1 ff5 fs17 fc0 sc0 ls0">
(3)
where 1
a
and 2
a
are thsol opte aeroical depths at 1
and λ
mon verts
t 440 nm and 870 nm for the four
st
sphere due to frequent
ra
ows the scatter
pl
2
The thly aage Angstrom wavelength exponen
derived from AOD a
, respectively.
ations, analyzed over the whole period of observations,
are shown in Figure 7. These data show that the
values
at Chiang Mai, Ubon Ratchathani and Nakhon Pathom
reach a maximum value in the dry season and minimum
value in the wet season. This suggests that fine aerosols
are more prevalent than large aerosols in the dry season,
while the coarse aerosols are more predominant in the
wet season. The dry season trend can be explained by the
large biomass burning that occurs as a by-product of
seasonal agricultural clean-up activities across the North,
Northeast and the Central region.
In comparison, the wet season has a reduction in bio-
mass burning aerosols in the atmo
in and a halt in agricultural clean-up activities. Most
biomass burning aerosols in the atmosphere are washed
out by the heavy rains. The Songkhla station shows no
seasonal peak of
, as is expected for a maritime station
with mostly sea salt aerosols year round.
The importance of particle sizes in driving the AOD is
further illustrated in Figure 8. Figure 8 sh
ot of daily average AOD and
at the four stations in
Thailand. The data have been organized into dry season
(from November to April), and wet season (from May to
October), for Chiang Mai, Ubon Ratchathani, and Nak-
hon Pathom (Figures 8(a)-(f)), whereas whole year data
were used for Songkhla station (Figure 8(g)). As may be
0.0
0.5
1.0
1.5
2.0
2.5
JANFEB MAR APR MAY JUNJULAUGSEPOCT NOV DEC
M
o
n
t
h
Angstrom wavelength exponent
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
So n
g
khl a
Figure 7. Variation of monthly average of Angstrom wave
length exponent for the four sites. -
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL.
Copyright © 2012 SciRes. ACS
447
observed, the relationships are very different between the
two seasons for Chiang Mai, Ubon Ratchathani and
Nakhon Pathom stations. During the dry season, there are
slight increases in
with a wide range of AOD and most
values of
are higher than those of the wet season. In
the wet season the
values vary greatly over a narrow
range of AOD. This indicates that the aerosols at these
inland stations are small in size during the dry season and
these sizes vary within a small range. This confirms that
the dominant aerosols at these stations in the dry season
are those produced from the biomass burning. In contrast,
during the wet season the
values vary greatly within a
small range of AOD, with most
values lower than those
of the dry season. This means that these stations have
a variety of aerosol sizes in the wet season and the varia-
tion of the aerosol sizes has little effect on AOD. For
Songkhla station, there is a small change in AOD corre-
sponding to changes in
value. Fine particles are scarce
in this environment and changes in
will not affect the
AOD substantially.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.01.0 2.03.0
AOD 500 n
m
Chiang Mai
Dry season (November - April)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0 1.0 2.0 3.0
AOD 500 n
m
Chiang Mai
Wet season (May - October)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0 1.0 2.0 3.0
AOD 500 n
m
Ubon Ratchathani
Dry season (November - April)
(a) (b) (c)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.01.02.03.
0
AOD 500 n
m
Ubon Ratchathani
Wet season (May - October)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.01.02.03.0
AOD 500 n
m
N
akhon Patho
m
Dry season (November - April)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0 1.0 2.0 3.0
AOD 500 n
m
N
akhon Patho
m
Wet season (May - October)
(d) (e) (f)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0
.01.02.03.0
AOD 500 n
m
So ngkhla
Whole year
(g)
Figure 8. Scattered plots of daily averaged aerosol optical th (AOD) and Angstrom wavelength exponent (
) for (a) dep
Chiang Mai in the dry season; (b) Chiang Mai in the wet season; (c) Ubon Ratchathani in the dry season; (d) Ubon
Ratchathani in the wet season; (e) Nakhon Pathom in the dry season; (f) Nakhon Pathom in the wet season; and (g) Songkhla
who le year.
S. JANJAI ET AL.
448
2.3. Single Scattering Albedo
ly referred as SSA, is Single scattering albedo, common
an important aerosol property that also varies with wave-
length. By definition it is the ratio of scattering efficiency
to total extinction efficiency of an aerosol particle that is
exposed to an incident electromagnetic wave as follows:
scatt
σ
SSA (4)
absp scatt
σσ
where SSA is single scattering albedo, is the scat-
icle Size Distribution
nd King [8]
mode the log-normal distribution is defined as:
scatt
σ
tering coefficient and absp
σ is the absorption coefficient.
Following the definitin, a purely scattering aerosol
would have a SSA of 1, while a pure absorber would not
scatter, thus SSA would equal to 0 [51]. Several studies
provide estimates of SSA in various regions of the world
covering different environments. Estimates range from
0.84 to 0.98 for the visible bands [15,49,52,53].
The method developed by Dubovik et al. [54] has been
us
o
ed to derive SSA for our four stations. Figure 9 com-
pares the SSA at 440, 675, 870 and 1020 nm for the four
stations. The SSA data have been organized into dry
season (from November to April), for Chiang Mai, Ubon
Ratchathani, and Nakhon Pathom, whereas whole year
data were used for Songkhla station. The results shows
the SSA values at Chiang Mai ranged from 0.82 to 0.88,
Ubon Ratchathani ranged from 0.88 to 0.92, Nakhon
Pathom ranged from 0.86 to 0.90, and Songkhla ranged
from 0.91 to 0.92. A low SSA relates to a high absorp-
tion value, with the lowest SSA value recorded in Chiang
Mai, it indicates that Chiang Mai has the highest absorp-
tion. The highest SSA values occur at Songkhla which
corresponds to the lowest absorption. The SSA values
show a different origin of aerosols. It can be suggested
that Chiang Mai is the largest city in the North of Thai-
land, and the second largest city in Thailand, so aerosols
in this area are a combination of both pollution from ve-
hicle exhaust emission, industrial processes, construction
activities and smoke from biomass burning. Biomass
burning and combustion of fossil fuels are known to
produce absorbing aerosols, with emissions contributing
to the formation of black carbon aerosols.
Ubon Ratchathani and Nakhon Pathom stations are
similar to Chiang Mai station, but have less pollution
than Chiang Mai, so the SSA values are a little higher.
For Songkhla station in the South of Thailand, the SSA
values are higher than the three inland sites (low absorp-
tion) because aerosols at this station are dominated by
maritime aerosols.
2.4. Aerosol Part
The inversion method described in Dubovik a
and Dubovik et al. [49,54] has been used to derive aero-
sol particle size distribution at the four stations. For each

2
ln
d1
exp
dln 2
2π
v
Vrr
C
V
r









(5)
ddlnVr
e column
where is the aerosol particle size distribution,
CV is thar volume of particles per u
section of atmospheric column, r is the particle radius, rv
olume me
sc
d in this study
by the monsoonal climates of Thai-
son starts in May with predominant
nit cross
is the vdian radius, and σ is the standard devia-
tion.
Figure 10 shows monthly averages of aerosol size dis-
tribution at the four stations. The outstanding feature is
the high frequency of fine particle mode in the inland
stations during the dry season, with maximum modal
values exceeding 0.13 μm3/μm2, and with a radius of
0.15 μm. Modal maxima in fine mode occurred in April
for Chiang Mai, and in March for both Ubon Ratchathani
and Nakhon Pathom.
At the height of the wet season both coarse and fine
peaks are much lower than their counterparts during the
dry season. At this time of the year much of the rain has
avenged out suspended particles also preventing the
majority of biomass burning, and frequent cloud cover
prevents photochemical reactions.
Levels are lower for Songkhla for all months in the
fine mode, not exceeding 0.06 μm3/μm2 and two distinct
peaks were observed throughout the year.
3. Discussion
Most of the processes and patterns discusse
are strongly affected
land. Its wet sea
southwest winds that sweep the country, bringing heavy
rain that intensifies in June/July and ends in October. The
dry season follows, which also coincides with the north-
ern hemisphere winter, bringing northerly air masses from
Siberia under high pressure and cooler in temperature,
especially to the north of the country. It is during this
time of the year that the air quality gradually deteriorates
0.72
0.76
0.80
0.84
0.88
0.92
0.96
Albedo
440 6758701,020
Wavelength (nm)
Single Scattering
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Son
g
khla
Figure 9. Single scattering albedo, SSA at Chiang Mai in
the dry season, Ubon Ratchathani in the dry season, Nak-
hon Pathom in the dry season and Songkhla w hole yea r.
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL. 449
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Son
g
khla
Januar y
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
dV/d(ln r) [m3/m2
Radius [m]
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Son
g
khla
F
ebruary
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius
[
m
]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
S ongkhla
Mar ch
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Son
g
khla
April
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
S ongkhla
May
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
Nakhon Pathom
]
S ongkhla
June
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Nakhon Pathom
S ongkhla
July
0.0 0
0.0 2
0.0 4
0.0 6
0.0 8
0.1 0
0.1 2
0.1 4
0.1 6
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Nakhon Pathom
S ongkh la
August
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
Chiang Mai
]
Ubon Ratchathani
Nakhon Pathom
S ongkhla
September
0. 00
0. 02
0. 04
0. 06
0. 08
0. 10
0. 12
0. 14
0. 16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Son
g
khla
October
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Songkhla
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.05
0.07
0.09
0.11
0.15
0.19
0.26
0.33
0.44
0.58
0.76
0.99
1.30
1.71
2.24
2.94
3.86
5.06
6.64
8.71
11.43
15.00
Radius [m]
dV/d(ln r) [m
3
/m
2
]
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
S ongkhla
December
N
ovember
Figure 10. Mean daily aerosol particle siztribution (dV/d(ln r)) for Chiang Mai, Ubon Ratchathani, Nakhon Pathom, an
Songkhla stations.
ass burning from agricultural clean-up into the atmosphere [48,55,56] and can be particularly
Nwofor et al. [21] in Ilorin,
e disd
as a result of biom
activities, both in Thailand and in neighboring countries,
consistent with forest fires and other human related
sources such as motor vehicle and industrial emissions.
This is especially true for biomass burning in the three
inland regions: the North, Northeast, and the Central ar-
eas of Thailand, where it is a common practice among
rice farmers. The burning of the residual rice straw (RS)
provides a low cost method of waste removal as well as
protection against pests and returning some nutrients to
the soil. This practice releases a large amount of aerosols
problematic during February when the atmospheric con-
ditions in these areas of Thailand are stagnant, leading to
a steady build-up of these aerosols in the atmosphere.
This problem is further compounded in these regions by
an increase in uncontrolled fires both naturally occurring
and from human error.
AODs gradually increased for all inland study sites
during this period, reaching their maximum value to-
wards the end of the dry season in February or March.
Similar studies such as
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL.
450
Ni
r the
fin
9].
Th
il or March for the three inland stations.
lower
th
r positioned at four
Thailand. They are located in the
try (Chiang Mai), in the Northeast
r environment, the AODs and α values
ar
erosols, but also involves synoptic
an
geria, Ogunjobi et al. [25] in West Africa and Pinker
et al. [57] in southwestern US also reported summer
maximum of AOD in late spring and summer.
Most of the increase in AODs for the three inland sta-
tions appears to be dominated by fine particles modes as
evidenced by the high seasonal range in monthly α and
the large changes in the modal maximum values fo
e particle mode. Maximum α values are largest in
March, at the end of the dry season, correlating closely to
the monthly highest AOD, and are lowest in June or July
during the wet season. These maximum monthly esti-
mates of α (1.68 - 1.52) are at the top when compared to
similar studies, such as Pinker et al. [57] in southwestern
United States (α = 1.60), Masmoudi et al. [6] in north
Africa (α = 0.88), Nwofor et al. [21] in Ilorin, Nigeria (α
= 0.70) Ogunjobi et al. [25] in five west African sites (α
= 1.07-0.93 for urban pollution), Eck et al. [58] in south-
ern Africa (α > 1.80 for biomass burning season) and
Schafer et al. [50] in Amazonia (α > 1.80 for smoke).
The lowest SSA (highest absorption) was observed at
440, 675, 870 and 1,020 nm at Chiang Mai (SSA ~0.88 -
0.82) which is similar to the African savanna, Zambia
region (SSA ~0.88 - 0.78) studies by Dubovik et al. [4
e SSA values for Nakhon Pathom (SSA ~0.90 - 0.86)
and Ubon Ratchathani (SSA ~0.92 - 0.88) are very simi-
lar to other biomass burning regions, such as Dubovik et
al. [49] in South American cerrado (SSA ~0.91 - 0.85).
In comparison with other biomass regions, the SSA
measured at Chiang Mai, Ubon Ratchathani, and Nakhon
Pathom are not very high. For example, the SSA at 440,
670, 870 and 1020 nm in the Amazonian forest of Brazil
has a range of 0.92 - 0.90 and Boreal forests of the
United States and Canada have a range of 0.94 - 0.91
[49]. This suggests that the values of SSA depend on the
type and origin of the smoke particles. For example, in
the South American cerrado regions smoke originates
from the combustion of cerrado vegetation and agricul-
tural pasture, while in both Brazil and Africa smoke
originates from savanna ecosystems [49]. For the three
inland regions of Thailand there is a combination of smo-
ke from forest fires and open burning of agricultural
residues such as paddy fields during the dry season as
well as pollution originating from traffic and anthropo-
genic activities. This leads to the values of SSA varying
according to the amount of each type of smoke presented.
For Songkhla station in the South of Thailand the SSA
values are higher than Chiang Mai, Ubon Ratchathani,
and Nakhon Pathom because aerosols at this station are
dominated by maritime aerosols. In comparison with
other oceanic regions, the SSA measured at Songkhla is
lower in value (SSA ~0.92 - 0.91) such as in Lanai, Ha-
waii, which has a range of 0.98 - 0.97 [49]. This can be
explained by biomass burning in Indonesia which causes
a smoke haze to reach and affect the South of Thailand
[59-62].
In agreement with the seasonal trends in α, the fine
mode dominates the pattern of the aerosol size distribu-
tion. Modal maximum values in the fine mode are high-
est in Apr
Songkhla shows different patterns from the rest of the
stations as it is a maritime station with no distinct dry
season. As a result, there is no pronounced maximum in
AOD during the dry season, AOD and α are much
an the other three stations, and fine particles do not
dominate the AOD. It is also interesting to note the sea-
sonal pattern of SSA is related to the higher scatter and
lack of a distinct seasonal minimum.
4. Conclusions
This study has examined data from four Cimel sunpho-
tometers and one Prede sunphotomete
different regions in
North of the coun
(Ubon Ratchathani), the Central part (Nakhon Pathom)
and the South (Songkhla). The AOD, Angstrom wave-
length exponent, SSA and aerosol size distribution over a
time span of about four to five years have been derived
from data obtained from these instruments. Results show
that for the three inland stations (Chiang Mai, Ubon
Ratchathani, and Nakhon Pathom), all optical data are
strongly influenced by the wet/dry climates of the region.
AODs reach their maximum towards the end of the dry
season in March or April, and their minimum in the wet
season from June to September. A similar pattern is ob-
served for the α with highest values in February or March
and lowest in June to September. Furthermore, the AOD
is governed by the fine particle mode, as evidenced by
the strong dependence of AOD on α for high values of
AOD and the large seasonal range in the modal maxima
of fine particles.
Different statistics were obtained for the maritime
southern station of Songkhla. It is not subject to the dry
season influences nor significant biomass burning and it
is a much cleane
e lower and do not show seasonal trends. Similarly, the
SSA does not exhibit a winter minima as does for the
other three stations.
These results point to the variability in aerosol proper-
ties that exist in the tropical environment of Thailand.
This variability stems not only from anthropogenic and
natural emissions of a
d regional weather patterns which are influenced by
the landscape and topography. Eventually, climatologies
need to be developed which will map this variability and
its relationship to the local geography. Until that time, it
is important to maintain and to expand sunphotometer
networks, such as the one described here, which provide
much needed data.
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL. 451
5. Acknowledgements
The authors would like to thank the Thailand Research
Fund for providing financial support to this research
work. The authors would also like to thank Dr. Brent
[2] V. Ramanathan, P. J. Crutzen, J. Lelieveld, A. P. Mitra, D.
Althausen, J. AOcean Experiment:
An Integratede Forcing and Ef-
Holben for valuable advice.
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[7] S. Ramachandran and A. Jayaraman, “Spectral Aerosol
Aerosol Optical Properties at Four Sites in Thailand
Atmospheric and Climate Sciences, 2012, 2, 441-453
http://dx.doi.org/10.4236/acs.2012.24038 Published Online October 2012 (http://www.SciRP.org/journal/acs)
Aerosol Optical Properties at Four Sites in Thailand
Serm Janjai1*, Manuel Nunez2, Itsara Masiri1, Rungrat Wattan1, Sumaman Buntoung1,
Treenuch Jantarach1, Worrapass Promsen1
1Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom, Thailand
2School of Geography and Environment Studies, University of Tasmania, Sandy Bay, Australia
Email: *serm@su.ac.th
Received June 5, 2012; revised July 8, 2012; accepted July 18, 2012
ABSTRACT
This paper presents column integrated aerosol optical properties including aerosol optical depth (AOD), Angstrom
wavelength exponent (), single scattering albedo (SSA), and size distribution from ground-based measurements at four
sites in Thailand: Chiang Mai (18.78˚N, 98.98˚E), Ubon Ratchathani (15.25˚N, 104.87˚E), Nakhon Pathom (13.82˚N,
100.04˚E), and Songkhla (7.2˚N, 100.60˚E). Results show a marked seasonal trend in AOD at 500 nm for the first three
stations, with the monthly average maxima of 0.92, 0.78 and 0.61 for Chiang Mai, Ubon Ratchathani and Nakhon
Pathom, respectively. These maxima occur in the dry season (November-April). Minimum values for these stations
were recorded during the wet season (May-October). A similar pattern is exhibited in the
for the three stations, with
maxima in the dry season and minima in the wet season. The lowest SSA values occur at Chiang Mai, which means this
station has the highest absorption, with the highest SSA values occurring at Songkhla which corresponds to the lowest
absorption. The southern station at Songkhla differs from the other three as it has less local pollution sources and is
subjected to the influence of the tropical maritime environment. AOD at Songkhla maintains a low and more constant
value year round with the maximum monthly average AOD of 0.27 and the minimum of 0.16. Diurnal changes in AOD
at the four stations are discussed and related to various external variables.
Keywords: Erosol Optical Properties; Measurements; Sunphotometers; Thailand; Tropical Environments
1. Introduction
Aerosols are small solid or liquid airborne mass, or parti-
cles, that remain suspended in the air and move with the
motion of the air within broad limits [1]. They typically
range in size from sub-micron for the smallest size to 10 -
100 microns for the giant particles. Their composition
may consist of sulphates, organics, black carbon and dust,
or a combination of them [2]. Biomass burning and fossil
fuel emissions contribute to the formation of black car-
bon aerosols. Sulphate aerosols can be both natural and
anthropogenic. Aerosols affect the global radiative bal-
ance and they may have a crucial role in affecting re-
gional and global climates. Sulphate aerosols predomi-
nantly scatter radiation, while organic and black carbon
aerosols both absorb and scatter solar radiation.
At the regional scale, they may affect atmospheric sta-
bility and the rate of photosynthesis by reducing the
amount of solar radiation reaching the earth’s surface. At
the global scale, they are believed to be efficient scatter-
ing agents, cooling the globe and partly counteracting the
effect of global warming [3]. The short residence time of
aerosols in the atmosphere, in the order of seven days,
and the complexity of aerosol impacts on cloud dynamics
and regional circulation make modeling of these pro-
cesses difficult [2,4].
An estimation of aerosol optical properties from
ground based measurements often involves sunphotome-
ter measurements of direct and diffuse solar radiation in
distinct narrow spectral bands. Relevant optical proper-
ties such as aerosol optical depth (AOD), Angstrom ex-
ponent (
), single scattering albedo (SSA), and size dis-
tribution can be derived from these measurements [5-8].
These properties are known to be highly variable in space
and time, making it difficult to obtain regional climatol-
ogy.
Due to the importance of aerosol optical properties, a
number of studies have been carried out for many parts
of the world [9-36]. However, limited works were fo-
cused on Southeast Asia [37-39]. In the case of Thailand,
a systematic investigation of aerosol optical properties
has been undertaken only for the Bangkok area [40]. The
information on aerosol optical properties in this tropical
country is still very limited, and not sufficient for at-
mospheric research and environmental studies. In respo-
nse to the demand for this information, we have estab-
lished four sunphotometer stations in four main regions
*Corresponding author.
C
opyright © 2012 SciRes. ACS
S. JANJAI ET AL.
442
of Thailand. The data collected from these stations was
analyzed and the results are presented in this paper.
2. Instruments and Data Acquisition
Four sunphotometers have been installed at each of our
four radiation monitoring stations located in different
climatic zones of Thailand. These stations are situated at
Chiang Mai (18.78˚N, 98.98˚E) in the Northern region,
Ubon Ratchathani (15.25˚N, 104.87˚E) in the Northeast-
ern region, Nakhon Pathom (13.82˚N, 100.04˚E) in the
Central region and Songkhla (7.20˚N, 100.60˚E) in the
Southern region (Figure 1). The sunphotometers at
Chiang Mai, Nakhon Pathom and Songkhla are fabri-
cated by Cimel (model CE-318). Two different sunpho-
tometers were employed at Ubon Ratchathani. In the pe-
riod from June 2008 to July 2009 a sunphotometer pro-
duced by Prede Co., Ltd. (model PGS-100) was used and
later replaced by a Cimel sunphotometer (model CE-318)
for the second period (October, 2009-December, 2011).
The Cimel sunphotometers take solar sky radiation
measurements with the almucantar scan at optical air
mass of 4, 3, 2 and 1.7 in both morning and afternoon.
The instruments measure direct solar radiation at the
nominal wavelengths of 340, 380, 440, 500, 675, 870,
940 and 1020 nm and diffuse sky radiances at 440, 675,
870 and 1020 nm, with a bandwidth of 10 nm except 2
nm in the UV [41].
The Prede sunphotometer was equipped with a sun
tracker (Kipp&Zonen, model 2AP). This sunphotometer
measured only the spectral direct solar radiation at the
wavelengths 350 - 1050 nm with a bandwidth of 3.6 nm.
It takes solar radiation measurements every 5 minutes.
This sunphotometer was calibrated by the manufacturer
before and after the measurement period.
All sunphotometers belong to our Department and they
were operated by our staffs. In order to standardize the
aerosol products, the Cimel sunphotometers were incor-
porated into the Aerosol Robotic Network (AERONET)
of NASA. These sunphotometers were calibrated by
AERONET every 1 - 1.5 years, resulting in AOD accu-
racy of ~0.01 - 0.02, with the higher errors in the UV
[42]. AOD data were screened for clouds by the algo-
rithm of Smirnov et al. [43] that relies on the greater
temporal variance of cloud versus aerosol optical depths.
Table 1 and Figure 1 provide more details, including
locations and periods of measurements.
This study examined main aerosol optical properties
captured by these sunphotometers. These are aerosol
optical depth, Angstrom parameters, single scattering
albedo and aerosol size distribution. As the Prede sun-
photometer measured only direct radiation, the data ob-
tained from this instrument was used to derive only AOD
and Angstrom parameters.
(May-October)
N
ortheast monsoon
(November-February)
A
B
C
D
Southwest monsoon
Cimel sunphotometer
at Chiang Mai
Cimel sunphotometer
at Nakhon Pathom
Cimel sunphotometer
at Songkhla
Cimel sunphotometer & Prede sunphotometer
at Ubon Ratchathani
Figure 1. Instruments and locations of the measurement sites. A, B, C and D indicate the main regions of Thailand, namely
the North, the Northeast, the Central and the South, respectively.
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL. 443
Table 1. Station details.
Station name Latitude Longitude Instrument and period of measurement
Chiang Mai 18.78˚N 98.98˚E Cimel sunphotometer (September 2006-July 2011)
Ubon Ratchathani 15.25˚N 104.87˚E Prede sunphotometer (June 2008-July 2009)
and Cimel sunphotometer (October 2009-December 2011)
Nakhon Pathom 13.82˚N 100.04˚E Cimel sunphotometer (August 2006-December 2011)
Songkhla 7.2˚N 100.60˚E Cimel sunphotometer (January 2007-December 2011)
2.1. Aerosol Optical Depth
a) Seasonal pattern
This work focuses on AOD at 500 nm, which is com-
monly used to show the effect of aerosols on solar radia-
tion.
Figures 2(a)-(d) illustrate the daily average AOD
values at 500 nm for Chiang Mai, Ubon Ratchathani, Na-
khon Pathom and Songkhla, respectively. The monthly
average AOD for these four stations are shown in Table
2. AOD values at the three inland stations: Chiang Mai,
Ubon Ratchathani and Nakhon Pathom (Figures 2(a)-(c))
reach their maximum in March then slowly decrease to
reach a minimum in July. The maximum monthly aver-
age AOD values for Chiang Mai, Ubon Ratchathani and
Nakhon Pathom are 0.92, 0.78 and 0.61, respectively.
The period of high values and low values of AOD corre-
spond respectively to the dry season (November-April)
and the wet season (May-October). This is due to the
climate of Thailand being influenced strongly by the
northeast monsoon (November-February) and the south-
west monsoon (May-October). The northeast monsoon
brings dry and cold air to the North, Northeast and Cen-
tral region (see Figure 1). Due to very few rainy days in
March and April, these months are also included in the
dry season. The dry season air temperature increases with
a peak temperature around 30˚C - 35˚C during the day,
which causes an increase in heat convection, leading to
an uplift of dust particles from the soil into the atmos-
phere. Moreover, at this time of the year these regions
and surrounding countries often carry out agricultural
burning and forest clearing activities which produce large
biomass burning smoke concentrations. Therefore, values
of AOD in the dry season are higher. Figure 3 shows the
map from MODIS Rapid Response
(http://aeronet.gsfc.nasa.gov) which reveals fire spots
caused mainly by biomass burning on a representative
day in the dry season.
The southwest monsoon blows from the Andaman Sea
causing overcast conditions and precipitation throughout
the country. Figure 4 shows the variation of rainfall
throughout the year at the four stations. The period:
May-October is known as the wet season for the North,
Northeast and the Central region. The rain causes aero-
sols in the atmosphere of these regions to be washed out
and also causes biomass burning to cease, leading to the
AOD in the wet season to be lower in value.
a) Chiang Mai
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0306090120 150 180210 240270 300330 360
Julian Da
y
AOD
2006
2007
2008
2009
2010
2011
b) Ubon Ratchathani
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0306090120 150180 210 240270 300 330 360
Julian Da
y
AOD
2008
2009
2010
2011
c) Nakhon Pathom
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0306090120150 180 210 240 270300 330 360
Julian Da
y
AOD
2006
2007
2008
2009
2010
2011
d) Songkhla
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0306090120 150 180 210240 270 300330 360
Julian Day
AOD
2007
2008
2009
2010
2011
Figure 2. Mean daily values of aerosol optical depth at the
500 nm wavelength for a) Chiang Mai, b) Ubon Ratchatha-
ni, c) Nakhon Pathom and d) Songkhla.
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL.
444
Table 2. Monthly average aerosol optical depth (AOD) at
500 nm for the entire period of records. The maximum val-
ues are marked in bold.
Month Chiang Mai Ubon
Ratchathani
Nakhon
Pathom Songkhla
January
February
March
April
May
June
July
August
September
October
November
December
0.33
0.51
0.92
0.86
0.36
0.20
0.13
0.13
0.22
0.34
0.30
0.32
0.22
0.59
0.78
0.65
0.36
0.21
0.18
0.16
0.14
0.35
0.21
0.24
0.59
0.58
0.61
0.43
0.31
0.30
0.15
0.13
0.20
0.44
0.40
0.44
0.27
0.23
0.22
0.23
0.25
0.18
0.27
0.18
0.27
0.25
0.16
0.22
Chiang Mai
Ubon Ratchathani
Nakhon Pathom
Songkhla
Figure 3. Map from MODIS showing fire spots in the dry
season on 7 March, 2010.
0
100
200
300
400
500
600
700
800
900
1000
JanFeb Mar AprMayJunJulAugSepOctNovDec
Month
Rainfall (mm)
Chiang Mai
Nakhon Pathom
Ubon Ratchathani
Songkhla
Figure 4. Monthly average of rainfall for the four sites.
Contrary to the pattern for the inland stations, the
southern station at Songkhla (Figure 2(d)) maintains a
low and more constant value of AOD all year round, with
the maximum monthly average of 0.27 and the minimum
of 0.16. It may be explained by the geography of the re-
gion which consists of a thin peninsula along the entire
length of southern Thailand, bordered on the west by the
Andaman Sea and the east by the Gulf of Thailand (see
Figure 1). This leads to an influx of maritime aerosols
carried by the northeast and southwest monsoons. In ad-
dition, the region also has longer period of the wet season,
which lasts until December due to the northeast monsoon
which brings moisture from the Gulf of Thailand to this
region. Consequently, the rain continuously removes
aerosols from the atmosphere during the wet season. Ad-
ditionally, this site is far to the south of the primary bio-
mass burning regions and it is unusual for the regional
wind systems to transport the aerosols to this far south.
b) Diurnal pattern
We used the observational data to compute the diurnal
variation of aerosol optical depth as a percentage depar-
ture from the daily mean during 8:00 - 16:00 h. The
method was similar to that used by Smirnov et al. [44]
and Peterson et al. [45]. Hourly departures from the daily
mean were calculated on a daily basis and further aver-
aged for each hour. The averaging periods for all stations
were performed over the entire years from 2006 to 2011
for Chiang Mai and Nakhon Pathom, 2009 to 2011 for
Ubon Ratchathani and 2007 to 2011 for Songkhla.
Diurnal variations at Chiang Mai (Figu re 5(a)) feature
peak values in the morning between 9:00 to 10:00 fol-
lowed by a slight decrease in optical depths. There are
several possible reasons for this pattern. Mid-morning
hours are usually associated with the break-up of the
daytime inversion which is featured in south-east Asia
[46,47]. This process may lead to rapid convection and
ejection of surface dust into the atmosphere at this time.
At later times concentrations may be governed by the
pattern of deposition vs ejection and is probably de-
pendent on the temperature structure and degree of tur-
bulence. A second possibility is that the departures are
related to motor vehicle emissions. As Chiang Mai is the
largest city in the North of Thailand with a population of
about 1.6 million inhabitants a correspondingly large
number of vehicles are found on its roadways for trans-
portation of goods, tourists and inhabitants. This leads to
the AOD in rush hours to be higher in value as the
greater number of vehicles at these busy times create
higher emissions.
The results for Ubon Ratchathani station is shown in
Figure 5(b). The AOD values is low in the morning with
slight decreases throughout the day until a slight increase
again in the late afternoon. Ubon Ratchathani is a sub-
rural city located in the Northeast of Thailand. The in-
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL. 445
-10 0
-80
-60
-40
-20
0
20
40
60
80
100
891011 12 13 1415 16
Local Time
Percent
d
eparture
f
rom
d
a
il
y
average of AOD at 500 nm (%
)
0.10
0.14
0.18
0.22
0.26
0.30
0.34
0.38
0.42
0.46
0.50
0.54
0.58
0.62
0.66
0.70
0.74
0.78
0.82
AOD
2006 2007
2008 2009
2010 2011
Ave ra
e
(a) Chiang Mai
(a) Chiang Mai
-10 0
-80
-60
-40
-20
0
20
40
60
80
100
891011 12 131415 16
Local Time
Percent departure from daily
average of AOD at 500 nm (%
)
0.04
0.08
0.12
0.16
0.20
0.24
0.28
0.32
0.36
0.40
0.44
0.48
0.52
0.56
0.60
0.64
0.68
0.72
0.76
AOD
2009 2010
2011 Avera
e
(b) Ubon Ratchathani
(b) Ubon Ratchathani
-100
-80
-60
-40
-20
0
20
40
60
80
100
891011 12 13 1415 16
Local Time
Percent departure from daily
average of AOD at 500 nm (%)
0.17
0.21
0.25
0.29
0.33
0.37
0.41
0.45
0.49
0.53
0.57
0.61
0.65
0.69
0.73
0.77
AOD
2006 2007
2008 2009
2010 2011
Avera
g
e
(c) Nakhon Pathom
(c) Nakhon Phthom
-100
-80
-60
-40
-20
0
20
40
60
80
100
8910 11 12 13 141516
Local Time
Percent departure from daily
average of AOD at 500 nm (%
)
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
AOD
2007 2008
2009 2010
2011 Average
(d) Songkhla
(d) Songkhla
Figure 5. Diurnal variation of aerosol optical depth com-
puted hourly as percent departure from daily average for:
(a) Chiang Mai; (b) Ubon Ratchathani; (c) Nakhon Pathom;
and (d) Songkhla. The continuous lines represent the
graphs of each year and the dash line s represent the graphs
strument of this stat
of the mean values.
ion is located near the airport and the
n of
Th
outh of Thailand.
Lo
AOD.
af
DIS
w
area is surrounded by scattered trees and private houses.
It is likely that some local activities in the after noon such
as garbage burnings and local transportations caused by
local people leading to high AOD in late afternoon.
Nakhon Pathom is located in the central regio
ailand. Aerosol optical depth diurnal variation is
within a range of about 20% (Figure 5(c)). In compari-
son with Chiang Mai and Ubon Ratchathani, the AOD
diurnal variation at Nakhon Pathom is relatively stable.
The AOD slightly increases from the morning to the
noon period then slightly decreases until the evening.
This suggests that air temperature increases from morn-
ing time, which causes an increase in heat convection,
leading to an uplift of dust particles from the soil into the
atmosphere causing the maximum AOD values at noon
time followed by a slight decrease until late afternoon.
Moreover, the instrument at Nakhon Pathom is installed
on the roof of the Science building, Faculty of Science,
Silpakorn University, and situated far from the main road.
This building is also surrounded by other buildings
therefore it has low vehicular traffic.
Songkhla station is located in the S
w aerosol loading is evident all year round (average
aerosol optical depth at 500 nm is only 0.22). Average
diurnal variation is about 20% from the mean (Figure
5(d)). The AOD values reach maximum values in the
early morning before slightly decreasing for the rest of
the day with some fluctuations. The relatively high AOD
in the morning is likely due to the local motor vehicle
emissions. When compared with the three inland regions,
this station has a relatively weak diurnal variation.
c) Relation between fire count from MODIS and
Biomass burning is prevalent in Thailand and occurs
ter harvest with the clearance of refuse agricultural
products and prior to new crops being sown. This opera-
tion is performed in the dry months from January to
April, and mainly occurs in the northern part of the
country. The effect of biomass burning on aerosol prop-
erties has been documented in the literature [42,48-50]
and generally leads to high AOD levels, higher percent-
age of fine mode particles, an increase in the Angstrom
exponent and a lowering of the single scattering albedo.
The seasonal AOD pattern in Figure 2 suggests that this
process is important in the region. In this section, we link
the AOD trends to MODIS data on bush fire events.
Images of fire pixels were obtained from the MO
eb site (http://lance-modis.eosdis.nasa.gov) with one
image being shown in Figure 3. Our procedure was to
draw a circle of 200 km around each of the four stations
and count the total number of fire pixels within the cir-
cles. These were averaged over the month so as to arrive
at a monthly average fire count for each of the four
months January to April, for each station and for each
Copyright © 2012 SciRes. ACS
S. JANJAI ET AL.
446
year.
Figure 6(a) shows monthly average fire counts for
C
2.2. Angstrom Wavelength Exponent
length expo-
hiang Mai featuring a maximum fire count in March
which is a similar trend to that observed with the AOD
for Chiang Mai (Figure 2(a)). Similar trends were ob-
served for Ubon Ratchathani and Nakhon Pathom but no
trends were observed for Songkhla due to the absence of
a dry season and infrequent fire burning in its vicinity.
The strong relationship between monthly average fire
counts and monthly average AOD (500 nm) is shown in
Figure 6(b).
The Angstrom parameters consist of wave
nent (
) and turbidity coefficient (
). The wavelength
exponent provides a general characterization of the aer-
osol size distribution. Large values of
represent smaller
particles, conversely smaller values of
represent larger
particles. Angstrom turbidity coefficient indicates aero-
sol concentration. Large values of
represent high aero-
sol and vise-versa. The
and
can be calculated ac-
cording to the classical equation of Angstrom [5]:
a

(1)
where a
is AOD at wavelengt
yinosol op-
tic
h
in microns.
Applg Equation (1) to the calculation of aer
al depth at 1
and λ2, one can obtain Equations relat-
0
500
1000
1500
2000
2500
3000
3500
JANFEB MAR APR
Mo n th
Fire Count
a)
(a)
R
2
= 0.7039
0.0
0.5
1.0
1.5
2.0
0500010000 15000 20000
Fire Count
Aerosol Optical Dept
h
b)
(b)
Figure 6. (a) monthly averaire counts for Chiang Mai ge f
which shows a maximum in March coinciding with highest
AOD levels; (b) Relation between aerosol optical depth and
fire count in the area with the radius of 200 km centered at
Chiang Mai, Ubon Ratchathani and Nakhon Pathom.
ing
and
to the aerosol optical depth as follows:
1
ln a



2
2
1
ln
a




(2)
and
1
1
a
or 2
2
a
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