This work investigates the individual and combined effects of temperature
and volatile organic compound precursor concentrations on the chemical
composition of particles formed in the dark ozonolysis of α-pinene.
All experiments were conducted in a 5 m3 Teflon chamber at an initial
ozone concentration of 100 ppb and initial α-pinene concentrations
of 10 and 50 ppb, respectively; at constant temperatures of 20, 0, or -15 ∘C; and at changing
temperatures (ramps) from -15 to 20 and from 20
to -15 ∘C. The chemical composition of the
particles was probed using a high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS).
A four-factor solution of a positive matrix factorization (PMF) analysis of
the combined HR-ToF-AMS data is presented. The PMF analysis and the
elemental composition analysis of individual experiments show that secondary
organic aerosol particles with the highest oxidation level are formed from
the lowest initial α-pinene concentration (10 ppb) and at the
highest temperature (20 ∘C). A higher initial α-pinene
concentration (50 ppb) and/or lower temperature (0 or -15 ∘C) results in a lower oxidation level of the molecules contained in
the particles. With respect to the carbon oxidation state, particles formed at 0 ∘C are more comparable to particles formed at -15 ∘C
than to those formed at 20 ∘C. A remarkable observation is that
changes in temperature during particle formation result in only minor
changes in the elemental composition of the particles. Thus, the temperature at
which aerosol particle formation is induced seems to be a critical
parameter for the particle elemental composition.
Comparison of the HR-ToF-AMS-derived estimates of the content of organic
acids in the particles based on m/z 44 in the mass spectra show good
agreement with results from off-line molecular analysis of particle filter
samples collected from the same experiments. Higher temperatures are
associated with a decrease in the absolute mass concentrations of organic
acids (R-COOH) and organic acid functionalities (-COOH), while the organic
acid functionalities account for an increasing fraction of the measured
particle mass.
Introduction
Atmospheric aerosol particles can alter air quality (WHO, 2016) and
visibility (Wang et al., 2009) on a regional scale. On a global scale,
particles affect cloud formation, the radiative balance, and thus climate
(IPCC, 2013).
Atmospheric particles are chemically diverse entities, often with a
significant mass fraction of organic compounds (Zhang et al., 2007; Jimenez
et al., 2009). Secondary organic aerosol (SOA) is formed from the condensation of
oxidation products of volatile organic compounds (VOCs) emitted from both
anthropogenic and biogenic sources (Sindelarova et al., 2014; Seinfeld and
Pandis, 2016). α-pinene is a biogenic VOC emitted from sources such as the foliage
of coniferous trees (Rasmussen, 1972), and it has been identified as the most
common monoterpene in boreal forests all year round (Hakola et al., 2003). In
the atmosphere, α-pinene is oxidized primarily by ozone (O3),
hydroxyl radicals (OH⚫), and nitrate radicals
(NO3⚫). Due to their low vapor pressures, some of the gas-phase oxidation products may partition onto already existing particles by
condensation or reactive uptake and contribute to particle growth
(Hallquist et al., 2009). In addition, some low-vapor-pressure oxidation
products of α-pinene are able to nucleate (Kirkby et al., 2016) and
likely play an important role in the initial growth of new particles in the
atmosphere (O'Dowd et al., 2002; Riipinen et al., 2012; Ehn et al., 2014;
Tröstl et al., 2016).
It is well established that the particle mass available for the condensation of
gases affects the partitioning of organic species between the gas phase and the
particle phase (Pankow, 1994a, b), although the traditional partitioning
theory is limited in relation to non-liquid, more viscous particles, such as
α-pinene-derived SOA formed at low relative humidity (Renbaum-Wolff
et al., 2013), because of slow diffusion (Cappa and Wilson, 2011; Pöschl,
2011).
The fraction (F) of a given semi-volatile species in the particle phase at a
given temperature has been formulated in an absorptive equilibrium
partitioning framework as
F=11+c∗/M,
where c* is the gas-phase mass concentration at saturation, and M is the mass
concentration of absorbing material (Kroll and Seinfeld, 2008). Thus, the
chemical composition of a particle that is in equilibrium with the
surrounding gas phase is affected by both c* and M. The c* of a gaseous
compound is generally inversely related to its level of oxidation (Jimenez
et al., 2009). The particle composition can be shifted towards species with
higher c* values (i.e., less oxidized, more volatile species) by increasing
the mass concentration of preexisting particles, i.e., the value of M;
conversely, lower M values result in particle-phase compositions that are
dominated by species with lower c* values (i.e., more oxidized, less volatile
species). This has been experimentally confirmed by studies such as Shilling et al. (2009), who showed that the oxidation level of SOA from α-pinene
ozonolysis decreases with increasing particle mass loadings.
The equilibrium partitioning of a gas with a given c* (volatility) also
depends on temperature, as demonstrated by Pathak et al. (2007), Saathoff et al. (2009), and Warren et al. (2009), based on chamber studies of α-pinene-derived particles at different constant temperatures between -30
and 45 ∘C. Partitioning has also been addressed in
chamber studies where the temperature was ramped after the initial (constant
temperature) formation of SOA. Stanier et al. (2007) increased the
temperature from 22 ∘C to a maximum of 40 ∘C and then decreased the temperature back to 22 ∘C in
some experiments. During
heating, they observed a decrease in SOA size, which is an indication of evaporation,
and during cooling, they observed an increase in SOA size, which is an indication of condensation.
In experiments by Warren et al. (2009), where the temperature was cycled in
the ranges of 5 to 27 and 27 to 45, respectively, heating was associated with a decrease in particle mass, and
cooling was associated with an increase in particle mass. In a recent study by
Zhao et al. (2019), where the temperature was cycled between 5
and 35 ∘C (RH varied between 10 % and 80 %), it was suggested
that condensation during cooling is lower than predicted by equilibrium
partitioning.
The chemical composition of the gas and particle phases in α-pinene
ozonolysis experiments is determined by a combination of thermodynamic and
kinetic aspects (Zhang et al., 2015; Kristensen et al., 2017). However, the effect of
temperatures below room temperature (∼ 20 ∘C), in
particular below 0 ∘C, on gas-phase oxidation products,
nucleation, SOA growth, and particle chemical composition remains
a largely unexplored area (Kristensen et al., 2017; Simon et al., 2020). As
low temperatures are of high atmospheric relevance (e.g., low temperatures
prevail at the latitudes of the boreal forests and at higher
elevation), it is important to quantify SOA formation and properties under
cold conditions. Furthermore, vertical transport can lead to changes in
temperature within short time frames, affecting reaction kinetics,
condensation processes, and properties relevant for the climate effect of
particles (Topping et al., 2013; Murphy et al., 2015).
The lack of knowledge on how the chemical composition of both the gas phase
and particle phase vary with temperature was the motivation behind the
Aarhus Chamber Campaign on Highly Oxygenated Organic Molecules and Aerosols (ACCHA) introduced in
Kristensen et al. (2020). The ACCHA campaign focuses on temperatures from
20 to -15 ∘C, corresponding to conditions relevant in
the boreal forest regions (Portillo-Estrada et al., 2013). As in most chamber
experiments, VOC concentrations in the ACCHA campaign (10–50 ppb) were 1–2 orders of magnitude higher than typical ambient conditions (Kourtchev et al., 2016). These conditions were chosen to speed up aerosol formation in the
experiments, and we believe that the data provide valuable and atmospherically
relevant information that is applicable to regions such as the boreal forest.
The impact of temperature on the yield of highly oxygenated organic
molecules (HOMs) is presented in Quéléver et al. (2019), and more
details on VOCs are presented in Rosati et al. (2019).
The goal of the current paper is to investigate and quantify the individual
and combined effects of α-pinene precursor concentrations and
temperature on the SOA mass concentration and chemical composition. For this
purpose, we describe and discuss a subset of the data collected during
the ACCHA campaign, focusing on results obtained from a high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS).
MethodsExperimental
This work is based on experiments conducted in the Aarhus University
Research on Aerosol (AURA) smog chamber: a ∼ 5 m3 bag made of
125 µm fluorinated ethylene propylene Teflon film located in an enclosure, where the temperature
is controllable between -16 and 26 ∘C. The AURA
chamber has been described in detail by Kristensen et al. (2017).
The experiments were conducted as part of the ACCHA campaign and focus on
SOA formed in dark ozonolysis of α-pinene at various temperatures.
An overview of the campaign is provided in Kristensen et al. (2020); therefore, only a short summary of the ACCHA campaign is
given here. A modified version of the overview table of the experiments
from Kristensen et al. (2020) is also presented in Table 1, where the focus is on the
parameters relevant in this work. At a constant respective temperature of 20, 0, or -15 ∘C, ozone was injected
into the chamber to a concentration of ∼ 100 ppb, followed by the
injection of either 10 ppb (low concentration) or 50 ppb (high
concentration) α-pinene. The chamber was operated at atmospheric
pressure, and neither seed particles nor OH scavengers were introduced.
Overview of experimental details for the ACCHA experiments
included in this work. The experiments are constant temperature (const.
temp.) experiments and/or temperature ramp (temp. ramp) experiments.
a Temperature and relative humidity probe failure after 59 min and
the rest of the constant temperature experiment. b Temperature and
relative humidity probe failure after 42 min. ∗ Averages of temperature and relative humidity are based on data from the
time of α-pinene injection to filter sampling and might differ
slightly from the values reported in Kristensen et al. (2020). Densities are
30 min averages obtained by the end of the experiments (1.1–1.5 and
3.1–3.3). NA denotes not available.
Three series of constant temperature experiments, all consisting of an
experiment at 20, 0, and -15 ∘C, were
conducted. In one of the series, 10 ppb α-pinene was injected into
the chamber (experiments 1.1–1.3), while two similar series of experiments
were performed at 50 ppb α-pinene (experiments 2.1–2.3 and 3.1–3.3).
Additionally, the series of 10 ppb α-pinene experiments includes two
temperature ramp experiments, where the temperature was decreased from 20 to -15 ∘C (experiment 1.4) and increased from -15
to 20 ∘C (experiment 1.5) ∼ 35 min after α-pinene injection, which corresponds to the period
during SOA formation and before mass peak.
In this work, we present data from a subset of instruments involved in the
ACCHA campaign: a temperature and humidity sensor (HC02-04) attached to a
HygroFlex HF320 transmitter (Rotronic AG) placed in the center of the
chamber; a scanning mobility particle sizer (SMPS), consisting of a
differential mobility analyzer (DMA; TSI 3082) and a nano water-based
condensation particle counter (CPC; TSI 3788); and a high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS; Aerodyne Research
Inc.) (Jayne et al., 2000; DeCarlo et al., 2006; Canagaratna et al., 2007). In
the following, the HR-ToF-AMS will be referred to as AMS. Both the SMPS and
AMS were placed at room temperature next to the chamber outlets, and the
connecting tubing was temperature insulated.
By the end of each experiment, a particle sample was collected on a Teflon
filter (0.45 µm pore size; CHROMAFIL). Particle samples were
extracted and analyzed by an ultrahigh-performance liquid
chromatograph–electrospray ionization quadrupole time-of-flight mass
spectrometer (UHPLC–ESI-qTOF-MS; Bruker Daltonics), as described in
Kristensen et al. (2020), where the analytical method and results are also
presented in detail. Herein, we compare the findings from the
UHPLC–ESI-qTOF-MS, hereafter referred to as LC-MS, to the AMS measurements.
Data analysis
Positive matrix factorization (PMF) (Paatero and Tapper, 1994; Paatero, 1997)
has traditionally been used to investigate contributions of different
sources to ambient particles, and the application of PMF to AMS data from
chamber experiments was first demonstrated by Craven et al. (2012). In the
present work, PMF analysis is applied to chemical composition data from SOA
particles that are produced in the ozonolysis of α-pinene but are
formed and aged under different temperatures and precursor concentrations
and, consequently, different particle loadings. High-resolution AMS mass
spectra of SOA particles from the various experimental conditions were
analyzed in one matrix, allowing for the spectral and elemental
chemical composition changes that occur to be monitored as conditions change. The PET tool
(V 2.09A) was used to perform the PMF analysis on high-resolution AMS mass
spectra, according to the principles described in detail by Ulbrich et al. (2009).
PMF is a model that can be used to express measured mass spectra as a linear
combination of factors that are the products of constant mass spectra and
related time profiles as follows:
xij=∑pgipfpj+eij.
The measured mass spectral data are the matrix X, an m×n matrix with n
ion masses measured at m different time points, and xij is an element of
this matrix. p is the number of factors chosen for the solution, gip is
an element of the matrix G containing time series of the factors, and
fpj is an element of the matrix F of constant factor mass spectral
profiles. The matrix elements eij correspond to the error matrix, E, of
residuals not explained by the model (Paatero, 1997; Ulbrich et al., 2009).
Equation (2) is solved using the PMF2 algorithm (Paatero, 1997), which uses
linear least-squares fitting and the constraints that the values
of matrix F and G have to be nonnegative. The solution is found by
minimizing the fit parameter Q:
Q=∑i=1m∑j=1neijσij2,
where σij is an element of a matrix containing the standard
deviations for each element of X (Paatero, 1997; Ulbrich et al., 2009).
The estimation of standard deviations was performed as outlined in Ulbrich et al. (2009) with “weak” ions (i.e., ions with signal/noise (S/N) < 2) being down-weighted by a factor of 2, and “bad” ions (i.e., ions with
S/N<0.2) being down-weighted by a factor of 10. Additional
sources of uncertainty that are not accounted for in the PMF analysis of
high-resolution mass spectra are uncertainties related to high-resolution
fitting, including errors in peak shape, and m/z calibrations (Cubison and Jimenez, 2015). The number of factors (p) is chosen based on a combination of the
evaluation of residuals, Q values, and a priori knowledge about the dataset
(Lanz et al., 2007; Ulbrich et al., 2009). In the result section, a
four-factor solution of the PMF analysis of high-resolution AMS data is
presented. Although the five-factor solution and six-factor solution have
lower Q/Qexpected (Qexpected≈m×n, i.e., the
number of points in the data matrix, Ulbrich et al., 2009) compared with the
four-factor solution, a larger number of factors is not selected because
it does not provide any more interpretable information about the particle
composition. The background for choosing the four-factor solution over the
five- and six-factor solutions is explained in more detailed in the
Supplement (Figs. S1–S11).
As previous laboratory experiments show that the collection efficiency
(CE) and relative ionization efficiency (RIE) of laboratory SOA are variable
(Docherty et al., 2013), the mass concentrations presented in the PMF
analyses are estimated from the total SOA mass concentration, as obtained
from integrated SMPS size distributions, assuming spherical particles and
densities calculated from the AMS-derived elemental ratios (Kuwata et al., 2011). Densities are derived as averages based on AMS data from the last 30 min of each experiment. Uncertainties related to the density calculation
are described in Kuwata et al. (2011).
Mass spectra of the factors from the PMF analysis are compared using the
methods described by Wan et al. (2002) and Ulbrich et al. (2009),
respectively. The comparisons focus on both the entire high-resolution mass
spectra, m/z 12 to m/z 115, and the range of m/z>44 to prevent
an impact from the most intense peaks, especially m/z 43 and m/z 44, which
are the ions associated with the largest variation between the factors.
Low SOA concentrations at the beginning of the experiments increase the
uncertainty of the AMS measurements. Therefore, the first 4 to 16 min of
the experiments (longest in the 10 ppb α-pinene experiments) are
omitted from the elemental analysis of the AMS data.
(a) Evolution of the SOA mass (density corrected) and the O:C
ratio during a typical experiment (50 ppb α-pinene, -15 ∘C, experiment 2.3) and (b) the mass spectra of the experiment obtained at the mass
peak (5 min average). gt denotes greater than.
Results and discussion
To provide an overview of the course of a typical experiment, Fig. 1a
shows the evolution of the particle mass concentration and the elemental
composition, illustrated by the oxygen-to-carbon (O:C) ratio in experiment
2.3 which is conducted at -15 ∘C with an initial α-pinene
concentration of 50 ppb. At the beginning of the experiment, both the mass
concentration and O:C ratio increase significantly, but the O:C ratio tends to stabilize after
∼ 50 min, whereas the
particle mass concentration continues to increase and peaks after
∼ 175 min (not corrected for wall loss). The reported
particle mass concentration is obtained using a density of 1.12 g cm-3.
According to Table 1 and Supplement Fig. S12, the AMS-derived SOA densities are of the order of 1.1–1.3 g cm-3 by the end of the
experiments. There are indications of a slight increase in density with higher
experimental temperature as well as a slightly higher density for the
particles formed at a low α-pinene concentration (10 ppb) compared with a
high α-pinene concentration (50 ppb). For reproducibility with
respect to SOA formation (mass concentration) as well as loss rates of
α-pinene and ozone, see Kristensen et al. (2020).
Figure 1b is a mass spectrum of experiment 2.3 obtained from the
high-resolution AMS data at the highest particle mass concentration (not corrected for
wall loss). It shows that fragments, which belong to the so-called
hydrocarbon family (CH), are distributed throughout the mass spectrum, with
some of the most prominent peaks (and ions) being m/z 39
(C3H3+), 41 (C3H5+), and 55
(C4H7+). The oxidized compounds, which belong to the CHO1 and
CHOgt1 (gt means greater than) families, dominate at m/z 28 (estimated from
CO2+ according to Aiken et al., 2008), 29 (CHO+), 43
(C2H3O+), 44 (C2H4O+, CO2+), 55
(C3H3O+), and 83 (C5H7O+). m/z 43 is the most
significant peak and also has the highest contribution of the CHO1
family, whereas the more oxidized CHOgt1 family dominates in m/z 44. Previous
studies have shown that these two peaks provide useful information about
particle oxidation level (Ng et al., 2011). The patterns described above are
also observed in the mass spectra of the other experiments, which are generally highly comparable across experimental conditions (Supplement
Figs. S13–S23) as well as comparable to mass spectra of particles formed
in dark ozonolysis of α-pinene in other chambers (Bahreini et al., 2005; Song et al., 2007; Shilling et al., 2009; Chhabra et al., 2010).
PMF analysis of aerosol mass spectra
While PMF analysis is traditionally utilized to identify distinct sources in
ambient measurements and the factors are named according to what they are
(e.g., oxygenated organic aerosol – OOA), here PMF analysis of the combined
dataset of α-pinene SOA experiments provides a tool to identify
subtle changes in the measured mass spectra across the different
experimental conditions and the factors are named according to the
conditions under which they dominate. The analysis was performed on a
combined dataset representing eight different experimental conditions: three
constant temperature conditions (20, 0, and -15 ∘C) with two initial α-pinene concentrations (10 and
50 ppb) – experiments 1.1, 1.2, 1.3, 3.1, 3.2, and 3.3 – and two temperature
ramps (from 20 to -15 and from -15
to 20 ∘C), both with an initial α-pinene concentration of
10 ppb – experiments 1.4 and 1.5.
The result of the four-factor solution from the PMF analysis is presented in
Fig. 2 (and Fig. S7), showing the changes in mass concentration of the factors
as a function of time in each experiment, and in Fig. 3, showing the
high-resolution mass spectra of the four factors (i.e., factor profiles). The
mass spectra are colored according to contributions from the various types
of elemental compositions (i.e., ion families) that appear at each ion
signal. As expected from the comparison of the mass spectra from the
individual experiments (Supplement Figs. S13–S23), the factor profiles
show a high degree of similarity (Figs. 3 and S24) with small differences
in the relative intensities of ions. Using the method described by Ulbrich
et al. (2009), on a scale from 0 to 1, with 1 indicating highest similarity,
the similarity is calculated to be between 0.86 and 0.97 across all factors
in the m/z range from 12 to 115 with Factor 3 and Factor 4 being the most
similar and Factor 1 and 3 being least similar. By focusing only on the m/z>44, similarities between 0.85 and 0.97 are obtained, and Factor
2 and Factor 3 are the most similar and Factor 1 and Factor 2 are least
similar. Figure S24 also shows the corresponding results of a comparison using the
method described by Wan et al. (2002).
Mass evolution (µgm-3) of the four factors
from the PMF analysis of the combined AMS dataset including six constant
temperature experiments with an initial α-pinene concentration of 10
or 50 ppb, conducted at 20 ∘C (experiments 1.1 and 3.1), 0 ∘C (experiments 1.2 and 3.2), and -15 ∘C (experiments
1.3 and 3.3), and two temperature ramp experiments with an initial α-pinene concentration of 10 ppb where the temperature was changed from 20 to -15 ∘C (experiment 1.4) and from -15 to 20 ∘C (experiment 1.5). Graphs in the left and
middle columns depict 10 ppb α-pinene experiments, and 50 ppb α-pinene experiments are shown in the right column. Each row of graphs
represents different initial temperatures. The reason for the bump/shoulder
around 50 min in experiment 3.3. is unclear.
Table 2 summarizes how the four factors differ with respect to the oxygen-to-carbon (O:C)
ratio; the hydrogen-to-carbon (H:C) ratio; the average carbon oxidation state
(OSC; Kroll et al., 2011); the ratios between the absolute
intensities of the fragment ions at m/z 43 (C3H7+,
C2H3O+) and m/z 44 (C2H4O+, CO2+),
respectively; and the corresponding total organic ion intensity (f43
and f44, respectively). Variations in these parameters can help explain
the relative contribution of each factor to the SOA mass under different
experimental conditions, i.e., temperatures and α-pinene
concentrations (particle mass loadings).
Characteristics of the four factors obtained from PMF
analysis of experiments (1.1, 1.2, 1.3, 1.4, 1.5, 3.1, 3.2, and 3.3)
according to Fig. 2. For each factor, the fraction of m/z 43 (f43)
and m/z 44 (f44) relative to the total mass spectra is given as a
percentage.
FactorDominating appearanceCharacteristicsf43f44O:C ratioH:C ratioOSCFactor 1High temperatureFraction decrease with decreasing temperature at both α-pinene concentrations1490.561.65-0.53Factor 210 ppb (low) α-pinene concentration, low particle mass loadingHigh fraction in all 10 ppb experiments; almost nonexistent in 50 ppb experiments780.391.59-0.81Factor 350 ppb (high) α-pinene concentration, high particle mass loadingHigh fraction in all 50 ppb experiments; appears only slightly in 10 ppb experiments1030.261.60-1.08Factor 4Low temperatureFraction decrease with increasing temperature at both α-pinene concentrations1340.341.71-1.03
As shown in Fig. 2, the SOA observed under each experimental condition
does not correspond to a single PMF factor. The SOA values obtained under different
experimental conditions are represented by a linear combination of multiple
factors instead. However, the factors are clearly distinguished from each other by consistent trends in their relative mass contributions to the
SOA observed under the different experimental conditions. These trends are
used in the interpretation and naming of the factors. According to their
appearance and relative contribution to total SOA mass, factors 1 and 4 will
be referred to as “temperature factors” in the following discussion, and
factors 2 and 3 will be referred to as “concentration factors”. For
example, according to Fig. 2,
Factor 1 makes up a significant fraction of the particle mass in the 20 ∘C experiments at both α-pinene concentrations, but it plays a minor role in the colder experiments.
Therefore, Factor 1 will be referred to as “high temperature factor”. The
significant contribution of Factor 1 to the SOA mass at high temperature is
in agreement with the fact that this factor is mostly dominated by ions from
oxidized species (i.e., high intensity of CHO1 and CHOgt1 ion groups at m/z 28, 29, 43, 44, 55, and 83) (Fig. 3). Among all factors, Factor 1 has the
highest O:C ratio (0.56), OSC (-0.53), f43 (14 %), and f44
(9 %) (Table 2); therefore, the chemical species represented by Factor
1 (i.e., related to high temperature) are likely the most oxidized entities
present in the SOA.
Factor 2 is dominant in all 10 ppb α-pinene experiments, but it is
almost nonexistent in the 50 ppb α-pinene experiments. Therefore, it will be referred to as a “low concentration factor” (Fig. 2). Among
all factors, Factor 2 (low α-pinene concentration) has the second
highest contribution of oxidized ions (CHO1 and CHOgt1 family) (Fig. 3)
as well as the second highest O:C ratio (0.39), OSC (-0.81), and
f44 (8 %) (Table 2). With f43 (7 %) being of a similar
magnitude as f44, it has the highest relative ratio of
f44 to f43 among the four factors. Furthermore, Factor 2 also has
strong contributions from hydrocarbons (CH family) such as m/z 39, 41, 55,
67, 69, 79, 81, and 91 (Fig. 3).
Mass spectra of the four factors from the PMF analysis
(see also Fig. 2 and Table 2) of the combined dataset of experiments
1.1–1.5 and 3.1–3.3. gt denotes greater than.
Factor 3 makes up a significant fraction of the particle mass formed in the
50 ppb α-pinene experiments at all temperatures. It plays a less
important role in the 10 ppb α-pinene experiments conducted at both
0 and -15 ∘C, whereas at 20 ∘C, where
the lowest particle mass is formed, Factor 3 is practically nonexistent
(Fig. 2). Based on this appearance, Factor 3 will be referred to as a
“high concentration factor”. Looking into the profile of Factor 3 (high
α-pinene concentration), it has relatively high contributions from
the CHO1 family at m/z 55 and m/z 83, and among all factors, it has the
highest contribution of m/z 91 from the CH family, which has been used as a
tracer of biogenic emissions in ambient measurements (Lee et al., 2016)
(Fig. 3). Factor 3 also has the lowest f44 (3 %), O:C (0.26), and
OSC (-1.08) among all factors, i.e., it represents the least oxidized
material (Table 2).
Factor 4 (low temperature) appears at low temperature in SOA formed in both
10 and 50 ppb α-pinene experiments (Fig. 2). It has around the
same level of f43 (13 %) as Factor 1 and relatively high
intensities of m/z 55 and m/z 83, which are the fragment ions larger than
m/z 43 that are most intense in the CHO1 ion family (Fig. 3). On the other
hand, Factor 4 is almost as low as Factor 3 in the more oxidized f44 (4 %) and also in O:C ratio (0.34) and OSC (-1.03) (Table 2).
It is generally seen that the factors related to temperature variation (factors 1 and 4) show a larger difference in the oxidation level than the factors
related to the α-pinene concentration, i.e., particle mass loading
(factors 2 and 3). This suggests that, within the investigated conditions,
differences in temperature (20 to -15 ∘C) have a
larger effect on the particle chemical composition than the VOC concentration (10
and 50 ppb α-pinene, respectively).
Overall, the four PMF factors represent different main
characteristics of the particle chemical composition associated with
temperature and VOC precursor concentrations, and they provide a useful framework
for discussing the effects of temperature and VOC concentration on SOA formation
and properties in chamber experiments. Figures 2 and S7 show that, within each
experiment, the relative contribution of the factors changes with time.
These changes in relative ratios likely reflect the changes in the SOA
composition from nucleation (beginning of experiment), condensational growth
(increase in mass concentration), and wall loss (decrease in mass
concentration towards the end of the experiment). In addition, ongoing gas-phase chemistry may also affect observed trends in composition (Kristensen
et al., 2020). Furthermore, recent studies (Pospisilova et al., 2020) have shown that particle-phase processing continues after condensation, but
more work is needed to understand the extent and mechanisms of such
processes; therefore, we cannot conclude on such effects.
In each chamber experiment, the correlation between the relative
contribution of each factor and the SOA mass concentration can be utilized
to infer information about the relative volatilities of the species in each
factor. For example, Figs. 2 and S7 show that the relative mass concentration ratio of Factor 2
to Factor 1 is largest at lower SOA mass concentrations within
experiments 1.1 and 1.2. This suggests that
the volatility of species related to Factor 2 is lower than Factor 1 species,
which is interesting as Factor 1 is more oxidized than Factor 2 (Table 2). For experiments 3.2 and 3.3, Figs. 2 and S7 show that the
trend in the relative mass concentration ratio of Factor 3 to Factor 4 is
largest at time periods in the experiments with lower SOA mass
concentrations. This indicates that the volatility of Factor 3 species is
lower than Factor 4 species. The relative volatilities of Factor 1 and
Factor 3 can be assessed by examining the time trends in experiments 3.1 and
3.2, where the fraction of Factor 1 is higher at low mass loading. This,
along with the fact that the relative ratio of Factor 3 to Factor 1 is
higher at lower temperatures, suggests that Factor 3 is more volatile than
Factor 1. Taken together, these results suggest that the volatility (c*) of
the four factors increases in the following way: Factor 2 < Factor 1
< Factor 3 < Factor 4.
While we do not see any systematic indications of a specific factor being
coupled to relative humidity in our experiments, we cannot rule out that the
changes in relative humidity during the experiments might have some impact
on SOA composition.
As PMF analysis is traditionally used on ambient AMS data, it is relevant to
compare the findings from the AURA chamber experiments to ambient studies.
Here, the analysis presented by Lee et al. (2016) is relevant, as they used
PMF analysis to explore the SOA sources in a coniferous forest mountain
region in British Columbia, where SOA concentrations reached up to 5 µgm-3 and the temperature varied from ∼ 5
to ∼ 25 ∘C, corresponding to the temperature in the
upper range of the experiments presented in this paper. PMF factors obtained
from the ambient AMS data showed a background source and two biogenic SOA
sources: BSOA1 from terpene oxidized by ozone and nitrate radical during
nighttime, and BSOA2 from terpene oxidized by ozone and OH radical during
daytime. Especially the BSOA1 O:C ratio (0.56), the H:C ratio (1.56), and the
overall distribution of peaks (particularly with respect to the relative
ratios of m/z 58 to m/z 55 and to the ions above m/z 60) in the mass
spectrum are comparable to Factor 1 (high temperature). As both ozone
and OH radicals are present in the ACCHA campaign experiments as discussed
by Quéléver et al. (2019), is it interesting that the comparability to
the Lee et al. (2016) BSOA1 factor representing ozone and nitrate radical at
nighttime is higher than the comparability to the BSOA2 factor representing
terpene oxidized by ozone and OH radical at daytime. This may be related to a
larger fraction of SOA being formed from ozonolysis rather than OH oxidation
in the ACCHA campaign experiments (Quéléver et al., 2019). Moreover,
it suggests that (BSOA1) ozonolysis might
have been the major SOA formation pathway in Lee et al. (2016) during nighttime.
The comparison and similarity between PMF factors from laboratory and
ambient observations indicates that the PMF analysis of chamber SOA chemical
composition, obtained under different temperature and loading conditions,
can be useful for the interpretation and understanding of ambient SOA
composition and vice versa.
Trends in chemical compositionElemental analysis
Studying the evolution of the elemental composition of SOA can provide
insight into the chemical changes occurring during chemical and physical
processes. Figure 4 illustrates the evolution of the O:C ratio during
constant temperature experiments and temperature ramps (standard errors are
shown in Fig. S25). The time series of the O:C ratio in both 10 ppb α-pinene experiments (1.1–1.5) and 50 ppb α-pinene experiments
(2.1–2.3 and 3.1–3.3) are shown. In accordance with expectations based on
the absorptive equilibrium framework (Eq. 1), previous work (Shilling
et al., 2009), and observations from the PMF analysis, SOA formed from a low
α-pinene concentration (10 ppb) and at higher temperature are
associated with higher O:C ratios, compared with SOA formed from a high α-pinene concentration (50 ppb) at lower temperatures. In all experiments,
an initial increase in the O:C ratios, which subsequently level off, is
observed – most significantly in the 50 ppb α-pinene experiments
(2.1–2.3), which is probably due to the higher reaction rate in these experiments.
Although the aging of oxidized organic particles in ambient measurements is
associated with an increase in the O:C ratio (Ng et al., 2011) at higher particle
mass concentrations, the O:C ratio is usually observed to decrease during
particle aging (Shilling et al., 2009; Chhabra et al., 2010; Denjean et al., 2015b) due to the increased partitioning of less oxidized semi-volatile
compounds into the particle phase.
Time series of the O:C ratio in 10 ppb α-pinene
experiments (1.1–1.5) and 50 ppb α-pinene experiments (2.1–2.3).
Every fifth data point is shown. Standard errors are shown in Fig. S25.
In the 10 ppb α-pinene experiment (experiment 1.4), where the temperature is
lowered 36 min after the start of the experiment, the temperature change from 20 to -15 ∘C is associated with a small decrease in
the O:C ratio, which corresponds to condensation of less oxidized (i.e., more
volatile) species (Fig. 4). Conversely, heating the particles from -15 to 20 ∘C at 34 min after the start of the experiment
(experiment 1.5) results in a slight increase in the O:C ratio as more volatile, less
oxidized species evaporate and increase the O:C ratio of the remaining
particle mass. For comparison, Denjean et al. (2015a) also observed a slight
increase in the O:C ratio when increasing the temperature by 6 ∘C in
the room temperature range.
An important outcome of Fig. 4 is that the O:C ratios at the end of the
temperature ramps are closer to the O:C ratios of the particles in the
experiments conducted at the temperature where the ramps start than at the temperatures where
they end. This observation suggests that the composition of α-pinene-derived SOA particles is, to a large extent, controlled by the temperature
at which they are initially formed and that subsequent changes in
temperature, even as dramatic as 35 ∘C during 100–130 min,
only affect the particle chemical composition to a minor extent. Even though
the newly formed particles are exposed to this change in temperature
(∼ 35 min after the start of the experiment and ∼ 1 h before SOA mass peak; Fig. 2), only slight changes in the chemical
composition are observed (Fig. 4).
It is also relevant to investigate the corresponding evolution of the O:C
ratio and the H:C ratio derived from the AMS data based on the so-called Van
Krevelen plot (Van Krevelen, 1950) (Aiken et al., 2007, 2008;
Canagaratna et al., 2015). Van Krevelen plots of the constant temperature
experiments (1.1–1.3, 2.1–2.3, and 3.1–3.3) are shown in Fig. 5 (the standard
errors of O:C ratios and H:C ratios can be found in Figs. S25 and S26,
respectively). The differences between the 50 ppb α-pinene
experiments conducted at similar temperature might be a result of
experimental uncertainty. The figures reveal interesting tendencies, both in
relation to elemental composition at particle mass peak (Fig. 5a) and to
evolution during the experiments (Fig. 5b). For comparison, the O:C ratio
and H:C ratio of the four factors from the PMF analysis, which were obtained
from the combined dataset of several experiments, are also shown in Fig. 5a and b. The O:C ratios and H:C ratios of the factors encompass the data
from the individual experiments, illustrating how the factors from the PMF
analysis capture and define the extremes in the diversity of the chemical
composition of particles from the individual experiments, as a result of
different experimental conditions. The largest differences in elemental
ratios are observed between the SOA particles produced under different
temperatures.
Van Krevelen plots (H:C ratio vs. O:C ratio) at (a) the SOA
mass peak (five-data-point average) and in the expansion (b) during the
constant temperature experiments in this work (arrows indicate the direction of
time). (c) The OSC during the experiments. The lines in the
Van Krevelen plots are based on Heald et al. (2010) and Ng et al. (2011).
Note the different scales of the axes in panels (a) and (b). The positions of
the four factors obtained from the PMF analysis are indicated by crosses:
F1 denotes Factor 1 (high temperature), F2 denotes Factor 2 (low concentration), F3 denotes
Factor 3 (high concentration), and F4 denotes Factor 4 (low temperature). Panels (b)
and (c) show every fifth data point. Standard errors are shown in Figs. S25 and S26.
Figure 5b shows that the O:C ratio and the H:C ratio are almost constant during all constant temperature 10 ppb α-pinene
experiments.
Interestingly, in the 50 ppb α-pinene experiments conducted at lower
temperatures (0 and -15 ∘C; experiments 2.2, 2.3,
3.2, and 3.3), the H:C and O:C ratios increase simultaneously during the
experiment. As this is not a commonly reported trend, neither in ambient
measurements (Ng et al., 2011; Lee et al., 2016) nor in chamber experiments
focusing on α-pinene-derived SOA (Chhabra et al., 2011), it
demonstrates the importance of investigating SOA particles at low,
atmospheric relevant, temperatures. Several mechanisms could potentially
explain the observed evolution of the SOA elemental composition in the Van
Krevelen plot, and it could in fact be due to a combination of different
simultaneous mechanisms, such as oxidation and oligomerization. As no
OH scavenger is added in our experiments, one explanation could be related
to OH chemistry. Qi et al. (2012) demonstrated that the exposure of ozonolysis-generated α-pinene SOA to OH radicals increases the O:C ratio and
also leads to higher H:C ratio, due to OH addition to the unsaturated
VOC. However, modeling suggests that the OH oxidation is not more
pronounced at low temperature (0 ∘C), compared with high
temperature (20 ∘C) (Quéléver et al., 2019), which makes
this a less likely explanation for the continuous increase in the O:C ratio
and H:C ratio in the cold experiments. More specifically, the ratio of VOC
oxidized by ozone relative to that oxidized by OH radicals was
∼2:1, independent of precursor concentration and temperature
(Quéléver et al., 2019).
While the simultaneous increase in the H:C ratio and O:C ratio could also be
associated with hydration reactions (Heald et al., 2010) of carbonyls (Axson
et al., 2010), the condensation of water does not influence the elemental ratios
derived from the AMS spectra, as the calculation does not directly
utilize measured H2O-related ion signals, because they typically have large
interferences from gas-phase H2O in air (Canagaratna et al., 2015). It
should be mentioned that the observed increase in the H:C ratio could
potentially be due to impurities condensing to the particle phase in the
cold experiments, although this seems highly unlikely, as the chamber was
cleaned thoroughly before each experiment (see Kristensen et al., 2020), and
the observed changes in the H:C ratios would need an excessive amount of
impurities because the particle mass is high (see Table 1, and Fig. 2).
The combined effect of the evolution of the O:C ratio and H:C ratio is shown
in Fig. 5c, depicting the average carbon oxidation state OSC (Kroll
et al., 2011) during the experiments. At all temperatures the 50 ppb α-pinene experiments reach a relatively stable OSC within
∼ 15 min. This suggests that the observed increase in the
H:C ratio during the cold 50 ppb α-pinene experiments (Fig. 5b)
only has a small effect on the oxidation state of the particles. Throughout
the 10 ppb α-pinene experiments conducted at constant temperature
(experiments 1.1–1.3) and during heating (1.5), a slight gradual increase in
OSC is observed. For both the 10 and 50 ppb α-pinene
experiments, the OSC is linearly correlated with the SOA density (Fig. S27).
Oxidized organic tracer ions
As described in relation to the mass spectra obtained from the PMF analysis
(Fig. 3), differences in VOC precursor concentrations (i.e., particle mass
loading) and temperature primarily result in intensity differences in the
dominant oxygen-containing ions, m/z 43 and m/z 44. m/z 43 (dominated by
C2H3O+ (CHO1 family)) likely derives from organic compounds
containing non-acid oxygen (Ng et al., 2010), whereas the signal at m/z 44
(primary CO2+ (CHOgt1 family)) arises from carboxylic acids
(Alfarra, 2004). Both the number of acid groups and the length and
functionalization of the carbon chain in the compounds affect the intensity
of the signal at m/z 44 (Alfarra et al., 2004; Canagaratna et al., 2015).
Figure 6a and b are “triangle plots” (Ng et al., 2010), showing f44
(the fraction of m/z 44 relative to the total mass in the spectra) as a
function of f43 (the fraction of m/z 43 relative to the total mass in
the spectra), obtained from unit mass resolution data from the AMS. Figure 6a shows the values at the peak of mass concentration (five-data-point
average) of constant temperature experiments (1.1–1.3, 2.1–2.3, and 3.1–3.3)
and temperature ramp experiments (1.4 and 1.5), whereas Fig. 6b shows the
evolution through the constant temperature experiments. As observed in the
Van Krevelen plots (Fig. 5a, b), data from the repeated 50 ppb
α-pinene experiments (2.1–2.3 and 3.1–3.3) conducted at similar
temperatures show overall reproducibility, although they are not identical.
The elemental composition of particles formed in
experiments 1.1–1.3, 2.1–2.3, and 3.1–3.3 depicted in triangle plots
(f44 vs. f43) (a) at the mass peak (five-data-point average) and in
the expansion (b) during the experiments (∼ 5 min time
resolution, arrows indicate the direction of time). Note the different scales
of the axes in panels (a) and (b). In the triangle plots, the lines (y=-6.0204x+0.4154 and y=-1.8438x+0.3319, where 0.069≤x≤0.18 and y≤0.295) define the common composition of oxygenated organic
aerosol (Ng et al., 2010).
The triangle plots show that particles formed at higher temperature have a
higher f44 (i.e., CO2+, acid-derived functionalities) than
particles formed at lower temperature. No clear trends with temperature are
observed for f43 (i.e., C2H3O+, non-acid-derived
functionalities). Particles formed at a lower α-pinene concentration
(10 ppb) have higher f44 and a lower f43 than particles formed at a
higher α-pinene concentration (50 ppb). This suggests that
acid-derived functionalities are more prevalent in α-pinene SOA
formed at a low precursor concentration (and thus low particle mass loading),
which is consistent with less partitioning of the more volatile, less
oxidized material to the particle phase. In all experiments, f44 values
are between 0.04 and 0.1, and f43 values are between 0.08 and 0.15.
These levels are comparable to values reported in the literature from
chamber experiments conducted at comparable α-pinene concentrations,
both at room temperature (Chhabra et al., 2011; Kristensen et al., 2017) and
at -15 ∘C (Kristensen et al., 2017). The slight continuous
increase in f43 (Fig. 6b) in the experiments conducted at -15 ∘C (experiments 1.3, 2.3, and 3.3) is in agreement with
observations by Kristensen et al. (2017) from experiments performed at -15 ∘C at identical α-pinene concentrations. Moreover, the
non-evolving f43 at 20 ∘C is also in agreement with
the literature exploring α-pinene SOA at comparable concentrations and
room temperature (Chhabra et al., 2011; Kristensen et al., 2017). As the
increase in f43 is only observed in the cold experiments, especially
-15 ∘C, this suggests that the formation of species that give rise to
high f43 values is highly temperature dependent.
Estimated particle content of organic acids
In AMS mass spectra, m/z 44 has been shown to be a good tracer for the
content of organic acids in SOA (Canagaratna et al., 2015; Yatavelli et al., 2015). Yatavelli et al. (2015) investigated how the mass concentration of
molecules (R-COOH) containing one or more acid functionalities, can be
related to the AMS-derived mass concentration of m/z 44 multiplied by
scaling factors. Yatavelli et al. (2015) estimated that 10 % to 50 % of the
organic particle mass in the Northern Hemisphere can be attributed to
molecules containing the carboxylic acid functionality. Inspired by
Yatavelli et al. (2015), we explore how the intensity of m/z 44 in the
AMS mass spectra compares to the mass concentration of organic acids
(R-COOH) and organic acid functionalities (-COOH) based on results from the
off-line LC-MS analysis of filter samples obtained by the end of the AURA
chamber experiments. As described in detail in Kristensen et al. (2020),
LC-MS analysis was performed to identify and quantify 10 carboxylic acids
formed in the dark ozonolysis of α-pinene (constituting 18 %–38 %
of the SOA mass concentration in the current experiments), as well as 30 dimer
esters (constituting in total 4 %–11 % of the total SOA mass concentration
in the current experiments).
For the 50 ppb α-pinene experiments 3.1, 3.2, and 3.3, conducted at
20, 0, and -15 ∘C, respectively,
Fig. 7a shows the mass concentration of organic acids (R-COOH) identified
from LC-MS analysis (Kristensen et al., 2020), and the mass concentration of
the m/z 44 signal in the AMS mass spectra, scaled to the SMPS mass
concentration and corrected for density, as previously described. For both
techniques (AMS and LC-MS), the mass concentration of organic acids is lower
at higher temperatures. The AMS m/z 44 mass concentrations are lower than
the organic acid concentrations obtained from the LC-MS by factors of 2.55,
4.11, and 4.65 at 20, 0, and -15 ∘C,
respectively. In the following, these numbers will be referred to as scaling
factors. For comparison, Yatavelli et al. (2015) reported that the m/z 44 AMS
signal was a factor of ∼ 2.32 lower than the mass
concentration of organic acids in SOA during summertime in a forest area
dominated by pine trees near Colorado Springs, USA. Their result is in very
good agreement with the scaling factor obtained in the experiment conducted
at 20 ∘C, which supports the hypothesis that the most important
organic acids in α-pinene SOA are determined by the LC-MS method.
The variation in scaling factors at the different temperatures likely
reflects that organic acids with different numbers of acid functionality
(-COOH) and/or different multifunctional moieties exhibit different degrees
of thermal decomposition to the m/z 44 signal in the AMS (Canagaratna et al., 2015; Yatavelli et al., 2015). The similarity of the scaling factors obtained
in the 0 ∘C (4.11) and -15 ∘C (4.65) experiments is
consistent with the fact that the SOA chemical composition at those
temperatures have a higher degree of comparability relative to the 20 ∘C experiment, where a lower scaling factor (2.55) is obtained
(recall Figs. 4 and 6).
LC-MS-derived organic acid (R-COOH) mass concentration
(a) and organic acid mass fraction (b) at -15, 0, and 20 ∘C. The organic acid functionality (-COOH) mass concentration and
mass concentration at the three temperatures are shown in panels (c) and (d),
respectively. Additionally, the corresponding results related to dimers are
shown. The panels also show m/z 44 – a tracer of organic acids – from the
AMS mass spectra and the scaling factors to be applied to reach the level of
organic acid (functionality) measured by the LC-MS. The particle filter
samples analyzed by the LC-MS are obtained by the end of the experiments
(see Table 1), whereas the AMS results are obtained right before the filter
sampling (10 min average). The figure is based on data from experiments
3.1–3.3.
As lower SOA mass is produced at the higher temperatures, it is also
relevant to investigate how the mass fractions of organic acids vary with
temperature (Fig. 7b). The mass fractions are obtained by dividing the
LC-MS and AMS results presented in Fig. 7a by the total SOA mass
concentration measured in the chamber, prior to the filter sampling, and
corrected as described previously. By application of the scaling factors
found above, the two techniques are in good agreement, although slight
differences appear at 20 and -15 ∘C. While the
mass concentration of organic acids (R-COOH) obtained from the LC-MS
decreased significantly with higher temperature (Fig. 7a), no trend was
observed in organic acid mass fractions (Fig. 7b). Interestingly, for m/z 44 from the AMS mass spectra, the temperature-dependent trend changes from
decreasing with higher temperature (Fig. 7a) to increasing when focusing
on the mass fraction of m/z 44 to total SOA mass concentration (Fig. 7b).
Some of the organic acids as well as the dimers observed from the LC-MS data
(Fig. 7a, b) contain multiple acid functionalities (-COOH)
(Kristensen et al., 2020). Therefore, it is also relevant to investigate how
the mass concentration and mass fraction of acid functionalities (from the
suggested molecular structures; Kristensen et al., 2020) relate to the m/z 44 signal obtained from the AMS. Lower masses of organic acid
functionalities (-COOH) are obtained at higher temperatures, and the scaling
factors of 1.15, 1.46, and 1.70 applied to the m/z 44 AMS signal at 20, 0, and -15 ∘C, respectively (Fig. 7c), are lower and less variable with temperature than those for the mass
concentrations of organic acids (R-COOH; Fig. 7a). The difference between
the scaling factors, related to the mass concentration of organic acids
(R-COOH; Fig. 7a) and organic acid functionalities (-COOH; Fig. 7c) at
the same temperatures, reflects the mass of the organic acid backbone (i.e.,
R in R-COOH). The observed trends in Fig. 7a and c suggest that organic
acids with heavier backbones are formed at temperatures below 20 ∘C.
Figure 7d shows that carboxylic acid functionalities (-COOH) account for a
greater fraction of the observed SOA mass concentration at higher
temperatures, consistent with the observation of higher f44 values at
higher temperatures in Fig. 6. This trend is the inverse of the temperature-dependent trends of the absolute mass concentration of organic acid
functionalities (-COOH, Fig. 7c) and organic acids (R-COOH), both in this
study (Fig. 7b) and in previous studies (Zhang et al., 2015; Kristensen et al., 2015).
Overall, the comparison of the m/z 44 signal from the AMS mass spectra and
SOA acid content obtained from LC-MS data shows that organic acids and
organic acid functionalities are important constituents of α-pinene-derived SOA and that it is relevant to investigate and compare different
techniques for their quantification.
Conclusions
The chemical composition of α-pinene-derived SOA was investigated
using HR-ToF-AMS in a series of experiments performed at different α-pinene concentrations (10 and 50 ppb) and temperatures (20, 0, and -15 ∘C, and ramps in the range of 20 to -15 and -15 to 20 ∘C). PMF analysis was applied to a combined AMS dataset
representing eight different experimental conditions. The PMF analysis
revealed that the chemical composition of the SOA particles could be
described by four factors, which differ in their dependence on VOC
concentration and experiment temperature. To our knowledge, this is the
first study using PMF analysis on AMS chamber data to reveal distinct
factors sensitive to temperature.
This analysis demonstrates that the α-pinene SOA oxidation level is
dependent on both temperature and the α-pinene concentration: SOA
oxidation level increases with higher temperature and with lower SOA mass
loading. The dataset suggests that particles formed at 0 ∘C are
more chemically similar to particles formed at -15 ∘C than to
particles formed at 20 ∘C. Temperature ramps over a range of 35 ∘C were only accompanied by slight changes in chemical
composition, with increasing oxidation levels during heating ramps and
decreasing oxidation levels during cooling ramps. The investigation
demonstrates that the temperature at which particles are formed is decisive
for their properties during α-pinene SOA lifetime. This is
interesting from an atmospheric perspective as secondary organic aerosol
particles are formed and age over a wide range of temperatures.
Code availability
TThe PET code (Ulbrich et al., 2009) is freely available from the
following website: http://cires1.colorado.edu/jimenez-group/wiki/index.php/PMF-AMS_Analysis_Guide (last access: 15 July 2021).
The PMF software is available from Pentti Paatero (Pentti.Paatero86@gmail.com).
Data availability
Data are available upon request from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-11545-2021-supplement.
Author contributions
MB, ME, MG, and HBP supervised the ACCHA campaign. KK and LNJ initialized the chamber for experiments. KK and LNJ measured and analyzed the aerosol phase. KK, BR, RT, and LLJQ measured and analyzed the VOCs and their oxidation products. LNJ performed the AMS experiments, and LNJ analyzed the AMSA data with guidance and assistance from MRC. LNJ prepared the paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by the Aarhus Universitets Forskningsfond (AUFF NOVA grant), the European Research Council (ERC grant no. 638703; COALA), and the Academy of Finland (project nos. 307331, 317380, and 320094).
Review statement
This paper was edited by Astrid Kiendler-Scharr and reviewed by two anonymous referees.
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