ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-20-8201-2020Predicting secondary organic aerosol phase state and viscosity and its
effect on multiphase chemistry in a regional-scale air quality modelPredicting secondary organic aerosol phase state and viscositySchmeddingRyanRasoolQuazi Z.https://orcid.org/0000-0001-6274-6236ZhangYuehttps://orcid.org/0000-0001-7234-9672PyeHavala O. T.https://orcid.org/0000-0002-2014-2140ZhangHaofeihttps://orcid.org/0000-0002-7936-4493ChenYuzhiSurrattJason D.https://orcid.org/0000-0002-6833-1450Lopez-HilfikerFelipe D.ThorntonJoel A.GoldsteinAllen H.https://orcid.org/0000-0003-4014-4896VizueteWilliamvizuete@unc.eduDepartment of Environmental Science and Engineering, The University of North
Carolina at Chapel Hill, Chapel Hill, NC 27516, USAOffice of Research and Development, Environmental Protection Agency,
Research Triangle Park, Durham, NC 27709, USADepartment of Chemistry, University of California at Riverside, Riverside,
CA 92521, USAAerodyne Research, Inc., Billerica, MA 01821, USADepartment of Atmospheric Sciences, University of Washington, Seattle, WA
98195, USADepartment of Environmental Science, Policy, and Management, University of
California, Berkeley, CA 94720, USADepartment of Civil and Environmental Engineering, University of California,
Berkeley, CA 94720, USApresent address: Department of Atmospheric and Oceanic Science, McGill
University, Montreal, H3A 2K6, Canadapresent address: Pacific Northwest National Laboratory, Richland, WA 99354, USApresent address: Tofwerk AG, 3600 Thun, Switzerland
Atmospheric aerosols are a significant public health hazard and have
substantial impacts on the climate. Secondary organic aerosols (SOAs) have
been shown to phase separate into a highly viscous organic outer layer
surrounding an aqueous core. This phase separation can decrease the
partitioning of semi-volatile and low-volatile species to the organic phase
and alter the extent of acid-catalyzed reactions in the aqueous core. A new
algorithm that can determine SOA phase separation based on their glass
transition temperature (Tg), oxygen to carbon (O:C) ratio and organic mass
to sulfate ratio, and meteorological conditions was implemented into the
Community Multiscale Air Quality Modeling (CMAQ) system version 5.2.1 and
was used to simulate the conditions in the continental United States for the
summer of 2013. SOA formed at the ground/surface level was predicted to be
phase separated with core–shell morphology, i.e., aqueous inorganic core
surrounded by organic coating 65.4 % of the time during the 2013 Southern
Oxidant and Aerosol Study (SOAS) on average in the isoprene-rich southeastern
United States. Our estimate is in proximity to the previously reported
∼70 % in literature. The phase states of organic coatings
switched between semi-solid and liquid states, depending on the
environmental conditions. The semi-solid shell occurring with lower aerosol
liquid water content (western United States and at higher altitudes) has a
viscosity that was predicted to be 102–1012Pas, which
resulted in organic mass being decreased due to diffusion limitation.
Organic aerosol was primarily liquid where aerosol liquid water was dominant
(eastern United States and at the surface), with a viscosity <102Pas.
Phase separation while in a liquid phase state, i.e.,
liquid–liquid phase separation (LLPS), also reduces reactive uptake rates
relative to homogeneous internally mixed liquid morphology but was lower
than aerosols with a thick viscous organic shell. The sensitivity cases
performed with different phase-separation parameterization and dissolution
rate of isoprene epoxydiol (IEPOX) into the particle phase in CMAQ can have
varying impact on fine particulate matter (PM2.5) organic mass, in
terms of bias and error compared to field data collected during the 2013 SOAS.
This highlights the need to better constrain the parameters that
govern phase state and morphology of SOA, as well as expand mechanistic
representation of multiphase chemistry for non-IEPOX SOA formation in models
aided by novel experimental insights.
Introduction
Particulate matter (PM) is one of six criteria pollutants regulated by the
United States Environmental Protection Agency (EPA)'s National Ambient Air
Quality Standards (NAAQS), established by the 1970 Clean Air Act. There are
two categories of PM regulated by NAAQS: fine PM (PM2.5) with particle
diameter less than 2.5 µm and coarse PM (PM10) with particle
diameter up to 10 µm. PM has adverse effects on the global climate
(Carslaw
et al., 2013; Grandey et al., 2018; L. A. Lee et al., 2016; Regayre et al.,
2015). PM2.5 also represents a substantial public health risk due to
its association with increased overall mortality, due to cardiorespiratory
diseases
(Hwang
et al., 2017; Jaques and Kim, 2000; Zanobetti and Schwartz, 2009). It has
been estimated that 20 %–60 % of PM2.5 are comprised of organic
aerosols (OAs) (Docherty et al., 2008).
These pollutant species are either directly emitted primary organic aerosols
(POAs) or secondary organic aerosols (SOAs), which form when volatile organic
compounds (VOCs) undergo chemical reactions that reduce their volatility to
the point that they partition into the aerosol phase
(Zhang et al.,
2007) or react heterogeneously with the existing particles (Riva et al.,
2019). Studies have found that SOA tends to form the bulk of observed OA
around the world (Nozière et al., 2015). The VOCs that
form SOA may be either from biogenic or anthropogenic sources and can vary
both spatially and temporally to areas as confined as the community level
(Yu et al., 2014).
Recent studies have shown that SOA may undergo phase separation under
atmospherically relevant conditions resulting in different morphologies.
These observations have included a “partially engulfed” organic–inorganic
morphology; an emulsified or “island” morphology, where discrete pockets of
SOA dot a larger inorganic particle; and a “core–shell” morphology,
characterized by an organic-rich outer “shell” and aqueous inorganic “core”
(Freedman,
2017; O'Brien et al., 2015; Price et al., 2015; Renbaum-Wolff et al., 2016;
Song et al., 2015, 2016; You et al., 2012; Y. Zhang et al., 2015, 2018). Pye
et al. (2018) applied the Aerosol Inorganic-Organic Mixtures Functional
groups Activity Coefficients (AIOMFAC) model
(Zuend et al., 2008) to predict the
thermodynamic favorability of phase separation in SOA using a box model and
found that aerosols over the southeastern United States may be
phase separated as frequently as 70 % of the time. Pye et al. (2017) used
the ratio of organic matter to organic carbon (OM:OC) and the ambient
relative humidity (RH) to predict phase-separation frequencies. They found
that phase separation was common at lower RH in urban areas with low OM:OC,
but lower phase-separation frequencies in rural areas were attributed to
increasing OM:OC except for late mornings when phase-separation frequency
increased due to low RH.
When aerosols form a core–shell morphology, experimentally observed
viscosities of the outer organic-rich shell and inner electrolyte-rich core
have been shown to differ by up to 3 orders of magnitude, resulting in
possible diffusion limitations on reactive uptake (Ullmann et al., 2019). It
has also been shown that the viscosity of the organic-rich shell and subsequently diffusivity of gaseous particles through it (Dorg) may vary as a
function of SOA composition (Grayson et al., 2017).
Laboratory experiments have been conducted to measure the viscosity of SOA
using poke-flow and bead mobility techniques (Reid et al., 2018;
Renbaum-Wolff et al., 2013; Song et al., 2015, 2016). These studies have
found that SOAs formed from anthropogenic precursors, such as benzene,
toluene and xylene, have similar Dorg values in the realm of
10-14–10-16m2s-1
(Grayson et al., 2016; Song et
al., 2015, 2016, 2018). Similar studies on biogenic SOA comprised of α-pinene
oxidation products found that its measured viscosities and
calculated diffusion coefficients are lower than those of anthropogenic SOA
by as much as 2 orders of magnitude in comparable conditions
(Song et al., 2016, 2018;
Zhang et al., 2015).
The most abundantly emitted biogenic VOC is isoprene
(2-methyl-1,3-butadiene), with average annual global emissions totaling
approximately 500–750 TgCyr-1
(Guenther
et al., 2006; Liao et al., 2015). Isoprene is known to react with hydroxyl
(OH) radicals under low-NOx (=NO+NO2) conditions to
form isoprene hydroxyhydroperoxides (ISOPOOH)
(Jacobs
et al., 2014; Krechmer et al., 2015). If the reaction pathway continues with
OH, ISOPOOH will react again to form isoprene epoxydiol (IEPOX)
(Bates
et al., 2014; Paulot et al., 2009; Surratt et al., 2010). It is possible for
IEPOX to form products with sufficiently low volatility to form SOA via a
reactive uptake onto acidified sulfate seed particles
(Bondy
et al., 2018; Surratt et al., 2006, 2007, 2010). IEPOX-derived SOA have been
observed to account for up to 36 % of biogenic SOA in the southeastern
United States during the summer
(Budisulistiorini
et al., 2016). Given the importance of this pathway, there has been increased
focus on the phase state of particles and its impact on reactive uptake
(Budisulistiorini et al., 2017).
Prior measurements of isoprene-derived SOA suggested that it would not be
viscous enough to exhibit diffusion limitations. There is still much
uncertainty with these measurements as those particles are mainly formed
through nucleation of semi-volatile species (Song et al., 2015).
IEPOX-derived SOA in the southeastern United States is found to exhibit
higher volatility than the remaining bulk OA, with saturation vapor pressures
for IEPOX-derived SOA being 2 to 8 orders of magnitude larger than the
remaining bulk OA. However, IEPOX-derived SOA has a low overall volatility,
with evaporation timescales >100h under atmospherically
relevant conditions (Lopez-Hilfiker et al., 2016). Specifically, acid-driven
multiphase chemistry of IEPOX with inorganic sulfate aerosol results in a
significant yield of organosulfates that have potentially higher viscosities
(Riva et al., 2019). Furthermore, RH
(Huang
et al., 2018; Pajunoja et al., 2014; Song et al., 2015, 2016; Zhang et al.,
2015, 2018a), temperature (Maclean et al., 2017), degree of
oligomerization and mass loading (Grayson et al., 2016) also
impact particle viscosity. Higher RHs may result in more water to partition
into the particle and act as a plasticizer which decreases its viscosity
(Song
et al., 2015, 2016, 2018; Zhang et al., 2011). Higher temperatures also
increase the diffusion coefficient
(Chenyakin,
2015). The degree of oligomerization increases the viscosity of SOA and
therefore reduces its Dorg as well (Grayson
et al., 2017). The reduced transport of semi- or low-volatile gas species
such as IEPOX onto particles also highlights the effects of phase separation
on aerosol formation by decreasing reactive uptake of IEPOX (Gaston et al.,
2014). This is due to increased resistance to diffusion of IEPOX through the
SOA coating (Y. Zhang et al., 2018). The experimental data provided from
previous studies highlight the urgency of incorporating those results into
regional and global models to accurately predict the effects of phase states
on aerosol formation in the ambient environment. A recent study by
Schmedding et al. (2019) used a
dimensionless (0-D) box model for phase-separated SOA formation at the Look
Rock site during the 2013 Southern Oxidant and Aerosol Study (SOAS). Our
prior work found that the inclusion of a phase-separation parameter could
either inhibit SOA due to diffusion limitations in the separated organic
phase or increase it by concentrating the electrolytes into the aqueous core,
leading to faster acid-catalyzed reactions. This resulted in decreasing
normalized mean error (NME) of the model from 83.4 % to 77.9 % and the
normalized mean bias (NMB) from -66.2 % to -36.3 % compared to a
previous work simulating the same dataset that assumed homogeneous aerosol
(Budisulistiorini
et al., 2016). The aforementioned model study
(Schmedding et al., 2019) highlighted
the significant impact of an organic coating layer on IEPOX-derived SOA
formation but lacked any quantification of conditions that result in phase
separation creating such organic coating and its phase state.
The inclusion of an explicit reaction pathway for the reactive uptake of
acid-catalyzed IEPOX-derived SOA in both regional- and global-scale chemical
transport models (CTMs), such as the Community Multi-Scale Air Quality Model
(CMAQ v5.2.1) and the Goddard Earth Observing System (GEOS-Chem v11-02-rc),
has substantially improved the performance of predicted SOA yields
(Marais
et al., 2016; Pye et al., 2013, 2017). These models do not
include parameters in their aerosol algorithms that account for aerosol
morphology or phase separation and its impact on SOA formation (Marais et
al., 2016; Pye et al., 2017, 2018), which can lead to potential deviations
of aerosol quantification. This work systematically examines formation of
coatings comprised of OA derived from a mixture of biogenic and
anthropogenic compounds. Besides predicting frequencies of core–shell
morphology, this work explores how organic coating impacts acid-catalyzed
multiphase reactions of IEPOX by implementing parameterizations to determine
the viscosity and phase state of particles (liquid or glassy) in CMAQ and
simulating for the continental United States.
MethodsPhase state and its impact on reactive uptake: overview
Particles that are in a liquid-like state may either be an internal
homogeneous mixture, or they can be phase separated in a core–shell
morphology with inorganic-rich core and the organic-rich shell also referred to
as liquid–liquid phase separation (LLPS). The occurrence of LLPS depends on
the average O:C ratio, organic mass to sulfate ratio, ambient temperature
and ambient RH (Song et al., 2018;
Zuend and Seinfeld, 2012).
Organic constituents of an aerosol may also exhibit a solid-like glassy
phase state when the ambient temperature is below the glass transition
temperature (Tg), which is a function of RH and the aerosol composition
(DeRieux et al., 2018).
A liquid phase state occurs when the Tg is lower than ambient
temperature. The difference in viscosity (ηorg) of the
organic-rich coating, below and above the Tg, may be as high as 8
orders of magnitude (Marsh et al.,
2018). Thus, Tg can be used to determine when aerosols are in a highly
viscous glassy state (ηorg≥1012Pas), a semi-solid
state (100≤ηorg<1012Pas) or a liquid state (ηorg<100Pas)
(Marcolli et al., 2004; Martin,
2000). Aerosols in a highly viscous or a semi-solid state can be homogeneous
or phase separated in a core–shell morphology, similar to particles with a
liquid-like state. For this study, we also ran a sensitivity simulation to
see the impact if highly viscous particles were phase separated at all times
(refer to Sect. 2.6). The need for this sensitivity is based on recent
observations showing higher-than-anticipated rebound fractions in OA
particles with viscosities >102Pas, implying a highly viscous
particle that can likely exhibit diffusive limitations in reactive uptake
(Reid et al., 2018). These viscous aerosols can be assumed to be in an
amorphous solid phase, homogeneous or phase separated, but unlike liquid
particles, they can only dissipate energy by rebounding, and criteria
governing phase separation in them is not well constrained (Bateman et al.,
2015a, b, 2017; Reid et al., 2018; Virtanen et al., 2010). The specific
conditions under which a particle will form a glassy rather than liquid-like
organic shell are unclear but thought to be driven by the same underlying
physical properties that drive viscosity. This led to a sensitivity
simulation with the consideration that semi-solid or glassy particles would
inherently adopt a core–shell morphology. This sensitivity case can be
thought of as an upper bound on the frequency of particles separating into
core–shell morphology. For the primary phase-separation criteria to be
broader, it was not assumed that a semi-solid state is always
phase separated, and instead the LLPS criteria were applied for conditions
that produce a low aerosol water content (refer to Sect. 2.3).
Phase state of an organic shell impacts reactive uptake by affecting the
diffusivity of a species through this outer organic shell (Dorg).
Dorg can be related to the viscosity of the organic shell (ηorg) using the Stokes–Einstein equation, as shown in Eq. (1)
(Ullmann et al., 2019):
Dorg=kbT6πηorgrdiffusive,
where kb is the Boltzmann constant, T is the ambient temperature,
ηorg is the organic shell viscosity, and rdiffusive is the
hydrodynamic radius of the molecule diffusing through the viscous organic
shell.
CMAQ-defined aerosol phase species (Pye et al., 2017; Murphy et
al., 2017) used in the calculation of the predicted organic-phase parameter
(overall SOA viscosity – ηorg) and their respective
organic mass to organic carbon ratio (OM:OC) (Pye et al., 2017), atomic
oxygen to carbon ratio (O:C), molar weight (Pye et al., 2017) and predicted
individual glass transition temperature (Tg) and viscosity at standard
temperature.
SpeciesDescriptionSourceOM:OCO:CMolar weightPredictedPredicted ηorg at T=298Knameratioratio(gmole-1)Tg (K)and ALW=0 (Pa s)AALK1SV alkane VOC SOAANTH1.560.3152252567.54×109AALK2SV alkane VOC SOAANTH1.420.203205.12335.34×107ABNZ1SV high-NOx SOA product from benzeneANTH2.681.2111612891.67×1014ABNZ2SV high-NOx SOA product from benzeneANTH2.230.8511342342.66×107ABNZ3LV low-NOx SOA product from benzeneANTH3.001.4671803221.00×1012AGLYGlyoxal/methylglyoxal SOABIOG2.130.77166.41601.71×103AISO1SV SOA product from isopreneBIOG2.200.827132.02301.20×107AISO2HV SOA product from isopreneBIOG2.230.851133.02332.26×107AISO3Acid-catalyzed isoprene SOA compounds (2-methyltetrols plus IEPOX organosulfate)BIOG2.801.307168.23011.00×1012ALVOO1LV oxidized combustion organic compoundsANTH2.270.8831362386.59×107ALVOO2LV oxidized combustion organic compoundsANTH2.060.7151362223.97×106AOLGAOligomer products of anthropogenic SOA compoundsANTH2.501.0672063031.00×1012AOLGBOligomer products of biogenic SOA compoundsBIOG2.100.7472483001.00×1012AORGCGlyoxal and methylglyoxal SOABIOG2.000.6671772511.33×109APAH1SV high-NOx SOA product from PAHsANTH1.630.371195.62391.58×108APAH2SV high-NOx SOA product from PAHsANTH1.490.259178.72162.80×106APAH3LV low-NOx SOA product from PAHsANTH1.770.483212.22601.97×1010APCSOPotential combustion SOAANTH2.000.6671702453.91×108ASQTSV SOA from sesquiterpenesBIOG1.520.2831351791.87×104ASVOO1SV oxidized combustion organic productsANTH1.880.5711352074.69×105ASVOO2SV oxidized combustion organic productsANTH1.730.4511351951.10×105ASVOO3SV oxidized combustion organic compoundsANTH1.600.3471341843.19×104AIVPO1Intermediate-volatility primary organic compoundsANTH1.170.0032662603.22×1010ALVPO1LV primary organic compoundsANTH1.390.1792182412.58×108ASVPO1SV primary organic compoundsANTH1.320.1232302457.00×108ASVPO2SV primary organic compoundsANTH1.260.0752412491.86×109ASVPO3SV primary organic compoundsANTH1.210.0352532546.63×109ATOL1SV high-NOx toluene SOAANTH2.260.8751632598.17×109ATOL2SV high-NOx toluene SOAANTH1.820.523175237.25×107ATOL3LV low-NOx toluene SOAANTH2.701.2271943091.00×1012ATRP1SV SOA product from monoterpenesBIOG1.840.5391772391.30×108ATRP2HV SOA product from monoterpenesBIOG1.830.5311982543.93×109AXYL1SV high-NOx SOA product from xyleneANTH2.421.0031742783.16×1012AXYL2SV high-NOx SOA product from xyleneANTH1.930.6111852521.85×109AXYL3LV low-NOx SOA product from xyleneANTH2.300.9072182971.43×1016Determining the glass transition temperature (Tg,org)
The combined Tg,org for anthropogenic species, biogenic species and
aerosol water associated with them was found using a modified version of the
Gordon–Taylor mixing rule, as represented by Eq. (2)
(DeRieux et al.,
2018; Gordon and Taylor, 1952):
Tg,org=wsTg,w+1kGTwaTg,a+wbTg,bws(RH)+1kGTwa+wb,
where Tg,w is the glass transition temperature of water (137 K)
(Koop et al., 2011). Tg,a and
Tg,b are the respective glass transition temperatures (K) for the
anthropogenic (also includes all combustion-generated POA in addition to
VOC-derived SOA; see Table 1) and biogenic (only includes VOC-derived SOA;
see Table 1) fractions of OA. KGT is the Gordon–Taylor constant, which
is assumed to be 2.5 based on Koop et al. (2011). wa and wb
are the mass fractions of anthropogenic and
biogenic OA species, respectively. ws(RH) or simply ws is the
mass fraction of organic aerosol water.
For this work, it was assumed that 10 % of the aerosol water was present
in the organic shell, which is a lower bound estimate of the range of
organic water reported by Pye et al. (2017). Approximately 10 % of total
aerosol water is associated with the organic phase during daytime when IEPOX
chemistry is more prevalent as indicated by the observations collected
during the 2013 SOAS campaign (Guo et al., 2015). To best replicate daytime
IEPOX chemistry, the 10 % value was chosen under the assumption that the
underprediction of nighttime organic water would negligibly impact overall
IEPOX-derived SOA. Naturally, this is not applicable for the rest of multiphase
chemistry and should be addressed accordingly in future work. In CMAQ v5.2.1,
the total aerosol water is predicted by ISORROPIA and only associated with
inorganic electrolytes such as ammonium bisulfate (Pye et al., 2017). As
represented by Eq. (3), the ws along with the wa and wb make
up the organic water component of the aerosol and add up to 1:
ws=1-wa+wb.
Shiraiwa et al. (2017) used 179 organic species to fit a relationship
between Tg, the molar mass (M) and O:C ratio
(Shiraiwa et al., 2017) . Following the same
relationship as in Eq. (4), the respective glass transition temperatures for
the anthropogenic and biogenic fractions (Tg,a and Tg,b) were
calculated using the weighted average molar mass (Mx) and O:C ratio
((O:C)x) for all individual anthropogenic and biogenic species
addressed in CMAQ (see Table 1), where x refers to anthropogenic (a) or
biogenic (b) OA and i refers to individual species:
Tg,x or Tx=-21.57+1.51Mx-0.0017Mx2+131.4O:Cx-0.25MxO:Cx,
where
Mx=∑(wi,xMi,x);(O:C)x=∑wi,x(O:C)i,x;wi,x=mass concentrationi,xtotal mass concentrationx.
When the ambient temperature is below the Tg,org, the viscosity of the
coating (ηorg) is assumed to remain constant at
1012Pas. When the ambient temperature is greater
than or equal to the calculated Tg,org, the viscosity of the organic
shell is calculated using a modified Vogel–Tamman–Fulcher equation
(DeRieux et al., 2018; Fulcher, 1925; Tamman and
Hesse, 1926; Vogel, 1921), as shown in Eq. (5) with experimentally fitted
parameters as shown in Eqs. (6) and (7):
5log10(ηorg)=-5+0.434T0DT-T06T0=39.17Tg,orgD+39.177D=14.4-2.3O:Cavg.T is the ambient temperature (K), T0 is an experimentally fitted
parameter of Eq. (5) that varies as a function of Tg,org and the
fragility parameter D, which is a function of the O:C ratio
(DeRieux
et al., 2018; Zhang et al., 2019). (O:C)avg refers
to the overall OA (including both POA – all anthropogenic and SOA –
anthropogenic and biogenic; see Table 1) O:C ratio given by CMAQ.
The effective diffusion coefficient for IEPOX through the organic coating
(Dorg) was then calculated using the Stokes–Einstein equation (refer
to Eq. 1), assuming that rdiffusive=1nm (Evoy et al.,
2019; Ullmann et al., 2019).
Rate constants used to calculate the effective first-order rate
constant for aqueous-phase IEPOX SOA formation catalyzed by H+,
HSO4- with water and SO42- as nucleophiles.
Rate constantValue (M-2s-1)ReferencekH+,water9.00×10-4Eddingsaas et al. (2010), Pye et al. (2013)kHSO4-,water1.31×10-5Eddingsaas et al. (2010)kH+,SO42-1.27×10-3Riedel et al. (2016), Budisulistiorini et al. (2017)
Brief summary of different simulations conducted in this work in
CMAQ v5.2.1.
SimulationsDetailsNonPhaseSepBase CMAQ v5.2.1 parameterization assuming homogeneous, internally mixed organic–inorganic fine aerosol, no phase separation (Pye et al., 2017)PhaseSep2Additional term in reactive uptake calculation to capture the impact of phase separation and organic coating described in Sect. 2.1–2.3 and 2.5; Zuend and Seinfeld (2012) phase-separation criteria for both liquid and solid or semi-solid particles (see Sect. 2.3)Emissions ReductionEPA-recommended emission reductions for the year 2025 of 34 % and 48 % for NOx and SO2 from base 2013 scenario (see Sect. 2.6)HighHorgHigher organic-phase Henry's law coefficient (3 orders of magnitude higher than in PhaseSep or PhaseSep2) (see Sect. 2.6)PhaseSepAll parametrization is the same as in PhaseSep2 except for the sensitivity that Zuend and Seinfeld (2012) phase-separation criteria are not followed for solid or semi-solid particles (see Sect. 2.6)Phase separation
Unlike the case of liquid particles, phase-separation frequencies in solid
or semi-solid particles are not well understood as stated in Sect. 2.1. We
predict LLPS to occur for aerosols with ηorg≤100Pas or Tg,org:T<0.8
(Shiraiwa et al., 2017) and when RH≤ separation relative
humidity (SRHLLPS) (Bertram et al., 2011; You
et al., 2014). Song et al. (2018) suggests that LLPS always happens when
O:Cavg≤0.56, which we implemented
to predict phase separation. When O:Cavg>0.56, phase separation (or rather LLPS) is predicted
based on the conditions specified in Eqs. (8) and (9). As a model
simplification, solid or semi-solid phase-separated particles (SSPSs) occur
following the aforementioned LLPS criteria, but when ηorg>100Pas
or Tg,org:T≥0.8, to create a
broader scenario referred to as PhaseSep2 (see Table 3). The SRHLLPS is
dependent on OA composition, as shown in Eqs. (8) and (9) based on Bertram et
al. (2011), Zuend and
Seinfeld (2012) and Song et al. (2018):
SRHLLPS=35.5+339.9O:Cavg-471.8O:Cavg2,
when 0.56<O:Cavg≤0.73 and
0.1<(OM:inorganicsulfate)≤15SRHLLPS=0,
when O:Cavg>0.73
and
0.1<OM:inorganicsulfate≤15.
Model description and implementation
All simulations were completed in CMAQ v5.2.1 for the SOAS campaign from
1 June to 15 July 2013, with 10 d of spin-up time starting on 21 May 2013.
Model inputs are described in Xu et al. (2018). The horizontal resolution
of the simulation was 12km×12km. Model
vertical extent between the surface and 50 hPa (representing possible
stratospheric influences) consisted of 35 layers of variable thickness.
The Advanced Research Weather Research and Forecasting model (ARW)
version 3.8 with lightning assimilation was used to generate the
meteorological inputs for the simulations
(Appel
et al., 2017; Heath et al., 2016). The National Emission Inventory (NEI)
2011 v2 produced by the EPA was used to generate anthropogenic emissions.
Biogenic emissions were determined using the Biogenic Emission Inventory
System (BEIS) v3.6.1 (Bash
et al., 2016). BEIS predicts lower emissions amounts for isoprene than the
Model of Emissions of Gases and Aerosols from Nature (MEGAN)
(Carlton and Baker, 2011). Therefore,
emissions of isoprene were increased in this work to 1.5 times their original
levels based on Pye et al. (2017), who found that this increase led to better
agreement with field measurements of isoprene and OH at the Centreville site
during the 2013 SOAS. Carbon Bond v6.3 (CB6r3) was used for the gas-phase
chemistry in the model (Emery et al.,
2015; Ruiz and Yarwood, 2013; Yarwood et al., 2010).
Reactive uptake
IEPOX-derived SOA is modeled with a first-order heterogeneous uptake
reaction that includes a new term that accounts for diffusion limitations
due to an organic coating when the aerosol phase state demands it, as
described below in Eqs. (10)–(13)
(Anttila
et al., 2006; Gaston et al., 2014; Ryder et al., 2014; Budisulistiorini et
al., 2017). The impact of organic coating was not considered in the original
IEPOX reactive uptake algorithm in CMAQ (Pye et
al., 2013):
IEPOX(g)→IEPOXSOA(aerosol).
This first-order heterogeneous-reaction rate constant (khet) is defined
as
khet=SArpDg+4νγ,
where SA is the aerosol surface area (µm2m-3), ν is
the mean molecular speed (ms-1) of gas-phase IEPOX estimated
by Eq. (12):
v=8RTπMWIEPOX.rp is the effective molecular particle radius including both the
inorganic core and organic shell (m), Dg is IEPOX diffusivity in
the gas phase (1.9⋅MWIEPOX-23m2s-1), MWIEPOX=118gmol-1
is the molecular weight of IEPOX and γ is the reactive uptake
coefficient:
1γ=1α+vrp24HinorgRTDarcore1qcothq-1q+vlorgrp4HorgRTDorg,effrcore.α is the accommodation coefficient (0.02).
Hinorg is Henry's law coefficient into the inorganic core
(3×107Matm-1). R is
the gas constant (0.08206 Latm-1K-1mol-1), and T is the ambient temperature
(K).
Da is the IEPOX diffusivity in the aerosol core (10-9m2s-1) and
q is the diffuso-reactive parameter as defined in Eq. (14):
q=rpkparticleDa.kparticle is the pseudo-first-order rate constant
(s-1) defined in Eq. (15) (Pye et al.,
2013), with parameters defined in Table 2:
kparticle=∑i=1N∑j=1Mki,jnuciacidj.Dorg,eff (m2s-1) is the effective
diffusivity of IEPOX through an organic coating compromised of the species
given in Table 1 and 10 % of the total aerosol liquid water.
The contribution of organic species to the volume of the core is assumed
negligible and water moves freely between the inorganic core and the organic
shell, leading to approximately 90 % aerosol water in inorganic core and
10 % in the organic shell for this work as described by Pye et al. (2017).
An extension of this assumption is that the inorganic ion species are
concentrated entirely within the aqueous core when calculating
kparticle. Horg(2×105Matm-1) is the
effective Henry's law constant for the organic coating and lorg is the
organic shell thickness given by Eq. (16) calculated at each time step based
on Riemer et al. (2009). rp is the
surface-area-weighted median particle radius based on surface area
distribution of different species and β is the ratio of inorganic
particle volume (90 % of the particle water and inorganic species) to the
total particle volume (all organic species, water and inorganic species):
lorg=rp(1-β13),rp is the effective aerosol radius (m), the same as in Eqs. (11), (13) and
(14), and rcore is the aerosol inorganic core radius
(m). rcore is defined based on Riemer et al. (2009)
below:
rcore=rpβ13.
Particles that did not have LLPS or SSPS morphology were assumed to form a
homogeneous mixture of organics and inorganics (i.e., lorg=0),
reducing Eq. (13) to the standard CMAQ treatment.
Sensitivity simulations
Three sensitivity simulations were performed, besides the phase state and
primary phase-separation prediction mechanism in CMAQ as detailed in
Sect. 2.1–2.5 (PhaseSep2; see Table 3). A sensitivity simulation
(Emissions Reduction) was conducted using the EPA's emission reductions
estimates for the year 2025 of 34 % and 48 % for NOx and
SO2, respectively
(Marais
et al., 2016; Eyth et al., 2014). A second sensitivity
(HighHorg) was conducted that used the same upper bound of Horg as
reported by Schmedding et al. (2019) increasing the value from 2×105
to 3×108Matm-1. To better understand the
effects of viscosity on particle morphology and phase separation, a third
sensitivity simulation (PhaseSep) was conducted. In PhaseSep, all particles
with ηorg>100Pas were assumed
to be automatically phase separated with a semi-solid outer core, also
referred to as SSPS morphology. While guidelines laid down in Sect. 2.3
(Eqs. 8 and 9 along with phase separation always happening at O:Cavg≤0.56) are now applicable only for
LLPS in PhaseSep. Table 3 gives a brief description of the NonPhaseSep
simulation (base CMAQ without phase state and organic coating impacts), the
updated PhaseSep2 proposed in this work and the three sensitivity
simulations: Emissions Reduction, HighHorg and PhaseSep.
Measurement comparisons
Field data were collected using a high-resolution time-of-flight chemical
ion mass spectrometer (HR-ToF-CIMS) coupled with a filter inlet for gases and
aerosols (FIGAERO) and a two-dimensional gas chromatography time-of-flight
mass spectrometer (GC × GC-ToF-MS) at the Centreville, AL, site during
the 2013 SOAS campaign (H. Zhang et al.,
2018). The combined measurements provide comprehensive and quantitative
characterization of particle-phase OA composition with over 800 OA
components in these data identified as SOA produced predominantly through VOC
oxidation, with a time resolution of 4 h. Chemical formulas were
assigned to all the species based on high-resolution peak fitting, and hence
their O:C ratios and molecular weights were known, which were then used to
empirically calculate the average Tg,org of the OA at the site for the
duration of the entire SOAS campaign (1 June–15 July 2013). The speciated
OA was estimated to account for 74 % of total fine OA mass during SOAS.
The uncharacterized fraction of fine OA (organosulfur compounds,
highly oxidized multifunctional molecules (HOMs), etc.) will likely have
some influence on the estimated Tg,org. Also note that both techniques
use thermal desorption approach to analyze OA composition which was recently
shown to cause thermal decomposition for certain species (Lopez-Hilfiker et
al., 2016; Cui et al., 2018). Thus, some interferences in Tg,org
estimation could be expected by thermal decomposition; however, at this time,
it remains unclear how substantial these interferences could be due to lack
of understanding of the degree of decomposition that occurs in these
analytical methods. Nevertheless, to our knowledge, this is the most
comprehensive molecular-level OA speciation dataset and thus is
appropriate to use for comparison with modeled Tg,org in this work.
We also use observed O:C ratios of various HOMs as reported by Massoli et
al. (2018) recorded at Centreville forest site, Alabama, during the 2013 SOAS
study to compare with the simulations. Massoli et al. (2018) used a
high-resolution time-of-flight chemical ionization mass spectrometer with
nitrate reagent ion (NO3- CIMS) for these observations. More
details are provided in the results section.
Model simulation results (PhaseSep2, HighHorg, Emissions Reduction and
PhaseSep) were compared to recorded values for PM2.5 organic carbon
mass concentration at monitoring stations that are a part of the South
Eastern Aerosol Research Characterization study (SEARCH)
(Hansen et al., 2003) to better constrain the
parameters used in the calculation of γIEPOX.
ResultsPredicted aerosol phase state and phase-separation criteriaPredicted phase state at surface
The ratio of Tg,org to the ambient T is the strongest indicator of the
phase state of the aerosol. The mean, maximum and minimum values for
Tg,org for all grid cells on the surface level and for all time steps
were similar for both PhaseSep2 and PhaseSep estimated to be around 207–209,
284–289 and 137 K, respectively. This suggests that particles would be
mostly semi-solid or liquid-like because of the similarity to the ambient
temperature. The values of Tg,b and Tg,a were also the same for both
PhaseSep2 and PhaseSep, and ranged from 160 to 301 and 230 to 311 K,
respectively. This indicates that anthropogenic species have a narrower
range of glass transition temperatures but overall higher values than
biogenic species; however, the maximum values of Tg,b and
Tg,a are more similar than their minimum values. This is attributed to the
abundant biogenic acid-catalyzed IEPOX-derived SOA species, such as
organosulfates and 2-methyltetrols, having a high Tg of 301 K and a
viscosity of 1012Pas as shown in Table 1.
For the simulation period, the diurnal variability (i.e., between day and
night) in the ambient T at any site was ∼10K, while
Tg,org varied by as much as ∼75K within a 24 h
period. This indicates that changes in the Tg,org:T ratio (i.e., phase
state) were driven by Tg,org (i.e., composition of the organics and
aerosol water in OA) rather than T.
Probability density distribution of glass transition temperature
to ambient temperature ratio (Tg,org:T) for all grid cells and time
steps for (a) the PhaseSep2 simulation and (b) the PhaseSep simulation at
the surface layer (red), lower troposphere (green), upper troposphere
(blue) and stratosphere (purple).
Predicted phase state: vertical distribution
Figure 1 gives the predicted probability density distribution of the
Tg,org:T ratio for both PhaseSep2 and PhaseSep cases across all grid
cells and time steps at different vertical layers of atmosphere: surface,
18th layer (lower troposphere ∼1.8km above ground
level), 28th layer (upper troposphere ∼8km above ground
level) and the 35th layer (lower stratosphere ∼17km
above ground level). At the surface, the PhaseSep simulation has a minimum
Tg,org:T ratio of 0.46 and a maximum of 0.99, while the corresponding
values of the PhaseSep2 simulation were 0.48 and 0.89, respectively. In the
surface layer, over 63.5 % of the Tg,org:T ratios were less than 0.8
in PhaseSep and 67.0 % for PhaseSep2, a value which is given as the
transition point from a semi-solid viscosity to a liquid-like viscosity
(Shiraiwa et al., 2017), with the remainder in a
solid or semi-solid phase state. PhaseSep as expected has a higher fraction
of solid or semi-solid particles, also with higher viscosity than
PhaseSep2. In the lower and upper troposphere, the majority of SOA in
PhaseSep and PhaseSep2 had Tg,org:T ratios between 0.8 and 1.0,
suggesting semi-solid behavior. Particles in the lower stratosphere for both
simulations exhibited Tg,org:T ratios >1 that suggested a
glassy state.
For all time steps and over the continental United States, the
average glass transition temperature to ambient temperature (Tg,org:T)
ratio at (a) the surface level, (b) 1.8 km above the surface layer (lower
troposphere), (c) 8 km above the surface layer (upper troposphere)
and (d) 17 km above the surface layer
(stratosphere) for the PhaseSep2 simulation.
Predicted phase state: spatial variability
Figure 2a shows a map of the average surface layer Tg,org:T ratio
across the continental United States for the duration of the PhaseSep2
simulation. The Tg,org:T ratios exhibited a bimodal distribution both
at the surface and in the lower troposphere (Fig. 1), where particles over
the oceans had substantially higher ws. Particles dominated by ws
had Tg,org similar to Tg,w, with reduced influence from wa
and wb, which decreased their Tg,org:T ratio as
Tg,w is substantially lower than the predicted Tg values for organic
species. These particles correspond to the peak at Tg,org:T at
over approximately 0.5 (Fig. 1). Semi-solid particles with a higher range of
Tg values (Fig. 2a) were concentrated over areas associated with
higher anthropogenic SOA (including anthropogenic POA listed in Table 1) and
a low RH, aerosol liquid water content and biogenic SOA, such as the
American southwest and Rocky Mountains. These higher Tg values pulled
the overall Tg,org value up closer to the ambient temperature and
thus brought the Tg,org:T ratio closer to 1, which is shown in the
cluster of peaks between Tg,org:T=0.8 and Tg,org:T=1.
Figure 2b, c and d show the spatial profiles of the mean Tg,org:T
ratio for each grid cell at the 18th layer of CMAQ (lower troposphere),
28th layer of CMAQ (upper troposphere) and the 35th layer of CMAQ
(stratosphere). The value of T drops with the decreasing pressure. The O:C
ratio of the particles are predicted to increase when compared to the
surface due to atmospheric oxidation. The mean O:C ratio of all particles at
the surface was 0.73, while across the troposphere (at layers 18 and 28) it
increased slightly to 0.75, and at layer 35 it was 0.77. The PhaseSep2
simulation followed a similar pattern of increasing O:C ratios as PhaseSep
but starting higher at surface ∼0.75 and increasing to 0.79
at layer 35. Species with high O:C (>1.6) parameterized in CMAQ
as anthropogenic OA collectively can be used as a surrogate for highly
oxygenated organic aerosol (OOA), specifically low-volatility oxygenated organic aerosol (LVOOA) and
semi-volatile oxygenated organic aerosol (SVOOA). Table 1 shows the species that led to
this specific modeled change in O:C with elevation. The mean mass fraction
of anthropogenic OA increased from ∼40 % at surface to
∼65 % at the upper troposphere and eventually
∼80 % at layer 35 in both PhaseSep and PhaseSep2, whereas
biogenic OA composed of isoprene-derived OA drops from ∼30 % at surface to 24 % at the upper troposphere and eventually
∼20 % at layer 35. The higher O:C in PhaseSep2 relative to
PhaseSep is connected to higher biogenic OA as well (Fig. 4c and e). This
is in agreement with the findings from airborne measurements in the
southeastern United States as part of the Southeast Nexus (SENEX) field
campaign that show a sharp drop in isoprene-derived OA and drastic increase
in OOA with rising altitude (Xu et al., 2016). The mean value of Tg,org
also increased from 207 K at the surface to 219 K at layer 18, 223.5 K at
layer 28 and 239 K at layer 35. This change in Tg,org was primarily
driven by decreases in organic water in the aerosol, which decreased from an
average 29 % at the surface to 1.4 % at layer 35. The removal of water
from the organic phase led to the disappearance of the bimodal Tg,org:T
ratio beyond the 28th layer (upper troposphere).
The mean Tg,org:T ratio was less than 1 in the lower troposphere for
PhaseSep and PhaseSep2. PhaseSep predicted 59.7 % of particles were likely
to be liquid based on the Tg,org:T ratio <0.8, while PhaseSep2
predicted that 45.4 % would likely be liquid-like. The remaining
semi-solid particles were still concentrated over the American southwest and
Rocky Mountains. The difference between Tg,org:T ratio was more
pronounced in the upper troposphere, where PhaseSep predicted 69.4 % of
particles would have semi-solid behavior, with the remainder almost evenly
split between liquid-like and glassy behavior. On the other hand, PhaseSep2
predicted over 99.6 % of particles in the upper troposphere would be
semi-solid. At the 35th layer of CMAQ, all particles in both
simulations had a Tg,org:T ratio >1, indicating glassy
viscosities. Particles with the highest Tg,org:T ratio at this altitude
were located over the southern half of the simulation area, with
Tg,org:T ratios approaching ∼1 in the northern half of
the simulation. Particles in the northern half of the continental United
States domain had higher concentrations of biogenic and anthropogenic SOA in
comparison to those in the southern half of the domain and therefore had
higher Tg,org values than their southern counterparts had.
Phase-separation frequency with different phase-separation criteria
Whether a particle is semi-solid or liquid, and whether it has LLPS or SSPS
morphology, is influenced by the proportions of SOA constituents, including
water. The overall phase-separation frequency using the PhaseSep criteria
was 68.5 %, where 54.8 % of predicted viscosities were greater than
100 Pas, indicating that they exhibited SSPS morphology. The median
viscosity of SSPS particles was on the order of 103Pas,
just above the threshold where particles start exhibiting a glassy state.
The remaining 13.7 % of the phase-separated particles exhibited a LLPS
morphology. The median viscosity of LLPS particles was on the order of
100Pas, indicating a very liquid-like state.
Dorg,eff, which is inversely related to ηorg as derived
from Eq. (1), had a mean and median of 3.40×10-12
and 3.94×10-11m2s-1, respectively. The mean O:C ratio
of LLPS particles was 0.68. The mean values of wa and wb in LLPS
were 37.7 % and 19.8 %. The largest observed contributions of
wa and wb to the total organic mass were 96.9 % and 82.1 %. This
suggests that anthropogenic aerosol components are more dominant than the
biogenic components, whereas biogenic components are likely more water soluble
with their average O:C being 0.72 compared to 0.59 for anthropogenic
constituents (Table 1). The mean fraction of the organic shell composed of
water (ws) was 42.4 % and a maximum of 99.9 %.
The removal of the hypothetical assumption that all particles with a
semi-solid viscosity were phase separated (PhaseSep2) decreased the overall
phase-separation frequency to 29 % from 68.5 % in the PhaseSep
simulation. The entirety of this reduction was from reductions in SSPS only.
The phase-separation frequency at the Centreville, Alabama, site decreased to
65.4 % from 79.3 %, which is still in agreement with the values reported
by Pye et al. (2017).
Diurnal pattern for SOAS (1 June–15 July) 2013 of the organic
glass transition temperature (Tg,org – orange), and contributions of
anthropogenic OA (red), biogenic OA (green) and aerosol water associated
with organics (blue) to Tg,org in the PhaseSep2 case at the
(a, c) Centreville, AL, site and the (b, d) Jefferson Street, Atlanta, GA, site.
Bars/shaded boxes indicate 25th to 75th percentiles. Extreme
bounds of whiskers indicate 5th to 95th percentiles (i.e., 95 %
confidence interval), and the line indicates the mean.
Diurnal influence of OA composition on phase state
Figure 3 shows the diurnal profile of model extracted Tg,org and
relative contributions of the model-predicted anthropogenic, biogenic and
water fractions to Tg,org for 1 June–15 July 2013 at two SEARCH
monitoring sites: a rural site in Centreville, Alabama (Fig. 3a and c),
and at an urban site at the Jefferson Street, Atlanta, Georgia (Fig. 3b and
d). These diurnal profiles are pretty much same for both PhaseSep2 and
PhaseSep cases. Both the Centreville and Atlanta sites (Fig. 3) had Tg,org
values that ranged ∼150–250 K (Fig. 3). The rural Centreville
site is dominated by biogenic OA (Fig. 3c), whose diurnal trend is similar
to that of overall Tg,org at this site (Fig. 3a), whereas the
Jefferson Street site has a significant presence of both anthropogenic and
biogenic OA, but the diurnal trend of Tg,org at this site (Fig. 3b) is
similar to anthropogenic OA which dominates slightly (Fig. 3d). Figure 3 also
gives the diurnal pattern of relative contribution of aerosol liquid water
(associated with organics) to Tg,org. The peaks in Tg,org
coincided with the daytime period of high emissions of VOCs and lower
contribution of aerosol liquid water (Fig. 3). At night and in the early
morning, due to higher contribution of aerosol liquid water, Tg,org is
lower than in daytime for both the sites (Fig. 3). Tg,org at the urban
Jefferson Street site (Fig. 3b) also shows a sharper contrast between day
and night compared to rural Centerville site (Fig. 3a), due to the more
pronounced diurnal variations in aerosol liquid water (Fig. 3d). Both sites
have a relatively high contribution of aerosol water (generally true for
eastern United States; see Fig. 2a) to the organic phase, especially at
night and in the early morning for 18:00–08:00 LT (Fig. 3).
For all grid cells and time steps at the surface layer, the
(a) probability distribution of the organic-phase viscosity and correlations of
particle viscosity (ηorg) with (b) relative humidity, (c) atomic
oxygen to carbon (O:C) ratio, (d) anthropogenic SOA weight fraction,
(e) biogenic SOA weight fraction and (f) organic-phase water content (ws) for
PhaseSep (blue) and PhaseSep2 (gold).
Predicted viscosity
Figure 4a gives the probability density distribution of ηorg at the
surface level for all grid cells and time steps for both the PhaseSep and
PhaseSep2 simulations. Both simulations predicted a bimodal distribution of
ηorg with comparable median values. Semi-solid and
glassy particles tended to have slightly higher viscosities in PhaseSep2
comparison to comparable particles in PhaseSep. Due to large differences in
the mean and median of the predicted ηorg in both simulations, we
chose to use the median value as a more robust measure of the central
tendency of the predicted ηorg for inter- and intra-simulation
comparisons. Figure 4b–f show the impacts of different external and
internal factors on ηorg RH was more strongly correlated with ηorg (r=0.68) than O:C ratio (r=0.62), which was very weakly
correlated with viscosity (Fig. 4b–c), The abundance of water relative to
organics (Fig. 4f) drives the variability in viscosity and phase separation.
Figure 4d–f show that ws (i.e., water related to organic shell) had
the strongest correlation with ηorg (r=0.79), followed by
wb (r=0.75) and wa (r=0.63).
Predicted glass transition temperature to ambient temperature
ratio (Tg,org:T) at the Centreville, AL, site during the 2013 SOAS
campaign based on OA composition reported by H. Zhang et al. (2018) (black).
Predicted Tg,org:T from this work is shown in blue for PhaseSep and
gold for PhaseSep2.
Comparison to observed data
Figure 5 shows the Tg,org:T ratio calculated from speciated organic
aerosol composition field data at the Centreville, Alabama, field site at
32.944∘ N, 87.1386∘ W (H. Zhang et al., 2018), during the
2013 SOAS period. Tg,org:T ratio derived from data collected by
H. Zhang et al. (2018) ranged from ∼0.63 to 0.88. Meanwhile, the
predicted Tg,org:T ratios using both PhaseSep2 and PhaseSep
ranged ∼0.47 to 0.88, slightly exceeding the range predicted
from observations. It should be noted that the H. Zhang et al. (2018)
observations were recorded every 4 h for ∼60 % of
the 2013 SOAS time period. Modeled Tg,org:T mostly captures the peaks
and drops, which the field-observation-derived Tg,org:T shows (Fig. 5).
Some mismatch can be attributed to the lack of an explicit mechanism to
compute organic aerosol water uptake in CMAQ and some unaccounted SOA
formation mechanisms. Further, H. Zhang et al. (2018) only accounted for
∼70 % of SOA species listed in Table 1. The mean
Tg,org:T ratio predicted from the 2013 SOAS Centerville site field
observations was 0.79 compared quite close to the corresponding value of
∼0.73 predicted by both PhaseSep2 and PhaseSep simulations in
CMAQ. The model-estimated Tg,org:T (both PhaseSep2 and PhaseSep)
compared to Tg,org:T range based on field observations at Centreville,
AL, for the 2013 SOAS gives a correlation coefficient of ∼0.64
between them. There is also a discernable consistent diurnal trend across
the 2013 SOAS period for Tg,org:T (Fig. 5), such as stronger
contribution of aerosol liquid water for 18:00–08:00 LT and a lowering of
the Tg,org:T during those hours (Fig. 3a and c).
Massoli et al. (2018) reported the observed O:C at the Centreville, AL, site during
the 2013 SOAS that ranged between ∼0.5 and 1.4 and averaged
0.91. Massoli et al. (2018) presents the first instance of ambient
measurements with a NO3- CIMS in an isoprene-dominated environment
and identified organic nitrates or organonitrates (ONs) originating from
both isoprene and monoterpene to be a significant component of the
NO3- CIMS spectra and dominating the observed SOA at the Centreville site
throughout the day, reflecting daytime and nighttime formation pathways.
Both isoprene- and monoterpene-derived ONs have very high O:C (>1)
and account for up to 10 % of total oxygen at the Centreville site (Xu et
al., 2015; B. H. Lee et al., 2016), explaining the high overall observed average
O:C. Our modeled O:C at the Centerville, AL, site during the 2013 SOAS
ranged between ∼0.5 and 1 and averaged ∼0.7
for both PhaseSep2 and PhaseSep. CMAQ v5.2.1 with carbon bond chemistry (used
in this study) uses aero6 aerosol mechanism, without any explicit
representation of formation pathways of isoprene- and monoterpene-derived
ONs. Specifically CMAQ with aero6 significantly underestimates monoterpene
oxidation that accounts for ∼50 % of organic aerosol in the
southeastern United States in summer (H. Zhang et al., 2018). Consideration of
explicit monoterpene organic nitrates and updated monoterpene photo-oxidation
yields in aero7 eliminates the CMAQ model–measurement bias (Xu et al., 2018).
The lack of explicit organic nitrates here can explain the lack of high O:C
(>1) predictions at the Centreville, AL, site during the 2013 SOAS
leading to the low correlation of model-estimated Tg,org:T with
observations.
Model-predicted SOA viscosity (ηorg) and experimental
data for ηorg from Y. Zhang et al. (2018) (red) and Song et al. (2016) (blue) at varying RH.
Figure 6 shows the predicted viscosity of our phase-separation
implementation for all days, grid cells and layers sorted into 10 % RH
bins. The trends in range of modeled ηorg are the same as those in
Fig. 4b, with higher mean and quantiles of ηorg corresponding to lower
RH, and vice versa for higher RH. Also shown in Fig. 6 are viscosities of
aerosols made in the laboratory. The red dots represent the viscosities of
α-pinene SOA measured by Y. Zhang et al. (2018), and the blue box
plots represent the range of viscosities of toluene SOA measured by Song et
al. (2016). Both laboratory-based experimental studies show good agreement
at atmospherically relevant RH ranges with the viscosities predicted by our
implementation. At lower RH ranges (∼30 %), the
experimentally measured viscosities are slightly higher than those predicted
by our study. This could be attributed to shattering of highly viscous SOA
(ηorg≥106Pas) for RH≤30 % that
inhibits their flow in laboratory measurements of ηorg
(Renbaum-Wolff et al., 2013; Zhang et al., 2015; Y. Zhang et al., 2018).
Huang et al. (2018) speculates that differences in physicochemical
properties of α-pinene SOA, including viscosity, can exhibit a
“memory effect” of the conditions under which the particle formed. This is
regardless of the subsequent conditions to which the particle is exposed.
This could lead to differences between model-predicted and experimentally
measured viscosities, as these memory effects are not well characterized.
Grayson et al. (2016) reports that the viscosity of α-pinene SOA may
vary as a function of the mass loading conditions with higher mass loading
leading to lower viscosity measurements. Unfortunately, there is also a lack
of experimental data on viscosity measurements at RH<60 %.
For all grid cells and time steps, the predicted (a) probability
distribution of γIEPOX at the surface level for the NonPhaseSep
(red), PhaseSep (blue) and PhaseSep2 (gold) simulations. For each grid cell,
the mean value of γIEPOX for the (b) NonPhaseSep, (c) PhaseSep
and (d) PhaseSep2 simulations.
Impact on model predictionsReactive uptake coefficient of IEPOX (γIEPOX)
Previous experimental studies show that phase separation forming semi-solid
organic aerosol coatings is expected to decrease IEPOX reactive uptake
(γIEPOX) and thus the resulting SOA (Y. Zhang et al., 2018).
Figure 7 clearly is in agreement with Y. Zhang et al. (2018), showing reductions
in γIEPOX with PhaseSep, and PhaseSep2 simulations relative to
the NonPhaseSep. Compared to the original CMAQ with no phase separation
considered (NonPhaseSep), PhaseSep had a ∼18 % decrease for
mean γIEPOX at the surface level, while PhaseSep2 led to a
reduction of only ∼2 % (Fig. 7a). For the southeastern United
States, a similar overall shift of higher γIEPOX values
>10-3 in NonPhaseSep to lower values ranging between
10-4 to 10-6 occurs with the introduction of phase separation and
phase state parameters in CMAQ, much more in PhaseSep than in PhaseSep2
(Fig. S1 in the Supplement).
Across the continental United States, for different locations as well, there
is a significant reduction in mean γIEPOX for the 2013 SOAS
period in PhaseSep compared to NonPhaseSep (Fig. 7b and c); however, it is much
more similar between PhaseSep2 and NonPhaseSep (Fig. 7b and d). There was
high variability in the value of γIEPOX between regions:
specifically, between the eastern and western United States. To understand
the drivers that influence changes in γIEPOX with the new
PhaseSep2 and PhaseSep simulations, grid cells that exhibited the maximum
increase and decrease relative to the NonPhaseSep were analyzed. When phase
separation was included, particles in grid cells and time steps with the maximum
reduction in γIEPOX were the result of a low Dorg,eff
of 5.83×10-19m2s-1 and an lorg as high as 100 nm,
i.e., thick organic coating with diffusion limitations. Particles in the grid
cell and time step with the highest increases in γIEPOX had a
Dorg,eff value of 7.34×10-16m2s-1 and lorg
of 0.67 nm, and were located over oceans with an abundant amount of aerosol
liquid water that were in close proximity to biogenic isoprene emission
sources (Fig. S2). These large increases in γIEPOX were
primarily caused by increases in kparticle due to added nucleophiles
(i.e., abundant aerosol liquid water) and a lack of diffusive limitations
through the organic shell. Regions with highest reductions in mean γIEPOX for the 2013 SOAS period across the continental United States
in PhaseSep (southwestern US and southern Canada; Fig. 7c) and PhaseSep2
(midwestern US; Fig. 7d) relative to NonPhaseSep (Fig. 7b) also had
higher lorg (Fig. S2). To summarize, the phase (which influences
Dorg,eff) and thickness (lorg) of the organic coating are the main
drivers of change in γIEPOX.
A recent study by Riva et al. (2019) demonstrated that the formation of
organosulfates during the IEPOX reactive uptake process leads to an organic
coating and thus a reduced γIEPOX. This manifests as a
self-limiting effect during the IEPOX-derived SOA formation. Atmospheric
models, including this work, do not consider this recently observed
self-limiting process yet, but accounting for it may lead to a further
reduction of the γIEPOX. It is also interesting to note that
the spatial maximum of mean organic coating thickness across the continental
United States for PhaseSep2 and PhaseSep cases came around the thin
(∼20nm) and thick (∼40nm) organic coating as
used in Riva et al. (2019), respectively (Fig. S2).
Spatial map of the mean percent relative change in IEPOX-derived
SOA for the (a) PhaseSep and (b) PhaseSep2 cases relative to the NonPhaseSep
simulation.
Predicted SOA mass
Variability in γIEPOX, in the PhaseSep2 and PhaseSep relative
to the NonPhaseSep (Fig. 7), was also reflected in the large geospatial
variations in the concentrations of IEPOX-derived SOA, i.e., organosulfates
and tetrols (Fig. 8). Higher reductions in the IEPOX-derived SOA for PhaseSep
relative to NonPhaseSep were also in regions such as the southwestern United
States and southern Canada (more pronounced near the Great Lakes), with
higher reductions in γIEPOX due to thick organic coatings
(Fig. S2). Although the southwestern United States shows a high reduction in
IEPOX-derived SOA (Fig. 8), it is not reflected for changes in biogenic SOA,
while the high reduction in IEPOX-derived SOA (Fig. 8) in southern Canada is
reflected in reductions in biogenic SOA in that region. This spatial
variability can be explained by the lower fraction of IEPOX-derived SOA in
total biogenic SOA on average in the southwestern United States compared to its
higher fraction in southern Ontario (Fig. S3), which is further reduced to
some extent in PhaseSep2 (Fig. S3c) and becomes negligible in the PhaseSep
case for the southwestern US (Fig. S3b). Hence, the magnitude of changes in biogenic
SOA and eventually PM2.5 organic carbon mass (Fig. S4) is dampened as
compared to changes in IEPOX-derived SOA mass with introduction of phase-separation parameters (Fig. 8).
On average, the largest reduction in biogenic SOA mass at any one grid cell
was 40.9 % and occurred over a forested region in southern Ontario near Lake
Superior which also exhibits high IEPOX-derived SOA contribution to total
biogenic SOA (Fig. S3). For the southeastern United States, modeled average
reductions for 2013 SOAS period in IEPOX-derived SOA ranged between
25 % and 30 %, translated to a 10 %–15 % reduction in total biogenic SOA (Fig. 8).
The highest average reduction in IEPOX-derived SOA was 74.06 % over
Colorado (Fig. 8a). This reduction matters less in terms of overall biogenic
SOA reduction due to negligible contribution of IEPOX-derived SOA to total
biogenic SOA in the American southwest (Fig. S3). In southern Ontario,
where the maximum biogenic SOA reduction in PhaseSep occurred, the average
reductions in IEPOX-derived SOA per grid cell ranged from 63 % to 66 %.
The southern Ontario region with maximum biogenic SOA reduction had average
particle viscosities per grid cell in the range of ∼103 to
106Pas. The total phase-separation frequency of
particles for southern Canada region was 86.3 % of which SSPS was
62.04 % and LLPS was 24.26 %. The combination of these factors led to
a 52.64 % average reduction in γIEPOX in PhaseSep, which can
be treated as a hypothetical upper bound.
PhaseSep2 led to predicted γIEPOX values that were more similar
to those of NonPhaseSep than PhaseSep; however, there is some small variation
in western states. Overall, biogenic SOA mass yields increased by an average
of 25.86 % from the PhaseSep simulation for the continental United States.
Table S1 in the Supplement shows a modest 4 % improvement in model performance for total
PM2.5 OC mass, in the isoprene-abundant southeastern United States with
PhaseSep2. The range of phase-separation frequency in semi-solid particles
per grid cell is still wide in PhaseSep2, i.e., 0.02 % to 55.8 % SSPS.
Increased frequency of bulk phase in semi-solid conditions in PhaseSep2
relative to PhaseSep causes much less resistance to reactive uptake, closer
to but still more than in NonPhaseSep. This is reflected in the similarity
of γIEPOX between PhaseSep2 and NonPhaseSep (Fig. 7c). Hence, a
smaller difference in IEPOX SOA and biogenic SOA in PhaseSep2 relative to
NonPhaseSep occurs, unlike much higher differences observed in PhaseSep
(Figs. 8 and S3). Particles in PhaseSep2 adopted a core–shell morphology
less frequently than those in PhaseSep, typically causing lower
kparticle (Eqs. 13 and 14), which led to reduced reactive uptake of
IEPOX compared to PhaseSep.
PM2.5 organic carbon (OC) mass (µgm-3) as a
function of hour of the day. Non-aggregated performance statistics – mean
bias (µgm-3), % normalized mean bias (NMB) and Spearman's
correlation coefficient (r2) of NonPhaseSep (green) and PhaseSep2
(blue) cases relative to observed (grey) PM2.5 OC mass for the (a) rural
Centreville, AL, site and (b) urban Jefferson Street, Atlanta, GA,
site. Bars/shading indicate 25th to 75th percentiles. Extreme
bounds of whiskers indicate 5th to 95th percentiles (i.e., 95 %
confidence interval). Lines indicate means (dashed line indicates PhaseSep2). n is the
number of observation points.
Comparison to observed data
Figure 9 and Table S1 show that different consideration of phase state and
separation in CMAQ can impact model performance differently. PhaseSep
slightly worsened the NMB based on comparison with hourly PM2.5 organic
carbon mass SEARCH observations at both the Centreville rural site and
urban Jefferson street, Atlanta, site by ∼-4 %. However,
this change was marginal in terms of mean bias change in PhaseSep relative
to the NonPhaseSep case being <0.1µgm-3. The sensitivity
cases that assumed a higher Horg (HighHorg) and considered LLPS
criteria in predicting SSPS (PhaseSep2) resulted in correcting the worsening
of model performance observed with the PhaseSep case (Fig. 9 and Table S1). The
initial assumption regarding phase separation at high viscosity seems to be
approximately as important as the assumption regarding Horg in
constraining the impact of phase state and morphology on reactive uptake of
IEPOX. This highlights the poorly constrained parameters in models such as
Horg assumed as a constant and less understood criterion that might
govern phase separation under low RH or low aerosol water at different O:C
ratios.
Relative change (%) in biogenic SOA mass at the surface level
from the PhaseSep parameterization for the (a)NOx and SO2
Emissions Reduction sensitivity simulation and (b) HighHorg sensitivity
simulation.
Sensitivities
The reduction in emission sources of NOx and SO2 impacted aerosol
composition and thus the Tg,org. The average Tg,org for the
Emissions Reduction simulation predicted a small but statistically
significant (p value =2×10-16) increase of 1.5 K from the
PhaseSep simulation, indicating the future emission reductions could result
in minor increases in viscosity and frequency of phase separation. The
overall phase-separation frequency for this sensitivity was 70.5 %
(57.0 % SSPS, 13.5 % LLPS), with predicted viscosities ranging from
6.13×10-3 to 1.73×1011Pas,
which was slightly narrower compared to the ηorg range from the
PhaseSep simulation (refer to Sect. 3.1.1). With the implementation of the
future reduced NOx and SO2 emissions, overall, there was a mean
7.85 % reduction in biogenic SOA at the surface level from the PhaseSep
simulation for the continental United States. As shown in Fig. 10a, the areas
with the largest reductions in SOA mass occurred in the American southeast,
while a marginal increase in SOA mass that occurred over the Atlantic Ocean
and in some sparse areas in northern Canada and the western United States. The
American southeast was highly sensitive to the Emissions Reduction
sensitivity due to the high concentrations of SO2 from coal-fired power
plants and the high concentrations of IEPOX-derived SOA, whose chemistry is
driven by particulate sulfate. Figure S5 shows that highest reductions in
particulate sulfate occurs in the American southeast, accompanied by a
reduction in aerosol liquid water. This drives the reductions in
IEPOX-derived SOA as shown in recent literature (Pye et al., 2017) and
hence the large reductions in biogenic SOA mass. Also, NOx reductions
in the NOx-limited southeastern United States region essentially
result in a larger decrease in biogenic SOA, as shown in Fig. 10a, which is
consistent with findings from SENEX aircraft (Edwards et al., 2017) and SOAS
ground measurements (Xu et al., 2015) in the southeastern United States.
When increases in Horg were simulated in the HighHorg scenario, it had
impacts in opposite directions compared to changes in the Emissions
Reduction scenario. This increase in biogenic SOA can simply be attributed
to the increased dissolution of IEPOX into the particle phase through the
organic coating with a Horg value 3 orders of magnitude higher than
that in the PhaseSep simulation. The average Tg,org in the HighHorg
simulation had a similarly small but also statistically significant increase
of 1.4 K, with particles being phase separated 68.3 % of the time
(55.8 % SSPS, 12.5 % LLPS). Predicted viscosities in this simulation
were comparable to the PhaseSep simulation and ranged from 5.94×10-3
to 6.1011Pas. Overall, biogenic
SOA mass increased by an average of 14.19 % at the surface level for this
simulation relative to PhaseSep for the continental United States. As shown
in Fig. 10b, the regions with the largest increases in biogenic SOA mass
were located over boreal forests in Ontario and Quebec, Canada, that
correspond to the regions with highest reactive uptake (Fig. 7b and c)
forming more homogeneous SOA with increased Horg.
While modest improvement in model performance of PM2.5 OC by the
aforementioned sensitivity simulations (HighHorg and PhaseSep2; see
Sect. 3.2.2: “Comparison to observed data”) occurs, it does not addresses other major issues in the base CMAQ
model performance. Firstly, these updates to phase state and
phase-separation considerations only translate to IEPOX SOA, the only explicit
parametrization of multiphase reactive uptake in CMAQ. IEPOX SOA also just
makes up approximately 12 % of the total PM2.5 OC mass simulated by
CMAQ on an average for the 2013 SOAS period. There are other more important
factors introducing major source of uncertainty in models across spatial
scales including CMAQ, a very prominent uncertainty being missing
representation of species like ONs reported as dominant in the 2013 SOAS by new
instrumentation providing higher molecular detail (B. H. Lee et al., 2016;
Massoli et al., 2018). Furthermore, field or laboratory studies on a wider
suite of SOAs are needed to explicitly parametrize their multiphase chemistry
and are still missing in CMAQ. It is a challenge to implement these
mechanistic representations of different SOA holistically, without increased
computational cost in CMAQ.
Discussion and atmospheric implications
Current chemical transport models have not accurately accounted for the
effects of aerosol composition on phase separation or viscosity. This work
has updated the CMAQ model to include parameters to calculate the
Tg,org based on the Gordon–Taylor equation for SOA. This implementation
used molar mass and O:C ratio of the species, but other parameters could be
used. For example,
DeRieux et al. (2018)
developed a calculation for Tg,i based on the number of
carbon–hydrogen and carbon–oxygen bonds in a molecule.
DeRieux et al. (2018)
showed their implementation to be in good agreement with implementation
provided in this work (Eq. 4) for species with molar masses in the range
of those used by CMAQ v5.2.1. This implementation also included parameters to
determine whether SOA was phase separated based on its viscosity, O:C ratio,
sulfate concentrations and the ambient RH. Our updated PhaseSep2 model
predicted up to 65.4 % of the time particles would exhibit phase
separation at the surface layer, which is in proximity with the
∼70 % predicted by Pye et al. (2017)
for the isoprene-rich Centerville site in the southeastern United States.
PhaseSep overestimated this phase-separation frequency at ∼79.3 %, indicating PhaseSep2 as a broader and accurate scenario for
future implementations as well. This implementation predicts that most of
the SOA in the middle and upper troposphere over the United States is
phase separated with more organics in a semi-solid or even glassy state with
increasing altitude. This is in agreement with previous fieldwork and
modeling studies which have found that SOA in the upper troposphere tends to
be in a glassy state
(Lienhard et al.,
2015; Shiraiwa et al., 2017). This work also
shows LLPS to be more dominant in the eastern US, with the semi-solid phase
state being more prevalent in the western US. This is in agreement with the
predominant role of aerosol liquid water driving the liquid phase state and
LLPS across the eastern United States, as observed in previous studies (Pye
et al., 2017, 2018). The factors driving LLPS and SSPS are also
an area that should be further studied due to the fact that the modifying
the conditions for LLPS and SSPS led to large differences in IEPOX-SOA.
The model predicted that SOA dominated by anthropogenic constituents
typically featured thick semi-solid organic coatings surrounding aqueous
cores, which caused the reactive uptake of IEPOX to become diffusion
limited. Regions that were predicted to have larger fractions of biogenic
SOA mass typically featured LLPS morphology that did not produce much of
diffusion limitations. These aerosols also resulted in a smaller inorganic
core volume increasing the concentrations of nucleophiles and acids, thus
enhancing the rate of reaction in presence of abundant aerosol water over
oceans but exhibited reduction in SOA over land, though not as much as
solid-like particles exhibited. The phase-separation parameters had the
largest impact over the Ohio River valley, southern Canada (more pronounced
near Great Lakes) and the American southeast. These areas were also the most
sensitive to future emission reductions of NOx and SO2.
Further experimental and modeling work is required to understand the effects
of aerosol phase state on the viscosity of the inorganic core that cause
variability in the value of Da and can subsequently alter the
reactive uptake of IEPOX. The combined effect of aerosol acidity and
aforementioned higher core viscosity because of IEPOX SOA formation that has
a self-limiting impact on IEPOX reactive uptake is also a caveat to be
explored further (Zhang et al., 2019). The conditions under which highly
viscous SOA will separate from inorganics in a particle or if the particle
will remain homogeneously mixed should be further explored as well, given
the differences in the frequency of predicted particle-phase separation
between the PhaseSep and PhaseSep2 simulations and the implications that
this has for the reactive uptake of IEPOX (Table S1). Constraining the
viscosity of SOA in low RH (<30 %) conditions is also an area
that should be further explored to improve model performance. Furthermore,
particle morphology in the event of phase-separated organic and inorganic species
as “core–shell”, “partially engulfed” or “emulsified” (smaller islands of
organics in the aqueous inorganic core) is driven by the differences in the
interfacial surface tensions (Gorkowski et al., 2017). However, developing a
computationally efficient method of modeling these alternative particle
morphologies in CTMs is an area of ongoing research and needs further
exploration. Recent studies have also shown that at very high RH ranges
(95 %–100 %), some particles will return to a core–shell morphology
(Ham et al., 2019;
Renbaum-Wolff et al., 2016). There is also little information on the
criteria that drive a particle to adopt a phase-separated morphology under
these conditions. Such variability in particle morphologies may modify the
value of kparticle by changing the core volume. It is imperative that
these parameters be better constrained in models. Furthermore, there is much
uncertainty in the organic shell Henry's law coefficient (Horg), where
higher Horg increases the dissolution of IEPOX into the aerosol. Some
of the newly proposed reaction mechanisms leading to the formation of
extremely low-volatile organic compounds (ELVOCs) and organosulfates may
also increase the viscosity of the particle but have not been incorporated
into this study.
This work paves the way for implementing a more accurate representation of
multiphase chemistry of different complex systems on the lines of explicit
representation of IEPOX SOA. Multiphase chemistry of other dominant SOA
apart from IEPOX SOA, such as monoterpene-derived SOA and ONs derived from
both isoprene and monoterpenes, is not incorporated in CMAQ v5.2.1 (Pye et
al., 2018; Slade et al., 2019) and should be a focus of future work. This
work showed that organic water fraction is the biggest driver of viscosity,
though the water abundance was set at a constant 10 % of the inorganic
water content to better reflect observed concentrations of organic water
during daylight hours relevant to IEPOX SOA chemistry. Organic water uptake,
even if higher than the amount assumed here, will still follow the diurnal
trend of RH since it is diverted from the aqueous core that is derived from
ISORROPIA-based aerosol water in CMAQ v5.2.1. In this work, the O:C ratio
of individual organic constituents as listed in Table 1 was used to
calculate Tg,org based on Shiraiwa et al. (2017). The O:C provides an
indication of hygroscopicity of different organic species (Pye et al.,
2017), but it is only a surrogate. Lack of explicitly representing
hydrophobicity or hygroscopic growth of various organic constituents is a
limitation in the CMAQ modeling framework that was used. More recently,
the degree of diffusivity or uptake of semi-volatile organic compounds (SVOCs)
such as isoprene oxidation products and ONs into more viscous or semi-solid
particle phase is found to differ. These changes in ηorg
profoundly impact both aerosol growth kinetics and their size distribution
dynamics (Vander Wall et al., 2020; Zaveri et al., 2020). To assess the
actual impacts of aging and hygroscopic growth under varying conditions,
updates in the CMAQ model are required by adding explicit reactive uptake
mechanisms for a wider range of non-IEPOX SOAs.
Performing sensitivity simulations in terms of different assumptions made on
determining phase separation or morphology (PhaseSep2 and PhaseSep) is as
important as constraining the Horg (HighHorg) factor in the
regions with abundant IEPOX-SOA such as the American and Canadian southeast.
Incorporating explicit kinetics coupled with thermodynamic calculation of
energies governing the mixing state of organic–inorganic aerosol mixtures
under different aerosol phase states, as observed from recent and ongoing
experimental findings, into atmospheric models such as CMAQ, would lead to
more scientifically sound representations of the impact particle-phase state
and morphology have on SOA mass predictions.
Code availability
US EPA makes the source code of CMAQ version 5.2.1 model publicly available
for download at https://github.com/USEPA/CMAQ/tree/5.2.1 (last
access: 6 November 2019) (US EPA Office of Research and Development;
10.5281/zenodo.1212601). Corresponding author William Vizuete can make the modifications
made to the CMAQ source code as part of this work available on request.
Data availability
The emissions and meteorological inputs along with other miscellaneous
inputs to run the CMAQ model for the 2013 SOAS episode (1 June 2013 to 15 July 2015)
across the continental United States can be downloaded from https://drive.google.com/open?id=1XR6Xp3bZzrZIzNBx-AgjcNCtC_HLlCkZ
(last access: 6 November 2019), made available by US EPA
and University of North Carolina – Institute of Environment (2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-8201-2020-supplement.
Author contributions
RS and QZR led the writing with WV. RS designed the new PhaseSep
methodology along with sensitivity cases to run in consistent consultation
with YZ, HOTP, YC, JDS, QZR and WV. QZR implemented the model code and
performed the simulations in the regional-scale model with reviews from
HOTP. RS analyzed results of simulations with QZR, WV and HZ.
FDLH,
JAT, AHG and HZ analyzed the SOAS field data. RS and QZR prepared the paper
with extensive reviews and edits from WV, YZ, YC, HOTP, HZ and JDS.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The U.S. Environmental Protection Agency through its Office of
Research and Development collaborated in the research described here. The
research has been subjected to Agency administrative review and approved for
publication but may not necessarily reflect official Agency policy. The
views expressed in this article are those of the authors and do not
necessarily represent the views or policies of the U.S. Environmental
Protection Agency.
Acknowledgements
A National Science Foundation (NSF) Postdoctoral Fellowship under
Atmospheric and Geospace Sciences (AGS-1524731) and the National Institute
of Health (NIH) training grant supported Yue Zhang. Haofei Zhang and Allen H. Goldstein were supported by
NSF grants AGS-1250569 and AGS-1644406. Felipe D. Lopez-Hilfiker and Joel A. Thornton were supported
by a grant from the US Department of Energy Atmospheric System Research
Program (DE-SC0018221). Jason D. Surratt, Yuzhi Chen and Yue Zhang
were supported by NSF-AGS grant 1703535. Claudia Mohr (Department of Atmospheric Sciences, University of
Washington, Seattle, WA; currently at Department of Environmental Science
and Analytical Chemistry, Stockholm University, 106 91 Stockholm, Sweden)
and Anna Lutz (Department of Chemistry and Molecular Biology, University of
Gothenberg, 41296 Gothenberg, Sweden) are acknowledged for providing the
FIGAERO-CIMS measurements from the 2013 SOAS campaign. Ben H. Lee
(Department of Atmospheric Sciences, University of Washington, Seattle, WA)
is also acknowledged for organizing the 2013 SOAS field data.
Financial support
This research has been supported by the NSF-AGS
(grant nos. AGS-1524731, AGS-1703535, AGS-1250569 and AGS-1644406)
and the US Department of Energy Atmospheric System Research Program
(grant no. DE-SC0018221).
Review statement
This paper was edited by Yafang Cheng and reviewed by two anonymous referees.
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