Our understanding of atmospheric oxidation chemistry has improved
significantly in recent years, greatly facilitated by developments in mass
spectrometry. The generated mass spectra typically contain vast amounts of
information on atmospheric sources and processes, but the identification and
quantification of these is hampered by the wealth of data to analyze. The
implementation of factor analysis techniques have greatly facilitated this
analysis, yet many atmospheric processes still remain poorly understood.
Here, we present new insights into highly oxygenated products from monoterpene
oxidation, measured by chemical ionization mass spectrometry, at a boreal
forest site in Finland in autumn 2016. Our primary focus was on the formation
of accretion products, i.e., dimers. We identified the formation of
daytime dimers, with a diurnal peak at noontime, despite high nitric oxide
(NO) concentrations typically expected to inhibit dimer formation. These
dimers may play an important role in new particle formation events that are
often observed in the forest. In addition, dimers identified as combined
products of NO3 and O3 oxidation of monoterpenes were also found
to be a large source of low-volatility vapors at night. This highlights the
complexity of atmospheric oxidation chemistry and the need for future
laboratory studies on multi-oxidant systems. These two processes
could not have been separated without the new analysis approach deployed in our
study, where we applied binned positive matrix factorization (binPMF) on
subranges of the mass spectra rather than the traditional approach where
the entire mass spectrum is included for PMF analysis. In addition to the
main findings listed above, several other benefits compared to traditional
methods were found.
Introduction
Huge amounts of volatile organic compounds (VOCs) are emitted to the
atmosphere every year (Guenther et al., 1995; Lamarque et al., 2010),
which play a significant role in atmospheric chemistry and affect the
oxidative ability of the atmosphere. The oxidation products of VOCs can
contribute to the formation and growth of secondary organic aerosols
(Kulmala et al., 2013; Ehn et al., 2014; Kirkby et al., 2016; Troestl et
al., 2016), affecting air quality, human health, and climate radiative
forcing (Pope et al., 2009; Stocker et al., 2013; Zhang et al.,
2016; Shiraiwa et al., 2017). Thanks to the advancement in mass spectrometric
applications, like the aerosol mass spectrometer (AMS) (Canagaratna et
al., 2007) and chemical ionization mass spectrometry (CIMS) (Bertram et
al., 2011; Jokinen et al., 2012; Lee et al., 2014), our capability to detect
these oxidized products, as well as our understanding of the complicated
atmospheric oxidation pathways in which they take part, has been greatly
enhanced.
Monoterpenes (C10H16), one major group of VOCs emitted in forested
areas, have been shown to be a large source of atmospheric secondary organic
aerosol (SOA). The oxidation of monoterpenes produces an abundance of
different oxidation products (oxygenated VOC, OVOC), including highly
oxygenated organic molecules (HOMs) with molar yields in the range of a few
percent, depending on the specific monoterpene and oxidant (Ehn et al.,
2014; Bianchi et al., 2019). Recent chamber studies have greatly advanced our
knowledge of formation pathways for monoterpene HOM products, e.g.,
monomers (typically C9-10H12-16O6-12) and dimers
(typically C19-20H28-32O8-18). Dimers, as shown by previous
studies, can contribute to new particle formation (NPF) (Kirkby et al.,
2016; Troestl et al., 2016; Lehtipalo et al., 2018), and they are thus of
particular interest.
In nearly all atmospheric oxidation chemistry, peroxy radicals (RO2)
are the key intermediates (Orlando and Tyndall, 2012). They form when VOCs
react with oxidants like ozone, or the hydroxyl (OH) or nitrate (NO3)
radicals, while their termination occurs mainly by bimolecular reactions
with nitric oxide (NO), hydroperoxyl (HO2), and/or other RO2.
RO2+R′O2 reactions can form ROOR′ dimers (Berndt et al.,
2018a, b), and this pathway competes with RO2+NO
reactions, meaning that NO, formed by photolysis of NO2, can
efficiently suppress dimer formation, as also seen from atmospheric HOM
observations (Ehn et al., 2014; Yan et al., 2016). Mohr et al. (2017) also
reported daytime dimers in the boreal forest in Finland, coinciding with NPF
events. A better understanding of the formation of these daytime dimers
would assist elucidating NPF and particle growth mechanisms.
At night, nitrogen oxides can also impact the oxidation pathways when
NO2 and O3 react to form NO3 radicals that can oxidize
monoterpenes. NO3 radicals are greatly reduced during daytime due to
photolysis and reactions with NO reducing their lifetime to a few seconds
(Ng et al., 2017). Yan et al. (2016) reported nighttime
HOMs initiated by NO3 in the boreal forest in Finland, but to our
knowledge there have been no laboratory studies on HOM formation from
NO3 oxidation of monoterpenes. However, there have been several studies
looking into the SOA formation in these systems, finding that certain
monoterpenes, like β-pinene, have very high SOA yields, while the
most abundant monoterpene, α-pinene, has negligible SOA forming
potential. It remains an open question as to what the role of NO3 radical
oxidation of monoterpenes, and the observed NO3-derived HOMs, in the
nighttime boreal forest is. Identification of these processes in the
ambient environment is fundamental for better understanding of NPF and
SOA.
The recent development of CIMS techniques has allowed researchers to observe
unprecedented numbers of OVOCs in real time (Riva et
al., 2019). This ability to measure thousands of compounds is a great
benefit, but it is also a large challenge for the data analyst. For this reason,
factor analytical techniques have often been applied to reduce the
complexity of the data (Huang et al., 1999), e.g., positive matrix
factorization, PMF (Paatero and Tapper, 1994; Zhang et al., 2011). The
factors have then been attributed to sources (e.g., biomass burning organic
aerosol) or processes (e.g., monoterpene ozonolysis) depending on the
application and ability to identify spectral signatures (Yan et al.,
2016; Zhang et al., 2017).
In the vast majority of these PMF applications to mass spectra, the mass
range of ions has been maximized in order to provide as much input as
possible for the algorithm. This approach was certainly motivated in the early
application of PMF by, for example, offline filters, with chemical information of
metals, water-soluble ions, and organic carbon and elemental carbon (OC and EC),
where the number of variables is counted in tens, and the number of samples
in tens or hundreds (Zhang et al., 2017). However, with gas-phase
CIMS, we often have up to a thousand variables, with hundreds or even
thousands of samples, meaning that the amount of data itself is unlikely to
be a limitation for PMF calculation. In this work, we aimed to explore
potential benefits of dividing the spectra into subranges before applying
factorization analysis. This approach was motivated by several issues, which
we expected to be resolvable by analyzing several mass ranges separately.
Firstly, the loss rate of OVOCs by condensation is strongly coupled to the
molecular mass (Peräkylä et al., 2020), likely giving very
different behaviors for the high and low mass ranges, even when produced by
the same source. Second, dimers are a product of two RO2 radicals, which can
have different sources, meaning that they may have temporal profiles unlike
anything observable for monomers. Finally, if one mass range contains much
less signal than another, it will have very little impact on the final PMF
results.
In this study, we applied PMF analysis on three different mass ranges of
mass spectra of OVOCs measured by a chemical ionization atmospheric pressure
interface time-of-flight (CI-APi-TOF; Jokinen et al.,
2012) mass spectrometer in the Finnish boreal forest. We utilized our
recently proposed new PMF approach, binPMF, to include as much of the
high-resolution information in the mass spectra as possible in a robust way
(Zhang et al., 2019). We show the benefits of the
subrange PMF approach to better separate chemical sources by reducing
disturbance from variable loss terms of the OVOCs. Much of the analysis
focuses on dimer formation pathways and the role of different nitrogen
oxides in these pathways. We find that both daytime dimers and dimers
resulting from the combination of different oxidants can be separated with
the subrange approach but not with the PMF applied to the full mass range.
We believe that this study will provide new perspectives for future studies
analyzing gas-phase CIMS data.
Methodology
The focus of this work is on retrieving new information from mass spectra by
applying new analytical approaches. Therefore, we chose a dataset that has
been presented earlier, though without PMF analysis, by
Zha et al. (2018) and was also
used in the first study describing the binPMF method
(Zhang et al., 2019). The measurements are described
in more details below in Sect. 2.1, while the data analysis techniques
used in this work are presented in Sect. 2.2.
MeasurementsAmbient site
The ambient measurements were conducted at the Station for Measuring
Ecosystem–Atmosphere Relations (SMEAR) II in Finland (Hari and
Kulmala, 2005) as part of the Influence of Biosphere-Atmosphere Interactions
on the Reactive Nitrogen budget (IBAIRN) campaign (Zha et al., 2018). Located
in the boreal environment in Hyytiälä, SMEAR II is surrounded by
coniferous forest and has limited anthropogenic emission sources nearby.
Diverse measurements of meteorology, aerosol, and gas-phase properties are
continuously conducted at the station. Details about the meteorological
conditions and temporal variations of trace gases during the IBAIRN campaign are
presented by Zha et al. (2018) and Liebmann
et al. (2018).
Instrument and data
Data were collected with a nitrate (NO3-)-based chemical
ionization atmospheric pressure interface time-of-flight mass spectrometer
(CI-APi-TOF, Jokinen et al., 2012) with about 4000 Th Th-1 mass-resolving power at ground level in September 2016. In our study, the mass
spectra were averaged to 1 h time resolution from 6 to 22 September for further analysis. We use the thomson (Th) as the unit for
mass-to-charge ratio, with 1 Th =1 Da/e, where e is the elementary charge. As all
the data discussed in this work are based on negative ion mass spectrometry,
we will use the absolute value of the mass-to-charge ratio, although the charge of
each ion will be negative. The masses discussed in this work include the
contribution from the nitrate ion, 62, unless specifically mentioned.
Furthermore, as the technique is based on soft ionization with
NO3- ions, any multiple charging effects are unlikely, and
therefore the reported mass-to-charge values in thomson can be considered
equivalent to the mass of the ion in dalton (Da).
The forest site of Hyytiälä is dominated by monoterpene emissions
(Hakola et al., 2006). The main feature of previous
CI-APi-TOF measurements in Hyytiälä (Ehn et al., 2014; Yan et al.,
2016) has been a bimodal distribution of HOMs, termed monomers and dimers,
as they are formed of either one or two RO2 radicals, respectively. For
the analysis in this study, we chose three mass-to-charge (m/z) ranges of 50 Th
each (Fig. 1), corresponding to regions between which we expect
differences in formation or loss mechanisms. In addition to regions with HOM
monomers and HOM dimers, one range was chosen at lower masses, in a region
presumably mainly consisting of molecules that are less likely to condense
onto aerosol particles (Peräkylä et al., 2020).
Positive matrix factorization (PMF)
After the model of PMF was developed (Paatero and Tapper, 1994), numerous
applications have been conducted with different types of environmental data
(Song et al., 2007; Ulbrich et al., 2009; Yan et al., 2016; Zhang et al.,
2017). By reducing the dimensionality of the measured dataset, the PMF model greatly
simplifies the data analysis process with no requirement for prior knowledge
of sources or pathways as essential input. The main factors can be further
interpreted with their unique or dominant markers (elements or masses).
The basic assumption for PMF modeling is mass balance, which assumes that
ambient concentration of a chemical component is the sum of contributions
from several sources or processes, as shown in Eq. (1).
X=TS×MS+R
In Eq. (1), X stands for the time series of measured concentration of
different variables (m/z in our case), TS represents the temporal variation of factor contributions, MS stands for factor profiles (mass spectral
profiles), and R is the residual as the difference of the modeled and the
observed data. The matrices TS and MS are iteratively calculated by a
least-squares algorithm utilizing uncertainty estimates to pursue a minimized
Q value as shown in Eq. (2), where Sij is the estimated
uncertainty, an essential input in the PMF model.
Q=∑∑RijSij2
The PMF model was conducted by multilinear engine (ME-2) (Paatero,
1999) and interfaced with Source Finder (SoFi, v6.3) (Canonaco et al.,
2013). Signal-to-noise ratio (SNR) was calculated as SNRij= abs (Xij)/abs
(Sij). When the signal-to-noise ratio (SNR) is below 1, the signal of Xij
will be down-weighted by replacing the corresponding uncertainty Sij by
Sij/SNRij (Visser et al., 2015). Future studies should
pay attention to the potential risk when utilizing this method since
down-weighting low signals element-wise will create a positive bias in the
data. Robust mode was chosen in the PMF modeling, where outliers |RijSij|>4 were significantly down-weighted
(Paatero, 1997).
binPMF
As a newly developed application of PMF for mass spectral data, binPMF has
no requirement for chemical composition information while still taking
advantage of the high-resolution (HR) mass spectra, saving effort and time
(Zhang et al., 2019). To explore the benefits of
analyzing separated mass ranges, we applied binPMF to the three separated
ranges. The three ranges were also later combined for binPMF analysis as
a comparison with the previous results. The PMF model requires both data
matrix and error matrix as input, and details of the preparation of data and
error matrices are described below.
Data matrix
Unlike normal unit mass resolution (UMR) or HR peak fitting, in binPMF, the mass spectra
are divided into small bins after baseline subtraction and mass axis
calibration. Linear interpolation was first conducted on the mass spectra
with a mass interval of 0.001 Th. Then the interpolated data were averaged
into bins of 0.02 Th width. We selected three ranges for further analysis
based on earlier studies (Ehn et al., 2014; Yan et al., 2016; Bianchi et
al., 2019; Peräkylä et al., 2020).
Range 1, m/z 250–300 Th, 51 unit masses ×25 bins per unit mass =1275 bins/variables, consisting mainly of molecules with five to nine carbon
atoms and four to nine oxygen atoms in our dataset.
Range 2, m/z 300–350 Th, 51×25=1275 bins, mainly corresponding
to HOM monomer products, with 9 to 10 C atoms and 7 to 10
O atoms.
Range 3, m/z 510–560 Th, 51×30=1530 bins, mainly corresponding
to HOM dimer products, with carbon numbers of 16 to 20 and 11
to 15 O atoms.
To avoid unnecessary computation, only signal regions with meaningful
signals in the mass spectra were binned (Zhang et
al., 2019). For a nominal mass N, the signal region included in further
analyses was between N-0.2 and N+0.3 Th for Range 1 and Range 2 and between
N-0.2 and N+0.4 Th for Range 3. The wider signal regions in Range 3 are
due to wider peaks at higher masses. The data were averaged into 1 h time
resolution, and in total we had 384 time points in the data matrix.
Error matrix
The error matrix represents the estimated uncertainty for each element of
the data matrix, and it is crucial for iterative calculation of the Q minimum.
Equation (3) is used for error estimation (Polissar et al., 1998),
Sij=σij+σnoise,
where Sij represents the uncertainty of m/zj at time i and σij
stands for counting statistics uncertainty and is estimated as follows:
σij=a×Iijts,
where I is the signal intensity term, in unit of counts per second (cps);
ts stands for length of averaging in seconds, and a is an empirical
coefficient to compensate for unaccounted uncertainties (Allan et al.,
2003; Yan et al., 2016) and is 1.28 in our study as previously estimated from
laboratory experiments (Yan et al., 2016). The σnoise term was
estimated as the median of the standard deviations from signals in the bins
in the region between nominal masses, where no physically meaningful signals
are expected.
ResultsGeneral overview of the dataset and spectrum
During the campaign, in autumn 2016, the weather was overall sunny and
humid with average temperature of 10.8 ∘C and relative humidity (RH) of
87 % (Zha et al., 2019). The average concentrations of NOx and O3
were 0.4 and 21 ppbv, respectively. The average total HOM concentration
was ∼108 molecules cm-3.
Example of mass spectrum with 1 h time resolution measured from a
boreal forest environment during the IBAIRN campaign (at 18:00 LT, Finnish
local time, UTC+2). The mass spectrum was divided into three parts, and
three subranges were chosen from different parts for further analysis in
our study. The nitrate ion (62 Th) is included in the mass.
Figure 1 shows the 1 h averaged mass spectrum taken at 18:00 LT (all times in this paper are in Finnish local time (UTC+2) unless stated otherwise) on 12 September, as an example of the analyzed dataset. In addition to exploring the
benefits of this type of subrange analysis in relation to different
formation or loss pathways, separating into subranges may also aid factor
identification for low-signal regions. As shown in Fig. 1, there is a
difference of 1–2 orders of magnitude in the signal intensity between Range
3 and Range 1–Range 2. If all ranges are run together, we would expect that the
higher signals from Range 1 and Range 2 will drive the factorization. While if
run separately, separating formation pathways of dimers in Range 3 will
likely be easier. As dimers have been shown to be crucial for the formation
of new aerosol particles from monoterpene oxidation (Kirkby et al.,
2016; Troestl et al., 2016; Lehtipalo et al., 2018), this information may even
be the most critical in some cases, despite the low contribution of these
peaks to the total measured signal.
binPMF was separately applied to Range 1, Range 2, Range 3, and a “Range combined” which
comprised all three subranges. All the PMF runs for the four ranges
were conducted from 2 to 10 factors and repeated 3 times for each
factor number, to assure the consistency of the results. Factorization
results and evolution with increasing factor number are briefly described in
the following sections, separately for each range (Sect. 3.2–3.5). It
is worth noting that the factor order in factor evolution does not
necessarily correspond to that of the final results. The factor orders
displayed in Figs. 2–5 have been modified for further comparison between
different ranges. More detailed discussion and comparisons between the
results are presented in Sect. 4.
binPMF on Range 1 (250–300 Th)
As has become routine (Zhang et al., 2011; Craven et al., 2012), we first
examined the mathematical parameters of our solutions. From 2 to 10
factors, Q/Qexp decreased from 2.8 to 0.7 (Fig. S1 in Supplement), and after three factors the decreasing trend was gradually
slowing down and approaching 1, which is the ideal value for Q/Qexp as a
diagnostic parameter. The unexplained variation showed a decline from 18 %
to 8 % from 2 to 10 factors.
In the two-factor results, two daytime factors were separated, with peak
time both at 14:00–15:00. One factor was characterized by large signals at
m/z 250, 255, 264, 281, 283, 295, and 297 Th. The other factor was
characterized by large signals at m/z 294, 250, 252, 264, 266,
268, and 297 Th. In Hyytiälä, as reported in previous studies,
odd masses observed by the nitrate CI-APi-TOF are generally linked to
monoterpene-derived organonitrates during the day (Ehn et al., 2014; Yan
et al., 2016). When the factor number increased to three, the two earlier
daytime factors remained similar to the previous result, while a new
factor appeared with a distinct sawtooth shape in the diurnal cycle. The
main marker in the spectral profile was m/z 276 Th, with a clear negative mass
defect. When one more factor was added, the previous three factors remained
similar as in the three-factor solution, and a new morning factor was
resolved, with m/z 264 and 297 Th dominant in the mass spectral profile and
a diurnal peak at 11:00.
As the factor number was increased, more daytime factors were separated,
with similar spectral profiles to existing daytime factors and various peak
times. No nighttime factors were found in the analysis even when the factor
number reached 10. We chose the four-factor result for further discussion,
and Fig. 2 shows the result of Range 1, with spectral profile, time
series, diurnal cycle, and averaged factor contribution during the campaign.
As shown in Fig. 2d, factors 1–3 are all daytime factors, while factor 4
has no clear diurnal cycle but a distinct sawtooth shape. Factor 4 comes
from a contamination of perfluorinated acids from the inlet's automated
zeroing every 3 h during the measurements (Zhang et al., 2019). The
zeroing periods have been removed from the dataset before binPMF analysis,
but the contamination factor was still resolved. This factor is discussed in
more detail in Sect. 4.1 and 4.4.
Four-factor result for Range 1 for (a) factor spectral profiles,
(b) averaged factor contribution during the campaign, (c) time series, and
(d) diurnal trend. Details on the naming schemes for the factors are shown in Table 1.
binPMF on Range 2 (300–350 Th)
This range covers the monoterpene HOM monomer range, and binPMF results have
already been discussed by Zhang et al. (2019) as a first example of the
application of binPMF on ambient data. Our input data here are slightly
different. In the previous study, the 10 min automatic zeroing every 3 h was not removed before averaging to 1 h time resolution, while here
we have removed these data. Overall, the results are similar as in our
earlier study, and therefore the results are just briefly summarized below
for further comparison and discussion in Sect. 4. Similar to Range 1, both
the Q/Qexp (2.2 to 0.6) and unexplained variation (16 % to
8 %) declined with the increased factor number from 2 to 10.
When the factor number was two, one daytime factor and one nighttime factor were
separated, with diurnal peak times at 14:00 and 17:00, respectively. The
nighttime factor was characterized by masses at 340, 308, and 325 Th
(HOM monomers from monoterpene ozonolysis; Ehn et al., 2014) and remained
stable throughout the factor evolution from 2 to 10 factors. With the
addition of more factors, no more nighttime factors got separated, while the
daytime factor was further separated and more daytime factors appeared,
peaking at various times in the morning (10:00), at noon, or in the early
afternoon (around 14:00 and 15:00). High contribution of m/z 339 Th can be
found in all the daytime factor profiles. As the factor number reached six,
a contamination factor appeared, characterized by large signals at m/z 339
and 324 Th, showing negative mass defects (Fig. S2). The
factor profile is nearly identical to the contamination factor determined in
Zhang et al. (2019), where the zeroing periods were not removed, causing
larger signals for the contaminants. In our dataset, where the zeroing
periods were removed, no sawtooth pattern was discernible in the diurnal
trend, yet it could still be separated even though it only contributed 3 %
to Range 2. More about the contamination factors from different ranges will
be discussed in Sect. 4.4. We chose to show the four-factor result below,
to simplify the later discussion and comparison. Figure 3 shows four-factor
result of Range 2, with spectral profile, time series, diurnal cycle, and
averaged factor contribution during the campaign.
Four-factor result for Range 2 for (a) factor spectral profiles,
(b) averaged factor contribution during the campaign, (c) time series, and
(d) diurnal trend. Details on the naming schemes for the factors are shown in Table 1.
binPMF on Range 3 (510–560 Th)
Range 3 represents mainly the monoterpene HOM dimers (Ehn et al., 2014).
Similar to Range 1 and Range 2, both the Q/Qexp (1.5 to 0.6) and
unexplained variation (18 % to 15 %) showed decreasing trend with the
increased factor number (2–10). As can be seen from Fig. 1, data in Range
3 had much lower signals compared to those of Range 1 and Range 2, explaining
the higher unexplained variation for Range 3.
In the two-factor result for Range 3, one daytime factor and one nighttime factor
appeared, with diurnal peak times at noon and 18:00, respectively. The
nighttime factor was characterized by ions at m/z 510, 524, 526, 542, 555, and 556 Th, while the daytime factor showed no dominant marker
masses, yet with relatively high signals at m/z 516, 518, and 520 Th. As
the number of factors increased to three, one factor with almost flat
diurnal trend was separated, with dominant masses of 510, 529, and 558 Th.
Most peaks in this factor had negative mass defects, and this factor was
again linked to a contamination factor. The four-factor result resolved
another nighttime factor with a dominant peak at m/z 555 Th and effectively
zero contribution during daytime. As the factor number was further
increased, the new factors seemed like splits from previous factors with
similar spectral profiles. We therefore chose the four-factor result also for
Range 3 (results shown in Fig. 4) for further discussion.
Four-factor result for Range 3 for (a) factor spectral profiles,
(b) averaged factor contribution during the campaign, (c) time series, and
(d) diurnal trend. Details on the naming schemes for the factors are shown in Table 1.
binPMF on Range combined (250–350 and 510–560 Th)
As a comparison to the previous three ranges, we conducted the binPMF analysis
on Range combined, which is the combination of the three ranges. The results
of this range are fairly similar to those of Range 1 and Range 2, as could be
expected since the signal intensities in these ranges were much higher than
in Range 3. As the number of factors increased (2–10), both the
Q/Qexp (1.3 to 0.6) and unexplained variation (16 % to 8 %)
showed a decreasing trend.
Four-factor result for Range combined for (a) factor spectral
profiles, (b) averaged factor contribution during the campaign, (c) time
series, and (d) diurnal trend. Details on the naming schemes for the factors are shown in Table 1.
In the two-factor result, one daytime factor and one nighttime factor were
separated. In the nighttime factor, most masses were found at even masses,
and the fraction of masses in Range 3 was much higher than that in the daytime
factor. In contrast, in the daytime factor, most masses were observed at odd
masses and the fraction of signal in Range 3 was much lower. During the day,
photochemical reactions as well as potential emissions increase the
concentration of NO, which serves as peroxy radical (RO2) terminator
and often outcompetes RO2 cross reactions in which dimers can be formed
(Ehn et al., 2014). Thus, the production of dimers is suppressed during the
day, yielding instead a larger fraction of organic nitrates, as has been
shown also previously (Yan et al., 2016).
With the increase in the number of factors, more daytime factors were
resolved with different peak times. When the factor number reached seven, a
clear sawtooth-shape diurnal cycle occurred, i.e., the contamination factor
caused by the zeroing. As more factors were added, no further nighttime
factors were separated, and only more daytime factors appeared. To simplify
the discussion and inter-range comparison, we also here chose the
four-factor result for further analysis. Figure 5 shows the four-factor
result of Range combined, with spectral profile, time series, diurnal cycle,
and averaged factor contribution during the campaign. The signals in the range
of 510–560 Th were enlarged 100-fold to be visible.
Discussion
In Sect. 3, results by binPMF analysis were shown for Range 1, Range 2, Range 3, and
Range combined. In this section, we discuss and compare the results from the
different ranges. To simplify the inter-range comparison, we chose
four-factor results for all four ranges, with the abbreviations shown in
Table 1. From Range 1, three daytime factors and a contaminations factor
were separated. In Range 2, three daytime factors and one nighttime factor
(abbreviated as R2F4_N) were resolved. The
R2F4_N factor was characterized by signals at m/z 308 Th
(C10H14O7⋅NO3-), 325 Th
(C10H15O8⋅NO3-), and 340 Th
(C10H14O9⋅NO3-), and they can be confirmed as
monoterpene ozonolysis products (Ehn et al., 2014; Yan et al., 2016). With
the increase in factor number to six, the contamination factor was separated
also in this mass range. In Range 3, one daytime factor, two nighttime
factors, and a contamination factor were separated. The first nighttime
factor (R3F2_N1) had large peaks at m/z 510 Th
(C20H32O11⋅NO3-) and 556 Th
(C20H30O14⋅NO3-), representing dimer products that
have been identified during chamber studies of monoterpene ozonolysis
(Ehn et al., 2014). The molecule observed at m/z 510 Th has 32 H-atoms,
suggesting that one of the RO2 involved would have been initiated by
OH, which is formed during the ozonolysis of alkenes such as monoterpenes at
nighttime (Atkinson et al., 1992; Paulson and Orlando,
1996). The other nighttime factor (R3F3_N2) was dominated by
ions at m/z 523 Th (C20H31O8NO3⋅NO3-)
and 555 Th (C20H31O10NO3⋅NO3-),
representing nighttime monoterpene oxidation involving NO3. As these
dimers contain only one N-atom, and 31 H-atoms, we can assume that they are
formed from reactions between an RO2 formed from NO3 oxidation and
another RO2 formed by ozone oxidation. These results match well with
the profiles in a previous study by Yan et al. (2016). The results of Range
combined are very similar to Range 2, with one nighttime factor and three
daytime factors. The contamination factor was separated with increase in
factor number to seven.
Summary of PMF results for the different mass ranges.
a Factor name is defined with range name, factor number, and name. For
example, RxFy represents factor y in range x. RC stands for Range combined.
For the factor name, D is short for daytime, N for nighttime, and C for
contamination. b The contamination factor in Range 1 shows a sawtooth pattern,
while Range 3 shows no diurnal pattern.
Time series correlation
In Fig. 6, the upper panels show the time series correlations among the
first three ranges. As expected based on the results above, generally the
daytime factors, and the two nighttime monoterpene ozonolysis factors
(R2F4_N and R3F2_N1), correlated well. However, the contamination factors did not show a strong
correlation between different ranges, even though they are undoubtedly from the same
source. More about the contamination factors will be discussed in Sect. 4.4. The lower panels in Fig. 6 display the correlations between the
first three ranges and the Range combined, and they clearly demonstrate that the
results of Range combined are mainly controlled by high signals from Range 1
and 2. More detailed aspects of the comparison between factors in different
ranges is given in the following sections. The good agreements between
factors from different subranges also help to verify the robustness of the
solutions.
Time series correlations among Range 1, Range 2, Range 3 (a–c),
and between the first three ranges and the Range combined (d–f). The abbreviations for different factors are the same as in Table 1, with
F for factor, D for daytime, N for nighttime, and C for contamination, e.g.
F1D1 for factor 1 daytime 1. The coefficient of determination, R2, is
marked in each subplot by a number shown in the right upper corners and by
the blue colors, with stronger blue indicating higher R2.
Daytime processesFactor comparison
As mentioned above, with increasing number of factors, more daytime
factors will usually be resolved, reflecting the complicated daytime photochemistry.
The three daytime factors between Range 1 and Range 2 agreed with each other quite
well (Fig. 6a). However, R1F1_D1 and R2F1_D1
did not show strong correlations with the only daytime factor in Range 3
(R3F1_D), while the other two daytime factors in both Range 1
and Range 2, i.e., R1F2_D2, R1F3_D3, and
R2F2_D2, R2F3_D3, correlated well with
R3F1_D from Range 3.
The first daytime factors from Range 1 and Range 2, R1F1_D1 and
R2F1_D1, were mainly characterized by odd masses: 255, 281, 283, 295, 297, 307, 309, 311, 323, 325, and 339 Th. The factors are dominated by organonitrates. Organic nitrate
formation during daytime is generally associated with the termination of
RO2 radicals by NO. This termination step is mutually exclusive with
the termination of RO2 with other RO2, which can lead to dimer
formation. If the NO concentration is the limiting factor for the formation
of these factors, the low correlations between the NO-terminated monomer
factors and the dimer factors are to be expected. In contrast, if the other
daytime factors mainly depend on oxidant and monoterpene concentrations,
some correlation between those, and the daytime dimer factor, is to be
expected, as shown in Fig. 6b and c.
All the spectral profiles resolved from Range combined binPMF analysis
inevitably contained mass contributions from m/z 510 to 560 Th, even the daytime
factor from Range combined (RCF1_D1) which did not show a clear
correlation with R3F1_D from Range 3 (Fig. 6e).
The second and third daytime factors in Range 1 and Range 2,
R1F2_D2, R1F3_D3, R2F2_D2,
R2F3_D3, had high correlations with R3F1_D in
Range 3. Daytime factors in Range combined (RCF2_D2 and
RCF3_D3) also showed good correlation with
R3F1_D in Range 3. However, if we compare R3F1_D and the mass range of m/z 510–560 Th of the daytime factors in Range combined, just with a quick look, we can readily see the difference. The
daytime factor separated in Range 3 (R3F1_D) has no obvious
markers in the profile. With the increase in factor number (up to 10
factors), no clearly new factors were separated in Range 3, but instead the
previously separated factors were seen to split into several factors.
However, the spectral pattern in R3F1_D is different from
that in the mass range of 510–560 Th in RCF2_D2. The
factorization of Range combined was mainly controlled by low masses due to
their high signals. The signals at high masses were forced to be distributed
according to the time series determined by small masses. Ultimately, this
will lead to failure in factor separation for this low-signal range.
Daytime dimer formation
Dimers are primarily produced during nighttime, due to NO suppressing
RO2+RO2 reactions in daytime (Ehn et al., 2014; Yan et al.,
2016). However, in this study, we found one clear daytime factor in Range 3
(R3F1_D, peak at local time 12:00, UTC+2) by subrange
analysis. With high loadings from even masses including 516, 518, 520, 528,
and 540 Th, this only daytime factor in dimer range correlated very well with
two daytime factors in Range 1 and Range 2 (R1F2_D2,
R1F3_D3, R2F2_D2, R2F3_D3)
(Fig. 6b and c). Table 2 includes the correlation matrix of all PMF and
factors and selected meteorological parameters. Strong correlation between
R3F1_D with solar radiation was found, with R=0.79 (Table 2). This may indicate involvement of OH oxidation in producing this factor.
Correlation between factors and meteorological parameters and gases.
As previous studies have shown, dimers greatly facilitate new particle
formation (NPF) (Kirkby et al., 2016; Troestl et al., 2016; Lehtipalo et
al., 2018), and this daytime dimer factor may represent a source of dimers
that would impact the initial stages of NPF in Hyytiälä.
Mohr et al. (2017) reported a clear diel pattern of dimers
(sum of about 60 dimeric compounds of C16-20H13-33O6-9)
during NPF events in 2013 in Hyytiälä, with minimum at night and
maximum after noon, and they estimated that these dimers can contribute
∼5% of the mass of sub-60 nm particles. The link between
the dimers presented in that paper and those reported here will require
further studies, as will the proper quantification of the dimer factor
identified here.
Nighttime processesFactor comparison
Since high-mass dimers are more likely to form at night due to photochemical
production of NO in daytime, which inhibits RO2+RO2 reactions,
Range 3 had the highest fraction of nighttime signals of all the subranges.
While Range 3 produced two nighttime factors, Range 2 and Range combined showed
one, and Range 1 had no nighttime factor. The difference between the two
results also indicates the advantage of analyzing monomers and dimers
separately.
The two nighttime factors in Range 3 can be clearly identified as arising
from ozonolysis (R3F2_N1) and a mix of ozonolysis and
NO3 oxidation (R3F2_N2) based on the mass spectral
profiles, as described above. The organonitrate at m/z 555 Th,
C20H31O10NO3⋅NO3-, is a typical
marker for NO3 radical-initiated monoterpene chemistry
(Yan et al., 2016). However, several interesting features
become evident when comparing to the results of Range 2 and Range combined.
Firstly, only one nighttime factor (R2F4_N,
RCF4_N) was separated in each of these ranges, and that shows
a clear resemblance with ozonolysis of monoterpenes as measured in numerous
studies, e.g., Ehn et al. (2012, 2014). Secondly, the high correlation
found in Fig. 6b between the ozonolysis factors (i.e., R2F4_N, R3F2_N1, RCF4_N) further supports the
assignment. However, factor R2F4_N is the only nighttime
factor in the monomer range, suggesting that NO3 radical chemistry of
monoterpenes in Hyytiälä does not form substantial amounts of HOM
monomers. The only way for the CI-APi-TOF to detect products of
monoterpene-NO3 radical chemistry may thus be through the dimers, where
one highly oxygenated RO2 radical from ozonolysis reacts with a less
oxygenated RO2 radical from NO3 oxidation.
In the results by Yan et al. (2016) the combined UMR-PMF of
monomers and dimers did yield a considerable amount of compounds in the
monomer range also for the NO3 radical chemistry factor. There may be
several reasons for this discrepancy. One major cause for differences
between the spring dataset by Yan et al. (2016) and the
autumn dataset presented here is that nighttime concentrations of HOMs were
greatly reduced during our autumn campaign. The cause may have been fairly
frequent fog formation during nights and also the fact that the concentration of ozone decreased nearly to zero during several nights
(Zha et al., 2018). It is also
possible that the NO3 radical-related factor by Yan et al. (2016) is probably a mixture of NO3 and O3 radical chemistry,
while the monomer may thus be attributed to the O3 part. Alternatively,
the different conditions during the two measurement periods, as well as
seasonal difference in monoterpene mixtures (Hakola et
al., 2012), caused variations in the oxidation pathways.
Dimers initiated by NO3 radicals
Previous studies show that NO3 oxidation of α-pinene, the most
abundant monoterpene in Hyytiälä (Hakola et
al., 2012), produces fairly little SOA mass (yields < 4 %), while
β-pinene shows yields of up to 53 % (Bonn and Moorgat, 2002; Nah
et al., 2016). The NO3+β-pinene reaction results in low-volatility organic nitrate compounds with carboxylic acid, alcohol, and
peroxide functional groups (Fry et al., 2014; Boyd et al., 2015), while
the NO3+α-pinene reaction will typically lose the nitrate
functional group and form oxidation products with high vapor pressures
(Spittler et al., 2006; Perraud et al., 2010). Most monoterpene-derived
HOMs, including monomers, are low-volatility molecules (Peräkylä et
al., 2020), and thus a low SOA yield indicates a low HOM yield. Thus, while
there are to our knowledge no laboratory studies on HOM formation from
NO3 oxidation of α-pinene, a low yield can be expected based on
SOA studies.
Time series of the NO3 oxidation dimer factor (blue line) and
the products of (a) [NO3]2× [monoterpene]2, (b) [O3]2× [monoterpene]2, and (c) [NO3]
× [O3] × [monoterpene]2, where [] represents
concentration in units of pptv for NO3 radicals and monoterpene and ppbv
for O3, while the scatter plots are shown as inserts, (d), (e), and (f),
respectively. The scatter plots and correlation coefficients, R, are only
calculated from nighttime data, which is selected based on solar radiation,
to eliminate the influence from daytime oxidation processes.
As discussed above, a dimer factor (R3F2_N2) was identified
as being a crossover between RO2 radicals initiated by NO3 radicals and O3. Figure 7 shows the time series of this factor,
as well as the products of [NO3]2× [monoterpene]2,
[O3]2× [monoterpene]2, and [NO3] ×
[O3] × [monoterpene]2. These products are used to mimic
the formation rates of the RO2 radicals reacting to form the dimers,
either from pure NO3 oxidation (Fig. 7a), pure O3 oxidation
(Fig. 7b), or the mixed reaction between RO2 from the two oxidants (Fig. 7c). The
NO3 concentration was estimated in
Liebmann et al. (2018) for the same campaign.
Monoterpenes were measured using a proton transfer reaction time-of-flight
mass spectrometer (PTR-TOF-MS). More details on measurement of NO3
proxy and monoterpene can be found in
Liebmann et al. (2018).
As shown in Fig. 7, the time series of the dimer factor tracks those of
[NO3]2×[monoterpene]2 and [O3]2×[monoterpene]2
reasonably well, but it shows the highest correlation with the product of
[NO3]×[O3]×[monoterpene]2. This further
supports this dimer formation as a mixed process of ozonolysis and
NO3 oxidation. The heterogeneity of the monoterpene emissions in the
forest, and the fact that no dimer loss process is included, partly explains
the relatively low correlation coefficients. The sampling inlets for PTR-TOF
were about 170 m away from the NO3 reactivity measurement
(Liebmann et al., 2018), which in turn was
some tens of meters away from the HOM measurements. Thus, this analysis
should be considered qualitative only.
The nitrate dimer factor (R3F2_N2) was dominated by the
organonitrate at m/z 555 Th, C20H31O10NO3⋅NO3-. However, unlike the pure ozonolysis dimer factor which had a
corresponding monomer factor (R=0.86 between factor R2F4_N and R3F2_N1), this NO3-related dimer factor did not
have an equivalent monomer factor. This suggests that the NO3 oxidation
of the monoterpene mixture in Hyytiälä does not by itself form much
HOMs, but, in the presence of RO2 from ozonolysis, the RO2 from
NO3 oxidation can take part in HOM dimer formation. This further
implies that, unlike previous knowledge based on single-oxidant
experiments in chambers, NO3 oxidation may have a larger impact on SOA
formation in the atmosphere where different oxidants exist concurrently.
This highlights the need for future laboratory studies to consider systems
with multiple oxidants during monoterpene oxidation experiments to truly
understand the role and contribution of different oxidants and NO3 in
particular.
Fluorinated compounds
During the campaign, an automated instrument zeroing every 3 h was
conducted. While the zeroing successfully removed the low-volatility HOMs and
H2SO4, the process also introduced contaminants into the inlet
lines, e.g., perfluorinated organic acids from Teflon tubing. Each zeroing
process lasted for 10 min. In the data analysis, we removed all the 10 min
zeroing periods, and averaged the data to 1 h time resolution, but
contaminants were still identified in all ranges by binPMF. However, the
correlation between contamination factors from different ranges is low
(Fig. 6c).
To further investigate the low factor correlations of the same source, three
fluorinated compounds with different volatilities,
(CF2)3CO2HF⋅NO3- (275.9748 Th),
(CF2)5C2O4H- (338.9721 Th), and
(CF2)6CO2HF⋅NO3- (425.9653 Th), were
examined in fine time resolution, i.e., 1 min. The time series and 3 h cycle
of the three fluorinated compounds were shown in Figs. S3 and S4. The correlation coefficients dropped greatly before and after
the zero period was removed: from 0.9 to 0.3 for R2 between m/z 276 and
339 Th and 0.8 to 0.1 between m/z 276 and 426 Th (Fig. S5a, b). A similar
effect is also found with the 1 h averaged data (Fig. S5c, d). It is evident
that the three fluorinated compounds were from the same source (zeroing
process), but due to their different volatilities, they were lost at
different rates. This, in turn, means that the spectral signature of this
source will change as a function of time, at odds with one of the basic
assumptions of PMF.
The analysis of the fluorinated compounds in our system was here merely used
as an example to show that volatility can impact source profiles over time.
In Fig. S5, it can be clearly seen that the profile of Range combined is
noisier than that of Range 3, probably due to the varied fractional
contributions of contamination compounds to the profile. In ambient data,
products from different sources can have undergone atmospheric processing,
altering the product distribution. This analysis highlighted the importance
of differences in the sink terms due to different volatilities of the
products. This may be an important issue for gas-phase mass spectrometry
analysis, potentially underestimated by many PMF users, as it is likely only
a minor issue for aerosol data, for which PMF has been applied much more
routinely. If failing to achieve physically meaningful factors using PMF on
gas-phase mass spectra, our recommendation is to try applying PMF to
subranges of the spectrum, where intermediate-volatility organic compounds (IVOCs), semivolatile organic compounds (SVOCs), and (extremely) low volatility organic compounds ((E)LVOCs) could be analyzed separately.
Atmospheric insights
Based on the new data analysis technique, binPMF, applied to subranges of
mass spectra, we were able to separate two particularly intriguing
atmospheric processes, the formation of daytime dimers and dimer
formation involving NO3 radicals, which otherwise could not have been
identified in our study.
With a diurnal peak around noontime, the daytime dimers identified in this
study correlate very well with daytime factors in the monomer range. Strong
correlation between this factor and solar radiation indicates the potential
role of OH oxidation in the formation of daytime dimers. By now, very few
studies have reported the observations of daytime dimers. As dimers are
shown to be able to take part in new particle formation (NPF) (Kirkby et
al., 2016), this daytime dimer may contribute to the early stages of NPF in
the boreal forest.
The second process identified in our study is the formation of dimers that
are a crossover between NO3 and O3 oxidation. Such dimers have
been identified before (Yan et al., 2016). However, we were not able to
identify corresponding HOM monomer compounds. This finding indicates that
while NO3 oxidation of the monoterpenes in Hyytiälä may not
undergo autoxidation to form HOMs by themselves, they can contribute to HOM
dimers when the NO3-derived RO2 reacts with highly oxygenated
RO2 from other oxidants. Multi-oxidant systems should be taken into
consideration in future experimental studies on monoterpene oxidation
processes.
Conclusions
The recent developments in the field of mass spectrometry, combined with
factor analysis techniques such as PMF, have greatly improved our
understanding of complicated atmospheric processes and sources. In this
study, we applied the new binPMF approach (Zhang et
al., 2019) to separate subranges of mass spectra measured using a chemical
ionization mass spectrometer in the Finnish boreal forest. By using this
method, we were able to identify a daytime dimer factor, presumably
initiated by OH/O3 oxidation of monoterpenes, forming from
RO2+RO2 reactions despite competition from daytime NO. This
compound group, showing a diurnal peak around noon, may contribute to new
particle formation at the site. In addition, we successfully separated
NO3-related dimers which would not have been identified from this
dataset without utilizing the different subranges. The NO3-related
factor was consistent with earlier observations (Yan et al., 2016), with the
exception that we did not observe any corresponding monomer factor. This may
be explained by the observed nitrate-containing dimers being formed from two
RO2 radicals, where one is initiated by oxidation by O3 and the other by
NO3. If the NO3-derived RO2 radicals are not able to form HOMs by
themselves, there will not be any related monomers observed. To validate
this hypothesis, future laboratory experiments that target more complex
oxidation systems will be useful in order to understand the role of NO3
oxidation in SOA formation under different atmospheric conditions.
Apart from these two major findings, we also found several other benefits of
applying PMF on separate subranges of the mass spectra. First, different
compounds from the same source can have variable loss rates due to
differences in volatilities. This leads to increased difficulty for PMF to
separate this source, but if the PMF analysis is run separately on lighter
masses (with higher volatility) and heavier masses (with lower volatility),
the source may become easier to distinguish. Secondly, chemistry or sources
contributing only to one particular mass range, e.g., dimers, can be better
separated. Thirdly, mass ranges with small, but informative, signals can be
more accurately assigned as their contribution becomes larger than if the
entire mass range was analyzed at once. Finally, running PMF on separate
mass ranges also allows for comparing the factors between the different ranges,
helping to verify the results. In summary, while we do not suggest that this
type of subrange analysis should always be utilized, we recommend other
analysts of gas-phase mass spectrometer data to test this approach in order
to see whether additional useful information can be obtained. In our
dataset, this method was crucial for identifying different types of dimers
and dimer formation pathways, which are of great importance for the
formation of both new particles and SOA.
Data availability
The data used in this study are available from the first
author upon request: please contact Yanjun Zhang (yanjun.zhang@helsinki.fi).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-5945-2020-supplement.
Author contributions
ME and YZ designed the study. QZ and MR collected the
data; data analysis and article writing were done by YZ. All coauthors
discussed the results and commented on the article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the tofTools team for providing tools for mass
spectrometry data analysis. The personnel of the Hyytiälä forestry
field station are acknowledged for help during field measurements.
Financial support
This research has been supported by the European Research Council (grant no. 638703-COALA), the Academy of Finland (grant nos. 317380 and 320094), and the Swiss National Science postdoc mobility grant (grant no. P2EZP2_181599).Open access funding provided by Helsinki University Library.
Review statement
This paper was edited by James Allan and reviewed by two anonymous referees.
ReferencesAllan, J. D., Jimenez, J. L., Williams, P. I., Alfarra, M. R., Bower, K.
N., Jayne, J. T., Coe, H., and Worsnop, D. R.: Quantitative sampling using
an Aerodyne aerosol mass spectrometer 1. Techniques of data interpretation
and error analysis, J. Geophys. Res.-Atmos., 108, 10.1029/2002JD002358, 2003.Atkinson, R., Aschmann, S. M., Arey, J., and Shorees, B.: Formation of OH
radicals in the gas phase reactions of O3 with a series of terpenes, J. Geophys. Res., 97, 6065–6073, 10.1029/92jd00062, 1992.Berndt, T., Mentler, B., Scholz, W., Fischer, L., Herrmann, H., Kulmala, M.,
and Hansel, A.: Accretion Product Formation from Ozonolysis and OH Radical
Reaction of α-Pinene: Mechanistic Insight and the Influence of Isoprene and
Ethylene, Environ. Sci. Technol., 52, 11069–11077,
10.1021/acs.est.8b02210, 2018a.Berndt, T., Scholz, W., Mentler, B., Fischer, L., Herrmann, H., Kulmala, M.,
and Hansel, A.: Accretion Product Formation from Self- and Cross-Reactions
of RO2 Radicals in the Atmosphere, Angewandte Chemie International Edition
in English, 57, 3820–3824, 10.1002/anie.201710989, 2018b.Bertram, T. H., Kimmel, J. R., Crisp, T. A., Ryder, O. S., Yatavelli, R. L. N., Thornton, J. A., Cubison, M. J., Gonin, M., and Worsnop, D. R.: A field-deployable, chemical ionization time-of-flight mass spectrometer, Atmos. Meas. Tech., 4, 1471–1479, 10.5194/amt-4-1471-2011, 2011.Bianchi, F., Kurtén, T., Riva, M., Mohr, C., Rissanen, M. P., Roldin, P.,
Berndt, T., Crounse, J. D., Wennberg, P. O., Mentel, T. F., Wildt, J.,
Junninen, H., Jokinen, T., Kulmala, M., Worsnop, D. R., Thornton, J. A.,
Donahue, N., Kjaergaard, H. G., and Ehn, M.: Highly Oxygenated Organic
Molecules (HOM) from Gas-Phase Autoxidation Involving Peroxy Radicals: A Key
Contributor to Atmospheric Aerosol, Chem. Rev., 119, 3472–3509,
10.1021/acs.chemrev.8b00395, 2019.Bonn, B. and Moorgat, G. K.: New particle formation during a- and b-pinene oxidation by O3, OH and NO3, and the influence of water vapour: particle size distribution studies, Atmos. Chem. Phys., 2, 183–196, 10.5194/acp-2-183-2002, 2002.Boyd, C. M., Sanchez, J., Xu, L., Eugene, A. J., Nah, T., Tuet, W. Y., Guzman, M. I., and Ng, N. L.: Secondary organic aerosol formation from the β-pinene+NO3 system: effect of humidity and peroxy radical fate, Atmos. Chem. Phys., 15, 7497–7522, 10.5194/acp-15-7497-2015, 2015.
Canagaratna, M., Jayne, J., Jimenez, J., Allan, J., Alfarra, M., Zhang, Q.,
Onasch, T., Drewnick, F., Coe, H., and Middlebrook, A.: Chemical and
microphysical characterization of ambient aerosols with the aerodyne aerosol
mass spectrometer, Mass Spectrom. Rev., 26, 185–222, 2007.Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., and Prévôt, A. S. H.: SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Atmos. Meas. Tech., 6, 3649–3661, 10.5194/amt-6-3649-2013, 2013.Craven, J. S., Yee, L. D., Ng, N. L., Canagaratna, M. R., Loza, C. L., Schilling, K. A., Yatavelli, R. L. N., Thornton, J. A., Ziemann, P. J., Flagan, R. C., and Seinfeld, J. H.: Analysis of secondary organic aerosol formation and aging using positive matrix factorization of high-resolution aerosol mass spectra: application to the dodecane low-NOx system, Atmos. Chem. Phys., 12, 11795–11817, 10.5194/acp-12-11795-2012, 2012.Ehn, M., Kleist, E., Junninen, H., Petäjä, T., Lönn, G., Schobesberger, S., Dal Maso, M., Trimborn, A., Kulmala, M., Worsnop, D. R., Wahner, A., Wildt, J., and Mentel, Th. F.: Gas phase formation of extremely oxidized pinene reaction products in chamber and ambient air, Atmos. Chem. Phys., 12, 5113–5127, 10.5194/acp-12-5113-2012, 2012.Ehn, M., Thornton, J. A., Kleist, E., Sipila, M., Junninen, H., Pullinen,
I., Springer, M., Rubach, F., Tillmann, R., Lee, B., Lopez-Hilfiker, F.,
Andres, S., Acir, I.-H., Rissanen, M., Jokinen, T., Schobesberger, S.,
Kangasluoma, J., Kontkanen, J., Nieminen, T., Kurten, T., Nielsen, L. B.,
Jorgensen, S., Kjaergaard, H. G., Canagaratna, M., Dal Maso, M., Berndt, T.,
Petaja, T., Wahner, A., Kerminen, V.-M., Kulmala, M., Worsnop, D. R., Wildt,
J., and Mentel, T. F.: A large source of low-volatility secondary organic
aerosol, Nature, 506, 476–479, 10.1038/nature13032, 2014.Fry, J. L., Draper, D. C., Barsanti, K. C., Smith, J. N., Ortega, J.,
Winkler, P. M., Lawler, M. J., Brown, S. S., Edwards, P. M., Cohen, R. C.,
and Lee, L.: Secondary Organic Aerosol Formation and Organic Nitrate Yield
from NO3 Oxidation of Biogenic Hydrocarbons, Environ. Sci. Technol., 48, 11944–11953, 10.1021/es502204x, 2014.Guenther, A., Hewitt, C. N., Erickson, D., Fall, R., Geron, C., Graedel, T.,
Harley, P., Klinger, L., Lerdau, M., McKay, W. A., Pierce, T., Scholes, B.,
Steinbrecher, R., Tallamraju, R., Taylor, J., and Zimmerman, P.: A
Global-model of natural volatile organic-compound emissions,
J. Geophys. Res.-Atmos., 100, 8873–8892, 10.1029/94jd02950, 1995.Hakola, H., Tarvainen, V., Bäck, J., Ranta, H., Bonn, B., Rinne, J., and Kulmala, M.: Seasonal variation of mono- and sesquiterpene emission rates of Scots pine, Biogeosciences, 3, 93–101, 10.5194/bg-3-93-2006, 2006.Hakola, H., Hellén, H., Hemmilä, M., Rinne, J., and Kulmala, M.: In situ measurements of volatile organic compounds in a boreal forest, Atmos. Chem. Phys., 12, 11665–11678, 10.5194/acp-12-11665-2012, 2012.
Hari, P. and Kulmala, M.: Station for Measuring Ecosystem–Atmosphere
Relations (SMEAR II), Boreal Environ. Res., 10, 315–322, 2005.Huang, S., Rahn, K. A., and Arimoto, R.: Testing and optimizing two
factor-analysis techniques on aerosol at Narragansett, Rhode Island,
Atmos. Environ., 33, 2169–2185, 10.1016/S1352-2310(98)00324-0, 1999.Jokinen, T., Sipilä, M., Junninen, H., Ehn, M., Lönn, G., Hakala, J., Petäjä, T., Mauldin III, R. L., Kulmala, M., and Worsnop, D. R.: Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF, Atmos. Chem. Phys., 12, 4117–4125, 10.5194/acp-12-4117-2012, 2012.Kirkby, J., Duplissy, J., Sengupta, K., Frege, C., Gordon, H., Williamson,
C., Heinritzi, M., Simon, M., Yan, C., Almeida, J., Troestl, J., Nieminen,
T., Ortega, I. K., Wagner, R., Adamov, A., Amorim, A., Bernhammer, A.-K.,
Bianchi, F., Breitenlechner, M., Brilke, S., Chen, X., Craven, J., Dias, A.,
Ehrhart, S., Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Hakala, J.,
Hoyle, C. R., Jokinen, T., Junninen, H., Kangasluoma, J., Kim, J., Krapf,
M., Kuerten, A., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S.,
Molteni, U., Onnela, A., Peraekylae, O., Piel, F., Petaejae, T., Praplan, A.
P., Pringle, K., Rap, A., Richards, N. A. D., Riipinen, I., Rissanen, M. P.,
Rondo, L., Sarnela, N., Schobesberger, S., Scott, C. E., Seinfeld, J. H.,
Sipilae, M., Steiner, G., Stozhkov, Y., Stratmann, F., Tome, A., Virtanen,
A., Vogel, A. L., Wagner, A. C., Wagner, P. E., Weingartner, E., Wimmer, D.,
Winkler, P. M., Ye, P., Zhang, X., Hansel, A., Dommen, J., Donahue, N. M.,
Worsnop, D. R., Baltensperger, U., Kulmala, M., Carslaw, K. S., and Curtius,
J.: Ion-induced nucleation of pure biogenic particles, Nature, 533, 521–526,
10.1038/nature17953, 2016.Kulmala, M., Kontkanen, J., Junninen, H., Lehtipalo, K., Manninen, H. E.,
Nieminen, T., Petäjä, T., Sipilä, M., Schobesberger, S., Rantala, P.,
Franchin, A., Jokinen, T., J?rvinen, E., Aijälä, M., Kangasluoma, J.,
Hakala, J., Aalto, P. P., Paasonen, P., Mikkilä, J., Vanhanen, J., Aalto,
J., Hakola, H., Makkonen, U., Ruuskanen, T., Mauldin, R. L., Duplissy, J.,
Vehkamäki, H., Bäck, J., Kortelainen, A., Riipinen, I., Kurtén, T.,
Johnston, M. V., Smith, J. N., Ehn, M., Mentel, T. F., Lehtinen, K. E. J.,
Laaksonen, A., Kerminen, V.-M., and Worsnop, D. R.: Direct Observations of
Atmospheric Aerosol Nucleation, Science, 339, 943–946, 10.1126/science.1227385,
2013.Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, 10.5194/acp-10-7017-2010, 2010.Lee, B. H., Lopez-Hilfiker, F. D., Mohr, C., Kurtén, T., Worsnop, D. R.,
and Thornton, J. A.: An Iodide-Adduct High-Resolution Time-of-Flight
Chemical-Ionization Mass Spectrometer: Application to Atmospheric Inorganic
and Organic Compounds, Environ. Sci. Technol., 48, 6309–6317,
10.1021/es500362a, 2014.Lehtipalo, K., Yan, C., Dada, L., Bianchi, F., Xiao, M., Wagner, R.,
Stolzenburg, D., Ahonen, L. R., Amorim, A., Baccarini, A., Bauer, P. S.,
Baumgartner, B., Bergen, A., Bernhammer, A.-K., Breitenlechner, M., Brilke,
S., Buchholz, A., Mazon, S. B., Chen, D., Chen, X., Dias, A., Dommen, J.,
Draper, D. C., Duplissy, J., Ehn, M., Finkenzeller, H., Fischer, L., Frege,
C., Fuchs, C., Garmash, O., Gordon, H., Hakala, J., He, X., Heikkinen, L.,
Heinritzi, M., Helm, J. C., Hofbauer, V., Hoyle, C. R., Jokinen, T.,
Kangasluoma, J., Kerminen, V.-M., Kim, C., Kirkby, J., Kontkanen, J.,
Kürten, A., Lawler, M. J., Mai, H., Mathot, S., Mauldin, R. L., Molteni,
U., Nichman, L., Nie, W., Nieminen, T., Ojdanic, A., Onnela, A., Passananti,
M., Petäjä, T., Piel, F., Pospisilova, V., Quéléver, L. L. J., Rissanen,
M. P., Rose, C., Sarnela, N., Schallhart, S., Schuchmann, S., Sengupta, K.,
Simon, M., Sipilä, M., Tauber, C., Tomé, A., Tröstl, J., Väisänen, O.,
Vogel, A. L., Volkamer, R., Wagner, A. C., Wang, M., Weitz, L., Wimmer, D.,
Ye, P., Ylisirniö, A., Zha, Q., Carslaw, K. S., Curtius, J., Donahue, N. M.,
Flagan, R. C., Hansel, A., Riipinen, I., Virtanen, A., Winkler, P. M.,
Baltensperger, U., Kulmala, M., and Worsnop, D. R.: Multicomponent new
particle formation from sulfuric acid, ammonia, and biogenic vapors, J. Sci. Adv., 4,
eaau5363, 10.1126/sciadv.aau5363, 2018.Liebmann, J., Karu, E., Sobanski, N., Schuladen, J., Ehn, M., Schallhart, S., Quéléver, L., Hellen, H., Hakola, H., Hoffmann, T., Williams, J., Fischer, H., Lelieveld, J., and Crowley, J. N.: Direct measurement of NO3 radical reactivity in a boreal forest, Atmos. Chem. Phys., 18, 3799–3815, 10.5194/acp-18-3799-2018, 2018.Mohr, C., Lopez-Hilfiker, F. D., Yli-Juuti, T., Heitto, A., Lutz, A.,
Hallquist, M., D'Ambro, E. L., Rissanen, M. P., Hao, L., Schobesberger, S.,
Kulmala, M., Mauldin III, R. L., Makkonen, U., Sipilä, M., Petäjä, T., and
Thornton, J. A.: Ambient observations of dimers from terpene oxidation in
the gas phase: Implications for new particle formation and growth, Geophys. Res. Lett., 44,
2958–2966, 10.1002/2017gl072718, 2017.Nah, T., Sanchez, J., Boyd, C. M., and Ng, N. L.: Photochemical Aging of
α-pinene and β-pinene Secondary Organic Aerosol formed from Nitrate
Radical Oxidation, Environ. Sci. Technol., 50, 222–231,
10.1021/acs.est.5b04594, 2016.Ng, N. L., Brown, S. S., Archibald, A. T., Atlas, E., Cohen, R. C., Crowley, J. N., Day, D. A., Donahue, N. M., Fry, J. L., Fuchs, H., Griffin, R. J., Guzman, M. I., Herrmann, H., Hodzic, A., Iinuma, Y., Jimenez, J. L., Kiendler-Scharr, A., Lee, B. H., Luecken, D. J., Mao, J., McLaren, R., Mutzel, A., Osthoff, H. D., Ouyang, B., Picquet-Varrault, B., Platt, U., Pye, H. O. T., Rudich, Y., Schwantes, R. H., Shiraiwa, M., Stutz, J., Thornton, J. A., Tilgner, A., Williams, B. J., and Zaveri, R. A.: Nitrate radicals and biogenic volatile organic compounds: oxidation, mechanisms, and organic aerosol, Atmos. Chem. Phys., 17, 2103–2162, 10.5194/acp-17-2103-2017, 2017.
Orlando, J. J. and Tyndall, G. S.: Laboratory studies of organic peroxy
radical chemistry: an overview with emphasis on recent issues of atmospheric
significance, J. Chem. Soc. Rev., 41, 6294–6317, 2012.
Paatero, P. and Tapper, U.: Positive matrix factorization: A
non-negative factor model with optimal utilization of error
estimates of data values, Environmetrics, 5, 111–126, 1994.Paatero, P.: Least squares formulation of robust non-negative factor
analysis, Chemometr. Intell. Lab., 37, 23–35,
10.1016/S0169-7439(96)00044-5, 1997.Paatero, P.: The Multilinear Engine–A Table-Driven, Least Squares Program
for Solving Multilinear Problems, Including the n-Way Parallel Factor
Analysis Model, J. Comput. Graph. Stat., 8,
854–888, 10.1080/10618600.1999.10474853, 1999.Paulson, S. E. and Orlando, J. J.: The reactions of ozone with alkenes: An
important source of HOx in the boundary layer, Geophys. Res. Lett., 23, 3727–3730,
10.1029/96gl03477, 1996.Peräkylä, O., Riva, M., Heikkinen, L., Quéléver, L., Roldin, P., and Ehn, M.: Experimental investigation into the volatilities of highly oxygenated organic molecules (HOMs), Atmos. Chem. Phys., 20, 649–669, 10.5194/acp-20-649-2020, 2020.Perraud, V., Bruns, E. A., Ezell, M. J., Johnson, S. N., Greaves, J., and
Finlayson-Pitts, B. J.: Identification of Organic Nitrates in the NO3
Radical Initiated Oxidation of α-Pinene by Atmospheric Pressure Chemical
Ionization Mass Spectrometry, Environ. Sci. Technol., 44,
5887–5893, 10.1021/es1005658, 2010.
Polissar, A. V., Hopke, P. K., Paatero, P., Malm, W. C., and Sisler, J. F.:
Atmospheric aerosol over Alaska: 2. Elemental composition and sources,
J. Geophys. Res.-Atmos., 103, 19045–19057, 1998.
Pope III, C. A., Ezzati, M., and Dockery, D. W.: Fine-particulate air
pollution and life expectancy in the United States, New Engl. J.
Med., 360, 376–386, 2009.Riva, M., Rantala, P., Krechmer, J. E., Peräkylä, O., Zhang, Y., Heikkinen, L., Garmash, O., Yan, C., Kulmala, M., Worsnop, D., and Ehn, M.: Evaluating the performance of five different chemical ionization techniques for detecting gaseous oxygenated organic species, Atmos. Meas. Tech., 12, 2403–2421, 10.5194/amt-12-2403-2019, 2019.Shiraiwa, M., Ueda, K., Pozzer, A., Lammel, G., Kampf, C. J., Fushimi, A.,
Enami, S., Arangio, A. M., Fröhlich-Nowoisky, J., Fujitani, Y., Furuyama,
A., Lakey, P. S. J., Lelieveld, J., Lucas, K., Morino, Y., Pöschl, U.,
Takahama, S., Takami, A., Tong, H., Weber, B., Yoshino, A., and Sato, K.:
Aerosol Health Effects from Molecular to Global Scales, Environ. Sci. Technol., 51, 13545–13567, 10.1021/acs.est.7b04417, 2017.
Song, Y., Shao, M., Liu, Y., Lu, S., Kuster, W., Goldan, P., and Xie, S.:
Source apportionment of ambient volatile organic compounds in Beijing,
Environ. Sci. Technol., 41, 4348–4353, 2007.Spittler, M., Barnes, I., Bejan, I., Brockmann, K. J., Benter, T., and
Wirtz, K.: Reactions of NO3 radicals with limonene and α-pinene: Product
and SOA formation, Atmos. Environ., 40, 116–127, 10.1016/j.atmosenv.2005.09.093, 2006.
Stocker, T., Qin, D., Plattner, G., Tignor, M., Allen, S., Boschung, J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P.: IPCC, 2013: Climate Change
2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
1535 pp., Cambridge Univ. Press, Cambridge, UK, and New York, 2013.Troestl, J., Chuang, W. K., Gordon, H., Heinritzi, M., Yan, C., Molteni, U.,
Ahlm, L., Frege, C., Bianchi, F., Wagner, R., Simon, M., Lehtipalo, K.,
Williamson, C., Craven, J. S., Duplissy, J., Adamov, A., Almeida, J.,
Bernhammer, A.-K., Breitenlechner, M., Brilke, S., Dias, A., Ehrhart, S.,
Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Gysel, M., Hansel, A.,
Hoyle, C. R., Jokinen, T., Junninen, H., Kangasluoma, J., Keskinen, H., Kim,
J., Krapf, M., Kuerten, A., Laaksonen, A., Lawler, M., Leiminger, M.,
Mathot, S., Moehler, O., Nieminen, T., Onnela, A., Petaejae, T., Piel, F.
M., Miettinen, P., Rissanen, M. P., Rondo, L., Sarnela, N., Schobesberger,
S., Sengupta, K., Sipila, M., Smith, J. N., Steiner, G., Tome, A., Virtanen,
A., Wagner, A. C., Weingartner, E., Wimmer, D., Winkler, P. M., Ye, P.,
Carslaw, K. S., Curtius, J., Dommen, J., Kirkby, J., Kulmala, M., Riipinen,
I., Worsnop, D. R., Donahue, N. M., and Baltensperger, U.: The role of
low-volatility organic compounds in initial particle growth in the
atmosphere, Nature, 533, 527–531, 10.1038/nature18271, 2016.Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and Jimenez, J. L.: Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9, 2891–2918, 10.5194/acp-9-2891-2009, 2009.Visser, S., Slowik, J. G., Furger, M., Zotter, P., Bukowiecki, N., Canonaco, F., Flechsig, U., Appel, K., Green, D. C., Tremper, A. H., Young, D. E., Williams, P. I., Allan, J. D., Coe, H., Williams, L. R., Mohr, C., Xu, L., Ng, N. L., Nemitz, E., Barlow, J. F., Halios, C. H., Fleming, Z. L., Baltensperger, U., and Prévôt, A. S. H.: Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter, Atmos. Chem. Phys., 15, 11291–11309, 10.5194/acp-15-11291-2015, 2015.Yan, C., Nie, W., Äijälä, M., Rissanen, M. P., Canagaratna, M. R., Massoli, P., Junninen, H., Jokinen, T., Sarnela, N., Häme, S. A. K., Schobesberger, S., Canonaco, F., Yao, L., Prévôt, A. S. H., Petäjä, T., Kulmala, M., Sipilä, M., Worsnop, D. R., and Ehn, M.: Source characterization of highly oxidized multifunctional compounds in a boreal forest environment using positive matrix factorization, Atmos. Chem. Phys., 16, 12715–12731, 10.5194/acp-16-12715-2016, 2016.
Zha, Q., Yan, C., Junninen, H., Riva, M., Sarnela, N., Aalto, J., Quéléver, L., Schallhart, S., Dada, L., Heikkinen, L., Peräkylä, O., Zou, J., Rose, C., Wang, Y., Mammarella, I., Katul, G., Vesala, T., Worsnop, D. R., Kulmala, M., Petäjä, T., Bianchi, F., and Ehn, M.: Vertical characterization of highly oxygenated molecules (HOMs) below and above a boreal forest canopy, Atmos. Chem. Phys., 18, 17437–17450, 10.5194/acp-18-17437-2018, 2018.Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Ulbrich, I. M., Ng, N. L.,
Worsnop, D. R., and Sun, Y.: Understanding atmospheric organic aerosols via
factor analysis of aerosol mass spectrometry: a review, Anal.
Bioanal. Chem., 401, 3045–3067, 10.1007/s00216-011-5355-y, 2011.
Zhang, Y., Lin, Y., Cai, J., Liu, Y., Hong, L., Qin, M., Zhao, Y., Ma, J.,
Wang, X., and Zhu, T.: Atmospheric PAHs in North China: spatial distribution
and sources, Sci. Total Environ., 565, 994–1000, 2016.
Zhang, Y., Cai, J., Wang, S., He, K., and Zheng, M.: Review of
receptor-based source apportionment research of fine particulate matter and
its challenges in China, Sci. Total Environ., 586, 917–929,
2017.Zhang, Y., Peräkylä, O., Yan, C., Heikkinen, L., Äijälä, M., Daellenbach, K. R., Zha, Q., Riva, M., Garmash, O., Junninen, H., Paatero, P., Worsnop, D., and Ehn, M.: A novel approach for simple statistical analysis of high-resolution mass spectra, Atmos. Meas. Tech., 12, 3761–3776, 10.5194/amt-12-3761-2019, 2019.