Cyclohexene (
HOMs were produced in a chamber from cyclohexene ozonolysis and measured with a chemical ionisation mass spectrometer (CIMS) using nitrate (
Under unperturbed ozonolysis conditions, known HOM peaks were observed in the chamber, including
The addition of seed aerosol increased the condensation sink, which markedly decreased the signals of all low-volatility compounds. Larger molecules were seen to have a higher affinity for condensation, but a more detailed analysis showed that the uptake was controlled mainly by the number of oxygen atoms in each molecule. Nitrates required higher mass and higher oxygen content to condense at similar rates as the non-nitrate HOMs. We also tested two existing elemental-composition-based parameterisations for their ability to reproduce the condensation observed in our cyclohexene system. Both predicted higher volatilities than observed, most likely due to the number of oxygen atoms enhancing the product uptake more than the models would suggest.
Secondary organic aerosol (SOA) plays an important role in the climate system, and its formation and growth have been a major focus of research in recent years
Many of the hydrocarbons emitted to the atmosphere have natural sources, mainly in vegetation
The lower the volatility of an oxidation product is, the more it has a tendency to condense onto existing aerosol particles
The most emitted monoterpene in the atmosphere is
In this work, we studied cyclohexene ozonolysis in an environmental chamber, with the main focus of understanding the formation and fates of HOM products under perturbations. NO was added to the chamber in order to investigate the resulting changes in the formation of HOMs, while introducing seed aerosol allowed us to examine the connection between volatility and HOM elemental composition. We also assessed the applicability of two existing volatility parameterisations on our cyclohexene system. This allows for investigating the link between elemental composition and volatility of cyclohexene HOM in the broader context of other volatile organic compounds (VOCs), such as the atmospherically relevant BVOCs.
The measurements were conducted during a 5 d period in the COALA chamber, which is described in detail by
The condensation of vapours was studied with a 9
Oxygenated gas-phase products were monitored by a chemical ionisation atmospheric pressure interface time-of-flight mass spectrometer
The CI-APi-TOF mass spectrometry data were analysed in MATLAB (R2016a) with the toolbox tofTools (R607)
The toolbox tofTools fits peaks based on a given list of compounds. Some of the compounds could potentially have very similar masses and fitting them correctly can sometimes prove challenging for an automatic routine
Including potentially false signals would be undesirable in the analysis. Individually estimating the quality of each peak fit at each time point is extremely time-consuming and presents an inherent challenge for high-resolution mass spectral analysis. This motivated us to attempt to use a quantitative measure to describe the quality of the automatically generated fits. By assigning this variable for each fit, signals with poorly fitted peaks could be easily identified and down-weighted or removed from the analysis.
The variable, which we will call the FitFactor, was used to quantify how well the fitted peak matched the spectra. The residual area, i.e. the difference between the spectral peak and the sum of all fitted peaks is compared to the surface area of the fitted peak of interest. This residual-to-peak ratio is subtracted from unity, and the reached value is the FitFactor of the peak fit.
Visual examples of the FitFactor determination from a few different peak fits are shown in Fig.
The mechanisms of cyclohexene ozonolysis, related autoxidation steps and other reactions leading to
Unimolecular termination of
Two example 10 min average spectra showing the largest product peaks that were all observed within the mass range 150–400 Th. Panel
A loss of
Peroxy radicals are also a part of the chemistry of pollutants such as
Only Reaction (
A fraction of
The left panel in Fig.
Injecting NO into the chamber has a significant impact on the chemistry within, which consequently leads to changed product signals. Although we expect NO to have the largest effect on the radical chemistry (Eqs.
Examples of signal changes due to increased
An example spectrum during a
A few examples of changes in product signals with increasing
For a broader overview of signal changes in response to
The effect of
The results in Fig.
This varied response in the monomer signals highlights how complicated the formation pathways can be, even when the initial reactant is a relatively simple molecule like cyclohexene. Speculating on the exact reasons for differences between individual signal responses is difficult. A single observed elemental composition may often contain a collection of isomers, each with distinct formation pathways. The reaction rate constants and different branching ratios of reactions between different combinations of
For these reasons, we will only briefly look at certain example molecules and compound groups in more detail.
The reason for the decreasing
The increase of the observed nitrate signals under high
Signal changes in N-containing molecules upon
Despite the difficulties with pre-existing signals, the formation of many nitrate products was clearly observed in the experiment.
The condensation sink of ABS seed aerosol was used to assess species volatilities. Compounds with low volatilities are expected to rapidly condense onto the particle surfaces, whereas the more volatile compounds should remain in the gas phase. The loss rate of the latter is mainly governed by flush out from the chamber, while the low-volatility compounds condense either onto the walls or onto particles (
Condensation behaviour of three
The fraction remaining (FR) at a condensation sink of approximately
In addition to observations, also predictions based on two different models are shown in Fig.
that was originally based on measurements with
The model for volatility used in Fig.
Here the additional constants are
The observed (opaque) and modelled (transparent) decrease in signal during an ABS seed experiment, for non-nitrate (green) and nitrate (blue) products. Panel
Many monomers had remaining fractions above 1, indicating that the signal increased. A likely explanation is that these compounds are semivolatile and were still equilibrating with the chamber walls, thus having not yet reached steady state. This type of behaviour is likely the largest source of uncertainty in our data, and the potential error will be different for all molecules and therefore difficult to quantify in any detail. A rough estimate is that this would at most cause an error of 10 %–20 % to the relative changes reported above for the relatively fast seed addition experiments. Another possible reason for the increased signal is that these molecules have particle-phase sources, which would explain the increased formation upon seed addition. Due to these associated uncertainties, we do not try to interpret these points further. Some molecules with FR
The product volatilities were closely related to the molecular mass. The condensation sink caused most of the non-nitrate signals with masses above 250 Th to drop to values less than 50 % of the original, suggesting irreversible condensation and low volatilities. At masses below 225 Th, on the other hand, compounds remained fairly volatile, as all signals either remained the same or increased. Two exceptions to this with FR
Similarly to non-nitrates, the volatility of nitrates decreases with mass.
However, the transition from high to low volatility appears to happen at a larger mass than for non-nitrates, which is in line with previously determined group additivity trends of volatility
Both the
As the models misplace the transition between non-condensing and irreversibly condensing compounds, they also over-predict the product volatilities in general. Since we cannot distinguish between the differences in volatilities of compounds that condensed irreversibly in our experiments, we are not able to confirm where the observed volatilities merge with the ones predicted by the models.
In order to better understand what controls the volatility of cyclohexene products and where these models might fail, we had a more detailed look into the effect of elemental composition on FR. In Fig.
Cyclohexene oxidation products with the lowest carbon and hydrogen contents hardly condensed, while the opposite was true for the molecules with the highest C and H contents, mostly associated with the dimers (Fig.
However, the fraction remaining as a function of the oxygen content of the molecule (Fig.
Fraction remaining (FR) after seed addition as a function of elemental composition. This figure is similar to Fig.
Elemental composition can be a good indication of the possible functional groups in the molecule that ultimately determine the volatility of a compound
Another possible explanation to some of the model–observation discrepancies is if the monomer HOM in our experiments not only condense but are also lost by reactive uptake on the particles due to labile hydroperoxide functionalities. Separating between these effects is not possible from our data but would be extremely important for understanding, and modelling, SOA formation. We hope our work will help spur future studies to design experiments where these open questions could be tackled. Currently, volatility estimates for HOMs are highly uncertain, with different computational methods predicting vapour pressures differing by many orders of magnitude for a given HOM
We have investigated the formation and fates of cyclohexene ozonolysis products, with a focus on the most oxidised species. These highly oxygenated organic molecules (HOMs) were measured with a CI-APi-TOF mass spectrometer. A statistical approach was utilised for evaluating the reliability of compound signals that had been determined from the spectra by means of an automatic peak-fitting procedure. By assigning each peak fit with a so-called FitFactor value that compared the fitted peak and spectral residual areas, we could fast and feasibly sort through the fitted compounds and identify the most reliable signals.
Perturbations to product signals by
The lower rate of
The condensation of the oxidation products was probed in experiments where ammonium bisulfate seed aerosol was added to the chamber. The concentration of lighter products either remained the same or even increased, while after a narrow transition zone, all non-nitrate products heavier than 250 Th condensed onto the particles at nearly equal rates, dropping to at least half of the initial signal. Nitrates condensed less than non-nitrates with similar masses. A breakdown of the elemental composition of the different products indicated that the main factor determining the volatility of cyclohexene HOM is the number of oxygen atoms in the molecule. Products that had seven or more oxygen atoms, all condensed irreversibly in our experiment. Carbon- and hydrogen-atom contents also correlated with volatility, but this was mostly limited to the different condensational behaviour of monomers and dimers, while the effect of C and H content specifically was somewhat ambiguous. For example, molecules with five or six C atoms were observed to range anywhere from non-condensing to irreversibly condensing. We also found that the effective O : C ratio is by itself not a good measure for volatility, as small monomers can have a much higher volatility than many dimers, even with an O : C ratio twice as high.
Several parameterisations with the aim of predicting product volatilities based on their elemental composition have been developed. Being typically applied to larger reactants, such as monoterpenes (
The highly species-specific effects of
The approximate reactant, oxidant and particle concentrations during the conducted experiments. Cyclohexene concentration is a very rough estimate based on changes in ozone concentration.
By applying the FitFactor, a majority of the very poorly fitted peaks, such as the one shown in Fig.
Examples of determining the FitFactor from the residual-to-peak ratio of four different compounds. The title compounds are indicated by green lines and shading. The FitFactor value for each is shown in the upper right corner of the subplots.
Similar to Fig.
Similar to Fig.
Scripts used in the analysis are available from corresponding author Meri Räty upon reasonable request.
Data are available at
The study was conceived by ME, MRiv and MRis. The measurements were performed by MRiv, OG and LQ. MRä did the main data analysis and visualisation under the supervision of ME with additional guidance from OP and MRis. MRä wrote the article, and it was reviewed and commented on by the other authors.
The authors declare that they have no conflict of interest.
We thank Liine Heikkinen for assistance during the experiments, Pontus Roldin for helpful discussion and the tofTools team for providing tools for mass spectrometry data analysis.
This research has been supported by the King Abdullah University of Science and Technology (grant no. OSR-2016-CRG5-3022), the European Research Council (grant no. 638703-COALA) and the Academy of Finland (grant nos. 307331, 317380, 320094 and 326948).Open-access funding was provided by the Helsinki University Library.
This paper was edited by Manabu Shiraiwa and reviewed by two anonymous referees.