Introduction
Water is a ubiquitous component of atmospheric aerosol , which can interact with organic compounds in a number of ways to influence
particulate matter (PM) mass and size, human health, and Earth's radiative
balance. While constituents such as sulfate and nitrate often drive aerosol
water concentrations, inorganic and organic compounds are internally mixed
under humid conditions , and hydrophilic organic compounds
promote the uptake of water . Uptake of water into the
organic phase increases particle size, making particles more effective at
interacting with radiation , obscuring visibility
, and forming clouds . Water can
serve as a medium for partitioning of soluble and
semivolatile gases, thus
contributing to particulate matter concentrations. Once in the
particle phase, organic compounds can participate in water-mediated reactions
such as hydrolysis, driving isoprene epoxydiol uptake to the particle
and loss of gas-phase organic nitrates
.
Organic–aerosol-water interactions have been examined in a number of
laboratory and field studies, and results are mixed. The lack of a consistent
relationship in laboratory work may be partially due to experimental
conditions such as high mass loadings that minimize the effect of water for semivolatile systems . Laboratory studies have
observed no significant change in yield with increasing relative humidity
(RH) , enhanced yields under dry conditions
, and higher yields with increasing aerosol water
depending on the precursor, oxidant, and seed. Trends in
ambient aerosol organic carbon are consistent with the trend in decreasing
aerosol water in the southeast US , and
observed episodic correlations of water-soluble organic carbon and water
vapor. However, found no well-defined relationship over
the entire summer in Atlanta, GA, and organic aerosol was not correlated with
liquid water content in Pittsburgh, PA .
found that the presence of organic compounds suppressed aerosol water in
urban locations. In the atmosphere, the relative roles of different secondary
organic aerosol (SOA) species change as a function of time and space, and each
species may have a different sensitivity to aerosol water.
The interaction of primary organic aerosol (POA), SOA from low-volatility and
semivolatile (Ci*<3000 µgm-3) compounds, SOA from
aqueous pathways, and the inorganic/water-rich phase is important for the
concentration of organic aerosol (OA) as partitioning is a function of the
availability of an absorptive medium. Current chemical transport models,
including the Community Multiscale Air Quality (CMAQ) model
, consider SOA to be exclusively or dominantly formed via
condensation of organic compounds in the absence of water. Individual model
studies have examined hydrophobic and hydrophilic SOA through
semi-mechanistic algorithms and surrogate structure information.
used a decoupled approach in which organic species partitioned
only to their dominant phase (aqueous vs. organic). allowed
compounds to partition to both phases in varying amounts based on their
properties. examined the implications of water uptake to
the organic phase and the effects on OA concentrations.
allowed organic compounds to interact with water and separate into two phases
if thermodynamically favorable. None of these approaches considered mixing of
the inorganic and organic phases and often required computationally intensive
calculations of activity coefficients. These models accounting for aerosol-water–organic interactions are not in widespread use and have not been
evaluated with recently available observations of aerosol water.
Figure shows the contribution of POA and water-soluble OA (determined from water-soluble organic carbon, WSOC; ) to
total OA as observed during the Southern Oxidant and Aerosol Study (SOAS) for
June 2013 in Centreville, AL. Ambient measurements of WSOC are highly
correlated with oxygenated organic aerosol (OOA) , and
water-soluble OA accounted for 90 % of total OA on average in the southeast
US during summer 2013 . WSOC has also been proposed
to contain SOA from aqueous pathways with evidence for reversible
and irreversible formation. CMAQ
tends to overpredict the concentration of POA by almost a factor of 2 during
SOAS . CMAQ predicts a relatively minor role for aqueous OA
with the dominant source of OA in CMAQ being dry processes (other SOA in
Fig. ).
Contribution of POA
observed biomass burning OA, BBOA;, SOA, water-soluble OA estimated as 2.1 × WSOC
from the Particle-into-Liquid Sampler (PiLS);, and aqueous (aq.) SOA (model only) to
total OA during June 2013 observed at CTR during SOAS and modeled by standard
CMAQ. Insoluble OA is the difference between measured total OA and
water-soluble OA. Modeled “other SOA” is formed via partitioning to a dry
organic phase.
Through a series of sensitivity simulations (outlined in
Sect. ), this work aims to understand if
interactions of aerosol water with semivolatile compounds can resolve
model–measurement discrepancies and to what degree OA predicted by models
should be classified as water soluble. Semiempirical SOA in the CMAQ model
(Sect. ) was connected to a consistent set of properties
useful for predicting atmospherically relevant behavior such as interaction
with aerosol water. In cases where a specific molecular species was not
already used as a surrogate, aerosol properties were linked to volatility and
parent hydrocarbon (Sect. ). These quantities allowed
molecular weights, organic matter to organic carbon (OM / OC) ratios,
Henry's law coefficients, deposition properties, hygroscopicity
(κi), phase separation (Sect. ), water uptake
(Sect. ), and deviations from ideality
(Sect. ) to be predicted semiempirically and influence
partitioning (Sect. ). In addition, the
fraction WSOC was estimated for model species (Sect. ),
and primary vs. secondary organic aerosol was estimated for monitoring
networks (Sect. ). The property updates will be
available in CMAQ v5.2, and their effects on model predictions are illustrated
in Sect. . The implications of the updates for OA and
particle-phase liquid water content (LWC) are examined in the context of
routine monitoring networks and Southern
Oxidant and Aerosol Study (SOAS) observations
(Sect. ).
Method
Simulations
CMAQ v5.1 with additional updates was run over the eastern
United States for June 2013 at 12 km by 12 km horizontal resolution using the
same domain and meteorological inputs as in the work of .
Anthropogenic emissions were based on the EPA National Emission Inventory
(NEI) 2011 v1. Isoprene emissions were predicted with the Biogenic Emission
Inventory System (BEIS) v3.6.1 . BEIS often predicts lower
emissions than the Model of Emissions of Gases and Aerosols from Nature
(MEGAN) , and isoprene emissions were increased by 50 %
in this work to better agree with observations of isoprene and OH at the SOAS
Centreville, AL (CTR), site (Fig. S1i–h in the Supplement).
A baseline simulation including surrogate property updates detailed in
Sect. (molecular weight, Henry's law coefficients,
etc.) and three sensitivity simulations examining the implications of aerosol
liquid water for SOA were conducted (Fig. ). In the
baseline simulation, POA and traditional SOA were designated hydrophobic and
did not interact with aerosol water or SOA produced through aqueous pathways
following common chemical transport model assumptions. Two sensitivity
simulations examined the implications of aerosol water on semivolatile
partitioning via increases in the partitioning medium assuming ideal mixing.
In one simulation (ideal Wi), POA, traditional SOA, aqueous SOA, and
water associated with inorganic constituents were assumed to form one ideal
phase when RH was above the separation relative humidity (SRH) and to undergo
liquid–liquid phase separation into organic-rich (POA and traditional SOA)
and water-rich (aqueous SOA and inorganic constituents) ideal phases
otherwise. When one phase was predicted to be present (SRH < RH), interactions
of semivolatile organic compounds and inorganic water were assumed to be ideal.
When phase separation occurred, semivolatile organic compounds did not
interact with water. In the second simulation, uptake of water to the organic
phase (Wo>0) was predicted based on its OM / OC and
κ-Köhler theory
(Sect. ). Thus, ideal Wi and Wo>0 simulations are meant to capture the effects of inorganic and organic
water under ideal conditions separately. The impacts of phase separation and
water uptake to organic species along with deviations from ideality determined via an
activity coefficient (γ) were simulated together in the third
sensitivity simulation (γ≠1).
Interactions of the inorganic phase (e.g., sulfate, nitrate,
ammonium, aerosol water), aqueous SOA, vapor-pressure-driven SOA, and POA in
the base and sensitivity simulations. Blue arrows depict water
partitioning/uptake. Red arrows indicate semivolatile partitioning
interactions via modified Raoult's law. The white dashed arrows indicate
aqueous SOA interaction with the inorganic phase (via liquid water, acidity,
and particle size).
CMAQ organic aerosol
CMAQ v5.1 contains several types of SOA with different sensitivities to
aerosol water:
traditional semivolatile SOA from Odum two-product representations,
nonvolatile SOA produced by volatile organic compound (VOC) reaction,
heterogeneously produced SOA parameterized by an uptake coefficient,
semivolatile organic nitrate SOA and its hydrolysis product, and
other contributions from cloud processing and accretion/oligomerization reactions
(Fig. , Table ). The traditional SOA
systems in CMAQ include SOA from isoprene, monoterpenes, sesquiterpenes,
benzene, toluene, xylene, alkanes, and polycyclic aromatic hydrocarbons
(PAHs) . The semivolatile SOA from these
precursors is allowed to oligomerize to a nonvolatile form on a 29 h
timescale . Currently, low-NOx oxidation of
aromatics leads to nonvolatile SOA in the traditional systems. Glyoxal (GLY),
methylglyoxal, and epoxides undergo heterogeneous uptake to form SOA
. Glyoxal SOA forms using a fixed uptake coefficient
of 0.0029 . Following the approach of ,
methylglyoxal's uptake coefficient was scaled to the glyoxal uptake
coefficient by the relative Henry's law coefficient (resulting in an uptake
coefficient of 2.6×10-4) in this work. Isoprene epoxydiol (IEPOX)
SOA is parameterized with an uptake coefficient calculated as a function of
aerosol phase constituents, including sulfate and water assuming an
acid-catalyzed mechanism . In this work, the IEPOX
organosulfate formation rate constant was updated to
8.83×10-3 M-2s-1 using the ratio of 2-methyltetrol
to organosulfate formation rate constants from and a
2-methyltetrol rate constant of 9×10-4 M-2s-1
. This organosulfate rate constant is more aggressive
(overall and relative) than predicted by . Overestimates of
the organosulfate in the model may compensate for missing IEPOX-derived SOA
species such as C5-alkene triols or additional
oligomers . In addition, the Henry's law coefficient
was updated to 3.0×107 Matm-1 ,
which improved model predictions of 2-methyltetrols (Supplement)
and total isoprene SOA. The diffusivity of IEPOX in the particle
(Da, cm2s-1) was predicted by fitting a line
through the data in the work of resulting in
Da=10(7.18RH-12.7)
for 0≤RH≤1. Semisolid organic aerosol
(Da<10-12 cm2s-1) is not expected in the humid
eastern US during summer . SOA from later-generation
high-NOx/high-NO2 SOA species (methacrylic acid epoxide and
hydroxymethyl-methyl-α-lactone) is relatively minor
, consistent with observations from SOAS ground
sites . All SOA produced through heterogeneous
uptake is assumed to be nonvolatile in CMAQ v5.1. SOA from isoprene and monoterpene
organic nitrates is semivolatile, but the nitrate groups hydrolyze in the
particle to produce SOA, which is assumed to be nonvolatile, and nitric acid
. SOA from cloud processing is predicted to result in less
than 3 % of total organic aerosol in CMAQ. POA and volatility-based SOA is
treated as hydrophobic by default, while aqueous and in-cloud SOA is assumed to be hydrophilic and resides in a water-rich phase (CMAQv5.1 aero6i
assumptions;
Table ).
Schematic of SOA treatment in current CMAQ-aero6i. Species are
described in Table . Species in grey boxes are nonvolatile.
Species with names in red make up POA (i.e., POA = POC + NCOM, where POC stands for primary organic carbon and NCOM stands for non-carbon organic matter).
Species with names in blue form in the model as a direct result of
interactions with water.
SOA and semivolatile organic compound (SVOC) species in CMAQ v5.1-aero6i
. CMAQ model species names are
generally preceded by the letter A to indicate aerosol. Semivolatile
surrogates have a corresponding gas-phase species whose name is preceded by
the letters SV.
Species
Species or production pathway description
Partitioning medium in CMAQ v5.1
ALK1
alkane + OH SOA/SVOC
Dry organic aerosol
ALK2
alkane + OH SOA/SVOC
Dry organic aerosol
BNZ1
benzene + OH high-NOx SOA/SVOC
Dry organic aerosol
BNZ2
benzene + OH high-NOx SOA/SVOC
Dry organic aerosol
BNZ3
benzene + OH low-NOx SOA
Dry organic aerosol
DIM
IEPOX-derived dimers
Aqueous aerosol
GLY
glyoxal + methylglyoxal SOA
Aqueous aerosol
IEOS
IEPOX-derived organosulfate
Aqueous aerosol
IETET
2-methyltetrols
Aqueous aerosol
IMGA
2-methylglyceric acid
Aqueous aerosol
IMOS
MPAN-derived organosulfate
Aqueous aerosol
ISO1
isoprene + OH SOA/SVOC
Dry organic aerosol
ISO2
isoprene + OH SOA/SVOC
Dry organic aerosol
ISO3
acid-catalyzed isoprene SOA*
Dry organic aerosol
ISOPNN
isoprene dinitrate
Dry organic aerosol
MTHYD
organic nitrate hydrolysis product
Aqueous aerosol (from dry organic aerosol parent)
MTNO3
monoterpene nitrate
Dry organic aerosol
OLGA
oligomers from anthropogenic SOA/SVOCs
Dry organic aerosol
OLGB
oligomers from biogenic SOA/SVOCs
Dry organic aerosol
ORGC
glyoxal+methylglyoxal SOA
Cloud droplets
PAH1
naphthalene + OH high-NOx SOA/SVOC
Dry organic aerosol
PAH2
naphthalene + OH high-NOx SOA/SVOC
Dry organic aerosol
PAH3
naphthalene + OH low-NOx SOA
Dry organic aerosol
SQT
sesquiterpene + OH, O3, NO3, O3P SOA/SVOC
Dry organic aerosol
TOL1
toluene + OH high-NOx SOA/SVOC
Dry organic aerosol
TOL2
toluene + OH high-NOx SOA/SVOC
Dry organic aerosol
TOL3
toluene + OH low-NOx SOA
Dry organic aerosol
TRP1
monoterpene + OH, O3, O3P SOA/SVOC
Dry organic aerosol
TRP2
monoterpene + OH, O3, O3P SOA/SVOC
Dry organic aerosol
XYL1
xylene + OH high-NOx SOA/SVOC
Dry organic aerosol
XYL2
xylene + OH high-NOx SOA/SVOC
Dry organic aerosol
XYL3
xylene + OH low-NOx SOA
Dry organic aerosol
* AISO3 contains the sum of 2-methyltetrols and
IEPOX-derived organosulfates in CMAQv5.1-aero6. It is not used in aero6i as
those species are represented individually. Prior to v5.1, AISO3 was
determined as an enhancement over AISO1 + AISO2 based on [H+]
.
Updating properties of semivolatiles
Molecular properties
For SOA systems, the molecular weight and OM / OC by mass must be
specified for mass-to-molecule number unit conversions within CMAQ and to
allow for post-processing of organic carbon (OC) from total SOA for
comparison to observations. The number of carbons per molecule
(nC) is also specified for the traditional semivolatile systems
to allow for oligomerization to conserve carbon .
Historically, in CMAQ model formulations (v5.1 and prior), the
nC, saturation concentration (Ci*), and OM / OC were
set independently with the OM / OC obtained from chamber experiments and
nC set to that of the parent hydrocarbon. The molecular weight
was calculated to be consistent with the number of carbons and OM / OC.
The OM / OC values were not a function of volatility or peroxy radical
(RO2) fate. Thus, all SOA species from a given parent hydrocarbon
were assumed to have the same properties (OM / OC, molecular weight,
number of carbons) regardless of their volatility. When viewed in the O : C
vs. Ci* space , this leads to some apparent
contradictions such as sesquiterpene SOA being more functionalized and having
a longer carbon backbone at a given vapor pressure than isoprene or
monoterpene SOA. This inconsistency is also seen in the molecular weight vs.
Ci* space (Fig. ). Most SOA constituents are
expected to show that molecular weight is correlated with vapor pressure
(Ci*) with more functionalized species having a shallower slope than
less functionalized species . Systems examined by
were found to reside between a line characteristic of
O : C =0 (alkane, CnH2n+2) and O : C =1 (sugar,
CnOnH2n-2). Sesquiterpene SOA in CMAQ v5.1 resides outside the
molecular corridor bounds that correspond to O : C =0 (OM / OC=1.17)
and O : C =1 (OM / OC =2.3 to 2.5). The CMAQv5.1 Odum two-product
isoprene SOA components imply an O : C >1
which is possible, but not
observed by Shiraiwa et al., 2014, and infrequent in the work of.
The volatility, molecular weight, and OM / OC of SOA species in
CMAQ. Nonvolatile species are arbitrarily plotted at a saturation
concentration of 0.01 µgm-3. The arrows start at the old
molecular weights assumed in CMAQ v5.1. The arrows end at the new (CMAQ v5.2)
molecular weights in Table . Lines indicate the
properties of alkanes and sugars. The molecular weight of sesquiterpene SOA in CMAQ v5.1
is off the scale at 378 g mol-1.
Structure–activity relationships or group contribution methods can be used to
relate vapor pressure, molecular weight, and OM / OC (or molar O : C).
developed a relationship between the saturation
concentration of a pure species (Ci* = C0,i*), number of
carbons per molecule, and number of oxygens per molecule (nO)
ignoring sulfate and nitrate for use with the 2-D volatility basis set (VBS):
log10C0,i*=0.475(25-nC)-2.3nO+0.6nCnO/(nC+nO).
Built into this relationship are assumptions about the functionality of
semivolatile organic compounds (specifically equal alcohols and ketones with
acid terminal groups), the volatility of a 25 carbon alkane (Ci*=1
µgm-3), and how a given functional group affects volatility
from the SIMPOL model;. Note that considerable
variability in atmospheric aging exists in terms of the addition of functional
groups as indicated on van Krevelen diagrams . The number of
oxygen is related to the molar O : C by
nO=nC(O:C).
O : C can be related to the mass-based OM / OC :
O:C=1215OMOC-1415,
which assumes only H, O, and C atoms and produces results consistent with
aerosol mass spectrometry (AMS)-determined relationships between O : C and OM / OC
. OM / OC was the focus of this work instead of
O : C since OM / OC values are directly used to post-process model
output for comparison to observation network measurements of OC. In addition,
OM / OC ratios are a useful quantity in reconstructing the total mass of PM
and could be available routinely from the Interagency Monitoring of Protected
Visual Environments (IMPROVE) network in the future using Fourier transform
infrared spectroscopy (FTIR) analysis . The
molecular weight (M̃) follows as
M̃i=12nCOMOC.
Equations () to () provide four equations for
six unknowns: nC, nO, O : C, OM / OC,
Ci*, and M̃i. Ci* was obtained from the Odum
two-product fits derived from laboratory data
and nC was set to that of the parent
hydrocarbon. The OM / OC and molecular weight were then calculated.
nO and O : C were not needed for CMAQ (but could be easily
obtained). undertook a similar exercise in which they
developed surrogates for each of the CMAQ v5.0 SOA species using SIMPOL and
plausible structures. Their information was used when available, and
Eqs. () to () were employed otherwise. For the
systems on which provide information, the results based
on Eqs. () to () are very similar. For SOA
from the explicit later-generation precursors (such as IEPOX, isoprene
dinitrates, and monoterpene nitrates), the molecular properties were already
tied to a specific surrogate identity. The CMAQ SOA species representing
actual compounds were not updated.
Deposition properties
The deposition-related properties of gases, such as their solubility,
diffusivity, and reactivity, are related to molecular structure and
composition. CMAQ uses a resistance in series method for dry deposition
. CMAQ v4.7 through v5.1 use adipic acid (Henry's law
coefficient, H = 2×108 Matm-1) as a wet deposition
surrogate for gas-phase semivolatile organic compounds (SVOCs). Default dry
deposition of SVOCs is based on acetic acid
(H = 4.1×103exp(63 000K(298-T)/(298T)) Matm-1;
gas-phase diffusivity (Dg)= 0.0944 cm2s-1; dry cuticular
resistance = 1200 sm-1; LeBas molar
volume = 63 cm3mol-1).
used the Generator of Explicit Chemistry and Kinetics of
Organics in the Atmosphere (GECKO) to predict products from various SOA
systems commonly represented in models. Henry's law coefficients were then
estimated based on the GROup contribution Method for Henry's law Estimate
(GROMHE) . GROMHE was found to reproduce Henry's Law
coefficients for organic–water systems with a mean absolute error of about 0.3
log units compared to 0.5 for HenryWin and 0.4 for SPARCv4.2 (SPARC Performs Automated Reasoning in Chemistry; ). For SOA systems, a strong relationship was
observed between saturation concentrations and Henry's law coefficients, with
chemically aged species being less volatile, more functionalized, and more
soluble than their parent hydrocarbon. Although the relationship between H
and Ci* was relatively robust, variability in H spanned many orders of
magnitude for a given Ci* bin without considering how inorganic species
may modify the Henry's law coefficient. The relationships derived by
were used to predict the Henry's law coefficients as a
function of Ci* for each SVOC surrogate in equilibrium with the
particle in the model. An enthalpy of solvation of 50 kJmol-1 was
also adopted to adjust the Henry's law coefficients for temperature. Note
that although the approach used by is also a group
contribution method, it potentially represents the functional groups present
in CMAQ SOA species with different groups than would be assumed by
Eqs. ()–().
Additional properties needed for deposition include the gas-phase diffusion
coefficient, which was calculated as a function of molecular weight via
Dg,i=1.9(M̃i)-2/3 cm2s-1
, and the LeBas molar volume (VLeBas), calculated
assuming ring-opened products :
VLeBas=14.8nC+ 7.4nO+ 3.7nHcm3mol-1,
where the number of hydrogens, nH, is calculated from the
molecular weight assuming only carbon, oxygen, and hydrogen. Modifications
were also made to the deposition parameters affecting H2O2, IEPOX,
and organic nitrates to produce results consistent with
(parameters available in the Supplement).
Predicting phase separation
The solubility of an organic compound in water generally decreases due to the addition of a salt with some exceptions, like glyoxal .
However, as atmospheric aerosols contain water, salts, and organic compounds,
there are likely conditions where the solubility of an organic is more or
less favorable in the water–inorganic-rich phase. Mixed organic–inorganic
solutions have been observed to phase-separate into an organic-rich and
inorganic-rich phase based on their degree of functionalization (as measured
by O : C) and relative humidity. The O : C serves as a proxy for molar
polarization, which dictates the magnitude of the salting-out effect through the
Setchenov equation . The relative humidity above which a
single combined phase exists is called the separation relative humidity. The
SRH is not expected to be a strong function of the organic-to-inorganic ratio
, molecular weight of the organic species, or
temperature between 244 and 290 K . However, the SRH is a
function of the type of salt present, with ammonium sulfate having higher SRH
(and less frequent mixing) than ammonium bisulfate, sodium chloride, and
ammonium nitrate for a given O : C. During SOAS, inorganic aerosol was
dominated by (NH4)2SO4 and NH4HSO4, and SRH was diagnosed
in CMAQ based on the experimental results for ammonium
sulfate. The relationship for SRH (fraction between 0 and 1) as a function of
O : C was recast in terms of OM / OC:
SRH=1+exp7.7OMOC-15.8-1.
Since ammonium sulfate has the highest SRH of the salts examined by
, choosing another salt would increase the frequency of
phase mixing and difference compared to the base simulation.
For simulations considering phase separation in CMAQ (ideal Wi and
γ≠1), when the ambient relative humidity was below the SRH, the
model separated the particle into a water-rich phase (containing aqueous SOA)
and an organic-rich phase (containing traditional SOA and POA). This separation
of aqueous SOA and traditional SOA at low RH is consistent with the work of
, who found that isoprene SOA surrogates unfavorably interacted with
α-pinene SOA even at 60 % RH.
Predicting water uptake to the organic phase
Water uptake to the organic phase (Wo>0 and
γ≠1 simulations) was predicted in CMAQ using κ-Köhler
theory and solving for the volume-equivalent diameter, D
:
RH-D3-Dcore3D3-Dcore3(1-κ)exp4σwM̃wRTρwD=0
and
Dcore=6π∑i≠WoVi1/3,
where Dcore is the volume (V) equivalent accumulation mode
diameter excluding water associated with organic species,
M̃w is the molecular weight of water,
ρw is the density of water, R is the universal gas constant,
T is temperature, and σw is the surface tension of water
(0.072 J m-2). In order to calculate the volume-equivalent
diameters, D and Dcore, particle density was needed. Density
values in CMAQ v4.7–v5.1 for organic constituents are generally on the order
of 2000 kgm-3. The densities of organic aerosol species were
updated to chamber-specific information when available
and to 1400 kgm-3 otherwise. The mass of
particle liquid water associated with organic compounds per volume of air
(Wo) was calculated from
Wo=πNpρw6(D3-Dcore3),
where Np was number of particles per volume air. Total aerosol
water in the model was computed as the sum of water associated with
inorganics (Wi) calculated with ISORROPIA v2.2
and Wo.
The hygroscopicity parameter, κ, was calculated as a volume-weighted
sum of the individual component κi ignoring
water associated with organics:
κ=∑i≠WoκiVi∑i≠WoVi.
Cloud condensation nuclei (CCN)-based κs were used following due to the
completeness of that study. The O : C values obtained by
were increased by 27 % to account for a low bias in old calibrations
. In addition, the relationship was recast in terms of
OM / OC, resulting in
κorg,i=0.11OMOC-0.10.
Equations in terms of O : C are available in the Supplement.
For subsaturated conditions, like those relevant to predicting water uptake,
the hygroscopic growth factor (hgf) κ is most relevant
; however, CMAQ simulations used CCN-based
κorg,i to predict water uptake. Hgf-based κs from
and were combined with data from
into a parameterization by . After
correcting the parameterization to use updated O : C, the parameterization
including hgf-based data resulted in one negative κ and three
κs higher than 0.6 (same as ammonium sulfate), which may be an upper
limit on κorg,i . Thus, contrary to the
typical trend of κCCN>κhgf, more than half of
the species had κCCN<κhgf. Variation from study
to study may be higher than κCCN vs. κhgf
variations, which have been found to be within 30 % for many compounds and
unable to be resolved using common measurement techniques
.
In the processing of model output, the following equation was used to determine
how errors in the concentration of organic compounds ([OA]),
κorg, and RH propagated to errors in Wo:
Wo=ρwρorg[OA]κorg1(1/aw-1),
with the activity of water (aw) defined as
aw=RHexp4σwM̃wRTρwD.
Representing the effect of water on semivolatile partitioning
Partitioning of semivolatile organic species into an absorbing medium can be
described by a modified Raoult's law :
Ai/MpGi=RTM̃pγiPisat,
where Ai is the aerosol phase concentration of species i (µgm-3air), Gi is the gas-phase concentration of i (µgm-3air), Mp is the mass of the partitioning medium
(µgm-3air), M̃p is the molecular
weight of the partitioning medium, γi is a mole-based activity
coefficient, and Pisat is the saturation vapor pressure of pure
i. This relationship (Eq. ) is true regardless of how
the partitioning coefficient (Ci* or Kp,i) is defined.
CMAQ, following , defines Ci* as
Ci*≡M̃iγiPisatRT,
where the relevant molecular weight is the individual species molecular weight in contrast to
the traditional definition of , which uses the partitioning medium's molecular weight:
Ci*′=1Kp,i≡M̃pγiPisatRT.
Model calculations in this work used the definition in Eq. thus:
Ci*=GiM̃iNAi,
where the total moles in the partitioning medium (N) are
N=Nother+∑i(Ai/M̃i).
Nother represents aerosol in the partitioning medium that is not
semivolatile during calculation. Including water in the partitioning medium
(either from uptake onto hydrophilic organic compounds or from the inorganic
phase) increases the moles of partitioning medium by contributing to
Nother. The inclusion of water, and even inorganic constituents, in the
absorbing phase has been encouraged for simplified models in order to
reproduce more detailed calculations .
One equation for one unknown can be derived, where Ti is the total
(Gi+Ai) mass of the semivolatile determined by the mass-based
stoichiometric coefficients and amount of parent hydrocarbon reacted
(αiΔHC):
f(N)=0=NotherN-1+∑iTiCi*+M̃iN.
Equation was solved for N in the model.
M̃p≈M̃i for the interpretation of data
from chamber experiments only, and it allows for Ci*′≈Ci* in a single-precursor chamber experiment so that the Odum
two-product fit can be determined. Table indicates this
was a realistic assumption for most systems as the two surrogate molecular
weights vary by less than 10 %. This assumption was not necessary within
the CMAQ model.
A priori SOA and SVOC properties: saturation concentration of pure
species (C0*), mass-based stoichiometric yield from parent hydrocarbon
reaction (α), organic matter to organic carbon ratio (OM / OC),
molecular weight (M̃), number of carbons per molecule
(nC), Henry's law coefficient (H), diffusivity in the gas phase
(Dg), LeBas molar volume (VLeBas), hygroscopicity parameter
(κ), density (ρ), activity coefficient at infinite dilution
(γ∞), solubility (S), and saturation concentration at
infinite dilution in water (CH*). All temperature-dependent parameters
given at 298 K.
Species
C0*
α
OM / OC
M̃
nC
H
Dg
VLeBas
κ
ρ
γ∞ f
S
CH* f
µgm-3
gg-1
gg-1
gmol-1
Matm-1
cm2s-1
cm3mol-1
kgm-3
gL-1
µgm-3
ALK1
0.1472a
0.0334
1.56
225
12
6.2×108
0.0514
280.5
0.07
1400
5600
2
8.3×102
ALK2
51.8775a
0.2164
1.42
205.1
12
4.5×106
0.0546
275.6
0.06
1400
2000
6
1.0×105
BNZ1
0.302b
0.0720
2.68c
161
5
2.1×108
0.0642
134.1
0.19
1400
5800
2
1.7×103
BNZ2
111.11b
0.8880
2.23c
134
5
2.0×106
0.0726
127.5
0.15
1400
1400
5
1.5×105
BNZ3
NA
0.370
3.00c
180
5
NA
NA
NA
0.23
1400
NA
<1
NA
DIM
NA
NA
2.07d
248.2
10
NA
NA
NA
0.13
1400
NA
NA
NA
GLY
NA
NA
2.13e
66.4
3
NA
NA
NA
0.13
1400
NA
NA
NA
IEOS
NA
NA
3.60d
216.2
5
NA
NA
NA
0.30
1400
NA
NA
NA
IETET
NA
NA
2.27d
136.2
5
NA
NA
NA
0.15
1400
NA
NA
NA
IMGA
NA
NA
2.50d
120.1
4
NA
NA
NA
0.18
1400
NA
NA
NA
IMOS
NA
NA
4.17d
200.2
4
NA
NA
NA
0.36
1400
NA
NA
NA
ISO1
116.01b
0.2320
2.20c
132
5
4.3×107
0.0733
126.3
0.14
1400
60
120
6.9×103
ISO2
0.617b
0.0288
2.23c
133
5
3.7×109
0.0729
123.8
0.15
1400
130
56
8.2×101
ISO3
NA
NA
2.80d
168.2
5
NA
NA
NA
0.21
1400
NA
NA
NA
ISOPNN
8.9e
NA
3.80e
226
5
4.5×108 e
0.0457e
206.8e
0.32
1400
130
98
1.1×103
MTHYD
NA
NA
1.54e
185
10
NA
NA
NA
0.07
1400
NA
NA
NA
MTNO3
12e
NA
1.90e
231
10
1.5×106e,f
0.0453e
251.2e
0.11
1400
29 000
0.4
3.5×105
OLGA
NA
NA
2.50c
206
7
NA
NA
NA
0.18
1400
NA
<1
NA
OLGB
NA
NA
2.10c
248
10
NA
NA
NA
0.13
1400
NA
>10
NA
ORGC
NA
NA
2.00b
177
7
NA
NA
NA
0.12
1400
NA
NA
NA
PAH1
1.6598a
0.2100
1.63
195.6
10
5.1×107
0.0564
235.7
0.08
1480
5300
2
8.8×103
PAH2
264.6675a
1.0700
1.49
178.7
10
7.2×105
0.0599
231.5
0.06
1480
2100
5
5.7×105
PAH3
NAa
0.7300
1.77
212.2
10
NA
NA
NA
0.09
1550
NA
<1
NA
SQT
24.984b
1.5370
1.52c
273
15
6.2×108
0.0451
346.5
0.07
1400
40
380
1.0×103
TOL1
2.326b
0.0580
2.26c
163
6
4.2×107
0.0637
153.7
0.15
1240
3800
2
8.9×103
TOL2
21.277b
0.1130
1.82c
175
8
7.3×106
0.0607
194.1
0.10
1240
2600
4
5.5×104
TOL3
NA
0.300
2.70c
194
6
NA
NA
NA
0.20
1450
NA
<1
NA
TRP1
14.792b
0.1393
1.84c
177
8
9.9×108
0.0603
194.9
0.10
1400
27
360
4.0×102
TRP2
133.7297b
0.4542
1.83c
198
9
1.4×108
0.0559
218.8
0.10
1400
25
450
3.3×103
XYL1
1.314b
0.0310
2.42c
174
6
6.2×107
0.061
154.6
0.17
1480
4900
2
6.4×103
XYL2
34.483b
0.0900
1.93c
185
8
4.0×106
0.0585
194.6
0.11
1480
3100
3
1.1×105
XYL3
NA
0.360
2.30c
218
8
NA
NA
NA
0.15
1330
NA
<1
NA
a . b .
c . d .
e . fA factor-of-100 increase in
MTNO3 Henry's law coefficient, factor-of-10 decrease in γ∞,
and factor-of-10 decrease in Cxw=1* produced better model
results in the γ≠1 simulation. See Supplement for a
posteriori γ≠1 simulation parameters. NA indicates not
applicable (nonvolatile species).
Estimating solubility and deviations from ideality
When deviations from ideality were considered, the saturation concentration
used in the modified Raoult's law was adjusted using an activity coefficient.
All organic–organic interactions were assumed to be ideal, and only the inclusion of
water drove deviation from ideality. Observations during SOAS indicate that
despite a factor-of-7 change in ambient aerosol water concentration from
night to day, xw (mole fraction of water in the partitioning medium)
typically varied over a narrow range (80 to 96 % by mole) throughout the
day.
The activity coefficient for each organic species, γi, was determined using a one-constant Margules equation:
ln(γi)=xw2ln(γi∞).
Since γi∞ (the temperature-dependent constant in the Margules
equation) corresponds to the activity coefficient at infinite dilution in
water (xw=1), it can be estimated based on Henry's law combined
with Raoult's law:
γi∞=M̃iρwHiC0,i*RTM̃w,
where C0,i* is the pure species saturation concentration at T.
γi∞ is related to solubility (Si) in mass per volume of
water:
Si=HiC0,i*RT.
The saturation concentration as a function of water becomes
Ci*=C0,i*(γi∞)Nw2/N2,
where Nw is the moles of aerosol water in the partitioning
medium. This equation applies across the entire organic-to-water spectrum and
shows that γi∞ represents Ci* of a species in water
(xw=1) normalized to the pure species C0,i*. Evaluating
Ci* for pure water provides, CH,i*, the saturation concentration
at infinite dilution:
CH,i*=M̃iρwHiRTM̃w.
Values are available in Table . The solubilities of
nonvolatile species derived from traditional precursors (oligomers/accretion
products) were estimated based on assuming a C0,i* between 10-2
and 10-5 µgm-3and the Henry's law coefficients of
.
This representation of deviations from ideality resulted in competing effects
due to the addition of aerosol water to the partitioning medium. Adding water
increased the partitioning medium as described in Sect. , which led to more SOA. However, adding water
also increased the activity coefficient via the Margules model (Fig. S2),
leading to higher Ci* and less favorable partitioning (Fig. S3). The
Margules model, combined with the fact that all deviations are observed to be
positive for the species examined here, indicated that large additions of water
reduced SOA due to the activity coefficient adjustment. Indeed, all
saturation concentrations for partitioning into pure water (CH,i*) are
higher than those into pure organic (C0,i*) by 1 to 4 orders of
magnitude (Table ). A priori assumptions
regarding the solubility and activity of monoterpene nitrates were so
nonideal that particulate nitrate was driven entirely out of the particle,
inconsistent with observations . As a result, the
Henry's law coefficient for monoterpene nitrates (MTNO3) was
increased by a factor of 100 and all activity coefficients were reduced by a
factor of 10 compared to a priori values in the CMAQ γ≠1
simulation. These adjustments, determined through a series of sensitivity
simulations, may have been necessary due to inaccuracies in the Henry's law
coefficients, effects of inorganics, pure species saturation concentrations,
molecular weights, Margules model, or a combination of all of the above. A
posteriori parameters used in γ≠1, which include a factor-of-100
increase in MTNO3 solubility and factor-of-10 decrease in activity
coefficients, are available in Table S6.
Estimating WSOC
WSOC is an operationally defined species measured by adding water to a system
and analyzing the dissolved compounds . Particulate
compounds with solubilities greater than 10 g L-1 tend to be measured
as WSOC regardless of the sampling and extraction method, while compounds with
solubilities less than 1×10-4 g L-1 are insoluble
. To determine the fraction of OA extracted as WSOC
(WSOCp), the particle phase can be modeled as an equilibrium
between two phases: a and b. The fraction of species, i, in phase a
compared to the total particulate species concentration is
fa,i=1+Ca,i*NbCb,i*Na-1,
where Na and Nb are the number of moles in phases a and
b, respectively. If phase b has no water and is ideal, while phase a is
dominated by water and obeys Henry's law, then the fraction of aerosol
species i extracted as WSOCp (fWSOC,i) is
fWSOC,i=1+γi∞WIOALWCM̃wM̃i-1,
where WIOA and LWC are concentration of water-insoluble OA and liquid water
in mass per volume of air. Thus WSOC depends on the amount of insoluble
material, liquid water, Henry's law coefficient, and pure species saturation
concentration.
Observations for evaluation
Simulations were evaluated by comparing to OC from IMPROVE, Chemical
Speciation Network (CSN), and SouthEastern Aerosol Research and
Characterization (SEARCH) network observations in the eastern US. For
comparisons to SEARCH observations, the Jefferson Street, Atlanta, GA (JST),
and Birmingham, AL (BHM), urban sites as well as Yorkville, GA (YRK), and
CTR, rural sites were considered. In order to estimate
secondary organic carbon (SOC), the method of , which uses
OC / EC (elemental carbon) ratios, was revised to account for the semivolatile nature of
POA. For estimating observed POA from total OA only, POA in CMAQ is assumed
to correspond to emissions of Ci* ≈ 3000 µgm-3
and lower-volatility compounds. The volatility distribution of gasoline
vehicle POA from and used by the CMAQ-VBS was
used to estimate how much POA is expected in the particle under ambient
conditions.
The fraction of POA in the particle (fP) for each observation
data point was estimated as
fp=∑i=15αi1+Ci*/OCobs(OM/OC)mod,
where the volatility profile is described by one nonvolatile and Ci*=1, 10, 100, and 1000 µgm-3 surrogate species in the
following mass-based abundance (αi): 0.27, 0.15, 0.26, 0.16, and
0.17. Observed SOC was estimated from each observed OC by
SOCobs=OCobs-fp(POC/EC)modECobs;
therefore,
POCobs=fp(POC/EC)modECobs.
This calculation only accounts for the effect of dilution and partitioning on
POC (primary organic carbon) and does not account for chemical processing that may convert POA to SOA.
In addition, compared to other volatility profiles such as diesel POA, this
profile tends to be weighted toward lower-volatility compounds. As a result,
this approach may be an upper bound on the amount of POC (lower bound on
SOC).
In addition to the routine monitoring network data, model predictions were
compared to data from the CTR (87.25∘ W, 32.90∘ N) and Look Rock, TN
(LRK; 83.94∘ W, 35.63∘ N) sites from the
SOAS field campaign in the southeast United States. Observations include
water-soluble organic carbon in both particle and gas phase
, aerosol LWC , OA
, and gas-phase species
. The Supplement provides
additional evaluation such as a comparison to OH ,
isoprene , and 2-methyltetrol
concentrations.
Conclusions
Current chemical transport models consider the dominant pathways to SOA to be
dry processes governed by condensation of low-volatility organic compounds in
the absence of water. In addition, models generally do not consider uptake of
water by organic species. In this work, the CMAQ model was updated to
consider aerosol water interactions with semivolatile SOA species and uptake
of water into OA with a focus on simulating conditions during the Southern
Oxidant and Aerosol Study of 2013. A method (γ≠1 simulation) was
developed to take into account deviations from ideality using an activity
coefficient calculated based on the species Henry's law coefficient, pure
species saturation concentration (C0,i*), and the mole fraction of water in
the particle that resulted in a normalized mean bias of -4, -10, and
-2 % for IMPROVE, CSN, and SEARCH SOC. Monoterpene nitrates were
predicted to be the least soluble semivolatile in the model, consistent with
SOA yields from β-pinene + NO3 being comparable under dry
and humid conditions . However, most biogenic
hydrocarbon-derived semivolatile SOA was highly soluble and predicted to be
measured as WSOC. Thus, even aerosol formed through dry processes in models
may be classified as WSOC as measured by instruments such as the PiLS.
Based on current observations, aerosol water cannot be added to the
partitioning medium for semivolatile organic compounds without simultaneously
accounting for deviations in ideality. Otherwise, aerosol liquid water and
aerosol carbon are overestimated at night. This finding is consistent with the
work by , who found that aerosol water concentrations would more
than double if ideality was assumed. also found that
organic–inorganic water-uptake experiments could not be modeled assuming
ideal, well-mixed liquids, and assuming ideality overpredicted
α-pinene SOA concentrations by 100–200 % in the work of
.
All simulations in this work, including the more aggressive ones assuming
ideality, could not reproduce daytime observed OA in the southeast US (at
SEARCH sites) solely by adding water to the partitioning medium. Including
water resulted in increased model error but could reduce the bias in OC.
Additional pathways (new precursors and/or new pathways) to OA, particularly
during the daytime, are still needed in models.
The updates described here are in three stages of model readiness:
Properties of semivolatile OA constituents can immediately be updated in models to be consistent with their assumed volatility and parent hydrocarbon.
Base model performance was good in terms of isoprene-OA and total OC compared to routine networks. Property updates in this work (Table 2) are scheduled for public release as part of CMAQv5.2.
Prediction of organic water is more uncertain, but OM / OC is a useful proxy and can be used to parameterize water uptake onto organic species via Eq. () and κ-Köhler theory.
The effects of water on semivolatile OA partitioning requires additional research as deviations from ideality are important. γi∞ or CH,i* are recommended as
useful parameters for characterizing solubility. Models such as the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model and
UManSysProp offer opportunities to perform detailed calculations.
In addition, these areas of model improvement are suggested for future work:
A treatment of semivolatile primary OA is needed to reproduce observed surrogates for POA. Factor-of-2 overestimates in POA were predicted to compensate for underestimates in SOA on the
order of 40 % in IMPROVE and CSN networks.
Improvements to sulfate and gas-phase isoprene chemistry will lead to an improved isoprene-OA representation in models as isoprene-OA is correlated with sulfate, but precursors to IEPOX-derived
SOA were overestimated at CTR during SOAS. Predictions of isoprene SOA could be further improved by considering the volatility of IEPOX-derived species (such as 2-methyltetrols and C5-alkene triols)
as well as formation of additional species .
Model-predicted aerosol LWC that includes water associated with organic compounds can be most improved by improving the concentration of OA, which may require a number of updates in different areas.
New precursors to SOA are likely needed, especially during the day when OA is underestimated and gas-phase semivolatile model species are less plentiful. Additional precursors for the isoprene system
may include multifunctional hydroperoxides .