Volatility and viscosity are important properties of organic aerosols (OA),
affecting aerosol processes such as formation, evolution, and partitioning of
OA. Volatility distributions of ambient OA particles have often been
measured, while viscosity measurements are scarce. We have previously
developed a method to estimate the glass transition temperature (Tg) of
an organic compound containing carbon, hydrogen, and oxygen. Based on
analysis of over 2400 organic compounds including oxygenated organic
compounds, as well as nitrogen- and sulfur-containing organic compounds, we
extend this method to include nitrogen- and sulfur-containing compounds
based on elemental composition. In addition, parameterizations are developed
to predict Tg as a function of volatility and the atomic
oxygen-to-carbon ratio based on a negative correlation between Tg and
volatility. This prediction method of Tg is applied to ambient
observations of volatility distributions at 11 field sites. The
predicted Tg values of OA under dry conditions vary mainly from 290 to 339 K
and the predicted viscosities are consistent with the results of ambient
particle-phase-state measurements in the southeastern US and the Amazonian
rain forest. Reducing the uncertainties in measured volatility distributions
would improve predictions of viscosity, especially at low relative humidity.
We also predict the Tg of OA components identified via positive matrix
factorization of aerosol mass spectrometer (AMS) data. The predicted viscosity of
oxidized OA is consistent with previously reported viscosity of secondary organic aerosols (SOA) derived
from α-pinene, toluene, isoprene epoxydiol (IEPOX), and diesel fuel.
Comparison of the predicted viscosity based on the observed volatility
distributions with the viscosity simulated by a chemical transport model
implies that missing low volatility compounds in a global model can lead to
underestimation of OA viscosity at some sites. The relation between
volatility and viscosity can be applied in the molecular corridor or
volatility basis set approaches to improve OA simulations in chemical
transport models by consideration of effects of particle viscosity in OA
formation and evolution.
Introduction
Organic aerosols (OA) contribute substantially to the mass loadings of
atmospheric fine particulate matter (Hallquist et al., 2009; Jimenez et al.,
2009). OA formed from various anthropogenic or biogenic precursors have
complex physicochemical properties (Goldstein and Galbally, 2007; Nizkorodov
et al., 2011; Ditto et al., 2018), which makes predictions of their role in
air quality, climate, and public health challenging (Kanakidou et al., 2005;
Shrivastava et al., 2017). Volatility and viscosity are important properties
of OA, both of which affect important aerosol processes such as
gas–particle partitioning, new particle formation and evolution of size
distribution, heterogeneous reactions, and cloud condensation and ice
nucleation pathways of OA, as summarized in recent review articles (Krieger
et al., 2012; Bilde et al., 2015; Pöschl and Shiraiwa, 2015; Knopf et
al., 2018; Reid et al., 2018).
Recent measurements have shown that OA can exist in liquid (low dynamic
viscosity η; η<102 Pa s), semisolid (102≤η≤1012 Pa s), and amorphous solid (η>1012 Pa s) states, depending on temperature (T), relative
humidity (RH), and chemical composition (Reid et al., 2018). Even though
there are several particle bounce measurements to infer ambient OA phase
state, there are limited ambient measurements of particle phase state or
viscosity (Virtanen et al., 2010; O'Brien et al., 2014; Bateman et al.,
2016, 2017; Pajunoja et al., 2016; Liu et al., 2017; Ditto
et al., 2019; Slade et al., 2019). Viscosity can be directly converted to
bulk diffusivity of organic molecules using the Stokes–Einstein equation,
which has been shown to work well for organic molecules diffusing through
low-viscosity materials (Price et al., 2016; Chenyakin et al., 2017). This
relation is inapplicable for predicting the bulk diffusivity of water and
small molecules, and it may also underestimate the diffusivity of organic
molecules in a highly viscous matrix, which can be corrected using a
fractional Stokes–Einstein equation (Price et al., 2016; Evoy et al., 2019).
Viscosity can be related to the glass transition temperature (Tg), at
which a phase transition between amorphous solid and semisolid states
occurs (Koop et al., 2011). Ambient temperature varies through 100 K
throughout the troposphere, greatly influencing the viscosity of the
mixture. When the ambient temperature is below Tg, an amorphous particle
behaves as a solid, while a particle would be semisolid or liquid when the
ambient temperature is above Tg. OA particles contain a number of
organic compounds and also a variable amount of liquid water depending on
RH, which can act as a plasticizer to reduce Tg: these mixture effects
can be estimated using the Gordon–Taylor relation (Mikhailov et al., 2009;
Koop et al., 2011; Dette et al., 2014). In addition, ambient OA may often be
internally mixed with inorganic species such as sulfate and nitrate, which
would further lower Tg and viscosity if they were well mixed in one
phase; when the phase separation occurs, the inorganic-rich and organic-rich
phases may undergo glass transition at different temperatures (Dette and
Koop, 2015).
For pure organic compounds with known molecular structure, viscosity can be
predicted by group contribution approaches (Cao et al., 1993; Bosse, 2005;
Y. C. Song et al., 2016; Rovelli et al., 2019; Gervasi et al., 2020); chemical
composition of ambient OA is complex, and molecular specificity is often
unavailable, which makes viscosity predictions of ambient OA challenging. We
have recently developed a set of semiempirical parameterizations using
molar mass (M) and atomic O:C ratio (Shiraiwa et al., 2017) or
elemental composition (DeRieux et al., 2018) to predict Tg for
compounds comprised of carbon, hydrogen, and oxygen (CHO compounds). These
parameterizations have been applied to high-resolution mass spectrometry
measurements to estimate viscosity of organic aerosols (DeRieux et
al., 2018; Schum et al., 2018; Ditto et al., 2019; Song et al., 2019) and
coupled into a thermodynamic model (Gervasi et al., 2020). Note that
heteroatoms and the effects of molecular structure and functional groups on
Tg are not considered in parameterizations of Shiraiwa et al. (2017) and DeRieux et al. (2018).
Viscosity of pure compounds has been found to be inversely correlated with
vapor pressure (Thomas et al., 1979). The molecular-corridor-based analysis (Shiraiwa et
al., 2014; Li et al., 2016) of hundreds of secondary organic aerosol (SOA) components has
shown that compounds with lower pure-compound saturation mass concentration (C0)
have higher Tg (Shiraiwa et al., 2017). Rothfuss and Petters (2017)
found that there is a similar trend between the sensitivity of viscosity to
functional group addition and the sensitivity of vapor pressure to
functional group addition. Measurements of the evaporation kinetics of
maleic acid showed that decreasing particle viscosity leads to a suppression
in the effective vapor pressure of maleic acid (Marshall et al., 2018).
Champion et al. (2019) found that SOA with higher
condensed-phase fractions of extremely low volatility organic
compounds (ELVOC) and low volatility organic
compounds (LVOC) showed an increased viscosity. Zhang et al. (2019) measured
Tg of isoprene SOA components including isoprene hydroxy hydroperoxide
(ISOPOOH), isoprene-derived epoxydiol (IEPOX), 2-methyltetrols, and
2-methyltetrol sulfates (2-MT-OS), observing a tight correlation between
Tg and vapor pressure.
Based on the above evidence showing a close relation between volatility and
viscosity, in this study we develop the parameterizations predicting
Tg as a function of C0 based on data from over 2000 compounds.
Functional group contribution approaches are often used to predict C0
(Capouet and Müller, 2006; Pankow and Asher, 2008; Compernolle et al.,
2011; O'Meara et al., 2014); thereby, using C0 to predict Tg would
include the molecular structure effect indirectly. The developed
parameterizations are applied to field observations of volatility
distributions to predict viscosity of ambient OA.
MethodsDataset of glass transition temperature
The training dataset used to develop the parameterizations of Tg include
2448 organic compounds classified into four classes (see the number of CH,
CHO, CHON, and CHOS compounds in Table S1 in the Supplement). Measured Tg values are
available for 42 CH compounds, 259 CHO compounds, 35 CHON compounds, and 1
CHOS compound (Koop et al., 2011; Rothfuss and Petters, 2017; Lessmeier et
al., 2018; Zhang et al., 2019), among which there are 168 compounds with
measured C0 available (Table S1). When Tg measurements are
unavailable, Tg is estimated from the melting temperature (Tm)
by applying the Boyer–Kauzmann rule of Tg=g⋅Tm (Kauzmann, 1948;
Boyer, 1954) with g=0.70085 (±0.00375) (Koop et al., 2011),
referred to as “estimated Tg” in this study (see good agreement of
measured and estimated Tg in Fig. S1a in the Supplement). There are 1187 compounds (391 CH, 537 CHO,
241 CHON, and 18 CHOS compounds) with both measured Tm and C0 (Tables S1, S2) adopted from the MPBPWIN program test sets (http://esc.syrres.com/interkow/EpiSuiteData.htm, last access: 9 July 2020) included in the EPI Suite software version 4.1 (Estimation Programs Interface; US EPA,
2015). Measured Tg, Tm, or C0 for CHOS compounds is sparse and
we adopt 850 CHOS compounds included in Li et al. (2016) with their
Tm and C0 estimated by the EPI Suite software (Table S2). There are
estimation limitations in the EPI Suite; for example, the disagreement
between measured and estimated C0 is larger for compounds with C0<∼10-2µgm-3 (Fig. S1b), which may
affect the Tg predictions for compounds with low volatility. However,
given the large number of data points with measured C0 included in the
training dataset, the estimation bias introduced by the EPI Suite may not
substantially impact the accuracy of the parameterization developed in this
study.
The test dataset used to validate the performance of the parameterizations
predicting Tg of SOA components includes 654 CHO compounds and 212 CHON
compounds found in SOA oxidation products (Shiraiwa et al., 2014). The
values of their C0 are estimated using the EVAPORATION model
(Compernolle et al., 2011). Their Tm values are adopted from the EPI
Suite. The Tg values predicted by our parameterizations are compared with the
Tg estimated from the Tm by applying the Boyer–Kauzmann rule in the
test dataset.
Parameterizations of Tg as a function of volatility
Figure 1a shows a dependence of Tg on C0 for 2448 organic
compounds in the training dataset. The compounds with lower C0 have
higher Tg, and the Tg appears to level off at around 420 K at C0<∼10-10µgm-3. The dependence of Tg
on the atomic O:C ratio is weaker (Figs. 1a and S2), in agreement with
previous studies (Koop et al., 2011; Shiraiwa et al., 2017). Note that a
tight correlation between Tg and the O:C ratio has been observed for
oxidation products formed from specific precursors including α-pinene (Dette et al., 2014), n-heptadecane, and naphthalene (Saukko et al.,
2012). Based on the trend shown in Fig. 1a, we develop a parameterization
(Eq. 1) to predict Tg as a function of C0 and O:C, which are the
parameters used in the two-dimensional volatility basis set (2D-VBS) framework (Donahue et
al., 2011).
Tg=289.10-16.50×log10(C0)-0.29×[log10(C0)]2+3.23×log10(C0)(O:C)
The coefficients in Eq. (1) are obtained by fitting the Tg of 2448
compounds in Fig. 1a with multilinear least squares analysis with 68 %
prediction and confidence intervals. The predicted Tg by Eq. (1) is
plotted in Fig. 1a with the O:C ratios of 0, 0.5, and 1, showing that the
predicted dependence of Tg on C0 follows the trend well in the
training dataset. The O:C ratio mainly affects the predicted Tg of
volatile compounds or extremely low volatility compounds. Figure 1b shows that the
Tg values of those compounds are predicted well by Eq. (1) as indicated
by a high correlation coefficient (R) of 0.92. The average absolute value of
the relative error (AAVRE; Aiken et al., 2007) is 12 %.
(a)Tg of organic compounds in the training dataset plotted
against C0. The lines show the predictions of Tg (Eq. 1) by
C0 and the O:C ratios of 0 (dashed), 0.5 (solid), and 1 (dotted). (b) Predicted Tg by C0 and the O:C ratio (Eq. 1) for compounds shown in
(a) compared to measured or otherwise estimated Tg from Tm. (c) Predicted Tg for SOA components (Shiraiwa et al., 2014) using Eq. (1)
plotted against estimated Tg from Tm with the Boyer–Kauzmann rule.
The correlation coefficient (R) and the average absolute value of the
relative error (AAVRE) are shown. In (b) and (c) the dashed and dotted lines
show 68 % confidence and prediction bands, respectively.
Equation (1) is further evaluated using the test dataset for SOA components.
Figure 1c compares Tg predicted by Eq. (1) with estimated Tg from
Tm by applying the Boyer–Kauzmann rule, showing that Eq. (1) also presents
a good performance for predicting Tg of these SOA components with R=0.96 and AAVRE =6 %. Note that C0 values of SOA components were
estimated using the EVAPORATION model (Compernolle et al., 2011). The
Tg values of individual SOA compounds can be predicted within ±20 K as indicated by the prediction band (dotted lines in Fig. 1c); however,
this uncertainty may be much smaller for multicomponent SOA mixtures under
ideal mixing conditions as indicated in the confidence band (dashed lines,
almost overlapping with the 1:1 line) (Shiraiwa et al., 2017; DeRieux
et al., 2018; Song et al., 2019).
We also develop a parameterization (Eq. 2) predicting Tg as a function
of C0 solely, which can be applied to the information available with the
one-dimensional VBS framework (1D-VBS; Donahue et al., 2006), and this can be
used when the O:C ratio is not available in measurements.
Tg=288.70-15.33×log10(C0)-0.33×[log10(C0)]2
The coefficients in Eq. (2) are obtained following the procedures developing
Eq. (1) and the same training dataset is used. Figures S3–S4 show that Eq. (2) gives very similar predictions as Eq. (1) particularly for the compounds
with low O:C ratio. As Eqs. (1) and (2) are developed based on the compounds
with their C0 higher than ∼10-20µgm-3,
Eqs. (1) and (2) may not be applicable for compounds with C0<∼10-20µgm-3 (Fig. 1a).
Predictions of Tg and viscosity of organic aerosols
For the application of Tg parameterizations in field observations of
volatility distributions, Tg for each volatility bin (Tg,i) is
calculated by Eq. (1). The term volatility refers to the effective
saturation mass concentration (C∗), and we assume ideal thermodynamic
mixing in which case C∗ is equal to C0 (Donahue et al., 2011).
Note that there may be additional uncertainty in application of Tg
parameterizations (which were developed based on pure compounds) to each
volatility bin representing a surrogate of complex multicomponent mixtures.
The isolines in Fig. 2 show the Tg,i predicted by Eq. (1) with the
C∗ and O:C defined in the 2D-VBS framework. Tg would be below
∼250 K for intermediate volatility organic compounds (IVOC;
300<C0<3×106µgm-3), from
∼260 to 290 K for semivolatile organic compounds (SVOC;
0.3<C0<300µgm-3), and higher than 300 K
for low-volatility organic compounds (LVOC; 3×10-4<C0<0.3µgm-3) and extremely low volatility organic
compounds (ELVOC; C0<3×10-4µgm-3).
The Tg increases as the O:C ratio increases for SVOC and IVOC, which is
consistent with previous studies (Koop et al., 2011; Saukko et al., 2012;
Berkemeier et al., 2014). The Tg slightly decreases as the O:C ratio
increases for LVOC and ELVOC compounds, which might be due to the
uncertainties in Eq. (1) which is derived from a dataset containing fewer
LVOC and ELVOC compounds as shown in Fig. 1a, which exhibits lower Tg
with higher O:C.
Predicted glass transition temperatures of organic aerosols under
dry conditions (Tg,org) during the SOAS campaign placed into the 2-D VBS
framework. The isopleths correspond to the Tg calculated using Eq. (1)
with the effective saturation mass concentration (C∗) and the O:C
ratio defined in the 2D-VBS. The markers represent the Tg,org of total
OA (TOA) and IEPOX SOA calculated from the volatility distributions
simulated by a global chemical transport model (EMAC-ORACLE; Shiraiwa et al.,
2017) or measured during the SOAS campaign (Hu et al., 2016; Saha et al.,
2017; Stark et al., 2017). Three methods (Formulas, Partitioning, and
Thermograms) are applied in Stark et al. (2017) to derive the C∗
distributions, where the Thermograms method provides the most credible
volatility distributions compared to Formulas and Partitioning (marker
edge lines in gray).
The glass transition temperatures of organic aerosols under dry conditions
(Tg,org) are calculated by the Gordon–Taylor equation (Gordon and
Taylor, 1952) by assuming the Gordon–Taylor constant (kGT) of 1 (Dette et
al., 2014):
Tg,org=∑iwiTg,i,
where wi is the mass fraction in the particle phase for each volatility
bin. The Gordon–Taylor approach has been validated for a wide range of
mixtures including SOA compounds (Dette et al., 2014; Lessmeier et al.,
2018). The Gordon–Taylor approach may fail in the case of adduct or complex
formation (Koop et al., 2011), which is highly unlikely in multicomponent
mixtures with myriads of SOA compounds with very small individual mole
fractions and when particular interactions between individual compounds are
more likely to average out (Shiraiwa et al., 2017); this aspect would need
to be investigated in future studies.
The phase state of aerosol particles strongly depends on their water content
(Mikhailov et al., 2009; Koop et al., 2011). Under humid conditions, the
water content in OA can be estimated using the effective hygroscopicity
parameter (κ) (Petters and Kreidenweis, 2007). The Tg of
organic–water mixtures (Tg(worg)) at given RH can be estimated using
the Gordon–Taylor equation (Gordon and Taylor, 1952):
Tg(worg)=(1-worg)Tg,w+1kGTworgTg,org(1-worg)+1kGTworg,
where worg is the mass fraction of organics in particles; Tg,w is
the glass transition temperature of pure water (136 K; Kohl et al., 2005),
and kGT is the Gordon–Taylor constant for organic–water mixtures which
is suggested to be 2.5 (Zobrist et al., 2008; Koop et al., 2011). Viscosity
can then be calculated by applying the Vogel–Tammann–Fulcher (VTF) equation
(Angell, 1991): η=η∞eT0DT-T0,
where η∞ is the viscosity at infinite temperature
(10-5 Pa s; Angell, 1991), D is the fragility parameter which is assumed
to be 10 (DeRieux et al., 2018), and T0 is the Vogel
temperature calculated as T0=39.17TgD+39.17.
Application in field observationsSouthern Oxidant and Aerosol Study (SOAS)
In this section we predict glass transition temperatures and phase state of
ambient OA during the SOAS (Southern Oxidant and Aerosol Study) campaign, which took place in the southeastern
United States (Centreville, Alabama) in summer 2013 (Carlton et al., 2018).
The Tg of organic aerosols under dry conditions (Tg,org) is
calculated using Eqs. (1) and (3) with measured volatility distributions.
Figure 2 shows the calculated Tg,org placed in the 2D-VBS framework
against the average log10(C∗) calculated by ∑iwilog10(Ci∗) (Kostenidou et al., 2018), and the
measured O:C ratio is from Xu et al. (2015).
Figure 2 shows that Tg,org of total OA (TOA) range from 232 to 334 K,
depending on volatility distributions measured by different methods, while
the most credible predicted Tg,org values span in the range of 313–330 K. The reasons are stated below by comparing the different methods deriving
the C∗ distributions. Stark et al. (2017) used three methods
(Thermograms, Partitioning, and Formulas) to derive volatility
distributions by applying the measurements of organic acids (which were shown
to account for about half of the total OA; Yatavelli et al., 2015) from a
high-resolution chemical ionization time-of-flight mass spectrometer
equipped with a filter inlet for gases and aerosols (Lopez-Hilfiker et al.,
2014; Thompson et al., 2017). In the Thermograms method, C∗ at 298 K is estimated from the desorption temperature after calibration with known
species (Faulhaber et al., 2009). This method results in 93 % of OA mass
distributed in the LVOC and ELVOC (Stark et al., 2017), and a high
Tg,org of 330 K is predicted (Fig. 2). While this method may be
influenced by thermal decomposition, the peak temperatures of decomposing
species can be expected to relate closer to actual volatilities than any of
the other two analysis methods (Stark et al., 2017). The result from the
thermogram method is consistent with those measured by an aerosol mass
spectrometer (AMS) with a thermodenuder, which also applied the thermogram
method to estimate the C∗ distributions (Hu et al., 2016). Saha et
al. (2017) applied an evaporation kinetic model (Lee et al., 2011) based on
the VBS approach to extract the C∗ distributions, and the effects of
enthalpy of vaporization and accommodation coefficient (α) are
considered, resulting in the estimated Tg,org of 313 K. This study
retrieved α of ∼0.5, which is consistent with recent
experiments (Krechmer et al., 2017; Liu et al., 2019).
The lower Tg,org values (<280 K) calculated from the C∗
distributions estimated from the Formulas and Partitioning methods
(Stark et al., 2017) are less atmospherically relevant. The Formulas
method used SIMPOL (simple group contribution method; Pankow and Asher, 2008) to
calculate vapor pressures from the composition of the identified ions. While
the specific functional group distributions needed for SIMPOL are unknown
from mass spectrometer measurements, some assumptions can be made, leading
to limits in the volatility distributions, all of which are showing the same
behavior of high volatilities (Stark et al., 2017). This is because many of
the detected species can be thermal decomposition products rather than
actual SOA molecules (Stark et al., 2015, 2017), which can
lead to overestimations of volatilities, resulting in the unlikely low
Tg,org of 232 K. The Partitioning method used the measured
particle-phase mass fractions of each species to estimate C∗ based
on the partitioning theory (Pankow, 1994). The estimated C∗ is
distributed mainly in the SVOC range (Stark et al., 2017), leading to a
Tg,org of 279 K (Fig. 2). This value is very close to the Tg,org
(281 K) simulated by a global chemical transport model, EMAC-ORACLE, in which
a narrow distribution of C∗ (1, 10, 102, and 103µgm-3) was applied (Shiraiwa et al., 2017). However, Stark et al. (2017)
note that the partitioning-based volatility distribution is likely too high
due to an artifact of signal-to-noise limitations, confining the C∗
characterizable by the partitioning method to a relatively narrow range
centered around the ambient OA concentration (by definition the
semivolatile range). These analyses indicate that the volatility
distributions derived from different methods, even when based on the same
measurements, significantly affect the predicted Tg,org, and the most
atmospherically relevant volatility distributions should be carefully chosen
to reasonably predict the glass transition temperature of ambient OA. In
summary, the Tg,org values during the SOAS campaign should be in the
range of 313–330 K.
Figure 2 also includes Tg,org of isoprene-derived epoxydiol SOA
(IEPOX-SOA) identified via positive matrix factorization (PMF) of AMS mass
spectra (Lanz et al., 2007). IEPOX-SOA is predicted to have a Tg,org of
345 K with very low volatility with the average C∗ lower than
10-4µgm-3 (Hu et al., 2016; Lopez-Hilfiker et al., 2016;
D'Ambro et al., 2019), which may be due to substantial formation of
organosulfates and other oligomers (Lin et al., 2012; Hu et al., 2015; Riva
et al., 2019). The predicted Tg,org of IEPOX-SOA is higher than
previously reported Tg,org of 263–293 K for monoterpene-derived
(α-pinene, 3-carene, myrcene, limonene, and ocimene) SOA
(Petters et al., 2019).
We further calculate the viscosity of OA based on the Tg,org of TOA
predicted above in order to compare with the ambient phase-state
measurements during the SOAS campaign. Figure 3a shows the predicted
viscosity of total OA at different RH; T is adopted as 298 K, the average
value during the SOAS campaign (Hu et al., 2016). The effective
hygroscopicity parameter (κ) is set to 0.14 for TOA based on
measurements (Cerully et al., 2015). The characteristic timescale of mass
transport and mixing by molecular diffusion (τmix) is also
calculated: τmix=dp2/(4π2Db)
(Seinfeld and Pandis, 2006), where dp is the particle diameter, and the
bulk diffusion coefficient Db is calculated from the predicted viscosity
by the fractional Stokes–Einstein relation (Evoy et al., 2019). We assume
a radius of the diffusing molecule of 10-10 m and a particle
diameter of 200 nm (Shiraiwa et al., 2011). Note that these estimated
timescales represent rough estimations, as molecular interactions in complex
mixtures are not considered.
(a) Predicted viscosity of total OA measured during the SOAS
campaign as a function of RH. (b) Diurnal variations of viscosity of total
OA predicted employing the measured RH and T (Hu et al., 2016) during the
SOAS campaign. Tg,org values are calculated using the volatility distributions
measured in Hu et al. (2016), Saha et al. (2017), and the Thermograms
method in Stark et al. (2017). Characteristic mixing timescales of organic
molecules with a radius of 10-10 m within 200 nm particles are also
shown on the right axis.
The viscosity of TOA at RH of 83 % (average RH during SOAS) is predicted
to be less than 102 Pa s with τmix less than 1 s, which is
consistent with the particle bounce measurements, suggesting that
organic-dominated particles were mostly liquid during the SOAS campaign
(Pajunoja et al., 2016). When RH was below ∼50 % in the
sampling inlet, the particles were found to adopt a semisolid state
(Pajunoja et al., 2016), which agrees with the predicted viscosity of
107–1011 Pa s, and τmix can be higher than 1 h at
50 % RH (Fig. 3a). The variations in Tg,org (313–330 K) due to the
different measured C∗ distributions (Fig. 2) have a more significant
impact on the predicted viscosity at low and medium RH (Fig. 3a). When RH is
higher than ∼70 %, the predicted viscosities calculated
from different Tg,org values are very close; at high RH the condensed-phase water has a larger influence on the phase state than the volatility
does, depending on the hygroscopicity of organic aerosols.
Figure 3b shows diurnal variations of predicted viscosity of total OA
using measured T and RH during the SOAS campaign (Hu et al., 2016). During
10:00–20:00 LT (local time) when RH <70 % and T>298 K, three
simulations using different Tg,org values predict that total OA occurs as
semisolid with the predicted viscosity of 102–107 Pa s and the
mixing times of less than 1 h. Particles are predicted to have a low viscosity
of <1 Pa s, adopting a liquid phase during nighttime. The lowest
viscosity occurs around 05:00–06:00 LT with RH >95 %. Here we
did not consider the effects of the diurnal variations of volatility
distributions, as they did not vary dramatically over the campaign period
(Saha et al., 2017). Besides T and RH, diurnal variation of ambient aerosol
phase state also depends on particle chemical composition and mixing states.
Organic particles in the Amazon were found to be more viscous at night than the
daytime due to the influence of biomass burning that may form nonliquid
particles (Bateman et al., 2017). Particles in a mixed forest in northern
Michigan, USA, were also found more viscous at night despite higher RH than the
daytime, due to the formation of high molar mass organic compounds and
smaller inorganic sulfate mass fractions (Slade et al., 2019). Phase-state
measurements during daytime and nighttime at Atlanta, USA, suggested that the
ambient particle phase state was influenced by OA composition, the presence
of inorganic ions, aerosol liquid water, and particle mixing state (Ditto et
al., 2019).
Tg,org at 11 global sites
Figure 4 summarizes Tg,org at 11 sites where the measured volatility
distributions with volatility bins of four or more are available (Table S3).
We did not include the data with narrower volatility ranges which may not
correctly characterize the properties of ambient SOA (Bilde et al., 2015),
and thus may not be appropriate for estimating volatility distributions or would result in unrealistically low Tg without considering
realistically low C∗ bins. Note that a narrow VBS may still be
useful for efficiency in three-dimensional chemical transport models for SOA
evaporation and condensation under a narrow range of ambient temperature
variations (Kostenidou et al., 2018).
Predicted glass transition temperatures of organic aerosols under
dry conditions (Tg,org) at 11 sites. The fill color of the markers
represents Tg,org(a) or the O:C ratio (b). The marker edge color
indicates the OA components identified via PMF of the AMS mass spectra. The
isopleths in (a) correspond to Tg calculated using Eq. (1) with
C∗ and O:C defined in the 2D-VBS. The vertical error bars correspond
to uncertainties in Tg,org considering parameterization uncertainties
and error propagation. The horizontal error bars for the Centreville site
correspond to the upper and lower limits of the average
log10(C∗) calculated from different volatility distributions
measured during the SOAS campaign (Hu et al., 2016; Saha et al., 2017; Stark
et al., 2017).
Figure 4a shows the 2D-VBS framework of O:C vs. log10C∗ with
the marker fill color representing Tg,org, whereas panel (b) shows
Tg,org vs. log10C∗ with the marker fill color representing
O:C. The marker edge color represents OA components identified via positive
matrix factorization of AMS mass spectra (Lanz et al., 2007), including
biomass-burning OA (BBOA), hydrocarbon-like OA (HOA), cooking OA (COA), and
oxygenated OA (OOA), which is sometimes further separated into more
oxygenated OA (MO-OOA) and less oxygenated OA (LO-OOA) factors. Note that these
different OA factors may often be internally mixed in ambient atmosphere, and
predicted Tg,org and particle viscosity would be irrelevant in such a
case. Nevertheless, these predictions can be useful when particles are
externally mixed or ambient OA are dominated by a certain OA factor.
Tg,org of total OA (TOA) varies from 290 to 339 K. The lower
Tg,org occurs at Beijing, China, in June 2018 (Xu et al., 2019). OA in
Beijing was found to be overall more volatile with the particle-phase
semivolatile fraction of 63 %. This may be due to the higher total OA
mass concentrations in Beijing (Xu et al., 2019), which facilitate greater
partitioning of SVOC compounds into the particle phase, leading to a lower
Tg,org. The predicted Tg,org of total OA at numerous other sites
range between 300 and 320 K, including Paris (Paciga et al., 2016), Mexico
City (Cappa and Jimenez, 2010), Centreville (Hu et al., 2016; Saha et al.,
2017; Stark et al., 2017), Raleigh (Saha et al., 2017), and Durham (Saha et
al., 2018) in southeastern US. The Tg,org value (316 K) at 220 m
downwind from a highway in Durham is higher than the Tg,org (309 K) at
10 m downwind from a highway due to the dilution and mixing of
traffic-sourced particles with background air and evaporation of
semivolatile species during downwind transport (Saha et al., 2018). The
Tg,org values are predicted to be high (>320 K) at the
sites in Athens (Louvaris et al., 2017), Pasadena (Ortega et al., 2016),
Colorado Rocky Mountain (Stark et al., 2017), and the Amazon (Hu et al., 2016).
The Tg,org values for MO-OOA in Mexico City and Paris are predicted to
be very high at ∼350 K, reflecting their very low volatility.
Figure 5 shows the OA viscosity variation of OA components against RH. The
hygroscopic growth is considered based on hygroscopicity (κ), which
is estimated as a function of the O:C ratio (Lambe et al., 2011) when
κ was not measured (Table S3). The κ values of OA factors
with low O:C ratio, i.e., HOA, COA, and BBOA, are estimated to be low
(<0.08); they are predicted to undergo glass transition at RH
between 25 % and 68 % and adopt a liquid phase only when RH is very
high (∼80 %). The predicted behavior of BBOA is in line
with bounce measurements observing that particles are semisolid in a biomass-burning plume (Bateman et al., 2017). OA factors with higher O:C ratios
including LO-OOA, MO-OOA, and IEPOX SOA tend to become liquid (viscosity
<102 Pa s) at intermediate RH (Fig. 5b).
Predicted viscosity of (a) HOA, COA, and BBOA and (b) LO-OOA,
MO-OOA, and IEPOX SOA in different locations at 298 K as a function of RH.
Experimentally measured viscosity of laboratory-generated SOA formed from
isoprene (Song et al., 2015), α-pinene (Abramson et al., 2013;
Renbaum-Wolff et al., 2013; Kidd et al., 2014; Pajunoja et al., 2014;
Bateman et al., 2015; Zhang et al., 2015; Grayson et al., 2016; Petters et
al., 2019), toluene (M. Song et al., 2016), and diesel fuel (Song et al., 2019)
are also shown. Predicted viscosity of IEPOX-derived OS mixtures (solid blue
line) is from Riva et al. (2019). Note that in the case when these OA factors are
internally mixed with other components, the predicted viscosity would not
represent real ambient complex organic mixtures.
There have been growing measurements of RH-dependent viscosity of
laboratory-generated SOA formed from different precursors, e.g., isoprene
(Song et al., 2015), α-pinene (Abramson et al., 2013; Renbaum-Wolff
et al., 2013; Kidd et al., 2014; Pajunoja et al., 2014; Bateman et al.,
2015; Zhang et al., 2015; Grayson et al., 2016; Petters et al., 2019),
toluene (M. Song et al., 2016), and diesel fuel (Song et al., 2019). As the OOA
factors characterized from ambient AMS observations may represent ambient
SOA (Jimenez et al., 2009), the predicted viscosities of OOA are compared
with laboratory measurements of SOA viscosities in Fig. 5b. It shows that
the majority of experimental values is well bounded by the predicted
viscosities of OOA, represented by the pink shaded area. One exception is
the measured viscosity of isoprene SOA is lower than the predicted viscosity
of IEPOX SOA at low RH (<30 %). One possible reason is that the
isoprene SOA in experiments was formed with high oxidant concentrations with
a short reaction time in an oxidation flow reactor in the absence of
inorganic seed particles (Song et al., 2015). In ambient environments
heterogeneous reactions with acidic sulfate particles forming oligomers are
suggested to be an important pathway (Surratt et al., 2010; Lin et al.,
2013; Hu et al., 2015, 2016). These particle-phase organosulfates
may contribute to a higher viscosity, as indicated by the predicted
viscosity of IEPOX-derived organosulfate mixtures with their Tg,org
estimated to be 313 K (Riva et al., 2019). Another reason could be the mass
concentrations of isoprene SOA are much higher (100–1000 µgm-3; Song et al., 2015) compared to ambient OA concentrations
(5 µgm-3 during SOAS; Stark et al., 2017). Higher mass
concentrations can lead to lower viscosity, as more semivolatile compounds
can partition into the particle phase (Grayson et al., 2016; Jain et al.,
2018; Champion et al., 2019).
Comparison with global simulations
Shiraiwa et al. (2017) simulated the global distribution of annual averages
of SOA phase state using the chemical transport model EMAC (Jöckel et
al., 2006) coupled with the organic aerosol module ORACLE (Tsimpidi et al.,
2014). ORACLE uses the 1D-VBS framework with four C∗ bins (1, 10,
102, and 103µgm-3). To estimate Tg, the values of
molar mass and O:C ratio were assigned for each volatility bin based on
molecular corridors (Shiraiwa et al., 2014). Note that the molar mass
assigned for the volatility bin of 1 µgm-3 was assumed to have
relatively high molar mass to partially compensate for the fact that ORACLE
does not consider lower volatility bins with higher molar mass. As shown in
Fig. 6, global distributions of Tg/T presented in Shiraiwa et al. (2017)
are converted to viscosity using the VTF equation. Figure 6 also includes the
viscosity of total OA at the 11 sites by applying measured volatility
distributions and the global model-simulated 5-year-average T and RH with
κ assumed to be 0.1 (Pringle et al., 2010). Figure 6b shows that the
predicted viscosities at the 11 sites generally agree with the global
simulations: the amorphous solid or semisolid phase occurs over relatively
dry areas, including the sites in western US, Mexico City, Beijing, and
coastal sites in Greece; the lower viscosity occurs in southeastern US and
Paris.
(a) Global distributions of SOA annually averaged viscosity at the
surface simulated by a global chemical transport model (Shiraiwa et al.,
2017) with the viscosity predicted by measured volatility distributions at
11 global sites (triangle, square, and circle represent remote, forested, and
urban sites, respectively, Table S3). The color code indicates viscosity on
a log scale. (b) Predicted viscosity based on measured volatility
distributions compared against the viscosity in global simulations. The
error bars correspond to uncertainties in viscosities calculated from
uncertainties in predicted Tg,org shown in Fig. 4.
The global simulations show that the particles are liquid in the Amazon,
while they occur as semisolid in our predictions based on measured
volatility distributions (Fig. 6a). The reason for this disagreement may be
mainly due to the substantial fraction of low volatility compounds observed
in ambient measurements largely missing from global simulations. Hu et al. (2016) observed that 90 % of OA has volatilities lower than 1 µgm-3, which is the lowest C∗ bin in the global simulations. The
ambient phase-state measurements show that for background conditions of the
Amazonian tropical forest particles are mostly liquid, while for the
anthropogenic influence including both urban pollution and biomass burning
they occur as semisolid or glassy (Bateman et al., 2016,
2017). The volatility distributions were measured in the dry season that is heavily
influenced by biomass burning (Hu et al., 2016), which can lead to the
higher predicted viscosity. Similar cases are observed in Athens and the two
sites in the western US, that our predictions based on volatility
distributions indicate the glassy phase state, while the global model
predicts the occurrence of a semisolid phase.
Conclusions and implications
We have developed parameterizations to estimate the glass transition
temperature of organic compounds using saturation mass concentration
(C0) and atomic O:C ratio. They can be applied to ambient observations
of volatility distributions to estimate viscosity of ambient organic
aerosols. The Tg and viscosity prediction method can be applied in the
volatility basis set or the molecular-corridor-based approach to improve OA
simulations in chemical transport models by consideration of effects of
particle viscosity on OA formation and evolution (Shiraiwa et al., 2017; Pye
et al., 2017; Schmedding et al., 2019). Most of the current chemical
transport models treat particles as a homogeneously well-mixed liquid without
considering particle-phase diffusion limitations, which can lead to bias in
simulations of SOA mass concentrations and evolution of size distributions
(Shiraiwa and Seinfeld, 2012; Zaveri et al., 2018). The SOA simulations
applying the VBS framework have not yet included the effects of viscosity on
SOA formation and evolution. When the gas–particle partitioning is limited
by bulk diffusion, kinetic treatments of SOA partitioning may need to be
applied (Perraud et al., 2012; Liu et al., 2016; Yli-Juuti et al., 2017; Li
and Shiraiwa, 2019). Some chamber experiments probing the mixing timescales
of SOA particles formed from isoprene, α-pinene, and limonene did
not observe significant kinetic limitations at moderate and high RH under
room temperature conditions (Loza et al., 2013; Ye et al., 2016), while kinetic
limitations of bulk diffusion of organic molecules in β-caryophyllene
SOA have been observed at 75 % RH (Ye et al., 2018), warranting further
investigations on the degree of kinetic limitations in ambient tropospheric
conditions. In addition, the interplay of diffusion limitations and phase
separation impacts heterogeneous and multiphase chemistry (Vander Wall et
al., 2018; DeRieux et al., 2019; Zhou et al., 2019) and gas–particle
partitioning (Zuend and Seinfeld, 2012; Shiraiwa et al., 2013; Freedman,
2017; Pye et al., 2017; Gorkowski et al., 2019a). The particle morphology
and the degree of nonideal mixing and liquid–liquid phase separation can
evolve upon atmospheric aging (Gorkowski et al., 2019b). These aspects may
also need to be considered for better representation of organic aerosols in
future studies.
Parameterizations of Tg based on elemental compositions
We recently developed a parameterization (Eq. A1) predicting Tg as a
function of the number of carbon (nC), hydrogen (nH), and oxygen
(nO) atoms (DeRieux et al., 2018), similar to the formulation
used to predict C0 (Donahue et al., 2011; Li et al., 2016):
Tg=(nC0+ln(nC))bC+ln(nH)bH+ln(nC)ln(nH)bCH+ln(nO)bO+ln(nC)ln(nO)bCO.
Values of the coefficients nC0, bC, bH,
bCH, bO, and bCO are 1.96, 61.99, -113.33, 28.74, 0, and 0 for CH
compounds and 12.13, 10.95, -41.82, 21.61, 118.96, and -24.38 for CHO
compounds. We broaden the parameterizations for CH and CHO compounds (Eq. A1) to the following equations applicable to CHON (Eq. A2) and CHOS
compounds (Eq. A3):
A2Tg=(nC0+ln(nC))bC+ln(nO)bO+ln(nN)bN+ln(nC)ln(nO)bCO+ln(nC)ln(nN)bCN+ln(nO)ln(nN)bON,A3Tg=(nC0+ln(nC))bC+ln(nO)bO+ln(nS)bS+ln(nC)ln(nO)bCO+ln(nC)ln(nS)bCS+ln(nO)ln(nS)bOS.
Values of the coefficients nC0, bC, bO, bN,
bCO, bCN, and bON in Eq. (A2) are 5.34, 31.53, -7.06, 134.96,
6.54, -34.36, and -15.35, respectively. Values of the coefficients nC0, bC, bO, bS,
bCO, bCS, and bOS in Eq. (A3) are 1.12, 68.41, 64.95, 35.77,
-12.32, -9.85, and 13.80, respectively. These values are obtained by fitting
the Tg of CHON and CHOS compounds included in the training dataset (Fig. 1a, Table S1) with multilinear least squares analysis. Figure A1a shows
a fair agreement between the predicted Tg using Eq. (A2) and the
measured or otherwise estimated Tg with R of 0.55 and relatively large
AAVRE of 16 % for CHON compounds in the training dataset. Figure A1b
shows a better prediction performance with R of 0.83 and AAVRE of 9 % for
212 CHON compounds included in the test dataset for SOA components with
their Tg estimated by the Boyer–Kauzmann rule using the EPI-estimated
Tm. Figure A1c shows that Eq. (A3) performs well for the CHOS
compounds included in the training dataset with their Tg estimated by
the Boyer–Kauzmann rule using the EPI-estimated Tm (R=0.87, AAVRE =8 %).
Figure S5 shows the comparison of Tg predicted by the elemental
composition (Eqs. A1–A3) with the Tg predicted as a function of
C0 and the O:C ratio (Eq. 1). The agreement between the two sets of
parameterizations for nitrogen- and sulfur-containing compounds is not as
good as that for CHO compounds, indicating that there are limitations of
predicting Tg by the elemental composition for nitrogen- and
sulfur-containing compounds with complex elemental compositions and
molecular structures. As volatility depends significantly on functional
groups contained in a molecule (Pankow and Asher, 2008; Compernolle et al.,
2011), predicting Tg by volatility (Eq. 1) indirectly incorporates the
molecular structure effects. As there are limited CHON and CHOS compounds
with measured Tg available, future experiments measuring more Tg
data for nitrogen- and sulfur-containing organics would help improve the
Tg parameterizations by elemental composition.
Tg predicted by elemental composition (Eq. A2) compared to
(a) measured or otherwise estimated Tg by the Boyer–Kauzmann rule using
measured Tm for CHON compounds in the training dataset and (b) estimated
Tg by the Boyer–Kauzmann rule with Tm estimated by the EPI Suite for
CHON compounds in the test dataset for SOA components. (c)Tg predicted
by elemental composition (Eq. A3) compared to estimated Tg by the
Boyer–Kauzmann rule with Tm estimated by the EPI Suite for CHOS
compounds in the training dataset. The dashed and dotted lines show 68 %
confidence and prediction bands, respectively. The correlation coefficient
(R) and the average absolute value of the relative error (AAVRE) are
included in each figure legend.
Comparison of Tg predictions with Zhang et al. (2019)
Recently Zhang et al. (2019) developed a semiempirical parameterization
(Eq. B1) using vapor pressure (p0 in atm) to predict Tg based on
measured Tg of 11 SOA compounds:
Tg=480.1-54395(log10p0-1.7929)2+116.49.p0 can be converted to C0 via C0=(106Mp0)/(RT), where
R is the ideal gas constant (R=8.2×10-5 m3 atm mol-1 K-1), M is the molar mass (g mol-1), and T is the
temperature (K). Figure B1 compares the measured Tg included in the
training dataset shown in Fig. 1a to Tg predicted by (a) C0 and the
atomic O:C ratio (Eq. 1), (b) elemental composition (Eqs. A1–A3), and (c) Eq. (B1)
by Zhang et al. (2019). While all three methods perform reasonably well, the
predictions using elemental composition (Eqs. A1–A3) show better performance
(Fig. B1b) with R of 0.93 and AAVRE of 11 %.
Comparison between measured Tg in the training dataset in
Fig. 1a and Tg predicted by (a)C0 and O:C (Eq. 1), (b) elemental
composition (Eqs. A1–A3), and (c) the parameterization (Eq. B1) in Zhang et
al. (2019). The solid line shows the 1:1 line. The correlation coefficient
(R) and the average absolute value of the relative error (AAVRE) are
included in each figure legend.
Predicted Tg by (a)C0 and O:C (Eq. 1), (b) elemental
composition (Eq. A1), and (c) the parameterization (Eq. B1) in Zhang et al. (2019) plotted against estimated Tg from Tm applying the
Boyer–Kauzmann rule. CHO compounds in (a)–(c) included in the training
dataset shown in Fig. 1a are with measured Tm and C0 values; CHO
compounds in (d)–(f) included in the test dataset for SOA components shown
in Fig. 1c are with Tm and C0 values estimated by the EPI Suite and
the EVAPORATION model, respectively. The correlation coefficient (R) and the
average absolute value of the relative error (AAVRE) are shown.
The prediction performance is influenced by the training dataset used to
develop parameterizations of Tg. The compounds shown in Fig. B1
contain mostly carboxylic acid and hydroxyl functional groups (Koop et al.,
2011; Rothfuss and Petters, 2017) and are included in the training dataset
used to develop Eqs. (1) and Eqs. (A1)–(A3). The training dataset used in Zhang
et al. (2019) included 11 organic compounds, and their parameterization
predicted Tg of isoprene SOA very well (Zhang et al., 2019), but
underpredicted some low-Tg compounds (Fig. B1c). For compounds with
their measured Tg higher than 200 K, predictions by Zhang et al. (2019)
show good performance and are consistent with the predictions given by Eq. (1) as a function of C0 and the O:C ratio. Predicted Tg values of 2-MT-OS
using the three methods are 297 K (Eq. 1, as a function of C0 and the
O:C ratio), 275 K (Eq. A3, as a function of the elemental composition), and
280 K (Eq. B1, Zhang et al., 2019), comparable with the measured Tg of
276±15 K (Zhang et al., 2019).
Note that predictions using elemental composition (Eq. A1) overestimate the
Tg of phthalate compounds (the star markers in Fig. B1). For instance,
the observed Tg of dioctyl phthalate is 194 K (Zhang et al., 2018), while
the prediction is higher than 300 K (Fig. B1b). The reason is that ester is
not an effective functional group to increase viscosity compared to
carboxylic acid and hydroxyl (Rothfuss and Petters, 2017). Parameterizations
using volatility (Eqs. 1 and B1) improve the predicted Tg of phthalate
compounds (Fig. B1a, c). Figure B2 shows, compared to the predictions using
Eq. (B1) provided in Zhang et al. (2019), that predictions by C0 and the
atomic O:C (Eq. 1) and elemental composition (Eq. A1) agree better with the
Tg estimated from the Boyer–Kauzmann rule. Future experiments measuring
more Tg data of SOA components would help verify the Tg predictions by
different parameterizations.
Data availability
The data used in this study are available in the
Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-8103-2020-supplement.
Author contributions
YL, JLJ, and MS designed the research. YL developed the
parameterizations. DAD, HS, and JLJ provided measured volatility
distributions for the SOAS campaign. YL and MS wrote the article. All
authors discussed the results and contributed to article editing.
Acknowledgements
We
thank Alexandra Tsimpidi, Vlassis Karydis, Spyros Pandis, and Jos Lelieveld for global
simulations of SOA concentrations used to calculate Tg/T (as presented in
Shiraiwa et al., 2017), which are converted into viscosity (Fig. 6). We also
thank Sergey Nizkorodov, Andreas Zuend, Yue Zhang, Jason Surratt, and Markus
Petters for stimulating discussions.
Financial support
This research has been supported by the National Science Foundation, Division of Atmospheric and Geospace Sciences (grant nos. AGS-1654104 and AGS-1822664) and the U.S. Department of Energy (grant nos. DE-SC0018349 and DE-SC0016559).
Review statement
This paper was edited by Neil M. Donahue and reviewed by two anonymous referees.
ReferencesAbramson, E., Imre, D., Beranek, J., Wilson, J. M., and Zelenyuk, A.:
Experimental determination of chemical diffusion within secondary organic
aerosol particles, Phys. Chem. Chem. Phys., 15, 2983–2991,
10.1039/c2cp44013j, 2013.Aiken, A. C., DeCarlo, P. F., and Jimenez, J. L.: Elemental analysis of
organic species with electron ionization high-resolution mass spectrometry,
Anal. Chem., 79, 8350–8358, 10.1021/ac071150w, 2007.Angell, C.: Relaxation in liquids, polymers and plastic
crystals – strong/fragile patterns and problems, J. Non-Cryst. Solids,
131–133, 13–31, 10.1016/0022-3093(91)90266-9, 1991.Bateman, A. P., Bertram, A. K., and Martin, S. T.: Hygroscopic influence on
the semisolid-to-liquid transition of secondary organic materials, J. Phys.
Chem. A, 119, 4386–4395, 10.1021/jp508521c, 2015.Bateman, A. P., Gong, Z., Liu, P., Sato, B., Cirino, G., Zhang, Y., Artaxo,
P., Bertram, A. K., Manzi, A. O., Rizzo, L. V., Souza, R. A. F., Zaveri, R.
A., and Martin, S. T.: Sub-micrometre particulate matter is primarily in
liquid form over Amazon rainforest, Nat. Geosci., 9, 34–37,
10.1038/ngeo2599, 2016.Bateman, A. P., Gong, Z., Harder, T. H., de Sá, S. S., Wang, B., Castillo, P., China, S., Liu, Y., O'Brien, R. E., Palm, B. B., Shiu, H.-W., Cirino, G. G., Thalman, R., Adachi, K., Alexander, M. L., Artaxo, P., Bertram, A. K., Buseck, P. R., Gilles, M. K., Jimenez, J. L., Laskin, A., Manzi, A. O., Sedlacek, A., Souza, R. A. F., Wang, J., Zaveri, R., and Martin, S. T.: Anthropogenic influences on the physical state of submicron particulate matter over a tropical forest, Atmos. Chem. Phys., 17, 1759–1773, 10.5194/acp-17-1759-2017, 2017.Berkemeier, T., Shiraiwa, M., Pöschl, U., and Koop, T.: Competition between water uptake and ice nucleation by glassy organic aerosol particles, Atmos. Chem. Phys., 14, 12513–12531, 10.5194/acp-14-12513-2014, 2014.Bilde, M., Barsanti, K., Booth, M., Cappa, C. D., Donahue, N. M.,
Emanuelsson, E. U., McFiggans, G., Krieger, U. K., Marcolli, C., Topping,
D., Ziemann, P., Barley, M., Clegg, S., Dennis-Smither, B., Hallquist, M.,
Hallquist, Å. M., Khlystov, A., Kulmala, M., Mogensen, D., Percival, C.
J., Pope, F., Reid, J. P., Ribeiro da Silva, M. A. V., Rosenoern, T., Salo,
K., Soonsin, V. P., Yli-Juuti, T., Prisle, N. L., Pagels, J., Rarey, J.,
Zardini, A. A., and Riipinen, I.: Saturation vapor pressures and transition
enthalpies of low-volatility organic molecules of atmospheric relevance:
from dicarboxylic acids to complex mixtures, Chem. Rev., 115, 4115–4156,
10.1021/cr5005502, 2015.Bosse, D.: Diffusion, viscosity, and thermodynamics in liquid systems, PhD
thesis,
available at: https://kluedo.ub.uni-kl.de/frontdoor/deliver/index/docId/1691/file/PhD-Bosse-published.pdf (last access: 9 July 2020),
2005.Boyer, R. F.: Relationship of first-to second-order transition temperatures
for crystalline high polymers, J. Appl. Phys., 25, 825–829,
10.1063/1.1721752, 1954.Cao, W., Knudsen, K., Fredenslund, A. and Rasmussen, P.: Group-contribution
viscosity predictions of liquid mixtures using UNIFAC-VLE parameters, Ind.
Eng. Chem. Res., 32, 2088–2092, 10.1021/ie00021a034, 1993.Capouet, M. and Müller, J.-F.: A group contribution method for estimating the vapour pressures of α-pinene oxidation products, Atmos. Chem. Phys., 6, 1455–1467, 10.5194/acp-6-1455-2006, 2006.Cappa, C. D. and Jimenez, J. L.: Quantitative estimates of the volatility of ambient organic aerosol, Atmos. Chem. Phys., 10, 5409–5424, 10.5194/acp-10-5409-2010, 2010.Carlton, A. G., de Gouw, J., Jimenez, J. L., Ambrose, J. L., Attwood, A. R.,
Brown, S., Baker, K. R., Brock, C., Cohen, R. C., and Edgerton, S.: Synthesis
of the southeast atmosphere studies: Investigating fundamental atmospheric
chemistry questions, B. Am. Meteorol. Soc., 99, 547–567,
10.1175/BAMS-D-16-0048.1, 2018.Cerully, K. M., Bougiatioti, A., Hite Jr., J. R., Guo, H., Xu, L., Ng, N. L., Weber, R., and Nenes, A.: On the link between hygroscopicity, volatility, and oxidation state of ambient and water-soluble aerosols in the southeastern United States, Atmos. Chem. Phys., 15, 8679–8694, 10.5194/acp-15-8679-2015, 2015.Champion, W. M., Rothfuss, N. E., Petters, M. D., and Grieshop, A. P.:
Volatility and viscosity are correlated in terpene secondary organic aerosol
formed in a flow reactor, Environ. Sci. Tech. Let., 6, 513–519,
10.1021/acs.estlett.9b00412, 2019.Chenyakin, Y., Ullmann, D. A., Evoy, E., Renbaum-Wolff, L., Kamal, S., and Bertram, A. K.: Diffusion coefficients of organic molecules in sucrose–water solutions and comparison with Stokes–Einstein predictions, Atmos. Chem. Phys., 17, 2423–2435, 10.5194/acp-17-2423-2017, 2017.Compernolle, S., Ceulemans, K., and Müller, J.-F.: EVAPORATION: a new vapour pressure estimation methodfor organic molecules including non-additivity and intramolecular interactions, Atmos. Chem. Phys., 11, 9431–9450, 10.5194/acp-11-9431-2011, 2011.D'Ambro, E. L., Schobesberger, S., Gaston, C. J., Lopez-Hilfiker, F. D., Lee, B. H., Liu, J., Zelenyuk, A., Bell, D., Cappa, C. D., Helgestad, T., Li, Z., Guenther, A., Wang, J., Wise, M., Caylor, R., Surratt, J. D., Riedel, T., Hyttinen, N., Salo, V.-T., Hasan, G., Kurtén, T., Shilling, J. E., and Thornton, J. A.: Chamber-based insights into the factors controlling epoxydiol (IEPOX) secondary organic aerosol (SOA) yield, composition, and volatility, Atmos. Chem. Phys., 19, 11253–11265, 10.5194/acp-19-11253-2019, 2019.DeRieux, W.-S. W., Li, Y., Lin, P., Laskin, J., Laskin, A., Bertram, A. K., Nizkorodov, S. A., and Shiraiwa, M.: Predicting the glass transition temperature and viscosity of secondary organic material using molecular composition, Atmos. Chem. Phys., 18, 6331–6351, 10.5194/acp-18-6331-2018, 2018.DeRieux, W.-S. W., Lakey, P. S. J., Chu, Y., Chan, C. K. K., Glicker, H.,
Smith, J. N., Zuend, A., and Shiraiwa, M.: Effects of phase state and phase
separation on dimethylamine uptake of ammonium sulfate and ammonium
sulfate–sucrose mixed particles, ACS Earth Space Chem., 3, 1268–1278,
10.1021/acsearthspacechem.9b00142, 2019.Dette, H. P., Qi, M., Schröder, D. C., Godt, A., and Koop, T.:
Glass-forming properties of 3-methylbutane-1,2,3-tricarboxylic acid and its
mixtures with water and pinonic acid, J. Phys. Chem. A, 118, 7024–7033,
10.1021/jp505910w, 2014.Dette, H. P. and Koop, T.: Glass formation processes in mixed
inorganic/organic aerosol particles, J. Phys. Chem. A, 119, 4552–4561,
10.1021/jp5106967, 2015.Ditto, J. C., Barnes, E. B., Khare, P., Takeuchi, M., Joo, T., Bui, A. A.
T., Lee-Taylor, J., Eris, G., Chen, Y., Aumont, B., Jimenez, J. L., Ng, N.
L., Griffin, R. J., and Gentner, D. R.: An omnipresent diversity and
variability in the chemical composition of atmospheric functionalized
organic aerosol, Commun. Chem., 1, 75,
10.1038/s42004-018-0074-3, 2018.Ditto, J. C., Joo, T., Khare, P., Sheu, R., Takeuchi, M., Chen, Y., Xu, W.,
Bui, A. A. T., Sun, Y., Ng, N. L. S., and Gentner, D. R.: Effects of
molecular-level compositional variability in organic aerosol on phase state
and thermodynamic mixing behavior, Environ. Sci. Technol., 53, 13009–13018,
10.1021/acs.est.9b02664, 2019.Donahue, N., Robinson, A., Stanier, C., and Pandis, S.: Coupled partitioning,
dilution, and chemical aging of semivolatile organics, Environ. Sci.
Technol., 40, 2635–2643, 10.1021/es052297c, 2006.Donahue, N. M., Epstein, S. A., Pandis, S. N., and Robinson, A. L.: A two-dimensional volatility basis set: 1. organic-aerosol mixing thermodynamics, Atmos. Chem. Phys., 11, 3303–3318, 10.5194/acp-11-3303-2011, 2011.Evoy, E., Maclean, A. M., Rovelli, G., Li, Y., Tsimpidi, A. P., Karydis, V. A., Kamal, S., Lelieveld, J., Shiraiwa, M., Reid, J. P., and Bertram, A. K.: Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes–Einstein and fractional Stokes–Einstein relations, Atmos. Chem. Phys., 19, 10073–10085, 10.5194/acp-19-10073-2019, 2019.Faulhaber, A. E., Thomas, B. M., Jimenez, J. L., Jayne, J. T., Worsnop, D. R., and Ziemann, P. J.: Characterization of a thermodenuder-particle beam mass spectrometer system for the study of organic aerosol volatility and composition, Atmos. Meas. Tech., 2, 15–31, 10.5194/amt-2-15-2009, 2009.Freedman, M. A.: Phase separation in organic aerosol, Chem. Soc. Rev., 46,
7694–7705, 10.1039/c6cs00783j, 2017.Gervasi, N. R., Topping, D. O., and Zuend, A.: A predictive group-contribution model for the viscosity of aqueous organic aerosol, Atmos. Chem. Phys., 20, 2987–3008, 10.5194/acp-20-2987-2020, 2020.Goldstein, A. H. and Galbally, I. E.: Known and unexplored organic
constituents in the Earth's atmosphere, Environ. Sci. Technol., 41,
1514–1521, 10.1021/es072476p, 2007.Gordon, M. and Taylor, J. S.: Ideal copolymers and the second-order
transitions of synthetic rubbers. i. non-crystalline copolymers, J. Appl.
Chem., 2, 493–500, 10.1002/jctb.5010020901, 1952.Gorkowski, K., Preston, T. C., and Zuend, A.: Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model, Atmos. Chem. Phys., 19, 13383–13407, 10.5194/acp-19-13383-2019, 2019a.Gorkowski, K., Donahue, N. M., and Sullivan, R. C.: Aerosol optical tweezers
constrain the morphology evolution of liquid-liquid phase-separated
atmospheric particles, Chem, 6, 1–17,
10.1016/j.chempr.2019.10.018, 2019b.Grayson, J. W., Zhang, Y., Mutzel, A., Renbaum-Wolff, L., Böge, O., Kamal, S., Herrmann, H., Martin, S. T., and Bertram, A. K.: Effect of varying experimental conditions on the viscosity of α-pinene derived secondary organic material, Atmos. Chem. Phys., 16, 6027–6040, 10.5194/acp-16-6027-2016, 2016.Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H., Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, Th. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R., and Wildt, J.: The formation, properties and impact of secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236, 10.5194/acp-9-5155-2009, 2009.Hu, W. W., Campuzano-Jost, P., Palm, B. B., Day, D. A., Ortega, A. M., Hayes, P. L., Krechmer, J. E., Chen, Q., Kuwata, M., Liu, Y. J., de Sá, S. S., McKinney, K., Martin, S. T., Hu, M., Budisulistiorini, S. H., Riva, M., Surratt, J. D., St. Clair, J. M., Isaacman-Van Wertz, G., Yee, L. D., Goldstein, A. H., Carbone, S., Brito, J., Artaxo, P., de Gouw, J. A., Koss, A., Wisthaler, A., Mikoviny, T., Karl, T., Kaser, L., Jud, W., Hansel, A., Docherty, K. S., Alexander, M. L., Robinson, N. H., Coe, H., Allan, J. D., Canagaratna, M. R., Paulot, F., and Jimenez, J. L.: Characterization of a real-time tracer for isoprene epoxydiols-derived secondary organic aerosol (IEPOX-SOA) from aerosol mass spectrometer measurements, Atmos. Chem. Phys., 15, 11807–11833, 10.5194/acp-15-11807-2015, 2015.Hu, W., Palm, B. B., Day, D. A., Campuzano-Jost, P., Krechmer, J. E., Peng, Z., de Sá, S. S., Martin, S. T., Alexander, M. L., Baumann, K., Hacker, L., Kiendler-Scharr, A., Koss, A. R., de Gouw, J. A., Goldstein, A. H., Seco, R., Sjostedt, S. J., Park, J.-H., Guenther, A. B., Kim, S., Canonaco, F., Prévôt, A. S. H., Brune, W. H., and Jimenez, J. L.: Volatility and lifetime against OH heterogeneous reaction of ambient isoprene-epoxydiols-derived secondary organic aerosol (IEPOX-SOA), Atmos. Chem. Phys., 16, 11563–11580, 10.5194/acp-16-11563-2016, 2016.Jain, S., Fischer, B. K., and Petrucci, A. G.: The Influence of absolute mass
loading of secondary organic aerosols on their phase state, Atmosphere, 9,
131, 10.3390/atmos9040131,
2018.Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang,
Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken,
A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L.,
Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y.
L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara,
P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J.,
Dunlea, E. J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P.
I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer,
S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A.,
Miyoshi, T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina,
K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A.
M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E.,
Baltensperger, U., and Worsnop, D. R.: Evolution of organic aerosols in the
atmosphere, Science, 326, 1525–1529,
10.1126/science.1180353, 2009.Jöckel, P., Tost, H., Pozzer, A., Brühl, C., Buchholz, J., Ganzeveld, L., Hoor, P., Kerkweg, A., Lawrence, M. G., Sander, R., Steil, B., Stiller, G., Tanarhte, M., Taraborrelli, D., van Aardenne, J., and Lelieveld, J.: The atmospheric chemistry general circulation model ECHAM5/MESSy1: consistent simulation of ozone from the surface to the mesosphere, Atmos. Chem. Phys., 6, 5067–5104, 10.5194/acp-6-5067-2006, 2006.Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J., Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J., Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati, E., Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate modelling: a review, Atmos. Chem. Phys., 5, 1053–1123, 10.5194/acp-5-1053-2005, 2005.Kauzmann, W.: The nature of the glassy state and the behavior of liquids at
low temperatures, Chem. Rev., 43, 219–256,
10.1021/cr60135a002, 1948.Kidd, C., Perraud, V., Wingen, L. M., and Finlayson-Pitts, B. J.: Integrating
phase and composition of secondary organic aerosol from the ozonolysis of
alpha-pinene, P. Natl. Acad. Sci. USA, 111, 7552–7557,
10.1073/pnas.1322558111, 2014.Knopf, D. A., Alpert, P. A., and Wang, B.: The role of organic aerosol in
atmospheric ice nucleation: a review, ACS Earth Space Chem., 2, 168–202,
10.1021/acsearthspacechem.7b00120, 2018.Kohl, I., Bachmann, L., Hallbrucker, A., Mayer, E., and Loerting, T.:
Liquid-like relaxation in hyperquenched water at ≤ 140 K, Phys. Chem.
Chem. Phys., 7, 3210–3220, 10.1039/B507651J, 2005.Koop, T., Bookhold, J., Shiraiwa, M., and Pöschl, U.: Glass transition and
phase state of organic compounds: dependency on molecular properties and
implications for secondary organic aerosols in the atmosphere, Phys. Chem.
Chem. Phys., 13, 19238–19255, 10.1039/C1CP22617G, 2011.Kostenidou, E., Karnezi, E., Hite Jr., J. R., Bougiatioti, A., Cerully, K., Xu, L., Ng, N. L., Nenes, A., and Pandis, S. N.: Organic aerosol in the summertime southeastern United States: components and their link to volatility distribution, oxidation state and hygroscopicity, Atmos. Chem. Phys., 18, 5799–5819, 10.5194/acp-18-5799-2018, 2018.Krechmer, J. E., Day, D. A., Ziemann, P. J., and Jimenez, J. L.: Direct
measurements of gas/particle partitioning and mass accommodation
coefficients in environmental chambers, Environ. Sci. Technol., 51,
11867–11875, 10.1021/acs.est.7b02144, 2017.Krieger, U. K., Marcolli, C., and Reid, J. P.: Exploring the complexity of
aerosol particle properties and processes using single particle techniques,
Chem. Soc. Rev., 41, 6631–6662, 10.1039/C2CS35082C, 2012.Lambe, A. T., Onasch, T. B., Massoli, P., Croasdale, D. R., Wright, J. P., Ahern, A. T., Williams, L. R., Worsnop, D. R., Brune, W. H., and Davidovits, P.: Laboratory studies of the chemical composition and cloud condensation nuclei (CCN) activity of secondary organic aerosol (SOA) and oxidized primary organic aerosol (OPOA), Atmos. Chem. Phys., 11, 8913–8928, 10.5194/acp-11-8913-2011, 2011.Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C., and Prévôt, A. S. H.: Source apportionment of submicron organic aerosols at an urban site by factor analytical modelling of aerosol mass spectra, Atmos. Chem. Phys., 7, 1503–1522, 10.5194/acp-7-1503-2007, 2007.Lee, B.-H., Pierce, J. R., Engelhart, G. J., and Pandis, S. N.: Volatility of
secondary organic aerosol from the ozonolysis of monoterpenes, Atmos.
Environ., 45, 2443–2452, 10.1016/j.atmosenv.2011.02.004,
2011.Lessmeier, J., Dette, H. P., Godt, A., and Koop, T.: Physical state of 2-methylbutane-1,2,3,4-tetraol in pure and internally mixed aerosols, Atmos. Chem. Phys., 18, 15841–15857, 10.5194/acp-18-15841-2018, 2018.Li, Y. and Shiraiwa, M.: Timescales of secondary organic aerosols to reach equilibrium at various temperatures and relative humidities, Atmos. Chem. Phys., 19, 5959–5971, 10.5194/acp-19-5959-2019, 2019.Li, Y., Pöschl, U., and Shiraiwa, M.: Molecular corridors and parameterizations of volatility in the chemical evolution of organic aerosols, Atmos. Chem. Phys., 16, 3327–3344, 10.5194/acp-16-3327-2016, 2016.Lin, Y.-H., Zhang, Z., Docherty, K. S., Zhang, H., Budisulistiorini, S. H.,
Rubitschun, C. L., Shaw, S. L., Knipping, E. M., Edgerton, E. S.,
Kleindienst, T. E., Gold, A., and Surratt, J. D.: Isoprene epoxydiols as
precursors to secondary organic aerosol formation: acid-catalyzed reactive
uptake studies with authentic compounds, Environ. Sci. Technol., 46,
250–258, 10.1021/es202554c, 2012.Lin, Y.-H., Zhang, H., Pye, H. O. T., Zhang, Z., Marth, W. J., Park, S.,
Arashiro, M., Cui, T., Budisulistiorini, S. H., Sexton, K. G., Vizuete, W.,
Xie, Y., Luecken, D. J., Piletic, I. R., Edney, E. O., Bartolotti, L. J.,
Gold, A., and Surratt, J. D.: Epoxide as a precursor to secondary organic
aerosol formation from isoprene photooxidation in the presence of nitrogen
oxides, P. Natl. Acad. Sci. USA, 110, 6718–6723,
10.1073/pnas.1221150110, 2013.Liu, P., Li, Y. J., Wang, Y., Gilles, M. K., Zaveri, R. A., Bertram, A. K.,
and Martin, S. T.: Lability of secondary organic particulate matter, P.
Natl. Acad. Sci. USA, 113, 12643–12648,
10.1073/pnas.1603138113, 2016.Liu, X., Day, D. A., Krechmer, J. E., Brown, W., Peng, Z., Ziemann, P. J.,
and Jimenez, J. L.: Direct measurements of semi-volatile organic compound
dynamics show near-unity mass accommodation coefficients for diverse
aerosols, Commun. Chem., 2, 98, 10.1038/s42004-019-0200-x, 2019.Liu, Y., Wu, Z., Wang, Y., Xiao, Y., Gu, F., Zheng, J., Tan, T., Shang, D.,
Wu, Y., Zeng, L., Hu, M., Bateman, A. P., and Martin, S. T.: Submicrometer
particles are in the liquid state during heavy haze episodes in the urban
atmosphere of Beijing, China, Environ. Sci. Tech. Let., 4, 427–432,
10.1021/acs.estlett.7b00352, 2017.Lopez-Hilfiker, F. D., Mohr, C., Ehn, M., Rubach, F., Kleist, E., Wildt, J., Mentel, Th. F., Lutz, A., Hallquist, M., Worsnop, D., and Thornton, J. A.: A novel method for online analysis of gas and particle composition: description and evaluation of a Filter Inlet for Gases and AEROsols (FIGAERO), Atmos. Meas. Tech., 7, 983–1001, 10.5194/amt-7-983-2014, 2014.Lopez-Hilfiker, F. D., Mohr, C., D'Ambro, E. L., Lutz, A., Riedel, T. P.,
Gaston, C. J., Iyer, S., Zhang, Z., Gold, A., Surratt, J. D., Lee, B. H.,
Kurten, T., Hu, W. W., Jimenez, J., Hallquist, M., and Thornton, J. A.:
Molecular composition and volatility of organic aerosol in the southeastern
U.S.: implications for IEPOX derived SOA, Environ. Sci. Technol., 50,
2200–2209, 10.1021/acs.est.5b04769, 2016.Louvaris, E. E., Florou, K., Karnezi, E., Papanastasiou, D. K., Gkatzelis,
G. I., and Pandis, S. N.: Volatility of source apportioned wintertime organic
aerosol in the city of Athens, Atmos. Environ., 158, 138–147,
10.1016/j.atmosenv.2017.03.042, 2017.Loza, C. L., Coggon, M. M., Nguyen, T. B., Zuend, A., Flagan, R. C., and
Seinfeld, J. H.: On the mixing and evaporation of secondary organic aerosol
components, Environ. Sci. Technol., 47, 6173–6180, 10.1021/es400979k, 2013.Marshall, F. H., Berkemeier, T., Shiraiwa, M., Nandy, L., Ohm, P. B.,
Dutcher, C. S., and Reid, J. P.: Influence of particle viscosity on mass
transfer and heterogeneous ozonolysis kinetics in aqueous-sucrose-maleic
acid aerosol, Phys. Chem. Chem. Phys., 20, 15560–15573,
10.1039/c8cp01666f, 2018.Mikhailov, E., Vlasenko, S., Martin, S. T., Koop, T., and Pöschl, U.: Amorphous and crystalline aerosol particles interacting with water vapor: conceptual framework and experimental evidence for restructuring, phase transitions and kinetic limitations, Atmos. Chem. Phys., 9, 9491–9522, 10.5194/acp-9-9491-2009, 2009.Nizkorodov, S. A., Laskin, J., and Laskin, A.: Molecular chemistry of organic
aerosols through the application of high resolution mass spectrometry, Phys.
Chem. Chem. Phys., 13, 3612–3629, 10.1039/c0cp02032j, 2011.O'Brien, R. E., Neu, A., Epstein, S. A., MacMillan, A. C., Wang, B., Kelly,
S. T., Nizkorodov, S. A., Laskin, A., Moffet, R. C., and Gilles, M. K.:
Physical properties of ambient and laboratory-generated secondary organic
aerosol, Geophys. Res. Lett., 41, 4347–4353,
10.1002/2014GL060219, 2014.O'Meara, S., Booth, A. M., Barley, M. H., Topping, D., and McFiggans, G.: An
assessment of vapour pressure estimation methods, Phys. Chem. Chem. Phys., 16,
19453–19469, 10.1039/c4cp00857j, 2014.Ortega, A. M., Hayes, P. L., Peng, Z., Palm, B. B., Hu, W., Day, D. A., Li, R., Cubison, M. J., Brune, W. H., Graus, M., Warneke, C., Gilman, J. B., Kuster, W. C., de Gouw, J., Gutiérrez-Montes, C., and Jimenez, J. L.: Real-time measurements of secondary organic aerosol formation and aging from ambient air in an oxidation flow reactor in the Los Angeles area, Atmos. Chem. Phys., 16, 7411–7433, 10.5194/acp-16-7411-2016, 2016.Paciga, A., Karnezi, E., Kostenidou, E., Hildebrandt, L., Psichoudaki, M., Engelhart, G. J., Lee, B.-H., Crippa, M., Prévôt, A. S. H., Baltensperger, U., and Pandis, S. N.: Volatility of organic aerosol and its components in the megacity of Paris, Atmos. Chem. Phys., 16, 2013–2023, 10.5194/acp-16-2013-2016, 2016.Pajunoja, A., Malila, J., Hao, L., Joutsensaari, J., Lehtinen, K. E., and
Virtanen, A.: Estimating the viscosity range of SOA particles based on their
coalescence time, Aerosol Sci. Tech., 48, i–iv,
10.1080/02786826.2013.870325, 2014.Pajunoja, A., Hu, W., Leong, Y. J., Taylor, N. F., Miettinen, P., Palm, B. B., Mikkonen, S., Collins, D. R., Jimenez, J. L., and Virtanen, A.: Phase state of ambient aerosol linked with water uptake and chemical aging in the southeastern US, Atmos. Chem. Phys., 16, 11163–11176, 10.5194/acp-16-11163-2016, 2016.Pankow, J. F.: An absorption model of gas-particle partitioning of
organic-compounds in the atmosphere, Atmos. Environ., 28, 185–188,
10.1016/1352-2310(94)90093-0, 1994.Pankow, J. F. and Asher, W. E.: SIMPOL.1: a simple group contribution method for predicting vapor pressures and enthalpies of vaporization of multifunctional organic compounds, Atmos. Chem. Phys., 8, 2773–2796, 10.5194/acp-8-2773-2008, 2008.Perraud, V., Bruns, E. A., Ezell, M. J., Johnson, S. N., Yu, Y., Alexander,
M. L., Zelenyuk, A., Imre, D., Chang, W. L., Dabdub, D., Pankow, J. F., and
Finlayson-Pitts, B. J.: Nonequilibrium atmospheric secondary organic aerosol
formation and growth, P. Natl. Acad. Sci. USA, 109, 2836–2841,
10.1073/pnas.1119909109, 2012.Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, 10.5194/acp-7-1961-2007, 2007.Petters, S. S., Kreidenweis, S. M., Grieshop, A. P., Ziemann, P. J., and
Petters, M. D.: Temperature- and humidity-dependent phase states of
secondary organic aerosols, Geophys. Res. Lett., 46, 1005–1013,
10.1029/2018GL080563, 2019.Pöschl, U. and Shiraiwa, M.: Multiphase chemistry at the
atmosphere-biosphere interface influencing climate and public health in the
anthropocene, Chem. Rev., 115, 4440–4475, 10.1021/cr500487s,
2015.Price, H. C., Mattsson, J., and Murray, B. J.: Sucrose diffusion in aqueous
solution, Phys. Chem. Chem. Phys., 18, 19207–19216,
10.1039/c6cp03238a, 2016.Pringle, K. J., Tost, H., Pozzer, A., Pöschl, U., and Lelieveld, J.: Global distribution of the effective aerosol hygroscopicity parameter for CCN activation, Atmos. Chem. Phys., 10, 5241–5255, 10.5194/acp-10-5241-2010, 2010.Pye, H. O. T., Murphy, B. N., Xu, L., Ng, N. L., Carlton, A. G., Guo, H., Weber, R., Vasilakos, P., Appel, K. W., Budisulistiorini, S. H., Surratt, J. D., Nenes, A., Hu, W., Jimenez, J. L., Isaacman-VanWertz, G., Misztal, P. K., and Goldstein, A. H.: On the implications of aerosol liquid water and phase separation for organic aerosol mass, Atmos. Chem. Phys., 17, 343–369, 10.5194/acp-17-343-2017, 2017.Reid, J. P., Bertram, A. K., Topping, D. O., Laskin, A., Martin, S. T.,
Petters, M. D., Pope, F. D., and Rovelli, G.: The viscosity of
atmospherically relevant organic particles, Nat. Commun., 9, 956,
10.1038/s41467-018-03027-z, 2018.Renbaum-Wolff, L., Grayson, J. W., Bateman, A. P., Kuwata, M., Sellier, M.,
Murray, B. J., Shilling, J. E., Martin, S. T., and Bertram, A. K.: Viscosity
of α-pinene secondary organic material and implications for particle
growth and reactivity, P. Natl. Acad. Sci. USA, 110, 8014–8019,
10.1073/pnas.1219548110, 2013.Riva, M., Chen, Y., Zhang, Y., Lei, Z., Olson, N. E., Boyer, H. C., Narayan,
S., Yee, L. D., Green, H. S., Cui, T., Zhang, Z., Baumann, K., Fort, M.,
Edgerton, E., Budisulistiorini, S. H., Rose, C. A., Ribeiro, I. O., e
Oliveira, R. L., dos Santos, E. O., Machado, C. M. D., Szopa, S., Zhao, Y.,
Alves, E. G., de Sá, S. S., Hu, W., Knipping, E. M., Shaw, S. L.,
Duvoisin Junior, S., de Souza, R. A. F., Palm, B. B., Jimenez, J.-L.,
Glasius, M., Goldstein, A. H., Pye, H. O. T., Gold, A., Turpin, B. J.,
Vizuete, W., Martin, S. T., Thornton, J. A., Dutcher, C. S., Ault, A. P., and
Surratt, J. D.: Increasing isoprene epoxydiol-to-inorganic sulfate aerosol
ratio results in extensive conversion of inorganic sulfate to organosulfur
forms: implications for aerosol physicochemical properties, Environ. Sci.
Technol., 53, 8682–8694, 10.1021/acs.est.9b01019, 2019.Rothfuss, N. E. and Petters, M. D.: Influence of functional groups on the
viscosity of organic aerosol, Environ. Sci. Technol., 51, 271–279,
10.1021/acs.est.6b04478, 2017.Rovelli, G., Song, Y.-C., Maclean, A. M., Topping, D. O., Bertram, A. K.. and
Reid, J. P.: Comparison of approaches for measuring and predicting the
viscosity of ternary component aerosol particles, Anal. Chem., 91,
5074–5082, 10.1021/acs.analchem.8b05353, 2019.Saha, P. K., Khlystov, A., Yahya, K., Zhang, Y., Xu, L., Ng, N. L., and Grieshop, A. P.: Quantifying the volatility of organic aerosol in the southeastern US, Atmos. Chem. Phys., 17, 501–520, 10.5194/acp-17-501-2017, 2017.Saha, P. K., Khlystov, A., and Grieshop, A. P.: Downwind evolution of the volatility and mixing state of near-road aerosols near a US interstate highway, Atmos. Chem. Phys., 18, 2139–2154, 10.5194/acp-18-2139-2018, 2018.Saukko, E., Lambe, A. T., Massoli, P., Koop, T., Wright, J. P., Croasdale, D. R., Pedernera, D. A., Onasch, T. B., Laaksonen, A., Davidovits, P., Worsnop, D. R., and Virtanen, A.: Humidity-dependent phase state of SOA particles from biogenic and anthropogenic precursors, Atmos. Chem. Phys., 12, 7517–7529, 10.5194/acp-12-7517-2012, 2012.Schmedding, R., Rasool, Q. Z., Zhang, Y., Pye, H. O. T., Zhang, H., Chen, Y., Surratt, J. D., Lee, B. H., Mohr, C., Lopez-Hilfiker, F. D., Thornton, J. A., Goldstein, A. H., and Vizuete, W.: Predicting Secondary Organic Aerosol Phase State and Viscosity and its Effect on Multiphase Chemistry in a Regional Scale Air Quality Model, Atmos. Chem. Phys. Discuss., 10.5194/acp-2019-900, in review, 2019.Schum, S. K., Zhang, B., Džepina, K., Fialho, P., Mazzoleni, C., and Mazzoleni, L. R.: Molecular and physical characteristics of aerosol at a remote free troposphere site: implications for atmospheric aging, Atmos. Chem. Phys., 18, 14017–14036, 10.5194/acp-18-14017-2018, 2018.Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics – From
air pollution to climate change, John Wiley & Sons, Inc., New York, 2006.Shiraiwa, M. and Seinfeld, J. H.: Equilibration timescale of atmospheric
secondary organic aerosol partitioning, Geophys. Res. Lett., 39, L24801,
10.1029/2012GL054008, 2012.Shiraiwa, M., Ammann, M., Koop, T., and Pöschl, U.: Gas uptake and chemical
aging of semisolid organic aerosol particles, P. Natl. Acad. Sci. USA,
108, 11003–11008, 10.1073/pnas.1103045108, 2011.Shiraiwa, M., Zuend, A., Bertram, A. K., and Seinfeld, J. H.: Gas-particle
partitioning of atmospheric aerosols: interplay of physical state, non-ideal
mixing and morphology, Phys. Chem. Chem. Phys., 15, 11441–11453,
10.1039/C3CP51595H, 2013.Shiraiwa, M., Berkemeier, T., Schilling-Fahnestock, K. A., Seinfeld, J. H., and Pöschl, U.: Molecular corridors and kinetic regimes in the multiphase chemical evolution of secondary organic aerosol, Atmos. Chem. Phys., 14, 8323–8341, 10.5194/acp-14-8323-2014, 2014.Shiraiwa, M., Li, Y., Tsimpidi, A. P., Karydis, V. A., Berkemeier, T.,
Pandis, S. N., Lelieveld, J., Koop, T., and Pöschl, U.: Global
distribution of particle phase state in atmospheric secondary organic
aerosols, Nat. Commun., 8, 15002, 10.1038/ncomms15002, 2017.Shrivastava, M., Cappa, C. D., Fan, J., Goldstein, A. H., Guenther, A. B.,
Jimenez, J. L., Kuang, C., Laskin, A., Martin, S. T., Ng, N. L., Petaja, T.,
Pierce, J. R., Rasch, P. J., Roldin, P., Seinfeld, J. H., Shilling, J.,
Smith, J. N., Thornton, J. A., Volkamer, R., Wang, J., Worsnop, D. R.,
Zaveri, R. A., Zelenyuk, A., and Zhang, Q.: Recent advances in understanding
secondary organic aerosol: Implications for global climate forcing, Rev.
Geophys., 55, 509–559, 10.1002/2016RG000540, 2017.Slade, J. H., Ault, A. P., Bui, A. T., Ditto, J. C., Lei, Z., Bondy, A. L.,
Olson, N. E., Cook, R. D., Desrochers, S. J., Harvey, R. M., Erickson, M.
H., Wallace, H. W., Alvarez, S. L., Flynn, J. H., Boor, B. E., Petrucci, G.
A., Gentner, D. R., Griffin, R. J., and Shepson, P. B.: Bouncier particles at
night: biogenic secondary organic aerosol chemistry and sulfate drive diel
variations in the aerosol phase in a mixed forest, Environ. Sci. Technol.,
53, 4977–4987, 10.1021/acs.est.8b07319, 2019.Song, M., Liu, P. F., Hanna, S. J., Li, Y. J., Martin, S. T., and Bertram, A. K.: Relative humidity-dependent viscosities of isoprene-derived secondary organic material and atmospheric implications for isoprene-dominant forests, Atmos. Chem. Phys., 15, 5145–5159, 10.5194/acp-15-5145-2015, 2015.Song, M., Liu, P. F., Hanna, S. J., Zaveri, R. A., Potter, K., You, Y., Martin, S. T., and Bertram, A. K.: Relative humidity-dependent viscosity of secondary organic material from toluene photo-oxidation and possible implications for organic particulate matter over megacities, Atmos. Chem. Phys., 16, 8817–8830, 10.5194/acp-16-8817-2016, 2016.Song, M., Maclean, A. M., Huang, Y., Smith, N. R., Blair, S. L., Laskin, J., Laskin, A., DeRieux, W.-S. W., Li, Y., Shiraiwa, M., Nizkorodov, S. A., and Bertram, A. K.: Liquid–liquid phase separation and viscosity within secondary organic aerosol generated from diesel fuel vapors, Atmos. Chem. Phys., 19, 12515–12529, 10.5194/acp-19-12515-2019, 2019.Song, Y. C., Haddrell, A. E., Bzdek, B. R., Reid, J. P., Bannan, T.,
Topping, D. O., Percival, C., and Cai, C.: Measurements and predictions of
binary component aerosol particle viscosity, J. Phys. Chem. A, 120,
8123–8137, 10.1021/acs.jpca.6b07835, 2016.Stark, H., Yatavelli, R. L. N., Thompson, S. L., Kimmel, J. R., Cubison, M.
J., Chhabra, P. S., Canagaratna, M. R., Jayne, J. T., Worsnop, D. R., and
Jimenez, J. L.: Methods to extract molecular and bulk chemical information
from series of complex mass spectra with limited mass resolution, Int. J.
Mass Spectrom., 389, 26–38, 10.1016/j.ijms.2015.08.011,
2015.Stark, H., Yatavelli, R. L., Thompson, S. L., Kang, H., Krechmer, J. E.,
Kimmel, J. R., Palm, B. B., Hu, W., Hayes, P. L., and Day, D. A.: Impact of
thermal decomposition on thermal desorption instruments: advantage of
thermogram analysis for quantifying volatility distributions of organic
species, Environ. Sci. Technol., 51, 8491–8500,
10.1021/acs.est.7b00160, 2017.Surratt, J. D., Chan, A. W. H., Eddingsaas, N. C., Chan, M., Loza, C. L.,
Kwan, A. J., Hersey, S. P., Flagan, R. C., Wennberg, P. O., and Seinfeld, J.
H.: Reactive intermediates revealed in secondary organic aerosol formation
from isoprene, P. Natl. Acad. Sci. USA, 107, 6640–6645,
10.1073/pnas.0911114107, 2010.Thomas, L. H., Meatyard, R., Smith, H., and Davies, G. H.: Viscosity behavior
of associated liquids at lower temperatures and vapor pressures, J. Chem.
Eng. Data, 24, 161–164, 10.1021/je60082a011, 1979.Thompson, S. L., Yatavelli, R. L. N., Stark, H., Kimmel, J. R., Krechmer, J.
E., Day, D. A., Hu, W., Isaacman-VanWertz, G., Yee, L., Goldstein, A. H.,
Khan, M. A. H., Holzinger, R., Kreisberg, N., Lopez-Hilfiker, F. D., Mohr,
C., Thornton, J. A., Jayne, J. T., Canagaratna, M., Worsnop, D. R., and
Jimenez, J. L.: Field intercomparison of the gas/particle partitioning of
oxygenated organics during the Southern Oxidant and Aerosol Study (SOAS) in
2013, Aerosol Sci. Tech., 51, 30–56,
10.1080/02786826.2016.1254719, 2017.Tsimpidi, A. P., Karydis, V. A., Pozzer, A., Pandis, S. N., and Lelieveld, J.: ORACLE (v1.0): module to simulate the organic aerosol composition and evolution in the atmosphere, Geosci. Model Dev., 7, 3153–3172, 10.5194/gmd-7-3153-2014, 2014.US EPA: Estimation Programs Interface Suite™ for Microsoft Windows
v4.1, United States Environmental Protection Agency, Washington, DC, USA,
2015.Vander Wall, A. C., Lakey, P. S. J., Rossich Molina, E., Perraud, V.,
Wingen, L. M., Xu, J., Soulsby, D., Gerber, R. B., Shiraiwa, M. and
Finlayson-Pitts, B. J.: Understanding interactions of organic nitrates with
the surface and bulk of organic films: implications for particle growth in
the atmosphere, Environ. Sci. Processes Impacts, 20, 1593–1610,
10.1039/C8EM00348C, 2018.Virtanen, A., Joutsensaari, J., Koop, T., Kannosto, J., YliPirilä, P.,
Leskinen, J., Mäkelä, J. M., Holopainen, J. K., Pöschl, U.,
Kulmala, M., Worsnop, D. R., and Laaksonen, A.: An amorphous solid state of
biogenic secondary organic aerosol particles, Nature, 467, 824–827,
10.1038/nature09455, 2010.Xu, L., Suresh, S., Guo, H., Weber, R. J., and Ng, N. L.: Aerosol characterization over the southeastern United States using high-resolution aerosol mass spectrometry: spatial and seasonal variation of aerosol composition and sources with a focus on organic nitrates, Atmos. Chem. Phys., 15, 7307–7336, 10.5194/acp-15-7307-2015, 2015.Xu, W., Xie, C., Karnezi, E., Zhang, Q., Wang, J., Pandis, S. N., Ge, X., Zhang, J., An, J., Wang, Q., Zhao, J., Du, W., Qiu, Y., Zhou, W., He, Y., Li, Y., Li, J., Fu, P., Wang, Z., Worsnop, D. R., and Sun, Y.: Summertime aerosol volatility measurements in Beijing, China, Atmos. Chem. Phys., 19, 10205–10216, 10.5194/acp-19-10205-2019, 2019.Yatavelli, R. L. N., Mohr, C., Stark, H., Day, D. A., Thompson, S. L.,
Lopez-Hilfiker, F. D., Campuzano-Jost, P., Palm, B. B., Vogel, A. L.,
Hoffmann, T., Heikkinen, L., Äijälä, M., Ng, N. L., Kimmel, J.
R., Canagaratna, M. R., Ehn, M., Junninen, H., Cubison, M. J.,
Petäjä, T., Kulmala, M., Jayne, J. T., Worsnop, D. R., and Jimenez,
J. L.: Estimating the contribution of organic acids to northern hemispheric
continental organic aerosol, Geophys. Res. Lett., 42, 6084–6090,
10.1002/2015gl064650, 2015.Ye, Q., Robinson, E. S., Ding, X., Ye, P., Sullivan, R. C., and Donahue, N.
M.: Mixing of secondary organic aerosols versus relative humidity, P.
Natl. Acad. Sci. USA, 113, 12649–12654,
10.1073/pnas.1604536113, 2016.Ye, Q., Upshur, M. A., Robinson, E. S., Geiger, F. M., Sullivan, R. C.,
Thomson, R. J., and Donahue, N. M.: Following particle-particle mixing in
atmospheric secondary organic aerosols by using isotopically labeled
terpenes, Chem, 4, 318–333, 10.1016/j.chempr.2017.12.008,
2018.Yli-Juuti, T., Pajunoja, A., Tikkanen, O.-P., Buchholz, A., Faiola, C.,
Väisänen, O., Hao, L., Kari, E., Peräkylä, O., Garmash, O.,
Shiraiwa, M., Ehn, M., Lehtinen, K., and Virtanen, A.: Factors controlling
the evaporation of secondary organic aerosol from α-pinene
ozonolysis, Geophys. Res. Lett., 44, 2562–2570,
10.1002/2016GL072364, 2017.Zaveri, R. A., Shilling, J. E., Zelenyuk, A., Liu, J., Bell, D. M., D'Ambro,
E. L., Gaston, C. J., Thornton, J. A., Laskin, A., Lin, P., Wilson, J.,
Easter, R. C., Wang, J., Bertram, A. K., Martin, S. T., Seinfeld, J. H. and
Worsnop, D. R.: Growth kinetics and size distribution dynamics of viscous
secondary organic aerosol, Environ. Sci. Technol., 52, 1191–1199,
10.1021/acs.est.7b04623, 2018.Zhang, Y., Sanchez, M. S., Douet, C., Wang, Y., Bateman, A. P., Gong, Z., Kuwata, M., Renbaum-Wolff, L., Sato, B. B., Liu, P. F., Bertram, A. K., Geiger, F. M., and Martin, S. T.: Changing shapes and implied viscosities of suspended submicron particles, Atmos. Chem. Phys., 15, 7819–7829, 10.5194/acp-15-7819-2015, 2015.Zhang, Y., Katira, S., Lee, A., Lambe, A. T., Onasch, T. B., Xu, W., Brooks, W. A., Canagaratna, M. R., Freedman, A., Jayne, J. T., Worsnop, D. R., Davidovits, P., Chandler, D., and Kolb, C. E.: Kinetically controlled glass transition measurement of organic aerosol thin films using broadband dielectric spectroscopy, Atmos. Meas. Tech., 11, 3479–3490, 10.5194/amt-11-3479-2018, 2018.Zhang, Y., Nichman, L., Spencer, P., Jung, J. I., Lee, A., Heffernan, B. K.,
Gold, A., Zhang, Z., Chen, Y., Canagaratna, M. R., Jayne, J. T., Worsnop, D.
R., Onasch, T. B., Surratt, J. D., Chandler, D., Davidovits, P., and Kolb, C.
E.: The cooling rate and volatility dependent glass forming properties of
organic aerosols measured by Broadband Dielectric Spectroscopy, Environ.
Sci. Technol., 53, 12366–12378,
10.1021/acs.est.9b03317, 2019.
Zhou, S., Hwang, B. C. H., Lakey, P. S. J., Zuend, A., Abbatt, J. P. D., and
Shiraiwa, M.: Multiphase reactivity of polycyclic aromatic hydrocarbons is
driven by phase separation and diffusion limitations, P. Natl. Acad. Sci.
USA, 116, 11658–11663, 10.1073/pnas.1902517116, 2019.Zobrist, B., Marcolli, C., Pedernera, D. A., and Koop, T.: Do atmospheric aerosols form glasses?, Atmos. Chem. Phys., 8, 5221–5244, 10.5194/acp-8-5221-2008, 2008.Zuend, A. and Seinfeld, J. H.: Modeling the gas-particle partitioning of secondary organic aerosol: the importance of liquid-liquid phase separation, Atmos. Chem. Phys., 12, 3857–3882, 10.5194/acp-12-3857-2012, 2012.