Secondary
organic aerosol (SOA) forms a major part of the tropospheric submicron
aerosol. Still, the exact formation mechanisms of SOA have remained elusive.
Recently, a newly discovered group of oxidation products of volatile organic
compounds (VOCs), highly oxygenated organic molecules (HOMs), have been
proposed to be responsible for a large fraction of SOA formation. To assess
the potential of HOMs to form SOA and to even take part in new particle
formation, knowledge of their exact volatilities is essential. However, due
to their exotic, and partially unknown, structures, estimating their
volatility is challenging. In this study, we performed a set of continuous
flow chamber experiments, supported by box modelling, to study the
volatilities of HOMs, along with some less oxygenated compounds, formed in the
ozonolysis of
Secondary organic aerosol (SOA) makes up a major fraction of the tropospheric
submicron aerosol world-wide
Most VOCs form organic peroxy (
Due to the high level of functionalization, HOMs are thought to be of low
volatility
Here, we investigate the volatility of HOMs, along with some less oxygenated
compounds, experimentally, in a manner similar to
In order to investigate the volatility of HOMs formed in the gas phase in a
controlled manner, we conducted a series of laboratory experiments in the
newly constructed COALA chamber at the University of Helsinki, previously
presented by
We injected dry air purified with a clean air generator (AADCO model 737-14,
Ohio, USA) into the chamber, along with gaseous reactants
In addition to the gaseous reactants, we injected size selected, 80 nm
inorganic seed particles, consisting of either ammonium sulfate (AS) or
ammonium bisulfate (ABS), into the chamber. ABS was used in order to promote
acidity-dependent particle-phase reactions: the effect of these on the
particle phase has been presented by
We monitored the chamber with a suite of online instruments measuring both
gas and particle phases. For measuring the responses of HOMs and other
oxidation products of
We used a proton transfer reaction time-of-Flight mass spectrometer
For the particle-phase measurements, we used a differential mobility particle
sizer
From the DMPS size distribution, we also calculated the dry condensation sink
(CS), describing the ability of a particle size distribution to remove
low-volatility vapours from the gas phase
In a typical experiment, we first continuously injected only gaseous
precursors
The time evolution of the gas-phase concentration of a compound in the
chamber is determined by its sinks (
In a continuous flow chamber, given that the inflow of reactants is kept
constant, a steady state is eventually reached. In a steady state, the
sources and sinks of a compound are equal, and thus its time derivative in
Eq. (
The volatility of a gas-phase compound affects the type of steady state it
forms in the chamber. Next we will qualitatively outline which terms in
Eq. (
For volatile reactants like
The injection rate of
Similarly to
The oxidation products of
Like IVOCs, oxidation products of low volatility, such as ELVOCs, are not
directly injected into the chamber, but produced from
We do not know the exact reaction rate coefficients between ELVOC species and
OH, or the OH concentration in the chamber. To get an upper limit for the
chemical loss, we can assume a collision limited reaction similarly to
The calculated fraction of ELVOC lost to different sinks, and their total lifetime in the gas phase as a function of the condensation sink caused by aerosol particles in the chamber. The wall loss lifetime of ELVOC is estimated to be 400 s. The chemical loss is an upper limit estimate, based on an OH concentration of around 0.1 ppt and collision limited reaction with ELVOC. The vertical broken lines at 0.002 and 0.01 s
Based on the example cases of IVOCs and ELVOCs, we can outline how seed
injections affect oxidation products of different volatilities. In the case
of IVOCs, the volatility of the product is high enough that there is
negligible net condensation. Thus, the sink of the compound is unchanged upon
seed addition. Assuming that the source of the compound stays constant, the
seed injection has no effect on the gas-phase concentration of the compound.
For ELVOCs, the gas-to-particle conversion is irreversible. Upon a typical
seed injection experiment, the condensation sink increases from around
Above, we have assumed that the source term of oxidation products stays constant upon seed injection. In the following section, we will present two important cases when this assumption does not hold, and discuss their effect on the method and the results. The first case is related to the loss of
An important class of intermediates in the formation of HOM from the
ozonolysis of
We have so far considered oxidation products originating directly from VOC
oxidation, through short-lived
So far we have only explicitly considered the formation of compounds in the
gas-phase oxidation. However, particle-phase processes can potentially affect
the gas-phase concentrations of compounds as well. A compound
This process can affect the gas-phase concentrations in two ways. First, the
concentration of compound
In order to quantitatively relate the volatilities of the formed oxidation
products to the behaviour of their gas-phase concentrations under the seed
injection, we performed a series of simulations using the ADCHAM model
The reversible wall losses of the condensable vapours were modelled using the
method proposed by
The Teflon walls are treated as a large organic aerosol concentration
(
The vast majority of the ions detected with the CI-APi-TOF were clusters of
analyte molecules with the nitrate ion,
In addition to the analyte molecules charged with the nitrate ion or its dimer, some molecules are also detected as deprotonated anions. For simplicity, we excluded these peaks from the analyses.
We processed the nitrate CI-APi-TOF data using
tofTools
In order to investigate the effect of seed injections on gas-phase
concentrations of
When injecting only gaseous precursors we observed formation of both HOM
monomers and dimers, as expected. The HOM formation was accompanied by the
formation of SOA. During the course of an experiment, both the HOM signals
measured by the CI-APi-TOF and the organic mass measured by the AMS stayed
stable, indicating they were in steady state (SS
Time series of both gas and particle-phase species during a typical seed injection (experiment 19, Table
Using the ADCHAM model, we found that in the conditions of the chamber, the
gas-phase concentrations of oxidation products with a saturation
concentration (
Modelled fraction remaining vs. the logarithm of saturation concentration from the ADCHAM model for experiment number eight (Table
The response of the HOMs to the seed injection was not uniform: some compounds
showed a larger fractional decrease than others. As an example, only a small
fraction of the original concentration of
Gas-phase HOM signal normalized to level before seed injection vs. condensation sink during a typical experiment (experiment 19, same as in Fig.
We will next analyse the behaviour of the measured gas-phase compounds in
experiments conducted in a dry chamber, without
Fraction remaining after seed injection vs. the molecular mass of the detected cluster. The area of the circles has been linearly scaled to the magnitude of the signal of each compound before the seed injection, capped at
Looking at the fraction remaining after seed injection as a function of the
mass of the detected cluster, we generally observed values close to 1 for
many of the lower mass compounds, up to 250 Da (including 62 Da from the
charging nitrate ion; Fig.
In addition to the closed shell products, we also investigated the effect of
the seed additions on the highly oxidized
Upon introduction of
Compared to the injections of ammonium sulfate, we did not find a large
difference in the behaviour of the gas-phase oxidation products upon
injections of the more acidic ammonium bisulfate. However, in the same set of
experiments,
In contrast, we did observe a difference between the experiments conducted at
To gain more insight into what determines the volatility of HOMs, we
constructed a statistical model explaining their condensation behaviour,
measured by the fraction remaining, in terms of their composition. From
Fig.
For the analysis, we chose to use the average of experiments 15 and 19
(Table
Compounds with less than six carbon atoms, or with a signal-to-noise ratio
below 10, were excluded from the model, in order to avoid the smallest
fragments and unreliable signals respectively. In addition, any compounds
with an FR value over 1.1 (meaning a 10 % increase upon seed addition) were
excluded due to the influence of particle-phase processes on them. The model
was weighted with the signal before seed addition, capped to the same value
as in Fig.
Measured fraction remaining vs. fraction remaining modelled on the chemical composition. The colouring of the circles is based on the carbon number of the compound, and the area of the circles is scaled linearly to the signal intensity, as in Figs.
We found that the fraction remaining could be well explained using the
composition (Fig.
The coefficients for each of the terms in the model, including the intercept, were highly statistically significant, with all
The addition of a carbon or a nitrogen atom to a molecule, with their
positive coefficients in the model, would act as to increase the volatility.
The addition of a hydrogen or an oxygen atom, on the other hand, would act as
to decrease it. The positive coefficients of carbon and nitrogen seem
counterintuitive at first. However, the addition or removal of a carbon or a
nitrogen atom is not independent of other elements. In the case of HOMs
formed here, the nitrogen most probably appears in the form of a nitrate
functional group (
As before, any compounds with a saturation concentration higher than
100
Comparison of different volatility estimates and parameterizations. SIMPOL and COSMO-RS are from
There are numerous existing parameterizations for assessing the volatility of
VOC oxidation products. Some, like the SIMPOL model by
In the SIMPOL model, any addition of oxygen to the molecule decreases its
volatility by almost an order of magnitude, at a minimum (for a ketone). A
hydroxy or a hydroperoxy group both lower the volatility by over 2 orders of
magnitude, as does a nitrate group. In our parameterization
(Eq.
Using quantum chemical calculations by the COSMO-RS model,
As a result of the lower sensitivity of the volatility to additional
functional groups,
For
To investigate the volatility of HOMs formed in the ozonolysis of the
monoterpene
We found that the behaviour of the compounds upon seed injection, and thus
their volatility, could be well explained in terms of their chemical
composition. We found carbon, hydrogen, oxygen and nitrogen numbers all to be
important in explaining the volatility, and the relationship could be
connected to molecular properties of the compounds. Based on this
relationship, we were able to develop a parameterization for the volatility of
HOMs monomers generated in
The results presented here are possibly specific to HOM from the ozonolysis
of
The data used in the figures, as well as the time
series of measured ozone,
The calculated fraction of
Experiment 3 modelled with ADCHAM.
Overview of experimental conditions. AS: ammonium sulfate; ABS: ammonium bisulfate; eff: effloresced seed; and
deli: deliquesced seed. Condensation sinks are calculated for
NA – not available
OP, MR and ME designed the study (conceptualization). OP performed the main data analysis and PR the analysis of the ADCHAM model results (formal analysis). ME acquired the funding for the project and OP acquired funding for himself. OP, MR, LH, LQ and ME performed the measurements (investigation). OP, MR and ME came up with the experimental set-up and related analyses, while PR designed the ADCHAM model (methodology). PR developed the ADCHAM model (software). ME supervised the project (supervision). OP verified that the results are consistent across experiments (validation). OP conceptualized and plotted the main text figures (visualization). OP wrote the original draft (writing – original draft). All coauthors read and commented on the manuscript (writing – review and editing).
The authors declare that they have no conflict of interest.
We would like to thank Olga Garmash and Chao Yan for helpful discussions, and Simon Schallhart for help in interpreting the PTR-TOF data. We thank the tofTools team for providing tools for mass spectrometry data analysis.
This research has been supported by the European Research Council (COALA (grant no. 638703)), the Academy of Finland (grant nos. 317380 and 320094), the Svenska Forskningsrådet Formas (grant no. 2018-1745), and the Vilho, Yrjö and Kalle Väisälä Foundation.Open access funding provided by Helsinki University Library.
This paper was edited by Jacqui Hamilton and reviewed by two anonymous referees.