Studies of the volatility distribution of secondary
organic aerosol (SOA) from aromatic compounds are limited compared with SOA
from biogenic monoterpenes. In this study, the volatility distribution was
investigated by composition, heating, and dilution measurements for SOA
formed from the photooxidation of 1,3,5-trimethylbenzene in the presence of
NOx. Composition studies revealed that highly oxygenated monomers
(C9H14Ox, x= 4–7) and dimers (C18H26Ox, x= 8–12) are the major products in SOA particles. Highly oxygenated
molecules (HOMs) with five or more oxygens were formed during photochemical
aging, whereas dimers degraded during photochemical aging. HOMs with five or
more oxygens may be produced from the photooxidation of phenol-type gaseous
products, whereas dimers in the particle phase may be photolyzed to smaller
molecules during photochemical aging. The results of composition, heating,
and dilution measurements showed that fresh SOA that formed from
1,3,5-trimethylbenzene (TMB) photooxidation includes low-volatility compounds with
<1µg m-3 saturation concentrations, which are
attributed to dimers. Similar results were reported for α-pinene SOA
in previous studies. Low-volatility compounds with <1µg m-3 saturation concentrations are not included in the
volatility distributions employed in the standard volatility basis-set (VBS) approach. Improvements
in the organic aerosol model will be necessary for the study of anthropogenic
SOA as well as biogenic SOA.
Introduction
Secondary organic aerosol (SOA) is a major component of atmospheric fine
particles (Zhang et al., 2007), affecting climate (IPCC, 2013) and human
health (Dockery et al., 1993; Shiraiwa et al., 2017). Aromatic hydrocarbons
are major sources of SOA in urban air (Hayes et al., 2015; Wu and Xie, 2018).
SOA formed from the photooxidation of aromatic hydrocarbons is predicted to
have a 33 % contribution to the global SOA production by atmospheric-model
calculations (Kelly et al., 2018).
Volatility basis-set (VBS) models have improved underestimation of
atmospheric organic aerosol levels by accounting for the decrease in organic aerosol volatility with photochemical aging (Robinson et al., 2007). Morino
et al. (2015) reported that a VBS model still underestimates organic aerosol
concentrations in urban environments, although predictions in remote
environments show better agreement with observations. The results reported
by Morino et al. (2015) suggest that current VBS models, as well as the emission
inventory, still include uncertainties. The volatility distribution of SOA,
a key property in the prediction of particle levels in VBS models, has been
investigated by several experimental techniques. Lane et al. (2008) used
laboratory SOA yield data (e.g., Izumi and Fukuyama, 1990; Odum et al.,
1997; Ng et al., 2007) to evaluate the volatility distributions for standard
VBS models. The yield curve analysis for laboratory data assumes gas-phase
single-step oxidation of volatile organic compounds (VOCs) during laboratory experiments and gas–particle
equilibrium. If these assumptions are not applicable, the volatility
distributions obtained from yield curve analysis may not be accurate.
Particle volatility distribution can also be studied by heating measurements
(Baltensperger et al., 2005; Kolesar et al., 2015; Docherty et al., 2018).
Heating measurements provide volatilities at high temperatures. The enthalpy
of vaporization is needed to determine the volatility distribution at
ambient temperatures. Furthermore, thermal decomposition may affect the
results obtained by this method. Another technique used to study volatility
distribution is the dilution method, which has been successfully applied to
the volatility studies of diesel exhaust particles (Robinson et al., 2007;
Fujitani et al., 2012). This technique is also used for SOA formed from the
α-pinene ozonolysis (e.g., Grieshop et al., 2007; Saleh et al.,
2013), but to the best of our knowledge, SOA from aromatic hydrocarbons has
not been investigated by this method. Saleh et al. (2013) reported that the
equilibration timescales for dilution of α-pinene SOA particles were
several tens of minutes. Data from dilution measurements may be affected by long
equilibration timescales due to low particle number concentrations and/or
low mass accommodation coefficients.
Chemical analysis can also provide the product volatility distribution as
well as formation mechanisms of products. Mass spectrometry combined with a
soft ionization method, such as electrospray ionization (ESI) and chemical
ionization, can identify a wide range of oxygenated organic molecules. These
techniques identified small organic acids, highly oxygenated molecules
(HOMs), and oligomers present in SOA particles from aromatic hydrocarbons
(Kalberer et al., 2004; Fisseha et al., 2004; Sato et al., 2012; Praplan et
al., 2014; Molteni et al., 2018). HOMs are formed through auto-oxidation
mechanisms, which include the intramolecular hydrogen abstraction of organic
peroxy radicals (Zhang et al., 2017), but it is still unclear whether or not
the suggested intramolecular reaction occurs for the peroxy radicals from
aromatic hydrocarbons. Oligomers may be produced by acid-catalyzed
oligomerization (Kalberer et al., 2004) and/or gas-phase bimolecular
reactions of peroxy radicals (Molteni et al., 2018). Recent studies on the
parameterization of the saturation concentration (Shiraiwa et al., 2014; Li et
al., 2016) and the sensitivity of ESI mass spectrometry (Kruve et al., 2013;
Heinritzi et al., 2016) are helpful in the evaluation of SOA volatility
using mass analysis data.
In this study, the photooxidation of 1,3,5-trimethylbenzene (TMB) in the
presence of NOx was investigated to evaluate the volatility
distribution of SOA. Although the volatility distribution of α-pinene SOA has been investigated in several laboratories (Sato et al.,
2018 and references therein), the number of studies addressing the
volatility of SOA from aromatic hydrocarbons is limited. Products formed
from the photooxidation of TMB were further exposed to OH radicals to study
the effect of photochemical aging. The volatility distribution of SOA was
evaluated by composition, heating, and dilution measurements. This work aims
to evaluate the volatility distribution of SOA from a typical anthropogenic
VOC and discuss SOA formation processes for improving organic aerosol modeling.
ExperimentsChamber experiment
A 6 m3 Teflon-coated stainless-steel chamber (Sato et al., 2007, 2018)
was used for all experiments. Figure S1 in the Supplement shows a schematic diagram of the
chamber system and analytical instruments. We listed initial concentrations
together with SOA mass concentrations and mean sizes observed immediately
before the sampling for the composition or dilution study (Table S1 in the Supplement). Prior to
each experiment, the chamber was filled with dry purified air (1 atm,
relative humidity <1 %). The temperature of the chamber was
controlled at 298±1 K. Required amounts of TMB, NO, and methyl
nitrite were added to the purified air in the chamber. A small amount
(∼0.01 ppm) of methyl nitrite was added because it was used
as an OH radical source for the initiation of photooxidation. The reaction
mixture in the chamber was irradiated by light from 19 xenon lamps (1 kW
each) through Pyrex filters. In normal photooxidation experiments (i.e.,
runs other than run 2), composition, heating, and dilution measurements of
SOA particles were conducted after 240 min irradiation. In an aging
experiment (run 2), ∼1 ppmv methyl nitrite was added at 239
and 299 min to expose photooxidation products to OH radicals. The
concentrations of TMB, NO, and methylglyoxal (MEGLY) were measured every 6 min by a Fourier
transform infrared (FT-IR) spectrometer (Nexus 670, Thermo Fisher Scientific, USA),
with a 221.5 m optical path. For dilution measurements, ∼20 ppmv CO was added to the reaction mixture as a dilution marker. The CO
levels before and after dilution were measured using a CO monitor (model 48i-TLE, Thermo Fisher Scientific, USA).
We investigated SOA formation under dry conditions only because the chamber
facility was designed for dry use (Akimoto et al., 1979). Present
experiments with dry air will simulate a dry urban atmosphere, in which
aqueous-phase chemistry during SOA formation will be suppressed (Kamens et
al., 2011; Zhou et al., 2011). In the present study, proton transfer
reaction mass spectrometer (PTR-MS) and dilution measurements required the
SOA mass concentrations to be >100µg m-3
in order to obtain sufficient signals (Table S1). In order to form the
desired amounts of SOA, we set the initial TMB concentrations to ppm levels.
We note that a TMB concentration higher than ambient levels may induce
RO2+RO2 reactions in comparison to ambient conditions,
although we observed no experimental evidence of the enhancement of these
reactions.
SOA mass concentration was measured every 3 min using an aerosol mass
spectrometer (AMS) (H-ToF-AMS, Aerodyne Research, USA) (Aiken et al., 2008).
The heating measurements were started immediately after sampling for the
composition or dilution study. A thermal denuder (TD) equipped with a bypass
line (Aerodyne Research, USA) (Huffmann et al., 2008; Faulhaber et al.,
2009) was combined with the AMS instrument for heating measurements. During
each heating measurement cycle, we used the bypass first for 9 min to
obtain the reference data and then used the TD for 15 min to obtain the data
for a specific temperature. Particle size distribution was observed every 3 min using a scanning mobility particle sizer (SMPS) (model 3034, TSI, USA).
The effective particle density was measured using a combination of a
differential mobility analyzer (DMA) (Sibata Scientific Technology, Japan),
an aerosol particle mass analyzer (APM) (Model 3600, Kanomax, Japan), and a
condensation particle counter (CPC) (Model 3772, TSI, USA). The effective
density of SOA was determined to be 1.40±0.28 g cm-3, which is close to the values found in the literature: 1.35–1.40 g cm-3 (Alfarra et al., 2006).
LC-TOF-MS analysis
Chemical composition analysis was conducted using positive-mode ESI liquid-chromatography time-of-flight mass spectrometry (LC-TOF-MS) (Agilent
Technologies, UK). The mass calibration and lock-mass correction were
conducted using G1969-85000 and G1969-85001 tuning mixtures (Agilent
Technologies, UK), respectively. A mass resolution of the mass spectrometer
(full width at half maximum) was > 20 000. SOA particles were
collected on a Fluoropore Teflon filter (Sumitomo Electric Industries,
Japan; 47 mm φ, pore size: 1 µm) at 16.7 L min-1 for 30 min. The filter sampling was performed at irradiation
times of 272 and 411 min in normal photooxidation (run 1) and aging
experiments (run 2), respectively. After sampling, the filter sample was
sonicated in 5 mL methanol for 30–38 min. The filter extract was
concentrated to near dryness under ∼1 L min-1
stream of nitrogen. A 1 mL formic-acid–methanol–water solution (v/v/v=0.05/100/99.95) was added to the concentrated extract to obtain the
analytical sample. A 10 µL aliquot of the analytical sample was
injected into the LC-TOF-MS instrument and separated with an octadecyl silica
gel column (Inertsil ODS-3, GL Science, Japan; 0.5 µm × 3.0 mm × 150 mm). A formic-acid–water solution (0.05 % v/v) and methanol
were used as mobile phases. The methanol fraction during each analysis was
set at 10 % (0 min), 90 % (30 min), 90 % (40 min), 10 % (45 min),
and 10 % (60 min). In our previous study (Sato et al., 2007), the recovery of
malic acid with a saturation concentration of 157 µg m-3 was determined to be >90 %, suggesting that
evaporation loss during pre-treatment is negligible for molecules with
saturated concentrations of 157 µg m-3 or less.
PTR-MS measurements
Gas and particle products were measured using a PTR-MS (PTR-QMS 500, Ionicon
Analytik, Austria) (Lindinger et al., 1998) to determine the saturation
concentration of each product (Inomata et al., 2014). To measure the
products in the gas phase, online measurements were taken from the filtered
chamber air at 240 and 359 min in the normal photooxidation (run 1) and aging
experiments (run 2), respectively. Afterwards, particles were collected on
another Fluoropore Teflon filter at 16.7 L min-1 for 30–50 min to measure products in the particle phase. The sample filter was placed
in a filter holder, which was then heated at 368 K under a stream of
nitrogen. The gases evaporating from the filter were measured using the
PTR-MS. The saturation concentration was calculated from the gas–particle
ratio determined by the PTR-MS for each mass-to-carbon ratio (m/z), assuming gas–particle equilibrium.
The procedure used for calculating the saturation concentration is described
in Sect. S1 in the Supplement.
Dilution measurement
Another 6 m3 fluorinated ethylene propylene (FEP) film bag was used as an external dilution chamber
(EDC) in runs 3, 4, 5, and 6. The temperature of the laboratory was
controlled at 298±1 K. Prior to each dilution experiment, the EDC
was filled with dry purified air (relative humidity <1 %). A
necessary amount of reaction chamber air was injected into the EDC using a
dilution ejector (FPS-4000, Dekati Ltd., Finland) at ∼240 min
of irradiation time. The dilution ratio (DR) was set to 20–86. Dry filtered
air was used as the carrier of the dilution ejector. The particle size
distribution, particle density, and CO concentration in the EDC were
monitored after the SOA dilution. The required duration for gas transfer
from the reaction chamber to the EDC was 13 min or less. During gas
transfer, CO might decrease due to either dilution effects and/or to the
reaction with OH radicals. We ignored the reaction of CO in the EDC because
the EDC was not irradiated. However, before gas transfer was complete, a
portion of diluted gas remained in the irradiated reaction chamber; the
decrease in this portion might lead to an overestimation of the DR. Even
though we assume a maximum level of OH radicals (107 molecules cm-3), CO decreases by only ≤2 % due to the
reaction with OH radicals for ≤13 min. Thus we ignored the
overestimation of the DR due to the reaction of CO with OH radicals. We
conducted dilution experiments under flow conditions using the dilution
ejector in run 7. We confirmed that negligible evaporation occurred
immediately after dilution, in accordance with the previous results reported
for α-pinene SOA (Grieshop et al., 2007).
Results and discussionTime series
Figure 1 shows a time series of (a) TMB and MEGLY concentrations observed by
the FT-IR, (b) SOA mass concentrations observed by the AMS, and (c) O/C the ratio
observed for SOA by the AMS during normal photooxidation (run 1) and aging
experiments (run 2). The MEGLY concentration increased with decreasing the
TMB concentration in runs 1 and 2. Methyl nitrite was added at irradiation
times of 239 and 299 min in run 2. The first injection of methyl nitrite was
aimed at completing the photooxidation of the remaining TMB, whereas the second
injection was aimed at ensuring that the photooxidation of gaseous secondary
products is promoted. The total OH exposures were determined to be 5.0×1010 and 6.2×1010 molecule cm-3 s-1 for the first (239–299 min) and second exposures
(299–359 min), respectively. The total OH exposures were calculated by
integrating the OH concentrations over irradiation time. The OH
concentration at each irradiation time was calculated assuming the steady
state between OH formation from the methyl nitrite photolysis and OH
reactions with TMB, MEGLY, formic acid, and NO2.
[OH]ss=k1[methyl nitrite]/(k2[TMB]+k3[MEGLY]+k4[formic acid]+k5[NO2]),
where we used present FT-IR data for the concentrations and literature
values for the rate coefficients. If we assume that the daytime OH radical
concentration is 106, the first and second exposures corresponded to
14 and 17 h photooxidation under ambient conditions. The TMB that remained
after the first addition of methyl nitrite was entirely consumed within 1 h. The concentration of MEGLY slightly increased after the first addition
of methyl nitrite due to formation from the remaining TMB and then decreased
by the photo-degradation of MEGLY. The decreasing rate of MEGLY increased
after the second addition of methyl nitrite, suggesting that the
concentration of OH radicals increased due to the second addition of methyl
nitrite.
Time series of (a) TMB and methylglyoxal (MEGLY) concentrations
observed by FT-IR, (b) SOA mass concentration observed by AMS, and (c)O/C
ratio observed for organic aerosol by AMS during normal photooxidation (run 1) and aging experiments (run 2). Methyl nitrite was added in run 2 at the
irradiation times indicated by vertical dotted straight lines.
SOA particles became detectable at an irradiation time of 55–58 min in runs 1 and 2. Afterwards, the SOA mass concentration increased with time,
reaching to ∼189µg m-3 at 239 min of
irradiation time for both runs. After the first addition of methyl nitrite
in run 2, the mass concentration of SOA increased to 259 µg m-3. SOA, forming after the first addition of methyl nitrite, could be
formed from the photooxidation of TMB as well as gaseous secondary products.
The mass concentration of SOA then decreased with a rate of (7.1±0.1)×10-5 s-1. The geometric mean particle size after
OH exposure was 558 nm, which was larger than for run 1 (395 nm). The wall
loss rates of ammonium sulfate particles with sizes of 279–322 and 372–429 nm were measured using the present reaction chamber to be (1.4±0.6)×10-5 and (6.1±3.5)×10-5 s-1,
respectively. Particle wall loss may explain the observed decrease in SOA
mass concentration. No great change in the decreasing rate was observed when
methyl nitrite was added for the second time.
In the early stages of SOA formation, the O/C ratio decreased with
increasing time, probably because new particle formation from HOMs was
followed by condensation of less oxygenated compounds from the gas phase.
After ∼120 min of irradiation time, the O/C ratio increased
because of photochemical aging. After the first addition of methyl nitrite
in run 2, the O/C ratio decreased, likely because of the formation of fresh SOA
from the photooxidation of the remaining TMB or an increase in the mass
concentration followed by the condensation of less oxygenated compounds. The
O/C ratio reached a minimum at 256 min and then again increased with time.
The O/C ratio increased continuously after the second addition of methyl
nitrite. Filter sampling of SOA for LC-TOF-MS analysis took place at irradiation
times of 272 and 411 min in runs 1 and 2, respectively. The O/C ratio
observed at the start time of filter sampling in run 2, 0.50, was higher
than that observed at the start time of filter sampling in run 1, 0.46,
indicating that the filter sample collected in the aging experiment must be
more highly oxygenated than that collected in the normal photooxidation
experiment.
LC-TOF-MS analysis
Figure 2 shows the mass spectra observed using the positive-mode ESI mass
spectrometer for SOA samples collected in (a) run 1 and (b) run 2. These
results were obtained by direct infusion of SOA samples into the ESI mass
spectrometer. Strong signals measured by the positive-mode ESI analysis were
identified as sodium-attached product ions. No sodium salt was added to the
mobile phase or analytical sample. Species that do not generate stable
positive ions through protonation were ionized by clustering with Na+
cations that are naturally present in the solvent chemicals and glassware
(Kruve et al., 2013; Zhang et al., 2017). The signal intensities of the
sodium-attached ions were confirmed to have a linear relationship with the
amount of the injected sample.
Mass spectra observed using positive-mode electrospray ionization
time-of-flight mass spectrometry for TMB SOA samples collected in (a) normal
photooxidation (run 1) and (b) aging experiments (run 2). These results were
obtained by direct infusion of the SOA samples to the ESI mass spectrometer.
Two series of mass signals with a regular mass difference of 16 were
measured in the regions m/z 209–257 and m/z 393–457. The signals observed in the
region m/z 209–257 were suggested to be C9H14Ox (x= 4–7)
molecules clustered with Na+. The C9H14Ox products are
HOMs with the same number of carbons as TMB and two more hydrogens than TMB.
The highest peak of m/z 271 was also observed in the analysis of blank samples,
suggesting that this peak had a contribution from contaminants or solvents.
Monomers detected in a previous online study, C9H14O5-11
(Molteni et al., 2018), are similar to those detected in our present offline
analysis, but monomers detected in the previous study have slightly more
oxygens than those detected in this study. A small portion of HOMs may
decompose during the present offline analysis.
The signals observed in the region m/z 393–457 were suggested to be
C18H26Ox (x= 8–12) molecules clustered with Na+. The
C18H26Ox products have twice as many carbons as TMB. These
products are, thus, attributed to dimeric products. Similar products were
observed between the samples collected in runs 1 and 2. Largely, monomer
signals observed in run 2 were higher than in run 1, whereas dimer signals
observed in run 2 were lower than in run 1.
Figure S2 shows the extracted-ion chromatograms (EICs) observed for (a) C9H14O4Na+, (b) C9H14O5Na+, and (c) C9H14O6Na+ by positive-mode LC-TOF-MS. We measured EICs
using LC columns because mass signals observed without the column may be
interfered from solvent and/or contaminant signals. Contaminant signals were
checked from measurements with blank samples, and these contaminant signals
were ignored from calculations of product signal intensity. Here, a blank
sample was prepared by extracting a new Teflon filter. The method of
extraction was similar to that used for the SOA samples. The chromatograms
observed for C9H14O4Na+, C9H14O5Na+,
and C9H14O6Na+ included at least 9, 8, and 11
chromatographic peaks, respectively, suggesting that each HOM product has a
number of structural isomers. The total peak area observed for the
C9H14O4 products in run 2 was lower than that observed in run 1, whereas the total peak areas observed for C9H14O5 and
C9H14O6 products in run 2 were higher than those observed in
run 1. Note that we collected similar amounts of SOA between runs 1 and 2
(i.e., 122 and 114 µg for runs 1 and 2, respectively).
Saturation concentration calculations
Table S2 summarizes the measured mass-to-charge ratio, suggested ion formula,
calculated molecular weight, measured total intensity of EIC peaks,
calculated O/C ratio, and predicted saturation concentrations for products
existing in SOA. A common logarithm of the saturation concentration of each
product (log10C∗) was calculated by using Eq. (1), which
predicts log10C∗ as a function of the number of carbon,
nitrogen, and oxygen atoms included in a referred molecule (Li et al.,
2016).
log10C∗=nC0-nCbC-nObO-2nCnO/nC+nObCO-nNbN,
where nC is the number of carbons, nO is the number of oxygens, nN is the
number of nitrogens, and nC0, bC, bO, and bN are
coefficients determined by fitting. Two different sets of coefficients were
used in this study. One was a set of coefficients determined by fitting to
log10C∗ data calculated by a SPARC (SPARC Performs Automated Reasoning in Chemistry) calculator (Hilal et al.,
2003) for CHO and CHNO products formed from the photooxidation of TMB. The
nC0, bC, bO, and bN values determined were 41.53,
0.202, 0.8805, -0.05239, and -1.715, respectively. The root mean square
error (RMSE) of predicted log10C∗ was determined to be 1.91,
which corresponds to the uncertainty of the predicted log10C∗.
The other set of parameters is that described by Li et al. (2016), who
fitted the equation to the log10C∗ results calculated for over
30 000 compounds.
Figure S3 shows a molecular corridor plotted for products from the
photooxidation of TMB, where a molecular corridor is a plot of molecular
weight as a function of the logarithm of the saturation concentration (Shiraiwa
et al., 2014). The saturation concentrations were calculated by a SPARC
calculator (Hilal et al., 2003) for products reported by previous
experimental studies (Smith et al., 1999; Kalberer et al., 2004; Fisseha et
al., 2004; Sato et al., 2012; Praplan et al., 2014) and the Master Chemical
Mechanism version 3.3.1 (MCM v3.3.1) (Jenkin et al., 2003). The SPARC
calculator predicts vapor pressures accounting for the chemical structure,
induction, resonance, and field effects. Table S3 shows simplified molecular-input line entry system (SMILES) code,
saturation concentrations determined by the SPARC calculator, and molecular
weights for products from the TMB photooxidation. The logarithm of the
saturation concentrations calculated for TMB photooxidation products ranged
from -7.99 to 9.86. The saturation concentrations were also determined
experimentally from the results of present PTR-MS measurements. Results
obtained for m/z≥150 by PTR-MS were only plotted in the molecular
corridor because the signals observed for m/z<150 were interfered with
by fragmentations from compounds with higher molecular weights. Saturation
concentrations calculated by the SPARC calculator showed agreement with
those determined by present PTR-MS observations.
Volatility distributions based on LC-TOF-MS data
Figure 3 shows volatility versus carbon number mapping obtained from LC-TOF-MS
results for particle-phase products in (a) run 1 and (b) run 2 and the
volatility distribution determined for the sum of gas- and particle-phase
products in (c) run 1 and (d) run 2. The size of the circle represents the
normalized total EIC peak intensity. The volatility versus carbon number
mapping shows that low-volatility compounds with log10C∗<0 are dimers. The color of the circle represents the O/C ratio of each
product. The O/C ratios of products were determined to be 0.24–0.89. The
saturation concentration was predicted by Eq. (1) and adapted to TMB oxidation
products. Compounds of 9-carbon (e.g., C9H14O5,
C9H14O6, C9H16O6, and C9H13NO8)
and 18-carbon compounds (e.g., C18H24O8,
C18H26O8, C8H26O9, and
C18H27NO12) are dominant monomer and dimer products,
respectively. As discussed previously, signal intensities of dimers
decreased during photochemical aging, whereas those of monomers with five or
more oxygens increased during photochemical aging. The weighted O/C average
determined in the aging experiment, 0.57, was higher than the normal
photooxidation experiment, 0.53, showing a qualitative agreement with the
results of the O/C ratio measured by the AMS. According to the LC-TOF-MS results,
the weighted O/C average increased, probably because monomers with five or more
oxygens are formed during photochemical aging.
Volatility versus carbon number mapping obtained from LC-TOF-MS
results for particulate products in (a) normal photooxidation (run 1) and
(b) aging experiments (run 2), and the volatility distribution determined
for the sum of gaseous and particulate products in (c) normal photooxidation
(run 1) and (d) aging experiments (run 2).
The volatility distributions with 10 bins were calculated for the gas- and
particle-phase products, using the results of LC-TOF-MS analysis. The total EIC
peak intensities were corrected assuming that the transmission efficiency
for time-of-flight mass spectrometry is proportional to m/z1/2 (Heinritzi
et al., 2016). We assumed that the corrected signal intensity is
proportional to the mass concentration in the particle phase. We divided the
region between log10C∗=-6.5 and 3.5 into 10 bins. The
mass fraction in the particle phase was calculated for each bin by summing
the corrected intensities among the products existing in the referred bin.
The mass fraction of the gas phase was calculated assuming gas–particle
equilibrium. The compound of log10C∗=3 existed
predominantly in the gas phase, whereas less volatility compounds existed
predominantly in the particle phase under present particle mass conditions.
The volatility distributions determined from LC-TOF-MS data were bimodal. Two
components of each volatility distribution were attributed to monomer and
dimer products. The mass fractions between log10C∗=0 and 2
increased during photochemical aging due to the increase in monomers with five
or more oxygens, whereas those between log10C∗=-3 and -1
decreased during photochemical aging, due to the decrease in dimers. The
weighted averages of log10C∗ were close between the normal
photooxidation (1.15) and aging experiments (1.16). Here, the weighted
averages are calculated using the total mass fraction as the weighting factor.
We note that the sensitivity of ESI mass spectrometry is compound specific,
thus the calculated distribution includes the uncertainties that result from
compound-specific sensitivities. In this regard, however sodium adduct
formation during ESI can ionize a wide range of oxygenated organic
compounds, including carbonyls, peroxides, and alcohols (Kruve et al., 2013;
Zhang et al., 2017).
Figure 4a–c compare the volatility distributions determined from LC-TOF-MS
data employing different methods. Figure 4a shows the volatility
distribution determined by predicting saturation concentrations (using Eq. 1 adapted to TMB photooxidation products) and correcting intensities
accounting for transmission efficiencies. The volatility distribution was
also calculated without correcting intensities to check the effect of the
intensity corrections (Fig. 4b). Similar bimodal volatility distributions
were obtained even if the signal intensities were not corrected. The average
log10C∗ values determined without accounting for intensity
corrections for normal photooxidation and aging experiments were 0.80 and
0.83, which were slightly lower than those determined with intensity
corrections. Again, average log10C∗ values are weighted by
the total mass fraction. The method used for saturation concentration
predictions was also checked. Figure 4c shows the volatility distributions
determined employing the unaltered equation proposed by Li et al. (2016).
The average log10C∗ values determined for normal
photooxidation and aging experiments were 0.34 and 0.39, respectively,
suggesting that the results obtained employing the method of Li et al. were
slightly lower than those determined from Eq. (1) adapted to TMB
photooxidation products. Even though we take into account uncertainties for
the saturation concentrations, all three results determined from LC-TOF-MS data
suggest that low-volatility products with log10C∗<0
exist in SOA. As shown in Fig. 4f, these low-volatility compounds are not
included in the volatility distribution determined by conventional yield
curve analysis (Lane et al., 2008).
Volatility distributions determined for products from TMB using
(a) LC-TOF-MS results, (b) LC-TOF-MS results obtained without intensity correction,
(c) LC-TOF-MS results obtained using equation by Li et al. (2016) for saturation
concentration calculations, (d) TD-AMS results, (e) dilution results, and
(f) results of previous yield curve analysis.
Reaction mechanism
Figure 5 shows mechanisms suggested for the formation and photochemical
aging of abundant products. In this study, we found the
C9H14O4-7 monomers and the C18H26O8-12 dimers
to be major products. The photooxidation of TMB is mainly initiated by the
addition of OH radicals to the aromatic ring. The produced adduct reacts
with an oxygen molecule to form a peroxy radical, which undergoes a bridging
reaction followed by an oxygen molecule addition to form a C9H13O5
peroxy radical (TM135BPRO2), according to the MCM mechanism. The TM135BPRO2 peroxy
radical can undergo an auto-oxidation process (Wang et al., 2017), namely,
intramolecular hydrogen abstraction and the following oxygen addition occur
to form a C9H13O7 peroxy radical. Molteni et al. (2018)
suggested the RO2+HO2→ROOH+O2 and RO2+RO2→ROOR+O2 reactions for the formation processes of
monomer products (e.g., TM135BPOOH of the MCM mechanism) and dimer products,
respectively, where RO2 is a highly oxygenated peroxy radical. Highly
oxygenated monomers and dimers may also be formed from ring-opening RO2
radicals (Molteni et al., 2018), which are formed from the following
reactions of bicyclic RO2 radicals (i.e., C9H13O5 and
C9H13O7).
Suggested mechanism for the formation and photochemical aging of
abundant products. Shaded products may exist predominantly in the particle
phase.
Since a relatively high concentration (i.e., ppm level) was used for the
initial TMB concentration in this study, the auto-oxidation of RO2
might be suppressed by fast RO2+RO2 reactions. Currently,
accurate rate constant values are not known for intramolecular hydrogen
abstraction from RO2 (k1) and for the RO2+RO2
reaction (k2). We conducted the following preliminary calculations: the
rate constant for the CH3O2+CH3O2 reaction (4.74×10-13 cm3 molecule-1 s-1; DeMore et al., 1997) was employed as k2. We assumed the
concentration of RO2 during the chamber experiments under ppm level
conditions to be <1010 molecule cm-3. If this is
the case, k2 [RO2] is determined to be <4.74×10-3 s-1, which is sufficiently lower than the k1 value
assumed for intramolecular hydrogen abstraction from RO2 (0.1 s-1;
Praske et al., 2018). These results suggest that RO2 auto-oxidation
will dominate even under ppm level conditions.
The mechanisms suggested by Molteni et al. (2018) can explain the number of
hydrogen atoms in C18H26Ox dimers because monomer peroxy
radicals have the structure C9H13Ox. For example, the
bimolecular reaction between TM135BPRO2 (C9H13O5) radicals
leads to the formation of C18H26O8 dimer +O2. The
RO2+RO2 reactions also have another product pathway to form a
combination of carbonyl and alcohol monomers (e.g., formation of TM135BP2OH + TM135BPOH in the MCM mechanism). The O/C ratios of the
C18H26O8-12 dimers, 0.44–0.67, were similar to those of the
C9H14O4-7 monomers, 0.44–0.78. The mechanisms suggested by
Molteni et al. (2018) are also consistent with the results of the O/C ratios; in this mechanism, the O/C ratios of monomers and dimers are
determined by those of abundant RO2 radicals present during
photooxidation.
If we assume ROOR-type structures for dimers, the dimers existing in
particle phase could be photolyzed during photochemical aging to form RO+RO. RO radicals, e.g., TM135BPRO in the MCM mechanism, may decompose to
smaller fragments, methylglyoxal, and a C6 counter product. These smaller
fragments existing in the particle phase may evaporate because their
volatilities are higher than the original ROOR dimers or RO radicals. The
volatility distributions determined from LC-TOF-MS data showed that dimers
comprise 25 %–49 % of the total product mass. The gas-phase RO2+RO2 reactions may not fully explain the mass ratio of dimer products because the rate constant for the RO2+RO2→ROOR+O2 reactions, 3.0×10-14 cm3 molecule-1 s-1 (Atkinson et al., 1989), was lower than that for
RO2+HO2→ROOH+O2 reactions, 5.6×10-12 cm3 molecule-1 s-1 (DeMore et al.,
1997). These results suggest that other dimer formation processes cannot be
excluded. The probable additional formation processes for dimers are
acid-catalyzed particle-phase reactions (Kalberer et al., 2004) or gas-phase
reactions of organic compounds with Criegee intermediates. Criegee
intermediates may be produced during photooxidation because ozone can react
with unsaturated photooxidation products (Sato et al., 2004). Present LC-TOF-MS
results show that C9H14O5 and C9H14O6 products
are produced during photochemical aging. These five- and six-oxygen monomers are
produced by the photooxidation of gaseous phenol-type compounds formed from
TMB photooxidation (Nakao et al., 2011). The compound
2-Hydroxy-1,3,5-trimethylbenzene (TM135BZOL of the MCM mechanism) is formed as a
primary product of TMB photooxidation. According to the MCM mechanism, this
phenol-type product reacts with OH radicals to form the TM135OLO2 peroxy
radical. This peroxy radical reacts with RO2 to form TM135OLOH or
reacts with HO2 to form TM135OLOOH, suggesting that the
C9H14O5 and C9H14O6 products are formed from
the photooxidation of TM135BZOL during photochemical aging. The peroxy radical,
TM135OLO2, may also react with NO2 to produce peroxy nitrate,
C9H13NO8.
TD-AMS measurements
Figure 6 shows the mass fraction remaining (MFR) observed for SOA as a
function of TD temperature (thermogram). Results obtained for SOA in normal
photooxidation experiments are averages from runs 1, 3, 4, 5, and 6. The error
bars quoted for results of the normal photooxidation experiments are
standard deviations. The MFR results observed between 50 and 150 ∘C in the aging experiment were slightly higher than those from normal
photooxidation experiments, but these differences may not be significant
because results of the aging experiment were obtained from only one experimental
run. The thermogram observed for SOA, formed from α-pinene
ozonolysis without photochemical aging in our previous study (Sato et al.,
2018), was similar to that observed for SOA from TMB in the aging experiment
in this study. The transmission efficiency of 50–250 nm sodium chloride
particles was reported to be >80 % in the region 298–498 K for a
TD with the same design as the present TD (Huffmann et al., 2008). We ignored
particle wall loss in the TD during the data analysis.
Mass fraction remaining (MFR) for SOA formed from the
photooxidation of TMB as a function of thermal denuder temperature
(thermogram).
Figure 4d shows the volatility distribution determined from the results of the
heating measurements. Thermogram data were converted into volatility
distributions using an empirical method developed by Faulhaber et al. (2009). Faulhaber et al. (2009) measured the relationship between the saturation
concentration and the TD temperature at which MFR becomes 0.5 for several
kinds of single-compound particles. We used the results of the calibration
curve from Faulhaber et al. (2009) directly, as the TD in our study has the same
design as their TD and has a residence time (∼13 s at 298 K)
close to that used for their TD (∼15 s 298 K). Furthermore,
we confirmed that their calibration curve agreed with our results obtained
for pinonic acid particles (Sato et al., 2018). The volatility distributions
determined from TD-AMS data included low-volatility products with log10C∗<0, showing agreement with results obtained from LC-TOF-MS
data. The weighted averages of log10C∗ determined from TD-AMS
data for normal photooxidation and aging experiments were 1.44 and 0.65,
respectively. The mass fractions determined between log10C∗=-4 and 0 for aging experiments were slightly higher than those
determined for normal photooxidation experiments, but again these
differences may not be significant.
Dilution measurements
Figure 7 shows the volume fraction remaining (VFR) observed for SOA as a
function of time after dilution. The number concentration and mean size of
SOA particles decreased with increasing time during dilution measurements.
We assumed that the decrease in the number concentration resulted from
particle wall loss, and the decrease in the mean size resulted from particle
evaporation. To remove the influence of wall loss, the VFR was determined by
r3/r03, where r is the geometric mean radius of particles and
r0 is the geometric mean radius immediately before dilution. We observed
the time series of the VFR at the DRs of 20, 40, 63, and 86. Although
evaporation is assumed to occur instantaneously in gas–particle equilibrium
partitioning models, the VFR of SOA particles decreased very slowly. The VFR
reached equilibrium more than 2 h after dilution, and in some cases, it did
not reach equilibrium at all within the observation duration.
Volume fraction remaining (VFR) for SOA particles formed from the
photooxidation of TMB as a function of time after dilution.
The equilibration timescale (τ) for gas–particle equilibrium could be
expressed as a function of the particle size, the particle number
concentration, and the mass accommodation coefficient (Saleh et al., 2013).
Figure S4 shows plots of effective saturation ratios as a function of
t/τ. Here, τ was calculated by Eq. (2).
τ=1+0.3773Kn+1.33Kn(1+Kn)/α/2πD(1+Kn)dN
In this expression, Kn is the Knudsen number, α is the mass
accommodation coefficient, D is the gas-phase diffusion coefficient, d is the
particle geometric mean diameter, and N is the particle number concentration.
The α value was treated as the fitting parameter. An effective
saturation ratio (SReff) in Fig. S4 is defined as the ratio of the
total vapor concentration in the gas phase to the vapor concentration at
equilibrium, as expressed by Eq. (3).
SReff=∑Cg,iCsat,eff=∑Cg,i∑xiγiCsat,i,
where Csat,eff is the effective saturation concentration, xi is mole
fraction, γi is the activity coefficient, and Csat,i is the
saturation concentration of component i. The time variation of SReff is
approximately a 1st-order variation if the change in the particle mass
concentration after dilution is less than several tens of percent, as
expressed by Eq. (4).
SReff=1-e-t/τ
Fitting Eq. (4) to experimental results, we determined the equilibration
timescales of DR = 20, 40, 63, and 86 to be 68±17, 174±40,
275±65, and 399±94 min, respectively. The current results of
a 68–399 min equilibration timescale was similar to or higher than our
previous results reported for α-pinene SOA, due to similar or lower
number concentrations (32–212 cm-3) during present dilution
experiments. We also determined the mass accommodation coefficient from the
fitting to be 0.7±0.3. The mass accommodation coefficient includes all
resistance to gas–particle partitioning other than gas-phase diffusion, for
example, surface accommodation and diffusion limitations in the particle
phase (Saleh et al., 2013). The existence of low-volatility materials in
SOA, kinetic inhibition, or some combined effect may explain an
accommodation coefficient of less than unity. The mass accommodation
coefficient determined for SOA from TMB was higher than that estimated for
α-pinene SOA in our previous study (0.1).
The normalized SOA yields determined for the observed dilution data and
predicted equilibrium points are plotted as a function of particle
concentration (Fig. S5). The normalized yield of the vertical axis is based on
the VFR determined from particle size, whereas the particle concentration of the
horizontal axis is the actual concentration affected by particle wall loss
in the EDC. The particle wall loss rate in the EDC was observed to be 2.5×10-5 s-1. We extrapolated observed dilution data to
predict normalized yields and particle concentrations at gas–particle
equilibria using results obtained by the present equilibration timescale
analysis. The observed dilution data and the predicted equilibrium data were
fitted by Eq. (2) in Grieshop et al. (2007) to determine the volatility
distributions, respectively. We determined the volatility distributions only
for log10C∗=-1, 0, 1, and 2 accounting for the mass
concentration region examined. The bin of log10C∗=-1
needed to have a non-zero mass fraction in order to explain observed
dilution data or predicted equilibrium data. Figure 4e shows the volatility
distributions determined from the results of dilution measurements. The
errors quoted for the volatility distributions were uncertainties in the
fitting analysis. The volatility distribution determined from dilution data
also shows that low-volatility products with log10C∗<0 are present, contradicting the volatility distributions predicted from
yield curve analysis.
Implications of data from different methods
There are not only statistical uncertainties for volatility distributions
obtained from each technique but also systematic uncertainties among
composition, heating, and dilution measurements. The volatility
distributions obtained from composition analysis will contain systematic
errors from sensitivity and saturation concentration parameterizations. In
addition, composition analysis may lose information for products with low
sensitivities, although positive-mode ESI analysis can detect major known
products. The results of heating measurements involve interference from
thermal decomposition and the uncertainties from the parameterizations for
the enthalpies of vaporization. The volatility distribution obtained from
dilution measurements also include large uncertainties under high DR
conditions, as discussed in the previous paragraph. Despite the probable
uncertainties in composition, heating, and dilution measurements, the
present results of all three methods suggest that TMB photooxidation
products include low-volatility compounds with log10C∗<0, which are not predicted from yield curve analysis. Possible
reasons for underestimations of low-volatility compounds by yield curve
analysis are the effects of semivolatile vapor wall loss generally enhanced
in batch experiments in a low-concentration region or constant product
yields assumed in a gas–particle partitioning model function. In the
standard VBS approach, the product volatility distributions determined by
yield curve analysis are employed (e.g., Robinson et al., 2007; Lane et al.,
2008). Currently, a limited number of non-standard treatments are available
only for α-pinene SOA; e.g., Trump and Donahue (2014) took into
account dimer formation in the particle phase, and Yli-Juuti et al. (2017)
employed the product volatility distribution determined from dilution data.
Further improvement in the atmospheric organic aerosol model will be
necessary not only for the study of biogenic SOA but also for anthropogenic SOA.
Conclusions
In this study, we investigated the volatility of SOA formed from TMB
photooxidation by composition, heating, and dilution measurements. To our
knowledge, this is the first study to determine the volatility distributions
from composition and dilution measurements of SOA formed from the
photooxidation of aromatic hydrocarbons. Results of the present composition
study showed that C9H14Ox monomers (x= 4–7) and
C18H26Ox dimers (x= 8–12) are major products in SOA
particles. These results are consistently explained by previously proposed
mechanisms in which highly oxygenated RO2 radicals are generated by
auto-oxidation mechanisms, and monomer and dimer products are formed by the
RO2+HO2 and RO2+RO2 reactions, respectively.
Dimer formation processes from particle-phase reactions or gas-phase
reactions of Criegee intermediates cannot be excluded because the rate
constants for the RO2+RO2→ROOR+O2 reactions in
the gas phase are lower than those for the RO2+HO2→ROOH+O2 reactions. Monomer products with five or more oxygens were newly
formed, and dimer products degraded during photochemical aging. Monomer
products with five or more oxygens may be produced from the photooxidation of
phenol-type gaseous products during photochemical aging, and dimer products
in the particle phase may undergo photolysis during photochemical aging.
The present results of composition, heating, and dilution measurements
suggest that TMB photooxidation products include compounds with volatilities
less than those predicted from conventional SOA yield curve analysis. In the
standard VBS approach, the product volatility distributions are determined
by SOA yield curve analysis, and the distributions employed in the standard
VBS approach do not include low-volatility products. A limited number of
non-standard treatments, accounting for the formation of low-volatility
products, are available, but non-standard treatments are examined only for
α-pinene ozonolysis. Further improvement of the atmospheric
simulation model will be necessary not only for the study of biogenic SOA
formation but also for anthropogenic SOA formation. Furthermore, the
volatility distribution of SOA was only studied under dry conditions in the
present study. Further studies would be necessary to understand the effects
of relative humidity on SOA volatility.
Data availability
All data used in this work can be found on the Figshare database
(10.6084/m9.figshare.10026131; Sato et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-14901-2019-supplement.
Author contributions
KS and TI designed and performed chamber experiments, TD-AMS measurements,
and LC-TOF-MS analysis. YF, AF, and YK designed and carried out dilution
measurements. SI and HT designed and carried out PTR-MS measurements. YM,
KT, and SS contributed to data interpretations from the viewpoint of
atmospheric modeling. TH, AS, and AT gave technical support for TD-AMS
measurements and also contributed to data interpretations.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank
Akio Togashi of Randstad, Yutaka Sugaya of NIES, and Tsuyoshi Fujii of
HORIBA Techno Service for their technical supports to dilution measurements.
Kei Sato thanks Shinichi Enami for a useful discussion on the formation mechanisms
of dimers and Yoshikatsu Takazawa for technical support in LC-TOF-MS analysis.
Financial support
This research has been supported by the Environmental Research and Technology Development Fund (grant no. 5-1408), JSPS KAKENHI (grant nos. JP25340021, JP16H06305, and JP17H01866), the Steel Foundation for Environmental Protection Technology (grant no. 14-15 Taiki-221), and the Sumitomo Foundation (grant no. 123449).
Review statement
This paper was edited by Neil M. Donahue and reviewed by three anonymous referees.
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