27

Oxidative Stress Induction Ability of Particles Emitting from
Agricultural Open Burning in Japan
Yuji FUJITANI1*, Akiko FURUYAMA1, Seishiro HIRANO1, Akihiro FUSHIMI1
Katsumi SAITOH1, 2, Ayako YOSHINO1, Kei SATO1 and Akinori TAKAMI1
1National Institute for Environmental Studies
16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
2
Environmental Science Analysis & Research Laboratory
1-500-82 Matsuo-yosegi, Hachimantai, Iwate, 028-7302, Japan*E-mail: fujitani.yuji@nies.go.jp

Abstract
Oxidative stress due to exposure to PM2.5 that generates reactive oxygen species (ROS) is one of thepathways leading to morphologic changes and lung function decrements that are linked to exacerbation ofasthma and chronic obstructive pulmonary disease. Biomass burning (BB) is one of the largest sources of PMwith its expected induction of ROS through exposure. Open burning of agricultural residues is widely
conducted in September and October in Japan, but it is unclear to what extent the oxidative stress induction
ability (OSIA) is increased during the active BB season compared to other seasons, when the effects of BB are
different. In this study we conducted heme oxygenase-1 assays on PM2.5 samples collected during the active
BB season (October 2015) in Tsukuba, Japan, to clarify the influence of BB on OSIA. On average, BB particles
were estimated to be responsible for 25% of PM2.5, and 80% of OSIA in October 2015. At that time, PM2.5, BB
particles and OSIA were up to two times, eight times and seven times higher, respectively, than during seasonsof low BB activity. Thus, elevated concentrations and OSIA are considered to arise mainly from BB particles.
The results indicate that management of BB activity is important for good air quality and public health.
Key words: agricultural open burning, dithiothreitol assay, heme oxygenase-1 assay, oxidative stress1. Introduction
Biomass burning (BB) is a major source of
atmospheric aerosols and a cause of global and regional
issues, including adverse effects on human health(Johnston, et al., 2012) and climate system perturbations(Myhre, et al., 2013). In Japan, agricultural open burning
is often conducted during the harvest season in rice fields
in September and October to reduce agricultural residues,such as rice straw and rice husks, as well as to alkalize
fields. Estimates of domestic annual PM10 and PM2.5
emissions (as of the year 2000) from open burning (notonly field burning but also waste incineration) were 25 Gg
and 18 Gg, representing 13% and 12% of total emissions
in Japan, respectively (Kannari, et al., 2007).
Respiratory tract oxidative stress after short term
exposure to PM2.5 resulting in reactive oxygen species(ROS) generation is one pathway leading to morphologicchanges and lung function impairment, which are linked
to exacerbation of asthma and chronic obstructive
Global Environmental Research
27/2023 27-35
printed in Japan

pulmonary disease (COPD) (U.S. EPA, 2019). Cells
protect themselves against oxidative stress by generating
antioxidant compounds such as glutathione andantioxidant enzymes such as heme oxygenase-1 (HO-1).
The gene expression underlying this enzyme synthesis is
the initial stage of oxidative stress (Hellack, et al., 2017).
Thus, oxidative stress induction ability (OSIA) can beassessed in vitro through an HO-1 cellular assay (Li et al.,2003) and has been evaluated against PM samples.
Acellular methods for determining oxidative potential(OP), such as a dithiothreitol (DTT) assay (Kumagai,
et al., 2002), have also been widely performed on PM
samples as a proxy indicator of potential of oxidative
stress in the last two decades (Shiraiwa, et al., 2017; Bates,
et al., 2019). These PM assays have revealed the emission
sources and chemical components of PM that cause
oxidative stress. Previous studies have shown that BB
emissions contain high levels of humic-like substances(Hoffer, et al., 2006) which induce large amounts of ROS(Lin and Yu, 2011; Verma, et al., 2012) and that, BB©2023 AIRIES28

Y. FUJITANI et al.

particles contribute greatly to the OP of PM (Verma, et al.,2015; Daellenbach, et al., 2016; Weber, et al., 2018;
Fushimi, et al., 2021) among various emission sources. In
addition, BB organic aerosol (BBOA) has been shown tohave higher OP per unit concentration (Verma, et al.,2015) as well as higher OSIA per unit concentration thanother sub-classes of organic aerosol such as secondaryorganic aerosols (Fujitani, et al., 2023).
While chemical composition data have been
accumulated historically with wide spatiality, in contrast,
data on oxidative stress assessment are scarce. Fujitani,
et al. (2023) showed that estimation of cellular OSIA andOP can be obtained from the chemical components of PM
using concentration data of metals and subclasses oforganic aerosol including BBOA. This would be useful inassessing oxidative stress from chemical composition datain areas where data on oxidative stress are not available
and for comparison with previous epidemiologicalfindings retrospectively. However, the predictability of
OSIA or OP from chemical composition needs to befurther validated with different seasonal data sets.
In this study, to assess the extent to which BB
particle emissions affect OSIA and OP, evaluations were
performed for PM2.5 collected during periods of highactivity in open burning of agricultural residue in Tsukuba,
Japan. The results were compared with those of samples
collected during other seasons with different magnitudesof BB activity at the same sampling site. Furthermore,
OSIA and OP were estimated from concentrations ofchemical components such as metals and sub-classes oforganic aerosol and compared to actual measurements.

2. Experimental Methodology
2.1 Site Descriptions and Sampling Methods
The observation site was located at the National
Institute of Environmental Studies (NIES) center in
Tsukuba (36.05°N, 140.12°E; population, about 200,000),
Japan, about 50 km northeast of Tokyo. The main harvest
season for rice tends to span September and October, andopen burning of rice husks and rice straw are often
conducted in these two months. During the year ofobservation, 2015, rice harvesting and the burning of ricehusks peaked in the middle of September and the burningof rice straw peaked in October (Tomiyama, et al., 2017).
Atmospheric observations were conducted between 7
October and 30 October 2015. Observations wereintermittent from 16 to 20 Oct because of power outages.
PM2.5 was concurrently collected both on 20.32 cm ×25.4 cm Teflon filters (TFH-R, Horiba, Kyoto, Japan) andon quartz fiber filters (2500QAT-UP, Pall, New York,
USA) by means of a high-volume air samplers(HV-1000R, Sibata Scientific Technology, Soka, Japan)
with PM2.5 impactor (Kaneyasu, 2010). All sampling was
conducted from around 10 a.m. on weekdays to 10 a.m.
two days later (i.e., 48 hours of sampling) and on

weekends from around 10 a.m. on Friday to 10 a.m. on
Monday (i.e., 72 hours of sampling), with a sampling flowrate of 740 l min-1. During sampling, the aerosol species
in PM1.0 were concurrently measured with ahigh-resolution time-of-flight particle aerosol mass
spectrometer (AMS, Aerodyne Research Inc., Billerica,
USA) and the sub-classes of organic aerosol were
determined by positive matrix factorization (PMF)
analysis. In PMF analysis, the measured mass spectrum is
decomposed for several factors, and the concentration andmass spectrum is obtained for each factor by determining
the residual matrix become minimum. Each PMF factor
can be assigned an emission source and particle type(sub-class of organic aerosol) by its diurnal pattern, mass
spectrum comparison of PMF factors obtained in previous
studies, and correlation of time variation with external
tracers. Atmospheric pollutants (i.e., PM2.5, NO, NO2,etc.) and meteorological data were obtained continuously
from the NIES Air Quality Research Station. These PM1.0
and atmospheric pollutant data are discussed in detail by
Fujitani, et al. (2020).
2.2 Chemical Analysis of PM2.5 Samples
The Teflon filter samples were analyzed for particle
mass, water soluble ions and elements. Particle mass was
obtained by incrementing the mass of the collection media
after sampling. Before weighing the Teflon filter samples,
the Teflon filters were placed in the chamber at 25°C and
50% humidity for at least 24 hours. An electronic balance(minimum reading 0.1 mg, LA130S-F, Sartorius AG,
Göttingen, Germany) was used for weighing. Each sample
was weighed at least twice, and the results were averaged
if the difference was within 1 mg; the mean value wasadopted as the weight of the sample. The water-soluble
ion component (F-, Cl-, NO2-, NO3-, SO42-, oxalate ion(C2O42-), Na+, NH4+) was analyzed by ion
chromatography (IC850, Metrohm, Herisau, Switzerland).
For the analysis, one-third of the Teflon filter was placedin a 15 ml polypropylene container, 10 ml of ultrapure
water was added, and ultrasonic extraction using an
ultrasonic bath (US-5A, ASONE, Osaka, Japan) was
performed for 10 min two times. The extract was filteredthrough a disposable membrane filter (hydrophilic PTFE)
with a pore size of 0.45 μm. Elements (Be, Na, Mg, Al, Si,
K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo,
Ag, Cd, Sb, Ba, Tl, Pb, Th, U) were analyzed byinductively coupled plasma–mass spectrometry (7700x,
Agilent Technologies, Santa Clara, USA). For the analysis,
samples were prepared using 1/32 of the Teflon filter, andpretreatment was performed by the ultrasonic-heat block
method. Carbon components (elemental carbon, organiccarbon and total carbon) were analyzed using quartz fiber
filter samples. For the analysis, 8 mmΦ of the filter waspunched out from two locations and analyzed using athermal optical carbon analyzer (Model 2001 Carbon
Analyzer, Desert Research Institute, Reno, USA) by

Oxidative Stress Induction Ability of Particles Emitting from Agricultural Open Burning in Japan29

means of the IMPROVE protocol and the two analyses
were averaged. Pyrolysis of the organic carbon during theanalysis was corrected using reflected light.

250 µg-PM ml-1, respectively. DTT was added to eachsample at a concentration of 160 μM. The solution was
incubated at 37°C for 15 min to complete the reaction.

2.3 Protocols for HO-1 and DTT Assays
OSIA and OP were determined by means of HO-1
assay and DTT assay according to the protocol of Fujitani,
et al. (2023). Briefly, for the HO-1 assay, a rat alveolar
epithelial cell line (SV40-T2 cells) was used. The samples
were extracted in two separate processes using distilled
deionized water (DDW) and dichloromethane (DCM) forwater-soluble and water-insoluble components of particles,
respectively, and the cells were exposed for three hours to
1 ml of the solution at PM concentrations of 50 µg-PM
ml-1 for each solution. For OP, the samples were extracted
by Tris-HCl buffer at pH 8.9 or DCM, and the solution
concentration was then adjusted to 30 µg-PM ml-1 and3. Results and Discussion
3.1 State of Open Burning of Agricultural Residue
and its Contribution to PM in Tsukuba
Figure 1 shows the PM2.5 concentration and major
chemical composition of the PM mass in the filter
samples, organic aerosol-PMF fraction to organic aerosol,
OSIA and OP. The PM2.5 concentrations were below theJapanese environmental standard (24-hour value of
35 µg m-3) for all samples. Note, however, that the
1-hr PM2.5 concentration (according to continuous
measurement) increased to a maximum of 100 μg m-3 on
October 10 due to intensive BB and then declined sharply

Fig. 1 Overview of the field campaign at the Tsukuba observation site for a) PM 2.5 concentrations and chemical compositions offiler samples; b) organic aerosol (OA) concentration and OA sub-fractions determined by positive matrix factorization(PMF) analysis, c) oxidative stress induction ability (OSIA), d) oxidative potential (OP), and e) the maximum andminimum of the daily number of open burning events during each sampling period in the visual inspection area of
Tsukuba City according to Tomiyama, et al. (2017), the C2H4O2+ fragment ion (m/z = 60.0211), which is a fragment ionof levoglucosan and tracers of biomass burning, and the wind speed. The OA fraction and C 2H4O2+ ion are averaged overthe filter sampling period. The x-axis label is the filter ID and indicates the sampling start date. The y axis in c) and d) for
OSIA and OP indicate air volume basis (OSIAv, OPv), while the size of the plots indicates the relative magnitude ofinduction fold change relative to control per PM mass (OSIArc) or OP of PM mass (OPm). Error bars in PM2.5, OA andC2H4O2+ represent standard deviation of the continuous measurement 1-hr averaged data during the sampling period. EC:
elemental carbon; OC: organic carbon; DMSO: dimethyl sulfoxide; MO-OOA: more-oxygenated oxygenated OA;
LO-OOA: less-oxygenated OOA; HOA: hydrocarbon-like OA; BBOA: biomass-burning OA.

30

Y. FUJITANI et al.

late in the evening of October 11 due to rain (Fujitani,
et al., 2020). As a result, the three-day average value waslower than the environmental standard; the larger standard
deviations of PM mass and organic aerosol concentrationsin ID 09Oct.2015 compared to the other samples resulted
from this variability.
For organic aerosol-PMF, each of the five factors is
associated with an emission source following Fujitani,
et al. (2020): freshly emitted BBOA (fresh BBOA), aged
BBOA, hydrocarbon-like organic aerosol (HOA),
more-oxygenated oxygenated organic aerosol (MO-OOA),
and less-oxygenated oxygenated organic aerosol(LO-OOA). The difference between fresh BBOA andaged BBOA is a feature of the difference in intensity of
the fragment ion of C2H4O2+ (m/z = 60.0211) which is a
fragment ion of levoglucosan. Aged BBOA has a lower
signal intensity for C2H4O2+ than fresh BBOA due toatmospheric aging through atmospheric evolution due to
OH radical reactions occurring either in the aqueous
phase or gas phase, or heterogeneous reactions at thegas-particle interface. It is estimated that the aged BBOA
had been aged for several hours in the atmosphere. HOA
is an indicator of vehicle tailpipe emissions, and
MO-OOA and LO-OOA are indicators of secondaryorganic aerosols.
Cases of open burning of agricultural residues wereobserved by visual inspection around the observation site,with 10 October having the highest number of cases and26 October having the second-highest number of cases(Tomiyama, et al., 2017). On these days, concentrationsof PM2.5, fresh and aged BBOA, and the fragment ion of
C2H4O2+ which is BB tracer were relatively high. A highcorrelation coefficient (0.82) was found between thenumber of burning events and the fresh BBOA fraction
with respect to organic aerosol for the entire observation
period data. These results indicate that BB had an impact
on PM mass and organic aerosol, and that BB caused
significant air quality perturbation. Averaged over theobservation period, the sum of fresh and aged BBOA
accounted for about 50% of organic aerosol (OA) (0.5 =BBOA/ OAenvironment). Considering that organic aerosol
accounted for about 29% of PM mass (0.29 =OAenvironment/PMenvironment) for the observation period, andthat organic aerosol from rice straw burning accounted forabout 57% of the PM mass in the emission source
experiment (0.57 = BBOA/PMBB) (Fujitani, et al., 2020),
which resulted in a contribution of BB aerosols (includingorganic aerosol, elemental carbon and other species) to
PM mass during the observation period, which wasestimated to be about 25% (0.25 = PMBB/ PMenvironment =OAenvironment/ PMenvironment ×BBOA/ OAenvironment/ BBOA/PMBB = 0.29 × 0.5/ 0.57). Metals constituted less than 6%of PM mass in most samples, and their fraction was
smallest among the major components, but some metals
exhibit OP and OSIA and are thus noteworthy
components.

OSIA can be represented as the induction fold change
relative to the control per unit air volume (denoted as
OSIAv: [fold change m-3] = [fold change μg-PM -1] ×[μg-PM m-3]) and OP can be represented as the DTT
consumption rate per unit air volume (OPv: [nmol-DTT
min-1 m-3] = [nmol-DTT min-1 μg-PM -1] × [μg-PM m-3]).
The size of the plots in Figs. 1c and 1d indicates therelative magnitude induction fold change relative to thecontrol per unit PM mass [OSIArc: fold change μg-1] or
DTT consumption rate per unit PM mass [OPm:nmol-DTT min-1 μg-1]. Sample ID 26Oct.2015 exhibitedthe highest values for all indices among the samples;
OSIA and OP were highest per unit air volume as well as
per unit PM mass for both solutions.
3.2 Comparison with Other Seasonal Samples withDifferent BB Impacts on OSIA and OP
The impacts of BB on OSIA and OP are discussed bycomparing the results of other seasons with different
degrees of BB impact. Fig. 2 shows comparisons of threeseasons; January 2017 and August 2017 where the periodsof observations were conducted in winter and summer,
respectively, at the same sampling site. Each observation
was conducted for about three weeks and the observation
protocols were mostly identical to those in this study.
According to the results of a year-round survey of BB
frequency in Tsukuba in 2016, BB was also observed in
January and August (Fig. 2b). In those months, however,monthly BB frequency was low and the contribution of
fresh BBOA to organic aerosol was as small as less than
10%. Comparing the three periods, not only fresh andaged BBOA but also PM2.5 and organic aerosol werehighest during October 2015. In particular, the increase inorganic aerosol concentration during October 2015 was
mostly identical to the increase in fresh and aged BBOA
concentration.
Boxplots of PM components exhibiting OSIA or OP
are shown in Fig. 2c. Since the predominant source varies
with the season and the chemical compositions of eachsource differ, the concentration of each component varies
with the season and is characteristic. In addition to freshand aged BBOA, Cu, Pb and Zn tended to be higher in
October 2015 than other periods, but the origins of these
metals are not clear (see the next section). January 2017
(winter) was the period with the highest contributions ofvehicle tailpipe emissions (HOA and elemental carbon)
and soil (Fe and Mn). Fe and Mn are considered to be
predominantly of soil origin at this location, given the
behavior of Al, an indicator of soil composition, in
combination with their high abundance in the soil
particles. Winter in Tsukuba has drier weather comparedto the other seasons, and the wind speeds are higher, sosoil particles tend to aerosolize. This may havecontributed to the high winter concentrations of Fe and
Mn. August 2017 (summer) was the period with thehighest contribution of heavy oil combustion and shipOxidative Stress Induction Ability of Particles Emitting from Agricultural Open Burning in Japan

31

Fig. 2 a) Concentrations and fractions of chemical compositions during each observation period at the Tsukuba sampling site,b) Numbers of open burning events for different month, obtained through annual observations in 2016 in Tsukuba(Tomiyama, et al., 2017), and boxplots for each observation period; c) Chemical components exhibiting OSIA or OP,d) Oxidative stress induction ability (OSIA) and oxidative potential (OP) expressed in per unit air volume and in per unit
PM mass. Error bars in a) represent the standard deviation of the data during the sampling period. The horizontal bar in
the boxes represents the median value. The upper and lower bounds of the boxes represent the 25th and 75th percentiles,
with whiskers extending to the highest and lowest data values. POA in a-2) indicates primary emitted OA other than
HOA and BBOA.

exhaust (V, Ni) and secondary organic aerosols (Fig. 2a).
Southerly winds tend to prevail during the summer,
resulting in influences from sources in industrial areas of
the coastal bay south of the measurement site (Fujitani,
et al., 2021). The OSIA and OP per unit air volume andper unit PM mass (Fig. 2d) for both extracted samples
were mostly highest in October 2015. In the next section,
the relationship between OSIA or OP and the chemicalcomponents is discussed.
3.3 Influences of Chemical Components on OSIA andOP
Figure 3 shows OSIA and OP values, the contribution
fraction of each component to OSIA and OP, andcompositional fraction of each chemical component
exhibiting OSIA or OP for each sample in October 2015
and the average values of each season. The fraction ofeach component to OSIA or OP was determined by
the concentrations of the chemical species responsible
for OSIA or OP and their unit values of OSIA [foldchange μg-component-1] or OP [nmol-DTT min-1
μg-component -1], the values of which were obtained by

Fujitani et al. (2023). The OSIA of each component wassummed assuming that a linear relationship was valid toobtain the total OSIA of all components, and the fractionof each component to the total OSIA was determined. The
fraction of each component to the total OP was obtainedby the same procedure. Water-soluble metals andPMF-organic aerosol were considered to exhibit OSIA or
OP in the calculations for the water-soluble samples,while PMF-organic aerosol and elemental carbon wereconsidered for the water-insoluble samples. Furthermore,among the metals, Mn, Fe, Ni and Cu, which showed OP
in reagent solution experiments, were considered, and forshowing OSIA, Zn, Pb, V and Cr were considered inaddition to the OP-exhibiting metals. For each metalcomponent, the concentration of water-solublecomponents was calculated using the measured total
concentration and the average water-soluble fraction
obtained from the summer and winter samples. Metals
and elemental carbon derived from vehicle tailpipe
emissions and BB emissions were calculated assuming anemission source profile, using those used in the Chemical
Mass Balance model (Takahashi et al., 2011) and the32

Y. FUJITANI et al.

results of agricultural residue combustion experiments(Fushimi et al., 2017). Soil-derived metals were
calculated using Al as an indicator, and the soil profile of
the average earth crust was used (Mason, 1966). The
remaining concentrations of metals and elemental carbon
that were not assigned to vehicle tailpipe and BB
emissions and soil, were assumed to originate fromsources other than those. As a result, it was estimated thatmost of the metal in terms of amount was from othersources not considered in this study and most of theelemental carbon by amount was attributable to vehicle
tailpipe emissions (Fig. 3e).
Averaged over the samples in each season, the OSIA
for the water-soluble samples was dominated by BB(fresh and aged BBOA) in October 2015, which
contributed nearly 80% (Fig. 3a). In other seasons, thecontribution of OOA and other metals increased and thecontribution of BB decreased slightly, to around
40%−50%. The contribution of BB to OSIA in thewater-insoluble samples was about 50% in October 2015
and about 20% in the other seasons (Fig. 3b). In the otherseasons, vehicle tailpipe emissions (HOA, vehicle tailpipe
emission-elemental carbon) was predominant (70%−80%).
The trend of the OP contribution for each solution was
generally consistent with the trend of OSIA (Fig. 3c, d).
These results indicate that OSIA and OP significantly
increased during the active open burning season, and the
majority of the increase was attributable to BB aerosols.
The contributions of components with larger
contributions to OSIA and OP (Fig. 3a−d) differed fromtheir mass-based contribution (Fig. 3e). In particular, thecontribution of metals was greater in the water-soluble
samples, because the unit values of OSIA and OP for
metals are orders of magnitude higher than those oforganic aerosol. However, BB had low metal content
according to the source profiles, and the BB-derived
metals present contributed little to OSIA or OP. Therefore,
in BB, organic matter rather than metals was found to be

Fig. 3 a) Oxidative stress induction ability (OSIA) and contribution of water-soluble (WS) chemical components to OSIA of
WS samples, b) OSIA and contribution of sub-classes of organic aerosols (OA) and elemental carbon (EC) in
water-insoluble (WIS) samples, c) Oxidative potential (OP) and contribution of chemical components to OP of WS
samples, d) OP and contribution of sub-classes of OA and EC of WIS samples, and e) Composition of chemical species
that exhibit OSIA or OP. Predicted values of total OSIA or OP were determined by multiplying the concentration of eachchemical component and the unit value of OSIA or OP (per unit of chemical component mass) and the sum of each
OSIA or OP value. The predicted values for cases without aged BBOA were determined by assuming the unit value of
OSIA or OP was zero for aged BBOA. The error bars indicate the standard deviation of the samples in each sampling
campaign. VTE indicates vehicle tailpipe emission.

Oxidative Stress Induction Ability of Particles Emitting from Agricultural Open Burning in Japanthe main causative component.
We focused on October 2015 samples in detail. As
described in Section 3.1, ID 09Oct.2015 and ID
26Oct.2015 were samples collected during especially
active open burning, while ID 07Oct.2015 and ID
26Oct.2015 were samples collected during windy(> 1.9 m s-1) days when it was largely soil particles that
tended to aerosolize. Accordingly, the contribution of soil
and BB to OSIA and OP increased in ID 07Oct.2015 andID 09Oct.2015, respectively. For ID 26Oct.2015, bothemissions exhibited simultaneous influence, likely
resulting in the highest OSIA and OP per unit air volume
and per unit PM mass (Fig. 1).
Figure 3 also shows the predicted values of total
OSIA and OP. For aged BBOA, no unit values of OSIA or
OP were available. Therefore, we determined thepredicted values of total OSIA and OP for the case inwhich the unit values of OSIA and OP for aged BBOA
were assumed to be the identical to those of fresh BBOA,
or for the case in which they were ignored (i.e., unit
values of OSIA and OP assumed zero). The predictedvalues generally followed the inter-sample variability
trend of the measured values in October 2015, and mostof the samples were predicted to be close to the measured
values, except for ID 26Oct.2015 for OSIA and ID
09Oct.2015 for OP. This result indicates that OSIA andOP values can be estimated retrospectively for most
samples using concentration data for chemicalcomponents such as metals and sub-organic aerosol
classes, and unit values of OSIA and OP for thosechemical components.
By comparing these two cases in which unit values of
OSIA and OP for aged BBOA are either identical tovalues for fresh BBOA or zero, it is possible to estimate
whether unit values of OSIA and OP in aged BBOA are
greater or lesser relative to their values in fresh BBOA.
Namely, the unit values of OSIA and OP of aged BBOA
for water-soluble samples are expected to be lower thanthose of fresh BBOA, since the total predicted values ofwater-soluble OSIA and water-soluble OP for most
samples exceed the measured values. On the other hand,the unit values of OSIA and OP for aged BBOA ofwater-insoluble samples are expected to be higher thanthose for fresh BBOA, since the total predicted values ofwater-insoluble OSIA and water-insoluble OP for most
samples are lower than the measured values. Accordingly,
the results indicate that oxidative stress induction changes
with aging, but the amount of substances that induce
oxidative stress or their unit values may have decreased in
the water-soluble
samples and increased in thewater-insoluble samples as a result of aging. For example,
although not an oxidative stress inducer, water-solublecomponents such as levoglucosan have also beensuggested to decrease with aging (Fujitani et al., 2020).
This hypothesis should be validated in laboratory
experiments in future challenges.

33

Underestimation of the predicted value of OSIA
compared to the measured value (ID 26Oct.2015) issimilar to the tendency seen in a previous study (Fujitani
et al., 2023): underestimation OSIA when it is high. Thiscan be explained by the following hypotheses. 1) The
high actual value is due to the presence of substances suchas endotoxins, rather than of chemical components; 2)
The HO-1 gene is expressed by the oxidation anddegradation of Kelch-like ECH-associated protein 1(Keap1). In addition, several other antioxidants such as
glutathione coordinate metals in the cell. In samples withhigh OSIA, antioxidants such as glutathione alone may
not be able to counteract oxidatively active substances,resulting in these greater quantity of oxidatively active
substances reacting with Keap1, leading to higher HO-1
expression.

4. Conclusions
To assess the extent to which BB particles affect
OSIA and OP, an evaluation was conducted of OSIA, OP
and chemical components in PM2.5 environmental
samples collected during periods of high BB activity, thatis, the burning of agricultural residue in Japan. Not only
were PM2.5 concentrations higher, but also OSIA and OP
per unit air volume and per unit PM mass in periods ofhigh open burning activity than in other seasons. The
results indicate that management of BB activity isimportant toward maintaining good air quality andprotecting public health.
Acknowledgments
This study received financial support from the
National Institute for Environmental Studies (2015–2017),
JSPS KAKENHI (Grant Number 17K00590), and theEnvironment Research and Technology Development
Fund (Grant Number JPMEERF20165005) administeredby the Ministry of the Environment, Japan. The authors
gratefully acknowledge the technical assistance provided
by Dr. Y. Kondo, Ms. M. Ihara, Ms. E. Shimaya, Mr. Y.
Sugaya, and Ms. M. Chiba, Mr. A. Togashi, Ms. S.
Takahashi, Ms. K. Higo, and Ms. M. Kobayashi (NIES),
Mr. T. Fujii and Mr. H. Konno (Horiba Techno Service),and Mr. M. Oikawa and Mr. M. Nakamura (Green Blue
Corp.). Meteorological data was obtained from the Air
Quality Research Station at NIES.
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Yuji FUJITANI
Yuji Fujitani is a chief senior researcher at the
National Institute for Environmental Studies(NIES). He completed his PhD (Engineering) in
Environment and Resource Engineering at theGraduate School of Engineering, Hokkaido
University, Japan. His current research interests
include the development of protocols for small animal whole-bodyinhalation exposure experiments and air-liquid interface cellular exposure
methods for micro- to ultrafine-particles to assess particle toxicities. He
also conducts toxicity-related particle characterization, exposure
assessments and source attribution for atmospheric aerosols, as well as
their combined analyses.

Oxidative Stress Induction Ability of Particles Emitting from Agricultural Open Burning in Japan

35

Akiko FURUYAMA

Ayako YOSHINO

Akiko Furuyama heads the Integrated Health
Risk Research Section at the National Institutefor Environmental Studies (NIES). She received
her PhD from the Graduate School of Arts andSciences at the University of Tokyo, Japan. Her
general research interests include elucidating theunderlying molecular mechanisms involved in PM 2.5-mediated toxicity,
developing a cell-based high-throughput screening technique for detecting
oxidative stress, and evaluating the health effects of air pollution using
laboratory animals. She has examined cell and tissue pathology, and cellbiology and matrix biology in humans and laboratory animals to better
understand how living cells respond to and function in the presence of
inhaled environmental contaminants.

Ayako Yoshino is a senior researcher at the
National Institute for Environmental Studies(NIES). She completed her PhD (Engineering) in
Atmospheric Chemistry at the Graduate Schoolof Engineering, Tokyo Metropolitan University,
Japan. Her current research interests include theeffects of air pollution on urban air quality and human health.

Seishiro HIRANO
Seishiro Hirano is a visiting research scientist at
the National Institute for Environmental Studies(NIES). After receiving his Bachelor’s andMaster’s degrees from the School of Scienceand Engineering at Waseda University, Japan, hestarted his career as a toxicological scientist at
NIES in 1981. He earned his PhD in Medical Science from the Universityof Tokyo in 1990. His research interests include: (1) the metabolism andtoxicity of arsenic, (2) the interaction of cellular membranes withparticulate substances, (3) inflammatory responses of macrophages tonanomaterials and secondary organic aerosols, and (4) the roles of
autophagy, inflammasome and phase separation in cells exposed toharmful substances.

Akihiro FUSHIMI
Akihiro Fushimi is a chief senior researcher at
the National Institute for Environmental Studies(NIES). He completed his Ph.D. (Engineering) in
Material Engineering at Yokohama National
University, Japan. His current research interests
include source apportionment, emission/formation mechanisms and toxicity/health risks of fine particles andnanoparticles emitted from various kinds of sources including vehicles,
agricultural open burning and aircraft. To accomplish this he has beendeveloping a sensitive organic analysis method based on techniques suchas thermal desorption gas-chromatography/mass spectrometry.

Katsumi SAITOH
Katsumi Saitoh is a visiting researcher at the
National Institute for Environmental Studies(NIES) and heads the Environmental Science
Analysis & Research Laboratory (ESAR). He
acquired his first PhD, in Medical Science, in
1994 from Akita University, Japan, and his
second PhD, in Environmental Science, in 2015 from the University of
Shiga Prefecture, Japan. His research interest lies in examining thecharacteristics of and factors in the generation of atmospheric aerosols
containing nanoparticles in terms of elemental and ionic components. He
is also developing high-sensitivity analysis, including sampling, foranalyzing the elemental composition and ionic content of nanoparticles.

Kei SATOKei Sato heads the Regional Atmosphere
Research Section of the National Institute forEnvironmental Studies (NIES). He acquired his
PhD (in Science) at the Interdisciplinary
Graduate School of Science and Engineering,Tokyo Institute of Technology, Japan. His current
research interests include laboratory chamber study of the formation andaging mechanisms of secondary organic aerosols from anthropogenic andbiogenic volatile organic compounds and field observation of the
dynamics of organic aerosols. He also conducts laboratory chamber
studies on the mechanisms of photochemical ozone formation.

Akinori TAKAMI
Akinori Takami is director of the Regional
Environment Conservative Division at the
National Institute for Environmental Studies(NIES). He completed his D.Phil at the
Department of Chemistry, Oxford University(UK). His current research interests include air
pollution and its impact on air quality, human health and climate change.
He also conducts research on sustainable society design.

(Received 4 July 2023, Accepted 26 December 2023)