α and β

59 Such a trend has been reported from many other observational sites in India and elsewhere (Dani et al., 2003; Devara et al., 2005; Satheesh et al., 2006; Xin et al., 2007; Madhanvan et al., 2008, Ganesh et al., 2008; Kumar et al., 2009; Sharma et al., 2011). During the observation period, it was found that angstrom wavelength exponent α varies in between 0.7 and 1.3. The polynomial fit between lnτ and lnλ provides further microscopic insight into aerosol size distribution. Significant variation was observed in a 1 and a 2 values (table 3.2); a 1 varies from -0.15 to -2.23 and a 2 values range from -0.94 to 0.76. In most of the cases the coefficient a 2 was positive, implying the dominance of coarse mode aerosols. A negative value of a 2 represents the domination of fine mode aerosols, and was observed for the month of April-May. Similar negative curvatures were reported from the tropical Indian coastal station Vishakhapatnam, during pre monsoon and summer monsoon periods (Madhavan et al., 2008). The polynomial fit analysis indicates that the region is influenced by both fine and coarse mode aerosols during February and March. But the concentration of fine mode aerosol dominates during April and it continues till monsoon activities are strengthened. Consequently during monsoon seasons this location is under strong marine influence and the aerosols are mainly coarse in nature. The air mass trajectory estimated using the HYSPLIT model during the period of observation are shown figure 3.4 of section 3.4 From this, it is obvious that the air mass movement during winter (December through February) and summer (March and April) is from east of this site, and during monsoon (June to August) and post monsoon (September to Early November) is from the western Arabian Sea. Further, during both winter and summer seasons, long range transport of continental air mass contributes to the observed AOD values, as the air masses appear to originate from the eastern part of

60 Kannur. Hence, it is presumed that this region is influenced by the transport of pollutants from industrialised region during winter and summer. As the experimental site is far from the industrial sources, the occurrence of fine-mode aerosol particles, on most of the observation days, could be due to long-range transport of aerosols from distant source regions. During the monsoon and post monsoon seasons, the movement of air mass trajectories originated over the Arabian Sea. As the air mass passed over the Arabian Sea, marine aerosols which are coarse in nature dominated at the lower level. Thus the air trajectory analysis further validates our conclusion regarding the seasonal variation of aerosols 3.6.4 Analysis of abrupt enhancement of AOD in festival occasions During our three year long period of observations, it was found that the variations in AODs were smooth, except during the months of April and May. Analysis of the results further revealed a sudden change in AOD, alpha and beta values during the burning of crackers associated with Vishu festival. Variations of AOD on pre and post Vishu episode is shown in the figure 3.17. The increase was found to be more in the lower wavelength region. The month April is earmarked with celebrations of festivals in Kannur District. Moreover, Vishu a major festival in Kerala falls on 14 th or 15 th of April every year. In north Kerala Vishu is celebrated with coordinated fireworks starting from the eve of Vishu to 2-3 days after it. Such continuous fireworks from majority of houses, public places and temples impair air quality (Attri et al., 2001) Nishanth et al., (2012a) reported the enhancement of organic and inorganic particulate matter in the atmosphere as a result of Vishu episode over Kannur. The variation of AOD and particle size distribution before and after Vishu festival was precisely analyzed with the aid of second order polynomial fit to ln λ versus ln τ graph.

61 0.6 Pre-Vishu Post-Vishu Aerosol Optical Depth 0.5 0.4 0.3 0.2 0.1 300 400 500 600 700 800 900 1000 Wavelength (nm) Figure 3.17: Variation of aerosol optical depth in the Vishu episode Table 3.3 shows the mean AOD, alpha and beta values, coefficient of variation (C.V.) (standard deviation/mean), calculated value of Students t test and one tailed p value during the pre-vishu and post Vishu days. Event Wavelength (nm) 340 440 625 870 1020 Alpha Beta Pre-Vishu 0.438 0.269 0.185 0.146 0.136 1.02 0.1305 Post-Vishu 0.523 0.387 0.296 0.158 0.14 1.28 0.143 C.V(pre-Vishu) 0.033 0.042 0.066 0.086 0.083 0.081 0.090 C.V(post-Vishu) 0.074 0.065 0.091 0.137 0.118 0.055 0.086 Value of t -5.04-10.5-9.2-1.1-0.05-6.04-1.90 P value (one tailed) 0.00035 1.13E-06 2.91E-06 0.135 0.029 7.36E-05 0.041 Table 3.3: The mean AOD, alpha and beta values, coefficient of variation, calculated value of Students t test and one tailed p value in the pre-vishu and post Vishu days

62 The positive curvature in the pre-phase (figure 3.18) and negative curvature in the postphase of Vishu (figure 3.19) indicates the domination of fine mode aerosols over coarse mode during firework festivals. Figure 3.18: ln λ vs ln τ (pre-vishu) norm of residuals for linar fit is 0.18 and for polynomial fit=0.082 Figure 3.19: ln λ vs ln τ (post-vishu) norm of residuals for linar fit is 0.28 and for polynomial fit is 0.18

63 This change in curvature may be due to the injection of fine mode particulate matter in to the air. Nishanth et al., (2012a) has reported an increase of PM 10 from 56 µg m -3 to 118 µg m -3 as a consequence of firework burning in April 2011. Student s t-test value and one tailed-p value reveals that, the enhancement of AOD in lower wavelength region is statistically significant to 95% confidence level. The higher value of coefficient of variation indicates that the aerosol variability is quite pronounced in the post Vishu period. The smoke from fireworks comprising mainly of fine particles that can cause respiratory problems and serious health issues (Uno et al., 1984). 3.7 AOD measurements in other geographical locations Aerosol characteristics vary with geographical locations as well. The concentration may vary over urban, rural, coastal and high altitude locations because of the geography of the environment. In this section we present the results of the field campaign measurements using MICROTOPS II over Ootty, a high altitude station in south India; Trivandrum, a coastal site and Ahmadabad an urban site surrounded by adjacent industrial areas. Ootty, (11.3 o N, 76.7 o E) is a hill station at an altitude of 2240m above MSL, generally lying in the boundary layer during day time and getting transformed into a region of free troposphere during night time. The observation site Doddabetta peak, the highest in the Nilagiri Mountains, is free from industrial and anthropogenic activities. Hence a free troposphere exists here which is free from hectic convective activities. The temperature is relatively low throughout the year, with average high temperature ranging from 17-20 o C and low temperature between 3-10 o C. The average rain fall is about 1250mm with drizzling throughout the year and the weather is quite pleasant in March.

64 The AOD measurements were conducted during the second week of February 2011 since the maximum number of clear sky days is available in February. Ponmudi (8.72 o N, 77.15 o E) another pristine location, is a hill station lying 61 km North East of Trivandrum city at an altitude of 1100 m. This hill region is a part of Western Ghat Mountains and is now transformed into a tourist spot in the state. As a part of exploring air quality over Trivandrum, AOD measurements were carried out at Ponmudi, in the month of March 2011. Trivandrum (8.54 o N, 77 o E) the capital city of Kerala State is located on South West coast of India which is close to the extreme south of the Indian subcontinent. The city has a tropical climate and therefore this location does not experience distinct seasons. The maximum mean temperature is about 34 o C and minimum 21 o C. Being a coastal site the humidity is high which rise to about 90% during monsoon seasons. The city enjoys about 1700 mm of rain per year. North East monsoon is more active at Trivandrum compared to other regions of Kerala. December-February is the winter season with coldest months, while March-May is the hottest period. The observations were carried out near Veli, 12 km away from Trivandrum city in the second week of March 2011. AOD measurements were carried out at Kannur town, the headquarters of Kannur District. The observation site was on the top of Science Park which is 500m away from the Arabian Sea shore, and close to the National highway (NH 17). The measurements were carried out in the fourth week of March 2011. Ahmadabad (23.2 o N, 72.53 o E) 55 m above MSL is located on the banks of Sabarmathi river in Gujarat State and the AOD were measured at Physical Research Laboratory Campus. The city is surrounded by sandy and dry area. The weather is hot through the months of March-June. The average summer maximum temperature goes up

65 to 40 o C and minimum 26 o C, and during November - February the average maximum temperature is about 30 o C and minimum 14 o C. The average rainfall over this location is about 900 mm. It is polluted by adjacent industrial areas and textiles mills. The measurements were carried out during the first week of April 2011. Figure 3.20 shows the wavelength dependence of aerosol optical depth retrieved from the field campaign over these five locations during February through April 2011. It was quite unique that AOD decreases with increase in wavelength at all these sites. The Angstrom parameters retrieved from both linear and polynomial fit are shown in table 3.4. Ootty is a hillock region and subsequently the boundary layer over this location exhibits a strong variation during day and night. The average diurnal spectral variation of AOD measured at Doddabetta on a clear sky day in February 2011 and the corresponding solar flux at these wavelengths are shown in figure 3.21 (a) and 3.21 (b) respectively. Aerosol Optical Depth 0.8 0.6 0.4 0.2 Ponmudi Veli Kannur Ahmedabad Ootty 0.0 300 400 500 600 700 800 900 1000 1100 Wavelength (nm) Figure 3.20: Comparison of AOD s at different geographical locations in India.

66 The AODs are quite low compared to that measured at KUC. It is further noticed that the AOD at 340 nm shows a dramatic increase in the afternoon due to the intense convective actives on the valley which induces a vertical air mass movement. Hence the asymmetry between the FN and AN AOD s is the highest in Ootty. Likewise, the enhancement of AOD in higher wavelengths is quite pronounced at this site due to the presence of fog in the atmosphere. The higher value of AOD at 1020 nm indicates the abundance of coarse aerosols over this region. One of the special observations found is the enhancement of AOD at 1020 nm over the AODs at 440, 675 and 870 nm. This is an indication of low level clouds and haze over this hill station throughout the day. The reason for higher magnitudes of AODs observed in the afternoon may be attributed to the long range transport of aerosols from surrounding valley. Backward air trajectory at Ooty (February 2011) during forenoon and after noon are shown in the figure 3.22 (a) and 3.22 (b) respectively. Date Place Linear fit Polynomial fit α β R 2 (a 1 ) (a 2 ) α = (a 2 a 1 ) β R 2 14 th Feb Ootty 0.49 0.09 0.55 14 th Mar Ponmudi 0.97 0.11 0.96-0.55 0.43 0.98 0.10 0.96 16 th Mar Veli (Tvm.) 1.09 0.13 0.98-1.52-0.39 1.13 0.13 0.99 28 th Mar Kannur 1.27 0.15 0.97-0.86 0.39 1.25 0.15 0.97 5 th April Ahmedabad 0.64 0.29 0.96-0.45 0.17 0.62 0.31 0.96 Table 3.4: Angstrom parameters (α and β at different locations in India) At Veli (Trivandrum), the coastal site which is free from hectic anthropogenic activities, the aerosol loading is higher (β=0.13) than that at Ponmudi, the hill station 61 km east of Trivandrum city. The low negative value of a 2 (-0.39) is an indication of equal contribution of both fine and accumulation mode. Being the air mass flow is from the

67 eastern side, (figure 2.23.a) it has to travel a long distance over the land before reaching the observation site. Thus this air flow through the thickly populated regions can contribute to fine and accumulation mode aerosol through secondary production mechanism in the warm and humid tropical environment. Ponmudi a high altitude site has a clean atmosphere. But the recent tourist activities and rapid urbanization induce possible threats to the air quality. Aerosol Optical Depth (AOD) 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 21 18 340 nm 440 nm 675 nm 870 nm 1020 nm (a) 9 10 11 12 13 14 15 16 17 340 nm 440 nm 675 nm 870 nm 1020 nm (b) Solar flux (W/m 2 ) 15 12 9 6 3 0 9 10 11 12 13 14 15 16 17 Time (IST) Figure 3.21: Diurnal variation of (a) AOD and (b) solar flux for different wavelengths at Ooty

68 Figure 3.22: Air mass trajectory analysis at Ootty (a) Forenoon (b) Afternoon Figure 3.23: Air mass trajectory analysis (a) at Veli (b) at Ponmudi The aerosol loading at Ponmudi is low (β = 0.11) as compared to Trivandrum. The air trajectory analysis (figure 3.23.b) further shows that the long range transport is from the eastern side and air mass travels comparatively short distance over land mass before reaching the observation site. The relatively high values of α (1.27) and a 2 value of 0.39, observed over Kannur town indicate a slight abundance of fine mode aerosols of anthropogenic origin over coarse mode. This may be due to vehicular emission and fine dust particles produced due to the deteriorated road conditions. The air trajectory analysis (figure 3.24a) indicates that the region has a mixed influence of land and ocean.

69 At low level, near the surface, the air mass flow is from the Arabian Sea, which brings marine aerosols over this region, whereas at 1500m altitude, the effluents is mainly from the land, which brings industrial pollutants to this region. The low positive a 2 value clearly validates the presence of bimodal aerosol. As we presumed, Ahmadabad shows high aerosol concentration owing to its industrial developments. The high β value (0.31) is an indication of dense aerosol loading whereas the low α value (0.64) designate coarse mode particles. Figure 3.24: Air mass trajectory analysis (a) at Kannur town (b) Ahmadabad The dust particles and pollution transported (figure 3.24b) from nearby industrial areas may further deteriorate the air quality of this region. The conversion of fine to accumulation mode dust particles, due to secondary production mechanisms can be the main contributors of coarse aerosols. Thus the results of the field campaign indicate, low values of AOD over Ootty, and very high value over Ahmadabad, and moderate value over Trivandrum as expected. The high value of AOD found over Kannur town than at KUC is really disturbing; and shows the influence of vehicular emission and local industries on aerosol concentration.