37

Chemical Properties of the Southeast Asian Haze fromIndonesian Peatland Fires
Yusuke FUJII*1 and Susumu TOHNO*2*1Osaka Metropolitan University
1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, 599-8531, Japan*2
Professor Emeritus, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan*1
E-mail: fujii.yusuke@omu.ac.jp, *2E-mail: tohno@energy.kyoto-u.ac.jp

Abstract
Anthropogenic activities such as forest degradation; the expansion of commodity crops, including oil
palm; and drainage of fire-prone peatlands have resulted in extensive peatland and forest fires in Indonesia.
Uncontrollable and intense peatland fires have frequently occurred in the Indonesian islands of Sumatra andKalimantan, particularly during the hot and dry seasons of the El Niño-Southern Oscillation. Air pollution in
the form of transboundary haze is an urgent issue in Southeast Asia. The physical and chemical properties of
the aerosols from peatland fires are fundamental to know when assessing their impact on human health and theenvironment, conducting source apportionment of haze events, and unraveling their dynamic state in theenvironment (e.g., secondary aerosol formation). In this paper, we conduct a holistic review of source profiles,
key indicators of Indonesian peatland fires at source and receptor sites, and transformation of haze during its
long-range transport (aging, secondary aerosol formation) as well as source apportionment in Southeast Asia,
using a comprehensive chemical component dataset. Knowledge of the chemical characteristics of particulatematter at Indonesian peatland fire sources and receptor sites is still limited, especially regarding controlling
factors (e.g., combustion conditions, peat composition and the effects of vegetative burning on peatland) that
determine the Indonesian peatland fire source profile of particulate matter. Additionally, the process of
secondary organic aerosol formation derived from Indonesian peatland fires during transport to receptor sites
remains largely unresolved.
Key words: chemical composition, haze, Indonesia, peatland fire, Southeast Asia

1. Introduction
Tropical peatlands are globally and regionally
important as carbon pools and carbon soil-atmosphereexchange process, and in provisioning ecosystem servicesof peat swamp forests (Page et al., 2011). The peatlandarea of Southeast Asia (below, “SEA”) covers 23.7 Mha,of which 20.7 Mha is in Indonesia (87.3%), followed by
2.59 Mha in Malaysia (Omar et al., 2022). Indonesia haseither the world’s largest tropical peatland area (Joosten,2009) or the world’s second largest tropical peatland area
(UNEP, 2022a), and small-scale land management fires
have been used for centuries in Indonesia (Simorangkir,2007). However, anthropogenic activities such as forest
degradation, the expansion of commodity crops including
oil palm, and drainage of fire-prone peatlands haveresulted in extensive peatland and forest fires in Indonesia.
Uncontrollable, intense peatland fires have frequently
Global Environmental Research
27/2023 37-48
printed in Japanoccurred in the Indonesian islands of Sumatra andKalimantan, particularly during the hot and dry seasons of
the El Niño-Southern Oscillation, since the early 1980s(Field et al., 2016; Yulianti et al., 2020; UNEP, 2022b).
The first catastrophic fire was in 1997 and subsequently,
intense fires have occurred in 2006, 2013 (a non-droughtyear), 2015 and 2019. Four million six hundred thousand
hectares of land in Indonesia burned in 2015 (Lohberger
et al., 2018), and CO2 emissions from the peatland fireexceeded the total annual emissions of a developed nation
as big as Japan for that year (World Bank, 2015; Field
et al., 2016; Crump, 2017). In addition to vast carbon
emissions affecting the global climate, large-scale smoke
generation (transboundary haze pollution) degrades theregional air quality significantly in SEA, resulting inadverse impacts on both ecosystems and human health(Crippa et al., 2016; Koplitz et al., 2016; Cheong et al.,2019; Phung et al., 2022; Yin, 2023). Furthermore, Hunag©2023 AIRIES38

Y. FUJII and S. TOHNO

et al. (2023) have discovered an intricate fire-weather
feedback loop that increases air pollution exposure
through the radiative effects of smoke aerosols(aerosol-cloud interaction) in Mediterranean and monsoon
climate regimes. The extensive fires have resulted inserious economic damage through its impact on various
environmental and social services. For example, theeconomic loss from the extensive peatland fires and hazein 2015 was estimated at 16.1 million USD in Indonesia(Glauber et al., 2016).
Air pollution in the form of transboundary haze is anurgent issue in SEA. Gaining information on the physicaland chemical properties of the aerosols from peatland
fires is fundamental to assessing their impact on humanhealth and the environment, conducting source
apportionment of haze events, and unraveling theirdynamic state in the environment (e.g., secondary aerosol
formation). Aerosols from biomass burning, includingfrom peatland fires, are mostly in fine size range (Reidet al., 2005). Human mortality due to exposure to ambientfine particulate matter (PM) with an aerodynamic
diameter less than 2.5 µm (PM2.5) is generally calculatedby an exposure-response function (Burnett et al., 2014)
based on a multitude of epidemiological studies that havefocused on long-term exposure effects (Pozzer et al.,2023). This function describes the relative risk
dependency on PM2.5 mass concentration, independent ofsource or composition. An increasing number of studies,
however, have suggested that sources and compositions offine particles may significantly influence the healtheffects (Zhang et al., 2023). Unequal toxicity of
individual PM2.5 components has been reported (Wyzga
and Rohr, 2015; Park et al., 2018), and chemicalcomposition is considered to be a major determinant of
the toxicity of airborne PM (Shiraiwa et al., 2017).
Chemical components of transboundary haze such as
heavy metals and polycyclic aromatic hydrocarbons(PAHs) are critically important species that are relevant inhealth risk assessment71, 88 (Each superscript corresponds
to a particular reference in the literature list in the separate
dataset that can be found in the Supplementary
Information. There is also a list of abbreviations in the“Appendix” sheet in the dataset. The dataset is describedat the end of this section and in the next section).
Aerosolradiative
effects
includedirectaerosol-radiation through direct scattering and absorptionof incoming solar and outgoing terrestrial (infrared)
radiation in the atmosphere, and indirect aerosol-cloudinteractions through alteration of cloud optical properties
and the formation of clouds and precipitation as cloud
condensation nuclei (CCN) (Penner et al., 2001).
Evaluating aerosol radiative forcing involves examining
the physical (e.g., size distribution, optical properties,
hygroscopicity) and chemical (e.g., water-solubility of
inorganic components and organic carbon as CCN)
properties of the aerosols. Therefore, the chemicalcomposition of haze (e.g., water-soluble ions andlight-absorbing carbonaceous components) is one of the
key parameters for evaluating its radiative effects.
Knowing the physical and chemical properties of thehaze from Indonesian peatland fires (below, “IPFs”) is
indeed indispensable to assessing its human health andenvironmental impacts. It should be noted, however, that
there exist complex interactions among environmental
and socio-economic factors hidden behind these peatland
fires (Sze et al., 2019; Shigetomi et al., 2020; Girkin et al.,2022; UNEP, 2022b). To manage and mitigate the fires’risk to human health and livelihoods, biodiversity and theglobal climate, visualization of the interactions through asystemic lens is crucial. A “drivers, pressures, state,
impact and response” (DPSIR) framework (Smeets andWeterings, 1999) has been used in modelling therelationship between environmental and human(socio-economic) systems. We have mapped the DPSIR
framework into the cause-effect chains of environmental
problems associated with IPFs, as shown in Fig. 1. In the“state” element of the DPSIR framework, we haveemphasized aerosols (haze) from IPFs, particularly their
physical and chemical properties to ascertain theirposition and interrelations with other components of theframework.
Many field and laboratory studies have been
conducted to investigate the chemical characteristics ofsmoke haze from IPFs at source and receptor sites in SEA.
Table S1 gives a summary of review papers relating tophysical (not including optical properties) and chemical
properties of haze from IPFs and other biomass burning,and source apportionment of PM (including contributionof IPFs, or IPF and other sources to PM concentrations)according to field observations in this region andlaboratory studies based on combustion of Indonesian or
Malaysian peat. Some papers also cover the climate,human health and economic impacts of the haze78, 99, 116.
They do not refer, however, to laboratory studies andcutting-edge analysis, nor do they cover sufficientchemical species for impact assessment, source profilingor ascertaining the dynamic state, and limit their target
area to a certain country. Fujii and Tohno104 carried out anarrative review focusing on the chemical compositionsof IPF-derived aerosols based on field and laboratory
studies and source apportionment of PM. Their review
contained information on various species but not from asystematic perspective.
Our research questions were fourfold: 1) Whatdifferences exist in chemical compositions between hazefrom IPFs and non-haze samples in SEA (source profiles
and contributions of other sources, particularly at receptor
sites), 2) What differences exist in chemical compositions
between haze sampled at source and at receptor sites(unique source profiles if available, key source indicators
and aging process of the haze), 3) Ways in which
advanced analytical techniques are applied inChemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires

39

Fig. 1 Schematic representation of the cause-effect chains of environmental problems associated with Indonesian peatland fires
according to a DPSIR framework. Emphasis is placed on aerosols from the fires in the “state” element of the framework.
(Revised from Fig. 1 in Fujii and Tohno104)

investigating the chemical properties of haze, and 4) The
extent to which smoke haze degrades the air quality in
SEA (the potential of peatland fires to contribute to andelevate ambient PM concentrations). Therefore, we
conducted a holistic review of source profiles, key
indicators of IPFs at source and receptor sites, andtransformation of haze during its long-range transport(aging, secondary aerosol formation) as well as source
apportionment in SEA, using a comprehensive chemicalcomponent dataset.
Van et al.116 discussed the definition of haze fromthese fires and proposed to use PM2.5 > 50 μg m-3,
visibility < 5 km and relative humidity (RH) < 90% as
criteria for a “haze day,” but we used whatever definitionof haze each respective article on field studies used as it
was. The origin of the haze in each article was confined to
IPFs using air mass backward trajectory analysis,
meteorological factors and other factors. That meant clear
differences in total PM mass concentration or visibility,
etc. were found between haze events from IPFs andnon-haze periods in each field study. Firstly, we created adetailed dataset of as many chemical compositions as
possible and size distributions of the haze as well as
possible indicators of peatland fires by collecting andreviewing the data from field and laboratory studies.
Contained therein is also the contribution of IPFs orbiomass burning to PM concentrations (annual or
long-term average) in SEA. Our dataset creation methods
and review process are described in the next section. Our
dataset enables a subsequent holistic review of thechemical compositions of haze from IPFs in SEA.

2. Methods
Our dataset is based on a literature search and review
to identify and extract relevant articles from scientific
databases under appropriate inclusion and exclusion
criteria. We aimed to find comprehensive chemical
properties of IPF-derived PM (haze) at source andreceptor sites in SEA with size distributions as well aslaboratory-generated PM from burning of Indonesian or
Malaysian peats. Hence, studies were included when they
were in peer-reviewed publications, written in English
with a full text article available, and 1) included massconcentration or mass fraction of chemical species in PM(all available species, carbonaceous components, watersoluble ions, elements, organic compounds, isotopes,
aerosol mass spectra, etc.) with the PM samples having
been collected during a haze period or at (near) firesources in SEA, 2) included mass concentration, emission
factor (below, “EF”), or emission ratio of generated PM
species in laboratory studies, or 3) presented thecontribution of IPFs or biomass burning to PM using
samples collected during haze periods in SEA. Studies
were excluded when they 1) focused on a specific purpose(e.g., optical properties, simulation models) and included
no observed chemical properties, or 2) were review
articles, book chapters, or reports, except for one
pioneering US-EPA report5.
Article identification and selection were conductedusing three databases, Web of Science, Scopus andScienceDirect, searching for publications that included thequeries in Table S2. The time range was from 1990 to 31

40

Y. FUJII and S. TOHNO

March 2023. We identified 1,769 suitable articles in the
databases. After removing duplicates and screening thetitles and abstracts, we had 232 articles remaining. We
reviewed the full-text articles using the study selection
criteria mentioned in the paragraph above. Ultimately, we
included 112 in our review, with 13 articles added throughsnowballing and nine review papers excluded. A
comparison of the nine review articles is shown in TableS1 as described in Section 1, and the articles are included
only in the literature list of the dataset in the
Supplementary Information. We found one newly
published article after completing our screening process,and included it in our dataset. Lastly we created a datasetof the 113 articles using a Microsoft Excel file with 13
sheets, consisting of: 1) a cover sheet, 2) carbonaceouscomponents, 3) water-soluble ions, 4) elements, 5)organic compounds excluding PAHs, 6) PAHs, 7) aerosol
mass spectra analyses, 8) isotopes, 9) lab. experiments,
10) size distributions, 11) source apportionments, 12) a
list of references and 13) an appendix. PM sampling
information (during haze periods, and partly including
non-haze periods), total PM mass concentration (or EF)
and mass concentration of each species (EF or emission
ratio) are contained in sheets 2) – 6). The contents of the
sheets 2) – 13) are listed and described in the cover sheet.
Species concentrations (or EF) are available for fivecountries: Indonesia, Brunei, Malaysia, Singapore andThailand, but are missing for other countries in SEA.

3. Chemical Compositions of Peatland Fire
Aerosols
3.1 Carbonaceous Components
When haze was induced by IPFs, a significant
increase in the concentrations of total carbon (TC),organic carbon (OC), water-soluble OC (WSOC) andcarbon content of humic-like substances (HULIS-C) was
typically observed at receptor sites as shown in the
Supplementary Information (“Carbonaceous Components”
sheet in the dataset). For example, the OC concentrationsin the total suspended particulates (TSP) during a haze
period in 1991 and a non-haze period in 1992 at Petaling
Jaya in Malaysia were 74 and 14 μg m-3, respectively2. In
Singapore, significantly higher OC concentrations wereobserved107 in the PM2.5 of smoke dominant samples (25 ±7 μg m-3) compared to those of non-smoke dominantsamples (5 ± 1 μg m-3) in 2012–2013 and 2015. In
Thailand, the OC concentrations in PM2.5 during haze andnon-haze periods in 2019 – 2020 were 10.1 and 2.1 ±0.2 μg m-3, respectively120. Although the WSOC data arelimited, there are some reports of an increase in WSOC
concentrations during haze events in Malaysia105,
Singapore91 and Thailand120,122. For HULIS-C, Fujii
et al.105 reported that the HULIS-C concentrations during
a strong haze event (17.5 μg m-3) at Bangi, Malaysia werehigher than those during a non-haze period (1.3 –4.4 μg m-3). In contrast to some of the above species suchas OC and WSOC, the elemental carbon (EC) and black
carbon (BC) concentrations showed no significant
increases during haze events in Malaysia2, 50, 55, 56, 105,
Singapore25, 43, 89, 91, 107 or Thailand120, 122. IPFs aresmoldering fires undergoing a slow, low-temperature,
flameless form of burning49, which produces muchsmaller EC (BC) emissions than OC emissions, as seen in
the laboratory experiments in the dataset. Therefore,
IPF-derived EC contributes less to non-haze levels of EC
at receptor sites according to dilution duringtransportation. The chemical speciation of brown carbon(BrC) constituents is described in Section S4.3 of the
Supplementary Information.
Based on laboratory chamber combustion
experiments using Indonesian or Malaysian peat, the EFsof OC, WSOC and EC were determined as 6.02–18.5,
3.1–3.84, and 0.04 – 0.57 g kg-fuel-1, respectively17, 26, 90, 98.
Iinuma et al.26 also reported EFs of water-insoluble
OC (WIOC) as 4.9 g kg-fuel-1. Stockwell et al.63 andJayarathne et al.77 presented EFs of OC and EC based on
peatland fire source sampling in Central Kalimantan,
Indonesia. Their EFs of OC (12.4 –16.0 g kg-fuel-1) andEC (0.24 g kg-fuel-1) were in the range of those based onpeat burning experiments17, 26, 90, 98. Chow et al.83 andWatson et al.90, respectively, reported the species
abundances in PM2.5 mass and EFs for carbonaceouscomponents (OC, EC and WSOC) based on aerosol datafrom laboratory chamber combustion of fresh and aged
Malaysian peat, separated by two- and seven-day
photochemical aging times simulated with an oxidation
flow reactor (below, “OFR”). The EF of OC for freshparticles was 17.9 – 18.2 g kg-fuel-1 and that for aged
particles decreased to 14.0 – 14.7 g kg-fuel-1 90. In particular,
reduction of OC1 (the lowest temperature OC fraction
obtained from thermal-optical method, see the “Appendix”
sheet in the dataset.) was revealed, due to the loss ofhigh-vapor-pressure semi-volatile organic compounds
upon aging. In contrast, the EF of WSOC, as well as masspercentage of WSOC in PM2.583, increased after oxidationin the OFR90. Based on the field observations in Malaysia,
Fujii et al.106 mentioned a high possibility of secondary
WSOC formation during transport from the IPF source toreceptor sites.
3.2 Water-Soluble Ions
During haze events induced by IPFs, a significant
increase in the concentrations of sulfate (SO42-) andammonium (NH4+) was typically observed at receptor
sites. For example, significant differences in the total
concentrations of water-soluble ions were observed
between haze (33 ± 6.5 μg m-3) and non-haze (5.5 ±2.6 μg m-3) periods in Pekanbaru, Indonesia85. They thenshowed the average concentrations of SO42- and NH4+
during haze events to have been 7.3 and 7.8 times higher
than those during non-haze periods. Highly elevatedChemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires

concentrations of SO42- (20.4 ± 18.4 μg m-3) and NH4+
(7.16 ± 6.66 μg m-3) were observed during haze events
compared to those during pre-haze periods (SO42-: 2.24 ±1.22 μg m-3, NH4+: 0.41 ± 0.35 μg m-3) and post-haze
periods (SO42-: 1.79 ± 0.98 μg m-3, NH4+: 0.16 ±0.12 μg m-3) in Kuala Lumpur, Malaysia71. Similar results
were also obtained at other locations in Malaysia such as
Petaling Jaya55 and Bangi56. In Singapore, significantly
higher concentrations of SO42- (15 ± 3 μg m-3) and NH4+
(6 ± 2 μg m-3) were observed107 in smoke-dominantsamples compared to those in non-smoke dominantsamples (SO42-: 4 ± 4 μg m-3, NH4+: 1.6 ± 1.3 μg m-3). At
other locations in Singapore, similar results were also
obtained16, 25, 30, 41, 43, 48, 53, 89. In Thailand, Chaisongkaew
et al.122 reported that the concentrations of SO42- and NH4+
during non-haze periods, which represented clean backgroundair, at Thepha beach were 0.658 and 0.198 μg m-3,
respectively, whereas during a haze period, those species’
concentrations increased (SO42-: 2.168 μg m-3 (partialhaze) and 2.248 μg m-3 (strong haze); NH4+: 0.825 μg m-3(partial haze) and 0.834 μg m-3 (strong haze)). Similar
results were also obtained in Hat Yai, Thailand120.
In general, potassium (K+) has been used as abiomass burning indicator (Chuang et al., 2013). Thus,
high K+ concentrations during IPF-induced haze eventsare expected at the receptor sites. In most cases of haze
induced by IPFs, however, partial overlapping of thevariation ranges of K+ concentrations were observed
between samples collected during haze and non-haze
periods as shown in the Supplementary Information (the“Water-soluble Ions” sheet in the dataset and Fig. S1).
Fujii et al.49 mentioned K+ was not appropriate as anindicator of IPFs due to an extremely low K+ fraction atsource (0.0423 ± 0.0400 wt.% of PM2.5), and a lower
fraction of 0.026 wt.% was obtained at a site near anotherfire source77, as shown in Fig. S2 in the Supplementary
Information.
Based on laboratory chamber combustion
experiments on Indonesian or Malaysian peat, chloride(Cl-), nitrate (NO3-) and SO42- were determined as themain ions in most cases26, 36, 83, 90. The EFs of Cl-, NO3and SO42- for fresh particles were reported as 25.5 – 48,
18.6 –28.6 and 15 –37.2 mg kg-fuel-1, respectively26, 90,
which were much smaller than the EFs of OC (6,020 –
18,500 mg kg-fuel-1) and WSOC (3,100–3,840 mg kg-fuel-1)(see Section 3.1). Although NH4+ was one of the main
water-soluble ions in PM collected at receptor sites duringhaze periods, the EF of NH4+ based on laboratorychamber combustion experiments26, 90 only came to 0.56 –0.608 mg kg-fuel-1. Chow et al.83 and Watson et al.90,
respectively, reported species abundances in PM2.5 massand EFs for water-soluble ions based on fresh and aged
aerosol data from laboratory chamber combustion of
Malaysian peat, separated by two- and seven-day
photochemical aging times simulated with an OFR. The
EFs of NO3- and SO42- for fresh particles were 18.6 ± 6.63

41

and 37.2 ± 3.18 mg kg-fuel-1, respectively, and those forparticles after two days of aging increased significantly to
171 ± 14.0 and 105 ± 6.58 mg kg-fuel-1, respectively.
After seven days of aging, further increases in the EFs of
NO3- (1200 ± 365 mg kg-fuel-1) and SO42- (502 ±32.2 mg kg-fuel-1) were revealed. Similar patterns werealso observed for NH4+ (EF: 0.411 – 0.608 mg kg-fuel-1 infresh particles, 159 ± 34.7 mg kg-fuel-1 after two days
aging and 1,210 ± 219 mg kg-fuel-1 after seven days
aging) and nitrite (NO2-) (EF: ~0.00 mg kg-fuel-1 in freshparticles, 0.148 ± 5.48 mg kg-fuel-1 after two days aging
and 3.89 ± 7.07 mg kg-fuel-1 after seven days aging). In
contrast to the above ions, the EF of Cl- in fresh particles
was 25.5 – 34.9 mg kg-fuel-1 and that in aged particles
decreased to 19.1–26.6 mg kg-fuel-1.
3.3 Elemental Components
There are many reports on elements in PM at
receptor sites (especially in Singapore) during periods of
IPF-induced haze. The number of detected elements in
those reports, however, ranged from 3 to 48, showing
considerable
differences
amongreports(see
Supplementary Information (“Elements” sheet in thedataset)). Based on field observations in Singapore,significant increases in the concentration of Al25, 37, 42, 43, 58,
K9, 25, 42, 58, Cr37, 47, 58, Mn37, 47, 58, Fe25, 37, 42, 43, 58, Co37, 47, 58,
Cu25, 37, 43, 58, Zn25, 37, 58, Cd37, 47, 58 and Pb37, 58, 118 werereported by several researchers. Although someresearchers also reported higher concentrations of otherelements such as Ca25, 42 and Ni37, 58 during haze events in
Singapore, the number of such reports is quite limited. In
Kuala Lumpur, Malaysia, Sulong et al.71 analyzed 18
elements in PM2.5 during pre-haze, haze and post-haze
periods. They then showed a significant increase of K
during haze (604 ± 301 ng m-3) compared to pre-haze(208 ± 118 ng m-3) and post-haze (195 ± 73.7 ng m-3)
periods. In Hat Yai, Thailand, Promsiri et al.120 analyzed
11 elements in PM2.5 from transboundary haze samples(only two samples) and background samples. There, thehighest concentrations of heavy metals (Cd, Co, Cr, Mn,
Ni and Pb) were found during haze events, but neither As
nor Cd were detected. They also mentioned K was about
three times higher than background levels during the haze
events. A similar pattern with K was also observed at
Thepha Beach, Songkhla Community College, the Wang
Yai Sub-district and Pattani City in Thailand122. At an IPF
source, Betha et al.34 showed higher concentrations of Al,
Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd and Pb in IPF
samples than in background air samples collected at thesame sampling point in the absence of IPFs.
The EFs of several elements based on laboratorychamber combustion experiments of Malaysian peat werereported by Watson et al.90. They showed, for example,
that the EFs of Al, K and Fe in fresh PM2.5 were 8.79 –
9.94, 6.13 –10.4 and 10.3 –17.0 mg kg-fuel-1, respectively.
Detailed information on other elements is provided in the42

Y. FUJII and S. TOHNO

Supplementary Information (“Elements” sheet in thedataset). In other experiments, Othman and Latif 36
analyzed eight elements (Cd, Cu, Zn, Fe, Al, Pb, Cr andNi) in PM10 from laboratory chamber combustion of
Malaysian peat soil, and indicated that the dominantelement was found to be Zn, followed by Al and Fe. Daset al.84 analyzed 14 elements (Mg, Al, Ca, V, Cr, Fe, Co,
Ni, Cu, Zn, Se, Sr, Sn and Pb) in PM from Indonesianpeat burning by controlled laboratory experiment. They
showed the emission ratios (mass fraction in PM) of Ca(516 ± 247 μg g-PM-1) to be highest, followed by those ofcrustal elements Fe (189 ± 117 μg g-PM-1) and Al (34 ±18 μg g-PM-1). Major elemental concentrations and massfractions in PM are shown in Figs.S3 and S4 in the
Supplementary Information.
According to the dataset, the Al/Pb ratios of PM2.5
collected during IPF-derived haze periods at receptor sites
and at a fire site were mostly much lower (3−50) thanthe Al/Pb ratios of TSP118 during haze (4,721) andnon-haze (669) periods, and the ratios of the freshlaboratory-generated particles from Indonesian and
Malaysian peat combustion ranged from 11 to 98 in thedataset. The discrepancy in the Al/Pb ratios between theabove TSP samples and fine particles in the dataset can be
explained: Because of uplifting and suspension of surface
soil particles during wildfires (wind erosion), mineral dust
is mainly confined in a coarse mode, while biomassburning-derived mineral particles are smaller (in a finemode) with different average bulk compositions (Jahnet al., 2021).
Atmospheric deposition is an important nutrient
source for freshwater and aquatic ecosystems, andexcessive loading of nutrients accelerates eutrophication(Swackhamer et al., 2004). The chemical composition ofnutrients (total N and P, organic N and P, and ions) in TSP(and rainwater) was determined during IPF-derived hazyand non-hazy days to quantify their dry (and wet)deposition fluxes to the coastal water of Singapore30. The
average concentrations of nutrients (N and P species)
increased by a factor of three to eight on hazy days whencompared with non-hazy days (total-N: 13 µg m-3, total-P:
0.5 µg m-3) 30. This finding reflects a remarkable elevationof total PM2.5 mass concentration on hazy days (24 to
113 µg m-3) because there is little difference in their PM2.5
mass percentages between hazy and non-hazy days. Bothof the estimated mean dry atmospheric fluxes of total N
and P during the hazy days were about tenfold higher
compared to the non-hazy days.
3.4 Organic Compounds
In this section, the target organic compounds areanalyzed by widely-used offline (conventional) analysis
methods such as gas chromatography-mass spectrometry(GC-MS). The results analyzed by online aerosol mass
spectrometer (AMS) are presented in Section 4. There areseveral reports of organic compounds aside from PAHs in

PM during IPF-induced haze events in Indonesia28, 89,
Malaysia2, 20, 21, 50, 56, 64, 76, 88, 105 and Singapore7, 16, 25, 27, 29,
40, 43, 65, 89, 107. The target compounds, however, havediffered considerably between researchers. More data on
levoglucosan (LG) are available compared to othercompounds. LG is a typical cellulose pyrolysis productand has been widely used as a useful biomass burningmarker (Simoneit et al., 1999). In Indonesia, Tham et al.89
conducted PM2.5 sampling near IPF sources(approximately 2 km downwind from IPF sites) in Jambi,
Sumatra Island, and a high LG concentration (840 ±700 ng m-3) in PM2.5 was observed. In Bangi, Malaysia,
Fujii et al.56 showed significant differences in LG
concentrations in TSP between haze (strong haze:
1,078 ng m-3, light haze: 352 ± 22.9 ng m-3) and non-haze(207 ± 39.3 ng m-3) samples. Similar results have also
been obtained at other locations in Malaysia such as
Kuala Lumpur20, 88 and Petaling Jaya50. In Singapore, Engling
et al.43 reported that the LG concentrations in TSP duringhaze events (1150.9 ± 917.2 ng m-3) were much higher
than those during the non-haze periods (15.3 ± 5.7 ng m-3).
Similar results were also obtained in Singapore29, 40, 89, 107.
Aside from LG, Fujii et al.50 quantified many organic
compounds derived from biomass burning and n-alkanes
through annual observations of PM2.5 in Petaling Jaya,
Malaysia, including IPF-induced haze periods. They thenshowed significant increases in the concentrations ofmannosan (MN), galactosan (GL), vanillin (V), vanillicacid (VA), p-hydroxybenzoic acid and n-alkanes withcarbon numbers from 23 (C23) to 33 (C33) for PM2.5
affected by IPFs at the receptor site. For
p-hydroxybenzoic acid, through intensive observations of
TSP in Bangi, Malaysia, Fujii et al.56 reported even during
light-haze
periods,thep-hydroxybenzoic
acid
concentrations were significantly higher than those during
non-haze periods, showing it to be a more useful indicatorfor IPFs compared to LG, MN and GL. Regarding
n-alkanes, through comparison of molecular distributionsof particulate n-alkanes obtained from IPFs andvehicle-related emission sources, Fujii et al.49 showed
particularly that C27, C28 and C29 mass fractions inparticulate total n-alkanes (C20-C33) of IPF-source
samples collected in Riau, Sumatra, Indonesia were 4.57–
26.6 times higher than those of other emissions.
Jayarathne et al.77 also agreed that n-alkanes could be
used to distinguish IPF emissions from other types ofbiomass burning or other combustion sources.
The EF of LG was reported as 4.21–2,500 mg kg-fuel-1
based on laboratory chamber combustion experiments ofIndonesian26 or Malaysian peat90, 98. The EFs of otherorganic compounds such as MN26, 90, GL26, lignin
pyrolysis products26, 98 and n-alkanes26, 98 are also
provided in the Supplementary Information (“Organic
Compounds” sheet in the dataset). Chow et al.83 andWatson et al.90, respectively, reported the species
abundances in PM2.5 mass and EFs of several organicChemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires

compounds based on fresh and aged aerosol data fromlaboratory chamber combustion of Malaysian peat,
separated by two- and seven-day photochemical aging
times simulated with an OFR. Although there was no
significant difference in the weight percentage of LG in
the PM mass between fresh (2.25 ± 0.016%) and two-day
aged (2.28 ± 0.99%) samples, such a difference wasobserved83 between fresh (4.38 ± 0.50%) and seven-day
aged (2.53 ± 0.19%) samples. For formic acid and oxalic
acid, Chow et al.83 reported the weight percentages of
formic acid and oxalic acid in the PM mass of freshparticles were 0.10 ± 0.014 and 0.26 ± 0.12%,
respectively, and those after two days aging increased to0.26 ± 0.049 and 1.14 ± 0.21%, respectively. After sevendays aging, further increases in the weight percentage of
formic acid (0.42 ± 0.10%) and oxalic acid (3.36 ±
0.28%) were revealed, indicating secondary organic
aerosol (SOA) formation related to these compounds.
3.5 PAHs
Some studies5, 29 have measured the concentrations of
PAHs in the gaseous and particulate phases, but not allphase concentrations were available, so the data were
excluded. Saha et al.70 analyzed 36 PAHs, but in mostcases, the concentrations of the US EPA 16 PAHs (see the“Appendix” sheet in the dataset) in PM during IPFderived haze periods have been analyzed in fieldmeasurements. The concentrations of all 16 PAHs in PM2.5(hereafter, “total PAHs level”) at receptor sites ranged
from (a) 0.15 ng m-3 in Thailand120 to (b) 61 ng m-3 in
Singapore (severe haze in 2010)37. However, theconcentration rose to (c) 562 ng m-3 100 m away from theboundary of a peat fire in Riau Province of Sumatra,Indonesia28. The mass fractions of the total of the 16
PAHs in PM corresponding to cases (a), (b) and (c) were30, 570 and 970 ppm (μg g-PM2.5-1), respectively. The
differences in mass fraction suggest differences in haze
intensity, as well as dilution and transformation, duringtransportation. The concentration varied significantly,
depending on IPF intensity, distance from the emission
source to the receptor site, meteorological conditions andlocal sources.
The total PAHs concentration increased during haze
periods as compared with during non-haze periods. For
example, the total PAHs level at Kuala Lumpur in
Malaysia88 was 3.40 ng m-3 during the 2015 haze period,
while the annual average concentration (including thehaze period) was 2.04 ng m-3. In Singapore37, the total
PAHs level increased from 24 ng m-3 during a non-haze
period to 61 ng m-3 during a severe haze event in 2010. In
Thailand, the average of total PAHs levels for four sites
was 0.20 ng m-3 during the 2019 haze period and0.13 ng m-3 in the non-haze period122 (Fig. S5 in the
Supplementary Information). High molecular weight
PAHs predominated in the haze period24, 88, 92, 119, 122. PAH
congeners that increased in concentration during the haze

43

period were Acy, Ace, Flu, Phe, Flt, Pyr, BaA, Chr, BbF,
BaP, Ind, BgP, and DhA (these abbreviations of congeners
are spelled out in the “Appendix” sheet of the dataset) for
Indonesian samples28. In Singapore37, an increase incongener concentrations was found for 13 congeners (Nap,
Flu, Phe, Ant, Pyr, BaA, Chr, BbF, BkF, BaP, Ind, BgP
and DhA) during the 2010 haze period. Among them, Pyr,
Chr, BbF, BkF, BaP, InD and DBA levels weresignificantly higher than usual37. In Kuala Lumpur88, thedominant PAHs in PM2.5 were BbF, BkF, BaP, Ind and
BgP during the 2015 haze event. In Thailand119, BkF, Act,
DhA, BbF were the dominant congeners in the 2019strong haze. Although the total PAHs concentration was
specified, there were limited concentration data available
on individual congeners (graphically depicted data only,
or not indicated). Therefore, the difference in species
between sampling sites was not significant.
In laboratory experiments, the EF of total PAHs in
PM10 from natural combustion of Indonesian peat was
20 mg kg-fuel-1, and the dominant congeners were Flu(11 mg kg-fuel-1), Phe (2.7), Pyr (1.5), Ant (1.1) and Flt(1.1)26. Sengupta et al.121 quantified the gas and PM EFs
of 133 PAHs and their derivatives from smoldering
combustion of Malaysian peat and Malaysian agricultural
peat (and four other fuels), and their chemicaltransformations during aging in an OFR. The EF of all
113 PAHs in fresh particles was 1.80 μg kg-fuel-1 and thatin aged particles was reduced to 0.25 μg kg-fuel-1 for
Malaysian peat. For fresh particles from the combustionof Malaysian peat, the dominant congeners were Phe(88.6 ng g-1), Flt (47.5) and BgF (20.8). In aged particles,
the dominant congeners were Ace (20.2 ng g-1), Flt (20.1)
and Phe (14.8). After oxidation of Malaysian peat
combustion emissions in the OFR, the EF of three-ringparticle-phase PAHs decreased by a factor of nearlyeight121.
PAH derivatives constitute a class of organic
compounds derived from parent PAH structures with avariety of functional groups or elements. Typically they
are nitrated (nitro) PAHs (NPAHs), oxygenated PAHs
(OPAHs), halogenated PAHs (XPAHs) or alkylated PAHs
(APAHs) (Peng et al., 2023). In field measurements, only
OPAHs were quantified, and the concentrations of thecongeners were in the range of 0.03 to 4.46 ng m3 during
the 2006 haze period in Singapore29, but the data were thesum of the gas- and TSP-phase OPAHs. Particulate
OPAHs were analyzed in the two laboratory experiments
mentioned in the above paragraph26, 121. No OPAHs weredetected in the experiment by Sengupta et al.121, but
Iinuma et al.26 quantified the EF of 9-fluorenone(0.75 ng g-1) and 9,10-anthracenedione (0.29). In our
survey, no other derivatives aside from OPAHs were
found.
Diagnostic ratios were used for source apportionmentof ambient PAHs in PM2.5 collected in Bangi, Malaysia
during the southwest monsoon (including haze) in 201351,

44

Y. FUJII and S. TOHNO

PM2.5 collected in Kuala Lumpur during different
monsoon seasons88, and PM0.1, PM1 and PM2.5 collectedin southern Thailand during normal, partial and strong
haze periods in 2019119. Bivariate plots of Flt/(Flt+Pyr)versus Ind/(Ind+BgP) indicated that the major sources ofPM during strong haze periods were predominantly frombiomass burning in the case of Thailand119. Two otherstudies indicated different sources without specifying
a definite source of the haze. Figure S6 in the
Supplementary Information shows three bivariate plots of
PAH ratios for haze and non-haze periods using ourdataset. In Fig. S6 (A), the three blue circles in the upper
right region (biomass burning region) are clearly
distinguished from the other data. The data are fromsamples collected at three sites in Riau, Indonesia during
the 2015 fire episode28, and the sampling sites were 100 m,
60 km and 220 km away from the fire source. However,no definite sources of haze were identified for the otherdata. Moreover, contributions from local sources candominate the contribution of IPF-derived haze15.
Unfortunately, the concentration of each PAH congener iseither indicated only as graphical data (Thailand data119
mentioned above) or not available in some reports. Morequantitative data may improve source identification of
IPF-derived haze.

4. Aerosol Mass Spectra Analysis for Chemical
Characterization
A time-of-flight aerosol chemical speciation monitor(ToF-ACSM) provides continuous online size andnon-refractory (NR) chemical composition data on
individual sub-micron aerosol particles without filter
sampling (Fröhlich et al., 2013). It produces
ensemble-average mass spectra for organic and inorganic
species, and organic aerosol (OA) mass spectra can be
deconvolved in combination with different statisticaltechniques to estimate sources, composition andatmospheric processing of OAs (e.g., Chen et al., 2020).
In recent years, a research group based at Nanyang
Technological University (hereafter “the Singapore
group”) used ToF-ACSM to characterize the physical andchemical properties of organic aerosols during the 2015
haze episode in Singapore 65, 73, 74. However, there are no
field studies of IPF aerosol using ToF-ACSM in other
ASEAN countries or at peatland fire sites in our dataset.
Laboratory experiments are a powerful approach for
determining in-depth chemical compositions of fresh andaged peat-burning aerosols and their influencing factors
using ToF-ACSM under controlled environmental
conditions. Laboratory experiments on aerosols fromIndonesian-peat and other biomass combustion have been
investigated by the Singapore group 65, 66, 69, 81, 82, 86, 102, 109, 112
using a flow-type reactor with ToF-ACSM, and by Ahern
et al.81 using batch-type dual chambers with an Aerodyne
high‐resolution AMS and a proton transfer reaction mass

spectrometer (PTR-MS) for volatile organic compound
measurement.
Detailed analyses of chemical compositions,secondary aerosol formation and the relationship between
chemical compositions and physical properties areprovided with additional documents in the Supplementary
Information.

5. Source Apportionment of PM during Haze
Episodes5.1 Key Indicators of Peatland Fire Aerosols
Several indicators for IPFs have been suggested fromthe diagnostic ratios of chemical species based on fieldobservation data (source and/or receptor sites) andlaboratory burning experiments. OC/EC, WSOC/OC and
HULIS-C/WSOC in PM provide a rough indication oforigins or characteristics of carbonaceous aerosols(Cao et al., 2005; Frka et al., 2018; Shen et al., 2014).
The
OC/EC
obtainedfromsource-dominatedfield observations28, 44, 63, 77, 106 and laboratory burning
experiments17, 26, 66, 83, 90, 95 were 2.42–223 and 14 –163,
respectively. The discrepancy among these data can be
attributed to differences in combustion conditions such asfuel load, moisture content (Chen et al., 2010),
smoldering kinetics and the reaction-zone structure of the
IPF (Huang and Rein, 2014). In addition, the use ofdifferent protocols such as NIOSH and IMPROVE for
OC/EC determination may also lead to uncertainties (Wuet al., 2016). Thus, to show the representative OC/EC for
an IPF is extremely difficult, as mentioned in Fujii
et al.106. At the receptor sites, obvious increases in
OC/EC during haze periods were consistently observed
compared to during non-haze periods in Malaysia2, 50, 56,
Singapore16, 25, 43, 89 and Thailand120. WSOC/OC obtained
from source-dominated field observations77, 106 andlaboratory burning experiments23, 26, 66, 83, 90 was 0.085 –0.16 and 0.0093 – 0.87, respectively. WSOC/OC obtained
from certain laboratory burning experiments23, 26 differedsignificantly from those obtained through fieldobservations. These laboratory burning systems may notaccurately represent real-world IPFs. Fujii et al.105
showed WSOC/OC in TSP collected in Bangi, Malaysia
during a haze period (strong haze: 0.34, light-haze: 0.39 ±0.034) was slightly lower than that during a non-haze
period (0.41 ± 0.064). Based on these data, Δ(WSOC/OC)
was calculated by subtracting the average concentrationsof OC and WSOC during the non-haze period from thoseof the haze period106. The resulting Δ(WSOC/OC) was
0.36 ± 0.055, which is higher than the WSOC/OC
obtained at the IPF source77, 106, suggesting secondary
WSOC formation during transport from IPF sources toreceptor sites. As an indicator, WSOC/OC is not useful for
IPFs. Regarding HULIS-C/WSOC, only one set of fieldobservation data is available105. Regarding that report,
although HULIS-C/WSOC during the haze period inChemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires

Bangi, Malaysia tended to be higher than that during a
non-haze period, some exceptions to this trend wereobserved.
Employing data on carbon fractions following the
IMPROVE_A protocol, OP/OC4 in TSP and (OC2 + OC3+ OP)/(EC2 + EC3) in PM2.5 were suggested as indicatorsof IPFs by Fujii et al.56 and Tham et al.89, respectively. OP
indicates pyrolyzed OC. Even for PM2.5 samples collectedin Malaysia, Fujii et al.50, 67 confirmed the effectiveness of
OP/OC4 as an indicator for IPFs. However, in Singapore,
there were no significant differences in OP/OC4 between
IPF-dominant smoke (2.1 ± 1.2) and non-smoke-dominantsamples (2.3 ± 1.6). We calculated (OC2 + OC3 +OP)/(EC2 + EC3) by using data on carbon fractions in
TSP collected in Bangi, Malaysia during strong haze, lighthaze and non-haze periods56. The results showedsignificant differences between haze (strong haze: 9.07 ±0.29, light haze: 3.72 ± 0.61) and non-haze samples (2.04± 0.69) even in Malaysia. The usefulness of this indicator
is demonstrated at source and receptor sites in the dataset.
Although further validation of this indicator is needed,(OC2 + OC3 + OP)/(EC2 + EC3) seems to be usefulcompared to OP/OC4.
MN/LG has been used in the source assignment ofspecific biofuels (e.g., Alves et al., 2010). Based on areport by Fabbri et al. (2009), differences in MN/LG
between hardwood burning (0.042 – 0.077) and softwoodburning (0.15 – 0.26) can be shown106. MN/LG in PM at
IPF sources44, 106 ranged widely from 0.021 to 0.095.
Whether obtained from laboratory burning ofIndonesian26 or Malaysian peat83, 90, these ratios were lessthan 0.088. Thus, IPFs can be roughly categorized as
hardwood burning. Based on data on LG and MN in TSP
collected in Bangi, Malaysia during haze and non-haze
periods reported by Fujii et al.56, LG/MN was calculated.
The results showed MN/LG during strong haze, light hazeand non-haze periods to be 0.12, 0.113 ± 0.015 and 0.108± 0.014, respectively, showing no obvious differences
between haze and non-haze samples. Other IPF indicators
have been suggested (e.g., SA/LG106 and SA/VA49) basedon data taken at IPF sources. The effectiveness of these
indicators, however, has not been fully verified at receptor
sites. The above-mentioned indicators are summarized inTables S6 and S7 in the Supplementary Information,
based on the dataset.
5.2 Isotopic Compositions as a Source Signature
Some studies have been conducted to investigate theisotopic composition of haze from IPFs as a potential
signature of these fires7, 15, 62, 80 and wind-blown mineral
dust particles during IPF events118.
For PM2.1 samples collected in Singapore during the1997 IPF and in Indonesia after the major fires had diedout, the carbon isotropic ratio of TC (δ13CTC, defined in
the “Appendix” sheet in the dataset) showed a decrease
from about −25.5‰ to −27.5‰ with an increase in TC

45

concentration (9 – 81 µgC m-3)7. The results suggest an
addition of OA from the combustion of C3 plants whoseδ13C is lower. The δ13C of individual PAHs in the haze
(TSP) in Malaysia from the 1997 IPF ranged from −17.7
to −27.9‰15. The isotopic ratios were similar to those ofsoot PAHs extracted from gasoline and diesel vehicles;
therefore the δ13C of the individual PAHs in the hazecannot serve as a signature of IPFs in Malaysian urban air
polluted by automobile exhaust.
209
Po and 210Po activities were determined for three
extracted fractions of Fe and Mn oxides, organic matterand residuals in PM10 (marine aerosol) sampled for two
years along the east coast of Malaysia 62. During thesouthwest monsoon period when strong transboundary
haze affects this area, a significant amount of excess 210Po
in fractions bound to organic matter (119 ± 85 μBq m-3,
cf. 54 ± 55 of ambient values), which are regulated byseasonal changes62, could be an IPF signature.
Wiggins et al.80 estimated the age and origin of
IPF-emitted PM2.5, utilizing the radiocarbon content (14C)of carbonaceous PM2.5 collected in Singapore during hazeand non-haze periods80. A Keeling plot (Mouteva et al.,2015) was used to separate urban backgroundcontributions, and the Δ14C for the haze period wassignificantly lower than atmospheric levels (−76 ± 51‰
vs. 25 ± 3‰), corresponding to a carbon pool of combusted
organic matter with a mean turnover time of 800 ± 420
years estimated by a radiocarbon box model80. Under anassumption about TC EF and peat Δ14C profiles, a Monte
Carlo analysis indicated that the smoke plumes reaching
Singapore originated primarily from peat burning (∼85 %),and not from deforestation fires or waste burning80.
Isotopic ratios of Pb (206Pb/207Pb and 208Pb/207Pb) in
TSP samples collected during the IPF-derived haze period
in Singapore in 2013 were 1.169 ± 0.012 and 2.448 ±0.013, respectively118. There was a statistically significant
difference between the two ratios during haze andnon-haze periods (1.159 ± 0.005 and 2.438 ± 0.006). The
difference in the Pb isotope ratios could be explained bymixing 10% – 70% of mineral dust (coarse) particles fromsuspension of crustal soil during fire events118. The
contribution of such mineral particles to TSP during thehaze period was supported by enhancement of the Al/Pb
ratio (average: 4,721) comparable to the ratio in crustal
material, with the average ratio during non-haze periods
being 669118.
5.3 Application of Receptor Models
In addition to chemical characterization, quantitativeknowledge of IPF source contribution to ambient PM in
SEA provides valuable information to policy makers for
mitigating IPF-haze pollution67. In this section, the resultsof IPF source apportionment by chemical mass balance(CMB) and positive matrix factorization (PMF) models
are described. Detailed information on CMB and PMF
models, as well as other receptor models, are provided in46

Y. FUJII and S. TOHNO

Seinfeld and Pandis (2016).
There are few reports regarding the results of PM
source apportionment of IPFs by the CMB model inIndonesia28, Singapore43 or Thailand120. In brief, Seeet al.28 reported that IPFs contributed 51% and 18.1% of
the PM2.5 mass during haze periods at receptor sites in
Belakang Rumah and Pekanbaru, respectively, in Sumatra,Indonesia. In Singapore, Engling et al.43 reported that
IPFs were the predominant source (75.6% of TSP mass)
during a haze event, followed by diesel exhaust (18.6%)
and ship emissions (5.3%). In Thailand, Promsiri et al.120
reported during a transboundary haze event, PM2.5 in Hat
Yai was dominated by IPF sources (50% of the PM2.5mass), followed by rubber wood burning (27%) and diesel
combustion (4%).
Several sets of data on PM source apportionment
by PMF model are available in Indonesia32,115 andMalaysia31, 51, 52, 55, 60, 61, 67, 71, 88, 108, 110, 111. However, only
Fujii et al.67 have discussed IPF contribution clearly. They
reported throughout their sampling periods (one year) that
IPFs contributed, on average, 6.1–7.0 μg m-3 to PM2.5 or~30% of PM2.5 mass. In particular, PM2.5 was dominantly
sourced from IPFs during the southwest monsoon season(51% – 55% of the PM2.5 mass on average). Other reports
have mentioned potential source contributions of IPFs at
receptor sites52, 55, 60, 61, 71, 88, 115.
Information from studies on source contributions
from CMB and PMF as well as principal component
analyses are summarized in the Supplementary
Information (“Source Apportionment” sheet in thedataset).

6. Size Distributions
As mentioned in Section 1, biomass burning aerosols,
including from peatland fires, are mostly in the fine size
range (Reid et al., 2005). Therefore, the total mass andchemical compositions of IPF-derived aerosols have been
widely determined for PM2.5 using a high volume air
sampler or impactor as in Table S8. Simple size
segregation such as coarse and fine PM sampling was
conducted by dichotomous114, 115 or Gent stacked filter
unit particle sampler9, 14, 31, 32, 52, 87, 97. To obtain higher
size-resolved (size distribution) mass and chemicalcompositions of IPF-derived haze, multi-stage cascade
impactors such as an Andersen sampler92, MOUDI(see the “Appendix” sheet in the dataset)25, 47, 48, Nanosamplers92, 96, 100, 101, 111, 119 and others have been employed.
A multi-stage impactor requires a longer sampling time,
especially to determine reliable size distributions of traceelements in haze, and is time-consuming andlabor-intensive. Recently, real-time characterization oforganic aerosols in haze (NR-PM1) has been conductedusing an AMS as described in Section 4. There are not
many, but real-time number size distributions have beenmeasured by SMPS23, 25, 53, 74, 81, 102, 109, OPC13, 74 or othermethods in field and laboratory experiments.
Details on the size distributions of total mass andchemical species are provided with additional documentsin the Supplementary Information.

7. Conclusions
In this paper, we have conducted a holistic review ofsource profiles, key indicators of IPFs at source andreceptor sites, and the transformation of haze during its
long-range transport (aging, secondary aerosol formation)
as well as source apportionment in SEA, using acomprehensive chemical component dataset. Knowledgeof the chemical characteristics of PM at IPF source andreceptor sites is still limited, especially regarding thecontrolling factors (e.g., burning conditions, peatcomposition and effects of vegetative burning on
peatlands) that determine the IPF source profile of PM.
Clarifying these factors can lead to more reliable andspeciated emission inventories of IPFs, enabling bothchemical transport and radiative forcing modeling andhealth risk assessments to advance. Additionally, theprocess of IPF-derived SOA formation during transport toreceptor sites remains largely unresolved. Recently
Lopez-Hilfiker et al. (2019) developed an extractive
electrospray ionization time-of flight mass spectrometer(EESI-ToF) with generally insignificant decomposition offragmentation products of OAs. Furthermore, Siemens
et al. (2023) revealed aging characteristics of BBOA
using multimodal MS techniques (combining AMS,EESI-ToF, offline HPLC-PDA-HRMS and tandem mass
spectrometry). Measurements of aerosols emitted atpeatland fire sites using cutting-edge techniques withprecursors measurements and contributions of aqSOA to
IPF aerosols would promote an understanding of theformation mechanism of SOA and OPOA, along with
SOA source apportionment.
Acknowledgments
This study was partially supported by JSPS Kakenhi(grant number: 21K17896).
Supplementary Information
Supplementary Information related to this article
can be found on Zenodo (https://doi.org/10.5281/
zenodo.10699445). Included are the newly created dataset
and supplementary documents (for Sections 4 and 6),
tables and figures.
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Yusuke FUJII
Yusuke Fujii is an associate professor at Osaka
Metropolitan University, Japan. His research
focuses on chemical characteristics of aerosols
derived from biomass burning and their impacts
on the atmospheric environment at the local andregional scales. In 2018, he was selected as amember of the Leading Initiative for Excellent Young Researchers(LEADER) project at the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT), JAPAN.

Susumu TOHNO
Susumu Tohno is a professor emeritus at Kyoto
University, Japan. His research interests involvethe dynamic state of atmospheric aerosols andcomprehensive evaluation of the hidden
environmental and social impacts of the humanactivities on the basis of life-cycle thinking. He
was the 16th president of the Japan Association of Aerosol Science andTechnology (JAAST).

(Received 2 November 2023, Accepted 28 December 2023)