Aerosol Optical Properties at Four Sites in Thailand

doi:10.4236/acs.2012.24038

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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.

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)

ortheast monsoon

(November-February)

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.

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

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

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

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.

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

0.22

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.

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-

S. JANJAI ET AL. 445

-10 0

-80

-60

-40

-20

100

891011 12 13 1415 16

Local Time

Percent

eparture

rom

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

(a) Chiang Mai

-10 0

-80

-60

-40

-20

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

(b) Ubon Ratchathani

-100

-80

-60

-40

-20

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

-100

-80

-60

-40

-20

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

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

outh of Thailand.

AOD.

DIS

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

S. JANJAI ET AL.

446

year.

Figure 6(a) shows monthly average fire counts for

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]:











 (1)

where a





 is AOD at wavelengt

yinosol op-

tic



in microns.

Applg Equation (1) to the calculation of aer

al depth at 1



and λ2, one can obtain Equations relat-

500

1000

1500

2000

2500

3000

3500

JANFEB MAR APR

Mo n th

Fire Count

(a)

= 0.7039

0.0

0.5

1.0

1.5

2.0

0500010000 15000 20000

Fire Count

Aerosol Optical Dept

(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:

ln a





























(2)

and















 or 2















 (3)

where 1







and 2







are thsol opte aeroical depths at 1



and λ

mon verts

t 440 nm and 870 nm for the four

sphere due to frequent

ows the scatter

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

Angstrom wavelength exponent

Chiang Mai

Ubon Ratchathani

Nakhon Pathom

So n

khl a

Figure 7. Variation of monthly average of Angstrom wave

length exponent for the four sites. -

S. JANJAI ET AL.

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



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



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



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.

AOD 500 n



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



akhon Patho

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



akhon Patho

Wet season (May - October)

(d) (e) (f)

0.0

0.5

1.0

1.5

2.0

2.5

3.0



.01.02.03.0

AOD 500 n

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

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

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

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



exp

dln 2

2π

Vrr



















(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

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

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.

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

/m

]

Chiang Mai

Ubon Ratchathani

Nakhon Pathom

Son

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

khla

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

/m

]

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

/m

]

Chiang Mai

Ubon Ratchathani

Nakhon Pathom

Son

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

/m

]

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

/m

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

/m

]

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

/m

]

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

/m

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

/m

]

Chiang Mai

Ubon Ratchathani

Nakhon Pathom

Son

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

/m

]

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

/m

]

Chiang Mai

Ubon Ratchathani

Nakhon Pathom

S ongkhla

December

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

S. JANJAI ET AL.

450

r the

fin

9].

il or March for the three inland stations.

lower

r positioned at four

Thailand. They are located in the

try (Chiang Mai), in the Northeast

r environment, the AODs and α values

erosols, but also involves synoptic

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.

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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