ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-5799-2018Organic aerosol in the summertime southeastern United States: components and
their link to volatility distribution, oxidation state and hygroscopicityOrganic aerosol in the summertime southeastern USKostenidouEvangeliaKarneziElenihttps://orcid.org/0000-0002-9781-2552Hite Jr.James R.BougiatiotiAikateriniCerullyKateXuLuhttps://orcid.org/0000-0002-0021-9876NgNga L.https://orcid.org/0000-0001-8460-4765NenesAthanasiosathanasios.nenes@gatech.eduhttps://orcid.org/0000-0003-3873-9970PandisSpyros N.spyros@chemeng.upatras.grInstitute of Chemical Engineering Sciences, Foundation for Research and Technology, Hellas, Patras, GreeceDepartment of Chemical Engineering, University of Patras, Patras, GreeceDepartment of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USASchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USASchool of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USAInstitute for Environmental Research and Sustainable Development, National Observatory of
Athens, Palea Penteli, Greecenow at: TSI, Inc., Shoreview, MN, USAnow at: Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USAAthanasios Nenes (athanasios.nenes@gatech.edu) and Spyros N. Pandis (spyros@chemeng.upatras.gr)26April20181885799581928October201723November201726March20183April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/5799/2018/acp-18-5799-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/5799/2018/acp-18-5799-2018.pdf
The volatility distribution of the
organic aerosol (OA) and its sources during the Southern Oxidant and Aerosol
Study (SOAS; Centreville, Alabama) was constrained using measurements from an
Aerodyne high-resolution time-of-flight aerosol mass spectrometer
(HR-ToF-AMS) and a thermodenuder (TD). Positive matrix factorization (PMF)
analysis was applied on both the ambient and thermodenuded high-resolution
mass spectra, leading to four factors: more oxidized oxygenated OA (MO-OOA),
less oxidized oxygenated OA (LO-OOA), an isoprene epoxydiol (IEPOX)-related
factor (isoprene-OA) and biomass burning OA (BBOA). BBOA had the highest mass
fraction remaining (MFR) at 100 ∘C, followed by the isoprene-OA, and
the LO-OOA. Surprisingly the MO-OOA evaporated the most in the TD. The
estimated effective vaporization enthalpies assuming an evaporation
coefficient equal to unity were 58 ± 13 kJ mol-1 for the LO-OOA,
89 ± 10 kJ mol-1 for the MO-OOA, 55 ± 11 kJ mol-1
for the BBOA, and 63 ± 15 kJ mol-1 for the isoprene-OA. The
estimated volatility distribution of all factors covered a wide range
including both semi-volatile and low-volatility components. BBOA had the
lowest average volatility of all factors, even though it had the lowest
O : C ratio among all factors. LO-OOA was the more volatile factor and
its high MFR was due to its low enthalpy of vaporization according to the
model. The isoprene-OA factor had intermediate volatility, quite higher than
suggested by a few other studies. The analysis suggests that deducing the
volatility of a factor only from its MFR could lead to erroneous conclusions.
The oxygen content of the factors can be combined with their estimated
volatility and hygroscopicity to provide a better view of their physical
properties.
Introduction
Population exposure to atmospheric particulate matter (PM) increases
premature mortality from cardiovascular and respiratory diseases (Pope et
al., 2002; IARC, 2016; Cohen et al., 2017). The same particles also modulate
the planetary radiative balance and hydrological cycle (IPCC, 2013; NASEM,
2016; Seinfeld et al., 2016). Organic aerosol (OA) constitutes a significant
part of submicron aerosol mass (Zhang et al., 2007) and it is characterized
by daunting chemical complexity (Kanakidou et al., 2005; Hallquist et al.,
2009). OA is directly emitted from anthropogenic and natural sources, but it
is also produced by condensation of products formed during the oxidation of
gas-phase organic compounds with O3, NO3 and OH radicals (secondary
organic aerosol, SOA; Kanakidou et al., 2005). OA formation can be further
promoted by the interactions of anthropogenic and biogenic compounds; in the
southeastern United States, anthropogenic sulfate enhances OA formation
through rapid reactive uptake of isoprene epoxydiol (IEPOX) to particles and aqueous-phase
reactions (Xu et al., 2015a, 2016a; Budisulistiorini et al., 2017).
Several approaches have been developed to unravel the sources and the degree
of atmospheric processing of aerosol sampled by the AMS. These include custom
principal component analysis (Zhang et al., 2005), multiple component
analysis (Zhang et al., 2007), positive matrix factorization (PMF) (Paatero
and Tapper, 1994; Lanz et al., 2007) and the multilinear engine (ME-2) (Lanz
et al., 2008; Canonaco et al., 2013). Applying the above source apportionment
techniques on AMS mass spectra, information about the aerosol sources and the
degree of the atmospheric processing can be derived. Important primary
components include hydrocarbon-like OA (HOA) (Zhang et al., 2005) and biomass
burning OA (BBOA) (Aiken et al., 2009). The most abundant and ubiquitous OA
component is the oxygenated OA (OOA), which often consists of a more
oxygenated (MO-OOA) and a less oxygenated OA (LO-OOA) factor (Lanz et al.,
2007). In the southeastern (SE) United States, MO-OOA and LO-OOA are dominant
factors, comprising 47–79 % of the total OA (Xu et al., 2015b). Factors
related to biogenic secondary OA have been identified in urban, suburban and
remote areas (Budisulistiorini et al., 2013; Chen et al., 2015; Kostenidou et
al., 2015). In the SE United States, an isoprene-derived OA factor
(isoprene-OA) linked to IEPOX uptake is present during warm periods,
contributing up to 36 % of the total OA in the summertime (Xu et al.,
2015b).
Central to understanding the atmospheric impacts of OA is constraining its
volatility and hygroscopicity (Kanakidou et al., 2005). Volatility
measurements are mostly carried out using heated laminar flow reactors, known
as thermodenuders (TDs) (Burtscher et al., 2001; An et al., 2007) or
isothermal dilution (Grieshop et al., 2009). In these systems, changes in OA
mass concentration are related to the OA evaporation rate and its volatility
can be estimated. The comparison of aerosol evaporation measurements across
studies and conditions with TD or isothermal dilution chambers is not
straightforward. The established proxy for volatility is the mass fraction
remaining (MFR), i.e., the mass of the aerosol remaining after a volatility
measurement (Huffman et al., 2009; Cerully et al., 2015; Xu et al., 2016b).
MFR has often been used as a relative measure of volatility, as it is assumed
that the volatility of particulate matter increases as MFR decreases for
similar particle sizes and TD operation conditions. Although clearly linked
to volatility, the MFR depends on the enthalpy of vaporization (ΔHvap), the aerosol concentration, the heating section residence
time, the particle size distribution, and potential particle-to-gas mass
transfer resistances. All these parameters therefore complicate the linking
of the measured MFR to the volatility. An additional complication is that
organic aerosol mixtures are characterized by a distribution of volatilities.
A number of studies have attempted to estimate this volatility distribution
with appropriate TD models (Cappa and Jimenez, 2010; Lee et al., 2010; Paciga
et al., 2016; Saha and Grieshop, 2016; Louvaris et al., 2017; Saha et al.,
2017).
Three studies have reported volatility distributions of the isoprene (or
IEPOX) SOA and the total OA for the southeastern United States.
Lopez-Hilfiker et al. (2016) suggested that the IEPOX SOA had a very low
saturation concentration with
C∗= 10-4µg m-3, based on the Filter Inlet
for Gases and AEROsols coupled to a Chemical-Ionization Mass
Spectrometer (FIGAERO-CIMS) signals of C5H12O4 and
C5H10O3. They assumed that these signals correspond to
2-methyltetrols and 3-MeTHF-3,4-diols and/or C5 alkene triols, which are
tracers for isoprene SOA. Using the total FIGAERO-CIMS signal
(CxHyOzN0-1) the same authors estimated an extremely low
total OA average volatility of
C∗= 3.7 × 10-7µg m-3 for the OA
with extremely low-volatility organic compounds (ELVOCs) representing
99 % of the total OA. This is the lowest reported volatility for ambient
OA in the literature. Hu et al. (2016) estimated an average volatility of
C∗=5.2× 10-5µg m-3 for the IEPOX SOA.
Their results were based on the MFR of the IEPOX SOA (calculated by PMF)
using ambient and thermodenuded AMS measurements. The volatility distribution
of IEPOX SOA was estimated applying the technique of Faulhaber et al. (2009).
The corresponding total OA volatility distribution covered the range from
C∗=10-9 to 1 µg m-3. Saha et al. (2017) used an
aerosol chemical speciation monitor (ACSM) and a thermodenuder to estimate an
average total OA volatility of C∗= 0.21 µg m-3
and a vaporization enthalpy of 100 kJ mol-1.
The two-dimensional volatility basis set (2D-VBS) framework, describing the
OA concentration as a function of its oxygen content and volatility, is a
promising approach to describe the partitioning and chemical evolution of the
thousands of compounds present in OA (Donahue et al., 2012). If expanded to
include hygroscopicity, the framework can be strengthened considerably.
Several studies have attempted to link hygroscopicity and volatility (Kuwata
et al., 2007; Asa-Awuku et al., 2009; Frosch et al., 2013) or hygroscopicity
and oxidation state (Masoli et al., 2010; Chang et al., 2010; Lathem et al.,
2013; Thalman et al., 2017); however, only a few focus on all the properties
combined (Jimenez et al., 2009; Tritscher et al., 2011; Cerully et al.,
2015). Jimenez et al. (2009) combined data from various studies and suggested
that hygroscopicity and oxidation state increase as volatility decreases. The
generality of this finding has been questioned by subsequent studies (Meyer
et al., 2009; Tritscher et al., 2011; Lathem et al., 2013). Recently,
Nakao (2017) proposed a theoretical framework, in which the hygroscopicity is
explicitly related to oxidation state and volatility. With this approach,
each OA “source” can have a unique set of volatility and hygroscopicity
parameters that evolve with atmospheric oxidative aging along a path that
requires further constraints from chemistry.
Xu et al. (2015a) estimated the contribution of different sources to the
measured OA, while Cerully et al. (2015) quantified the OA hygroscopicity
during the Southern Oxidant and Aerosol
Study (SOAS) field campaign at Centreville, Alabama. In this work we build
upon these studies and attempt to constrain the volatility distributions and
effective vaporization enthalpy of each PMF factor of OA sampled during the
same field campaign. We then proceed to associate the hygroscopicity
parameters estimated by Cerully et al. (2015) with the volatility
distributions and test their consistency with the Nakao (2017) theoretical
framework.
ExperimentalMeasurement site and campaign
The measurements were performed in Centreville, Alabama,
(32∘54′11.81′′ N, 87∘14′59′′ W). The station was
located in an area significantly influenced by biogenic emissions (Liao et
al., 2007; Spracklen et al., 2011). Anthropogenic emissions also affect the
site. The measurements were conducted during the Southern Oxidant and Aerosol
Study, which was part of the Southern Atmosphere Study (SAS;
http://www.eol.ucar.edu/projects/sas) from 1 June to 15 July 2013. A
summary of important findings can be found in Carlton et al. (2018), while
additional results relevant to our study can be found in Xu et al. (2015a),
Cerully et al. (2015), Guo et al. (2015) and Saha et al. (2017).
Instrumentation
The aim of the specific measurements was to characterize both the ambient and
the water-soluble fraction of the non-thermally and thermally denuded
PM1. For the vaporization a thermodenuder (Cerully et al., 2014)
was used. A particle-into-liquid sampler (PILS) (Weber et al., 2001) was used
to collect the water-soluble aerosol components and then the solution was
nebulized. The aerosol passed every 12 or 15 min through four lines: ambient
bypass, ambient TD, PILS bypass and PILS TD. In this work we used the ambient
denuded measurements only. Details about the experimental setup can be found
in Cerully et al. (2015).
The sampling instrumentation included an Aerodyne HR-AMS (DeCarlo et al.,
2006), a scanning mobility particle sizer (SMPS, Classifier model 3080, DMA
model 3081, CPC model 3022A, TSI) and a cloud condensation nuclei counter
(Droplet Measurement Technologies) (Roberts and Nenes, 2005). The TD used in
this campaign has been characterized by Cerully et al. (2014). Briefly, the
TD consisted of a heating and a cooling section. The first part was a
stainless steel tube of 30 in length and 0.68 in inner diameter. The cooling
section was removed during this campaign, as the recondensation of the vapors
is minimal when the ambient mass concentration is low, which was the case for
this campaign (Cappa and Jimenez, 2010; Saleh et al., 2011; Cerully et al.,
2014). The temperature in the TD was 60, 80 and 100 ∘C. The total
flow rate passing though the TD was 1.5 L min-1 and so the average TD
residence time was approximately 7 s.
Data analysisPMF and elemental ratios
PMF (Lanz et al., 2007) was applied to both ambient bypass and TD HR organic
mass spectra according to the procedure of Ulbrich et al. (2009). Details
about the PMF solution are provided in the Supplement (Figs. S1 and S2). The
O : C and H : C elemental ratios were estimated using the approach of
Canagaratna et al. (2015). Xu et al. (2015a) also used the Canagaratna et
al. (2015) O : C approach; however, Cerully et al. (2015) applied the
older algorithm of Aiken et al. (2008). For any comparisons between this work
and previous studies we converted the old O : C to the new O : C
ratios using the corresponding f44 fraction according to the following equation:
O : C = 0.079 + 4.31 f44 (Canagaratna et al., 2015).
Collection efficiency (CE)
Xu et al. (2015a) estimated the AMS CE using the composition-dependent
approach of Middlebrook et al. (2012). The average bypass CE was estimated to
be 0.65 ± 0.12, while the average TD CE was slightly higher at
0.7 ± 0.11. The difference was statistically significant with a p
value less than 0.0001. These estimates can be more uncertain than their
variability suggests, due to their sensitivity to aerosol ammonium and
neutralization. The sensitivity of our results is discussed in Sect. 5.3.
TD losses
The thermodenuded OA was corrected for particle losses due to sedimentation,
diffusion and thermophoresis inside the thermodenuder. More details about the
thermodenuder characterization are provided by Cerully et al. (2014).
Average ambient concentration of each factor and total OA, and the
corresponding fraction of the data above the threshold
(0.2 µg m-3).
Average ambientconcentrationPercent of measurementsFactor(µg m-3)above the thresholdMO-OOA1.9692LO-OOA1.6696Isoprene-OA0.976BBOA0.542Total OA5.0299MFR
For the MFR calculations only data with ambient OA concentration higher than
0.2 µg m-3 were used in order to avoid extreme variations of
the MFR. For such low concentrations the corresponding TD concentrations can
be very low, introducing significant error in the MFR calculation. The
fractions of the data for each factor above the threshold of
0.2 µg m-3 are given in Table 1. For the total OA, MO-OOA and
LO-OOA, this fraction was above 92 % but for the isoprene-OA and BBOA was
lower (76 and 42 % respectively). The four (or five) consecutive
ambient and TD measurements during each hour were averaged. The variability
of the four (or five) averaged values was 4–16 %.
Volatility distribution estimation
The dynamic mass transfer model of Riipinen et al. (2010) was used to
estimate the OA volatility distributions. The model simulates the particle
evaporation inside the thermodenuder solving the corresponding system of
differential equations describing the mass transfer between the particle and
gas phases:
dmpdt=-∑i=1nIi,dCidt=IiNtot,
where mp is the organic particle mass, Ci is the gas-phase
concentration of compound i, Ntot is the total number
concentration of the particles, n is the number of the assumed organic
aerosol components, and Ii is the mass flux of the compound i given by
the Vesala et al. (1997) equation:
Ii=2πdppMiDiβmiRTTDln1-pip1-pi0p,
where dp is the particle diameter, R the molar gas constant,
and Mi and Di the molar mass and the diffusion coefficient of
compound i at temperature TTD. The diffusion coefficient
(Di) depends on the temperature and is calculated according to Chen and
Othmer (1962) and βmi is the correction factor given by
Fuchs and Sutugin (1970). p is the total gas pressure, while pi and
pi0 are the partial vapor pressures of the compound i at the
particle surface and far away from the particle respectively. pi0 is
given by
pi0=xiγipsat,iexp4MiσRTpρdp=xmiCi∗RTTDMiexp4MiσRTpρdp,
where xi is the mole fraction of i, γi the activity
coefficient of i in the particle, psat,i the pure component vapor
pressure of i over a flat surface, Tp the particle temperature
(we assume that Tp=TTD), xmi the mass fraction
of i in the particle, ρ the particle density and σ the
particle surface tension. Ci∗ is the effective saturation
concentration of i at 298 K.
The change in the vapor pressure with temperature is calculated by the
Clausius–Clapeyron equation:
Ci∗(TTD)=Ci∗(298K)expΔHvap,iR1298-1TTD298TTD,
where ΔHvap is the vaporization enthalpy of component i.
The model inputs include the loss-corrected MFR, the thermodenuder
temperature and residence time, the bypass average particle size, the average
ambient OA concentration and the aerosol density (assumed 1.4 g cm-3
for all cases). The output of the model is the OA volatility distribution in
terms of effective saturation concentrations (C∗) at 298 K, in
combination with its effective vaporization enthalpy (ΔHvap) and the mass accommodation (evaporation) coefficient
(am). We fit the measured thermograms using a consecutive three-bin
C∗ distribution, with varying mass fraction in each bin. The bins
corresponded to saturation concentrations of 0.1, 1 and
10 µg m-3 at 298 K. The enthalpy of vaporization (ΔHvap) was also estimated, while the accommodation coefficient was
assumed to be equal to unity. The best (optimum) solutions and the
corresponding uncertainties are calculated using the algorithm of Karnezi et
al. (2014). The Karnezi et al. (2014) approach searches the full parameter
space for solutions that are consistent with the measured thermograms, within
a predetermined error consistent with the experimental uncertainty. The
algorithm usually finds a number of such solutions. It then calculates a
weighted average (the closer a solution is to the data the higher its weight)
and a weighted standard deviation using all these “acceptable” solutions.
In this study for the comparison between volatilities we will also use the
average volatility based on mass-fraction-weighted log10C∗.
Hygroscopicity
Using a CCN counter Cerully et al. (2015) estimated the hygroscopicity
parameter κ of the total and water-soluble ambient and thermodenuded
PM1 OA. The same authors performed linear regression of the ambient
water-soluble κorg with the PMF factors of the ambient
water-soluble OA. During the periods of the water solubility measurements the BBOA
concentration was too low to allow the separation of the factor, so its
hygroscopicity was not determined. The PMF results of the ambient total and
the ambient water-soluble data were practically the same. Additional details
about the hygroscopicity analysis can be found in Cerully et al. (2015).
(a) Loss-corrected MFR of the total OA. The purple circles
correspond to the measurements and the uncertainties to 1 standard
deviation of the mean. It is assumed that MFR = 1 at T= 24 ∘C. The
black line is the model fit estimated using the approach of Karnezi et
al. (2014). (b) The total OA volatility distribution. The uncertainties have
been estimated according to the algorithm of Karnezi et al. (2014). (c) The
predicted volatility distribution after passing through the thermodenuder as
a function of the temperature.
Results and discussionVolatility of organic aerosol
The average OA mass concentration was 5 µg m-3. The
loss-corrected OA MFR is depicted in Fig. 1a. Half of the total OA evaporated
at 100 ∘C (T50=100∘C). The estimated volatility
distribution (Fig. 1b) indicates that 46 % of the organic aerosol was
semi-volatile organic compounds (SVOCs) (compounds with 1 ≤C∗≤ 100 µg m-3) and 54 % was
low-volatility organic compounds (LVOCs) (0.001 ≤C∗≤ 0.1 µg m-3). Part of the material assigned to the
0.1 µg m-3 bin has volatility less than this value. The fact
that there were no measurements above 100 ∘C does not allow us to
constrain further the contributions of the LVOCs and ELVOCs. The number of
bins that can be used in the analysis of thermodenuder data is in general
determined by the ambient OA concentration (the bin range can extend up to an
order of magnitude higher than the measured values), the number of
temperature steps used in the analysis (the number of bins cannot be higher
than the number of data points available for fitting), and the maximum
fraction of the OA evaporated during the analysis. In theory, the
thermodenuder approach can go down to concentrations as low as
10-5µg m-3 or even lower if a high enough temperature
is used. For example, Louvaris et al. (2017) used temperatures up to
400 ∘C. The availability of measurements at 25, 60, 80 and
100 ∘C means a maximum of four bins are possible; however, since the OA
was on the order 5 µg m-3, the thermograms contain little
information on the partitioning of compounds with saturation concentration
exceeding 100 µg m-3. These two constraints together resulted
in the choice of three volatility bins: 0.1, 1 and 10 µg m-3.
The average volatility based on mass-fraction-weighted log10C∗
values was C∗= 0.55 ± 0.29 µg m-3. Please
note that this value is useful only for comparisons of volatility
distributions in the same VBS volatility range. The mass fraction of each
volatility bin is provided in Table S1 in the Supplement. The effective
vaporization enthalpy of the total OA was 86 ± 9 kJ mol-1.
MFRs of the loss-corrected (a) MO-OOA, (b) LO-OOA,
(c) isoprene-OA and (d) BBOA. The circles represent the
measurements with the 1 standard deviation of the mean. The black line
corresponds to the best-predicted MFR using the algorithm of Karnezi et
al. (2014).
OA mass fractions of the ambient and ambient and TD PMF factors.
MO-LO-Isoprene-DataOOAOOAOABBOAused(%)(%)(%)(%)Ambient only39321810Ambient and TD4329199
(a–d) Predicted volatility distributions of the OA PMF factors.
The error bars correspond to the uncertainties derived using the approach of
Karnezi et al. (2014), (e) vaporization enthalpies comparison between the
four OA factors and (f) volatility compositions comparison between the four
OA factors.
The predicted composition in terms of C∗ for
(a) MO-OOA, (b) LO-OOA, (c) isoprene-OA and
(d) BBOA after passing through the thermodenuder as a function of
the temperature. The model predicts, as expected, that the less volatile
material with C∗= 0.1 µg m-3 dominates the
composition of the remaining aerosol after the TD as the temperature
increases for all factors. However, there are significant differences in the
evolution of the composition of the various factors.
Volatility of OA components
The PMF analysis using both the ambient and TD measurements suggested four
factors. The OA consisted of 43 % more oxidized OOA (MO-OOA), 29 %
less oxidized OOA (LO-OOA), 19 % isoprene-OA and 9 % biomass burning
OA (BBOA). The same four factors and OA composition were obtained by Xu et
al. (2015a) using only the ambient AMS HR mass spectra (Table 2). Details
about their characteristics, correlation with external tracers and
justification of their names are provided by Xu et al. (2015a). The ambient
OA factor time series were practically the same in the two analyses with
R2 > 0.93; the mass spectra were also similar with angle
θ equal to 3–4∘ for LO-OOA, MO-OOA and isoprene-OA and
12∘ for the BBOA factor (Fig. S3 in the Supplement). Thus, our PMF
results are robust and quite consistent with the previous analysis.
The loss-corrected MFRs of the four factors are depicted in Fig. 2. BBOA
evaporated less, as its MFR was close to unity at all temperatures. The BBOA
factor was quite oxygenated with an O : C of 0.58 compared to previous
studies (e.g., Crippa et al., 2013; Florou et al., 2017). The corresponding
BBOA could be chemically aged or PMF may be mixing the BBOA with aged
background OA. Even though BBOA and isoprene-OA had similar O : C ratios
(0.58 and 0.59 correspondingly), the isoprene-OA MFR was lower. Surprisingly
the MFR of MO-OOA was lower than that of LO-OOA, even though MO-OOA had a
higher a O : C ratio (0.99) than LO-OOA (0.63). Relying only on MFR one
would reach the conclusion that MO-OOA was more volatile that LO-OOA.
The predicted thermograms for each factor are also depicted in Fig. 2 and the
resulting volatility distributions are shown in Fig. 3a–d. Figure 3e and
f show the comparison of the volatility compositions and the vaporization
enthalpies between the four OA factors. The mass fractions of each volatility
bin (in the aerosol phase), average volatility (C∗) and the
vaporization enthalpy of each factor are given in Table S1 in the Supplement.
The average LO-OOA mass concentration was 1.66 µg m-3 and
this factor based on the model was composed of 73 % SVOCs and 27 %
LVOCs. Its average volatility was
C∗= 1.88 ± 0.32 µg m-3 and its effective
vaporization enthalpy 58 ± 13 kJ mol-1. The average MO-OOA mass
concentration was 1.96 µg m-3. According to its volatility
distribution 56 % of the MO-OOA was SVOCs and 44 % was LVOCs. Its
effective vaporization enthalpy was 89 ± 10 kJ mol-1 and its
average volatility 0.95 ± 0.31 µg m-3. According at
least to the model the MO-OOA was less volatile on average than the LO-OOA
even if it evaporated more in the TD. This counterintuitive behavior is
explained by the TD model by the higher effective vaporization enthalpy of
the MO-OOA, probably due to the contribution of dicarboxylic and
tricarboxylic acids which have vaporization enthalpies higher than
100 kJ mol-1 (e.g., Saleh et al., 2008, 2010; Kostenidou et al.,
2018). In addition, the C∗ distributions as a function of the mass
fraction and the temperature indicate that, as the temperature increases,
MO-OOA is composed of a higher fraction of less volatile species
(C∗= 0.1 µg m-3) compared to LO-OOA (Fig. 4a
and b). This supports our finding that the MO-OOA factor contains less
volatile species than LO-OOA.
Our results suggest that deducing the volatility of a component using only
its MFR or its O : C ratio may lead to incorrect conclusions. It has
often been assumed that a lower MFR means more volatile OA and vice versa.
However, this applies to the temperature of the measurement. The volatility
of an OA component at a given temperature in the TD depends not only on its
volatility at ambient conditions, but also on its enthalpy of vaporization. A
high enthalpy of vaporization leads to drastic increases in the volatility as
the temperature increases and substantially affects the slope of the
thermogram over the full temperature range. The Karnezi et al. (2014)
algorithm looks at all potential explanations for the observed behavior and
it reports them. These results are shown in Fig. 3. The model finds that the
observed behavior of the thermograms is probably related to differences in
the effective enthalpy of vaporization (higher value for the MO-OOA than for
the LO-OOA). This difference appears to be robust, considering the estimated
uncertainties (Fig. 3e). In addition, Xu et al. (2016b) observed
contradictions between the O : C ratio and MFRs and they suggested that
different O : C distributions could result in the same bulk O : C but
different volatility distributions, which may lead to particles with the same
O : C but different MFR.
BBOA was the less abundant factor with average mass concentration equal to
0.5 µg m-3. According to the TD model, 53 % of the BBOA
consisted of SVOCs and the other 47 % was LVOCs. Its average volatility
was C∗= 0.59 ± 0.22 µg m-3 and its
effective vaporization enthalpy was 55 ± 11 kJ mol-1. The BBOA
volatility distribution did not change significantly with temperature
(Fig. 4d). Finally, the average isoprene-OA mass concentration was
0.9 ± 0.5 µg m-3 and contained of 59 % SVOCs and
41 % LVOCs. Its estimated average volatility was
C∗= 1.05 ± 0.30 µg m-3 and its
vaporization enthalpy was 63 ± 15 kJ mol-1. Even though
isoprene-OA had a very distinct thermogram compared to that of MO-OOA, their
estimated volatility distribution at 25 ∘C was similar. However, at
higher temperatures (e.g., at 100 ∘C), the remaining MO-OOA after
the TD was composed almost entirely of
C∗= 0.1 µg m-3, while the remaining isoprene-OA
included material of higher volatility.
These results suggest that all factors contained components with a wide range
of volatilities and vaporization enthalpy. Based on their average volatility,
BBOA was the least volatile, followed by MO-OOA, isoprene-OA and finally
LO-OOA. The availability of measurements at only three temperatures above
ambient, however, introduces uncertainty in the above results. A detailed
sensitivity analysis is presented in Sect. 5.
The correlation between the MFR of each factor at each temperature with the
RH, temperature, O3, NO, NO2, acidity and OA loading was also
investigated. There was a tendency of the MFR of all factors at higher
temperatures to increase as the ozone concentration increased. For example,
the R2 between O3 and the MFR of MO-OOA at 80 ∘C was 0.25,
R2=0.36 for the MFR of LO-OOA at 100 ∘C, R2=0.26 for the
MFR of isoprene-OA at 100 ∘C and R2=0.22 for the MFR of BBOA at
100 ∘C. This suggests that when the photochemistry is more intense
the OA evaporates less in the TD. The R2 between acidity and the MFR of
LO-OOA at 100 ∘C was 0.26, suggesting that acidity may be also
affecting the MFR. The MFR of BBOA at 100 ∘C on the other hand was
anti-correlated with the NO and NO2 concentrations (R2 of 0.23 and
0.37 correspondingly). This indicates that at lower NOx levels (away
from the source) BBOA evaporated less, suggesting that this factor may
contain both fresh and aged BBOA or fresh BBOA aerosols mixed with aged
background. This is also supported by the relatively high O : C ratio of
this factor (0.58). All the other R2 values examined were lower than
0.2. There was no distinct diurnal profile for the MO-OOA, BBOA and
isoprene-OA MFR. For LO-OOA MFR at 80 and 100 ∘C there was a slight
increase (with considerable noise though) between 11:00 and 16:00 LT. As a result,
a significant diurnal variation of the MFR of the various factors was not
observed.
Sensitivity analysisEffective enthalpy of vaporization (ΔHvap)
We estimated the volatility distributions for three fixed vaporization
enthalpies – 50, 80 and 100 kJ mol-1 – for all factors (Table S2 in the
Supplement). While the corresponding thermograms do not reproduce as well the
corresponding measurements, it is instructive to examine the corresponding
volatility distributions taking into account this time the measurement
uncertainties.
The 80 and 100 kJ mol-1 values lead to thermograms for MO-OOA
consistent with the measurements given the uncertainty of the latter
(Fig. A1, Appendix). The resulting MO-OOA volatility distributions (Fig. A2,
Appendix) are within the uncertainty range of the distributions shown in
Fig. 3. The LVOC content of the factor varies from 35 to 60 % as the
ΔHvap varies from 80 to 100 kJ mol-1. The optimum
(base case) solution suggested a 44 % LVOC content.
The situation is a little more complex for LO-OOA due to the higher
variability of the corresponding MFR measurements. All three ΔHvap values lead to solutions that are consistent with the
observations within experimental uncertainty. This results in a wide range of
volatility distributions with the LVOC content varying from 25 to
90 % (Fig. A2). The best (base case) solution suggested 27 % LVOCs,
so the sensitivity analysis suggests that the LO-OOA may have been
significantly less volatile.
Only the 50 and 80 kJ mol-1 values lead to acceptable thermograms for
the isoprene-OA (Fig. A1). The LVOCs are predicted to contribute to the
factor from 35 to 75 % (Fig. A2) as the assumed ΔHvap
varies from 50 to 80 kJ mol-1. The optimum (base case) solution
corresponded to 41 % LVOCs.
Finally, for the BBOA as the ΔHvap varies from 50 to
80 kJ mol-1 (the 100 kJ mol-1 value does not lead to acceptable
solutions) the LVOC content increases from 65 to 87 % (Fig. A2), values
that are higher than the estimated 47 % LVOCs in the optimum (base case)
solution.
Accommodation coefficient
It has been assumed in the analysis so far that there were no resistances to
the evaporation of the OA in the TD and that the accommodation coefficient,
am, was equal to one. We performed two sensitivity tests using
accommodation coefficients of 1 and 2 orders of magnitude lower (0.1,
0.01). The volatility distributions, the average volatility C∗ and
the vaporization enthalpy of each factor are given in Table S1 in the Supplement. The
corresponding MFRs are illustrated in Fig. A3 and the volatility
distributions in Fig. A4.
A value of am equal to 0.01 is inconsistent with the measured
thermograms of MO-OOA, isoprene-OA and total OA (Fig. A3). For LO-OOA and
BBOA the predicted thermograms are within the experimental error of the
measured values and the resulting volatility distributions are quite close to
those of the base case. For example, for LO-OOA the LVOC content is 40 %
(Fig. A4) compared to 27 % in the optimum solution. This rather
surprising insensitivity of the volatility distribution is due to the fact
that the model balances the effects of the lower am by increasing
the predicted ΔHvap. In the case of the LO-OOA the
estimated enthalpy of vaporization increases to 121 kJ mol-1.
The intermediate value of am=0.1 leads to predicted MFR values
within the experimental error for LO-OOA, isoprene-OA and BBOA, but not for
MO-OOA or total OA (Fig. A3). For the acceptable cases the average volatility
of the OA components decreases by a factor of 2–3 and the effective ΔHvap increases by 30–40 kJ mol-1. The LVOC content of
LO-OOA increases from 27 to 52 %, while the increase in the isoprene-OA
and BBOA LVOCs is small (from 41 to 47 % and from 60 to 64 %)
respectively (Fig. A4). For the MO-OOA and the total OA only the
am=1 simulations provided results consistent with the
observations.
The above analysis suggests that the estimated volatility distributions have
a surprisingly low sensitivity to the assumed accommodation (evaporation)
coefficient, but the ΔHvap is quite sensitive to this
value. This result is quite different from other studies (e.g., Lee et al.,
2010; Cappa and Jimenez, 2010; Riipinen et al., 2010) and is due to the
limited temperature range of the measurements in the present work.
TD collection efficiency
In this case we repeated the calculations assuming a lower AMS CE for the
aerosol that passed through the TD. Assuming a 10 % lower CE in the TD,
the volatility distribution of MO-OOA and isoprene-OA changed by less than
10 % (Table S1 in the Supplement). However, the volatility distribution
of LO-OOA and BBOA shifted towards lower values with the average volatility
decreasing by around a factor of 2. The reasons for this behavior could be
the high LO-OOA MFR uncertainty and the low mass concentration of the BBOA.
The corresponding thermograms and volatility distribution are shown in
Figs. S4 and S5 in the Supplement.
Comparisons with other studies
MO-OOA and LO-OOA: the volatility distributions of the MO-OOA
and LO-OOA were similar to those of the aged aerosol in Finokalia (FAME-08)
(Lee et al., 2010) in which the SVOCs accounted for 60 % and LVOCs for
40 % of the OA using an am=0.05 and ΔHvap=80 kJ mol-1 (Fig. S6 in the Supplement). The SOAS LO-OOA appears to
be a little more volatile than the summertime SV-OOA in Paris (Paciga et al.,
2016) and Mexico City (Cappa et al., 2010), while the MO-OOA is a lot more
volatile than the low-volatility oxygenated OA (LV-OOA) in these locations. These summertime OOA components
in SOAS were more volatile compared to the wintertime OOA in Paris and Athens
(Louvaris et al., 2017), which had a lower SVOC content (45 % for Paris
and 31 % in Athens).
BBOA: Figure S6b in the Supplement illustrates the volatility comparisons between the
BBOA factor and the BBOA factors from Mexico City, Paris (winter) and Athens
(winter). The estimated SVOC content of all four BBOA factors was
surprisingly similar around 50 % with the Mexico City BBOA having the
higher fraction (70 %). The differences in LVOCs and ELVOCs are at least
partially due to the temperature ranges used in the corresponding
measurements. The corresponding O : C ratios of the factors were quite
different: 0.58 for SOAS, 0.4 for Mexico City, 0.29 for Paris, and 0.23 for
Athens (all estimated using the Canagaratha et al., 2015, approach). Part of
the reason of the discrepancy may be hidden in the least volatile components
of BBOA that were not examined in the present study.
Isoprene-OA: Lopez-Hilfiker et al. (2016) suggested that the IEPOX
SOA had much lower saturation concentration,
C∗= 10-4µg m-3, compared to the volatility
of the isoprene-OA estimated here. However, Lopez-Hilfiker et al. (2016)
results are strictly for the IEPOX SOA which is a subset of the isoprene-OA
investigated here. So, a quantitative comparison of the corresponding
volatilities is not possible. Also, the analysis of Lopez-Hilfiker et
al. (2016) does not account for the effect of the vaporization enthalpy.
There is also a potentially important experimental difference in this case,
as in our work the OA just evaporates in the TD, while the Lopez-Hilfiker et
al. (2016) experimental approach involves collection of the OA on a filter
and then heating and desorption. As a consistency test, we used the
volatility distribution of Lopez-Hilfiker et al. (2016) as input to the code
of Riipinen et al. (2010) varying the enthalpy of vaporization. The best
result was obtained for an abnormally high value of ΔHvap= 208 kJ mol-1 and even then the model
underestimates the observed evaporation of isoprene-OA (Fig. S7 in the
Supplement). Using more reasonable values of ΔHvap for such
compounds the discrepancies between our measurements and the predictions are
even larger, suggesting that the Lopez-Hilfiker et al. (2016) volatility
estimates are not consistent with our results and appear not to represent the
full volatility range of isoprene-OA.
A similar discrepancy exists with the low estimated volatility for the IEPOX
SOA by Hu et al. (2016) which is even lower than that of Lopez-Hilfiker et
al. (2016) (Fig. S6c in the Supplement). Even though Hu et al. (2016) used
the same AMS–thermodenuder technique, their approach for the measurement
interpretation was very different. Hu et al. (2016) used the empirical method
of Faulhaber et al. (2009) and not an aerosol dynamics model for the
estimation of the volatility distributions from their MFR measurements. Their
method was based on a relationship between TD temperature and organic species
saturation concentration at 298 K (C∗) that has been obtained using
five compounds (acids) with known saturation concentration. This approach is
applicable to organic compounds with similar properties (e.g., enthalpy of
vaporization) to the five known compounds, but it may encounter significant
difficulties for OA that are quite different from the model compounds. A
related weakness of that approach is that it does not account for the
enthalpy of vaporization as the model used in this work does.
These discrepancies clearly show that there is need for additional
investigation of the volatility of the various components of the isoprene
SOA in the atmosphere.
Total OA: Fig. S6d in the Supplement compares the total OA
volatility estimated in this study to those of Lopez-Hilfiker et al. (2016),
Hu et al. (2016) and Saha et al. (2017) for the same location (Centreville)
and period. To facilitate the comparison, given that different temperature
ranges were used in the above studies, the
C∗= 0.1 µg m-3 bin is used to represent
compounds of even lower volatility than this value. Our results are quite
consistent with those of Saha et al. (2017), especially considering the
differences in both the TD design and modeling of the results. Saha et
al. (2017) obtained the total OA thermogram using a thermodenuder system and
then estimated the corresponding volatility distribution using an aerosol
dynamics model and the volatility basis set (Donahue et al., 2006; Lee et
al., 2011; Saha et al., 2015; Saha and Grieshop, 2016). Their experimental
and data analysis approach is a lot closer to ours compared to Hu et
al. (2016) and Lopez-Hilfiker et al. (2016) and their results for the total
OA are quite consistent with ours. Their model takes into account the
vaporization enthalpy as well and this is probably the key difference among
the various approaches.
Average carbon oxidation state OSC (left y axis) and O : C
ratio (right axis) versus the saturation concentration in terms of
log10C∗. The horizontal bars are the volatility distributions
of the SOAS PMF factors: MO-OOA (green), LO-OOA (blue), isoprene-OA (yellow)
and BBOA (red). The darker the color of the horizontal bars the higher the
mass fractional contribution for the corresponding C∗ bin. The
diamonds represent the average log10C∗ value for a given PMF
factor. The green, light blue and pink dashed areas are the locations of the
LV-OOA, SV-OOA and BBOA PMF factors as proposed by Donahue et al. (2012).
Link to the 2D-VBS framework
Figure 5 shows the location of our factors in the 2D-VBS framework of Donahue
et al. (2012). The PMF source locations in the 2D-VBS were estimated using
the elemental ratios derived by the method of Aiken et al. (2008) for
consistency with the original figure. The O : C of the MO-OOA, LO-OOA,
isoprene-OA and BBOA factors was 0.8, 0.46, 0.44 and 0.46 correspondingly.
The MO-OOA factor is in the proposed LV-OOA area but it includes an SVOC
component that does not exist in the original 2D-VBS. The LO-OOA factor is
quite consistent with the proposed SV-OOA area. The isoprene-OA is also
located in the SV-OOA area based on our results. Finally, the BBOA factor has
the expected volatility range, but is in the upper border of the 2D-VBS BBOA
area due to its high oxidation state observed during SOAS.
Linking the hygroscopicity of OA components to their O : C ratio and
volatility
Cerrully et al. (2015) estimated the hygroscopicity κ parameter for
each factor for the SOAS campaign for supersaturation s= 0.4 %
using PMF analysis on the PILS aerosol. The resulting values were
κMO-OOA= 0.16 ± 0.02,
κLO-OOA= 0.08 ± 0.02 and
κisoprene-OA= 0.20 ± 0.02. During the periods of
the PILS measurements the BBOA contribution was very low and PMF could not
resolve this factor. The isoprene-OA factor had a higher κ than
MO-OOA, but its O : C ratio was lower (0.62) than MO-OOA (1.02). This
contradicts Jimenez et al. (2009), who proposed that the hygroscopicity
increases linearly as the O : C ratio increases and the recent study of
Thalman et al. (2017), which suggested that for OOA factors the relationship
between the hygroscopicity and the O : C is linear. A possible
explanation for this contradiction could be that the O : C–hygroscopicity
relationship may not be monotonic, but there may be systems for which the
relationship may be highly nonlinear. For example, Cain and Pandis (2017)
showed that the hygroscopicity could exhibit a maximum at intermediate
volatilities.
A recent study by Nakao (2017) proposed a theoretical description for the
linkage between the O : C ratio, volatility and hygroscopicity. Figure S8
in the Supplement illustrates the experimental saturation concentrations and
κ parameters for known compounds found in the literature (Table S3 and
S4 in the Supplement) together with the Nakao (2017) estimations. The
isolines in this figure represent the intrinsic κ which corresponds to
the upper limit of κ assuming that the organic species are entirely
soluble. The location of the selected known compounds was generally in
agreement with the suggested by Nakao (2017) intrinsic κ isolines for
κ higher than 0.1. For κ lower than 0.1 the experimental values
were lower than the theoretical κ. This discrepancy could be due to
the fact that the compounds in the area with κ above 0.1 are more
water soluble than those in the area with κ below 0.1. For example,
the solubility of malonic acid is 1161 g L-1 (Saxena and Hildemann,
1996), while the water solubility of suberic acid is 2.46 g L-1
(Bretti et al., 2006).
O : C ratios versus the average volatility as
log10C∗. The black isolines correspond to the theoretically
intrinsic κ suggested by Nakao (2017). The triangles denote the SOAS
PMF factors. The hygroscopicity of the SOAS PMF factors has been transformed
into the intrinsic κ, using the water solubility results of Xu et
al. (2017). The open cyan triangle corresponds to the isoprene-OA with a
hypothetical O : C = 0.9.
Xu et al. (2017) calculated the water solubility of the MO-OOA, LO-OOA and
isoprene-OA in Centreville during the SOAS campaign and found it to be 100, 47 and
83 % correspondingly. Thus, the intrinsic κ of MO-OOA, LO-OOA and
isoprene-OA is correspondingly 0.16 ± 0.02, 0.17 ± 0.04 and
0.24 ± 0.03. Figure 6 shows the intrinsic κ values of our
factors in the 2D-VBS and the Nakao (2017) frameworks. The MO-OOA and LO-OOA
values are close to the Nakao (2017) proposed intrinsic κ isolines.
However, the isoprene-OA experimental intrinsic κ (0.24) is higher
than the theoretical (0.13). One reason for this disagreement could be the
O : C estimate by the AMS. Canagaratna et al. (2015) measured the
O : C ratio of a racemic mixture of δ-isoprene epoxydiols
(C5H10O3) and found it to be around 0.4, which is 1.5 times lower
than the theoretical (0.6). If the isoprene-OA factor behaves similarly to
the racemic mixture, its O : C may in fact be as high as 0.9,
corresponding to a higher theoretical (Nakao, 2017) intrinsic
κ= 0.19, which is closer to the experimental value (0.24).
Although our results cannot be fully explained by the theoretical framework
of Nakao (2017), they denote that the relationship between the hygroscopicity,
volatility and O : C ratio is rather complicated. The model of
Nakao (2017) is based on numerous assumptions that may not always be valid
and which could introduce errors in the κ isolines estimation.
Recently, Rastak et al. (2017) concluded that the hygroscopicity should be
described using more than a single parameter. In addition, Cain and
Pandis (2017) suggested that the hygroscopicity could exhibit a maximum at
intermediate volatilities.
Conclusions
The volatility distribution of the OA factors found during the SOAS campaign
was estimated using measurements by a thermodenuder coupled with a HR-AMS.
Using both the ambient and the thermodenuder data, the same four sources were
identified compared to the ambient-only PMF analysis. The four sources were
attributed to MO-OOA, LO-OOA, isoprene-OA and BBOA. The contribution, the
times series and the mass spectra of each factor were similar to the case of
the ambient-only PMF. Using the MFRs and the thermodenuder model of Riipinen
et al. (2010), the volatility distribution and the vaporization enthalpy of
each factor was estimated assuming an accommodation coefficient of unity.
MO-OOA was significantly more oxygenated than LO-OOA, but, in contrast with
previous studies, its MFR was much lower. According to the model, the MO-OOA
was less volatile than the LO-OOA and the implausible behavior of the
measured MFR was due to their different effective enthalpies of evaporation:
89 ± 10 kJ mol-1 for the MO-OOA and
58 ± 13 kJ mol-1 for the LO-OOA. Isoprene-OA had a similar
volatility distribution with MO-OOA, but its vaporization enthalpy was lower
at 63 ± 15 kJ mol-1. BBOA had the lowest O : C ratio but it
was the least volatile OA component with a vaporization enthalpy of
55 ± 11 kJ mol-1. All factors included components with a wide
range of volatilities, both semi-volatile and low volatility. The use of a
relatively modest highest temperature (100 ∘C) did not allow the
characterization of the least volatile components of the various factors. The
above results suggest that variations in the enthalpy of vaporization can
introduce significant variability in the links between the measured MFR and
the estimated volatility. We strongly recommend the use of higher
temperatures in additional steps in future studies.
The contradicting result of the higher MFR of the MO-OOA compared to that of
LO-OOA denotes that depending on the study the behavior of the OOA factors
can be quite variable. It shows that OOA factors are composed of organic
compounds with a wide range of volatility distributions, which may overlap a
lot with each other. One possible reason could be the existence of small
highly oxygenated molecules. However, the high-resolution time-of-flight
aerosol mass spectrometer (HR-ToF-AMS) cannot provide detailed
information about the identity of the compounds in each volatility bin and
so the use of other chemical analysis techniques is required. The direct
comparison of the MFR of OOA factors from different or even from the same
study is risky since MFR depends on the TD operation and characteristics,
the aerosol size distribution, the volatility, etc. The effective enthalpy
of vaporization is a parameter that has to be taken under consideration when
we estimate volatility distributions. It may explain why the relationship
between MO-OOA and LO-OOA MFR and volatility is complex and the apparent
similarity between the MO-OOA and isoprene-OA volatility distributions.
However, in the second case the uncertainties of the isoprene-OA volatility
distribution for all bins were significant. There are solutions for which
the MO-OOA is a lot less volatile than the isoprene-OA. So the measurements
in this case are not sufficient to compare the volatilities of the two
factors.
The counterintuitive findings of Cerully et al. (2015), that isoprene-OA was
more hygroscopic than MO-OOA even though it had a lower O : C ratio but
similar volatility distribution, are close but not fully explained by the
framework proposed by Nakao (2017). The proposed relationship of Jimenez et
al. (2009) may not apply to all environments and especially when multiple
aerosol sources and types are present. This suggests that the relationship
between the hygroscopicity and the volatility may also be nonlinear. Future
studies are necessary for a comprehensive understanding of the relationship
between the hygroscopicity, volatility and O : C ratio.
The data from this work are available upon request from
Spyros Pandis (spyros@chemeng.upatras.gr).
MFRs of the loss-corrected PMF OA factors and total OA for fixed
values of the vaporization enthalpy. The circles denote the measurements with
the 1 standard deviation of the mean, the dash lines correspond to the base
case, the grey lines represent the case of a constant ΔHvap
of 50 kJ mol-1, the magenta lines stand for the case of a constant
ΔHvap of 80 kJ mol-1 and the pink lines correspond
to the case of a constant ΔHvap of 100 kJ mol-1.
Predicted volatility distributions of the OA PMF factors and total
OA for fixed vaporization enthalpy. The error bars are estimated using the
approach of Karnezi et al. (2014). The grey bars represent the results of a
constant ΔHvap of 50 kJ mol-1, the magenta bars
correspond to the solution of a constant ΔHvap of
80 kJ mol-1 and the pink bars are the results for the case of a
constant ΔHvap of 100 kJ mol-1. The green, blue,
orange, red and purple bars stand for the base case solutions of MO-OOA,
LO-OOA, isoprene-OA, BBOA and total OA.
MFRs of the loss-corrected PMF OA factors and total OA. The circles
denote the measurements with the 1 standard deviation of the mean, the
green lines represent the best-predicted MFR for am=1 (base
case), the cyan lines correspond to the best-predicted MFR for
am=0.1 and the pink lines stand for the predicted MFR for
am=0.01.
Predicted volatility distributions of the OA PMF factors and total
OA. The error bars are estimated using the approach of Karnezi et al. (2014).
The green bars represent the results for am=1 (base case), the
cyan bars correspond to the solution for am=0.1 and the
pink bars are the results for am=0.01.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-5799-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the National Oceanic and Atmospheric Administration
CPO Award NA10OAR4310102 and the US Environmental Protection Agency
(EPA-STAR) through grants RD-835410 and RD-835405. This research was also
supported by the European Research Council Project PyroTRACH (Pyrogenic
TRansformations Affecting Climate and Health) grant agreement 726165.
Athanasios Nenes, Lu Xu, and Nga L. Ng acknowledge support from an NSF grant
(1242258). Lu Xu and Nga L. Ng acknowledge support from EPA STAR grant
RD-83540301. The authors acknowledge the Atmospheric Research and Analysis
Institute (ARA) for providing meteorological and gas-phase species data. The
contents of this publication are solely the responsibility of the authors and
do not necessarily represent the official views of the US EPA. Further, the
US EPA does not endorse the purchase of any commercial products or services
mentioned in the publication. Edited by: Manabu
Shiraiwa Reviewed by: two anonymous referees
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