The oxidation of biogenic volatile organic compounds
(VOCs) represents a substantial source of secondary organic aerosol (SOA) in
the atmosphere. In this study, we present online measurements of the
molecular constituents formed in the gas and aerosol phases during
Airborne particulate matter has significant impacts on global climate (Hallquist et al., 2009), human health (Dominici et al., 2006) and visibility (Husar et al., 1981). Organic compounds typically comprise around 50 % of submicron aerosol mass (Jimenez et al., 2009). Most of this is secondary and biogenic in origin (Hallquist et al., 2009); the oxidation of biogenic volatile organic compounds (VOCs) such as monoterpenes and isoprene represents a major source of atmospheric secondary organic aerosol (SOA) (Kroll and Seinfeld, 2008; Ziemann and Atkinson, 2012). However, SOA formation processes remain highly uncertain and this is regarded as a major weakness in the current understanding and model representation of atmospheric aerosols (Boucher et al., 2013). The chemistry involved is complex, and the range of organic compounds present in the atmosphere is extremely diverse (Goldstein and Galbally, 2007). Understanding how SOA components form and react is therefore a conceptual and analytical challenge (Noziere et al., 2015).
Identifying and quantifying individual organic components in this complex mixture is commonly achieved using mass spectrometry (MS). A great deal of insight into SOA formation and aging from monoterpenes has been provided by established instruments such as the aerosol mass spectrometer (AMS; Aiken et al., 2008; DeCarlo et al., 2006; Jayne et al., 2000). Of particular utility for understanding organic reaction mechanisms are so-called “soft” ionisation techniques, which retain molecular structure during ion formation (Hoffmann et al., 2011). Most conventional soft ionisation MS is “offline”, where chemical analysis is performed subsequent to sampling. Techniques such as electrospray ionisation (ESI) MS have been applied widely in atmospheric chemistry and have yielded extensive insight into aerosol chemical processes (e.g. Claeys et al., 2009; Edney et al., 2005; Kampf et al., 2012; Kourtchev et al., 2014). However, there are drawbacks to offline ESI-MS: the time resolution of measurements depends on the frequency at which new aerosol samples are collected, which is typically an hour or more for chamber and ambient sampling. There is also potential for sampling artefacts (Turpin et al., 2000) and a lack of analyte quantification when used in a direct-infusion mode.
A number of recent studies have therefore developed online or semi-continuous
atmospheric-pressure ionisation (API) MS techniques (Bateman et al., 2009;
Brüggemann et al., 2015; Clark et al., 2014; Nah et al., 2013; Pereira et
al., 2014; Vogel et al., 2013; Zhang et al., 2015; Zhao et al., 2017b). In
this paper, we focus on the application of one such approach to organic
aerosol analysis, namely extractive electrospray ionisation (EESI) MS. The
EESI process predominantly forms molecular ions ([M
Our earlier study reported the first quantification of the EESI aerosol
extraction process for carboxylic acid particles (Gallimore and Kalberer,
2013). The detected MS peak abundance scaled in direct proportion with the
aerosol mass concentration and was independent of particle diameter in the
ranges studied (3–600
In the current study, we evaluate in detail the use of EESI-MS for
atmospheric chamber experiments. Large-volume atmospheric chambers have
proven to be a valuable means of exploring volatile organic compound (VOC)
oxidation mechanisms because a simplified subset of reactions can be
investigated under well-defined conditions (Cocker et al., 2001; Gallimore et
al., 2017b; Paulsen et al., 2005). We investigate SOA formation from the
ozone-initiated oxidation of
Ultra-high-resolution mass spectra of
An aerosol generation system, described in detail by Gallimore and Kalberer (2013), was used to produce model aerosols for quantifying the extraction and ionisation of organic compounds in the presence of inorganic salts (Fig. S1 in the Supplement).
Aerosols were produced from aqueous solutions using a custom-made
constant-output atomiser. Solutions containing L-tartaric acid (99 %,
Aldrich) and ammonium sulfate (99.5 %, Fluka) in water (HPLC grade,
Rathburn) were prepared. The total solute concentration was held constant at
0.1 mol L
A silica diffusion dryer was used to produce dry particles
(< 10 % RH). The dried polydisperse particles were size-selected
in the range 50–200 nm prior to EESI-MS analysis using a differential
mobility analyser (DMA) (TSI model 3081). The outflow from the DMA was split,
with 0.3 L min
Experiments on the oxidation of biogenic VOCs were performed in the newly commissioned Cambridge Atmospheric Simulation Chamber (CASC), which is characterised in detail in Gallimore et al. (2017b). Aspects of the chamber operation relevant to the results in this paper are described briefly here (see also Fig. S2).
The chamber consists of a 5.4 m
Aerosol formation was investigated for the dark reaction between
Conditions used in the chamber experiments in this paper. In all
cases, the chamber humidity was adjusted to 60 % RH and
An air “sprinkler” system, consisting of a 2 m PTFE tube with a series of
small holes along its length, was supplied with high-pressure bursts of air
to mix the chamber constituents without recourse to a fan. The air sprinkler
leads to the addition of
Batch sampling system used to supply the EESI source with chamber air in a two-step process. Blue lines: air is drawn from the chamber into an intermediate 10 L reservoir, during which time the EESI source is flushed with zero air and a blank spectrum is acquired (3.5 min). Green lines: chamber air from the flow tube is pushed through to the EESI source to acquire a sample spectrum (3.5 min).
The EESI source was described in detail in Gallimore and Kalberer (2013). Briefly, it consists of a custom-built aerosol injector and housing which is interfaced with a commercially available ESI source. The primary solvent electrospray can generate droplets with positive or negative charges depending on the potential difference between the ESI probe and the mass spectrometer. Particle–droplet collisions dissolve the aerosol analytes, which are ionised and ejected into the gas phase by a Coulomb explosion mechanism.
Here the primary solvent was a water–methanol
The commercial ESI housing was found not to be air tight, so a batch sampling procedure was adopted to introduce particles from the chamber into the EESI source (Fig. 1).
Air was drawn from the chamber at 10 L min
Repetition of this cycle allowed batch sampling with a time resolution of
7 min. Particle losses using the sampling system in this way were
characterised using an SMPS and were
Filter samples of SOA were collected during the same experiments as the
online composition measurements. The sampling and analysis protocol was based
on that described in Kourtchev et al. (2014). Briefly, particles were drawn
through a charcoal denuder and collected onto cleaned quartz fibre filters
(Pallflex® Tissuquartz 2500QAT-UP, 47 mm
diameter) 1 h after the introduction of ozone to the chamber. Chamber air
was collected for 30 min at 15 L min
The EESI and nanoESI sources were coupled to an ultra-high-resolution mass
spectrometer (Thermo Scientific LTQ Orbitrap Velos). Mass spectra were
acquired in the range
Mass spectra generated from EESI and offline nanoESI samples were analysed
using a method similar to that described in Zielinski et al. (2017). Briefly,
possible formulae were assigned to the spectra using XCalibur 2.1 software
(Thermo Scientific). Evaluation of these initial assignments was performed
using an in-house code run in Mathematica 10 (Wolfram Research Inc.). This
removes formulae which fall outside a 2 ppm mass tolerance and those deemed
implausible based on their atomic ratios. By strictly limiting permitted
elements, we reduce the number of erroneous permutations of formulae that
coincide with the measured
Time series of individual particle-phase ions were also extracted from
Xcalibur. These raw time series were processed by removing the transitions
between sample and blank periods (
Gas-phase VOC concentrations were measured using a PTR
mass spectrometer (PTR-ToF-MS 8000, Ionicon Analytik, Innsbruck, Austria) in
the range
Data analysis for the PTR-MS was carried out using PTR-MS Viewer 3.2 (Ionicon
Analytik). Mass calibration was adjusted using H
The complete reaction scheme for the degradation of
Simulations were performed using the box model AtChem
(
AtChem simulates gas-phase chemistry, but not aerosol formation. To compare
gas-phase concentrations from the simulation with EESI-MS aerosol
measurements, we neglected possible in-particle chemistry and focused on
major aerosol components from previous studies referenced in Table 2.
Gas–particle partitioning considerations are discussed in Sect. 3.3.2. The
AtChem output concentrations (molecules cm
EESI-MS approaches have demonstrated excellent tolerance to very complex
sample matrices compared to direct ESI-MS (Chen et al., 2006). Here we
investigate the possible impact of inorganic salts on the EESI-MS peak
abundance of organic ions in mixed aerosol particles. Specifically, we
establish the potential impact of inorganic seed particles on the relative
quantification of organic acids (detected as [M
Peak abundance of
As for the single-component aerosol in Gallimore and Kalberer (2013), the detected mass spectrum signal abundance scales linearly with the organic aerosol mass concentration, this time over a range of organic/inorganic aerosol fractions. The best-fit curve for the entire data set in Fig. 2 follows a power law with an exponent of 0.97, close to the value of 1 expected for such a linear relationship.
The data in Fig. 2 are colour-coded according to the mole fraction of
tartaric acid present,
This is not problematic for the current application because the conditions
used in the chamber experiments when EESI-MS measurements were made involved
low ammonium sulfate concentrations (
EESI-MS (particle phase) and PTR-MS (gas phase) were deployed during dark
Tentative assignments of a selection of major ions detected by
PTR-MS and EESI-MS during dark
Compounds assigned
in other studies were used to provide possible assignments here:
The average carbon oxidation state of compounds detected via EESI-MS
and PTR-MS as a function of
The major products identified by EESI-MS and PTR-MS following data treatment
compare well to previous literature. Assigning PTR-MS spectra is slightly
complicated by fragmentation; abundant products such as pinonaldehyde appear
mostly as fragment ions (Wisthaler et al., 2001). Since fragmentation
patterns for most VOCs are not known, we have not assigned ions
<
A positive characteristic of EESI-MS is that most species are detected as
intact quasi-molecular ions (Table 2). Furthermore, the two ion polarities
allow detection of complementary compound classes. EESI(
Taken together, EESI(
The ensemble average
Aside from
Compounds detected via EESI(
A comparison of
Figure 4 shows a comparison in the same
There is good agreement between the two data sets in Fig. 4 in terms of the
range of
Mass spectra obtained during the dark ozonolysis of
Left
The peaks absent from EESI(
We now focus on the ability of our online MS techniques to monitor relative
concentration changes of individual species during
As configured, the sampling setup enables a blank and chamber measurement to be obtained in a 7 min cycle, a substantially higher time resolution than most other semi-continuous sampling methods (Bateman et al., 2009; Pereira et al., 2014) or collection onto filters. It is comparable to the recent highly time-resolved particle-into-liquid-sampling measurements of Zhang et al. (2015). The signal abundances vary by less than 15 % across a sample window (3.5 min) (Fig. 6), with most of the decrease attributed to particle deposition in the intermediate reservoir volume and sampling lines. An advantage of flushing the EESI source during each blank period is that baseline changes can be monitored and accounted for. The signals in Fig. 6 rapidly return towards the baseline recorded at the start of the experiment as the source is flushed. Small increases in this baseline (e.g. due to particle deposition in the source) are subtracted during data processing. Importantly, we avoid the major EESI source contamination problems reported in other applications (McCullough et al., 2011). We suspect that this is due to a combination of using relatively low analyte concentrations, the optimised source parameters from Gallimore and Kalberer (2013) and this regular flushing procedure.
The semi-volatile nature of SOA means that both gas- and particle-phase
species will be present in the chamber. We examined whether gas-phase species
contribute to our observed EESI(
The aerosol mass loading in the chamber (
An important application of simulation chamber experiments is to better
constrain and validate atmospheric reaction mechanisms, particularly for
complex VOC chemistry. We compare here individual species measured during
the chamber experiments using PTR-MS and EESI-MS to predictions from the
AtChem chamber box model using the near-explicit
We first benchmark the simulations to measurements of [
The “sigmoidal” shape of the
We now demonstrate a comparison of aerosol-phase EESI(
Comparison between EESI-MS peak abundances (left
The overall agreement between MS abundances and MCM simulations is very encouraging. The measurements and model compare well in two respects: the time dependence of product formation, and the relative concentrations of a given product in the low, medium and high conditions. Note that the MS abundances of the three compounds cannot be directly compared without calibration due to the species' different IEs.
The product time series reflect the rate of consumption of
The individual product yields after 1 h (when the
Although pinonic acid, pinic acid and OH-pinonic acid are reported as major
particle-phase oxidation products in a range of studies (Table 2), the
discussion above assumes that the gas-phase concentrations from the MCM are a
good proxy for particle-phase concentrations assessed by EESI-MS.
Particle-phase reactions are unlikely to be a large source or sink of these
major products, although they may be important for a range of high-molecular-weight species (Camredon et al., 2010). The equilibrium partitioning of
products between the gas and particle phases will favour the particle phase
for pinic acid and OH-pinonic acid because their saturation concentrations
(< 10
Pinonic acid is thought to have a significantly higher saturation
concentration (in the range 10
Despite the potential limitations of this comparison, Fig. 8 provides further
evidence that EESI-MS can be used for relative quantification of individual
species in organic aerosols. Moreover, it extends this applicability to
scenarios where the particles contain a complex mixture of components, and
where the particle composition, size and total mass are evolving.
Specifically, we have demonstrated here that the influence of the bulk
aerosol “matrix” on EESI ionisation appears to be negligible up to SOA
loadings of
Figure 9 shows the measured EESI(
Correlation between MS peak abundance and MCM mass across three different experiments for OH-pinonic acid.
This representation is analogous to the plots for tartaric acid shown in Fig. 2, except that the MS signal is compared to the model rather than a known analyte concentration. In principle, a direct calibration curve such as Fig. 2 would allow MS abundances to be converted to absolute concentrations for any species where authentic standards are available. In practice, however, the number of species present in aerosols, and the general unavailability of suitable standards, makes this approach impractical for routine quantification. Comparison to modelled concentrations in this way may therefore provide an approximate indicator of IE for a range of different species in aerosols. However, as discussed above, care is required in interpreting aerosol-phase concentration changes for the many species where in-particle reaction or gas–particle partitioning may be significant. A coupled model of aerosol- and gas-phase chemistry (Gallimore et al., 2017a; Shiraiwa et al., 2010) would therefore be a desirable tool to use alongside future chamber experiments.
Online measurements of particle- and gas-phase chemistry in the new Cambridge Atmospheric Simulation Chamber (CASC) have been achieved simultaneously using complementary “soft” ionisation mass spectrometry techniques: EESI and PTR. The results for EESI-MS are encouraging and prompt continuing use and further development of the technique in future. Its principal advantages over conventional electrospray techniques are (1) the sample and blank measurements are obtained online, providing highly time resolved information with fewer potential artefacts, and (2) that ion abundance can be used as a relative measure of concentration due to the lack of matrix interference, and in principle converted to an absolute concentration via calibration.
The lack of matrix interference in EESI-MS compared to direct ESI-MS has been
noted in other applications. The mechanistic differences are not fully
understood, but a likely rationale is that the primary electrospray
conditions are constant during EESI, but vary substantially depending on the
dissolved analytes in direct ESI. Our work (here and in previous studies)
shows a correlation between MS signal and total analyte mass which does not
appear to saturate at the upper end of the concentration range tested
(
Improving EESI-MS sensitivity would be an advantage in future atmospheric chemistry applications. The downside of our Orbitrap mass spectrometer is that the instrument's ion collection and transmission properties are less efficient relative to other instruments. Coupling the EESI ion source to an alternative mass analyser is an area of active investigation. Pre-concentrating the airborne particles using a virtual impactor system similar to Vogel et al. (2013) may also provide an order-of-magnitude boost to sensitivity.
Although out of the scope of the current study, our molecular composition measurements from the chamber may be amenable to detailed process modelling. A model which includes descriptions of gas–particle partitioning, alongside reactions in both phases, may be better able to capture the dynamic evolution of particle-phase components and probe multiphase processing and extended aging of the initial products.
Data presented in this study can be obtained by contacting the corresponding authors.
The Supplement contains details related to operation of the atmospheric chamber and EESI sampling system, further details about MCM modelling, full EESI-MS and PTR-MS spectra, and additional figures comparing the online MS measurements to MCM simulations.
The authors declare that they have no conflict of interest.
This work was funded by the European Research Council (grant 279405), the UK Natural Environment Research Council (grant NE/H52449X/1) and the Velux Foundations (project number 593). Edited by: Sergey A. Nizkorodov Reviewed by: two anonymous referees