The formation of secondary organic aerosols (SOAs) from
the photooxidation of three monoalkylbenzenes (toluene, ethylbenzene, and
n-propylbenzene) in the presence of inorganic seeds
(SO42-–NH4+–H2O system) under varying NOx
levels has been simulated using the Unified Partitioning Aerosol Phase
Reaction (UNIPAR) model. The evolution of the volatility–reactivity
distribution (mass-based stoichiometric coefficient, αi) of
oxygenated products, which were created by the near-explicit gas kinetic
mechanism, was integrated with the model using the parameters linked to the
concentrations of HO2 and RO2 radicals. This dynamic distribution
was used to estimate the model parameters related to the thermodynamic
constants of the products in multiple phases (e.g., the gas phase, organic
phase, and inorganic phase) and the reaction rate constants in the aerosol
phase. The SOA mass was predicted through the partitioning and aerosol
chemistry processes of the oxygenated products in both the organic phase and
aqueous solution containing electrolytes, with the assumption of
organic–inorganic phase separation. The prediction of the time series SOA
mass (12 h), against the aerosol data obtained from an outdoor
photochemical smog chamber, was improved by the dynamic αi set
compared to the prediction using the fixed αi set. Overall, the
effect of an aqueous phase containing electrolytes on SOA yields was more
important than that of the NOx level under our simulated conditions or
the utilization of the age-driven αi set. Regardless of the
NOx conditions, the SOA yields for the three aromatics were
significantly higher in the presence of wet electrolytic seeds than those
obtained with dry seeds or no seed. When increasing the NOx level, the
fraction of organic matter (OM) produced by aqueous reactions to the total
OM increased due to the increased formation of relatively volatile organic
nitrates and peroxyacyl-nitrate-like products. The predicted partitioning
mass fraction increased as the alkyl chain length increased but the organic
mass produced via aerosol-phase reactions decreased due to the increased
activity coefficient of the organic compounds containing longer alkyl
chains. Overall, the lower mass-based SOA yield was seen in the longer
alkyl-substituted benzene in both the presence and absence of
inorganic-seeded aerosols. However, the difference of mole-based SOA yields of three
monoalkylbenzenes becomes small because the highly reactive organic species
(i.e., glyoxal) mainly originates from ring opening products without an alkyl
side chain. UNIPAR predicted the conversion of hydrophilic, acidic sulfur
species to non-electrolytic dialkyl organosulfate (diOS) in the aerosol.
Thus, the model predicted the impact of diOS on both hygroscopicity and
acidity, which subsequently influenced aerosol growth via aqueous reactions.
Introduction
Anthropogenic volatile organic compounds (VOCs) have significant impacts on
urban and regional atmospheric chemistry, despite fewer global emissions
compared with biogenic VOCs (McDonald et al., 2018). As an important
group of anthropogenic VOCs, aromatic hydrocarbons (HCs) are emitted from
automobile exhaust (Zhang et al., 2018) and solvent use (Cheng et
al., 2018) and are known to be precursors for secondary organic aerosols (SOAs),
which are formed during the process of photooxidation (Seinfeld
and Pandis, 2016). In polluted areas (e.g., urban areas in Asia), aromatic
HCs occupy 11 % to 25 % of the total nonmethane HC emissions (67.0 Tg
in 2010) (Li et al., 2017) and traditionally comprise approximately 15 %
of SOA formation (Ait-Helal et al., 2014), which contributes to the
urban budget of fine particulate matter (Wood et al., 2010).
SOA formation has attracted substantial interest from scholars because of
its vital role in affecting climate change (IPCC, 2015; Seinfeld and
Pandis, 2016), urban visibility (Chen et al., 2012; Ren et al., 2018), and
health (Requia et al., 2018). The prediction of SOA formation was first
fulfilled by a gas–particle partitioning model. The partitioning-based SOA
model uses two surrogate products (Odum et al., 1996) or
several semivolatile surrogates (e.g., volatility basis set, VBS)
(Donahue et al., 2006), with semiempirical parameters (e.g.,
the product stoichiometric coefficient, α; and gas–particle
partitioning coefficient, Kp) for each HC system under a given
NOx condition. Due to its simplicity and high efficiency, the
partitioning-based model has been widely used in regional and global models.
Nonetheless, the models and their predecessors are limited to predict SOAs
formed from in-particle chemistry due to the loss of product structures,
which govern the reactivity of organic species in the aerosol phase.
Overall, regional air quality models have historically underestimated fine
particulate matter in summertime (Appel et al., 2017; Huang et al., 2017)
due to the lack of in-particle chemistry, particularly in the presence of an
aqueous phase containing electrolytes (Ervens et al., 2011; Tsigaridis et
al., 2014; Kelly et al., 2018).
A few models have attempted to implement in-particle chemistry into SOA
models. For instance, Johnson et al. (2004, 2005) simulated aromatic SOA chamber data, with a modified
Kp, to obtain experimentally comparable results, while the delayed
simulated SOA mass indicated the occurrence of chemical reactions in the
aerosol phase. McNeill et al. (2012) developed the
Gas–Aerosol Model for Mechanism Analysis (GAMMA) to predict the formation of
SOAs via aqueous-phase chemistry, which was further applied to the
production of isoprene SOAs. Im et al. (2014) advanced the
Unified Partitioning Aerosol Phase Reaction (UNIPAR) model, which predicted
the SOA mass from partitioning processes and aerosol-phase reactions
(reactions in both organic and inorganic phases and organosulfate (OS)
formation). In that study, toluene and 1,3,5-trimethylbenzene SOAs were
modeled using near-explicit products with the organic–inorganic phase
separation mode. Beardsley and Jang (2016) extended UNIPAR to
simulate isoprene SOAs in the single homogeneously mixed phase
(organic–inorganic mixture). Despite the reasonable prediction of SOA
masses, UNIPAR faced inaccuracies in predicting time series SOA data due to
the use of a fixed (nonage-driven) mass-based stoichiometric coefficient (αi) set.
Age-driven functionalization and fragmentation alter the volatility and
reactivity of products and their molecular structures (Donahue et al.,
2006; Rudich et al., 2007; Shilling et al., 2007; Hartikainen et al., 2018),
which, in turn, varies the in-particle chemistry. Cappa and
Wilson (2012) employed tunable parameters to kinetically demonstrate the
evolution of SOA mass and the bulk oxygen-to-carbon atomic ratio (O:C ratio)
during photochemical aging. However, oligomerization reactions in the
aerosol phase were excluded. Donahue et al. (2011)
developed a 2D-VBS method, which represented product aging by remapping the
volatility and polarity (O : C ratio) of the products in 2-D space. Zhao et
al. (2015) reported a discrepancy in the simulated toluene SOAs and
α-pinene SOAs within the same 2D-VBS configuration, which may result
from the different reactivities of the oxidation products of the precursors
in aerosol-phase reactions. In this study, we have attempted to improve the
UNIPAR model by using dynamic (age-driven) αi and applying the
resulting model to predict the SOA formation of three monoalkylbenzenes
(i.e., toluene, ethylbenzene, and n-propylbenzene) under a wide range of
environmental conditions (i.e., NOx, temperature, humidity,
sunlight, and aerosol acidity). To consider the effect of the aging process on SOA
formation, model parameters related to the organic molecular structures
(i.e., the molecular weight, MW; and O : C ratio) and the αi set
are calculated as the system ages, allowing for the internally dynamic
estimation of the activity coefficient of the products (lumping species) in
the aqueous phase containing electrolytes. Hence, the model is able to
dynamically compute the partitioning coefficient of organics in the
inorganic phase (Kin) by reflecting the photochemical evolution of the
products in the gas phase and, consequently, improving SOA prediction.
Organosulfate (OS), which has been identified in both laboratory and field
studies (Hettiyadura et al., 2015; J. Li et al., 2016; Estillore et al.,
2016; Chen et al., 2018), is an important chemical species due to its low
volatility and ability to modulate the hygroscopicity of sulfate
constituents. In the presence of acidic sulfate constituents, UNIPAR also
predicts the production of non-electrolytic sulfates (i.e.,
dialkyl-substituted OS, diOS) and the ensuing modification of aqueous-phase
reactions. The feasibility of unified rate constants for aerosol-phase
reactions was evaluated by extending the preexisting rate constants, which
has been employed for toluene and 1,3,5-trimethylbenzene (Im et
al., 2014) and isoprene (Beardsley and Jang, 2016), to the three
monoalkylbenzenes in this study.
Experimental conditions and resulting SOA chamber data from the
monoalkylbenzenes photooxidation experiments performed under various
NOx conditions with/without inorganic-seeded aerosol in the
dual outdoor UF APHOR chambers.
Exp.DatebInitial condition YSOAeRHTemp.NotefIDaHCNOxSeededHC/NOx(%)(%)(K)(ppb)(HONO)aerosold(ppbC/ppb)(ppb)(µg m-3)Tol16 Jan 2012 Ec190110 (40)5012.118.918–81280–306Fig. 6aTol26 Jan 2012 Wc19095 (35)–14.813.318–81280–306Fig. 6aTol39 Feb 2012 Ec175245 (35)465.015.321–83280–307Fig. 6dTol49 Feb 2012 Wc180246 (35)–4.59.321–84280–307Fig. 6dTol520 Jun 2012 Ec165110 (15)35 (SA)10.515.627–83295–317Fig. S7aTol616 Dec 2017 E198132 (79)–10.58.623–58283–300Figs. 3a and S7bTol725 Feb 2018 W154170 (22)–6.43.320–44293–313Figs. 3b, S3a, and S7cTol830 Apr 2018 E127306 (47)70 (SA)2.913.114–57289–317Figs. 3c, 5a, and S7dTol914 Jun 2018 W135361 (80)130 (wAS)2.619.051–98295–319Fig. S7eEB15 Dec 2017 E12671 (32)4314.215.418–57287–310Fig. 6bEB25 Dec 2017 W13474 (38)–14.412.225–66288–310Fig. 6b and S3bEB34 Jan 2018 E132175 (13)506.021.830–85267–291Fig. S7fEB44 Jan 2018 W131175 (22)–6.012.848–93267–289Fig. S7fEB510 Dec 2017 E131363 (13)392.910.120–83271–298Fig. 6eEB610 Dec 2017 W128363 (15)–2.84.133–86272–295Fig. 6eEB719 Feb 2018 W12581 (36)80 (SA)12.325.619-46292–315Figs. 5b and S7gEB819 Feb 2018 E11263 (36)35 (dAS)14.311.013–39292–314Fig. S7gEB919 Jan 2018 W169106 (30)40 (wAS)12.728.620–87269–302Fig. S7hPB14 Mar 2018 E10087 (19)5710.47.411–54279–306Fig. 6cPB24 Mar 2018 W109108 (24)–9.15.417–59279–305Figs. 6c and S3cPB328 Mar 2018 E87264 (36)543.07.111–43285–312Fig. 6fPB428 Mar 2018 W88248 (33)–3.24.616–51285–312Fig. 6fPB55 Apr 2018 W10176 (35)70 (SA)12.015.730–93282–312Figs. 5c and S7iPB617 Apr 2018 E101149 (141)70 (SA)6.111.914–85278–313Fig. S7jPB717 Apr 2018 W101155 (126)70 (wAS)5.918.140–91279–310Fig. S7jPB814 Jun 2018 E83353 (148)90 (SA)2.110.722–90294–322Fig. S7k
a “Tol”, “EB”, and “PB” represent toluene,
ethylbenzene, and n-propylbenzene oxidation experiments, respectively.
b “E” or “W” that follows the experiment date represents the
east or west chamber for the UF APHOR, respectively. c SOA data
obtained from Im et al. (2014). d “SA”, “wAS”, and “dAS”
denote directly injected sulfuric-acid-seeded aerosol, wet ammonium-sulfate-seeded aerosol, and
dry ammonium-sulfate-seeded aerosol, respectively (dry:
RH < ERH; wet: RH > ERH). For those without indication, SO2
(in the unit of ppb) was injected into the chamber to generate sulfuric acidic
seeds under the sun light. e SOA yield is estimated using
YSOA=ΔOM/ΔHC, where ΔOM is
formed organic matter and ΔHC is consumed HC. Yield in the table was
estimated where SOA mass reached to the maximum over the course of the experiments.
f This column denotes in which
figures the corresponding data were used. The accuracy of relative humidity (RH) is 5 %. The accuracy of temperature is 0.5 K.
Experimental techniques
The SOA formation from the photooxidation of monoalkylbenzenes was
conducted in the University of Florida Atmospheric PHotochemical Outdoor
Reactor (UF APHOR) (Table 1). The concentrations of HCs, trace gasses
(Ox, SO2, and O3), inorganic ions, aerosol acidity, and
organic carbon (OC) of particles were monitored, as were the meteorological
factors (i.e., relative humidity; temperature; and ultraviolet, UV, radiation).
The configurations of the chamber and instrumentations were
described by Im et al. (2014), J. Li et al. (2016),
Beardsley and Jang (2016), Yu et al. (2017), and
Jiang et al. (2017). Aerosol acidity ([H+], mol L-1 of
aerosol) is monitored using colorimetry integrated with the reflectance
UV–visible spectrometer (C-RUV) technique (Li et al., 2015) (Sect. S1 in
the Supplement). The diOS concentration (µmol m-3)
in an aerosol is estimated by the difference [H+] obtained
from ion chromatography (IC) interfaced with a particle-into-liquid sampler (PILS)
(Li et al., 2015) and C-RUV method. Each HC was studied under at
least two NOx levels (high NOx: HC/NOx< 5.5; low
NOx: HC/NOx> 5.5) with or without inorganic-seeded
aerosols (i.e., sulfuric acid, SA; or ammonium sulfate, AS). HONO was added
into the system as a reaction initiator. To investigate the effect of the
liquid water content (LWC) on AS-seeded SOA, two RH conditions were applied:
(1) dry: RH < efflorescence RH (ERH) of the AS seed; (2) wet:
RH > 50 % to prevent crystallization of AS seed. The ratio of
organic matter (OM) to OC was experimentally determined to be 1.9 (Table 1,
EB4), which was similar to the reported value of 2.0 for a series of
toluene–NOx oxidation study (Kleindienst et al., 2007).
Simplified scheme of the UNIPAR model. [HC]0 represent the
initial hydrocarbon (HC) concentration. The dynamic mass-based stoichiometric
coefficient (dynamic αi), the consumption of HCs (ΔHCs), the
concentration of hydroperoxide radical ([HO2]), and the concentration
of organic peroxyl radical ([RO2]) are simulated from the gas kinetic
mechanism (MCM v3.3.1). The aging scale factor (fA) is represented
as a function of [HO2], [RO2], and [HC]0, which is
detailed in Sect. 3.1. C and K denote the concentration and the partitioning
coefficient of organic compounds, respectively, in gas phase (g), organic phase (or),
and inorganic phase (in). kor,i denotes the reaction rate constant
of oligomerization of organic compounds in the organic phase. kAC,i denotes
the reaction rate constant of acid-catalyzed oligomerization of organic
compounds in the inorganic phase and is determined as a function of aerosol acidity ([H+])
and ambient humidity (RH). “OM” represents the concentration of organic matter.
Subscripts “AR”, “P”, and “T” indicate OM formed from aerosol-phase
reactions, OM formed from the partitioning process, and total OM, respectively.
Subscript i represents each lumping species. diOS represents the concentration
of organosulfate – dialkyl sulfate (diOS) in this study.
Model descriptions
The structure of the UNIPAR model is illustrated in Fig. 1. The simulation
of aromatic SOA formation in the aqueous phase containing electrolytes was
performed under the assumption of complete organic–inorganic phase
separation. Bertram et al. (2011) modeled the separation RH (SRH) in the
liquid–liquid phase of the mixture of organic and AS using the bulk O : C
ratio. When ambient RH < SRH, the system undergoes organic–inorganic phase separation. The reported O : C ratios of the toluene, ethylbenzene, and
n-propylbenzene SOAs were 0.62 (Sato et al., 2012), 0.55 (Sato et al., 2012), and 0.45 (L. J. Li et al., 2016),
respectively, which caused the corresponding SRH values to be 65 %, 80 %,
and 93 %, respectively. Most RH for active photooxidation of HCs
under ambient sunlight were under 65 %, which supported the assumption of
organic–inorganic phase separation. In addition, as less soluble oligomers
formed in the aerosol phase, an SRH higher than 65 % was more likely to be yielded.
Atmospheric evolution of lumping species
The gas-phase oxidation of HCs is simulated using the near-explicit
gas-phase chemistry mechanism (Master Chemical Mechanism, MCM, v3.3.1)
(Jenkin et al., 2012) integrated with the Morpho chemical solver
(Jeffries et al., 1998). To represent the polluted urban and clean
environments, the gas-phase oxidation is simulated under various NOx
levels (HCppbC/NOxppb= 2–14) for given meteorological conditions
(e.g., sunlight, temperature, and RH on 14 June 2018 near the summer solstice,
with a clear sky in Gainesville, Florida). The resulting oxygenated products
are lumped into 51 species within a 2-D set with eight levels of volatility
(1–8: 10-8, 10-6, 10-5, 10-4, 10-3, 10-2, 10-1,
and 1 mm Hg) and six levels of aerosol-phase reactivity (very fast: VF, fast: F,
medium: M, slow: S, partitioning only: P, and multi-alcohol: MA) plus
three additional reactive species (glyoxal, GLY; methylglyoxal, MGLY; and
epoxydiols, IEPOX, isoprene products) with their own vapor pressure. The
detailed lumping criteria and αi equations are described in
Sect. S2 along with the major product structures (Tables S1–S3 in the Supplement).
To simulate age-dependent SOA formation, αi is reconstructed over
time by a weighted average method using a pair of gas-phase oxidation
compositions with different aging statuses: fresh composition and highly
oxidized composition. The weighting factor at time =t is related to an
aging scale factor (fA(t)), which is defined as
fA(t)=logHO2+RO2[HC]0,
where [RO2] and [HO2] represent the concentrations (ppb) of
RO2 and HO2 radicals, respectively, and [HC]0 represents the
initial HC concentration (ppbC). The lower boundary of fA(t)
(t= fresh) to determine the fresh αi set is equal to -7.2 at
HC/NOx= 2 (high NOx levels) and -3.7 at HC/NOx= 14 (low
NOx levels) for all three HCs. The upper boundary of fA(t)
(t= highly aged) to determine the highly aged αi set is equal
to -5.2 and -2.9 under the same high and low NOx levels, respectively.
Both the fresh αi and highly aged αi are functions of
HC/NOx. fA(t) is further converted into a fractional aging
scale (fA′(t) ) ranging from 0 (fresh composition) to 1 (highly aged
composition) using a weight average method
fA′(t)=fA(highlyaged)-fA(t)fA(highlyaged)-fA(fresh)
at each NOx level. Then, αi is dynamically reconstructed based on fA′(t) under
varying NOx conditions.
αi=1-fA′(t)freshαi+fA′(t)highlyagedαi
The molecular structures, including O : Ci, MW (MWi), and
hydrogen bonding (HBi) parameters, of each species (i) are also
dynamically represented by a similar method, as shown in Sects. S3 and S4.
SOA formation: partitioning
The partitioning coefficient (KP) from the gas (g) phase to the
organic (or) phase (Kor,i, m3µg-1) and from the g phase to the
inorganic (in) phase (Kin,i, m3µg-1) of each species
is estimated using the following gas–particle absorption model (Pankow, 1994).
Kor,i=7.501RT109MWorγor,ipl,ioand3Kin,i=7.501RT109MWinγin,ipl,io,
where R represents the gas constant (8.314 J mol-1 K-1).
T represents the ambient temperature (K). MWor and
MWin represent the average MW (g mol-1) of organic and inorganic aerosols,
respectively. pl,io represents the subcooled liquid vapor
pressure (mm Hg) of a species, i. In the organic phase, we assume that the activity
coefficient (γor,i) of a species (i) is unity (Jang
and Kamens, 1998). In the inorganic phase, γin,i is semi-empirically
predicted by a regression equation, which was fit the theoretical activity
coefficients of various organic compounds to RH, fractional sulfate (FS),
and molecular structures (i.e., MWi, O : Ci, and HBi).
FS is a numerical indicator for inorganic compositions related to aerosol
acidity FS=SO42-SO42-+NH4+, where
[SO42-] and [NH4+] are the concentration of the total
sulfate and the total ammonium, respectively). The theoretical activity
coefficients were estimated at a given humidity and an aerosol composition
through a thermodynamic model (Aerosol Inorganic-Organic Mixtures Functional
Groups Activity Coefficients, AIOMFAC) (Zuend et al., 2011).
γin,i=4e0.035⋅MWi-2.704⋅lnO:Ci-1.121⋅HBi-0.330⋅FS-0.022⋅(100⋅RH)
The statistical information for Eq. (4) is shown in Sect. S4 and
Fig. S1 in the Supplement. The resulting Kor,i and Kin,i are employed to
calculate the concentration (µg m-3) of the lumping species in
multiple phases (Cg,i, Cor,i, Cin,i, and
CT,i=Cg,i+Cor,i+Cin,i).
Schell et al. (2001) developed a partitioning model to predict SOA
formation. This model was reconstructed by Cao and Jang (2010) to
include OM formed via aerosol-phase reactions (OMAR,i) for a
species (i), which is estimated in Sect. 3.3. OM formed during the partitioning
process (OMP) is estimated by utilizing the mass balance shown in the
following equation.
OMP=∑ijCT,i-OMAR,i-Cg,i*5Cor,iMWi∑ijCor,i′MWi+OMAR,iMWoli,i+OM0Cg,i* (1/Kor,i) is the effective saturation concentration
and OM0 represents the concentration (mol m-3) of the
preexisting OM. MWoli,i represents the average MW of oligomeric
products. Equation (5) is solved via iterations using the globally converging
Newton–Raphson method (Press et al., 1992).
SOA formation: aerosol-phase reactions
The formation of OMAR,i is processed in both the organic and inorganic phases:
oligomerization in the organic phase to form OMAR,or,i and
oligomerization in the inorganic phase to form OMAR,in,i based on the
assumption of a self-dimerization reaction (i.e., second-order reaction)
(Odian, 2004) for organic compounds in media. Oligomerization in an
aqueous phase can be accelerated under acidic environment
(Jang et al., 2002). The oligomerization rate
constants (L mol-1 s-1) in the organic phase and inorganic phase are ko,i
and kAC,i, respectively, and the kinetic equations for oligomerizations are
written as follows.
6dCor,idt=-ko,iC′or,i2MWiOMTρor1037dCin,idt=-kAC,iC′in,i2MWiMinρin103
The bracketed terms in the equations indicate the conversion factors from
aerosol-based concentrations (Cor,i′ and Cin,i′:
mol L-1) into air-based concentrations (µg m-3). The detailed
derivations are shown in Sect. S5 and are illustrated in Fig. S2.
ρor and ρin represent the density of the aerosol of organic and
inorganic aerosol. ρor was experimentally determined (EB4 in Table 1) to be
1.38 g cm-3, which was similar to the reported value of 1.4 g cm-3
for aromatic SOA (Nakao et al., 2011; Chen et al., 2017; Ng et al., 2007).
ρin is obtained from a regression equation through the extended
aerosol inorganic model (E-AIM) (Clegg et al., 1998). Due to atmospheric
diurnal patterns (high RH at nighttime to low humidity during daytime), it
is likely that the RH changes would be based on inorganic aerosol ERH.
UNIPAR internally predicts the ERH using the equation derived by Colberg et al. (2003).
kAC,i in Eq. (7) is estimated based on a semiempirical model developed by
Jang et al. (2005) as a function of species reactivity (Ri), protonation
equilibrium constant (pKBH+i), excess
acidity (X), water activity (aw), and proton concentration ([H+]),
which are estimated by the E-AIM.
kAC,i=100.96Ri+0.0005pKBH+i+0.96⋅X+logawH+-2.56
In the organic phase, ko,i is estimated by excluding the X
and aw [H+] terms. The formed OMAR can be calculated as a sum
of OMAR,or,i and OMAR,or,i for each species assuming that
OMAR is irreversibly formed and nonvolatile (Kleindienst et al.,
2006; Cao and Jang, 2010).
Organosulfate formation
In the presence of aqueous acidic sulfate, UNIPAR predicts the formation of
diOS ([diOS]model) to compute the change in aerosol hygroscopicity
and acidity. At each time step, free electrolytic sulfate ([SO42-]free),
which is the sulfate that is unassociated with
ammonium ([NH4+]), is represented as ([SO42-] - 0.5 [NH4+]).
[SO42-]free is then applied to the
semiempirical equation tested previously for several SOA systems (Im et
al., 2014; Beardsley and Jang, 2016) to predict [diOS]model, as
described below:
[diOS]modelSO42-free=1-11+fdiOSNdiOSSO42-free,
where fdiOS represents the diOS conversion factor introduced by
Im et al. (2014), which was semi-empirically determined to be 0.071
in this study. NdiOS represents the numeric parameter for scaling
lumping groups based on the effectiveness of the chemical species to form
diOS. For example, the diOS scale factor is 1 for each alcohol and aldehyde
group and 2 for each epoxide group (see Tables S1–S3 for functional groups).
Then, NdiOS is summed at each time step and applied to Eq. (9).
Operation of the UNIPAR model
The variables, which include HC consumption (ΔHC), [HO2],
[RO2], HC/NOx, RH, temperature, and the inorganic concentration
(i.e., Δ[SO42-] and Δ[NH4+]), were input
to the UNIPAR model every 6 min (Δt=6 min).
Results and discussionPrediction of SOA mass under the evolution of oxygenated products
As reported in former studies, the kinetic mechanism tends to underestimate
the decay of aromatic HCs because of the low prediction of OH radicals
(Johnson et al., 2005; Bloss et al., 2005). In this study, the addition of
artificial OH radicals varies with the HC/NOx ratio by fitting the
predicted decay of HCs using the kinetic mechanism in the experimental
measurements. The time profiles of the decays of the three HCs are shown in
Fig. S3 (Sect. S6). When the NOx level is very low, the
maximum additional OH radical production rate for monoalkylbenzenes is
2×108 molecules cm3 s-1, which is less than
4×108 (Bloss et al., 2005) but similar to the value
reported by Im et al. (2014). When HC/NOx< 3, no
addition of artificial OH radicals is needed for the chamber simulation of
the decay of monoalkylbenzenes. For the make-up OH production rate constants
of all three HCs under varying NOx, the mathematical weighting equation
is written below:
dynamicmakeupOHrate=e0.6×HC/NOxe0.6×HC/NOx+5010×2.0×108moleculescm3s-1.
In our model, we assume that the oxidation of products progresses in the gas
phase. Lambe et al. (2012) reported that the transition point
of n-C10 SOAs from a functionalization-dominant regime to a
fragmentation-dominant regime is approximately 3 d (photochemical equivalent age under
an atmospheric OH exposure of 1.5×106 molecules cm-3).
Under this criterion, we exclude the aging of nonvolatile aerosol
products (OMAR). However, the oxidation of aerosol products for longer periods
of time may decrease the volatility (George and Abbatt, 2010; Jimenez et al., 2009).
The mass-based stoichiometric coefficients (αi) of each
lumping species, i, from toluene oxidation (i) under low NOx
level (simulation based on the sunlight of Exp. Tol6, HC/NOx= 10.5,
16 December 2018) at (a) fresh condition and (b) highly aged
condition and (ii) under high NOx level (simulation based on the
sunlight of Exp. Tol8, HC/NOx= 2.9, 30 April 2018) at
(c) fresh condition and (d) highly aged condition, where
fA is the aging scale factor as derived in Eq. (1) in Sect. 3.1.
The oxygenated products predicted by the explicit gas kinetic model are lumped
as a function of vapor pressure (eight groups: 10-8, 10-6, 10-5,
10-4, 10-3, 10-2, 10-1, and 1 mm Hg) and aerosol-phase
reactivity (six groups), i.e., very fast (VF: tricarbonyls and α-hydroxybicarbonyls),
fast (F: two epoxides or aldehydes), medium (M: one epoxide or aldehyde), slow
(S: ketones), partitioning only (P), and multialcohol (MA). MGLY (methylglyoxal)
and GLY (glyoxal) were lumped separately due to the relatively high reactivity.
Figure 2 illustrates the evolution of the volatility–reactivity-based
distribution of the mass-based stoichiometric coefficient (αi)
of toluene at the two different NOx levels (HC/NOx= 2.9
and 10.5). Collectively, most αi values at both NOx levels
tend to decline as the reaction time lapses (Fig. 2a vs. Fig. 2b;
Fig. 2c vs. Fig. 2d) since the evolution of some semivolatile organic
compounds (SVOCs) forms very volatile molecules (i.e., CO2, formic
acid, and formaldehyde). For example, the αi values of highly
reactive carbonyls with high volatility (GLY and MGLY in Table S1 of
Sect. S7) are high under the fresh condition and significantly decline as the
system ages, because they undergo fast photolysis under sunlight (George
et al., 2015; Henry and Donahue, 2012). Consequently, the decay of these
highly reactive species leads to the decrease in the production of OMAR.
The high NOx level delays the oxidation of gas-phase
products. Similar trends in the αi set can be found for
ethylbenzene and n-propylbenzene, as shown in Sect. S7 (Tables. S2 and S3,
Figs. S4 and S5). The αi of highly reactive species
(e.g., GLY, 8VF, 3M, and 5S) decreases by increasing the NOx level due
to the suppression of the HOx cycle via the reaction of NO2 with
OH radicals. As seen in Fig. 2d, some medium reactivity species – i.e.,
2-methyl-4-oxo-3-nitro-2-butenoic acid (3M), 2-methyl-4-oxo-2-butenoic acid (6M),
and acetyl-3-oxopropanoate (7M) – start to form as NOx decreases.
Comparison between simulated SOA mass using the fixed αi
(αi is obtained when precursor being consumed make up half
of the total simulated consumption) and dynamic αi (αi evolving as photooxidation)
under (a) low-NOx condition (Exp. Tol6, HC/NOx= 10.5),
(b) moderate-NOx condition (Exp. Tol7, HC/NOx= 6.4),
and (c) high-NOx condition (Exp. Tol8, HC/NOx= 2.9 with sulfuric
acid (SA)-seeded aerosol). (d, e, f) represent the time-dependent SOA
growth curve (SOA mass concentration against consumed HC in the unit of
µg m-3) corresponding to the experimental conditions of (a, b, c),
respectively. The solid circle represents the experimental measurements. The
SOA mass is corrected for particle loss to the chamber wall. The experimental
conditions are available in Table 1.
In Fig. 3, the comparison between the simulations of SOA formation from
toluene oxidation is based on two different αi-reconstruction
strategies: dynamic αi and fixed αi. A clear
improvement in the prediction of SOA formation is demonstrated when
comparing the SOA mass using dynamic αi to that using
fixed αi. The aged SOA growth from the three systems (i.e., low
NOx level, Fig. 3a and d; moderate NOx level, Fig. 3b
and e; and high NOx level with an inorganic seed, Fig. 3c
and f) is even smaller than that predicted with the less-aged
αi set, which is obtained when precursors being consumed make up half
of the total simulated consumption. Our model simulation against the chamber data
suggests that while aging may alter aerosol compositions (Fig. 2), it does
not always increase SOA yields. Traditionally, the SOA mass has been
predicted using fixed thermodynamic parameters (i.e., Kp and
αi), which is inadequate when reflecting upon practical scenarios,
where oxygenated product distributions vary dynamically with oxidation.
Effects of aerosol acidity and LWC on SOA formation
In the model, aerosol acidity was expressed using a fractional free sulfate (FFS),
which is defined as FFS = ([SO42-] - 0.5[NH4+])/[SO42-]. Humidity can influence both aerosol
acidity and LWC, which are the model parameters in UNIPAR. Thus, UNIPAR has
the capability to decouple the effect of aerosol acidity and humidity, as
shown in Fig. 4 for toluene SOA. The impact of aerosol acidity and humidity
on the yields of SOAs derived from ethylbenzene and n-propylbenzene is
illustrated in Fig. S6 (Sect. S8). The dramatic difference in SOA yields
appears between the RH above ERH and the RH below ERH. The LWC disappears
below ERH, and there are no aqueous reactions. For example, the observed SOA
yield of ethylbenzene with effloresced AS was significantly smaller than
that with wet AS: 11 % (EB8 in Table 1) vs. 30 % (EB9 in Table 1).
Kamens et al. (2011) and Liu et al. (2018) reported a significantly
lower yield of toluene SOA for dry AS-seeded aerosols compared with its wet
counterpart. The partitioning of polar carbonaceous products increases with
increasing LWC and, thus, aqueous reactions. In the presence of wet
aerosols, SOA yields gradually increase with increasing FFS (increasing
acidity) at a given RH due to acid-catalyzed oligomerization. The oxygenated
products of toluene are relatively more polar than those of ethylbenzene or
propylbenzene and positively attributed to the increase in OMAR.
Simulated toluene SOA yields (YSOA=ΔOM/ΔHC at
the end of the simulation, where ΔOM is formed organic matter and
ΔHC is consumed HC) as a function of relative humidity (RH: 0.1–0.9)
and fractional free sulfate (FFS: 0–1), where
FFS = ([SO42-] - 0.5[NH4+])/[SO42-],
which is another numerical indicator that is used to estimate aerosol acidity ([H+])
in the inorganic thermodynamic model. The RH and FFS are fixed in the simulations.
The gas-phase simulations are based on the experimental condition of 14 June 2018
(Exp. Tol9 in Table 1) (initial HC concentration = 5 ppb, HC/NOx= 2,
preexisting OM (OM0) mass concentration = 2 µg m-3,
sulfate mass concentration = 20 µg m-3, and the mass ratio
of the consumed HC to sulfate (ΔHC : sulf) = 1).
Compared to isoprene SOAs reported by Beardsley and Jang (2016),
the impacts of humidity and acidity on the SOA yields of monoalkylbenzenes
in this study are relatively weaker above the ERH (Fig. 4), except for the
highly acidic conditions under high humidity. In this study, aromatic SOA
mass is attributed to a few highly reactive species, such as GLY. Other
aromatic oxidation products partitioned in the aerosol phase have moderate
solubility and they are slow to react in the aqueous phase. Isoprene
products are more hygroscopic than aromatic products and even mixable with
an aqueous phase containing electrolytes. The reactions of medium reactivity
polar products that formed during isoprene oxidation can be accelerated by
an acid catalyst with higher sensitivities to acidity and humidity.
Organosulfate: simulation vs. measurement
Figure 5 illustrates the time profiles of the predicted concentrations of diOS
([diOS]model) and protons ([H+]) with the measured concentrations
of diOS ([diOS]exp), [NH4+], and [SO42-] for
different aromatic HCs under given experimental conditions (Fig. 5a–c).
For the three SA-seeded SOA experiments, the fractions of diOS to the total
sulfate amount are 0.09, 0.15, and 0.06 for toluene (Exp. Tol8,
HC/NOx= 2.9, FS changing from 0.64 to 0.39), ethylbenzene (Exp. EB7,
HC/NOx= 12.3, FS changing from 0.82 to 0.46), and n-propylbenzene
(Exp. PB5, HC/NOx= 14.4, FS changing from 0.76 to 0.38), respectively.
The [diOS]model reasonably agrees with [diOS]exp. The aerosols in
Exp. Tol8 and Exp. PB5 show the cessation in diOS formation at approximately
10:00 EST since they became effloresced due to the neutralization of SA with
ammonia under the reduction in humidity during the daytime. The diOS
fraction in Exp. EB7, which contained wet acidic aerosols, was higher than
those in Exp. Tol8 and Exp. PB5, indicating that the acidic condition was
favorable for the formation of diOS (Surratt et al., 2010; Lin et al., 2013).
Time profiles of measured inorganic sulfate concentration ([SO42-]exp),
ammonium concentration ([NH4+]exp), diOS concentration
([diOS]exp), the predicted proton concentration ([H+]),
diOS concentration ([diOS]model), and the maximum diOS concentration
([diOS]max) (assuming there is no ammonia neutralization in the
system) for SOA generated from (a) toluene (Exp. Tol8, HC/NOx= 2.9,
OM-to-sulfate mass ratio (OM : sulf) = 1.4), (b) ethylbenzene
(Exp. EB7, HC/NOx= 12.3, OM : sulf = 1.4), and
(c)n-propylbenzene (Exp. PB5, HC/NOx= 14.4,
OM : sulf = 0.7). The degree of neutralization is indicated by FS,
ranging from 1 (for sulfuric acid) to 0.33 (for ammonium sulfate). “SA” stands
for experiment with direct-injection sulfuric-acid-seeded aerosols. The ions
and diOS concentrations were corrected for the particle loss to the chamber wall.
The experimental conditions are available in Table 1.
Beardsley and Jang (2016) reported that the diOS fraction for
isoprene SOAs was 0.26 (HC/NOx= 32.5, FS changing from 0.69 to 0.47),
which was more than that for toluene SOAs, indicating that the oxidation
products of isoprene may contain more reactive species to form diOS. For
example, IEPOX products in isoprene SOAs are known to be reactive to SA
(Budisulistiorini et al., 2017). Additionally, isoprene aerosol products
are mixable with aqueous solutions containing electrolytes, and they can
more effectively form diOS compared to the aromatic products in
liquid–liquid phase separation.
To estimate the potential upper boundary of the concentration of diOS ([diOS]max)
in monoalkylbenzene SOA, the aerosol composition was
predicted by the model in the presence of SA aerosols (without
neutralization with ammonia) under the given experimental conditions shown
in Fig. 5. The resulting diOS fractions were 0.29 (OM-to-sulfate mass ratio
(OM : sulf) = 1.4), 0.25 (OM : sulf = 1.4), and 0.12 (OM : sulf = 0.7) for
toluene, ethylbenzene, and n-propylbenzene, respectively. The aerosol acidity
of the ambient aerosol is generally lower than ammonium hydrogen
sulfate (AHS), and, thus, the diOS fraction in ambient air would be much lower than
the estimated upper boundary. Figure 5 suggests that the change in both
aerosol acidity and hygroscopicity by the formation of non-electrolytic
sulfate is important to predict SOA mass.
Effect of NOx on SOA formation in the presence of an aqueous phase containing electrolytes
Figure 6 shows the impact of NOx on the three aromatic SOAs in this study
by producing SOAs at two different NOx levels in the presence and
absence of SO2. Overall, regardless of the inorganic seed conditions,
both the chamber observation and model simulation suggest that increasing
the NOx level leads to the decreased formation of SOAs. This trend in
the absence of inorganic seed aerosols has also been observed multiple times
(Li et al., 2015; Ng et al., 2007; Song et al., 2005). By increasing
[NOx], the path of an RO2 radical progresses to the formation of
organonitrate and peroxyacyl nitrate (PAN) products, which are less reactive
to aerosol-phase reactions. They are relatively volatile and, thus,
insignificantly attributed to partitioning SOA mass.
Time profiles of measured and modeled SOA mass concentrations
(µg m-3) for toluene, ethylbenzene, and n-propylbenzene SOA
under low-NOx(a–c)/high-NOx(d–f) conditions in the presence (red color)/absence (green color) of
SO2-derived sulfuric-acid-seeded aerosol. Solid, dashed, and dotted
lines denote the total organic matter (OMT), the OM from partitioning
only (OMP), and the OM from the aerosol-phase reactions (OMAR),
respectively. The degree of ammonia neutralization with sulfuric acid is
indicated by the FSend, which is the FS at the end of the experimental
run. The FSend ranges from 1 (for sulfuric acid) to 0.33 (for
ammonium sulfate). The uncertainty associated with experimentally measured OM
is about 9 %. The SOA mass was corrected for the particle loss to the chamber
wall. The experimental conditions are available in Table 1.
For example, the SOA yields under the low NOx level
(HC(ppbC)/NOx(ppb)= 9.1–14.8, Table 1) in the presence
of SO2, with a similar degree of ammonia titration (i.e., similar
FS values by the end of the experiments), were higher than those without seeded
aerosols: 42 % for toluene (Exp. Tol1, FS = 0.44), 26 % for
ethylbenzene (Exp. EB1, FS = 0.37), and 66 % for propylbenzene (Exp. PB1,
FS = 0.43). The impact of aerosol acidity was even greater for SOAs produced
under a high NOx level (HC(ppbC)/NOx(ppb)= 2.8–5.0):
65 % for toluene (Exp. Tol3, FS = 0.43), 146 % for ethylbenzene
(Exp. EB5, FS = 0.39), and 77 % for propylbenzene (Exp. PB3, FS = 0.40).
SOA formation under high-NOx conditions is generally more
sensitive to aerosol acidity compared to that at low NOx levels
(Fig. 6a–c vs. Fig. 6d–f). The fractions of medium reactivity products
are relatively high in high NOx levels, and their reactions in aerosol
phase can be accelerated by an acid catalyst. Figure S7 (Sect. S9) also
illustrates the simulation of SOA mass against chamber data under varying
humidity, NOx levels, and aerosol acidity (Table 1).
The simulated SOA mass (i) for toluene (Tol),
ethylbenzene (EB), and n-propylbenzene (PB) under different conditions. The simulation conditions
are listed in the top table. The initial concentrations of monoalkylbenzenes,
preexisting OM (OM0), NH4HSO4 (AHS)-seeded aerosol, and
(NH4)2SO4 (AS)-seeded aerosol are 20 ppb, 2, 20, and
20 µg m-3, respectively. The gas-phase simulation used the sunlight
on 14 June 2018 (Exp. Tol9 in Table 1). OMP (diagonal stripes fill)
and OMAR (solid fill) represent the organic matter from the
partitioning process and aerosol-phase reactions. (ii) shows the
time-dependent SOA growth curve for three monoalkylbenzenes under corresponding
simulation conditions (top table). The concentrations that follow the legends
refer to the mass concentrations of the consumed monoalkyl-substituted benzenes
in each simulation under the high or low-NOx conditions.
Sensitivity of SOA formation to humidity, temperature, aerosol acidity, precursor HCs, and NOx level
Figure 7 illustrates the sensitivity of the SOA mass simulated at relatively
low concentration of HCs (20 ppb) (panel i) for three monoalkylbenzenes to
important variables – i.e., humidity (Ai vs. Bi for AHS and Ci vs. Di for AS),
temperature (Ai vs. Ei for AHS and Fi vs. Gi without inorganic
aerosol), aerosol acidity (Ai vs. Ci at RH = 45 % and Bi vs. Di at
RH = 65 %), and NOx levels (Ai vs. Hi with AHS-seeded aerosols and
Fi vs. Ii without inorganic-seeded aerosols). The most drastic change
appears by changing the temperature from 298 K (Ai) to 273 K (Ei). The SOA
yield is known to increase by 20 %–150 %, which results from a 10 K
decrease in temperature (Sheehan and Bowman, 2001). For all
SOAs, noticeable changes are shown between the absence (Fi) and
presence (Ai) of wet inorganic seeds, while a minor change appears between wet
AHS (Ai) and wet AS (Ci). Within the wet acidic aerosols (Ai vs. Bi and Ci
vs. Di), the effect of RH is insignificant in our simulation, as discussed
in Sect. 4.2. Although the impact of NOx (Ai vs. Hi and Fi
vs. Ii) is less than that of temperature and inorganic seeds, SOA yields are
still significantly altered, as discussed in Sect. 4.4.
The panel ii series in Fig. 7 illustrates SOA growth curves under various
conditions shown in panel i. Overall, the simulated SOA yields (slopes)
increase with a decreased alkyl chain length
(toluene > ethylbenzene >n-propylbenzene), which is consistent with our
chamber observations (Table 1). Although the decrease in the vapor pressure of
products benefits the increases in OMP as the alkyl chain length increases,
the increase in the activity coefficient of the organic products containing
longer alkyl chains in aqueous phase is unfavorable to the formation of OMAR via
aqueous reactions. However, the difference of mole-based SOA yields of three
monoalkylbenzenes becomes small because the highly reactive organic species
(i.e., glyoxal), which are produced through ring opening reactions without
an alkyl side chain, are significantly attributed to OMAR. Fig. 7ii confirms
that the effect of an aqueous phase containing electrolytes on SOA yields is
more critical than that of the NOx level under our simulated conditions.
Sensitivity of model prediction to major variables and model uncertainty
To determine the model sensitivity to these parameters, simulations
(Exp. Tol9 in Table 1) were performed by increasing/decreasing vapor
pressure (Vp), the enthalpy of vaporization (Hvap), γin,i, and
kAC,i by factors of 1.5, 1.1, 2, and 2, respectively. The corresponding
changes in the SOA mass are -7.2 %/6.8 %, -1.9 %/1.7 %,
-8.8 %/4.7 %, and 2.5 %/-3.5 %, respectively. The change in SOA mass
from the reference for each simulation is shown in Fig. S8 (Sect. S10).
The uncertainty associated with the group contribution method used for
Vp estimation is a factor of 1.45 (Zhao et al., 1999).
Hvap has a reported error of 2.6 % (Kolska et al.,
2005). γin,i is estimated as a function of O : C, MW, RH, and FS
(Eq. ). kAC,i is semi-empirically calculated based on [H+], LWC,
and species reactivity (Eq. 8). The E-AIM is performed to estimate the LWC,
which is reliable and based on a broadly used water activity dataset
(Zhang et al., 2000). Yet, the inorganic thermodynamic models including
E-AIM performed inadequately in the prediction of [H+] under low-RH and
ammonia-rich conditions (FS < 0.55) (Li and Jang, 2012).
Although most identified toluene products have been included, such as
methyl-cyclohexene (3S), 2-methyl-5-nitrophenol (5P), 2-methyl-benzoquinone
(6S), 2-methyl-4-oxo-2-butenoic acid (6M), o-cresol (7P),
3-hydroxy-1,3-propandial (7VF), 3-methyl-2(5H)-furanone (8P), MGLY, and GLY
(Forstner et al., 1997; Jang and Kamens, 2001; Sato et al., 2007;
Gomez Alvarez et al., 2007; Huang et al., 2016), a large number of toluene
oxidation mechanisms and involved products remain unstudied. A similar trend
can be found in ethylbenzene and propylbenzene. Evidently, the addition of
artificial OH radicals in the gas-phase simulation suggests missing
mechanisms in the MCM v3.3.1 or an improper branching ratio of reactions.
Additionally, the diverse reactions of the RO2 radicals might be
oversimplified in the gas-phase simulation by employing surrogate coefficients.
In the model, non-electrolytic diOS was predicted and applied to the prediction
of LWC and [H+], which subsequently affect aerosol growth via aqueous
reactions. Typically, the monoalkyl sulfate is identified as a product of
the esterification of SA with reactive species (Hettiyadura et al.,
2015; J. Li et al., 2016; Estillore et al., 2016; Chen et al., 2018). It is
possible that monoalkyl sulfates can influence LWC and aerosol acidity
differently from sulfuric acid, although they are strongly acidic and
hygroscopic. Although Noziere et al. (2010) reported that
OS could be produced by the reactions of GLY and sulfate radicals in the
presence of aqueous AS under UV light, the amounts of formed monoalkyl OS
and their influence on aerosol hygroscopicity are still not clear.
Some other factors in recent investigations, such as organic vapor wall loss
and aerosol viscosity, have not been accounted for by the UNIPAR model. The loss
of organic vapor to the Teflon chamber wall can compete with the
gas–particle partitioning process and the reactions in both the gas phase
and aerosol phase to initiate a negative bias in the experimental
measurements (Zhang et al., 2014; Mcvay et al., 2014). The modeling of the
gas–wall process of semivolatile organic compounds can improve the
prediction of SOA mass in regional scales. In addition, an increased aerosol
viscosity via aging could modify the diffusivity of the partitioned organic
molecules (Abramson et al., 2013) and the reaction rate constant
for oligomerization in the aerosol phase.
Conclusions and implications
Despite numerous studies in SOA characterization and formation mechanisms,
substantial biases between the simulated and field-measured SOA mass were
still found (Hodzic et al., 2016) due to the inadequacy of handling the
dynamic multigenerational aging (Jathar et al., 2016) and
aqueous reactions of the oxygenated products in the presence of an aqueous
phase containing electrolytes (Ervens et al., 2011). In this
study, the UNIPAR model addressed those issues using a dynamic age-driven
αi set, multiphase partitioning of organic compounds, and
in-particle chemistry. Although the utilization of the age-driven αi
set improves the time series prediction of SOA mass, as shown in
Fig. 3, the photochemical evolution of the gas-phase products via
monoalkylbenzene oxidation (Figs. 2, S4, and S5) does not increase
the SOA mass, as is commonly suggested. Overall, the effect of an aqueous
phase containing electrolytes on SOA formation was more critical than that
of the NOx level under our simulated conditions. By adding a wet
inorganic seed to the non-seed SOA system, the mass-based SOA yields under
high NOx levels increase more than those under low-NOx conditions
(Fig. 6 in Sect. 4.4). The vapor pressure of volatile organonitrate and
PAN-like species, which are formed at high NOx levels, is not low
enough to increase partitioning SOA mass (Fig. 7Aii). Thus, SOA yields
decreased by increasing NOx levels. Overall, both simulation and
chamber data show that monoalkylbenzene SOA yields increase with a decreased
alkyl chain length: toluene > ethylbenzene >n-propylbenzene.
This difference is most noticeable in the presence of an
inorganic seed at high NOx levels (Fig. 7Aii and Hii)
(Colberg et al., 2003).
Due to the pervasiveness and relatively high concentration of toluene in the
urban situation, where HC/NOx< 5.5 and wet inorganic seeds
typically exist, the importance of toluene SOAs to the urban SOA burden can
increase. The oxidation products from aromatic HCs can also involve cloud
condensation nuclei activity due to their high reactivity via heterogeneous
chemistry (Molteni et al., 2018), resulting in a change in
the properties of clouds and fog and the urban radiation balance (Gordon
et al., 2016). The unified aerosol-phase reaction rate constants for three
monoalkylbenzenes represent the feasibility of applying the UNIPAR model to
more aromatic systems (dialkyl benzenes and trialkyl benzenes) and the complex urban mixture.
Data availability
The chamber data and simulation results as part of this
publication are available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-5719-2019-supplement.
Author contributions
MJ designed the experiments and CZ and ZY carried them out.
MJ developed the model, and CZ performed the calculation of model parameters
and the simulations. CZ and MJ prepared the manuscript with contributions from ZY.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by the National Strategic Project-Fine
particle of the National Research Foundation of Korea (NRF) funded by the Ministry of
Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of
Health and Welfare (MOHW) (2017M3D8A1090654).
Review statement
This paper was edited by Christopher Hoyle and reviewed by
Kyle Gorkowski and one anonymous referee.
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