13

Characterization and Source Apportionment Studies of PM2.5

Characterization and Source Apportionment Studies of
PM2.5 Using Organic Marker -based Positive Matrix
Factorization
Kimiyo KUMAGAI1＊ and Akihiro IIJIMA2
1

Gunma Prefectural Institute of Public Health and Environmental Sciences
378 Kamioki, Maebashi, Gunma, 371─ 0052, Japan
2
Takasaki City University of Economics
1300 Kaminamie, Takasaki, Gunma, 370─ 0801, Japan＊E-mail : k-kimiyo＠pref.gunma.lg.jp

Abstract
In this study, organic marker compounds in fine particulate matter（PM2.5）were observed during four
seasons at forested（Akagi）, suburban（Maebashi）and urban（Saitama）sites in the Kanto region of Japan,
and the source contributions of PM2.5 were evaluated. Organic compounds were analyzed using an
extraction-derivatization GC/MS method. PM2.5 samples were also analyzed for ion components, organiccarbon, elemental carbon and metallic elements. At both urban and suburban sites, the concentrations oflevoglucosan（a biomass burning marker）increased in winter and fall, whereas dicarboxylic acids（photooxidation products）increased during warm seasons. At the forested site, the concentration of 2-methyltetrols（a biogenic secondary organic aerosol（BSOA）marker from isoprene）tended to increase during warm
seasons, especially in summer. The levels of organic markers for cooking and vehicle emissions were higher
at the urban site than the other sites. As a result of positive matrix factorization analysis using a PM2.5
component data set, it was possible to apportion 80-90％ of PM2.5 mass concentration to 12 sources,
including sources of organic particles such as biogenic primary/secondary origin, biomass burning andcooking. The contribution ratios of these sources involved in organic aerosols in PM2.5 were estimated to be
41％ at the urban, 39％ at the suburban, and 46％ at the forested site. These results will provide basic datafor planning of future PM2.5 reduction measures in Japan.
Key words : biogenic source, biomass burning, levoglucosan, organic carbon, secondary organic aerosol,
source contribution

1. Introduction
In Japan, air quality standards for fine particulatematter （PM2.5）（35 µg/m3 for 24-hour mean and15 µg/m3 for annual mean）were introduced in 2009 to
reduce the adverse effects on human health. Following
that, monitoring of PM2.5 concentrations has been
conducted by local governments. The results ofnationwide PM2.5 monitoring show the air quality
standard achievement ratio to be not so high. Therefore,
measures to reduce PM2.5 concentration are required.
There is also concern about transboundary PM2.5 air
pollution from East Asia to Japan. To create policies toachieve air quality standards, it is necessary to know the
major sources and formation mechanisms of PM2.5.
PM2.5 can be classified into primary particles
directly emitted from the sources as particulate matterand secondary particles which are formed from gaseous
substances by photochemical reactions. In Japan,
Global Environmental Research
22/2018 : 013─020
printed in Japan

regulations on vehicle exhaust emissions have been
gradually tightened, whereby PM concentration has
declined moderately over the last two decades（Iijima &Kumagai, 2012）. On the other hand, the proportion of
secondary particles has become relatively large. To
ameliorate PM2.5 pollution, deepening our understandingof the dynamics of the secondary particles and source
contributions will be indispensable.
Although organic aerosols （OA） are the mostimportant constituents of PM2.5 in Japan, the chemical
properties, environmental behavior and the contributionof potential sources have not been sufficiently elucidated
due to chemical complexity. OA is not only directly
emitted from potential sources but also formed byphotochemical reactions of volatile organic compounds
in the atmosphere（Turpin et al., 2000）. OA is classified
into primary organic aerosols （POA） and secondaryorganic aerosols （SOA）, and further classified into
anthropogenic OA（APOA, ASOA）and biogenic OA©2018 AIRIES14

K. KUMAGAI and A. IIJIMA（BPOA, BSOA）from the viewpoint of its origin. It is
important to clarify the source contributions of organic
particles in PM2.5. Recently, organic tracer-based source
apportionment methods have been employed todetermine the contributions of specific primary organic
sources to ambient OA concentrations（Schauer et al.,1996 ; Kleindienst et al., 2007 ; Fabbri et al., 2009）. For
example, levoglucosan, which is formed by combustionof cellulose （Simoneit, 2002）, is well known as abiomass burning tracer（Simoneit, et al., 1999 ; Kleeman
et al., 2008 ; Fabbri et al., 2009）. Dicarboxylic acids are
related to photo-oxidation products of organic precursors
of both anthropogenic and biogenic origin（Kawamura
et al., 1996 ; Kerminen et al., 2000）. Sugars, such as
glucose and arabitol, are derived from pollen and fungal
spores, so they are treated as biogenic primary
compounds（Schauer et al., 2002 ; Graham et al., 2003 ;Medeiros et al., 2006 ; Bauer et al., 2008）. 2-methytetols
are formed from the oxidation of isoprene （Claeys,
2004）, and pinonic acid is formed from the oxidation of
α-pinene（Kleindienst et al., 2007 ; Fu et al., 2010）. The
ambient behavior of organic marker compounds will help
elucidate the sources and formation of OA. In Japan,
many studies and monitoring surveys on PM2.5
components have been done, but knowledge aboutorganic compounds in PM2.5 remains deficient.
In this study, we focused on the characteristics ofsource-specific molecular markers of organic aerosols
with other components in PM2.5. Multicomponent
analysis of each organic marker of various sources, suchas biomass burning, photo-oxidation products, BSOA,
BPOA, cooking and vehicles, was developed using an
extraction-derivatization GC/MS（gas chromatography –mass spectrometry） method. Then PM2.5 source
apportionment was carried out using positive matrix
factorization. One more purpose of this study was toprovide an organic marker measurement method andsource apportionment method for policy making in Japanthat can be applied to monitoring methods in Japan.

2. Methods
2.1 Observation Sites
We conducted PM2.5 observations at the threedifferent locations, forested, suburban and urban, in the
Kanto region of Japan （Fig. 1）. The suburban site,
Maebashi（36.4054°N, 139.0945°E）, was selected in a
suburban city situated inland on the Kanto Plain, about
100 km northwest of central Tokyo. The sampling site
was surrounded by residential areas and agricultural
fields. The forested site, Akagi（36.5380°
N, 139.1818°E）
was selected in a mountainous area north of the Kanto
Plain. The sampling site was surrounded by deciduoustrees and had snow cover in winter. There were noanthropogenic sources around the Akagi sampling site.
The straight-line distance between Akagi and Maebashi
was approximately 16 km. The urban site, Saitama（35.8642°N, 139.6079°E）, was selected in the Tokyo
metropolitan area on the Kanto Plain, characterized by aFig. 1 Locations of the observation sites : Akagi（forested site）,
Maebashi（suburban site）, and Saitama（urban site）. The
shading of the enlarged map represents the topographic
image.

large population and heavy traffic. Its location was about
30 km northwest of central Tokyo.
Regarding the atmospheric environment, the Kanto
Plain, where these sampling sites were located, hascharacteristic meteorological conditions. On this plain, asea breeze produces a southerly wind during the daytime
during the warm seasons. This wind can bring polluted
air from the Tokyo metropolitan area. Contrastively, astrong, dry northwest wind frequently blows down fromthe mountainous area during the cold seasons.
2.2 Sampling and Analytical Methods
PM2.5 sampling was conducted during four seasons ;winter（from December 3 to 18, 2014）, spring（from May
26 to June 8, 2015）, summer（from August 7 to 21, 2015）,
and fall（from October 5 to 19, 2015）. Samples werecollected on quartz fiber filters（φ47 mm, 2500 QAT-UP,
Pallflex, USA） and PTFE （Polytetrafluoroethylene）
membrane filters（φ47 mm, R2-PJ047, Pallflex, USA）
with low-volume air samplers （FRM2025, Thermo
Fisher Scientific Inc., USA, 16.7 L/min, or MCAS-SJ,
Murata Keisokuki Service Co., Ltd., Japan, 30.0 L/min）,
respectively. Although high-volume air samplers areoften used in field studies targeting organic particulatematter（Alves et al., 2010 ; Offenberg et al., 2011 ; Wanget al., 2011）, we used low-volume samplers because they
are commonly used in PM2.5 monitoring surveys inJapan. The sampling period was 24 hours, starting at 10
a.m. Japan Standard Time（JST）. The quartz filters werepre-cleaned to remove carbonaceous components present
originally in the filters by baking for one hour at 350℃.
We obtained particulate mass with the PTFE filters by
the gravity method under conditions of constanttemperature and humidity （21±1.5℃ and 35±5％
RH）.

15

Characterization and Source Apportionment Studies of PM2.5

The PM2.5 filter samples collected at the three sites
were analyzed for organic marker compounds, ion
components, carbonaceous components, and metallic
elements. The detailed components are summarized inTable 1. Ion components, oxalic acid（C2）and watersoluble organic carbon（WSOC）on the PTFE filters
were ultrasonically extracted with ultrapure water, andsubsequently their solutions were filtered throughhydrophilic syringe filters. The ion components and
WSOC were measured with an ion chromatograph（Dionex ICS1100, Thermo Fisher Scientific Inc., USA）
and a total carbon analyzer（TOC-V, Shimadzu, Japan）,
respectively. Organic carbon（OC）and elemental carbon（EC） on the quartz filters were determined using athermal/optical carbon analyzer （DRI Model 2001,
Atmoslytic Inc., USA） by following the IMPROVE
protocol（Chow et al., 2001）. Metallic elements on the
PTFE filters were determined by acid digestion ICP-MS.
The sample preparation procedure is described in detailelsewhere（Iijima et al., 2009）. Metallic elements（e.g.,
Al, V, Zn, Fe, Cu）were determined by ICP-MS（7500cx,
Agilent Technologies Inc., Japan）. Blank filters wereanalyzed for all components, and blank values weresubtracted for some detected components.
Organic marker compounds were determined by aderivatization GC/MS method（Kumagai et al., 2010）.
This method is widely used for measurement oflevoglucosan, and can also be used for measuring otherpolar organic compounds（e. g., Fabbri et al., 2009, Fu &Kawamura, 2011）. We carried out preliminary
observations of PM2.5 at Maebashi and Akagi by using
high-volume air samplers and determined the operational
conditions for multicomponent analysis. The organic
compounds we targeted were as follows : levoglucosan,
mannosan and β-sitosterol as biomass burning tracers ;dicarboxylic acids（e. g., malonic acid, C3 ; succinic, C4 ;malic, hC4, azelaic, C9） as photo-oxidation products ;pinonic acid as a BSOA marker of α-pinene ;2-methyltetrols（2-methylthreitol and 2-methylerythritol）
as BSOA markers of isoprene ; arabitol and glucose as
BPOA markers ; oleic acid, linoleic acid and cholesterol
as cooking markers ; and hopanes （17α（H）21β（H）30-norhopane, HP29, and 17α（H）21β（H）-hopane,HP30）as vehicle markers. One half of each quartz filter
was extracted with a dichloromethane and methanol
mixture （2 : 1, v/v） by ultrasonic agitation for 15
minutes. Each sample was spiked with internal standards（d7 -levoglucosan and cis-ketopinic acid）. The extracts
were filtered and dried in a nitrogen stream and thenderivatized with 50 µL of N, O-bis （trimethylsilyl）
trifluoroacetamide （BSTFA） containing 10％
trimethylchlorosilane. The samples were heated to 70°C
for two hours for the derivatization reaction. Organic
compounds were analyzed using a GC/MS instrument（7890GC/5975MSD, Agilent Technologies, USA）
. The
instrument was equipped with a fused silica capillarycolumn（DB5MS, 60 m×0.25 mm-i.d.×0.25 µm film
thickness, Agilent Technologies, USA）
. For further
details on the GC/MS analysis conditions, see Kumagai

et al.（2017）.
2.3 PMF ModelingSource apportionment was conducted using a
positive matrix factorization（PMF）model（EPA PMF
5.0）. In this model, a speciated data set can be viewed asa data matrix X of i by j dimensions, in which i numberof samples（i＝1,…, m）and j chemical species（ j＝1,…,n）have been measured, with uncertainty u（US EPA,
2014）. The goal of PMF is to solve the mass balance
between measured species concentrations and source
profiles, as shown in Equation （1）, with number offactors p（1,…, k）, the species profile f of each source,
and the amount of mass g contributed by each factor toeach individual sample :pxij＝ Σ gik fkj＋eij

…（1）

k＝1

where xij is the concentration of species j in sample i andeij is the residual of each sample/species. Factor
contributions and profiles are derived by the PMF model
minimizing the objective function Q as defined in
Equation（2）:

（ ）

m
n
eij 2
Q＝ Σ Σ
i＝1 j＝1 uij…（2）where uij is the uncertainty of each sample/species. If themodel is appropriate, then（eij /uij）2 approximates 1 andthe expected Q（Qexp）will correspond to the degree of
freedom（mn－p（m＋n））of the fitted dataset.
Our dataset for PMF modeling consisted of 167
samples with 38 selected species : PM 2.5 massconcentration, seven ions, OC, EC, 10 elements, and 18
organic markers, as shown Table 1. Uncertainties of theTable 1 Summary of measured components in PM2.5 and species
selected for the PMF（positive matrix factorization）model.
Compounds
＊

Mass
Ions

Carbonaceouscomponents
Metallic
Elements

Organic
markers＊

Cl－＊, NO3－＊, SO42－＊, Na＋＊, NH4＋＊, K＋＊,
Mg2＋, Ca2＋＊
OC＊, EC＊, WSOC
Li, Be, B, Na, Mg, Al＊, K, Ca, Sc, Ti, V＊, Cr,
Mn＊, Fe＊, Co, Ni, Cu＊, Zn＊, Ga, Ge, As＊, Se,
Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd＊,
Sn, Sb, Te, Cs, Ba＊, La, Ce, Pr, Nd, Sm, Eu,
Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re,
Ir, Pt, Au, Pb＊, Tl, Bi, Th, U
Levoglucosan＊, Mannosan, β-Sitosterol＊,Dicarboxylic acids（oxalic acid, C2＊ ; malonic,
C3＊ ; succinic, C4＊ ; malic, hC4＊, glutaric, C5＊ ;adipic, C6＊ ; pimelic, C7 ; suberic, C8 ; azelaic,
C9＊ ; maleic acid, phthalic acid＊）, Pinonic
acid＊, 2-Methyltetrols＊（2-methylthreitol and2-methylerythritol）, Arabitol＊, Glucose＊, Oleic＊
Linoleic
acid,
Cholesterol＊,acid ,（H）21β（H）-30Hexadecanoic acid＊, 17α（H）21β（H）-hopane
norhopane＊（HP29）, 17α（HP30）

Species selected for the PMF model.

16

K. KUMAGAI and A. IIJIMA

respective data were calculated using the error estimation
function（Polissar et al., 1998）with obtained method
detection limits and error fraction of 10％. The extra
modeling uncertainty was set at 5％. The uncertainties of
duplicated variables（PM2.5 mass and OC）and HP29
with low S/N ratios were down-weighted by setting themas “weak” in the modeling. By considering the Q/Qexp
ratio, correlation coefficient between obtained andmodeled data, distribution of residuals and bootstrap
error estimation, we could resolve a twelve-factor
solution using PMF.

3. Results and Discussion
3.1 Chemical Characterization of PM2.5 at the Three
Sites
3.1.1 PM Mass Concentration and Major
Components
The average concentrations of PM2.5 mass obtainedin the four seasons from December 2014 to October 2015
were 7.2（minimum 0.5 ─ maximum 21.9）, 12.3（1.9 ─31.9）, and 12.7（3.5 ─ 36.0）µg/m3 at Akagi, Maebashi
and Saitama, respectively. The relatively most abundantcomponent in PM2.5 was OC, with average concentrationsof 1.3, 2.8, and 3.9 µg/m3 ; then SO42－, with average
concentrations of 1.8, 2.1, and 2.4 µg/m3 at Akagi,
Maebashi, and Saitama, respectively. Overall, theconcentrations of PM2.5 mass and its chemicalcomponents were higher at the urban and suburban sites
than at the forested site.

Figure 2 shows the temporal variations inconcentrations of PM2.5 mass and major chemicalcomponents during the observation period. In winter, at
Maebashi and Saitama, the range of PM2.5 concentrations
was larger than during other seasons. Highconcentrations of PM2.5 observed in winter were caused
by increasing OC and NO3－ concentrations at Maebashi
and Saitama, as shown in Fig. 2. EC and Cl－ also tended
to increase at Saitama in winter. This was considered toresult from a strong influence by anthropogenic
emissions. On the other hand, the PM2.5 concentration
remained low in winter at Akagi, attributed to a relatively
low influence of air pollution from human activities in
the area due to the prevailing north wind. In spring, massconcentrations of PM2.5 were observed to be relatively
high at all the sites, the major components being OC andSO42－. High concentrations（approximately 100 ppb）of
photochemical oxidants （Ox） were observed several
times during our sampling period. The increase in OC
and SO42－ could be attributed to secondary formation
through photochemical activity. In summer,
photochemical activity is generally high, but the massconcentrations of PM2.5 were relatively low during thesampling period, except for August 7, on which day highOx concentrations were observed. The PM 2.5
concentrations may have remained low due to rain fromtyphoons. OC and SO42－ were predominant components
in PM2.5 in summer. In fall, relatively high concentrationsof PM2.5 were observed at Maebashi, when OC was thedominant component. The proportions of SO42－ andNO3－ were high, second to OC. OC was commonly

Fig. 2 Temporal variations in PM2.5 mass, ion components, OC （organic carbon） and EC（elemental carbon）during the sampling period（winter : December 2014, spring : May–
June 2015, summer : August 2015, fall : October 2015）at the three sites specified in Fig. 1.

Characterization and Source Apportionment Studies of PM2.5

17dominant, but there was no increase in NO3－ at Saitama
or Akagi. OC was the dominant component of PM2.5
throughout the four seasons.
3.1.2 Seasonal Variation of Organic Markers in PM2.5
Figure 3 shows seasonal average concentrations oforganic marker compounds in PM2.5 collected at the threesites. Biomass burning markers, levoglucosan andmannosan increased in concentration during the coldseasons（winter and fall）in urban and suburban areas.
The average concentrations of levoglucosan were foundto be 175 ng/m3 in winter and 96 ng/m3 in fall at Saitama,and 93 ng/m3 in winter and 94 ng/m3 in fall at Maebashi,
respectively. The levoglucosan concentrations obtained
were strongly correlated with OC concentrations at
Maebashi and Saitama during these seasons（r＝0.920.99）. A similar relationship between OC andlevoglucosan at Maebashi also was observed in ourprevious study （Kumagai et al., 2010）. The
concentration of levoglucosan at Akagi increased slightly
when the main wind direction was southerly on the
Kanto Plain, but overall the concentration was lower thanat the other sites. From these results, biomass combustion
should be noted as an important regional source oforganic particles during the cold seasons.
The most abundant dicarboxylic acid in PM2.5 was
oxalic acid, which was common at all the sites and its
average concentrations in spring were the highest among
all seasons : 111 ng/m3 at Maebashi, 108 ng/m3 at Akagi,
and 82 ng/m3 at Saitama, respectively. The highest
concentrations of the other dicarboxylic acids were also
observed in spring, as shown in Fig. 3. The Ox
concentration level during the sampling period washigher in spring than in summer, indicating photooxidation activity was higher during the spring period.
Concentrations of low-molecular diacids such as C3-,
C4-, and hC4- diacid were found to be significantly highat Akagi, followed by Maebashi. Low-molecular-weight
diacids are most likely to be generated by photochemical
reactions（Kawamura & Ikushima, 1993）. These results
show that SOA formation increases during the warm
seasons（spring and summer）. At Saitama, C9 diacid wasfound at a relatively higher abundance than the otherdiacids. C9 diacid is an oxidation product of unsaturated
fatty acids（Kawamura & Ikushima, 1993）. Long-chaindiacids can be oxidized to short-chain diacids byphotochemical reactions （Kawamura & Yasui, 2005）.
Therefore the differences in compositions of dicarboxylic
acids observed at each site were thought to indicate
differences in aging of organic aerosols. In the Kantoarea, as the southerly wind blew dominantly in thedaytime during the warm seasons, as previously
mentioned, the air mass was transported from themetropolitan area as photochemical reactions occurred.
The organic aerosols inland in the Kanto region（thesuburban and forested sites in this study）are considered
to have undergone advanced aging compared to thosewithin the metropolitan area.
2-methyltetrols, which are oxidation products ofFig. 3 Seasonal average concentrations of organic marker
compounds in PM2.5 at the three sites specified in Fig. 1.
Lev : levoglucosan, Chol : cholesterol, HPs : hopanes

isoprene, increased notably during the warm season. The
average concentration of 2-methyltetrols was 15.1 ng/m3
in summer at the forested Akagi site, which was 3─5times higher than at Maebashi and Saitama. In this study,
we conducted simultaneous measurement of VOC when
the isoprene concentration level increased significantly insummer at Akagi. Our observation results suggested thatthe high concentration of 2-methyltetrols was related toisoprene emissions and photochemical activity. Similarseasonal trends in isoprene oxidation products werereported by Kleindienst et al.（2007）. The concentrationof pinonic acid, which is an oxidation product of
α-pinene, was high in spring at Maebashi but was detected
throughout all seasons. In contrast with isoprene,
α-pinene existed at the same order of concentration
during all seasons. These results suggest that isoprene
BSOA increases during the warm seasons and α-pinene
BSOA is present during all seasons.
Oleic acid and cholesterol, as organic markers forcooking（Schauer et al., 1999）, and hopans, as specificmarkers for vehicular emissions（Schauer et al., 2002）,
were detected at relatively higher concentrations at
Saitama than at Maebashi, and increased in winter. The
influence of anthropogenic emissions is thought to havearisen strongly under the stable meteorological

18

K. KUMAGAI and A. IIJIMA
Table 2 Assignment of decomposed factors by the PMF model to their respective sources.
Factor

SourceIndicated components

F1
F2
F3
F4
F5
F6
F7
F8
F9
F10
F11
F12

BPOA
BSOA from monoterpene
BSOA from isoprene
Cooking
Urban site organic aerosol
Biomass burning
Vehicular and road dust
NitrateSulfate from fuel combustion
Sulfate from coal combustion
Transboundary pollution
Soil

Arabitol, Glucose
Pinonic acid, C5-C6 diacids
2-methyltetrols, C3-C4 diacids
Cl－, Linoleic acid, Oleic acid, β-sitosterol
C16 acid, C9 diacid, Na＋
K＋, Levoglucosan
Ca2＋, EC, Mn, Cu, Zn, Ba, Hopane
NO3－, NH4＋
V, C2 diacid, SO42－
NH4＋, SO42－, As
As, Cd, Pb
Ca2＋, Al, Fe

Comparison
between sites1）
MA＞＞ST＞AK
MA＞AK≒ST
AK＞＞MA＞ST
ST＞＞MA≒AK
ST＞＞MA≒AK
MA＞ST＞＞AK
ST＞MA＞AK
MA＞ST＞AKST≒MA＞AKMA≒ST＞AKMA≒AK≒ST
MA＞＞AK≒ST

Seasonal
characteristics2）
Sp, Sm, Fl
Wn, SpSp, SmWn
Sp, Sm
Fl, WnAll seasons
Wn, FlSp, SmSm, FlSp, FlSp1）Predominant sites in order of contribution. MA : Maebashi, AK : Akagi, ST: Saitama.
2）Predominant seasons with higher contributions. Wn : winter, Sp : spring, Sm : summer, Fl : fallconditions of winter. In many samples collected at Akagi,
these compounds were below the detection limit.
As described above, the concentration levels of
various organic markers had different characteristics
according to the season and surrounding environment at
each site.
3.2 Source Apportionment by PMF
Each of the 12 factors has a distinctive grouping ofspecies that can be associated with a specific source
sector（F1 : BPOA, F2 : BSOA from monoterpene, F3 :BSOA from isoprene, F4 : cooking, F5 : urban siteorganic aerosol, F6 : biomass burning, F7 : vehicular androad dust, F8 : nitrate, F9 : sulfate from fuel combustion,
F10 : sulfate from coal combustion, F11 : transboundary
pollution, and F12: soil）. These are summarized in Table 2.
Conventional studies on PM2.5 source contribution by
PMF analysis based on datasets with ion components,
carbon components and metallic elements showed six toseven factors, typically including nitrate, sulfate,transboundary pollution, vehicle exhaust, oil burning,soil, road dust, etc.（Iijima & Kumagai, 2012 ; Belis etal., 2013 ; Toyonaga et al., 2017）. In many cases, it wasdifficult to identify certain sources that were supposed to
be present as origins of organic particles. By usingorganic markers for receptor modeling, six factors ; F1F6 related to OA could be determined in this study.
Source contributions of PM2.5 at Akagi, Maebashi
and Saitama are shown in Fig. 4. OC concentrations wereconverted to organic matter concentrations bymultiplying them with a coefficient of 2.1 at Akagi and
Maebashi, and 1.6 at Saitama（Turpin & Lim, 2001）.
Although the PM2.5 mass concentrations wereapproximately comparable at Maebashi and Saitama, thesource contributions were found to be significantly
different for each site, as shown in Fig. 4. At Akagi, atthe forested site, the most abundant factor was F3（BSOA from isoprene, 27％）
, followed by F10（15％）.
At Maebashi, the suburban site, the most abundant factor

Fig. 4 Average factor contributions to PM2.5 concentrations（upper）and percentages（bottom）at Akagi, Maebashi,and Saitama. F1 : BPOA, F2 : BSOA from monoterpene,
F3 : BSOA from isoprene, F4 : cooking, F5 : urban siteorganic aerosol, F6 : biomass burning, F7 : vehicular androad dust, F8 : nitrate, F9 : sulfate from fuel combustion,
F10 : sulfate from coal combustion, F11 : transboundary
pollution, F12 : soil.

was F10（sulfate from coal combustion, 17％）, followed
by of F1 （BPOA, 14％）
, F9 （sulfate from fuelcombustion, 14％）, and F6（biomass burning, 13％）. At
Saitama, the contribution of F5 （urban site organic
aerosol, 17％）was dominant, followed by F10（16％）
and F9（13％）.

Characterization and Source Apportionment Studies of PM2.5

Regarding the source contribution of OA, thefollowing characteristics were observed when focusing
on seasonal trends. Biomass burning was presumed to be
the most influential source during the cold seasons at
Maebashi and Saitama. The contribution of BSOA
derived from isoprene was remarkably high at Akagi inspring and summer, and it was characteristic at theforested site. The contributions of urban site organic
aerosols were the predominant source during the warm
season at Saitama, but the specific source was unclear.
More detailed seasonal characteristics of source
contributions are now under consideration. Source
contribution ratios of organic aerosols, which areassociated with the total of F1 to F6, could be estimatedto 39％ at Maebashi, 46％ at Akagi, and 41％ at
Saitama. Thus, organic markers were shown to be usefulfor source apportionment of PM2.5. It was concluded thatcountermeasures against sources of organic aerosols
would be effective for reducing PM2.5 massconcentration.

4. Conclusions
In this study, we observed organic marker
compounds and other chemical components in PM2.5 atforested （Akagi）, suburban （Maebashi） and urban（Saitama） sites in the Kanto region of Japan, andevaluated the source contribution of PM2.5. As a result of
our observation, we could clarify the seasonal andregional characteristics of various organic marker
compounds at these sites. The concentration oflevoglucosan significantly increased during the coldseasons （winter and fall） at Saitama and Maebashi.
Organic markers of cooking and vehicles had relatively
higher concentrations at Saitama than at the other sites.
Dicarboxylic acids had high concentrations during thewarm seasons because of photochemical activity.
Furthermore the dicarboxylic acid compositions weredifferent at each site, suggesting aging of the organic
particles. 2-metyltetrols, BSOA markers derived fromisoprene, tended to increase during the warm seasons,significantly at the forested site, Akagi. We found that,together with α-pinene BSOA, naturally occurring
secondary-generated organic particles exist in nonnegligible amounts.
As a result of PMF analysis using a PM2.5
component dataset, it was possible to apportion 80─90％
of PM2.5 mass concentration among 12 sources. By
measuring various organic marker components inaddition to the ion components, carbonaceouscomponents and elements, we were able to evaluate thecontribution rate of the sources of organic particles suchas BPOA, BSOA, biomass burning and cooking. In
particular, when we focused our attention on the source
contribution rate of OA, we found the following
characteristics. At Maebashi in spring, fall and winter,
the influence of biomass burning was great, affecting thegain in organic particle concentrations. In spring andsummer at Akagi, the contribution of BSOA derived from19

isoprene was remarkably high. The influence of local OA
in urban areas during the warm seasons at Saitama andbiomass burning in the cold season were characteristic.
The contribution ratio of sources involved in OA in PM2.5
was 39％ at Maebashi, 46％ at Akagi, and 41％ at
Saitama. Thus measures against sources of organic
particles would be effective for reducing PM 2.5
concentrations.
We established an organic marker measurement
method and receptor modeling that can be applied tomonitoring methods already implemented in Japan. Our
observations can provide a model case for PM2.5
monitoring surveys. These results can serve as basic datafor planning future PM2.5 reduction measures in Japan.

Acknowledgments
This work was supported by the Environment
Research and Technology Development Fund（5─1403）
of the Ministry of the Environment, Japan. The authors
would like to thank our project members : Dr. Shinji
Kudo of the University of Shiga Prefecture, Dr. Hiroshi
Tago, Mr. Yoshinori Saito of Gunma Prefectural Instituteof Public Health and Environmental Sciences, and Assoc.
Prof. Kazuhiko Sekiguchi of Saitama University, for theirsupport and encouragement.
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Kimiyo KUMAGAI
Kimiyo Kumagai is a researcher at the Gunma
Prefectural Institute of Public Health andEnvironmental Sciences, Japan. She received her
Ph. D. from the Graduate School of Science andEngineering, Saitama University, Japan. She has
worked in the Atmospheric Gunma Prefectural
Institute of Public Health and Environmental
Sciences where she performs PM2.5 and hazardous air pollutant monitoring
surveys. Her current research focuses on atmospheric organic particulatematter based on field measurements.

Akihiro IIJIMA
Akihiro Iijima is a Professor of Environmental
Science at the Department of Regional
Development, Faculty of Regional Policy,
Takasaki City University of Economics. He
received his Ph. D. in Applied Chemistry from
Chuo University, Japan. His current researchinterests include environmental statistics, with aparticular focus on receptor modeling of atmospheric aerosols for
elucidating potential sources.
（Received 11 May 2018, Accepted 1 September 2018）