The impact of aerosols on climate and air quality remains poorly understood
due to multiple factors. One of the current limitations is the incomplete
understanding of the contribution of oxygenated products, generated from the
gas-phase oxidation of volatile organic compounds (VOCs), to aerosol
formation. Indeed, atmospheric gaseous chemical processes yield thousands of
(highly) oxygenated species, spanning a wide range of chemical formulas,
functional groups and, consequently, volatilities. While recent mass
spectrometric developments have allowed extensive on-line detection of a
myriad of oxygenated organic species, playing a central role in atmospheric
chemistry, the detailed quantification and characterization of this diverse
group of compounds remains extremely challenging. To address this challenge,
we evaluated the capability of current state-of-the-art mass spectrometers
equipped with different chemical ionization sources to detect the oxidation
products formed from α-Pinene ozonolysis under various conditions.
Five different mass spectrometers were deployed simultaneously for a chamber
study. Two chemical ionization atmospheric pressure interface time-of-flight
mass spectrometers (CI-APi-TOF) with nitrate and amine reagent ion
chemistries and an iodide chemical ionization time-of-flight mass
spectrometer (TOF-CIMS) were used. Additionally, a proton transfer reaction
time-of-flight mass spectrometer (PTR-TOF 8000) and a new “vocus” PTR-TOF
were also deployed. In the current study, we compared around 1000 different
compounds between each of the five instruments, with the aim of determining
which oxygenated VOCs (OVOCs) the different methods were sensitive to and
identifying regions where two or more instruments were able to detect
species with similar molecular formulae. We utilized a large variability in
conditions (including different VOCs, ozone, NOx and OH scavenger
concentrations) in our newly constructed atmospheric simulation chamber for
a comprehensive correlation analysis between all instruments. This analysis,
combined with estimated concentrations for identified molecules in each
instrument, yielded both expected and surprising results. As anticipated
based on earlier studies, the PTR instruments were the only ones able to
measure the precursor VOC, the iodide TOF-CIMS efficiently detected many
semi-volatile organic compounds (SVOCs) with three to five oxygen atoms, and the
nitrate CI-APi-TOF was mainly sensitive to highly oxygenated organic (O > 5) molecules (HOMs). In addition, the vocus showed good
agreement with the iodide TOF-CIMS for the SVOC, including a range of
organonitrates. The amine CI-APi-TOF agreed well with the nitrate CI-APi-TOF
for HOM dimers. However, the loadings in our experiments caused the amine
reagent ion to be considerably depleted, causing nonlinear responses for
monomers. This study explores and highlights both benefits and limitations
of currently available chemical ionization mass spectrometry instrumentation
for characterizing the wide variety of OVOCs in the atmosphere. While
specifically shown for the case of α-Pinene ozonolysis, we expect
our general findings to also be valid for a wide range of other VOC–oxidant
systems. As discussed in this study, no single instrument configuration can
be deemed better or worse than the others, as the optimal instrument for a
particular study ultimately depends on the specific target of the study.
Introduction
Atmospheric aerosols, a mixture of solid and liquid particles consisting of
organic and inorganic substances suspended in the air, have a significant
impact on climate (Albrecht,
1989; Hallquist et al., 2009; Intergovernmental Panel on Climate Change,
2014; Twomey, 1977). They are also recognized to adversely impact air
quality and human health, nowadays representing the fifth-ranking human
health risk factor globally (Gakidou et al., 2017). Depending
on the region, organic aerosol contributes on average 20 %–90 % to the
submicron aerosol mass (Jimenez
et al., 2009), with secondary organic aerosol (SOA) as the largest source of
atmospheric organic aerosol (Hallquist
et al., 2009; Jimenez et al., 2009). SOA is predominantly formed through the
gas-phase oxidation of volatile organic compounds (VOCs), producing
oxygenated VOCs (OVOCs), which can subsequently condense onto pre-existing
aerosol particles. Generally, the more oxidized the OVOCs, the lower its
volatility is and the greater the probability of this compound to
partition to the particle phase. Recently, studies have provided new
insights into how highly oxygenated organic molecules (HOMs) can form faster
than previously expected and at high enough yields to make them a major
source of condensing or even nucleating compounds
(Ehn et al., 2014;
Jokinen et al., 2015; Kirkby et al., 2016; Wang et al., 2018).
The quantitative assessment of the impact of aerosol on climate remains
poorly understood due to a number of factors, including an incomplete
understanding of how VOC oxidation processes contribute to new particle and
SOA formation (Glasius and Goldstein, 2016). Indeed, atmospheric
oxidation processes can lead to the formation of thousands of oxidized
products from a single precursor (Glasius and Goldstein,
2016; Goldstein and Galbally, 2007). As a result of these complex oxidation
processes, atmospheric organic species span an extremely wide range of
chemical formulas, structures and physicochemical properties. Volatilities
range from volatile species present only in the gas phase, via low- and
semi-volatile organic compounds (LVOCs and SVOCs), to extremely low volatility
organic compounds (ELVOCs) present mainly in the particle phase
(Donahue et al., 2012). The chemical complexity of OVOC
poses a major challenge in detecting, quantifying and characterizing such a
large number and wide variety of organic compounds.
Mass spectrometric techniques, which can detect a large range of species
simultaneously, are well-suited to tackling these challenges. This is
underlined by the major role of the mass spectrometers in improving our
understanding of the atmospheric chemical composition over the last 20
years (Breitenlechner
et al., 2017; Ehn et al., 2014; Jokinen et al., 2012; Krechmer et al., 2018;
Lindinger et al., 1998; Yuan et al., 2017). Proton transfer reaction (PTR)
has been one of the most used medium-pressure ionization techniques since
the mid-1990s (Lindinger et al., 1998). Since then, the
PTR-MS technique has been greatly improved in terms of sensitivity,
detection limit and mass resolution by introducing the PTR-TOF-MS
(Yuan et al., 2017). The latest version has detection limits
as low as 107 molecules cm-3. While such techniques can
characterize VOCs, the PTR-MS technique has not been able to measure more
oxygenated organic species. This is mostly due to losses of these low
volatile compounds in the sampling lines and on the walls of the inlet
(caused, for example, by very low flow rates), as the instrument was designed to
primarily measure volatile compounds.
Several different chemical ionization mass spectrometry (CIMS) methods have
been developed, including medium-pressure systems like CF3O--CIMS
for specific detection of oxygenated VOCs and SVOCs including hydroperoxides
(Crounse et al., 2006), acetate CIMS for selective detection
of organic acids (Bertram et al., 2011) and
the iodide adduct ionization CIMS for the detection of a wider range of OVOCs,
including alcohols, hydroperoxides and peroxy acids
(Lee et al., 2014; Riva et al., 2017). These
instruments, based on negative ion chemistry, can detect oxygenated gas-phase compounds at concentrations as low as ∼106
molecules cm-3. Finally, the discovery of the HOMs was possible due to
the development of a nitrate chemical ionization source connected to an
atmospheric pressure interface time-of-flight mass spectrometer (CI-APi-TOF)
(Ehn et al., 2014; Jokinen et al.,
2012). The selectivity and high sensitivity for molecules containing many
functional groups (detection limit below 105 molecules cm-3) of
the nitrate CI-APi-TOF makes this instrument perfect for detecting HOMs and
even certain radicals (e.g., peroxy radicals). As part of the rapid
development in gas-phase mass spectrometry, several new reagent ion
chemistries have been tested over the last few years. With improvements in
sensitivity and/or selectivity, new methods are now able to detect a wide
variety of oxygenated species, including radicals and stabilized Criegee
intermediates (Berndt et
al., 2015, 2017, 2018; Breitenlechner et al., 2017; Hansel et al., 2018;
Krechmer et al., 2018).
Campaign overview, including the concentration of
O3, NO, NOx(a) as well as α-Pinene measured by the
vocus
and the PTR-TOF and pinonaldehyde measured using the vocus (b).
Concentrations of pinonic and pinic acids (vocus and iodide) are presented
in (c), example of HOM monomers from nitrate(d) and example of HOM dimers
from amine and nitrate(e). Concentrations for all the gaseous species are
in molecules cm-3; see text for details on quantification. The
experiments were separated into five types: I is α-Pinene +O3,
II is α-Pinene +O3+CO (as an OH scavenger), III is tests
(NO2 injection, H2O2 injection for generating
HO2), IV is α-Pinene +O3+NO and V is α-Pinene +O3+NO+CO. Concentrations of NO and C10H16O8NO3-
are scaled for clarity.
The selectivity and sensitivity of the different ionization chemistries
makes it impossible for one mass spectrometer to be able to measure the full
range of VOCs and OVOCs present in the atmosphere. Hence, only a simultaneous
deployment of several mass spectrometry techniques can provide a
comprehensive chemical characterization of the gaseous composition. While
such a multi-instrument approach maximizes the fraction of organic species
measured
(Isaacman-VanWertz et
al., 2017, 2018), a number of questions and limitations can arise in both
laboratory and field measurements. For instance, the extent to which
instruments can (i) measure species with identical molecular composition,
(ii) cover the entire range of oxygenated species and (iii) provide
constant sensitivity across different conditions, have to be determined.
Most studies are typically limited to one, or perhaps two, mass
spectrometers, and then it is also important to know which fraction of the
OVOC distribution these instruments are sensitive to. To our knowledge,
systematic comparisons of the most commonly used or recently developed
gas-phase mass spectrometers are not yet available. In this work, we
compared the suitability of five different chemical ionization methods
(including iodide TOF-CIMS, nitrate and amine CI-APi-TOFs, a PTR-TOF and the
newly developed vocus PTR-TOF) for the detection of OVOCs formed from α-Pinene ozonolysis during a comprehensive chamber study with varying VOCs,
O3 and NOx concentrations. We characterized the time evolution
of around 1000 compounds and explored the capability of these instruments to
measure OVOCs of different oxygenation levels within different compound
groups.
Experimental sectionChamber experiments
Experiments were performed at the University of Helsinki in a 2 m3
atmospheric simulation Teflon (FEP) chamber. The COALA chamber (named
after the project for which it was constructed: Comprehensive
molecular characterization of secondary Organic
AerosoL formation in the Atmosphere) was operated
under steady-state conditions, meaning that a constant flow of reactants and
oxidants were continuously added to the chamber, while chamber air was
sampled by the instruments. Under the conditions used in this study, the
average residence time in the chamber was ∼30 min, and the
majority of conditions were kept constant for 6 to 12 h before changing
to new conditions. These experiments focused on the characterization of the
oxidation products arising from the α-Pinene (C10H16)
ozonolysis. α-Pinene was used for the generation of oxidation
products because it is the most abundant monoterpene emitted by the boreal
forests and is one of the most important SOA precursors on a global scale
(Jokinen et al., 2015; Kelly et al., 2018).
The experiments were conducted at room temperature (27±2∘C) and under dry conditions (RH < 1 %). An overview of the
measurements, as well as the experimental conditions, are presented in Fig. 1. α-Pinene was introduced to the chamber from a gas cylinder, and
steady-state concentrations of α-Pinene were varied from 20 to 100 ppb. As alkene ozonolysis yields OH radicals
(Atkinson et al., 1997), in some experiments,
∼1500 ppm of carbon monoxide (CO) was injected to serve as the OH
scavenger. Also, 10 to 50 ppb of O3 was generated by injecting purified air
through an ozone generator (Dasibi 1008-PC) and monitored over the process
of the campaign using a UV photometric analyzer (Model 49P,
Thermo-Environmental). In the experiments performed in the presence of
NOx, 400 nm LED lights were used to generate NO in the chamber from the
photolysis of the injected NO2. The purified air ([O3/NOx]
and [VOC] reduced to less than 1 ppb and 5 ppb, respectively), generated by
an air purification system (AADCO, 737 Series, Ohio, USA) running on
compressed air, was used as a bath gas. Temperature, relative humidity (RH)
and pressure were monitored by a Vaisala Humidity and Temperature Probe
(INTERCAP® HMP60) and a differential pressure sensor
(Sensirion SDP1000-L025).
Overview and characteristics of the mass spectrometers
deployed during the campaign at the COALA chamber.
a The reagent ion is used to denote the instrument name. b Type of ionization method used for each instrument.
c Corresponds to the mass resolution of the instruments under the conditions used in this study.
d IMR is the ion–molecule reaction chamber, i.e., the region where sample molecules are mixed with reagent ions.
The IMR has a different design in each of the instruments, except for the nitrate and amine, which are identical.
Mass spectrometers
We deployed five chemical ionization schemes to the COALA chamber in order
to characterize the chemical composition of the gas-phase oxidation products
formed from α-Pinene ozonolysis. In this section, we briefly
present each instrument, summarized in Table 1. As each mass spectrometer
has slightly different working principles, references to more detailed
descriptions are provided. Specific benefits and limitations, which were not
often discussed in earlier studies, are reviewed in Sect. 2.4. Each of the
mass spectrometers were equipped with a mass analyzer manufactured by
Tofwerk AG, either an HTOF (mass resolving power ∼5000) or
long TOF (LTOF, mass resolving power ∼10000) version.
In the analysis, we focused primarily on the relative behavior of the ions
measured by the different mass spectrometers. An absolute comparison was
also performed, but this approach has a larger uncertainty, as the sensitivity
towards every molecule is different in each of the mass spectrometers,
depending on molecular size, functionality, proton affinity, polarizability,
etc. We attempted a rough estimate of absolute concentrations for each
instrument, despite the fact that, with around a thousand ions analyzed,
it is evident that we make no claim for them to all be accurate. As will be
shown, the concentrations of gas-phase VOCs and OVOCs vary up to 7 orders of
magnitude, and therefore useful information can still be obtained even in
cases where concentration estimates could be off by an order of magnitude.
Details about instruments used in this study as well as calibrations and
instrumental limitations are discussed in the following sections.
PTR-TOF
α-Pinene concentration was measured in the COALA chamber by a proton
transfer reaction time-of-flight mass spectrometer (PTR-TOF 8000, Ionicon
Analytik Gmbh) – later referred to as PTR-TOF. The technical details
have been described in detail elsewhere (Graus
et al., 2010; Jordan et al., 2009). The sample air from the COALA chamber
was drawn to the instrument using 2 m long PTFE tubing (6 mm o.d, 4 mm i.d.)
and a piece of 20 cm capillary PEEK tubing (1.6 mm o.d., 1 mm i.d.), with
a sampling flow of 0.8 L min-1 (liters per minute). The instrument was
operated using a drift tube at a pressure of around 2 mbar and a drift
tube at a temperature of 60∘ (∘C). Drift tube
voltage was kept at 600 V, leading to E/N=145 Td, where E is the
electrical field strength and N is the gas number density. With these settings,
the primary ion isotope (H318O+, 21.0221 Th) level stayed at
4500 cps (counts per second), and the mass resolving power of the HTOF mass
analyzer was ∼4500. Data were recorded using a time
resolution of 10 s. The background of the instrument was measured
approximately every day with VOC-free air generated using a custom-made
catalytic converter heated to 350 ∘C
(Schallhart et al., 2016).
Vocus
The vocus PTR-TOF (proton transfer reaction time-of-flight mass
spectrometer, Tofwerk AG/Aerodyne Research, Inc.), later referred to as a
vocus, is based on a new PTR-inlet design (i.e., focusing ion–molecule
reactor, FIMR) with sub-ppt detection limits (Krechmer
et al., 2018). Sample air was drawn to the instrument using 1 m long PTFE
tubing (6 mm o.d, 4 mm i.d.), with a flow rate of 4.5 L min-1. Most of the
sample air was directed to the exhaust, while the actual flow to the vocus
was around 0.15 L min-1. The instrument was operated with 1.0 mbar drift tube
pressure, the voltages being 350 and 400 V for axial and radial voltages,
respectively and E/N=120 Td. The vocus was operated at a higher water
flow than in Krechmer et al. (2018), resulting in a decrease in the OVOC
(e.g., HOMs) fragmentation but also in a lower sensitivity. The signal level
of the instrument had some instability during the campaign, thus the primary
ion signal (H3O+, 19.0178 Th) varied from a few hundred to few
thousand cps and the isotope of the second water cluster
(H218OH2OH3O+, 57.0432 Th) was around 104–105 cps. The much lower signal at H3O+ was due to a high-pass
band filter that removes most of the ions < 35 Th
(Krechmer et al., 2018). The mass resolving power of
the LTOF mass analyzer was 12 000–13 000 for the whole campaign. Data were
recorded using a time resolution of 10 s. Zero air was produced with a
built-in active carbon filter and background was measured hourly except
during 15–17 December due the malfunctioning of the zero-air pump.
Iodide
Another deployed instrument was a time-of-flight chemical ionization mass
spectrometer (TOF-CIMS, Tofwerk AG/Aerodyne Research, Inc.), equipped with
iodide (I-) reagent ion chemistry – later referred to as iodide.
While the molecules could be detected as deprotonated species or as adducts
with I-, we restricted the analysis in this work to ions containing
only an iodide adduct, which guarantees detection of the parent organic
compounds without substantial fragmentation. Iodide TOF-CIMS has been
described previously and has high sensitivity towards (multifunctional)
oxygenated organic compounds (Iyer et al., 2017; Lee
et al., 2014). The instrument was operated at 1 L min-1 reagent flow rate into
the ion–molecule reaction (IMR) chamber of the instrument. Iodide ions were
generated from methyl iodide (CH3I) using a polonium (Po-210) source.
Sample air was drawn to the instrument using 1 m long PTFE tubing (6 mm o.d,
4 mm i.d.) with a flow rate of 2 L min-1. The IMR was temperature controlled
at 40 ∘C and operated at a nominal pressure of 100 mbar. The
instrument, equipped with an HTOF mass analyzer, was configured to measure
singularly charged ions from 1 to 1000 Th with a mass resolving power and
time resolution of 4000–5000 and 10 s, respectively.
Amine and nitrate
Two chemical ionization atmospheric pressure interface time-of-flight mass
spectrometers (CI-APi-TOF, Tofwerk AG/Aerodyne Research, Inc.) were also
deployed (Ehn et al., 2014; Jokinen et al.,
2012). The inlet was designed to minimize wall losses through the use of
coaxial sample (10 L min-1) and sheath flows (∼30 L min-1) in
order to sample (extremely) low-volatile species which are easily lost to
the walls. Two types of ionization schemes were utilized: the promising new
amine reagent ion chemistry (Berndt et al., 2017, 2018) and
the more commonly used nitrate chemistry – later referred to as amine and
nitrate, respectively. The amine has been shown to be sensitive towards a
very wide range of OVOCs and both closed-shell species and peroxy radicals, from
molecules with a few oxygen atoms all the way to HOMs (Berndt et al., 2018).
Previous work have shown that protonated amines are effective reagent ions,
forming stable clusters with OVOCs (Berndt et al., 2018). The nitrate, on the
other hand, has mainly been used for detection of HOMs (Ehn
et al., 2014).
Sample air was drawn to the instruments using a common 1 m long PTFE inlet
line (19.05 mm o.d, 16 mm i.d.) with the flow rate being ∼20 L min-1 (∼10 L min-1 for each mass spectrometer). Nitrate
(NO3-) ions were formed from nitric acid (HNO3) using an
X-ray source, while protonated butylamine (C4H12N+) ions were
produced using butylamine with a 7.5 MBq Am-241 source. NO3- or
C4H12N+ ions enter the ion reaction zone together with a
clean sheath airflow, concentric with the sample flow, and the two do not
mix turbulently. The ions are then guided into the sample flow by an
electrical field. The residence time in the IMR was ∼200 ms.
The main reagent ions were NO3- (mass to charge of 62 Th),
HNO3NO3- (125 Th) and (HNO3)2NO3- 188 Th)
for the nitrate and C4H12N+ (74 Th) for the amine. Both
instruments were equipped with LTOF mass analyzers, providing a mass
resolving power of 9000–10 000.
Calibration of the mass spectrometers
In order to estimate absolute concentrations of all detected molecules, each
instrument's signals, using an averaging period of 15 min, were normalized
to the reagent ion signals (to eliminate the influence of changes affecting
all signals in the instruments, e.g., due to a degrading response from the
detector), followed by multiplication with a scaling factor. The reagent ion
quantity used for normalization is described below, separately for each
instrument. Normalized ion count rates are reported as normalized cps and normalized counts per second (ncps).
The scaling factors were derived differently for each instrument (details
provided below). For iodide, nitrate, and amine, the same factor was used for
all ions in the spectrum, while for the PTR instruments the factors were
different depending on the type of molecule (e.g., VOC or OVOC). For the
PTR instruments and the iodide, a duty cycle correction was applied to
compensate for mass-dependent transmission due to the orthogonal extraction
of the mass analyzers. The amine and nitrate were calibrated by scaling a
wide range of mass to charge based on earlier studies, where duty cycle
corrections had not been performed. Therefore, we did not apply such a
correction for the atmospheric pressure ionization mass spectrometers.
Finally, we emphasize that the scaling factors should not be compared
between instruments as a measure of sensitivity, since multiple factors
impact these values, including, for example, the specific normalization approach and
the chosen extraction frequency of the mass analyzers.
The PTR-TOF was calibrated twice using a calibration unit consisting of a
calibration gas mixture of 16 different VOCs (Apel-Riemer Environmental Inc.,
USA) that was diluted with clean air purified by a catalytic converter (1.2 L min-1 of zero air and 8 sccm of standard gas), producing VOC mixing ratios
of around 7 ppb (parts per billion) (Schallhart et al., 2016).
Sensitivities were calculated to be 12.31, 27.92, and 30.51 ncps ppb-1 based on the concentrations of
monoterpenes, MVK (methyl vinyl ketone) and m-/o-xylenes.
PTR-TOF signals were normalized using the sum of the first primary ion
isotope at 21.0221 Th and the first water cluster isotope at 39.0327 Th
(e.g., Schallhart et al., 2016). According to common practice, the
sensitivities above were scaled to correspond to a situation where the total
reagent ion signal equaled 106 cps.
The vocus was calibrated four times during the campaign using the same
calibration gas mixture as used for the PTR-TOF. There was variability in
the sensitivity during the campaign and therefore the uncertainty in the
vocus results are slightly larger than normal. Sensitivities were highest
for acetone, at maximum around 1800 and around 650 cps ppb-1 for monoterpenes. α-Pinene concentration was retrieved
using the authentic standard, while the concentrations of the OVOC and
C10H14H+ were estimated using the calibration factor of the
MVK and sum of m-/o-xylenes, respectively. MVK and m-/o-xylene sensitivities
was around 1700 and 700 cps ppb-1, respectively. Vocus
signals were normalized using the primary ion signal at 19.0178 Th only, as
the water clusters have a negligible effect on the ion chemistry inside the
FIMR (Krechmer et al., 2018). Due to the high-pass filter that removes
almost all the signal at 19.0178 Th, we do not report the normalized
sensitivities (i.e., in ncps ppb-1) for the vocus in order to avoid
direct comparisons with the PTR-TOF. Instead, the sensitivities above are
given without normalization, although a normalization was used for the final
data. For the uncertainty estimates, the same applies as listed above for
the PTR-TOF.
The uncertainties for the compounds that were directly calibrated are
estimated to be ±20 % for PTR-TOF and vocus. For other compounds, the
uncertainties are much higher due to uncertain ionization efficiencies and
potential fragmentation of the compounds with unknown structures. For
example, we used sensitivity of MVK for all oxygenated monoterpenes, even
though all those compounds may have very different fragmentation patterns,
transmission rates and/or proton transfer reaction rates.
Therefore, we refrain from quantitative estimates of the uncertainties for
these species.
The iodide was calibrated twice during the campaign (15 and 23 December) by
injecting known amounts of formic acid into the instrument. Due to unknown
reasons, the response of the iodide decayed throughout the campaign, and
therefore only data measured before 17 December, when a stronger drop
occurred, were included for the direct comparison of the nonnitrate OVOCs.
While normalization should compensate for this type of behavior, this
particular instrument utilized a time-to-digital converter (TDC) acquisition
card, which meant the primary ion peak was heavily saturated. Lacking any
isotopic signatures for I-, we found that utilizing a region of the
rising edge of the I- signal (126.5–126.65 Th) provided a reasonable
correction to our data. The sensitivity without normalization was 1.0 cps ppt-1 for formic acid, and following the normalization, this
sensitivity was applied for all ions throughout the period where iodide data
were included in the analysis. We acknowledge that this brings with it a
large uncertainty, as the iodide has sensitivities ranging over a few orders
of magnitude depending on the specific molecule (Lee et al., 2014), and
refrain from quantitative uncertainty estimates, as in the case for the PTR
instruments above.
Standards for OVOC compounds measurable by the nitrate are still lacking,
and this instrument was therefore not directly calibrated during the
campaign. However, to be able to roughly estimate concentrations, a
calibration was inferred by assuming that the molar yield of HOMs, i.e.,
molecules with six or more oxygen atoms, was 5 % during α-Pinene ozonolysis
experiments. Different values have been reported for the HOM yield
in this system, ranging from slightly above to slightly below 5 %
(Ehn et al., 2014; Jokinen et al., 2014, 2015).
Clearly such an approach yields large uncertainties, and we estimated it
here to roughly ±70 %. Earlier work with more direct calibrations
reported an uncertainty of ±50 % (Ehn et al.,
2014) and the added 20 p.p. in this work reflects the increased uncertainty
in scaling the sensitivity based on expected HOM yields. This method
requires knowledge of the wall loss rate of HOMs in the COALA chamber, which
was estimated to be 1/300 s-1 in our study. This estimate is based on a
rough scaling to a slightly smaller chamber (1.5 m3) with active mixing
by a fan, where the loss rate was measured to be 0.01 s-1 (Ehn et al.,
2014). As our chamber is larger, and our mixing fan was only spinning at a
moderate speed, we estimated the loss rates to roughly 3 times lower. The
resulting calibration coefficient was 2×1010 molecules cm-3 ncps-1,
which is similar to that in previous studies (Ehn et al., 2014; Jokinen et al., 2012).
As for nitrate, the amine was also not calibrated directly, and in order to
achieve an estimate of the concentrations measured by this instrument, we
scaled the sensitivity of the amine to match that of the nitrate for
specific HOM dimers (C19H28/30O12-17 and
C20H30/32O12-17), which were found to correlate very well
between the two instruments (as described in more detail in the Results
section). This approach gave a calibration factor of 6×108 molecules cm-3 ncps-1, with similar uncertainty estimates
to the nitrate. In the CI-APi-TOFs, the calibration factor is generally close
to 1010 molecules cm-3 ncps-1, but as discussed later in
Sect. 3.1, the amine reagent ion was considerably depleted during the
experiments, which led to the relatively low calibration factor. As
mentioned earlier, the scaling factors should not be compared directly
between instruments. The lower value for the amine is a result of the
normalization rather than an indication of higher sensitivity. This reagent
ion depletion also means that the most abundant species were most likely no
longer responding linearly to concentration changes, and therefore their
concentrations can be off by an order of magnitude or more.
Instrumental limitations and considerations
In this section we aim to highlight some of the limitations involved when
characterizing and quantifying OVOCs measured by online mass spectrometers.
The list below is not exhaustive but addresses several issues that are
relevant for the interpretation of our results.
Mass resolving power
One major limitation for all of the mass spectrometers described above is
the mass resolving power, ranging from 4000 to 14 000. Even though the new
generation of LTOF mass analyzers with higher resolving power can enhance
the separation of measured ions, it remains challenging to accurately
identify and deconvolve the elemental composition of many ions. Indeed, it
is common for one CIMS mass spectrum to include more than 1000 different
ions. For high-resolution (HR) peak identification and separation, firstly
one needs to generate a list of ions, i.e., a peak list. Its construction
can be time consuming, even if only based on one single spectrum, and once
conditions change, different ions may appear. For measurements lasting weeks
or months, it is nearly impossible to ensure that all ions are correctly
identified and fitted. If the peak list contains too few ions compared to
reality, signals from nonfitted ions will assign the adjacent ions with
artificially high signals. On the contrary, if too many closely lying ions
are included in the peak list, even small errors in the mass axis
determination can cause the signal to be fitted to specific ions even though
their signals are nonexistent. In such extreme cases, with closely
overlapping ions, traditional HR analysis becomes impossible.
While less selective detection techniques can sound more useful for
monitoring and characterizing OVOCs, spectra acquired using such ionization techniques
(e.g., PTR, iodide or amine) pose a significant challenge for data analysis
and may ultimately provide even less useful information. Statistical
analysis techniques can be used in order to better constrain the
uncertainties associated with peak fitting, as recently proposed
(Cubison and Jimenez, 2015; Stark et al.,
2015). These previous studies pointed out that the uncertainties related to
the peak fitting can become significant if the overlapping peaks are
separated by less than a full-width at half maximum (Cubison
and Jimenez, 2015). This is very often the case for CIMS instruments, and
the more the ions overlap, the larger the uncertainty is. Peak fitting
becomes increasingly problematic as molecular masses increase, since the
number of potential ions increases dramatically with mass.
Ionization, declustering and fragmentation
The response of a mass spectrometer to a certain compound is to first
approximation a result of two factors: the ionization probability of the
neutral molecule and the detection probability of the formed ion. The
ionization process is largely controlled by the stability of the products
compared to the primary ions, whether a question of adduct formation or
(de)protonation processes. Different reagent ion chemistries have been
studied computationally in recent years, successfully reproducing several
observations (Berndt et al., 2017;
Hyttinen et al., 2015, 2018; Iyer et al., 2016). While a neutral molecule
can bind to a reagent ion at the collision limit, the adduct can undergo
collision-induced dissociation (i.e., declustering) during transport through
interfacing with the high vacuum in the mass analyzer. Ultimately, the
binding strength of the adduct and the energy of the collisions in the mass
spectrometer will define the survival probability of the ions. To address
this issue, procedures have been proposed, for example to probe the response of
adducts to different collision energies
(Isaacman-VanWertz et al., 2018;
Lopez-Hilfiker et al., 2016), providing critical information on the
sensitivity of the instrument.
Similarly to declustering, (de)protonated compounds can undergo
fragmentation reactions where molecular bonds are broken. For example, the
detection of monoterpenes (C10H16) using PTR instruments often
shows equally large signals at the parent ion (C10H17+) and
at a fragment ion (C6H9+). Also, iodide adducts have been
shown to cause molecules to fragment, as in the case of peroxy acids
decomposing to carboxylate anions (Lee et al., 2014). Both declustering and
fragmentation processes are associated with the optimization of the voltages
of each instrument, which is performed by the instrument operator
(Breitenlechner et al., 2017; Krechmer
et al., 2018; Lopez-Hilfiker et al., 2016). While using voltage scans to
probe such processes is possible, and even desirable, performing,
interpreting and utilizing the results across the mass spectrum and across
different conditions remains challenging and has only been utilized in a
few studies to date (Isaacman-VanWertz et
al., 2018; Lopez-Hilfiker et al., 2016).
Quantification
For quantification, the instrument sensitivity is generally determined via
calibration standards, while a background level was measured by zero air.
The challenges involved in these procedures are highly dependent on the type
of compounds to be quantified. As an example, we discuss three kinds of
molecules with different volatilities: VOCs, SVOCs and ELVOCs.
VOCs: volatile species are relatively easy to quantify since they can be
contained in gas bottles or easily evaporated from standard samples in known
quantities. Their responses are also fast due to negligible
adsorption and evaporation from the walls.
SVOCs: many semi-volatile organic compounds (SVOCs) are commercially available
and can be evaporated in known amounts from liquid standards into the gas
phase. However, the nature of SVOCs results in both condensed and gas phases
for these species, meaning that once clean air is introduced, the signal of
SVOCs will often show a gradual decay over minutes or even hours due to
evaporation of the “leftovers” from surfaces in the inlet lines and the inlet
itself (Pagonis et al., 2017). The procedure used to determine the
“correct” blank is not trivial, and the blank will look different
depending on whether it is done at the entrance of the instrument or at the
sampling inlet and depending on the duration of the blank measurement
itself. Another related challenge for SVOC quantification is that
temperature fluctuations of a few degrees may cause net evaporation
(temperature increasing) or condensation (temperature decreasing) of SVOCs
from sampling lines and the inlet.
ELVOCs: for ELVOCs, finding standard compounds for calibration remains
extremely difficult. Most organic compounds, including hydroperoxide or
acid, with low volatility are likely to decompose before evaporating.
Thus, their quantification is often inferred from other similar compounds.
For the nitrate CI-APi-TOF, sulfuric acid is often used for calibration by
being formed in situ from SO2 (Kürten et al., 2012). This
is, to some extent, a similar approach to the one we took for the nitrate in this
work and scaled to the estimated HOM yield, as both methods require
knowledge of formation rates from the initial precursors and loss rates of
the formed compound of interest. Other studies have used permeation sources
of perfluorinated carboxylic acids, which are semi-volatile yet found to
bind strongly to nitrate ions (Ehn et al., 2014;
Heinritzi et al., 2016). However, while the calibration is complicated, the
blank measurements are often not even needed for exactly the same reasons.
Whatever contaminants might be present in the system, most are irreversibly
lost to instrument surfaces and unable to evaporate into the gas phase due
to the extremely low vapor pressures. Potential oxidation processes
occurring inside the mass spectrometer may be an exception, but to our
knowledge, this has not been reported to be a large concern for ELVOCs.
In addition to the list above, the response of an instrument to specific
molecules may vary according to the conditions at which they were sampled.
Temperature (change) was listed as one consideration and water vapor, or
relative humidity (RH), is another important limitation for several mass
spectrometers described above
(Breitenlechner et al.,
2017; Krechmer et al., 2018; Kürten et al., 2012; Lee et al., 2014; Li
et al., 2019). For chemical ionization techniques, the water vapor can
either compete with the OVOC ionization, leading to a decrease in
sensitivity, or stabilize the adduct, resulting in an increase in the
sensitivity. Alternatively, if a compound forms a very stable complex, it
may have an adduct formation efficiency that is independent of water vapor.
If the sensitivity is RH dependent, calibrations and blanks should optimally
be performed at the same RH as the sampling in order to be representative.
This, in turn, may cause considerable practical challenges for both RH
control and calibration and blank cleanliness.
In summary, recent computational and experimental work has shown that many
approaches exist for optimizing the ability of CIMS instruments to quantify
OVOCs, including different blanks, calibration methods, voltage scans, etc.
However, all these approaches are very rarely utilized in a single study,
simply due to the immense time and effort required, both during the
experiments and during the data analysis, where the results of all steps
need to be incorporated. Ultimately, each study needs to prioritize
producing larger amounts of data (i.e., performing more measurements) with
less capability for detailed quantification or producing a smaller amount
of data with more accurate quantification.
Results and discussion
We applied our five CIMS instruments at the COALA chamber over a period of
nearly 1 month, where we tried to provide different types of
atmospherically relevant oxidation conditions for α-Pinene. With
such high variability in the conditions, we compared signals between the mass
spectrometers more robustly, even though certain limitations were
inevitable. For example, it is often the case that mass spectra will show
some signal at almost every mass, which can be due to multiple reasons, and
it is important to separate when the signal is truly from the sampled air
and not from some internal background or contamination. Similarly, one needs
to assess whether the instrument is measuring the majority of the species
with the same elemental composition or only detecting a small subset of
those compounds due to specific selectivity for one isomer. In addition, an
instrument may be able to detect a certain molecule, but the resulting
signal remains unreliable. This may be the case if the sensitivity is
extremely low for the molecule or if the peak is close to a much larger
unrelated signal, which will create large interferences when performing HR
fitting. In both cases the signal is likely to be influenced by different
types of noise.
First, we performed correlation analyses in order to identify signals which
were physically meaningful. We conducted the analysis with the whole data set
(a total of ∼1000 ions in each instrument) rather than
selectively focusing on individual ions. This comprehensive approach
utilized more data but also resulted in larger uncertainties as not all
fitted ions could be validated for all CIMS. From the correlation analysis
we identified when two instruments agree, i.e., observing identical
elemental compositions and having a similar temporal behavior, concerning
some group of compounds. From a subsequent absolute comparison, we estimated
which chemical ionization method was likely to be detecting a certain group
of compounds more efficiently.
Instrument comparisons: correlationsMedium pressure ionization mass spectrometers
Peak fitting was performed by utilizing the Igor-based Tofware or Matlab-based
TofTools software (Junninen et al., 2010) for ion
mass to charge up to ∼600 Th, depending on the mass
spectrometers. To select which ions to fit (i.e., include in the peak
lists), both the exact masses and the isotopic distributions were used as
criteria. A Pearson correlation coefficient R was calculated between
molecules with the same elemental composition measured by different
instruments. As a practical example, the time series of
C10H16O3 measured by vocus and iodide are shown in Fig. 1c,
and the time series correlation for this compound between the two
instruments was R=0.85. For later comparisons we will use R squared and, in
this case, R2=0.73. For iodide, the data set covered only the first
half of the campaign, but the other instruments covered nearly the whole
period. This includes a wide variety of conditions, with and without
NOx, and therefore high correlations are very suggestive of two
instruments measuring the exact same compound(s) at that specific elemental
composition. However, as an increase in α-Pinene is likely to
increase almost all measured OVOC signals to some extent, low positive
correlations can arise artificially and should not be overinterpreted. Due
to the selectivity and the sensitivity of the ionization methods, not all
ions were observed in all the different instruments, and thus only a certain
fraction of the identified compounds can be compared between mass
spectrometers.
Mass-defect plots showing the compounds for which a time
series correlation (R2 > 0.2) was observed by the
medium-pressure chemical ionization mass spectrometers, (a) PTR-TOF,
(b) vocus and (c) iodide. Each circle represents a distinct molecular
composition and the marker area represents the correlation (R2, legend
shown in a) of the time series of that molecule between two different CIMS
instruments. The color of each marker depicts the instrument against which
the correlation is calculated.
Figure 2 shows the correlation analysis for the medium-pressure chemical
ionization mass spectrometers, with marker size scaled by R2. In those
figures, the abscissa represents the measured mass-to-charge ratio of the
compounds and the y axis their mass defect, which is calculated as the exact
mass of the compound minus the mass rounded to the closest integer
(Schobesberger
et al., 2013). For example, the mass of C10H16O3 is 184.110 Da, and the mass defect is +0.110 Da. The contribution of the reagent ions
has been removed in the different figures. A mass-defect diagram helps to
separate the molecules into two dimensions and allows some degree of
identification of the plotted markers.
As expected, the PTR-TOF and the vocus are strongly correlated for compounds
with low (0–3) oxygen number (Fig. 2a). Contrariwise, only a few compounds
were identified by the PTR-TOF and the iodide with a fairly good correlation
(i.e., R2 > 0.5). The correlating compounds included small
acids such as formic and acetic acid. As discussed earlier, the inlet of the
PTR-TOF is not well enough designed to sample OVOCs with low volatility, which
explained the lack of correlations for larger and more oxidized products
between the PTR-TOF and the nitrate CI-APi-TOF. The molecules with the
lowest correlations (R2 < 0.2) were not included in the plots,
as the intention is to show regions where instruments agree. If an ion is
included in a peak list, it will always be fit, and thereby a value of
R2 > 0 is always expected, filling markers throughout the
MD-mass space.
In addition to VOCs, the vocus was able to measure a large range of OVOCs
(150–300 Th) as revealed in Fig. 2b, displaying a very good correlation
with species identified by the iodide. Indeed, most of the identified
compounds have R2 > 0.7. As noted earlier, several different
experimental conditions were tested (Fig. 1), and these high correlations
indicate that both instruments were likely sensitive to the same compounds.
In other words, a good correlation was seen in this mass range for nearly
all compositions, the iodide and the vocus did not seem to be strongly
impacted by the exact chemical conformation of the organic compounds.
Interestingly no dimers (mass to charge > 300 Th) were observed
with the vocus, which suggests some potential limitation of the instrument
or the used settings. As a result, a very limited correlation was observed
between compounds measured by the vocus and the amine or nitrate
CI-APi-TOFs. The two main exceptions were C5H6O7 (178.011 Da) and C7H9NO8 (235.033 Da). Note that the latter is less
clear, as the correlation is nearly identical between three instruments
(nitrate, vocus, and iodide). The lack of correlation was not only due to
lack of ion transmission at higher masses in the vocus, since the instrument
was able to detect some ions up to 400 Th, including
C10H30O5Si5H+ and C19H29O6NH+.
One possibility was that since the compounds above ∼300 Th
were likely to contain hydroperoxides, or in the case of dimers, organic
peroxides, the ions may have fragmented before detection in the vocus,
either during the protonation or due to the strong electric fields in the
vocus FIMR. In the case of HOM monomers with more than seven oxygen atoms, an
additional limitation comes from more abundant and closely overlapping ions
in the spectra, impacting the accurate fitting of these ion signals in the
vocus. From our data set, it was not possible to determine the exact
cause(s) for this lack of sensitivity for larger molecules in the vocus, but
it is possible that changes in instrument operating conditions can extend
the range of molecules detectable using the vocus in future studies.
As shown in Fig. 2c, the iodide was capable of measuring ions with larger
masses (i.e., above 300 Th), indicating the detection of more complex (e.g.,
dimers) and oxygenated compounds than the vocus. This was the case in spite
of the lower flow rate for the iodide than the vocus and thus less optimal
for sampling of low-volatile species (Table 1). The iodide seemed to have
the widest detection range of the mass spectrometers deployed in this study,
showing high correlation with other instruments for organic molecules, from
C1 (like formic acid) to C20, as long as the molecules had at
least two oxygen atoms. This is in line with earlier findings that the
iodide is sensitive to most species that are polar or have polarizable
functional groups (Iyer et al., 2017; Lee et al.,
2014). However, the correlation with the CI-APi-TOFs was still somewhat
limited (R2 < 0.7) for HOM monomers and dimers. One reason may
have been that these HOMs contain peroxy acid functionalities, which have
been shown to undergo reactions in the iodide TOF-CIMS (Lee
et al., 2014). In this work, we only analyzed the ions containing I-, as
these were believed to be the ones where the parent molecule remained
intact. Another reason for lower correlation was the fact that I- is
less selective than other ionization methods, resulting in many overlapping
peaks at the same integer mass and ambiguous peak fitting
(Lee et al., 2014; Stark
et al., 2015, 2017), similar to the case in the vocus. This means that,
although the iodide and/or the vocus might be able to charge a specific
molecule, and it would not fragment before detection, the ion may remain
unquantifiable due to a highly ambiguous peak fitting as a result of multiple
overlapping signals.
Atmospheric pressure interface mass spectrometers
Figure 3 shows similar comparisons to those in Fig. 2 for the
nitrate
(Fig. 3a) and the amine (Fig. 3b). Interestingly, these two instruments
show excellent correlation (R2 > 0.9) for dimeric products
(molecules within 350–500 Th) but showed mostly low correlations
(R2 < 0.6) with other instruments in the monomer range. The
nitrate had some agreement with the iodide for certain monomer compounds,
but in the HOM-monomer range where the nitrate generally saw its largest
signals (C10 molecules with 7 to 11 oxygen atoms; Ehn et al., 2014),
none of the other instruments showed strongly correlating signatures.
Mass-defect plots showing the compounds for which time
series correlation (R2 > 0.2) was observed by the
atmospheric-pressure chemical ionization mass spectrometers, (a) nitrate and
(b) amine. Each circle represents a distinct molecular composition and the
marker area represents the correlation (R2, legend shown in Fig. 2a) of
the time series of that molecule between two different CIMS instruments. The
color of each marker depicts the instrument against which the correlation is
calculated.
Despite showing signals at almost all OVOCs, the amine presented low
correlations for all OVOCs except the dimers. In the amine the reagent ion
was greatly depleted due to the relatively high signals (Fig. 4), likely
leading to a nonlinear response for most of the OVOCs, apparently with the
exception of the HOM dimers. It may be that the amine reagent ion formed
extremely stable clusters with these dimers, and thus any collision
involving these dimers with the reagent ion (regardless of whether already
clustered with an OVOC) in the IMR led to an amine–dimer cluster. While the
amine showed very low correlation with the other instruments for most
molecules, it has been demonstrated to be an extremely useful detector of
both radicals and closed-shell OVOCs under very clean, low-loading flow tube
experiments (Berndt et al., 2017, 2018). In other words, it
can provide information on a wide variety of OVOCs, but to obtain
quantitative information, the amine CI-APi-TOF has to be used in a very
diluted system (with very clean air) and at low loadings. Determining the limitations more
explicitly requires further studies, but as a rough
approximation, the typical CI-APi-TOF sensitivity of ∼1010 molecules cm-3 ncps-1 means that when sampling
detectable molecules at 1010 molecules cm-3 (∼0.4 ppb), these molecules will have ion signals of equal abundance to the
reagent ions. Consequently, once the concentration of measurable molecules
exceeds roughly 100 ppt, the CI-APi-TOF may no longer be an optimal choice.
For the nitrate CI-APi-TOF, which mainly detects HOMs with short lifetimes
due to their low volatilities, this has rarely been a limitation, but for
less selective reagent ions, like amines, this can be an important
consideration.
Contribution of the reagent ion, sum of ions from 150 to
350 Th and sum of ions from 350 to 600 Th to total ion count throughout the
campaign for the amine CI-APi-TOF. Only a negligible fraction of the signal
was found below 150 Th (excluding C4H12N+).
Instrument comparisons: concentration estimates
Concentrations of the identified compounds were estimated for all the
different instruments, as described in Sect. 2.6. It should be noted that
no separate inlet loss corrections were applied. The estimations for the
results of PTR-TOF and the vocus are the most reliable as both instruments
were calibrated using authentic standards with a proven method, while larger
uncertainties in the total measured concentrations are expected for the
iodide and the CI-APi-TOFs.
With around 1000 identified ions for each instrument, except for the
PTR-TOF, we decided to focus our attention in this section on a few
particular compound groups: the most abundant C10 monomers (i.e.,
C10H14/16On), C10 organonitrates
(C10H15NOn) and dimers (C20H32On). For the
nonnitrate compounds, the concentrations were measured during
steady-state conditions on 9 December from 15:30 to 23:00 with
[O3] =25 ppb and [α-Pinene] =100 ppb) during period I (Fig. 1 in
blue). The organonitrate concentrations were compared using steady-state
conditions from 20 December, from 02:45 to 07:45 with [O3] =35,
[α-Pinene] =100 and NO =0.5 ppb, during period IV (Fig. 1 in purple). Figure 5a–d show the concentrations of the selected species
as a function of oxygen number in the molecules. While we again emphasize
that all the concentrations were only rough estimates, these plots painted a
similar picture to the correlation analysis, as described in more detail in
the next paragraphs.
Focusing first on the nonnitrate monomers (Fig. 5a–b), for compounds
with zero or one oxygen atoms, the PTR-TOF agreed well with the
concentration estimated by the vocus, while molecules with more than two
oxygen atoms were already close to, or below, the noise level of the
PTR-TOF. In contrast, as the number of oxygen atoms in the molecule reached
two or more, the iodide signal increased and for most compounds showed
concentrations similar to the vocus. These two instruments agreed on
concentration estimates fairly well all the way up to an oxygen content of
around nine oxygen atoms, where the measured signals were close to the
instruments' noise levels. However, when comparing to the nitrate, which is
assumed to have good sensitivity for HOMs with seven or more oxygen atoms, the
concentrations suggested by the vocus and iodide for the O7 and O8
monomers were very high. We preliminarily attributed this to an
overestimation of the concentrations of HOMs by these two instruments,
possibly due to higher sensitivities towards these molecules compared to
the compounds used for calibration (i.e., MVK). We also did not correct for
potential backgrounds using the blanks for the iodide, although they were measured,
since the variability in the blank concentrations (see also discussion in
Sect. 2.4) was large enough to cause artificially high fluctuations in the
final signals. Therefore, we opted to not include such a correction but
also note that, even if half the signal at a given ion was attributable to
background in the iodide, then it would only have a small impact on the
logarithmic scales used in Fig. 5. Other possible reasons for this
discrepancy was that the iodide and vocus were able to detect isomers that
the nitrate was not, or that the nitrate sensitivity was underestimated.
However, considering that the nitrate HOM signal was scaled to match a 5 %
molar HOM yield, it was unlikely that the HOM concentrations can be
considerably higher than this. Other estimated parameters involved in the
formation and loss rates of HOMs also had uncertainties, but we did not
expect any of them to be off by more than 50 %. This concentration
discrepancy thus remained unresolved and will require more dedicated future
studies.
Estimated concentrations of the main α-Pinene
C10-monomer oxidation products (a, b), C10-monomer
organonitrates (c) and α-Pinene dimers (d) by the different mass
spectrometers deployed in this study. The average concentrations were
estimated when the system reached steady state in two experiments: without
NO (a, b, d), 9 December (15:30–23:00), and with NO (c),
20 December (02:45 to 07:45). See text for more details. Data are plotted
only for ions for which the average concentrations were higher than 3 times
the standard deviation during the campaign.
Finally, the quantities estimated using the amine are significantly lower
(1–2 orders of magnitude) for all monomers when compared to the other
instruments. This was presumably related to the titration of the reagent
ion, which meant that the majority of charged OVOCs will undergo multiple
subsequent collisions with other OVOCs, potentially losing their charge in
the process. The nitrate had, as expected, very low sensitivity towards less
oxygenated compounds and its highest detection efficiency for HOMs (i.e.,
molecules with at least six oxygen atoms).
The organonitrate comparison in Fig. 5c suggested that both the vocus and
the iodide were efficient at detecting these compounds, as both instruments
agreed well (R2 > 0.7) for C10 organonitrates with 5 to
10 oxygen atoms. While organonitrates have been detected before using the
iodide (Lee et al., 2016), this was the first
observation in which the vocus also detected such compounds efficiently.
However, we cannot exclude such compounds undergoing fragmentation within
the drift tube as commonly observed in other PTR instruments
(Yuan et al., 2017). For larger oxygen content, the nitrate
again seemed to be most sensitive, showing clear signals above 10 oxygen
atoms, where the previous instruments were already close to noise levels.
The amine seemed worse at detecting organonitrates compared to nonnitrate
monomers.
Neither of the PTR instruments were able to detect any dimers in this study
within their measurement ranges (up to 320 Th for PTR-TOF). The amine and the
nitrate were able to quantify the widest range of HOM dimers, while the
iodide was able to detect less oxidized dimers (Fig. 5d). Based on the
concentration estimates, the amine detection range also extended to less
oxidized dimers than the nitrate, as has already been shown by Berndt et al. (2018). Dimers measured by the iodide were more abundant than the ones
detected by the amine, but from the monomer comparisons we
speculated that the amine might be underestimating concentrations, while the
iodide might be overestimating them. With the data available to us, we can
only speculate on the relative sensitivities of the instruments able to
detect dimers, especially with the vocus providing no support to the
comparison.
One aspect lending credibility to the amine dimer data, in addition to the
good time series correlation with the nitrate, was the odd–even oxygen atom
patterns visible both in the amine and nitrate data. Such a pattern is to be
expected, since the 32 hydrogen atoms in the selected dimers indicate that
they have been formed from RO2 radicals, one of which had 15 hydrogen atoms
(which is what ozonolysis will yield, following OH loss)
(Docherty et al., 2005; Lee et al., 2006;
Ziemann and Atkinson, 2012), while the second RO2 had 17 hydrogen atoms
(which is the number expected from OH oxidation of an alkene where OH adds
to the double bond). The first RO2 from ozonolysis had four oxygen atoms,
and further autoxidation will keep an even number of oxygen atoms, while the
opposite was true for the OH-derived RO2, which started from three oxygen
atoms. In other words, the major dimers from this pathway should contain an
odd number of oxygen atoms after they are combined. In the case of
C20H30On dimers, mainly formed from two ozonolysis RO2,
the pattern was expected to show peaks at even numbers, which is also the case
(not shown).
Odd–even patterns for the oxygen content were not visible in the iodide,
but the reason remained unknown. It was possible that the dimers detected by
the iodide might be formed via other pathways, where such a selectivity did
not occur. This topic should be explored further in future studies, since
dimers formed from the oxidation of biogenic compounds are important for
new-particle formation, and it is therefore critical to accurately identify
and quantify the formation and evolution of different types of dimers. To
date, both dimers measured by iodide (Mohr et al., 2017) and
nitrate (Tröstl et al., 2016) have been found to
be important for particle formation from monoterpenes.
Performance in detecting oxygenated species
Figure 6 summarizes our results and depicts the performance of each mass
spectrometer in detecting monomer and dimer monoterpene oxidation products.
Molecules of C10H16On, C10H15NOn and
C20H30On were provided as examples. We emphasized that the
oxygen content alone was not the determining factor for whether a certain
type of mass spectrometer will detect a compound, but we utilized this
simplified representation in order to provide an overview of the
performances of the different chemical ionization schemes. The results were
primarily based on the correlation analysis from Sect. 3.1, and as
apparent from the y axis, this comparison was only qualitative. However, our
aim was to provide an easy-to-interpret starting point, especially for new
CIMS users wanting to compare different available techniques.
Estimated detection suitability of the different CIMS
techniques for α-Pinene and its oxidation products, plotted as a
function of the number of oxygen atoms. Each panel symbolizes a compound
group: monomers (a), organonitrate monomers (b) and dimers (c). The figures
are indicative only, as none of the reagent ion chemistries are direct
functions of the oxygen atom content in the molecules. See text for more
details.
For monomer compounds without N atoms, shown in Fig. 6a, the PTR-TOF was
limited to the detection of VOCs, while the vocus was additionally able to
measure a large range of OVOCs, up to at least five to six oxygen atoms. The
iodide
detected OVOCs with oxygen content starting from ∼3 atoms but
did not seem to efficiently observe HOM monomers (i.e.,
C10HxO>7). While being a very promising instrument for a
broad detection of OVOCs, the performance of the amine was limited in our
study due to a significant drop in the reagent ion to ∼40 %
of the total signal. Therefore, the amine was marked with a shaded region
rather than a line, with the lower limit based roughly on its usefulness
under the conditions we probed, while the upper limit was an estimate based
on findings in a cleaner system with low loadings (Berndt et
al., 2018). Finally, the nitrate was mainly selective towards HOMs. The
detection and quantification of monomeric OVOCs containing five to eight oxygen
atoms remained the most uncertain, since there were inconsistencies in
both concentration and correlation between the nitrate, measuring the more
oxygenated species, and the vocus and iodide, which detected the less oxidized
compounds.
In Fig. 6b, the suitability for the different instruments was plotted for
organonitrate monomers. The vocus efficiently detected the less oxidized
organonitrates, while the iodide displayed good sensitivity for the same
compounds, with the exception of the least oxygenated ones. For larger
number of oxygens, the nitrate again seemed the most suitable method. For
dimers (Fig. 6c), neither of the PTR techniques showed any ability to
detect these compounds in our study. We did not extend the lines all the way
down to n=0 for the compounds, as it was still possible that these
methods can be able to detect the least oxidized and most volatile C20
compounds, which might not have been present during our experiments. The
iodide showed some correlation with the nitrate but had good signals mainly
in the range of dimers with four to eight oxygen atoms. The amine and
nitrate
correlated well for the most oxidized dimers, suggesting good suitability
for dimer detection of HOM dimers. The amine concentrations stayed high,
with the expected odd–even pattern in oxygen number, even at lower oxygen
content than the nitrate, and therefore the suitability extended further
towards lower O-atom contents. Again, the shaded area was based on a
combination of our findings and those of Berndt et al. (2018).
The results in Fig. 6 are based on the α-Pinene ozonolysis system.
While we will not speculate too much about the extent to which these
findings can be extrapolated to other systems, certain features will remain
similar for other atmospherically relevant reactions. For example, the
most oxidized gaseous HOM species will likely have been formed through
autoxidation processes, which means that they will contain hydroperoxide
functionalities and could thus be detectable by the nitrate. Likewise, the
HOMs, and in particular the dimers, will very likely have low volatilities,
requiring high sample flows with minimal wall contact, as in the case of the
Eisele-type CI inlets used in the nitrate and amine. Several other key
features are also expected to be valid in different VOC–oxidant systems, and
therefore we believe that our findings are also relevant for many other
reaction partners.
Concentration (in ppbC) of the sum of the compounds
measured by each instrument (vocus, iodide, amine and nitrate) throughout
the campaign compared to the amount of reacted carbon through α-Pinene oxidation. Large uncertainties remain in the quantification of the
OVOCs for all instruments, but it is clear that the iodide and vocus are able
to measure a large fraction of the reacted carbon in the gas phase.
As a final test for each instrument, we estimated how much of the reacted
carbon (in ppbC) the different mass spectrometers can explain. As shown in
Fig. 7, both the iodide and vocus seemed to capture most of the reacted
carbon within uncertainties. The concentration determined using the
vocus
was overestimated, explaining more carbon than was reacted. Out of the
largest contributors to the reacted carbon, pinonaldehyde
(C10H16O2) was not efficiently detected by iodide, but
otherwise most of the abundant molecules were quantified by both vocus and
iodide. Any carbon lost by condensation to walls or particles would not have
been quantifiable by any of the instruments in this study. While the
nitrate
was calibrated with an assumption that it can measure 5 % of the reacted
α-Pinene, it only detected less than 0.1 of that amount. The
reason was that the HOMs it can detect were quickly lost to walls (or
particles), and thus the gas-phase concentration was not equivalent to the
branching ratio of the VOC oxidation reaction. In fact, and as revealed by
the slow changes in the times series in Fig. 7d, most of the carbon
ultimately measured by the nitrate was semi-volatile, as such compounds
accumulated and reached higher concentration in the chamber, unlike HOMs.
Thus, while the nitrate was able to detect a critical group of OVOCs from an
aerosol formation perspective, i.e., HOMs, for carbon closure studies
(Isaacman-VanWertz et
al., 2017, 2018), it will be of limited use. This again highlights the need
to first determine the target of a study before deciding which CIMS
technique is the most useful. For the closure comparison in our study, the
overestimations emphasized the need to perform calibration with an extensive
set of OVOCs, ideally with monoterpene-oxidation products, in order to better
constrain the sensitivity of the products of interest. The study by
Isaacman-VanWertz et al. (2018), as the only
study to achieve full carbon closure during chamber oxidation of α-Pinene by OH, also successfully utilized voltage scanning to determine
sensitivities of each compound.
Conclusions
The primary goal of this work was to evaluate the performance of five chemical
ionization mass spectrometers (PTR-TOF, vocus PTR, iodide TOF-CIMS, amine
CI-APi-TOF and nitrate CI-APi-TOF) in the identification and quantification
of a wide variety of products formed in the ozonolysis of α-Pinene.
In addition, we wanted to estimate the capabilities of the newly developed
vocus PTR in measuring OVOC species. By comparing the regions of coverage of
the instruments across multiple experimental conditions (i.e., in different
O3, VOC, NO and OH radical concentrations), we demonstrated that the current
instrumentation captures nearly the entire range of OVOCs, spanning from VOCs
to ELVOCs. The PTR-TOF was only able to measure the most volatile compounds,
while the vocus appeared to be able to measure both VOCs and most of the
OVOCs up to five to six oxygen atoms. In combination with the iodide and nitrate, most
of the OVOC range can be measured. The iodide showed good overlap with the
vocus for most SVOCs with three to five oxygen atoms, while the nitrate mainly detected
products with six or more oxygen atoms. No dimer species were observed
with either of the PTR instruments, which might be due to wall losses
(likely at least for the PTR-TOF) and/or potential fragmentation in the
instruments. The amine CI-APi-TOF is a promising technique, as shown in
earlier studies, but it likely requires low loadings in order to not titrate
the reagent ion, limiting its utility for many chamber experiments and,
potentially, atmospheric observations. The large uncertainties in attempting
a quantification of the wide variety of species measurable with these mass
spectrometers underline the urgent need to develop robust, simple and
complete calibration methods in order to obtain a better estimation of the
concentrations. Finally, it is important to underline that the experimental
and analytical procedures performed by the user will ultimately impact the
sensitivity, the selectivity and the interpretability of the results
attainable from each instrument.
Data availability
Mass spectrometry data are available upon request to the corresponding authors.
Author contributions
MR and ME designed the experiments. Instrument deployment, operation,
and data analysis were carried out by MR, PR, JEK, OP, YZ, LH,
OG, CY and ME; MR, PR, OP and ME interpreted the compiled
data set. MR, PR and ME wrote the paper. All co-authors discussed the
results and commented the manuscript. The authors declare that they have no
conflict of interest.
Competing interests
JEK and DW both work for Aerodyne.
Acknowledgements
This work was supported by the European Research Council (ERC-StG COALA,
grant no. 638703). We gratefully acknowledge Pasi Aalto, Petri Keronen,
Frans Korhonen, and Erkki Siivola for technical support. Olga Garmash thanks Doctoral
Programme in Atmospheric Sciences (ATM-DP) at the University of Helsinki for
financial support. Otso Peräkylä would like to thank the Vilho, Yrjö and Kalle
Väisälä Foundation. We thank the TofTools team for providing
tools for mass spectrometry data analysis.
Review statement
This paper was edited by Keding Lu and reviewed by three anonymous referees.
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