Chemical Characterisation of Organic Functional Group Compositions in PM2.5 Collected at Nine Administrative Provinces in Northern Thailand during the Haze Episode in 2013

Abstract

Along with rapid economic growth and enhanced agricultural productivity, particulate matter emissions in the northern cities of Thailand have been increasing for the past two decades. This trend is expected to continue in the coming decade. Emissions of particulate matter have brought about a series of public health concerns, particularly chronic respiratory diseases. It is well known that lung cancer incidence among northern Thai women is one of the highest in Asia (an annual age-adjusted incidence rate of 37.4 per 100,000). This fact has aroused serious concern among the public and the government and has drawn much attention and interest from the scientific community. To investigate the potential causes of this relatively high lung cancer incidence, this study employed Fourier transform infrared spectroscopy (FTIR) transmission spectroscopy to identify the chemical composition of the PM2.5 collected using Quartz Fibre Filters (QFFs) coupled with MiniVolTM portable air samplers (Airmetrics). PM2.5 samples collected in nine administrative provinces in northern Thailand before and after the "Haze Episode" in 2013 were categorised based on three-dimensional plots of a principal component analysis (PCA) with Varimax rotation. In addition, the incremental lifetime exposure to PM2.5 of both genders was calculated, and the first derivative of the FTIR spectrum of individual samples is here discussed.

Full text

Asian Pacic Journal of Cancer Prevention, Vol 14, 2013 3653
DOI:http://dx.doi.org/10.7314/APJCP.2013.14.6.3653
Chemical Characterisation of PM2.5 in Northern Thailand during a Haze Episode
Asian Pacic J Cancer Prev, 14 (6), 3653-3661
Introduction
During the rst two weeks of March 2013, agricultural
waste burning, coupled with forest fires in northern
regions of Thailand, steadily enhanced the levels of air
pollution above the safety limit and produced an eye-
stinging, throat-burning, yellow-tinged haze that reduced
visibility to less than 1,000 metres. A profusion of ne
particles, measuring less than 10 micrometres in diameter
and known as PM10, pervaded the atmosphere, reaching
a peak on March 21st, 2013, at 428 µg m-3 at Mae Hong
Son province. This value was over 3.6 times the acceptable
safety ceiling of 120 µg m-3 and eventually prompted
authorities to issue warnings against outdoor exercise. The
monthly average concentrations of PM10 in March 2013
detected by the Pollution Control Department (PCD) of the
Ministry of Natural Resources and Environment (MNRE)
at Mae Hong Son (186±110 µg m-3), Chiang Rai (122±71
1NIDA Research Center of Disaster Prevention Management, School of Social and Environmental Development, National Institute
of Development Administration (NIDA), 2Bara Scientic Co., Ltd., Bangkok, Thailand, 3SKLLQG, Institute of Earth Environment,
Chinese Academy of Sciences (IEECAS), Xi’an, China *For correspondence: pongpiajun@gmail.com
Abstract
Along with rapid economic growth and enhanced agricultural productivity, particulate matter emissions in
the northern cities of Thailand have been increasing for the past two decades. This trend is expected to continue
in the coming decade. Emissions of particulate matter have brought about a series of public health concerns,
particularly chronic respiratory diseases. It is well known that lung cancer incidence among northern Thai
women is one of the highest in Asia (an annual age-adjusted incidence rate of 37.4 per 100,000). This fact has
aroused serious concern among the public and the government and has drawn much attention and interest
from the scientic community. To investigate the potential causes of this relatively high lung cancer incidence,
this study employed Fourier transform infrared spectroscopy (FTIR) transmission spectroscopy to identify the
chemical composition of the PM2.5 collected using Quartz Fibre Filters (QFFs) coupled with MiniVol™ portable
air samplers (Airmetrics). PM2.5 samples collected in nine administrative provinces in northern Thailand before
and after the “Haze Episode” in 2013 were categorised based on three-dimensional plots of a principal component
analysis (PCA) with Varimax rotation. In addition, the incremental lifetime exposure to PM2.5 of both genders
was calculated, and the rst derivative of the FTIR spectrum of individual samples is here discussed.
Keywords: PM2.5 - FTIR - PCA - incremental lifetime exposure - northern provinces of Thailand
RESEARCH ARTICLE
Chemical Characterisation of Organic Functional Group
Compositions in PM2.5 Collected at Nine Administrative
Provinces in Northern Thailand during the Haze Episode in
2013
Siwatt Pongpiachan1*, Chomsri Choochuay1, Jittiphan Chonchalar1, Panatda
Kanchai1, Tidarat Phonpiboon1, Sornsawan Wongsuesat1, Kanokwan
Chomkhae2, Itthipon Kittikoon2, Phoosak Hiranyatrakul2, Junji Cao3, Sombat
Thamrongthanyawong1
µg m-3), Lampang (123±75 µg m-3), Phrae (123±58 µg m-3)
and Chiang Mai (124±11 µg m-3) were slightly higher than
the PCD 24-h standard. Several scientic studies have
revealed that both long- and short-term exposure to PM2.5
cause premature death and adverse cardiovascular effects,
including enhanced hospitalisations and emergency
department visits for heart attacks and strokes.
Atmospheric aerosol particles range in size over more
than four orders of magnitude, from freshly nucleated
clusters containing a few molecules to cloud droplets and
crustal dust particles up to ten microns in size. Average
particle compositions vary with size, time, and location,
and the bulk compositions of individual particles of a given
size also vary signicantly. The size ranges of particles
measured in urban centres are characterised according to
three modes, with a minimum size between 1.0 µm and
3.0 µm. Particles with size ranges larger than the minimum
size (super-micron particles) are referred to as “coarse”,

Siwatt Pongpiachan et al
Asian Pacic Journal of Cancer Prevention, Vol 14, 2013
3654
whereas smaller particles are called “ne”. The three
modes correspond to the nuclei mode (particles below
0.1 µm), accumulation mode (0.1<Dp<1 µm), and coarse
mode (Dp>1 µm) (Whitby and Sverdrup, 1980). Thus,
ne particles include both the accumulation and nuclei
modes. Because the denition of modes has been based
only on the mass (or volume distribution) of particles,
the boundaries between these modes are not precise. The
location of modes is dependent upon whether the modes
are based on the particle number or surface distribution.
In contrast, aerosols in rural areas are primarily of natural
origin but are moderately inuenced by anthropogenic
sources (Hobbs et al., 1985). Recent studies have shown
an association between PM2.5 and adverse health effects,
such as lung cancer (Pope et al., 2002) and heart attacks
(Brook et al., 2002). Large particles (Dp>20 µm) deposit
in the nasal passages, where they are removed by sneezing
or swallowing. Finer particles, approximately 10 µm in
diameter, can enter the respiratory system, and particles
with diameters of less than 2.5 µm can penetrate even
deeper into the lungs to the alveolar region, where
they can remain for a long period of time (Harrison
and Perry, 1986). In the urban atmosphere, polycyclic
aromatic hydrocarbons (PAHs) are strongly associated
with ne particles (Allen et al., 1996; Cecinato et al.,
1999), although little data are available concerning the
distribution of oxy PAHs within particulate matter (Allen
et al., 1997). It is well known that PAHs are considered
to pose potential health hazards because some PAHs are
known carcinogens (Braga et al., 1999; Farmer et al.,
2003). At ambient temperatures, two-ring naphthalene
exists almost entirely in the gas phase, whereas ve-ring
PAHs and higher-ring PAHs are predominantly adsorbed
onto particles. The intermediate three- and four-ring PAHs
are distributed between the two phases (Zielinska et al.,
2004).
Studies on the distribution of PAHs within the full size
range of urban atmospheric particles found that 97% of
the PAHs with ve or more rings, which were all more
or less completely particle-bound in the atmosphere,
were associated with particles with aerodynamic
diameters (dac)<2.9 µm (Kauup et al., 2000). In the urban
aerosols near the road measured in Guangzhou, China,
approximately 57% of n-alkane and 62% of PAHs were
found in the particle fraction with diameters<0.49 µm (Bi
et al., 2004). The substance loading decreased steadily
with increasing particle size. An average of only 1.2%
of PAHs with ve or more rings were found on large
particles with dac>8.6 µm. In Pasadena, California, Miguel
and Frielander (1978) reported that 72.7% and 76.8%
of B[a]P and 75.7% and 93% of Cor sampled between
October and December 1976, respectively, were present
in particles with aerodynamic diameters in the range of
0.05-0.26 µm. In Hamilton, Ontario, the total particulate
PAHs ranged between 87% and 95% and were found on
particle sizes of 1.1 µm<D<7.0 µm (Katz and Chan, 1980).
The relatively high proportion of particulate PAHs (63%
to 85%) measured in London were found in aerosols with
aerodynamic diameters of less than 1.1 µm, and up to
95% of particulate PAHs in London were associated with
particles under 3.3 µm in diameter (Baek et al., 1991).
Unlike in other developed countries, there are a limited
number of studies concerning the chemical characterisation
of gaseous species and aerosols in Thailand (Thumanu et
al., 2009; Pongpiachan, 2013a; 2013b; Pongpiachan
et al., 2010, 2012a; 2012b; 2012c, 2013a; 2013b). In
this study, the authors postulate that the use of Fourier
Transform Infrared Spectroscopy (FTIR), combined
with numerous analogies of statistical analysis, assist in a
better understanding of the distribution pattern of organic
functional compositions of PM2.5, which can be subjected
to variations in sources and meteorological conditions
in northern Thailand. It is the objective of this study to
demonstrate the general application of organic functional
group analysis by FTIR as an innovative indicator to
chemically characterise PM2.5 at nine provinces in the
northern region of Thailand “before” and “after” the haze
episode in 2013. In addition, the incremental lifetime
exposure to PM2.5 and the application of FTIR spectral
features as an alternative “Biomass Burning” proxy will
be reviewed and discussed.
Materials and Methods
Monitoring sites and sampling period
Monitoring of PM2.5 was conducted at nine locations
and over several time periods. In all instances, the
duration of each sample collection was 24 h. The sampling
campaigns can be grouped according to the observation
period during which they were conducted: campaign І
was carried out before the “haze episode” in the winter
of 2012 (i.e., from the 7th to 22nd of December 2012),
whereas monitoring during campaign П was conducted
in March 2013 (i.e., from the 4th to 19th of March 2013;
see Table 1). Both monitoring campaigns were performed
at nine observatory sites, namely, Chiang-Rai Province
Observatory Site (CROS; The Eight Hotel; E: 593783,
N: 2258302); Chiang-Mai Province Observatory Site
(CMOS; Yupparat School; E: 498805, N: 2077713); Nan
Province Observatory Site (NPOS; Thewarat Hotel; E:
687123, N: 2077209); Phayao Province Observatory Site
(PYOS; Arunothai Coffee House Homestay; E: 594420,
N: 2119215); Mae Hong Son Province Observatory Site
(MHOS; Mae Hong Son Provincial Forestry Office;
E: 391834, N: 2134869); Phrae Province Observatory
Site (PHOS; Nana Charoenmuang Hotel; E: 620935, N:
2006155); Lampang Province Observatory Site (LMOS;
Maemoh Training Center; E: 568200, N: 2020017);
Lamphun Province Observatory Site (LPOS; Lamphun
Provincial Administration Organization Stadium; E:
500441, N: 2052987); and Uttaradit Province Observatory
Site (UTOS; OUM Hotel; E: 615923, N: 1948269) (Figure
1). There were no obstructions in the vicinity of the
sampling equipment, which was strategically positioned
to be accessible to winds from all directions. Monitoring
at group-1 monitoring stations (CROS, PYOS and NPOS),
group-2 monitoring stations (LMOS, PHOS and UTOS)
and group-3 monitoring stations (MHOS, CMOS and
LPOS) was conducted synchronously every day from the
28th of November to the 4th of December 2012, from the
7th to 13th of December 2012 and from the 16th to 22nd of
December 2012, respectively, as summarised in Table 1.

Asian Pacic Journal of Cancer Prevention, Vol 14, 2013 3655
DOI:http://dx.doi.org/10.7314/APJCP.2013.14.6.3653
Chemical Characterisation of PM2.5 in Northern Thailand during a Haze Episode
Sampling equipment
MiniVol™ portable air samplers (Airmetrics) were
used to collect PM2.5 over a 24 h period at nine sampling
sites. The MiniVol’s pump draws air at 5 litres minute-1
through a particle size separator (impactor) and then
through a 47 mm lter. A 2.5 micron particle separation
is achieved by impaction. Neither PM10 nor TSP samples
were collected for this study.
FTIR analysis
It is well known that Fourier Transform Infrared
(FTIR) spectrophotometry provides at least three major
advantages. These advantages include a multiplex
advantage (i.e., the accumulation of the results is
accomplished by measuring a spectrum of all wavenumbers
with relatively high scanning speed); an aperture
advantage (i.e., the sensitivity of FTIR results depends
on the aperture area and the incident angle of light); and
a wavenumber accuracy advantage (i.e., the laser emits
extremely stable monochromatic light, generating a
spectrum with high wavenumber accuracy). In this study,
IR-Afnity-1 Shimadzu was chosen because it possesses
the following traits: (i) high measurement sensitivity,
(ii) measurement of samples with low transmittance,
(iii) higher speed measurement, (iv) higher wavenumber
accuracy and (v) highly accurate spectrum subtraction.
The process of FTIR transmission spectroscopy
involves detecting the absorption of an infrared beam that
is passed directly through the sample and through Quartz
Fibre Filters (QFFs). Because the resulting absorption
bands are unique to specic functional groups and because
these bands are proportional to the amount of sample
present, a functional group analysis can be performed
using FTIR to provide quantitative and qualitative
information. Subtraction of the blank (i.e., the spectrum of
the empty QFFs) from the ltered sample was performed
for each sample. Each spectrum was determined by
averaging 16 scans at a resolution of 2 cm-1. The spectra
describe the absorbance of radiation as a function of
wavenumber, which ranges from 4,000-400 cm-1. For
more details on the quantication of identied functional
groups in PM2.5 samples collected on QFFs, please refer to
a previous publication by Krost and McClenney (1994).
Statistical analysis
SPSS version 13 was used to calculate the average
values, standard deviations and principal component
analysis with Varimax rotation of the data set.
Results and Discussion
PM2.5 concentrations
All PM2.5 samples were identied successfully in both
sampling campaigns (n=126). Table 1 summarises the
average concentrations of PM2.5 measured in the seven
samples taken before and after the “Haze Episode” at
CROS, PYOS, NPOS, LMOS, PHOS, UTOS, MHOS,
CMOS and LPOS. The atmospheric concentrations of
individual PM2.5-before the “Haze Episode” collected from nine
observatory sites varied from 1.38 to 100.69 μg m-3, with
an average of 35.16±19.59 μg m-3, whereas the PM2.5-after
the “Haze Episode” samples ranged from 20.69 to 517.24 μg m-3,
with an average of 96.14±47.27 μg m-3. The seven-day
PM2.5 average was found to be the highest at MHOS after
the “Haze Episode,” with an atmospheric concentration of
209.85±91.67 μg m-3, whereas the lowest concentration
was observed at CROS, with an average value of
14.98±6.09 μg m-3. It is interesting to note that the PM2.5-
Figure 1. Maps of Air Quality Observatory Sites at Nine Provinces of Upper Part of Northern Thailand

Siwatt Pongpiachan et al
Asian Pacic Journal of Cancer Prevention, Vol 14, 2013
3656
Table 1. PM2.5 Concentrations Before and After the “2013 Haze Episode” at Nine Provinces of Northern Part
of Thailand
Sampling
Site
PM2.5 before the
“haze episode”
PM2.5 after the
“haze episode”
Change in PM2.5 Mass Concentration
Sampling
Date
PM2.5
(μg m-3)
Sampling
Date
PM2.5
(μg m-3)
PM2.5 after-PM2.5 before
(μg m-3)
Percentage Change
(%)
CROS 11/28/12
11/29/12
11/30/12
12/1/12
12/2/12
12/3/12
12/4/12
12.41
15.17
24.83
8.28
20.69
13.79
9.66
2/23/56
2/24/56
2/25/56
2/26/56
2/27/56
2/28/56
3/1/56
85.52
59.31
74.48
84.14
99.31
102.07
137.93
73.10
44.14
49.66
75.86
78.62
88.28
128.28
Increase
Increase
Increase
Increase
Increase
Increase
Increase
589
291
200
917
380
640
1,329
Average 14.98±6.09 91.82±30.90 76.85±30.47 Increase 513±350
PYOS 11/28/12
11/29/12
11/30/12
12/1/12
12/2/12
12/3/12
12/4/12
15.17
9.66
22.07
16.55
8.28
17.93
34.48
2/23/56
2/24/56
2/25/56
2/26/56
2/27/56
2/28/56
3/1/56
62.07
62.07
93.79
97.93
99.31
140.69
142.07
46.90
52.41
71.72
81.38
91.03
122.76
107.59
Increase
Increase
Increase
Increase
Increase
Increase
Increase
309
543
325
492
1,100
685
312
Average 17.73±9.30 99.70±41.37 81.97±34.81 Increase 462±305
NPOS 11/28/12
11/29/12
11/30/12
12/1/12
12/2/12
12/3/12
12/4/12
24.83
20.69
17.93
27.59
13.79
26.21
35.86
2/23/56
2/24/56
2/25/56
2/26/56
2/27/56
2/28/56
3/1/56
46.90
59.31
52.41
64.83
102.07
107.59
106.21
22.07
38.62
34.48
37.24
88.28
81.38
70.34
Increase
Increase
Increase
Increase
Increase
Increase
Increase
89
187
192
135
640
311
196
Average 23.84±9.66 77.04±36.95 53.20±31.03 Increase 223±174
LMOS 12/7/12
12/8/12
12/9/12
12/10/12
12/11/12
12/12/12
12/13/12
12.41
33.10
35.86
26.21
33.10
23.45
38.62
3/4/56
3/5/56
3/5/56
3/6/56
3/7/56
3/8/56
3/9/56
57.93
55.17
64.83
84.14
66.21
75.86
146.21
45.52
22.07
28.97
57.93
33.10
52.41
107.59
Increase
Increase
Increase
Increase
Increase
Increase
Increase
367
67
81
221
100
224
279
Average 28.97±11.37 78.62±34.16 49.66±28.09 Increase 171±116
PHOS 12/7/12
12/8/12
12/9/12
12/10/12
12/11/12
12/12/12
12/13/12
52.41
64.83
71.72
100.69
78.62
66.21
56.55
3/4/56
3/5/56
3/5/56
3/6/56
3/7/56
3/8/56
3/9/56
30.34
53.79
67.59
49.66
517.24
91.03
120.00
-22.07
-11.03
-4.14
-51.03
438.62
24.83
63.45
Decrease
Decrease
Decrease
Decrease
Increase
Increase
Increase
42
17
6
51
558
38
112
Average 70.15±28.69 132.81±155.33 62.66±148.37 Increase 89±222
UTOS 12/7/12
12/8/12
12/9/12
12/10/12
12/11/12
12/12/12
12/13/12
49.66
46.90
73.10
64.83
68.97
17.93
28.97
3/4/56
3/5/56
3/5/56
3/6/56
3/7/56
3/8/56
3/9/56
20.69
23.45
75.86
68.97
67.59
88.28
78.62
-28.97
-23.45
2.76
4.14
-1.38
70.34
49.66
Decrease
Decrease
Increase
Increase
Decrease
Increase
Increase
58
50
4
6
2
392
171
Average 50.05±24.58 60.49±31.99 10.44±33.24 Increase 21±132
MHOS 12/16/12
12/17/12
12/18/12
12/19/12
12/20/12
12/21/12
12/22/12
64.83
22.07
33.10
38.62
23.45
33.10
26.21
3/13/56
3/14/56
3/15/56
3/16/56
3/17/56
3/18/56
3/19/56
171.03
212.41
204.14
204.14
184.83
182.07
310.34
106.21
190.34
171.03
165.52
161.38
148.97
284.14
Increase
Increase
Increase
Increase
Increase
Increase
Increase
164
862
517
429
688
450
1,084
Average 34.48±17.50 209.85±91.67 175.37±85.26 Increase 509±619
CMOS 12/16/12
12/17/12
12/18/12
12/19/12
12/20/12
12/21/12
12/22/12
20.69
38.62
22.07
52.41
53.79
27.59
46.90
3/13/56
3/14/56
3/15/56
3/16/56
3/17/56
3/18/56
3/19/56
60.69
53.79
52.41
48.28
57.93
88.28
131.03
40.00
15.17
30.34
-4.14
4.14
60.69
84.14
Increase
Increase
Increase
Decrease
Increase
Increase
Increase
193
39
137
8
8
220
179
Average 37.44±16.78 70.34±35.96 32.91±31.05 Increase 88±85

Asian Pacic Journal of Cancer Prevention, Vol 14, 2013 3657
DOI:http://dx.doi.org/10.7314/APJCP.2013.14.6.3653
Chemical Characterisation of PM2.5 in Northern Thailand during a Haze Episode
before the “Haze Episode” concentrations, in decreasing order, are
PHOS (70.15±28.69 μg m-3)>UTOS (50.05±24.58 μg
m-3)>LPOS (38.82±17.81 μg m-3)>CMOS (37.44±16.78 μg
m-3)>MHOS (34.48±17.50 μg m-3)>LMOS (28.97±11.37
μg m-3)>NPOS (23.84±9.66 μg m-3)>PYOS (17.73±9.30
μg m-3)>CROS (14.98±6.09 μg m-3), whereas those after the
“Haze Episode” are MHOS (209.85±91.67 μg m-3)>LPOS
(140.69±62.27 μg m-3)>PHOS (132.81±155.33 μg
m-3)>PYOS (99.70±41.37 μg m-3)>CROS (91.82±30.90 μg
m-3)>LMOS (78.62±34.16 μg m-3)>NPOS (77.04±36.95 μg
m-3)>CMOS (70.34±35.96 μg m-3)>UTOS (60.49±31.99
μg m-3) (see Table 1). It is also worth mentioning that
the percentage increases of PM2.5, in descending order,
are CROS (513±350%)>MHOS (509±619%)>PYOS
(462±305%)>LPOS (262±277%)>NPOS
(223±174%)>LMOS (171±116%)>PHOS
(89±222%)>CMOS (88±85%)>UTOS (21±132%).
Characteristics of FTIR spectra and their rst derivatives
As illustrated in Figure 3 and Figure 4, PM2.5-before the
“Haze Episode” has more complicated FTIR spectra coupled
with their rst derivatives than those of PM2.5-after the “Haze
Episode”. The main IR absorption band of PM2.5-before the “Haze
Episode” in samples collected at PYOS, CMOS, NPOS and
MHOS appears at approximately 405 cm-1, 430 cm-1,
463 cm-1 and 496 cm-1, respectively. These FTIR spectra
characteristics reect more complicated chemical mixtures
released from the unique emission sources of each
province before the “Haze Episode”. In normal conditions,
both “anthropogenic activities” and “natural emissions”
appear to play an important role in governing the organic
functional group content of PM2.5. In contrast, the major
IR absorption band of PM2.5-after the “Haze Episode” in samples
collected at MHOS, PYOS, PHOS and CROS occurs at
approximately 405 cm-1, 409 cm-1, 409 cm-1 and 486 cm-
1, respectively. These results indicate that PM2.5-after the “Haze
Episode” is comparatively more homogeneous than PM2.5-before
the “Haze Episode”. This homogeneity was possibly caused by
the overwhelming amount of biomass-burning aerosols
present in the nine administrative northern provinces after
the “Haze Episode”.
Occupational exposure of outdoor workers to PM2.5
To assess the health risks associated with the
occupational exposure of outdoor workers to PM2.5, the
incremental lifetime particulate matter exposure (ILPE)
model was employed and dened as follows:
ILPE=C5IR5t5EF5ED Equation 1
ILPE=Incremental lifetime particulate matter exposure
(g), C=PM2.5 concentrations (µg m-3), IR=Inhalation rate
(m3 h-1), t=Daily exposure time span (6 h d-1, for two
shifts), EF=Exposure frequency (250 d year-1 a, upper-
bound value), and ED=Exposure duration (25 yearsa,
upper-bound value). Note: aAdapted from Human Health
Evaluation Manual (US EPA, 1991).
According to the methods for the derivation of
inhalation dosimetry (US EPA, 1994), the inhalation
rates of male and female outdoor workers were estimated
to be 0.89 and 0.49 m3 h-1, respectively. The ILPE model
was adapted from the probabilistic incremental lifetime
cancer risk (ILCR) model, which was used to assess
trafc policemen’s exposure to PAHs during their work
in China (Hu et al., 2007). The estimated ILPE levels
in outdoor workers are summarised in Table 2. The
predicted ILPE of PM2.5 was consistently highest in both
genders at MHOS after the “Haze Episode”, with average
values of 7,004±3,059 mg and 3,856±1,684 mg for PM2.5
Figure 2. Spatial and Temporal Distribution of Wind Directions and Hot Spots in Thailand from 28th February
to 30th March 2013 (ASEAN Regional Specialized Meteorological Centre, Singapore)

Siwatt Pongpiachan et al
Asian Pacic Journal of Cancer Prevention, Vol 14, 2013
3658
accumulated in male and female workers, respectively,
over an exposure duration of 25 years. These increased
concentrations of PM2.5 after the “Haze Episode” may
have several explanations, including not only vehicular
and other industrial emissions but also agricultural
waste burning in suburban regions. It is well known that
uncontrolled biomass burning and forest res have been
found to be signicant sources of ambient PM2.5 in Mae
Hong Son and other northern cities over recent decades,
particularly during cold periods. An inversion is more
likely to arise during the winter when the angle of the sun
is very low, predominantly in mountainous provinces such
as Mae Hong Son, and could theoretically be accountable
for the rise of PM2.5 content during the observation periods
from the 13th to 19th of March 2013. The relatively low
ILPE of PM2.5 observed at CMOS (i.e., 2,348±1,200 mg
and 1,292±661 mg for males and females, respectively)
highlights the effects of anti-burning and smog prevention
programs proactively enacted by local administration
organisations since November 2011. It is also interesting
to note that the ILPE of PM2.5 at CROS after the “Haze
Episode” (i.e., 3,064±1,031 mg and 1,687±568 mg for
males and females, respectively) is approximately six
times higher than that during normal conditions (i.e.,
500±203 mg and 275±112 mg for males and females,
respectively). This difference in the ILPE of PM2.5 at CROS
can be explained by the occurrence of comparatively more
hot spots during the monitoring period after the “Haze
Episode” (Figure 2).
Pearson correlation analysis
Pearson correlation coefcients of averaged FTIR
spectra of PM2.5 collected before and after the “Haze
Episode” are displayed in Table 3. Only three combinations,
namely CROS-LMOS (R=0.83), CROS-LPOS (R=0.88)
and LMOS-LPOS (R=0.71), have R-values higher than
0.7 before major biomass burning events (Table 3).
Interestingly, relatively high correlations between FTIR
spectra of CROS-LMOS (R=0.98), CROS-LPOS (R=0.95),
CROS-CMOS (R=0.76), CROS-NPOS (R=0.95), LMOS-
LPOS (R=0.97), LMOS-CMOS (R=0.72), LMOS-NPOS
(R=0.98), LPOS-CMOS (R=0.72), LPOS-NPOS (R=0.99),
CMOS-NPOS (R=0.71) and PYOS-PHOS (R=0.99)
were observed after the forest re events. As previously
mentioned, these ndings are consistent with the FTIR
spectra results together with their corresponding rst
derivatives, emphasising the strong dominance of biomass
burning over the chemical characteristics of organic
functional groups in PM2.5 (Figure 3 and Figure 4). It is
worth mentioning that the R-values higher than 0.7 are
combinations of administrative provinces that share the
same border lines and/or vegetation types. In contrast,
relatively low R-values (less than 0.3) were observed at
administrative provinces that have strong dissimilarities
in both geographical conditions and forest types, such
as CMOS-MHOS (R=0.20), MHOS-PYOS (R=0.25),
MHOS-PHOS (R=0.25) and MHOS-UTOS (R=0.22) (see
Table 3).
Principal component analysis
PCA is employed as a multivariate analytical tool
Table 2. Statistical Description of the Inhaled
Particulate Mass of PM2.5 at Nine Administrative
Provinces in Northern Part of Thailand*
Before the “Haze Episode” After the “Haze Episode”
PM2.5 (mg) PM2.5 (mg) PM2.5 (mg) PM2.5 (mg)
Male Female Male Female
CROS 500±203 275±112 3,064±1,031 1,687±568
LMOS 967±379 532±209 2,624±1,140 1,445±628
LPOS 1,296±594 713±327 4,696±2,078 2,585±1,144
CMOS 1,250±560 688±308 2,348±1,200 1,292±661
MHOS 1,151±584 634±322 7,004±3,059 3,856±1,684
PYOS 592±310 326±171 3,327±1,381 1,832±760
NPOS 796±322 438±178 2,571±1,233 1,416±679
PHOS 2,341±958 1,289±527 4,433±5,184 2,440±2,854
UTOS 1,670±820 920±452 2,019±1,068 1,112±588
*Inhaled particulate mass over exposure duration of 25 years. **Values represent
average±standard deviation
Figure 3. FTIR Spectrum of PM2.5 from Wave Number
400 to 600 cm-1
Figure 4. First Derivative of FTIR Spectrum of PM2.5
from Wave Number 400 to 600 cm-1

Asian Pacic Journal of Cancer Prevention, Vol 14, 2013 3659
DOI:http://dx.doi.org/10.7314/APJCP.2013.14.6.3653
Chemical Characterisation of PM2.5 in Northern Thailand during a Haze Episode
0
25.0
50.0
75.0
100.0
Newly diagnosed without treatment
Newly diagnosed with treatment
Persistence or recurrence
Remission
None
Chemotherapy
Radiotherapy
Concurrent chemoradiation
10.3
0
12.8
30.0
25.0
20.3
10.1
6.3
51.7
75.0
51.1
30.0
31.3
54.2
46.8
56.3
27.6
25.0
33.1
30.0
31.3
23.7
38.0
31.3
0
25.0
50.0
75.0
100.0
Newly diagnosed without treatment
Newly diagnosed with treatment
Persistence or recurrence
Remission
None
Chemotherapy
Radiotherapy
Concurrent chemoradiation
10.3
0
12.8
30.0
25.0
20.3
10.1
6.3
51.7
75.0
51.1
30.0
31.3
54.2
46.8
56.3
27.6
25.0
33.1
30.0
31.3
23.7
38.0
31.3
Table 3. Pearson Correlation Coefcients of Averaged FTIR Spectrum of PM2.5 Collected during Periods of “before” and “after” the Haze Episode
*B-CROS B-LMOS B-LPOS B-CMOS B-MHOS B-PYOS B-NPOS B-PHOS B-UTOS A-CROS A-LMOS A-LPOS A-CMOS A-MHOS A-PYOS A-NPOS A-PHOS A-UTOS
B-CROS 1.000
B-LMOS 0.831 1.000
B-LPOS 0.876 0.705 1.000
B-CMOS 0.014 0.105 -0.093 1.000
B-MHOS 0.086 0.116 -0.091 0.200 1.000
B-PYOS -0.046 0.046 -0.182 0.240 0.308 1.000
B-NPOS 0.031 0.111 -0.059 0.239 0.282 0.218 1.000
B-PHOS 0.030 0.052 0.048 0.222 0.396 0.309 0.362 1.000
B-UTOS 0.031 0.094 -0.033 0.162 0.198 0.296 0.172 0.466 1.000
**A-CROS 0.817 0.830 0.663 0.091 0.109 0.097 0.017 0.008 -0.004 1.000
A-LMOS 0.764 0.790 0.580 0.134 0.154 0.210 0.031 0.017 0.013 0.975 1.000
A-LPOS 0.742 0.794 0.523 0.160 0.209 0.209 0.055 0.028 0.063 0.948 0.973 1.000
A-CMOS 0.758 0.791 0.609 0.032 0.037 0.043 0.005 -0.052 0.195 0.760 0.724 0.717 1.000
A-MHOS 0.189 0.281 0.054 0.013 0.028 0.605 0.055 -0.053 -0.021 0.311 0.406 0.362 0.202 1.000
A-PYOS 0.373 0.440 0.236 0.028 0.056 0.286 -0.001 -0.072 0.011 0.390 0.422 0.404 0.451 0.246 1.000
A-NPOS 0.736 0.774 0.509 0.145 0.209 0.246 0.049 0.032 0.056 0.953 0.978 0.986 0.710 0.397 0.428 1.000
A-PHOS 0.388 0.448 0.248 0.033 0.049 0.267 -0.001 -0.084 0.004 0.399 0.429 0.406 0.455 0.251 0.991 0.437 1.000
A-UTOS 0.650 0.662 0.503 0.233 0.090 0.037 0.014 -0.039 0.099 0.692 0.653 0.650 0.741 0.218 0.393 0.643 0.403 1.000
*B: Before, **A: After
Figure 5. Three-Dimensional Plots of PC1, PC2 and
PC3 Using FTIR Spectrum from Wave Number 400
to 4000 cm-1
to reduce a set of original variables (measured FTIR
spectra in PM2.5 samples) and to extract a small number
of latent factors (principal components, PCs) to analyse
relationships among the observed variables. Data
submitted for this analysis were arranged in a matrix,
where each column corresponds to one FTIR spectrum and
each row represents the number of samples. Data matrixes
were evaluated through PCA, allowing the summarised
data to be further analysed and plotted. To enable further
interpretation of potential sources of organic functional
groups in PM2.5, a PCA model with three signicant
principal components (PCs) was calculated. Each PC of
this model represented 43.06%, 13.67% and 9.85% of the
variance, thus accounting for 66.58% of the total variation
in the data.
These correlation coefcients of each PC are quite
useful and provide valuable information that can be
used to identify biomass-burning aerosols. However,
these correlation coefcients, as well as source proles,
should be used with great caution because physiochemical
processes can alter chemical composition distribution
patterns during transport from the emission source to
the receptor site. To minimise the above-mentioned
uncertainties, the plots of three-dimensional PCs can
be used as a tool to characterise aerosol types during
the sampling period. The clearest features displayed
in Figure 5 are described as follows: (i) 3D plots of
B-CMOS, B-MHOS, B-PYOS, B-NPOS and B-PHOS,
suggesting that the majority of PM2.5 before the “Haze
Episode” had similar organic functional compositions;
(ii) 3D plots of groups of PM2.5 after the “Haze Episode”
are highly clustered, indicating that the majority of PM2.5
shared comparable chemical compositions during the
biomass-burning events; (iii) there are distinctly different
sources of PM2.5 before and after the “Haze Episode”,
indicating that FTIR spectra, coupled with PCA, can
successfully distinguish “background aerosols” from
“biomass-burning aerosols”; and (iv) the 3D plot of the
B-LPOS strongly deviates from the group in both sampling
campaigns, implying that other unique local emission

Siwatt Pongpiachan et al
Asian Pacic Journal of Cancer Prevention, Vol 14, 2013
3660
sources might be signicant in Lamphun province.
In conclusions, FTIR spectra of PM2.5 collected before
and after the “Haze Episode” at nine administrative
provinces in northern Thailand were analysed by IR-
Afnity-1 Shimadzu. In general, PM2.5-before the “Haze Episode”
samples have more complicated FTIR spectra than those
of background aerosols, emphasising that “Biomass
Burning” played an important role in governing aerosol
chemical compositions in March 2013. The predicted
ILPE of PM2.5 indicated maximum values at MHOS in
both genders after the “Haze Episode”, with average
values of 7,004±3,059 mg and 3,856±1,684 mg for PM2.5
accumulated in male and female workers, respectively,
over an exposure duration of 25 years. The relatively low
ILPE of PM2.5 observed at CMOS (i.e., 2,348±1,200 mg
and 1,292±661 mg for males and females, respectively)
highlights the effects of anti-burning and smog prevention
programs proactively enacted by local administration
organisations since November 2011. In conclusion, 3D
plots of PCA, coupled with FTIR spectra, successfully
discriminated “Biomass-Burning Aerosols” from
“Background Aerosols”, suggesting that FTIR analysis
can be used as an alternative tool to characterise types of
aerosols.
Acknowledgements
This project was nanced by National Institute of
Development Administration (NIDA) Research Center.
The author acknowledges Assist. Prof. Dr. Torpong
Kreetachart from School of Energy and Environment
(SEEN), University of Phayao for their contributions
on laboratory works. The authors thank the kind support
from Pollution Control Department, Ministry of Natural
Resources and Environment for providing meteorological
data.
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