Effects of NOx and seed aerosol on highly oxygenated organic molecules (HOM) from cyclohexene ozonolysis

Cyclohexene (C6H10) is commonly used as a proxy for biogenic monoterpenes, when studying their oxidation mechanisms and secondary organic aerosol (SOA) formation. The ozonolysis of cyclohexene has been shown to be effective at producing highly oxygenated organic molecules (HOM), a group of molecules known to be important in the formation of SOA. Here, we provide an in depth look at how, on a molecular level, the HOM formation and fate changed with perturbations from NOx and seed particles. 5 HOM were produced in a chamber from cyclohexene ozonolysis, and measured with a chemical ionisation mass spectrometer (CIMS) using nitrate (NO3 ) as reagent ion. As high-resolution CIMS instruments provide mass spectra with numerous ion signals and a wealth of information that can be hard to manage, we employed a primarily statistical approach for the data analysis. To utilise as many individual HOM signals as possible, each compound was assigned a parameter describing the quality of the observed signal. These parameters were then used as weights or to determine the inclusion of a given signal in 10 further analyses. Under unperturbed ozonolysis conditions, known HOM peaks were observed in the chamber, including C6H8O9 as the largest HOM signal, and C12H20O9 as the largest “dimer” product. With the addition of nitric oxide (NO) into the chamber, the spectrum changed considerably, as expected. Dimer product signals decreased overall, but an increase in dimers with nitrate functionalities was seen, as a result of NO3 radical oxidation. The response of monomer signals to NO addition varied, and 15 while nitrate-containing monomers increased, non-nitrate signals either increased or decreased, depending on the individual molecules. 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. All non-nitrate compounds with at least 7 20 oxygen atoms were observed to condense onto the seed aerosol at close to equal rates. Nitrates required higher mass and higher oxygen content to condense at similar rates as the non-nitrate HOM. A comparison to experiments with α-pinene reported earlier, showed quite a similar relationship between elemental composition and volatility, although products from α-pinene ozonolysis appeared to require slightly higher oxygen numbers for the same decrease in volatility. In addition, two models 1 https://doi.org/10.5194/acp-2020-694 Preprint. Discussion started: 28 August 2020 c © Author(s) 2020. CC BY 4.0 License.


Chamber conditions and instrumentation
The measurements were conducted during a 5-day-period in the COALA chamber which is described in detail elsewhere (Riva 60 et al., 2019a;Peräkylä et al., 2020). The chamber is 2 m 3 in volume and has fluorinated ethylene propylene (FEP) as its wall material. The cyclohexene ozonolysis was performed under dark conditions. Temperature in the chamber was 27 ± 2 • C, and relative humidity 29±2%. Mass flow controllers (MKS, G-Series, 0.05-50 Lpm, Andover, MA, USA) were used to control the injections of all reactants. Ozone was generated from purified air with an ozone generator (Dasibi 1008-PC), and cyclohexene was produced by flowing air through a glass bubbler with liquid cyclohexene. Both were continuously injected into 65 the chamber, together with purified air Ohio,USA). The resulting steady state concentrations of ozone was approximately 18 ppb, while a rough estimate for cyclohexene concentration was about 100 ppb. As the concentration of cyclohexene was not measured it was estimated from the difference in ozone concentration with and without cyclohexene (with rate constant by Stewart et al., 2013). The average chamber residence time was approximately 50 minutes. Ozone and NO x levels were monitored with gas analysers (UV photometric ozone monitor, Model49p, Thermo Environmental Instruments;  Table 1. NO x experiments were conducted by injecting NO into the chamber at concentrations shown in table 1. As NO quickly reacts with ozone to form NO 2 , the resulting NO/NO x ratio in the chamber was fairly low, approximately 3±1% during highest injection rates. For most of the time, the concentration of NO in the chamber was not high enough to exceed the 75 detection limit of the used analyser. Therefore, only the total NO x was used in the analyses. The condensation of vapours was studied with a 9 µg m −3 loading of ammonium bisulfate (ABS) ((NH 4 )HSO 4 ) particles that were produced by nebulising aqueous ammonium sulfate solution with sulfuric acid. They were dried and size selected (80 nm) with a differential mobility analyser (DMA) before injection. The condensation sink was calculated for sulfuric acid, from aerosol size distribution data (Maso et al., 2005) measured with a Differential Mobility Particle Sizer (DMPS, Aalto et al., 80 2001). Experiments with ammonium sulfate were also carried out, but in these the loading was not sufficiently high to create a significant change that would allow for conclusions to be drawn. Therefore, these were left out of the analysis. Due to the acidity of the ammonium bisulfate aerosol, it should be noted that there is a possibility that a fraction of the overall sink might be a result of reactive uptake (Zhang et al., 2015). However, in their similar experiments with α-pinene, Peräkylä et al. (2020) did not observe the uptake of HOM to differ significantly between AS and ABS seeds.

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Oxygenated gas phase products were monitored by a Chemical Ionisation Atmospheric Pressure interface Time-of-flight mass spectrometer (CI-APi-TOF, TOFWERK AG, Aerodyne, Junninen et al., 2010;Jokinen et al., 2012). It consists of three components, a chemical ionisation inlet (CI), an atmospheric pressure interface (APi) and the Time-Of-Flight (ToF) mass spectrometer. Nitrate ions produced by subjecting an air stream containing nitric acid (HNO 3 ) to soft x-rays, are used to charge the sample molecules drawn from the chamber into the CI inlet. After charging, ions are directed into the APi. Quadruple ion 90 guides and an ion lens stack focus and guide the ions through the APi chambers where the air is pumped out, with the pressures ultimately reaching 10 −6 mbar in the TOF, where the ions' mass-to-charge ratio is determined. Nitrate ions have been shown to cluster readily with HOM molecules (Ehn et al., 2014;Bianchi et al., 2019). The clustering is not necessarily equally efficient between all compounds however, and signals are therefore not necessarily directly representative of the actual relative concentrations of the compounds. This selectivity of the nitrate ions has been described by e.g. Hyttinen et al. (2015).

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The CI-APi-TOF mass spectrometry data was analysed with a Matlab (R2016a) based toolbox, tofTools (R607) (Junninen et al., 2010). In further analysis the signals at different times were made comparable by normalising all time series by dividing them with the sum of the three reagent ion time series (NO − 3 , Jokinen et al., 2012). As quantification of HOM remains challenging (Riva et al., 2019b), we opted for analysing only the relative changes to HOM concentrations in this work. 100 2.2 Peak Fit Analysis 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 (Cubison and Jimenez, 2015;Zhang et al., 2019). Problems can also arise if the spectra are noisy, or if an observed ion is not listed in the compound list but something with a close mass is. These types of issues may result in fitting a peak either too small or too large.

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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. 110 4 https://doi.org/10.5194/acp-2020-694 Preprint. Discussion started: 28 August 2020 c Author(s) 2020. CC BY 4.0 License.
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.
Thus, the better a peak fit is, the closer the value is to 1 (Figure 1 a.). Values close to zero or even negative values are conversely 115 associated with poor fits (Figure 1 d.). Something in between can be reasonable (1 c.), but as the value decreases, uncertainty generally increases. Each compound has a single FitFactor value assigned to it. Spectra with signal below 50% of the time series maximum were ignored, because analysing for example a fit of a nitrate compound is not relevant during times when no NO is being added to the chamber. The final compound FitFactor was the median of individual FitFactor calculations from the rest of the spectra.

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By applying the FitFactor, a majority of the very poorly fitted peaks, such as the one shown in Figure 1 d., can easily be identified and removed from the analysis. However, some compounds may still have a relatively good FitFactor, despite the fit being somewhat questionable, like in the case of C 5 H 9 NO 5 NO − 3 shown in Figure 1 b. These types of fits are especially tricky. The automatic peak fitting routine is capable of producing a sum peak that matches the spectra relatively well, which translates into a high FitFactor value. However, as so many peaks (3 compound peaks, 1 large isotope peak) have been closely fitted onto 125 the same spectral peak, there is a great deal of uncertainty related to the individual peak fits. Thus, the FitFactor might not be optimal for this type of multiple fit problems, but thanks to the selectivity of the nitrate CI-APi-TOF, such peaks are not very prevalent in the spectra. The problem could be avoided by setting a minimum separation between fitted peaks, which we did not do in this work. Since the peak list was generated semi-automatically by simply listing all compositions within chosen element number limits, setting a minimum FitFactor value limit, is however, a helpful way to eliminate most, although not all, 130 of the poorest fits. The analysis can then be focused on the most reliable signals.

Peroxy radical chemistry and product formation
The oxidation and HOM formation pathways of cyclohexene ozonolysis is described in detail by Rissanen et al. (2014). In this section, we briefly outline the main steps considered when interpreting the results in this work. First, ozone oxidises cyclohexene by attaching itself on the double bond, leading to ring-opening. After rearrangement, loss of an OH radical, and 135 an addition of an O 2 molecule, a primary peroxy radical (RO 2 ) of the chemical composition C 6 H 9 O 4 , is formed . Peroxy radicals can undergo several steps of autoxidation (Crounse et al., 2013), producing an increasingly more oxygenated RO 2 radical, that may eventually terminate into a highly-oxidized end-product (i.e., HOM). However, the ultimate fate of the primary and potential subsequent RO 2 radicals will depend on the exact conditions under which the oxidation takes place. This will, subsequently, greatly impact the final product molecules and their properties, such as volatility. Therefore, we 140 outline the relevant main pathways below. a FitFactor close to 1. b.) Accurate determination of the fit quality is tricky, when this many compounds and/or isotopes are fitted to the same spectral peak. Automatic fitting can artificially produce a good sum fit, which translates into a good FitFactor, but single compound fits may lack credibility. c.) The two fitted peaks match the spectra relatively well, resulting in a reasonably good FitFactor for the bigger peak. d.) A very poor fit results in low or even negative FitFactor.
Before reaching termination, some radicals may be susceptible to a decrease in carbon number following a loss of a CO group (Mereau et al., 2001;Rissanen et al., 2014): A loss of HO 2 is another previously suggested mechanism that has since been shown not to be likely under atmospheric temperatures (Hyttinen et al., 2016). Bimolecular reactions between RO 2 and HO 2 , or between two RO 2 radicals also produce closed-shell species (Orlando and Tyndall, 2012;Rissanen et al., 2014). The former can typically produce ROOH molecules (Eq. (R3)), whereas the latter can lead to a production of a dimer molecule (ROOR) (Eq. (R4)) or two monomers (ROH and 150 R CHO) (Eq. (R5)). Alternatively, bimolecular reactions can also produce reactive open-shell alkoxy radicals (RO) (Eq. R6).
Peroxy radicals are also a part of the chemistry of pollutants such as NO x (i.e. NO and NO 2 ). The reactions between NO x and RO 2 usually take one of the following forms (Orlando and Tyndall, 2012): Typically only reaction (R8) is significant in producing closed-shell end-products, as products formed in reaction (R9) are thermally unstable, unless under very cold conditions (Orlando and Tyndall, 2012). Under short reaction times a high concentration of NO 2 has been shown to lead to dimer suppression and formation of highly oxidised nitrates in the cyclohexene ozonolysis system (Rissanen, 2018). However, here the longer reaction times and the much lower NO x concentration are not 165 likely to allow for a significant nitrate pool from NO 2 reactions due to the equilibrium of reaction R9 residing strongly on the reactant side under our experimental conditions. A fraction of NO 2 radicals may react with ozone to produce NO 3 , which is also one of the main oxidising species in the atmosphere. This oxidation of a BVOC compounds by NO 3 radicals produces nitrogen containing peroxy radicals, which can form both monomer and dimer species (Yan et al., 2016).

Results and Discussion
170 Figure 2 shows a HOM spectrum from cyclohexene ozonolysis (without NO addition), with ten of the biggest observed oxidation product peaks labelled. Other large peaks shown are the nitrate dimer at 188 Th, and an unidentified peak at 234 Th.
Strong signals of the HOM monomers C 6 H 8 O 7 and C 6 H 8 O 9 are observed, which has been typically reported also in other studies (Rissanen et al., 2014;Berndt et al., 2015). A number of the largest signal peaks are from relatively little oxidised compounds, with 4-5 oxygen atoms. Based on their elemental formulas, these species are most likely semi volatile organic 175 compounds (SVOC) (Peräkylä et al., 2020), which might accumulate in the chamber making them much more abundant than the low-volatile HOM, which are efficiently removed by condensation. This might explain the large signals even though the charging efficiency of such compounds is generally low (Hyttinen et al., 2015). In addition to the monomers, also one dimer signal, C 12 H 20 O 9 , reaches the top ten. One potential formation pathway for this dimer is a bimolecular reaction (R4) between radicals C 6 H 9 O 8 and C 6 H 11 O 3 . The latter is the primary peroxy radical formed form OH-oxidation of cyclohexene (Berndt 180 et al., 2015).  . An example 10-minute average spectrum of cyclohexene ozonolysis in the presence of NOx (approx. 9 ppb). Ten of the biggest product peaks (darker blue) are labelled with their chemical composition. All products are observed as clusters with NO − 3 , the mass of which is included in the masses shown in the x-axis.

Influence of NO on cyclohexene oxidation products
Injecting NO into the chamber has a significant impact on the chemistry within, which consequently leads to changed product signals. Although only NO mainly has an impact on the radical chemistry (see Eq. (R7)-(R9)), due to the aforementioned technical limitations leading to a lack of separate measurements of NO, we compare signals to the measured NO x . An example 185 spectrum during a NO x experiment is shown in Figure 3. The ten biggest signals are otherwise the same as in the absence of NO x (Figure 2), except for the dimer signal (C 12 H 20 O 9 ) having dropped to a fraction of the original and thus off the list, while a nitrate signal, C 6 H 9 NO 9 (Eq. R8), has replaced it. The monomer signal peaks are also on average larger than in Figure 2.  Figure 4. Species with comparable signal sizes are purposely selected (except for the nitrogen-containing dimer that has a much smaller signal). Shown are the 190 increasing monomer C 6 H 8 O 9 signal, decreasing C 6 H 8 O 7 monomer signal, decreasing C 12 H 20 O 9 dimer signal and increasing C 6 H 9 NO 9 nitrate and C 12 H 19 NO 11 nitrogen-containing dimer signals. When NO x is present in the chamber, the occurrence of RO 2 + NO reactions (Eq.(R7)-(R8)) reduce the likelihood for other bimolecular reactions (Eq. (R3)-(R6)).
Nitrogen containing species are only expected to form during the NO x experiments, as they are either formed in bimolecular reactions between RO 2 and NO x (Eq. Eq. R8-R9), or from radicals oxidised by NO 3 . Nitrogen containing dimers form in 195 bimolecular reactions (Eq. (R4)), where one of the reacting radicals has been oxidised by a nitrate radical.
10 https://doi.org/10.5194/acp-2020-694 Preprint. Discussion started: 28 August 2020 c Author(s) 2020. CC BY 4.0 License. For a broader overview of signal changes in response to NO x loading, Figure 5 shows the relative change of all non-nitrate products signals (nitrate-containing molecules are presented in Fig. 6 and discussed later) that were fitted. Signals with 9 ppb of NO x were compared to signals without NO x addition. All compounds with even hydrogen numbers are closed shell products, whereas ones with an odd number are radicals.

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The results in Figure 5 paint a complicated picture on the formation of the non-nitrate compounds. While some features are expected and easy to explain, others remain less clear. Here we summarise some aspects of the findings. As adding NO into the chamber leads to peroxy radicals reacting with NO (Eq. (R7)-(R8)), other bi-molecular reactions (Eq. (R3)-R6) consequently decrease. The most clear effect is the general decrease of almost all dimers, as the RO 2 cross reactions forming them decrease.
For monomers, the effects are more complicated, with some increasing and some decreasing. For example, the signals of the 205 two largest HOM, C 6 H 8 O 7 and C 6 H 8 O 9 showed opposite behaviour, with the former decreasing and the latter increasing.
Speculating the reason for this difference is difficult, especially as the reaction rate constants and different branching ratios of reactions between different combinations of RO 2 are unknown, and can vary by orders of magnitude depending on the specific structures (Shallcross et al., 2005;Berndt et al., 2018). Most of the C 6 H 10 O x compounds showed a decrease, which may be explained by a potential decrease in the bimolecular reactions R3 and (R5). The NO x will also function as a sink for OH, 210 although the cyclohexene should still dominate the OH reactivity in the chamber even at the highest NO x concentration. Most of the less oxidised SVOC showed relatively small changes.
The increase of the observed nitrate signals under high NO x (9 ppb) conditions is shown in Figure 6. Many compounds were associated with an unexpectedly high signal even outside of NO x experiments, and the size of this background signal is also shown in the figure. We opted to not simply subtract the background, i.e. the signal at zero added NO, because in some 215 cases the background was much larger than the signal increase, which would have caused large uncertainties when subtracting two relatively large numbers. In this way, the size of the background is also visible, and one can identify more clearly which increases are most relevant, i.e. the markers with the thickest yellow lines.
Unsurprisingly, nitrates increased with higher NO x . C 6 H 9 NO 9 was clearly produced the most, and likely formed in the reaction between NO and the radical C 6 H 9 O 8 , which is one of the most abundant highly oxygenated RO 2 radicals in this 220 system  (Eq. (R8)). Nitrogen-containing dimers are also detected. This implies that some of the cyclohexene had been oxidised by NO 3 , which had led to the formation of nitrogen containing radicals (Yan et al., 2016). These radicals were then terminated by RO 2 radicals from ozonolysis reactions, as the observed nitrate-containing dimers only had one nitrate functionality. The most abundant dimer was C 12 H 19 NO 11 . It can form in the reaction of the C 6 H 9 O 8 radical, from ozonolysis, together with the primary RO 2 radical C 6 H 10 NO 5 , from NO 3 oxidation of cyclohexene.

Condensation properties of cyclohexene oxidation products
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-volatile compounds condense either onto the walls or onto particles (k loss ≈ k CS + k wall ). For a more detailed discussion on the dynamics of the chamber and this method, see  Figure 7 shows the signal response of three species against the condensation sink (CS) of the injected ABS particles. The signals are normalised so that the median of the initial signal value before the seed experiment equals 1.
As a result, the fraction that remains following the seed addition is easy to observe. The three compounds in Figure 7 vary only by their oxygen number. The most highly oxygenated vapour, that is, C 6 H 10 O 8 , condenses most readily and thus can be described as being the least volatile.

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The fraction remaining (FR) at a condensation sink of approximately 0.85×10 −2 s −1 was calculated for all compounds fitted in the mass spectra. These are plotted in Figure 8 against the mass of each compound. The methodology here closely follows  figure 5. Yellow squares show the size of the signal under high NOx (9 ppb) conditions. The "holes" in the square correspond to the size of the background signal, i.e. the signal before NOx addition. The large background for some peaks may be due to small amounts of NOx being introduced to the chamber, or that these molecules were residues from chamber wall following earlier experiments.
that outlined by Peräkylä et al. (2020). Only the clearest signals are shown, with the aim to bring only the most meaningful signal into focus while minimising noise. In practice, this means compounds with FitFactor values of at least 0.7, which was 13 https://doi.org/10.5194/acp-2020-694 Preprint. Discussion started: 28 August 2020 c Author(s) 2020. CC BY 4.0 License. deemed to strike a good balance between quality (see Figure 1) and quantity. Non-nitrate and nitrate products are both plotted, 240 while radicals are not, since condensation is only a minor sink for these species because of their high reactivities with other radicals (Peräkylä et al., 2020).
In addition to observations, also predictions based on two different models are shown in Figure 8 log 10 (C * ) = 0.18 × n C − 0.14 × n H − 0.38 × n O + 0.8 × n N + 3.1 that was originally based on measurements with α-pinene oxidation products. n C , n H , n O , and n N , correspond to the numbers of carbon, hydrogen, oxygen and nitrogen atoms in the molecule (charging ion excluded).
The model for volatility used in the lower panel figure is given by Bianchi et al. (2019), although the model we used was further modified to account for nitrate functionalities. In nitrates, 3 oxygen atoms are bonded in the nitrate group, which as a whole reduces volatility by a factor of 2.5. We assumed an activity coefficient (γ) of 1, so that C*=γC 0 =C 0 (Donahue et al., 2011). 250 Thereby, the equation used in this work is Many monomers had remaining fractions above 1, indicating that the signal increased. The likely explanation for this is that these compounds are semivolatile and were still equilibrating with the chamber walls, and thus not in steady-state. Another possibility is that these molecules have condensed phase sources, causing an increased formation during the seed addition.
Due to these uncertainties, we do not try to interpret these points further, but note that although some of the molecules with 265 FR<1 may also have seen similar effects, the effects are probably smaller as these molecules are less likely to evaporate from particles or walls.
The higher the mass of the molecule, the more inclined it was to be taken up by the injected particles. 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. Such a high net condensation is a clear indication of a low volatility. At masses below 225 Th, compounds were fairly volatile, 270 as all signals remained either the same or increased. The exception to this are the two small green points around 200 Th, with FR<0.7. These are C 5 H 10 O 4 and C 5 H 10 O 5 , which are less oxidised than molecules typically observed with the nitrate adduct CI-APi-TOF. This may mean that they contain multiple hydroxyl and/or carboxylic acids groups (which may improve their clustering ability with NO − 3 ) that decrease their volatility more than if the O-atoms were incorporated as carbonyl and hydroperoxide functionalities, which is more typical for HOM. Figure 8. The observed (opaque) and modelled (transparent) decrease in signal during an ABS seed experiment, for non-nitrate (green) and nitrate (blue) products. The top panel compares our observations with the model presented by Peräkylä et al. (2020), while the bottom panel compares to the model by Bianchi et al. (2019). Only compounds with the best peak fits (FitFactor≥0.7) are shown. The circles sizes are based on the relative sizes of the initial signals before the ABS aerosol injection, with the exception of the two biggest signals, which were slightly scaled down. The condensation sink was approximately 0.85 × 10 −2 s −1 , meaning that these points correspond to the black edged circles in Figure 7. All molecules were charged with a nitrate ion, which has a mass-to-charge ratio of 62 Th.
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 (Pankow and Asher, 2008;Kroll and Seinfeld, 2008). A clear outlier is C 5 H 9 NO 5 (225.04 Th), as the signal comparison suggests an unexpectedly low volatility for the compound. This might however be an artefact from issues with the fitting of multiple peaks, as shown specifically for this ion in Figure 1 b.

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Both the Peräkylä et al. (2020) and Bianchi et al. (2019) models successfully predict the high volatility of the least oxidised monomers. The predicted volatilities of larger monomers are however in both cases higher than the observed. Both models predict the strongest volatility transition in the region between our observed monomers and dimers, although we observe many of the monomers to condense nearly irreversibly. Peräkylä et al. (2020) model predicts a more gradual decrease in volatility as a function of mass than the Bianchi et al. (2019) model. 285 Next, the link between elemental composition and volatility is analysed further. In Figure 9, the fraction remaining is plotted separately against carbon, hydrogen and oxygen numbers, as well as the "effective O:C ratio" (n O − 2n N )/n C . The incentive for choosing this ratio, rather than simply O:C, is the same as for the modification of the Bianchi et al. (2019) model, i.e. to account for nitrate functionalities. For non-nitrates this expression reduces to an O:C ratio. For comparison, also α-pinene products from an experiment by Peräkylä et al. (2020) are plotted on the background. The data is from an experiment with dry 290 ABS seed aerosol and NO x . As the reactant concentrations and chamber conditions differed between our and their experiments, the signals are not comparable in magnitude. In Figure 9, the observations have been scaled to the same marker size and FR range. Due to the higher SOA concentration in the Peräkylä et al. (2020) experiment, the remaining fractions of α-pinene products are somewhat lower than they would be at our experiment's SOA concentration.
Molecules with the lowest carbon and hydrogen content hardly condensed, while the opposite was true for the molecules 295 with the highest C and H content, mostly associated with the dimers (Fig. 9a and 9b). However, in the regions between these extremes, e.g. around C 6 and H 8 , respectively, molecules range from volatile to effectively non-volatile, and thus purely Cor H-atom content is not a good predictor for volatility. As a function of O-atom content (Fig. 9c), the volatility seems to be much more monotonically decreasing. The transition from non-condensing to condensing occurs at O-atom contents of 6-7 for cyclohexene HOM, with nitrates again being slightly more volatile than non-nitrates. Finally, figure 9d demonstrates that the 300 effective O:C ratio is the worst predictor for volatility out of the parameters plotted. Despite O:C ratios of organic aerosol often being used as a reference for volatility, for a single molecule it does not have a good correlation. This is to be expected, as high molecular weight dimers require only a relatively small number of oxygen atoms (thus having low O:C) to condense, while small molecules with 1-3 C-atoms are unlikely to condense regardless of their O:C ratios. In all figures (9a-d), the α-pinene and cyclohexene products showed similar behaviour.

4 Conclusions
We have investigated the formation and fates of cyclohexene oxidation products, with a focus on the most oxidised species.
These highly oxygenated organic molecules (HOM) were measured with a CI-APi-TOF mass spectrometer. A statistical approach was utilised for evaluating the quality of the identification of different ions in the mass spectra. In this approach, a so-called FitFactor parameter was set up to compare the spectral residual and the peak area. By this simple method, evaluating (O-2N)/C. In addition to the cyclohexene (C6H10) oxidation products, α-pinene (C10H16) ozonolysis products from a seed experiment by Peräkylä et al. (2020) are also included. As the signals between the two studies are not directly comparable, the α-pinene products have been scaled to the same limits of FR, and to the same range of marker sizes. thousands of peak fits was fast and feasible. Ultimately, this allowed discarding or down-weighing signals with poorly fitted peaks.
Perturbations to product signals by NO x were analysed species-by-species, revealing that NO x enhanced the formation of most of the observed monomer (C 3 -C 6 ) species, although some also experienced an opposite effect. These effects are likely driven by increased termination of RO 2 radicals by NO, leading to more alkoxy radicals, but with the current data, the detailed 315 reasons for the individual differences can only be speculated. However, more easily interpretable effects were also observed, namely the formation of organic nitrates, and the decrease of dimer (C 11 -C 12 ) species, due to enhanced RO 2 + NO reactions.
This reaction, taking place at the expense of RO 2 cross reactions, caused a decrease in dimers without a nitrate functionality.
Dimers containing a nitrate group conversely experienced a small increase. This is likely due to NO 3 radical (formed from NO 2 + O 3 ) oxidation forming RO 2 with nitrate functionalities, which can the react with other RO 2 to form dimers as long as 320 the NO concentration is not high enough to completely dominate the RO 2 termination in the system. Monomer nitrates, on the other hand, see a monotonically increasing trend as more NO is added to the chamber, as expected.
The volatility of the oxidation products was probed in experiments where ammonium bisulfate seed aerosol was added to the chamber. This resulted in a clear decrease in most HOM signals, with all non-nitrate products heavier than 250 Th condensing to the particles at nearly equal rates. Nitrates were slightly more volatile than non-nitrates with similar masses. Besides mass, 325 a break-down of the elemental composition of the different products indicated that the most significant factor determining the volatility of the observed cyclohexene oxidation products is the oxygen number. Products having an oxygen atom content of 7 or more, were all at the lowest end of the observed volatility range. Carbon and hydrogen atom content also correlated with volatility, but were clearly more ambiguous indicators for volatility than the oxygen content. For example, molecules with 5 or 6 C-atoms can range anywhere from volatile to effectively non-volatile. We also found that the O:C ratio is by itself not a 330 good measure for volatility, as molecules like C 3 H 4 O 4 have a much higher volatility than e.g. C 12 H 20 O 8 , despite having an O:C ratio twice as high.
There have been a few models presented recently for predicting the volatility of a compound based on its elemental composition. We tested our observed volatilities against two, the model by Bianchi et al. (2019) with a modification for nitrates, and another by Peräkylä et al. (2020). They are based on larger initial reactants than cyclohexene, such as α-pinene. While 335 both could predict the generally higher volatility of monomers compared to dimers, they were not able to produce the observed irreversible condensation of larger monomers. This highlights the difficulty in estimating volatilities with simple parametrisations. As cyclohexene is used as a surrogate for monoterpenes like α-pinene, we also compared the volatilities of HOM from α-pinene and cyclohexene against the different compositions. The results for both VOC imply that a high oxygen number is the most essential parameter for lowered volatility. Thus, the ability to undergo efficient autoxidation may be more important 340 for SOA formation than the size of the initial VOC reactant. Data availability. Data will be available at https://doi.org/10.5281/zenodo.4001577 Author contributions. 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 visualization under the supervision of ME and with additional guidance from OP and MRis. MRä wrote the manuscript, and it was reviewed and commented by the other authors.