chapter 3 - shodhgangashodhganga.inflibnet.ac.in/.../101357/13/13_chapter3.pdfhyderabad (kaskaoutis...

Variation of Physical and Optical Properties of Aerosols Over Gadanki Chapter 3

55

Chapter 3

Variation of Physical and Optical Properties of Aerosols Over

Gadanki.

3.1. Seasonal variations of Aerosol Optical Depth

Aerosol Optical Depth measurements are retrieved from the Sky-radiometer for a

period of 3 years from April 2008 to March 2011. As discussed earlier, the aerosol

properties such as AOD along with the other aerosol properties like single scattering

albedo and aerosol size distribution are derived from SkyRad.pack algorithm, which is

based on the Nakajima et al. (1996). The detailed description of the retrieval process of

the aerosol properties and the associated errors are described in Chapter 2. The average

AOD at 500 nm from April 2008 to March 2011 is 0.46 ± 0.23. Here onwards unless

specified AOD corresponds to the AOD value at 500 nm. When compared to other places,

AOD values over Gadanki are comparable or higher with respect to the studies conducted

over major cities in South Asia e.g. Pune (Devara et al., 2005; Pandithurai et al., 2007),

Ahmedabad (Ganguly et al., 2006), Dibrugarh (Pathak et al., 2010), Indore (Gupta et al.,

2003), Thiruvanthpuram (Moorthy et al., 2007), Visakhapatnam (Nirajan et al., 2011),

Hyderabad (Kaskaoutis et al., 2009) and Karachi (Khan et al., 2011). The comparison

table of AOD at 500 nm over Gadanki with other locations is shown in Table 3.1.

The histograms of the AOD for different seasons are shown in Figure 3.1. Spring

is taken from March to May, Summer (June to August), Autumn (September to

November) and Winter (December to February). The average value of AOD in Spring is

0.54 ± 0.24. The high values of AOD in Spring are usually attributed to the biomass

burning and agricultural waste burning. The high values of AOD in Spring are

comparable to values over Hyderabad (Badarinath et al., 2007). Low values of AOD with

a value of 0.38 ± 0.21 are associated with Winter and are attributed to the lower boundary

layer height (Basha and Ratnam, 2009). Lower boundary layer height cannot assist more

particles per unit column. The average AOD for Summer and Autumn is 0.46 ± 0.24 and

0.41 ± 0.20 respectively.


56

Location Mean AOD Value (500 nm) Reference

Ahmedabad (Urban) 0.3 to 0.45 Ganguly et al., 2006

Pune (Urban) 0.42 (Mar to May)

0.38 (Dec to Feb) Pandithurai et al., 2007

Indore (Urban) 0.4 to 0.6 Gupta et al., 2003

Manora peak

(Nainital, Himalaya) 0.157 Sagar et al., 2004

Kanpur (Urban) 0.6 Singh et al., 2004

New Delhi (Urban) 0.56 to 1.22 Singh et al., 2010

Trivandrum

(Coastal Urban) 0.29 to 0.43 Moorthy et al., 2007

Hyderabad (Urban)

0.38 to 0.56

0.46 to 0.65

Latha and Badarinath, 2006

Kaskaoutis et al., 2009

Dibrugarh (Urban) 0.69 (Mar to May) Pathak et al., 2010

Visakhapatnam

(Coastal Urban) 0.3 to 0.6 Niranjan et al., 2011

Chennai, Mumbai,

Kolkata, New Delhi

(Urban)

0.4, 0.4, >0.55, >0.55 Ramachandran, 2007

Bay of Bengal, Arabian Sea

0.36, 0.25

Kedia and Ramachandran,

2008

Arabian Sea

0.25

Kalapureddy and Devara,

2010

Arabian Sea 0.25 Kaskaoutis et al., 2010

ISRO GBP road campaign 0.2 to 0.6 Jayaraman et al., 2006

Gadanki

(This study; rural site) 0.46±0.23 (overall mean); 0.54±0.24 (Spring mean)

Table 3.1. Comparison of AOD values at 500 nm observed over Gadanki with respect to the

AOD values reported for other study regions.


57

a)

b)

c)

d)

Figure 3.1. Frequency distribution of AOD (500 nm) in each season over Gadanki for

the period 2008 April to 2011 March: a) Spring (March to May), b) Summer (June to

August), c) Autumn (September to November) and d) Winter (December to March).

Mean and median values are also shown in the figure.

Along with the fluctuations of local boundary layer, the long range transportation

of the particles also play an important role in modulating the aerosol properties (Rajeev et

al., 2000; Ramanathan et al., 2007; Badarinath et al., 2009,2010). The variations in the


58

size of aerosols present in the atmospheric column can be predicted by the spectral

behavior of AOD (Eck et al., 1999). The spectral variations in AOD for different seasons

over Gadanki are shown in the Figure 3.2 (a).

Interesting conclusions can be drawn from this figure. It is observed that steep

spectral variations are observed in Spring showing the dominance of fine mode aerosols.

For Spring, the AOD values for the shorter wavelength are much higher than compared to

the longer wavelengths. Such dependence on wavelength was also observed over Delhi

(Singh et al., 2005). During Summer, the spectral characteristics are rather flat when

compared to other seasons and is indicative of the presence of bigger particles. The AOD

at higher wavelengths for Summer are high as compared to Spring, Autumn and Winter.

Comparison of spectral AOD over Gadanki with various places in India is shown in

Figure 3.2 (b). AOD values over Gadanki are median of full data-set for the period from

April 2008 to March 2011 and the vertical bars over data points are their inter-quartile

range.

Values for other locations are plotted as they have been reported in the article

cited or they are reconstructed from angstrom exponent (to be explained in next section

3.2) and AOD at 500 nm values reported in the article. For the sake of clarity, inter-

quartile ranges or standard deviations are not plotted for other locations in the figure.

AOD over Hyderabad (Kaskaoutis et al., 2009) and Delhi (Soni et al., 2010) are

significantly higher than AOD over Gadanki whereas AOD over hill stations Mohal

(Guleria et al., 2011) and Manora Peak (Sagar et al., 2004) in Himalaya and remote

location such as Antarctica (Gadhavi and Jayaraman, 2004) are significantly lower than

the Gadanki AOD values. As mentioned earlier Gadanki AOD is comparable or higher

than the AOD values over cities like Indore (Gupta et al., 2003), Pune (Devara et al.,

2005), Ahmedabad (Ganguly et al., 2006) and Dibrugarh (Pathak et al., 2010). As one

moves from cities towards remote location the spectral characteristics also changes.

Within the group of cities having AOD values comparable to Gadanki values, Dibrugarh

and Gadanki have similar spectral characteristics but Ahmedabad, Pune and Indore are

characterized by lower slope.


59

a)

b)

Figure 3.2. a) Spectral variation of aerosol optical depth (AOD) plotted for different

seasons and b) Comparison of spectral aerosol optical depth over various places in

India with respect to Gadanki. Data points in case of Gadanki are median values from

the period April 2008 to March 2011.


60

Slope of the curve depends on size distribution of the particles. It indicates that

fine-mode of aerosols dominant over Gadanki and Dibrugarh in comparison to

Ahmedabad, Pune and Indore. Gadanki and Dibrugarh share one common geographic

characteristics that is they are located in regions which are densely vegetated with high

amount of annual rain-fall in contrast to this, the cities Ahmedabad, Pune and Indore are

located in arid or semi-arid regions of India.

The seasonal variation of AOD over Gadanki with monthly statistics shown as

box-and-whisker plot representation is shown in Figure 3.3. AOD over Gadanki has

discernible seasonal cycle but no significant variation from year to year. It has high intra-

month variability as characterized by longer inter-quartile range. It increases from

November to May and decreases from June to September. This is in contrast to the

seasonal variation over Ahmedabad, where AOD is minimum during December to March

(Ganguly et al., 2006) and over Kanpur, where the minimum is during March (Singh et

al., 2004). Also the intra-month variability is significantly small compare to Gadanki over

these two locations. Ganguly et al. (2006) attribute high AOD during pre-monsoon season

(April to May) to dust transport from arid region around Ahmedabad and low AOD

during Winter to shallow boundary layer as it allows less space for aerosols. The high

AOD season over Gadanki is in April which is one month ahead compared to

Ahmedabad. The rainfall associated with the South-West monsoon prevents further

increase of AOD after May over Gadanki.

Spectral features of AOD during April are significantly different from that found

over Ahmedabad during high AOD season there and indicative that the high AOD over

Gadanki is due to fine-mode aerosols. In contrast to that, high AOD over Ahmedabad is

due to the presence of coarse-mode dust particles. There is high intra-month variability

and in comparison to it seasonal variations are relatively small, which makes it difficult to

ascertain the causative mechanism for seasonal variation through correlation analysis.

However, changes in air trajectories with seasons, rainfall and biomass burning activity

appear to have influence on seasonal variation over Gadanki.


61

Figure 3.3. Seasonal variation of AOD at 500 nm over Gadanki shown as box-and-

whisker plot representation. The representation of box statistics is also shown together

in the figure.

3.2. Seasonal variations of Angstrom Exponent (α)

Though there is a small seasonal variation in AOD, there are systematic and very

prominent changes in spectral characteristics of AOD, which suggest the drastic changes

in aerosol chemical and micro-physical properties with seasons. Wavelength dependence

of AOD can be modeled using power law of the form shown in Equation 3.1.

τ=βλ-α (3.1)

The exponent 'α' is known as Angstrom exponent after Anders Ångström (1929) and 'β' is

the turbidity parameter which is usually the AOD at one micrometer. 'τ 'and 'λ' are AOD

and wavelength respectively. The angstrom exponent is calculated from the slope of the

linear least squares fit of the AODs and wavelength on log-log scale. Parameter α is

highly sensitive to size distribution of aerosol (Schuster et al., 2006). High values of α

represents the dominance of fine mode aerosols and low value of α represents the

dominance of coarse mode aerosols in aerosol size distribution. Soot and Sulfate aerosols


62

have value of α close to 1.6 and coarse mode particles such as mineral dust and sea salt

have α value close to 0 (d'Almeida et al., 1991; Hess et al., 1998 and references therein).

The average Angstrom exponent for the entire period is 1.06 ±0.37. The seasonal

variation of Angstrom exponent is shown in Figure 3.4.

Figure 3.4. Seasonal variation of the Angstrom Exponent calculated using the

wavelengths from 400 to 1020 nm. The representation of box statistics is also shown

together in the figure.

A strong seasonal cycle can be observed from the angstrom exponent, with low

values during Summer (June-August) and high values during March. This indicates that

the overall coarse mode particles are increasing from March to June and vice versa. The

size distribution is an important characteristic of sources of aerosol. Natural aerosols are

generally bigger size particles and anthropogenic aerosols are generally smaller size

particles. Depending on season, Gadanki experiences different air-masses which can

bring aerosols from pollution plumes in one season and natural aerosols from oceans in

other seasons. Five days air-back-trajectories ending over Gadanki are calculated using

HySPLIT model (Draxler and Rolph, 2013; Rolph, 2013). Back- trajectories for every

Thursday between April 2008 and March 2011 are shown for different seasons in

Figure 3.5.


63

a)

b)

c)

d)

Figure 3.5. Seasonal separation of five days air back trajectories arriving at 0.5, 1.5,

2.5 km altitude over Gadanki for every Thursday during the period April 2008 to

March 2011 for different seasons (a) Spring, (b) Summer, (c) Autumn and (d) Winter.

Shown in the figure are the air-back trajectories plotted over Gadanki for 3

different heights 500, 1500 and 2500 m. It was observed that during Winter and Spring

(December to March) most trajectories originate in Indo-Gangetic Basin and travel over

Bay of Bengal along eastern coast of India before reaching Gadanki. Some trajectories

are found to have the source points in African continent. These trajectories pass over the

Arabian Sea and enter Indian region nearly 20O N and cross the Deccan plateau before

reaching Gadanki. Winter seems to be having the majority of the trajectories from the

Indo-Gangetic basin which are rich in anthropogenic pollution. But in the Spring we see a


64

wider spread in the sources of trajectories. Several trajectories pass over the arid-regions

of the western India and reach Gadanki via central India. These air-masses contain

aerosols that are rich in mineral dust. Some air-masses have their sources in the central

Bay of Bengal and North-Eastern countries. During Summer the scenario is different

where the majority of the trajectories have sources in the Indian Ocean and is evidenced

at all the 3 heights plotted. These air-masses contain natural aerosols of marine origin.

Some of these air-masses can also contain signature of mineral dust. Earlier studies

reported the presence of mineral dust over the Arabian Sea and Arabian Peninsula

(Badarinath et al., 2010) and over Indian Ocean (Rajeev et al., 2000). In the Autumn

period most of the air-back trajectories have source points from the North-Eastern Asian

countries. This period is characterized by changing in the wind direction. Like Winter air-

back trajectories these air-masses can also contain significant amount of anthropogenic

pollution especially black carbon (BC).

Besides changes in aerosols sources arising from change in wind trajectories,

changes in local aerosol sources such as agricultural waste burning and forest fires are

also highly seasonal and can be responsible for the changes in the variation of the aerosol

properties. Origin points of five-day air-back-trajectories are shown in Figure 3.6.

Different colors and shapes in the figure are plotted depending on values of daily mean α

on the day when trajectory reaches Gadanki. Low values of α are associated with air

trajectories originating in Indian Ocean whereas high α values are associated with

trajectories originating in South-Central India and Bay of Bengal. Kanpur, an Industrial

area in the Northern part of India showed similar seasonal variation of Angstrom

exponent as that of Gadanki, Showing a decreasing value of α from April to June. The

decreasing value of α over Kanpur suggests that there is increasing number of coarser

particles (Singh et al., 2004). In their study they attribute this to the increasing amount of

dust. In pre-monsoon period, the average value of α over Kanpur is 0.6 ± 0.31 (Singh et

al., 2004), where as the average value of α over Gadanki is 1.11 ± 0.35. This clearly

suggests that Gadanki is highly influenced by the biomass burning activities whereas

Kanpur is highly influenced by the coarser particles. But in the monsoon period both

Kanpur and Gadanki has low value of Angstrom Exponent, indicative of the presence of

coarser particles.


65

Figure 3.6. Source point of five days air-back trajectories arriving at 1.5 km altitude

over Gadanki with color and shape plotted according to values of angstrom exponent.

Variations of α over Gadanki for Winter and Summer periods are comparable to

variations found over Dibrugarh (Bhuyan et al., 2005), Visakhapathnam (Niranjan et al.,

2004) and Trivandrum (Moorthy et al., 2007). As noted earlier, low α values are

indicative of natural aerosols and high α values are indicative of anthropogenic aerosols.

Influence of source region on aerosol loading of air-masses can clearly be seen in this

plot. Regions which are known for less anthropogenic activities such as Indian Ocean,

clearly has less number of trajectories associated with high α values and vice-versa.

These results are consistent with the findings of Corrigan et al. (2006) who have found

that over Hanimaadhoo (6.776O N, 73.183O E) when air-back-trajectories are from Indian

subcontinent and South-East Asia, there is a substantial increase in particle number

concentration, scattering coefficient, absorption coefficient and AOD attributable to

anthropogenic activities over these regions.


66

3.3. Absorption Aerosol Optical Depth

As noted earlier, one of the inversion products of Sky-radiometer is SSA (single

scattering albedo). Using SSA, one can separate AOD in to absorption AOD (AAOD) and

scattering AOD using equation AAOD = AOD*(1 – SSA). The seasonal variation of

Single Scattering albedo will be discussed in detail in Chapter 4 and in Chapter 5.

Absorption aerosol optical depth (AAOD) is particularly important to identify the

presence of BC or dust particles in the atmosphere. Accuracy of absorption optical depth

depends on the accuracy of both aerosol optical depth and single scattering albedo. There

has been several studies that compare AERONET (Aerosol Robotic Network; Holben et

al., 1998) based and SKYRAD.PACK based SSA (Che et al., 2008; Valenzuela et al.,

2012; Estellés et al., 2012). AERONET and SKYRAD.PACK SSA are found to be

agreeing within 3 to 13% depending on wavelength and aerosol amount. For high aerosol

optical depth and lower wavelengths, the match is better. Stated absolute uncertainty of

AERONET SSA is 0.03 (for AOD440 > 0.4). Kim et al. (2004) put extreme case

(AOD500nm < 0.2 and solar zenith angle <= 30) error at 0.08 and less than 0.02 for the

cases when AOD500nm >= 0.5.

Monthly statistics are shown in form of box-whisker plot for AAOD in Figure 3.7.

AAOD is high during Spring (March-May) and low during Autumn. If AAOD is

contributed by dust particles the particle size will be bigger and hence the Angstrom

exponent will be small and if AAOD is contributed by BC particles then the particle size

will be small and Angstrom exponent will be high. Seasonal variation of AAOD over

Gadanki is correlated with Angstrom exponent as well as biomass burning season,

indicative of the fact that AAOD over Gadanki is mainly because of BC particles.

Dominance of BC vs Dust in AAOD over Gadanki is consistent with modeling studies

(Bond et al., 2013 and references therein). However, models (AeroCom median model)

significantly underestimate the AAOD for South India. A factor of 6.20 difference

between AERONET and AeroCom Median Model is reported for South Asia (Bond et al.,

2013). Mean AAOD based on daily median AAOD at 500 nm is 0.047 over Gadanki,

whereas AeroCom median model AAODs at 550 nm over South India are in range of

0.006 to 0.012 which are about a factor from 4 to 8 lower than the observations.


67

Figure 3.7. Seasonal variation of the Absorption Aerosol Optical Depth (AAOD) at

550nm. The representation of box statistics is also shown together in the figure.

3.4. Surface aerosol number concentration and aerosol size distribution

The particle size bin segregated number concentration of the particles and as a

whole the total number concentration of the particles are derived from the APS. The

retrieval method of the micro-physical properties of aerosols along with the associated

error is discussed in Chapter 2. The seasonal variation of aerosol total number

concentration is shown in the Figure 3.8. Shown in the figure are the average total

number concentration values with standard deviation as the error bar. The seasonal

variation is well captured from the figure. There are data gaps in the figure due to the

instrument failure. There are several reasons that account for the variability of the total

number concentrations. The average number concentration is less during the Summer and

Autumn. The low number concentrations during these seasons are mostly related to the

effects of rain out of the particles. The variations of the boundary layer (Basha and

Ratnam, 2009) with respect to the variations of the solar insolation also play a major role

for the variation of the particle number concentration. Along with these effects, the

sources of aerosols also change from season to season.


68

Figure 3.8. Time series of the daily average of the total number concentration with ±

standard deviation as the error bar.

During the Spring, biomass burning can be a major source for aerosols. Spring is

also associated with high winds that can help to aloft the bigger particles into the

atmosphere. Summer and Autumn are associated with the long range transportation of

particles. Winter is a dry season and conducive for forest fires and other burning

activities. The variation of the total number concentration of the particles depends on the

variation of the particle modes and size distribution.

A good insight on the production and the growth of the particles in fine mode are

discussed by Heintzenberg, (1989). Fine mode particles are produced by the process of

condensation of vapors on the pre-existing particles. The particles are also produced by

the process of nucleation and coagulation of smaller particles to form bigger particles

(Heintzenberg, 1989). These processes cease for the particle diameter greater than 1 µm

and are effective only in the sub-micron range. The processes will result in the formation


69

of the accumulation mode (0.1 to 1 µm diameter) in which further mass transfer will be

minimized so as the case with the removal processes. These accumulation mode particles

have long residence times (Willeke and Whitby, 1975).

In a simulation study conducted by Jacobson and Turco, 1995 showed that

Brownian coagulation affects the particles under sub-micrometer range and the turbulent

motions are responsible for the coagulation of the particles in the super-micrometer

range. Accumulation mode particles mask the nuclei mode by coagulating with the

smaller particles. The condensate vapors prefer to stay with the pre-existing particles in

the accumulation mode and hence reduce the nuclei mode number.

A study by Wu et al. (2008) discussed that the nucleation events determine the

total number concentration. The affects of the wind speed on the variation of the particle

number concentration were also discussed by Wu et al. (2008), revealing that low number

concentration of the nucleation mode particles under low wind speed are related to the

strong coagulation scavenging produced by the presence of high number concentration of

the accumulation mode particles. Similar description was given in another study by

Monkkonen et al. (2004a). More emphasis will be given while we discuss the effects of

wind speeds on the total number concentration.

The average seasonal diurnal variation of the particle number concentration is

shown in Figure 3.9 (a). The seasons are taken as Summer, Winter, Spring and Autumn.

All the seasons show two peaks one in the day time and one in the night time. From the

figure, it is observed that the diurnality is well pronounced in Winter, Autumn and Spring

but not well pronounced in Summer. The highest particle number concentration is

observed in Winter agree with the studies of Baxla et al. (2009) over Kanpur. Winter is

associated with low atmospheric boundary layer height (Basha and Ratnam, 2009) and as

a result there are more number of particles within the boundary layer. Spring is usually

very hot over this part of the region and with higher solar irradiance at the surface. The

atmospheric boundary layer height is also high and due to this we see a low particle

number concentration during the afternoon time. The average values of the total number

concentration followed by standard deviations for different seasons are shown in Table

3.2. The effect of the occasional thunderstorms during late afternoon hours can also affect

the number concentration of the particles.


70

Autumn and Summer seasons are associated with particles from two different air-

masses. Summer is associated with the air-masses coming from the Indian Ocean and

Autumn is associated with the air-masses coming from Indo-Gangetic basin (IGB) and

from Bay of Bengal. The change in the aerosol type (Coarser or Finer) with respect to the

seasons and the amount of particle production could be a causative mechanism for the

variability of the particle number concentration. Rain washout of the particles does play a

role in modulating the aerosol size distribution. Surface size distribution for the four

seasons is shown in Figure 3.9 (b). Shown in the figure is the median size distribution

with error bar being the inter-quartile range. During Winter there is an increase in

accumulation mode particles (particles with diameter < 1 µm) and decrease in coarse

mode particles (particles with diameter >= 1 µm). Whereas during South-West monsoon

(June to August) reverse is true i.e. a decrease in accumulation mode and increase in

coarse-mode particle concentration. On seasonal scale the variation is similar to

variations which are found in columnar size distribution (Figure 3.10).

The production mechanism of the both smaller and bigger particles vis-à-vis the

affects of the wash out processes plays a major role for such seasonal variability of

particle size distribution. However, there is very little correlation between surface and

columnar size distribution as discussed in next section (3.5) on hourly or daily scale.

Using the log-normal distribution function as discussed in Chapter 2, it is possible to

identify the number of modes that fits the overall observed size distribution, which in-

turn depends on the information of total number concentration, modal diameter and width

of the distribution. It was observed that the overall mean aerosol size distribution

observed for both surface and column can be reconstructed by considering 3 different

modes in the size distribution. This is shown separately for both surface and column in

Figure 3.11.


71

a)

b)

Figure 3.9. (a) Average diurnal variation of the total number concentration for

different seasons and (b) Median particle number size distribution with inter-quartile

range as the error bar plotted for different seasons.


72

Figure 3.10. Seasonal variation of the columnar number size distribution plotted for different seasons. The size distribution values plotted are seasonal median values for the respective aerosol diameter bins with error bar as inter-quartile range.

The median surface number size distribution observed over Gadanki are compared

with the number size distribution values observed over the Arabian sea, Indian Ocean and

Coastal India, Bay of Bengal and Ahmedabad and are shown in Figure 3.12. The

concentration (dn/dlogr) in lowest size-bin (i.e. 0.54 µm) varies from 20 to 200

particles/cm3 and it is lower than the concentration found over Bay of Bengal (Sinha et

al., 2011; Ganguly et al., 2005) and Coastal India (Ramachandran and Jayaraman, 2002)

during Winter and Spring. However, it is comparable to Ahmedabad (Ganguly et al.,

2006). The size distribution curves for the other study regions are plotted basing on the

information such as the total number concentration, median diameter and width of the

distribution provided from the study for the respective locations mentioned in the figure.

However, the major difference in size distribution and previously mentioned locations is

at the particle size of the diameter 4 µm. Particle concentration over Gadanki is about two

orders of magnitude higher than the other locations for 4 µm diameter and decreases fast


73

with increasing diameter. High concentration and narrow spread for coarse mode range

are typical features of freshly generated biogenic aerosols.

a)

b)

Figure 3.11. Overall mean size distribution observed for (a) surface and (b) column over Gadanki, fitted along with the three individual log-normal curves.

Huffman et al. (2010) have found biogenic aerosols with geometric mean

diameter of 3.2 µm and geometric standard deviation (σ) of 1.3 over a semi-urban site in

Mainz, Germany. Similar features are found in terms of size-distribution for biogenic

aerosols in tropical rain-forest region (Central Amazonia) by Huffman et al. (2012). The

ICON observatory is located very close to forest. Also there is a significant land under

agriculture around Gadanki and have plantation throughout the year. They can be the

potential sources of biogenic aerosols in this region. The size-distribution curves shown

in Figure 3.12 for the places other than Gadanki are obtained for Winter or early Spring

season. Lesser amount of smaller size particles prevailed over Gadanki as compared to

Bay of Bengal. During these seasons, Gadanki is down-wind to Bay of Bengal and are

likely to be associated with the long range transportation of particles. Aerosols have

short-life time and as air-parcel takes time to cover distance, aerosol concentration in

parcel gets reduced.


74

Temporal variation {(Xi,j – Xi )/Xi } of daily median aerosol number concentration

is shown in Figure 3.13, where Xi,j is a daily median value of dN/dlogR for size-bin i and

j. Xi is average of all the variable values of dN/dlogR for size-bin i. The variation on

certain days is found to be as high as 15 times the Xi value, however plotting is restricted

to the values between -1 and 1 to bring-out gross features in their variation. Changes in

accumulation mode and coarse mode aerosols are anti-correlated. The onset of changes in

size distribution is characterized by sharp boundaries. Sudden changes in aerosol size

distribution with season are noticed by Moorthy et al. (2007) over Thiruvananthapuram (a

coastal city of India on West coast of India). The observed changes are sharp implying

that the size distribution changes are happening because of changes in sources of aerosols

linked with wind trajectory.

Figure 3.12. Comparison of the surface median size distribution for the entire period

of this study (2008 October to 2012 April) with respect to size distribution measured

over Bay of Bengal (Ganguly et al., 2005), Arabian Sea, Indian Ocean and Coastal

India (Ramachandran and Jayaraman, 2002) and Ahmedabad (Ganguly et al., 2006).


75

Figure 3.13. Temporal variation of surface aerosol number size distribution. The color

represents the value (X(i,j)- X(i)/X(i)), where X(i,j) is daily mean value of dN/dlogR

for diameter Di and Day Tj. X(i) is average of X(i,j) over time dimension for entire

period.

3.5. Comparison of Surface and Columnar Size Distribution

A comparison of surface aerosol size distribution observed using APS and

columnar size distribution estimated using Sky-radiometer is shown in Figure 3.14.

Figure 3.14 (a) shows the comparison of volume concentration for particles having

diameter in the range of 0.5 to 1 µm and Figure 3.14 (b) shows the comparison for

particles having diameter in range 1 to 10 µm. Spearman's rank correlation (ρ) is used to

see if there is any relationship between surface concentration and columnar

concentration. There is near zero correlation (ρ = -0.01) in case of smaller size particles

but a moderate correlation (ρ = 0.43) is found in the case of coarse-mode particles.


76

(a)

(b)

Figure 3.14. Scatter plot of columnar volume concentration Vs surface volume concentration for (a) particles of radius range 0.5 to 1 µm and (b) particles of radius range 1 to 10 µm.

This highlights the vertical in-homogeneity of aerosols. Bigger size particles owing to

their short residence time and high settling velocity remain confined to lower layer of the

atmosphere whereas smaller size particles can be lifted at higher altitudes and can get


77

transported to long-distances. Elevated layer of aerosol will produces a little or no

correlation with surface aerosols. Poor correlation is also possible when the bottom most

layer has insignificant contribution in comparison to total columnar aerosol

concentration. A poor (near zero) correlation between surface and columnar volume

concentration for smaller size particles which are optically very important is indicative

that localized sources have very less contribution in total concentration over Gadanki.

3.6. Surface aerosol mass concentration

Aerosol mass concentration measurements inform the standards of air quality. The

mass concentrations are computed in this study with a density of 1 g/cm3 which is a value

used in models for soot and with significant moisture content (Hess et al., 1998). The

median mass concentration is estimated to be 28 µg/m3 with 97% of the values (one

minute averages) being less than 100 µg/m3. The aerosol mass concentration over

Gadanki is well under the limits of NAAQ (National Ambient Air Quality, India)

standards which are 60 µg/m3 for PM2.5 and 100 µg/m3 for PM10 for 24 hour exposure

(CPCB, 2009). The average values of the particulate matter concentrations (PM1, PM2.5

and PM10) followed by the standard deviation values are shown in Table 3.2 for different

seasons. The surface mass concentration over Gadanki is significantly lower in

comparison to many other locations. The mass concentrations for PM10 are reported in

range from 40 to 106 µg/m3 over Ahmedabad (Ganguly et al., 2006) and from 50 to 671

µg/m3 over Delhi (Monkkonen et al., 2004). Nair et al. (2006) have reported PM10 mass

concentrations from 50 µg/m3 to 100 µg/m3 over several places in South India during a

road campaign.

S. No Category Total number

concentration (#/cc)

Particle mass concentration (µg/m3)

PM1 PM2.5 PM10

1 Over all 31.04±20.68 8.87±6.52 14.92±8.79 32.06±19.36

2 Spring 28.60±17.77 7.94±4.82 14.6±8.45 36.12±22.58

3 Summer 12.81±9.95 2.46±1.48 9.48±7.57 30.38±23.71

4 Autumn 23.70±19.83 9.79±7.25 14.28±9.21 23.83±13.77

5 Winter 44.58±20.11 13.97±5.94 19.34±7.47 32.05±10.41

Table 3.2. Average values of the total number concentration and mass concentration of the particles followed by the standard deviation for different seasons.


78

The monthly average of aerosol mass concentrations over Gadanki for different

particulate modes is shown in Figure 3.15 (a). From the figure it is observed that PM2.5

and PM10 showed a maximum in Spring where such maximum is not observed for PM1.

Although the seasonal variations are captured in all the modes, It is in PM1 that showed a

very smooth variation with maximum values attained in Autumn and minimum in

Summer. Such minimum values are not attained for the other modes PM10 and PM2.5

although there is a decreasing pattern observed for PM2.5 values. This indicates that the

coarser particles are predominant in Summer. At the same time wind speeds are high

during Spring and Summer which can help to aloft dust particles in to the atmosphere.

The second maximum for PM10 and PM2.5 is observed in Autumn and consistent with the

variations of PM1. The similar pattern was observed over a rural site in and around 10 km

radius of city Agra (Kulshrestha et al., 2009). In their study, the values for rural site and

urban site doesn't change much indicative that the rural site is continuously under the

influence of urban pollution. But from this study the values of PM modes are much lesser

than Agra, indicative that this location is relatively cleaner. The seasonal variation of the

PM modes is in comparison to that of Agra. This might be the influence of the synoptic

weather systems on the ambient aerosol pollution.

It is also possible that during Autumn the rain out of particles from all the size

bins might be removed. This may be the reason why sudden jumps in the higher PM

modes are not visible in Autumn. This may not be happening for the particles prevailing

in Spring and Summer. The in-homogeneity of the rainfall and high wind speeds present

in Spring and Summer seasons could be the possible reasons for the sudden jumps in

higher PM modes.

The monthly mean variation of the ratio of PM2.5 to PM10 is shown in Figure 3.15

(b). It is observed from the figure that ratio decreases from Winter to Summer and

increases again from Summer to Winter. This again is in agreement to the observations

over Agra (Kulshrestha et al., 2009). This suggests that coarser particles are more in

Summer and Spring. Harrison et al. (2001) and Dingenen et al. (2004) also showed in

their study that the re-suspension of coarser particles due to increasing wind speeds in

Summer might be the reason for the higher PM10 values where as the inter annual

variations in weather are the reasons for the variations of PM2.5 values.


79

a)

b)

Figure 3.15. (a) Monthly average variations of the particle mass concentration and (b)

monthly average variations of the ratio of PM2.5 to PM10 mass concentrations.


80

3.7. Dependence of aerosol properties on wind speed and rain

The variations of the AOD, aerosol number concentration and aerosol size

distribution with respect to the changes in wind speed and rainfall were also investigated

over Gadanki. As discussed earlier, Gadanki is in the Southern peninsular India and is

influenced by both the South-West monsoon (June to August) and the North-East

monsoon (October to November).

September is a transition phase of the South-West to the North-East monsoon.

Gadanki receives rainfall in both the monsoons. High wind speeds are associated with

Spring and Summer. So an attempt has been made to study the effects of wind speed and

rainfall on the measured micro-physical properties of the aerosols. Wind speeds and

rainfall data were obtained on a continuous basis from an automated weather station

installed in our observatory. The observations were available at every one hour local time

(LT). The aerosol measurements are available at a much higher resolution. So, we have

averaged the aerosol measurements to the hourly basis and co-allocated measurements

for the weather parameters are taken for inter-comparison. The seasonal variations of all

the weather parameters for a period of 4 years (2008 to 2011) and is shown in Figure 1.7.

Since more emphasis is given to the wind speed and rainfall in this study, we will

be discussing only on these two parameters. Wind speeds are high in Spring and Summer

and low in Winter and Autumn. The monthly average values of the weather parameters

with error bar being the standard deviation is shown in the Figure 1.7. Rainfall plotted in

the figure is the monthly accumulated rainfall. The rainfall is maximum in Summer and

Autumn and minimum in December and Spring.

Spring is associated with occasional thunder storms. The diurnal variations of the

accumulated rainfall are shown in the Figure 3.16. It was observed from the figure that

both Summer and Autumn received most of the rainfall but it is in the Autumn that rained

most and is observed from the total amount of rainfall provided in the figure. A

considerable change was observed in the diurnal variation of the rainfall from season to

season. All the seasons except Autumn showed low rainfall between 5 to 11 hours local

time. Most of the rainfall for Summer and Spring occurred from 15 hours local time

onwards. It is in the Autumn season where rainfall was observed almost every hour and

the hourly accumulated rainfall never went below 20 mm. Although the seasonal diurnal


81

variations of the rainfall for all the seasons give insight on overall rainfall pattern, there is

a strong day to day variability. The current study is focused more on a seasonal scale

rather than on a particular event.

Figure 3.16. Diurnal variation of the total rainfall plotted for different seasons during

the period January 2008 to December 2011.

Figure 3.17 (a) shows the scatter plot between the daily mean wind speed and

total number concentration. It was observed from the figure that wind speed and total

number concentrations are correlated with an R value of -0.26. Although the correlation

is not high, it gives impression that total number concentration is decreasing with

increasing wind speeds. The effects of particle number dilution with increased wind

speeds are discussed by Harrison et al. (2004) and Hussein et al. (2005, 2006). If wind

speeds are low then the condensable vapors prefer to stick to the particles of the

accumulation mode and result in the loss of nucleation mode particles. With increasing

wind speeds the accumulation mode particles decrease and the nucleation mode particles

increase (Wu et al., 2008). Interestingly in Summer a 'U-type' dependence between wind

speed and total number concentration was noticed and was shown in Figure 3.17 (b).


82

The particle number concentration decreased with increased wind speeds up to

~4m/s and then started increasing from there onwards. The similar kind of relation

between wind speed and number concentration was also observed by Wu et al. (2008)

over Beijing, China. Another study by Harrison et al. (2001) over UK urban locations

also reported the same dependence between wind speed and particle mass concentration.

Their study showed that the dilution effects of PM2.5 and at the same time an increase of

PM10 with increasing wind speeds. The ratio of PM10 to PM2.5 showed an increasing

tendency. In this study, the ratio of (Ncoarse/Nfine , where 'N' represents the number

concentration of particles) was plotted against wind speed for the Summer season and an

increasing tendency was observed with a positive spearman's rank correlation of 0.28 and

is shown in Figure 3.17 (c). The relationship between the wind speeds on variation of the

aerosol properties over land is rather weak as compared to the oceans. The prominent

dependence of wind speed on the observed aerosol properties was evident from the

studies by Gong et al. (1997), Ramachandran and Jayaraman, (2002), Vinoj and Satheesh,

(2003), Smirnov et al. (2003) and Ganguly et al. (2005). Most of the studies revealed that

an exponential kind of relationship exists between the wind speed and observed aerosol

properties. Moreover, the winds are not obstructed over oceans whereas over the land the

changes in terrain have influence on the wind speeds.

The effect of the rainfall on the particle size distribution was also studied

and reported here in this study. In order to study the effect of rainfall on the ambient

particles, all the rainy events are separated into seasons. An assumption is made here that

for a particular season rainfall characteristics will not change much although the rainfall

intensity may vary from event to event. The variation of total number concentration for

both the rain and non rainy days is shown in Figure 3.18. The diurnal average of the total

number concentration for the rainy and non-rainy events for all the seasons namely the

Spring, Summer, Autumn and Winter is shown in the figure. In order to bring out the

gross features the error bar is not represented. It is observed from the figure that for

Spring (Figure 3.18(a)) the rainy day total number concentration is higher than the other

days (non rainy days) of the same season especially between 5 to 11 hours local time. In

Spring the rainfall is high around 15 to 20 hours local time (Figure 3.16). During Spring,

the solar insolation is high and there is lift of moisture into the atmosphere that results in


83

(a)

(b)

(c)

Figure 3.17. (a) Scatter plot between daily average wind speed and daily average total

number concentration. (b) Scatter plot between hourly wind speed and hourly total

number concentration for Summer (June to August). (c) Scatter plot between the

hourly wind speed and ratio (Ncoarse/Nfine) for Summer (June to August)


84

a convective system. Most of the rainfall occurs usually at late afternoon hours between

14 to 16 hours local time. After the rain event lot of moisture is released into the

atmosphere and after the rainy day the enhanced level of moisture favors the new particle

formations and finally results in the increase of particle number concentration. In

Summer (Figure 3.18(b)) seasons the scenario is different. This season is having lot of

rainfall that resulted in lower particle number concentrations most of the time. So a rainy

event has negligible effect on the ambient particle number concentration. Chate, (2005) in

their study discussed that during the rain event the rain drops after splashing into the

surface can result in additional particles into the atmosphere.

This could be the reason for the additional number concentration during the rainy

events as and when compared with the other non rainy day behavior. At the same time the

advections do play a role in modulating the particle number concentration (Chate, 2005).

Winds can bring the aerosols from a polluted source to a cleaner site. In Autumn (Figure

3.18(c)) season we observe a cleansing effect and the rainy day pattern represents almost

the same manner as the non-rainy pattern. During this season the air-masses come from

the Bay of Bengal and from the North-Eastern part of India. These air-masses are rich in

anthropogenic pollution and results in the increase of particle number concentrations. So

the effect of rainfall was observed more in Autumn as compared to Summer where the

ambient particle number is concentration is low enough to observe any more cleansing

effect. Winter season is having the least amount of rainfall and most of the time it is clear.

During the clear sky days the affect of boundary layer is more prominent and can be

observed from the Figure 3.18(d). The non rainy days of Winter show a typical diurnal

behavior. During a cloudy day or a rainy day the boundary layer collapses that results in

the built up of surface pollutants and as a reason we could observe high number

concentration of particles during the rainy days of Winter.


85

(a)

(b)

(c)

(d)

Figure 3.18. Diurnal variation of the average total particle number concentration for

both rainy days and non-rainy days (Other days) in different seasons (a) Spring, (b)

Summer, (c) Autumn and (d) Winter.

3.7.1. Wet-deposition of Aerosol

Wet deposition is one of the major sinks, responsible for removal of aerosol from

the atmosphere. The wet deposition has two sub-processes known as rain-out and wash-

out. Rain-out is a process in which aerosol serve as cloud condensation nuclei or captured


86

by cloud droplets through diffusion and ultimately removed when cloud droplet falls as a

rain-drop. Wash-out is a process in which falling rain-drop captures particles which are in

the atmosphere below cloud during its journey toward Earth. Both the rain-out and wash-

out affects aerosol optical depth but only wash-out process affects surface aerosol number

concentration. Relative changes in surface number concentration of aerosols on next day

of rain event with respect to previous day of rain event are shown as function of rain-fall

amount of intervening day in Figure 3.19 (a). Contrary to expectation no systematic

decrease is found. Red dotted line are the values expected based on empirical

parameterization used in some models (e.g. FLEXPART; Stohl et al., 2004, 2010 and

references therein) for below cloud scavenging. Day to day variability is too high to

discern any direct relationship in observations. Similar exercise is also done for hourly

average data using concentrations before and after raining hour. Like daily mean data,

variability in hourly mean data is also found to be too high to derive direct relationship

between rain amount and aerosol scavenging. In contrast, relative changes in AOD

calculated in similar manner show systematic decrease (Figure 3.19 (b)).This is because

APS is unable to measure particles smaller than 0.3 µm diameter and the size-range that

APS measures has minimum scavenging efficiency for below-cloud scavenging

(Pruppacher and Klett, 1996). However, the atmosphere particles of diameter smaller

than 0.3 µm are very efficient in scattering light and constitute significant part of AOD.

Blue solid line in Figure 3.19 (b) is least square fit to the median values after

removing one outlier data point. The dashed line is model values of AOD-reduction based

on the in-cloud and below-cloud scavenging schemes being used in FLEXPART while

assuming cloud thickness 1 km, cloud base height 2 km and all aerosol being well mixed

in altitude range zero to three km. Note that for thicker clouds, scavenging will increase,

whereas cloud occurring at higher altitude the scavenging will decrease. Scavenging or

post rain decrease in AOD estimated in another dispersion model CALPUFF (Scire et al.,

2000; shown with dotted line in Figure 3.19 (b)) are far greater than those found in

observations. Such overestimation of scavenging may result in underestimation of mass

of a species in the atmosphere.


87

(a)

(b)

Figure 3.19. Relative changes in (a) surface number concentration and (b) AOD as a function of rain-fall amount in a day. Vertical bar over data points are inter-quartile range for relative change in different rain events. Yellow points are data points excluding data for the category 15 to 17.5 mm rain. Blue solid line is linear regression for yellow points. Dashed line is rain-scavenging estimated from a scheme used on FLEXPART model (Stohl et al., 2004 ; Stohl et al., 2010) and the dotted line is rain-scavenging of aerosol based on scheme used in CALPUFF model (Scire et al., 2000) The difference is calculated for previous and next day of the rain event.


88

3.8. Characterization of aerosols and source segregation

3.8.1. Aerosol Optical Depth vs Angstrom Exponent

Aerosols are observed in various sizes and types and hence the properties of the

aerosols are dependent on the type of the underlying sources. Indentifying the sources is a

step forward in this direction. However, the identification of the sources needs proper

aerosol characterization and aid of chemical analysis. In this study we have used the

method of aerosol characterization basing on the conventional methods established in the

literature that uses the combined information of AOD and Angstrom exponent (Eck et al.,

1999; O'Neill et al., 2001; Schuster et al., 2006; Kaskaoutis and Kambezidis, 2006;

Kaskaoutis et al., 2007; Cazorla et al., 2013). Such characterization was implemented in

several studies over Lampedusa in central mediterranean (Pace et al., 2006), Huelva,

Spain (Toledano et al., 2007), Hyderabad (Kaskaoutis et al., 2009) and over Arabian Sea

(Kalupureddy et al., 2009). The observed results are not different from the existing

methods but follow the one that implemented over Hyderabad (Kaskaoutis et al., 2009)

more closely. Although the method is same but basing on the observed aerosol properties,

the source segregation can be different. Moreover, Hyderabad and Gadanki are having

mostly comparable aerosol optical depth even though Gadanki is a rural site which is

most striking. Sources over Hyderabad and Gadanki can differ as one is urban type and

the other is a rural type. But the study over Hyderabad suggests that the aerosol properties

are too influenced by the local aerosol sources such as biomass burning from the near

rural sites. As discussed earlier, α is a good indicator of the particle size (Schuster et al.,

2006).

In order to discriminate the aerosol types for different seasons, a scatter plot is

considered between AOD and Angstrom exponent. Angstrom exponent used for the

characterization is calculated using the wavelengths from 400 to 870 nm inorder to be

consistent with the other studies. This conventional method was used to separate the

aerosol types. The α - AOD scatter plot for different seasons over Gadanki location was

shown in Figure 3.20. Both AOD and α are divided into 0.1 incremental steps and

number density for each AOD-α pair is plotted. It is observed from the figure that most of

the data points lie below 0.5 AOD and at the same time a large variation in the angstrom


89

exponent was observed with respect to the seasons.

a)

b)

c)

d)

Figure 3.20. AOD (500 nm) versus angstrom exponent (400-870) for each season over Gadanki during the period April 2008 to March 2011. Shown in the bracket is the respective number of observations for that season.

High density cluster in Spring is observed between 0.3 and 0.7 AOD and between

1 and 1.5 α. This is a clear indication of presence of finer particles that resulted in high

aerosol loading. During the same period, one smaller cluster is centered at 0.5 AOD and

between 0.5 and 1 α, suggesting that coarser particles also existed in the overall aerosol

loading. In Summer large number of data points are observed between 0.3 and 0.5 AOD

and between 0 and 0.5 α and are indicative of the prevalence of coarser particles probably


90

sea salt. There are some data clusters which are higher than 0.6 AOD and less than 0.5

alpha and showed the possible influence of mineral dust. In Winter a large data cluster is

between 0 to 0.5 AOD and between 1 and 1.5 α . This again showed a clear indication of

the presence of finer particles. In Autumn, the scenario is anti-correlated to that of

Summer. Here the large number of data points is less than 0.5 AOD and greater than 0.3

AOD and between 1 and 1.5 α thereby showing again the existence of smaller particles.

There are no major burning activities in Autumn and the presence of such smaller

particles could be due to the long range transportation process. The tail of the cluster

comes down to less than 1 α and is indicative of the presence of bigger particles of

possible marine influence. We have used this information of α-AOD scatterer to classify

the aerosol types over Gadanki. However, the classification depends on the set of

threshold values assumed for AOD and for α respectively. Over Gadanki it was observed

that 56% of the AOD cases at 0.5 µm are greater than 0.4 and in which 38% of the total

AOD cases are more than 0.5. So it is quite understandable that AOD over Gadanki is

high and in par with the AOD values measured over other cities like Indore (Gupta et al.,

2003), Pune (Devara et al., 2005), Ahmedabad (Ganguly et al., 2006), Dibrugarh (Pathak

et al., 2010) and Hyderabad (Kaskaoutis et al., 2009).

In this study we have taken the same criteria as suggested by Kaskaoutis et al.,

2009. Marine influenced (MI) aerosols are characterized for the cases when AOD < 0.3

and α < 0.9. Urban industrial and biomass burning aerosols (UI/BB) are characterized

when AOD > 0.5 and α > 1. Desert dust (DD) cases are identified when AOD>0.6 and α

< 0.7. The cases which don’t belong to any of these are left as mixed type (MT). The

seasonal discrimination of these aerosol types is shown in Figure 3.21. It is observed

from the figure that for all the seasons the contribution of mixed type is quite large (>55

%). However, the scenario is balanced well in the period of Summer. 19% of the cases in

Summer are contributed due to DD type whereas 20% are contributed due to MI type.

Only 2% of the cases in Summer are contributed due to UI/BB type. During Spring, the

contribution due to UI/BB type is 34% and quite high as compared to any other season.

This is understandable as Spring is associated with lot of burning activities and amount of

aerosols produced due to this burning activity is also large. Next to Summer, the

contribution of the different aerosol types is balanced again in Autumn, where 36% of the


91

cases are contributed by both MI and UI/BB type. The Scenario of Winter is somewhat

unpredictable as 79% of the particles were identified as MT and 18% of the cases are

identified as UI/BB type and rest of aerosol type contributions are negligible in Winter.

a) b)

c) d)

Figure 3.21. Fractional contribution of different aerosol types in overall aerosol burden for Gadanki in different seasons a) Spring, b) Summer, c) Autumn and d) Winter. In figure MI is for marine influenced, UI/BB is for urban industrial/biomass burning, DD is for desert dust and MT is for mixed type.

Although the information retrieved from AOD-α is helpful to classify the aerosol

types it cannot reveal the information about the aerosol processes such as condensation

and coagulation that aerosols undergo during their transit. More over atmospheric

aerosols are highly variable in space and time and the spectral characteristics of AOD


92

changes with respect to the particle type. The determination of α is based on the spectral

range selected and hence the information retrieved from the AOD- α scatter is difficult to

interpret (El-Metwally et al., 2008; Kaskaoutis et al., 2009). The relative mixing of the

finer and coarser particles result in the curvature and hence the usage of the narrower

spectral range for the determination of α can be useful for the aerosol type identification

(El-Metwally et al., 2008 and references there in). In the present study, graphical frame

work scheme was implemented as proposed by Gobbi et al. (2007) over this location to

verify the associated aerosol processes.

3.8.2. Identification of associated aerosol processes

The spectral variations of the AOD provide the information about the aerosol size

distributions (King et al., 1978). The relationship between AOD and α were explored by

several researchers and discussed that presence of different type of aerosols in the aerosol

size distribution results in a non linear relationship and hence results in a curvature (Eck

et al., 1999). In such cases simple Angstrom exponent will not be helpful and hence the

second derivative of the Angstrom exponent (α') was used to determine the relative

influence of fine mode to the coarser mode particles in the aerosol size distribution. The

presence of positive curvature show the presence of the finer particles and decrease in the

curvature show the presence of the coarser particles (Eck et al., 1999). Hence both α in

conjunction with α' are used to determine the particle type. These relationships were also

discussed in several other studies (Reid et al., 1999; O'Neill et al., 2001; Schuster et al.,

2006).

Gobbi et al. (2007) proposed a graphical frame work scheme in which the

difference of the angstrom exponent (dα; where dα=α(440,675)-α(675,870)) was taken as

the Angstrom exponent curvature. Such visualization scheme was useful to identify the

contribution of the fine mode aerosol in the total aerosol loading and also to determine

the modal radius of the particle (Rf). In their method the AOD data were separated into

several groups by their incremental values and corresponding values of α and dα of each

group are over laid on a reference grid. The reference grid was computed using the Mie

calculations for urban/industrial type that uses a refractive index of 1.4-0.001i, fine mode

fraction of the AOD (η) at 675 nm and Rf for the bimodal particle size distribution.


93

The sensitivity of this classification scheme to the refractive index of the aerosol

type was also discussed by Gobbi et al. (2007). In their study it was shown that there is a

clock wise rotation about the origin of the constant radius curves for increasing refractive

index and was observed to be weaker in the curves of the fine mode fraction η. The

maximum Rf indetermination is of the order of ±25% for refractive index varying

between 1.33-0.0i and m=1.53-0.003i whereas the fine mode fraction η varies of the

order of ±10%.

Within this level of indetermination, this scheme is robust enough to provide an

operational scheme of the aerosol properties. This technique was implemented over

Hyderabad (Sinha et al., 2012) and over the Arabian Sea to determine the relative

influence of coarser and finer particles in the total aerosol loading (Kaskaoutis et al.,

2010). This technique was implemented in this current study to classify the aerosol type

and also to determine the processes such as cloud contamination and hydration associated

with the aerosols. The α and dα (where dα = α (400,675)- α(675,870)) pairs separated for

different seasons is shown in the Figure 3.22. They are overlaid on the reference grid for

the urban/industrial type as discussed by Gobbi et al. (2007). The lines indicated in black

are mode radii of the fine mode aerosols (Rf) and the lines indicated in red are the fine

mode fraction curves (η). In Spring, we observe a wide spread of points on the reference

grid.

Spring typically showed the presence of both coarser and finer particles as

identified by the two sets of the data cluster one for α < 1 and dα > 0 and the other for α >

1 and dα < 0. Majority of the points lie between Rf of 0.1 and 0.15 µm with fine fraction

varying over a wide range upto 90%. The presence of the data cluster for high AOD > 0.6

with fine fraction greater than 70% and Rf between 0.1 and 0.15 µm suggest the presence

of biomass burning aerosols. There is a small cluster of points move towards the higher

values of Rf close to 0.2 µm with corresponding fine fraction varying between 70 and

90% and is indicative of the possible growth of the fine mode aerosols. Spring time

observations has close resemblance to that of Beijing (Gobbi et al., 2007) as both the sites

showed the bimodal particle behavior in terms of coarse and fine mode particles. Beijing

showed better curvature as compared to that of Gadanki. Kanpur (Gobbi et al., 2007)

didn't show much of the aerosol growth as compared to that of Gadanki.


94

The possible explanation for the growth of the aerosols observed over all these

mentioned sites are possibly due to the hygroscopic and coagulation of the aged fine

mode aerosols probably soot (Gobbi et al., 2007; Zhang et al., 2008; Yoon et al., 2012)

and Gadanki scenario shouldn't be different from the above explanation. A few data

points with high AOD values during Spring showed a movement towards to the origin on

the grid box. This suggests that there could be a possible cloud contamination in the

observed AOD.

As compared to the observations during the Spring period, Summer showed a

relative dominance of the coarser particles. The majority of the data cluster in Summer is

present for the α < 1 and dα > 0. Autumn period also showed a bimodal behavior

indicating the presence of both the coarser and finer particles in the total aerosol amount.

However, the majority of the data wing lies between fine fraction 50 % and 90% with the

fine mode radii Rf between 0.1 and 0.2 µm. There is again a movement of the data points

towards the higher Rf and indicated the possible growth of the aerosols. This Autumn

behavior has close resemblance to that of Ispra and Beijing (Gobbi et al., 2007).

Certain data points in Autumn with high AOD > 1 move towards the origin as an

indicative of the cloud contamination in the AOD data. Winter period clearly shows the

dominance of the fine mode aerosols. Majority of the data points associated with AOD >

0.6 are observed when the fraction is greater than 70% and less than 99% with Rf varying

between 0.1 to 0.3 µm. This is a clear indication of the growth of the aerosols. This

prominent behavior is not observed in Spring or Autumn. As compared to Hyderabad

(Sinha et al., 2012) and Kanpur (Gobbi et al., 2007) the hygroscopic growth and/or

coagulation of the particles are much evident in the Winter period over Gadanki.


95

a)

b)

c)

d)

Figure 3.22. Angstrom exponent difference (dα = α(400,675)-α(675,870)) versus α plotted for different seasons over Gadanki. The color code is based on the AOD values at 500 nm.

3.9. Summary

In this chapter the temporal variations of Physical and Optical properties of aerosols

are discussed in detail. The aerosol properties such as AOD, SSA, size distribution for a

period of 3 years from April 2008 to March 2011 are retrieved from the Sky-radiometer

and the micro-physical properties of the surface aerosols such as the aerosol number

concentration, mass concentration, aerosol number size distribution and particle effective

radius are derived from the Aerodynamic Particle Sizer (APS). In spite of being a rural

site, the average AOD at 0.5 µm for the entire period of study is high with a value of 0.46


96

± 0.23 and is comparable to many other urban places in South Asia. The AOD value

reaches to a further high value of 0.54 ± 0.24 during the peak biomass burning season.

The spectral variations of the AOD are highly seasonal with a steep curve response in

Spring (March to May) and a flat curve response in Summer (June to August) and are

indicative of the presence of the finer mode and coarser mode aerosols respectively.

Angstrom exponent (α) which is an indicator of the spectral dependence of AOD and a

proxy for the particle size indicator is also highly season dependent with the monthly

median values vary from 0.3 during Summer to 1.5 during Winter. Properties of aerosols

exhibited high seasonality and are shown to respond to the changes in the direction of air-

masses and the dynamics of the atmosphere. An attempt has been made in the study to

classify the aerosol types using the information of AOD and Angstrom Exponent. Further,

aerosol associated processes such as condensation and coagulation are further

investigated by implementing a graphical frame work scheme proposed by Gobbi et al.

(2007) that uses the information of α and dα (dα=α(400,675)-α(675,870)).

Biomass burning season exerts considerable influence on aerosol optical

properties. Monthly median absorption optical depth at 500 nm varies from near zero in

post monsoon period to 0.075 in peak biomass burning season. Surface and columnar

number size-distribution is characterized by abrupt changes in size-distribution spectra

with respect to the seasons. The median surface aerosol size distribution showed a

maximum in the Winter and Summer for sub-micrometer and super-micrometer size

range respectively. Peak around 4 µm in the aerosol size distribution during Summer

showed the possible prevalence of biological particles. Multiple peaks at higher particle

diameter in Spring suggest the prevalence of dust particles. In a comparison of surface

size distribution and columnar size distribution, there is no correlation found for smaller

size particles but a moderate correlation is found for coarse-mode particles indicating a

vertical in-homogeneity in aerosol types. High values of the total number concentration

are observed in Winter and low values are observed in Summer. The diurnal variations of

the surface total particle number concentrations for different seasons showed two peak

behavior, one in day time (5:00 to 11:00 LT) and one in night time (19:00-22:00 LT). The

median mass concentrations derived from APS for PM10 and PM2.5 for the entire period

of the study is less as compared to many other places, with the mean surface total mass


97

concentration is 28 µg/m3.

The local meteorological variations in wind and rain has influence on the observed

variations of the aerosol properties. The affects of winds on the particle number

concentration showed a dilution effect for the entire period of study. Summer showed U-

Type dependence between the wind speed and the total number concentration. The affects

of rain on the particle number concentration show a seasonal dependence. Spring and

Summer produced more particles during the rain events where as the Autumn showed the

cleansing effect of rain on the ambient particle number concentration. Below-cloud

scavenging of aerosols on temporal scale from hours to days is ineffective in reducing

aerosol number concentration in size range 0.3 µm to 10 µm. However, a systematic and

significant decrease is found in AOD between before and after rain event. This is possible

because AOD is reduced by both in-cloud and below-cloud scavenging and there is

significant contribution of aerosol of diameter less than 0.3 µm which are efficiently

removed in wet-scavenging. Nevertheless the observed scavenging is significantly lower

than model values.

chapter 3 - shodhgangashodhganga.inflibnet.ac.in/.../101357/13/13_chapter3.pdfhyderabad (kaskaoutis...

Documents