Introduction
Atmospheric aerosols influence the Earth's climate both directly and
indirectly through affecting the radiation balance, and altering the albedo,
lifetime and precipitation patterns of clouds (IPCC, 2013). However,
uncertainty in the spatial and temporal variability in the aerosol size
distribution, chemical composition and physicochemical properties make it
difficult to quantify the aerosol climate effects. The physicochemical
properties of atmospheric aerosol populations vary (e.g., Jimenez et al.,
2009). In terms of aerosol chemical composition measurements, one of the
greatest challenges is the presence of a vast number of different organic
components in the particles (Kanakidou et al., 2005; Goldstein and Galbally, 2007);
Kroll et al., 2011; Donahue et al., 2013). Understanding of the chemical and
physical properties of these organic compounds remains incomplete (Hallquist
et al., 2009).
One of the key physicochemical properties of atmospheric organic compounds
is their volatility, which determines their partitioning between the gas and
particle phase (Pankow, 1994; Bilde et al., 2015). Atmospheric
aerosol particles are mixtures of organic and inorganic compounds with
different volatilities. Volatilities of the common inorganic species are
relatively well known, while information on the volatility of organic
species, especially on extremely low volatility organics (Ehn et al., 2014;
Bilde et al., 2015), is still incomplete.
Different compounds evaporate differently at different temperatures
depending on their volatilities, described with saturation vapor
concentrations and enthalpies of vaporization (Kreidenweis et al., 1998).
Therefore, measuring the evaporation of particles at different temperatures
provides indirect information on the volatility of particles. Thermodenuders
(TD) where particle populations are heated, often coupled with a tandem
differential mobility analyzer (TDMA), are often used to obtain volatility
information on particles. More quantitative information on the volatility
distribution can be further obtained by coupling the measurement data with a
kinetic evaporation model (e.g., Riipinen et al., 2010; Cappa and Jimenez, 2010)
that describes the evaporation rate of aerosols inside the TD. While the
combination of different TD setups has been applied to quantify the
volatility of laboratory-generated aerosol particles (e.g., Häkkinen et
al., 2014) as well as field observations (e.g., Lee et al., 2010; Cappa and Jimenez, 2010; Häkkinen et al., 2012), it has not been utilized to determine
the volatility distribution of ambient organic aerosol in a boreal
environment. Here, it needs to be noted that the volatility distribution of
ambient aerosols does not represent the volatility distribution of the
condensing organic compounds in the gaseous phase. However, it provides
insights into the evaporation potentials of the compounds that are present
in the particle phase. Furthermore, it will be useful for closure studies
combining this information with condensation studies aiming to derive how
the aerosol size distributions are affected by given gaseous species.
Finally, measuring the evaporation of aerosols is also essential for testing
the applicability and limitations of TD setups for inferring the volatility
of aerosols.
Positive matrix factorization (PMF) is one of the widely used factor
analysis techniques for environmental applications. PMF allows separating
organic aerosol (OA) mass spectra into individual groups based on their bulk
chemical characteristics, providing information on the OA sources and
atmospheric processing (Lanz et al., 2007; Huffman et al., 2009; Zhang et
al., 2011). Typical organic groups determined using the PMF analysis include
e.g., hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and cooking OA
(COA) or oxygenated OA (OOA). OOA can be further separated into low-volatility OOA (LV-OOA) and semi-volatile OOA (SV-OOA). Even though there
have been multiple studies using PMF to identify different organic OA groups
from ambient data (Ulbrich et al., 2009; Hildebrandt et al., 2010; Ng et
al., 2010), especially the SV-OOA and LV-OOA groups, to our knowledge there
are only few studies (Cappa and Jimenez, 2010; Paciga et al., 2016) that have
attempted to directly connect the oxygenation levels from these two OOA
groups with the volatility of OA obtained by other methods. Comparing the
volatility distribution obtained using a mass transfer model and VTDMA data
to the oxidation level derived from the AMS data using PMF can help in
quantifying the volatilities of SV-OOA and LV-OOA.
In this study, we provide quantitative information on volatility
distributions of organic species of ambient aerosol in a boreal forest
environment. The sensitivity of the kinetic model was tested towards
different parameters of organic compounds, including density, molar mass,
saturation vapor concentration, diffusion coefficient and vaporization
enthalpy values. More specifically, the sensitivity result to assumed
vaporization enthalpy values of organics is discussed. The VTDMA-derived
volatility distributions are compared with the ones obtained from the
statistical analysis of the AMS.
Methods
Measurements site
The measurements were performed at the Hyytiälä SMEAR II (Station
for Measuring Ecosystem-Atmosphere Relations II) between 14 April and 31 May 2014. The SMEAR II station, located in southern Finland, is surrounded by a
54-year-old pine forest (Hari and Kulmala, 2005). The closest large city is Tampere, with a population
of around 213 000 and about 48 km to the southwest of the measurement
station.
A series of ambient parameters – e.g., particle number size distribution of
3–1000 nm particles (Aalto et al., 2001); ambient meteorological conditions
such as temperature, relative humidity, solar radiation, wind speed and wind
direction; and gas phase concentrations of, for example, SO2, O3,
NOx – are continuously measured at the station.
Schematic view of the VTDMA system.
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Particle volatility
The evaporation behavior of submicron aerosols was investigated using a
volatility tandem differential mobility analyzer (VTDMA), which is part of a
volatility–hygroscopicity tandem differential mobility analyzer (VH-TDMA)
system (Hong et al., 2014). A brief schematic view of the VTDMA is shown in
Fig. 1. In brief, a monodisperse aerosol population (particle diameter of
30, 60, 100 and 145 nm; RH < 10 %) was selected by a Hauke-type
differential mobility analyzer (DMA; Winklmayr et al., 1991). The aerosol
flow was then heated by a thermodenuder at a set temperature, after which
the remaining aerosol material was introduced into a second DMA followed by
a condensation particle counter (CPC, TSI 3010 and TSI 3772), where the
number size distribution of the aerosol after heating was measured. The
spread of the number size distribution of the aerosol was taken into account
in the data inversion using the piecewise linear inversion approach (Gysel
et al., 2009). The thermodenuder is a 50 cm stainless steel tube. No
adsorptive material for removing the gas phase was used after the heating
section. The residence time inside the thermodenuder was around 2.5 s. The
heating temperature of the setup ramped up from 25 to 280 ∘C with a
time resolution of about an hour. It was assumed that
the particles were instantaneously thermally equilibrated with the
surrounding gas phase, as the system was under atmospheric pressure.
The major particle losses during the heating process are from thermophoresis
and Brownian diffusion (Wehner et al., 2002; Häkkinen et al., 2012).
According to Ehn et al. (2007), who used a similar TD, the losses for
aerosol particles above 15 nm in diameter were observed to be less than
20 % when heated to 280 ∘C. Due to these losses, the
VTDMA-measured data underestimate the mass concentration of the
monodisperse aerosol particles after heating. However, this study focused on the change in particle size, which should not be greatly affected by the losses. Hence, the effect of the particle losses on the study
results can be considered negligible.
The VTDMA measures the particle diameter (and concentration) after heating
at each temperature for particles of certain initial size. From this
information volume fraction remaining (VFR) after the heating of particles
of diameter DP can be defined as
follows:
VFRDP=Dp3TDp3Troom=GFV3(T).
GFV describes how much of the particles shrink in size upon heating.
With VFR = 1 at a given temperature, particles are considered to not
evaporate, while with VFR = 0 particles fully evaporate upon heating at
that temperature. The mass fraction remaining (MFR) after the heating was
assumed to be equivalent to VFR assuming that particle density was constant
upon heating (Häkkinen et al., 2012).
Data during a running time window (5 h) were inserted into the model with
a time resolution of half an hour to make sure a full thermogram, i.e., the
VFR or MFR as a function of temperature, could be obtained. The
corresponding results represented the conditions (VFR or MFR) at the median
time of the 5 h time window.
Particle chemical composition
A high-resolution aerosol mass spectrometer (HR-AMS, Aerodyne Research Inc.,
Billerica, USA) was used to determine the chemical composition of aerosol
particles during the experimental period. Detailed description of the
instrument, measurement and data processing can be found in other
publications (DeCarlo et al., 2006; Canagaratna et al., 2007). A Sunset
semi-continuous OC / EC analyzer was deployed to determine the mass
concentrations of organic carbon (OC) and elemental carbon (EC)
concentrations in aerosols using a thermal–optical protocol (Bauer et al.,
2009).
Pairing of inorganic species
The neutral inorganic salts were calculated from the molar concentration of
all ions measured by the HR-AMS based on ion-pairing schemes introduced by
Reilly and Wood (1969) and Gysel et al. (2007). SO42- was first
neutralized by NH4+, and the excess of NH4+ was then
used to neutralize NO3-. The simplified ion-paring scheme was
introduced as below:
nH2SO4=max0,nSO42--nNH4+,nNH4HSO4=min2nSO42--nNH4+,nNH4+,n(NH4)2SO4=minmaxnNH4+-nSO42-,0,nSO42-,nNH4NO3=minmaxnNH4+-2nSO42-,0,nNO3-,
where n denotes the number of moles. This should naturally be treated only as
a rough estimation, as the scheme assumes perfectly internally mixed
particles, and the competing bonding of NH4+ between
SO42- and NO3- in particle phase is not fully described.
Positive matrix factorization (PMF) of organic aerosol
composition
Factor analysis is commonly used to de-convolve the time-dependent OA
concentrations and mass spectra into their basic components, based on a
linear algebraic model explaining the observed variance. The resulting
components, i.e., factors, are interpretable as separate organic sub-groups.
The sum of these organic groups' concentrations should closely match the
measured organic aerosol mass. PMF (Paatero, 1997) is one of these component analysis techniques, constrained so
that only positive concentration and mass spectra are obtained. In this
study, PMF was applied by using the PMF2 algorithm implemented with the
user interface Sofi by Canonaco et al. (2013) to the organic aerosol data
measured by the HR-AMS.
Kinetic evaporation model
A time-dependent evaporation model (Riipinen et al., 2010) was used to
simulate the evaporation of a monodisperse aerosol population in a heated
flow tube by solving the relevant mass transfer equations. The TD
temperature profile, residence time, initial particle size and the
thermophysical properties of the aerosol particles were used as input to the
model. The volatility of the aerosol constituents was described by the
effective saturation concentration, C∗, at standard conditions.
Properties of six particle components used as input for the
evaporation model.
Ammonium
Ammonium
Elemental
Model input parameter
ELVOA
LVOA
SVOA
nitrate (AN)
sulfate (AS)
carbon (EC)
Molar mass, MW (g mol-1)
300
200
150
80
132
280
Density, ρ (kg m-3)
1900
1700
1400
1720
1770
1900
Surface tension, σ(N m-1)
0.05
0.05
0.05
0.05
0.05
0.05
Diffusion coefficient, D (10-6 m2 s-1)
5
5
5
5
5
5
Temperature-dependent factor for D, μ
1.75
1.75
1.75
1.75
1.75
1.75
Saturation vapor concentration, C∗ (µg m-3)
1 × 10-5
1 × 10-2
10
76
2.0 × 10-3
1 × 10-30
Enthalpy of vaporization, ΔHVAP (kJ mol-1)
–a
–a
–a
152
94
100
Mass accommodation coefficient, αm
1
1
1
1
1
1
Activity coefficient, γ
1
1
1
1
1
1
Particle mass for the monodisperse aerosols, mP (µg m-3)b
0.1
Particle mobility diameter, DP(nm)
100
a The chosen enthalpy values of three groups of organics are summarized
in Table 2. b The particle mass concentration in particle size bin of
90–110 nm from DMPS is used to represent the particle mass concentration of
the monodisperse aerosols (i.e., DP= 100 nm).
According to Donahue et al. (2013) and Murphy et al. (2014), compounds with
different effective saturation vapor concentrations can be classified into
extremely low volatility (ELVOC; C∗ < 10-4 µg m-3),
low-volatility (LVOC; 10-3 µg m-3 < C∗ < 10-1 µg m-3),
semi-volatile (SVOC; 10-0.5 µg m-3 < C∗ < 102.5 µg m-3) and intermediate-volatility (IVOC;
102.5 µg m-3 < C∗ < 106.5 µg m-3) organic compounds. In the model, we assume the OA to consist of
three organic groups with their individual characteristic saturation
concentration of 10-5 (ELVOA), 10-2 (LVOA) and 10 µg m-3 (SVOA), corresponding to 10-10, 10-7, and 10-5 Pa or
104, 107, and 1010 molec cm-3: the aim being to obtain the
particle mass fractions of each of the organic group. The ambient particles
were assumed to be a mixture of six species, including the aforementioned
organic groups and three inorganic components, namely ammonium nitrate (AN),
ammonium sulfate (AS) and EC. AN and AS were assigned with their own
characteristic effective saturation vapor concentration and effective
vaporization enthalpies obtained from laboratory measurements (see Table 1).
EC was assumed to be non-volatile in the temperature range used in this
study (assuming C∗ of 10-30 µg m-3). As a result, the
corresponding average volatility distribution of the ambient aerosol was
obtained by letting the difference between the measured and modeled
evaporation of the ambient aerosol to reach a minimum with a certain pair of
mass fractions of these three organic groups together with known mass
fractions of AS, AN and EC from HR-AMS and OC / EC measurements. The MATLAB
optimization function fmincon with constraints was used to obtain the optimal fit
between the measured and modeled thermograms. This optimization method was
constrained by setting the sum of mass fraction of organics from the model to
be equal to the mass fraction of OA measured by HR-AMS and the mass
fraction of each individual organic group to be larger than zero but lower
than the total measured mass fraction of OA.
The input parameters, including the physicochemical properties of the six
components used for the model as well as particle properties, are summarized
in Table 1. Specifically, a mass accommodation coefficient of unity was used
along the whole study, thus yielding the maximum estimates for C∗ s. To best
match the overlapping size ranges of the instruments (VTDMA 30–145 nm and
HR-AMS 60–1000 nm), in this study we focus on modeling the evaporation of
100 nm particles.
Lee et al. (2010) reported that the modeled MFR is likely to depend strongly
on the vaporization enthalpy values. Hence, sensitivity tests towards this
variable were performed. In the sensitivity analysis the vaporization
enthalpy values of organics with different volatilities were either assumed
to be the same or varied for the different organics, e.g., [100 80 60] kJ mol-1.
Epstein et al. (2010) fitted the average ΔHVAP as a
function of log10C∗ to a set of surrogate organic compounds and obtained
the following relationship:
ΔHVAP=-11∗log10C∗+129,
where ΔHVAP and C∗ are in units of kJ mol-1 and µg m-3,
respectively. This vaporization enthalpy (ΔHVAP) of Epstein et al. (2010) (Eq. 3) was also tested in the model calculations. The
combinations of enthalpy values of all these three organic groups used in
this study are summarized in Table 2.
The combinations of vaporization enthalpy values used as an input
for the evaporation model.
ELVOA
LVOA
SVOA
Combination 1
60
60
60
Combination 2
80
80
80
Combination 3
100
100
100
Combination 4
100
80
60
Combination 5
120
100
80
Combination 6
130
110
80
Combination 7
160
130
80
Combination 8
140
125
100
Combination 9
Eq. (3)
Eq. (3)
Eq. (3)
Results and discussion
Inorganic volatility
Figure 2 illustrates the measured and model-interpreted thermograms (i.e.,
MFR as a function of the heating temperature) of ammonium nitrate and
ammonium sulfate. Vallina et al. (2007) reported that for 150 nm AN and AS
particles, the volatilization temperatures (temperature of full particle
evaporation) are around 60 and 180 ∘C,
respectively, by using a similar VTDMA system with a residence time of
around one second. According to the experimental curves (black line) in Fig. 2, AN and AS evaporated completely at around 45
and 180 ∘C, respectively. These results are close to those of Vallina et al. (2007) when the effects of faster evaporation for smaller particles and
longer residence time of this study are taken into account.
Thermograms of ammonium nitrate and ammonium sulfate using the
VTDMA (black lines) and the modeled evaporation using saturation vapor
pressures and enthalpies of vaporization corresponding to the best fit with
the experimental data (red lines).
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Modeled thermograms for both AN and AS were obtained by treating the
saturation vapor pressures and enthalpy of vaporization as fitting
parameters. The optimum C∗–ΔHVAP pair was obtained by minimizing
the difference between the measured and model-interpreted thermograms (red
lines in Fig. 2). The measured evaporation of AN was reproduced using C∗ and
ΔHVAP of 76 µg m-3 (corresponding to 2.6 × 10-3 Pa)
and 152 kJ mol-1, respectively. The obtained ΔHVAP is 1.5 times higher than reported previously (Brandner et al.,
1962; Hildenbrand et al., 2010a, b; Salo et al., 2011), and the saturation vapor
concentration is of the same magnitude as in previous studies (Brandner et al.,
1962; Chien et al., 2010). For AS, C∗ and ΔHVAP of
2 × 10-3 µg m-3 and ΔHVAP of 94 kJ mol-1 reproduced the
measurements best. Chien et al. (2010) reported an observation of AN
partially decomposing to NH3 and HNO3 upon heating. Huffman et al. (2009) similarly suggested that AS might decompose to ammonium bisulfate and
ammonia when heating to around 90–140 ∘C. The evaporation
mechanisms of these inorganics might be different from the evaporation of
organics, where the ΔHVAP of Epstein et al. (2010) was obtained
since, besides sublimation, decomposition might also occur during the
evaporation of inorganics. Hence, the vaporization enthalpy from Eq. (3) is
not used for the simulation of the evaporation of inorganics. In short, even
though there have been aforementioned earlier studies reporting the C∗ and
ΔHVAP of AN and AS, we selected the ones shown by the red curves
in Fig. 3 from our VTDMA technique for the model input to simulate the
evaporation of ambient aerosols. Moreover, according to the saturation vapor
concentration obtained for AN and AS in this study, we can conclude that AN
and AS can be considered as semi-volatile and low-volatility compounds,
respectively.
An example of measured (black dots) vs. modeled (green, magenta
and red lines) thermograms assuming different vaporization enthalpies of the
organics.
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The measured thermogram and corresponding evaporation mechanism of ammonium
bisulfate (NH4HSO4) are not available at present. In order to
neglect the effect of ammonium bisulfate on particle evaporation behavior,
only data with the mass fraction of ammonium bisulfate less than 10 % of
total aerosol mass (calculated from Eq. 2) were analyzed.
Performance of the model for TD data on the organic mixtures
Figure 3 shows example fits to the observed thermograms using different
combinations of organic vaporization enthalpies (Table 2). The different
simulated evaporation behavior indicates that the model is sensitive to
ΔHVAP values. The median norm of residuals, which describes the
difference between the fit and observed thermograms, was the largest when
the ΔHVAP of Epstein et al. (2010) (e.g., Combination 9 in Table 2)
for organics were applied in the model. As ΔHVAP increases,
the sensitivity of C∗ to temperature changes also increases, requiring also
lower C∗ values to match observations (see the red curve in Fig. 3). This is
also in line with Cappa and Jimenez (2010), who suggested that value of C∗ as
low as 10-15 µg m-3 for extremely low volatility material is
required to match the observations when C∗-dependent vaporization enthalpy
values of Epstein et al. (2010) are used.
By using the other vaporization enthalpy values (e.g., Combinations 1 to 8 in
Table 2), better agreement between the fitted and observed thermograms (Fig. 3) was obtained. Donahue et al. (2006) pointed out that artificially low
ΔHVAP values are expected when we present the complex organic
mixture aerosol with one single organic compound or of very few components.
The artificially low ΔHVAP values should thus be rather referred
to effective enthalpy of vaporization (see, e.g., Offenberg et al., 2006).
According to the performance of the model to TD data, the model was observed
to be sensitive to ΔHVAP values. Low ΔHVAP
values (i.e., ΔHVAP= 60–80 kJ mol-1) are suggested to be used
in the model in order to reproduce the measured thermograms.
Mass spectrum of SVOA and LVOA obtained from the PMF analysis (two-factor solution).
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Mass fractions of SVOA and LVOA of the total organic mass obtained
from VTDMA data vs. the ones from the PMF analysis. Here, the y axis
represents the VTDMA results interpretation using the kinetic model and the
x axis represents the AMS results interpretation using the statistical model
(PMF). Model results were obtained by using a constant enthalpy value for
all organics, corresponding to Combination 1 (a and b), Combination 2
(c and d) and Combination 3 (e and f) in Table 2. The LVOA_VTDMA
here is the sum of LVOA and ELVOA mass fractions. The colors of the data
points illustrate the inorganic mass fraction in the particles. Correlation
coefficient and equation for the line fitted to the data points are given in
the legends.
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AMS-derived volatility distribution using PMF
Two organic aerosol groups (SVOA and LVOA) with different volatilities were
separated from the AMS data using the PMF method (Sect. 2.3.2). This common
two-factor separation is driven by the relative fractions of m/z 44 (f44) and m/z 43
(f43), connected to the oxidation state (e.g., Aiken et al., 2008). Higher
factor solutions associated with other organic groups, commonly determined
by PMF analysis, such as biomass burning organic aerosol or hydrocarbon-like
organic aerosol, were not pursued. Since this study focuses on the
volatility distribution of organics using a complex kinetic model, we chose
to limit the PMF OA components to the main ones clearly connected with
oxidation state.
The mass spectra of the two organic groups are shown in Fig. 4. The LVOA
mass spectrum shows a highly abundant m/z 44 signal, which mostly corresponds
to the CO2+ ion (Aiken et al., 2008). The mass fraction of m/z 44
shows a good correlation with the O : C ratio in the organic aerosols (Aiken
et al., 2008). The SVOA mass spectrum has a high signal at m/z 43,
corresponding to the C2H3O+ ion, which is often considered as a
proxy for less oxidized organic aerosol. Hence, the relative abundances of
ions at m/z 43 (f43) and m/z 44 (f44) are our main indicators to separate these two
organic groups with different volatilities arising from their different
degrees of oxygenation.
Paciga et al. (2016) studied the volatility distribution of an LVOA factor
determined by the PMF analysis and found that a significant amount of the
LVOA mass was attributable to ELVOCs with effective saturation
concentrations ≤ 10-3 µg m-3. Hence, probably further
advances in the PMF analysis would be needed to assign more than two groups
of OA. We tested a three-factor application of PMF, based on the ratio of
masses of ions between m/z 44 and m/z 43, and compared the resulting three
organics factors with the mass fractions of different organics from the
VTDMA data. There was no correlation (R=0.02) between the mass fraction of
LVOA from the model and any of three factors from PMF analysis. We are not confident to
explain the reason behind this, but it seems possible that the mass
spectral statistics based on the PMF classification does not match with the
actual volatility grouping. The following discussion thus only focuses on
the well-established two-factor PMF solution (SVOA, LVOA) for the organic
components.
Median organic volatility distribution of the ambient aerosols of
this study obtained from the VTDMA data interpreted by the kinetic
evaporation model (Riipinen et al., 2010) and the AMS data derived from the
PMF analysis. ΔHVap= 80 kJ mol-1 was used in the kinetic
evaporation model.
![]()
Comparison between organic aerosol volatility from VTDMA and PMF
analysis
General results
In Fig. 5, we compare the organic volatility distributions obtained from the
VTDMA data using constant ΔHVAP values (Combinations 1 to 3 in
Table 2) with PMF analysis results. Since we used PMF-derived two-factor
results, we summed up the mass fractions of LVOA and ELVOA from the VTDMA
for the comparison. The correlation coefficients for the two data sets were
relatively similar with ΔHVAP values of 60 kJ mol-1 (R=0.48) and
80 kJ mol-1 assumed for all organic groups (R=0.41). Using ΔHVAP of 100 kJ mol-1 for all organic groups leads to a clearly worse
correlation (R=0.25) and the model interpreted that the particles were
solely consisting of low volatility organics besides the inorganic species.
Using the enthalpy value of 60 kJ mol-1 for all organic groups, the modeled
mass fraction of SVOA was higher than the SVOA from the PMF analysis. The
opposite was true for LVOA; while using ΔHVAP values of 100 kJ mol-1 for all organic groups, the comparison results differed significantly
from the 1:1 line. With the enthalpy value of 80 kJ mol-1 for organics, the
VTDMA-based OA composition was approximately equal to the ones from the PMF
results, however, with a linear correlation coefficient of only 0.4. This
relatively low correlation coefficient suggests that additional information
on each of the methods is needed for analyzing the potential links between
the AMS and volatility data. Moreover, Paciga et al. (2016) studied the
volatility distribution of the PMF-derived organics and estimated that
almost half of the SVOCs, which were determined from PMF, are semi-volatile,
while 42 % are low-volatility and 6 % are extremely low volatility. This
suggests that the two PMF-derived organic groups, commonly labeled for their
oxidation levels, might not be directly linked to their actual volatilities.
Time series of
particle chemical composition obtained from HR-AMS (a), mass
fractions of VTDMA- and PMF-derived SVOA (b) and mass fraction of
VTDMA- (the sum of LVOA + ELVOA) and PMF-derived LVOA (c) on
21 April 2014.
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Time series of
particle chemical composition obtained from HR-AMS (a), mass
fractions of VTDMA- and PMF-derived SVOA (b) and mass fraction of
VTDMA- (the sum of LVOA + ELVOA) and PMF-derived LVOA (c) on
1 May 2014.
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The agreement between the VTDMA- and PMF-based OA volatility distributions
depends on the inorganic mass fractions. The agreement tended to be somewhat
better when the inorganic mass fraction was lower (see Fig. S1 in the Supplement).
Interestingly, when the inorganic mass fraction was lower than 0.3, the
modeled results correlated well with the PMF results, with ΔHVAP values of 100 kJ mol-1 used in the model. Results of Häkkinen et al. (2014) suggested that relatively more particle-phase processing,
i.e.,
condensed-phase reactions, take places within organic–inorganic aerosol
mixtures having a higher aerosol inorganic mass fraction – which could be
consistent with our results as well.
The use of varying ΔHVAP values for ELVOA, LVOA and SVOA did not
improve the correlation with the PMF results (see Figs. S2 and S3).
Specifically, using ΔHVAP values from Eq. (3) would result in
particles exclusively consisting of low-volatility organics besides the
inorganic species. Lee et al. (2010) reached a similar conclusion. A single
effective ΔHVAP value can thus well represent the OA mixture.
Cappa and Wilson (2011) studied the volatility of secondary organic aerosol
from the oxidation of α-pinene and reached a similar conclusion:
α-pinene SOA behaved as if it were comprised of a single
“meta-compound”.
As discussed in Sect. 3.1 we would expect the ΔHVAP of Epstein
et al. (2010) to be the physically most correct of the alternatives tested
– at least when it comes to simple reversible evaporation. However, if
there are other processes in addition to evaporation taking place in the
particle phase upon heating, such as the molecular decomposition or
dissociation of unstable functional groups, the model might not be able to
capture the measured thermogram using Eq. (3). In this case we might end up
with an overestimate in the mass fraction of extremely low-volatility
organics. Donahue et al. (2006) and Riipinen et al. (2010) also discussed
that the evaporation of a mixture is best approximated with considerably
lower effective vaporization enthalpy than the one of a pure component
aerosol. For VTDMA measurements of ambient aerosols with various
compositions and external conditions, the relation between the C∗ and
vaporization enthalpy values might be nonlinear and species- and/or
system-dependent. Moreover, Saleh et al. (2013) reported that the
evaporation of particles in laboratory experiments could be simulated using
a mass accommodation coefficient much less than one. Tong et al. (2011)
concluded that the diffusion coefficient of a viscous solution might affect
the kinetics of evaporation of non-liquid particles, as aerosol particles in
boreal forest environment are expected to be viscous according to Virtanen
et al. (2010). Hence, non-unity mass accommodation coefficients of a
mixture and the particle-phase diffusion limitation on evaporation can also add
uncertainties to the interpretation of the TD data.
Finally, we compared the median volatility distributions of the organics
during the whole campaign using the two methods (Fig. 6). A constant ΔHVAP value of 80 kJ mol-1 for all organics was chosen here as the kinetic
model input. According to the PMF results, the SVOA contribution to the
total organic aerosol mass was around 30 %, which is somewhat lower than
the SVOA contribution (approximately 40 %) obtained based on the VTDMA
results. The model estimated that the mass fractions of LVOA and ELVOA of
the total OA mass were 34 and 26 %, respectively.
Time-dependent case studies
Figures 7 and 8 show two case studies for 21 April and 1 May 2014. Time
series of mass fractions of the particle constituents from HR-AMS, organic
mass fractions from the VTDMA (using Combinations 1–3 in Table 2) and PMF
analysis are shown.
When the ambient aerosol was dominated by organics (Fig. 7), the modeled
SVOA mass fraction followed the temporal pattern of the one determined from
PMF analysis. The elevated SVOA mass fraction in the early morning is
probably due to the condensation of SVOC onto the particles when temperature
was still low, and the following decrease in SVOA after the early morning
could be caused by the evaporation of SVOA after the ambient temperature
increased. The model-interpreted SVOA mass fraction using ΔHVAP values of 80 kJ mol-1 seemed to have a somewhat time-delayed effect
compared with the one from the PMF analysis.
When the inorganic species dominated the ambient aerosol mass (Fig. 8), a
clear diurnal pattern could also be seen from for both the VTDMA and the
PMF-derived SVOA and LVOA mass fractions. However, the VTDMA-based mass
fraction followed the PMF-derived ones better when using ΔHVAP values of 60 and 80 kJ mol-1 compared the one using ΔHVAP values of 100 kJ mol-1 (see also Fig. 5). The relative amount of
inorganic species in the particle phase might thus affect the particle-phase
processing. Conclusively, these two case studies suggest that an effective
ΔHVAP value of 60–80 kJ mol-1 represents the boreal forest
organic aerosols best and this effective ΔHVAP value should be
assumed in the model when comparing with the PMF results.
Summary and conclusions
The volatility of ambient aerosol particles formed and undergone aging was
studied with a volatility tandem differential mobility analyzer (VTDMA) in a
boreal forest environment in Hyytiälä from April to May 2014. A
kinetic evaporation model was used to further interpret the results and
quantify the mass fraction of organics with different volatilities.
When testing the performance of the model against the experimental
volatility data, the model was observed to be sensitive to the vaporization
enthalpy values of the organics. C∗-dependent vaporization enthalpies based on
a semi-empirical formula by Epstein et al. (2010) were applied, but the
modeled thermograms failed to reproduce the measurements in this case.
The best correlation between the VTDMA results and the PMF-derived mass
fractions of organics was obtained when ΔHVAP= 80 kJ mol-1
was assumed for all organic groups in the model, with a linear correlation
coefficient of around 0.4. This relatively low correlation coefficient
indicates that we need to acquire additional information on each of the
method to address the potential relation between the AMS and volatility
data.
With the use of a considerably lower enthalpy value (80 kJ mol-1) than the
semi-empirical ones, the model can best approximate the VTDMA data and the
PMF results. Potential explanations to why artificially low vaporization
enthalpy values provide the best approximation include a thermal decomposition
process in addition to evaporation in the particle phase, mixture effects
and different mass accommodation coefficients for aerosol mixtures rather
than for a pure component system (Riipinen et al., 2010). The interpretation
of the VTDMA data using the kinetic evaporation model cannot provide an
accurate, definitive volatility distribution for boreal forest aerosols due
to the uncertainties in ΔHVAP and other potential issues
mentioned above. However, using a proper effective ΔHVAP value
for OA, the VTDMA-model results nevertheless, for the first time, provide a
rough estimate of the volatility for boreal forest aerosols,
revealing that around 26 % of the monodisperse (100 nm) OA mass is extremely low
volatility.