Using GECKO-A to derive mechanistic understanding of SOA formation from the ubiquitous but understudied camphene

Camphene, a dominant monoterpene emitted from both biogenic and pyrogenic sources, has been significantly understudied, particularly in regard to secondary organic aerosol (SOA) formation. When camphene represents a significant 10 fraction of emissions, the lack of model parameterizations for camphene can result in inadequate representation of gas-phase chemistry and underprediction of SOA formation. In this work, the first mechanistic study of SOA formation from camphene was performed using the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A). GECKO-A was used to generate gas-phase chemical mechanisms for camphene and two well-studied monoterpenes, α-pinene and limonene; and to predict SOA mass formation and composition based on gas/particle partitioning theory. The model simulations represented 15 observed trends in published gas-phase reaction pathways and SOA yields well under chamber-relevant photooxidation and dark ozonolysis conditions. For photooxidation conditions, 70 % of the simulated α-pinene oxidation products remained in the gas phase compared to 50 % for limonene; supporting model predictions and observations of limonene having higher SOA yields than αpinene under equivalent conditions. The top 10 simulated particle-phase products in the α-pinene and limonene simulations represented 37-50 % of the SOA mass formed and 6-27 % of the hydrocarbon mass reacted. To facilitate comparison of camphene 20 with α-pinene and limonene, model simulations were run under idealized atmospheric conditions, wherein the gas-phase oxidant levels were controlled. Metrics for comparison included: gas-phase reactivity profiles, time-evolution of SOA mass and yields, and physicochemical property distributions of gasand particle-phase products. The controlled-reactivity simulations demonstrated that: (1) in the early stages of oxidation, camphene is predicted to form very low volatility products, lower than α-pinene and limonene, which condense at low mass loadings; and (2) the final simulated SOA yield for camphene (46 %) was relatively high, 25 in between α-pinene (25 %) and limonene (74 %). A 50/50 (α-pinene/limonene) mixture was then used as a surrogate to represent SOA formation from camphene; while simulated SOA mass and yield were well represented, the volatility distribution of the particle-phase products was not. To demonstrate the potential importance of including a parameterized representation of SOA formation by camphene in air quality models, SOA mass and yield were predicted for three wildland fire fuels based on measured monoterpene distributions, and published SOA parameterizations for α-pinene and limonene. Using the 50/50 surrogate mixture 30 to represent camphene increased predicted SOA mass by 43-50 % for black spruce and by 56-108 % for Douglas fir. This first detailed modeling study of the gas-phase oxidation of camphene and subsequent SOA formation provides an opportunity for future measurement-model comparisons and lays the foundation for developing chemical mechanism and SOA parameterizations for camphene that are suitable for air quality modeling. https://doi.org/10.5194/acp-2020-829 Preprint. Discussion started: 11 September 2020 c © Author(s) 2020. CC BY 4.0 License.

Monoterpenes have a wide range of molecular structures, atmospheric lifetimes, and secondary organic aerosol (SOA) 50 formation potentials. The molecular structures of monoterpenes can be acyclic or cyclic (with variability in the size and number of rings) and can include one to three C=C double bonds (Atkinson and Arey, 2003b;Calogirou et al., 1999;Jacobson et al., 2000;Lee et al., 2006a). The reaction rate constants of monoterpenes with atmospheric oxidants vary by orders of magnitude (Atkinson and Arey, 2003a;Geron et al., 2000), and their atmospheric lifetimes vary from minutes to days (Atkinson and Arey, 2003b).
Monoterpenes can react with atmospheric oxidants to form less-volatile oxidation products leading to the formation of SOA. SOA 55 composes a significant fraction of atmospheric fine particulate matter (PM2.5), which adversely affects air quality and impacts climate (Almatarneh et al., 2018;Hallquist et al., 1999;Jacobson et al., 2000;Kanakidou et al., 2004). The extent of SOA formation from monoterpenes can vary significantly, due to the differences in their structures, reaction rates, and volatility of their oxidation products and propensity to form accretion products (Barsanti et al., 2017;Griffin et al., 1999;Ng et al., 2007;Zhang et al., 2015).
Over the past two decades, laboratory studies have been performed using monoterpene precursors to elucidate their 60 potential to form SOA under conditions approximating atmospheric relevance. For example, Griffin et al. (1999) used a series of outdoor chamber experiments to establish the SOA formation potential of 14 biogenic compounds, including nine monoterpenes.
Since then, several chamber studies under varying experimental conditions have been conducted for individual monoterpenes including α-pinene, -pinene, 3-carene, limonene, and myrcene (e.g., Amin et al., 2013;Boyd et al., 2017;Fry et al., 2014;Hatfield and Huff Hartz, 2011;Lee et al., 2006a;Ng et al., 2007;Presto et al., 2005;Presto and Donahue, 2006;Zhao et al., 2018). 3 monoterpenes has a large contribution to total emissions. Camphene is one monoterpene that has been observed in the atmosphere 75 but has little to no published data regarding SOA formation. Previous experimental and theoretical studies of camphene focused on the gas-phase reactions of camphene and product identification (e.g., Atkinson et al., 1990;Gaona-Colmán et al., 2017;Hakola et al., 1994). Recently, a density functional theory (DFT) approach was also used to investigate the oxidation of camphene and the fate of product radicals under atmospherically relevant conditions (Baruah et al., 2018). While this approach identified plausible reaction pathways of camphene photooxidation and associated gas-phase products, formation of SOA was not considered.

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In this work, a mechanistic study of SOA formation from camphene was conducted using the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A). GECKO-A has been previously used to study SOA formation from a number of precursors (e.g., Camredon et al., 2007;La et al., 2016;McVay et al., 2016;Valorso et al., 2011). GECKO-A was used here to generate nearly explicit mechanisms for camphene and the well-studied monoterpenes α-pinene and limonene.
Model simulations were run under chamber-relevant conditions ("chamber reactivity simulations") to capture trends in simulated 85 SOA mass and composition and compared with published observations using commonly reported metrics including SOA yields and oxygen/carbon (O/C) ratios. Model simulations were also run under idealized atmospheric conditions ("controlled reactivity simulations") to facilitate a direct comparison of camphene with α-pinene and limonene; including comparisons of gas-phase oxidation pathways, gas-phase reactivity profiles, time-evolution of SOA mass and yields, and physicochemical property distributions of gas-and particle-phase products. The feasibility of using α-pinene or limonene as a surrogate for camphene was 90 assessed. Based on these analyses, the feasibility of using α-pinene or limonene as a surrogate for camphene was assessed, and implications for air quality model predictions and opportunities for future studies were identified.

GECKO-A Model description
SOA formation from three monoterpene precursors (α-pinene, limonene, and camphene) was modeled using GECKO-A.

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A description of GECKO-A is given by Aumont et al. (2005). GECKO-A is a modeling tool that generates nearly explicit gasphase oxidation mechanisms for individual or multiple organic compounds under general atmospheric conditions (Aumont et al., 2005(Aumont et al., , 2012Camredon et al., 2007), as well as the properties to represent the gas/particle partitioning of the stable organic compounds present in the mechanisms (Camredon et al., 2007;Valorso et al., 2011). The nearly explicit chemical mechanism is generated using experimental data and a predefined protocol (Aumont et al., 2005(Aumont et al., , 2012Camredon et al., 2007). The protocol is 100 described in Aumont et al. (2005) and includes updates described in Aumont et al. (2013), La et al. (2016, and Valorso et al. (2011). In the absence of experimental data, reaction rate constants and products, as well as their physicochemical properties, are estimated based on structure-activity relationships (SARs) (Aumont et al., 2005). Autoxidation, leading to the formation of highly oxygenated molecules (HOM) in the gas phase (e.g., Bianchi et al., 2019;Ehn et al., 2014), is not currently represented in GECKO-A. A SAR to predict the rate coefficients of peroxy radical (RO2) H migration reactions (H-shifts) that 105 lead to the formation of HOM was recently published by Vereecken and Nozière (2020).. TThe straight implementation of this SAR into GECKO-A would lead to a non-manageable number of species and reactions. Therefore, rReduction protocols are thus currently under development to consider the autoxidation reactions in subsequent model versions. For the application presented herein, limitations and implications of the absence of HOM formation via RO2 autoxidation are discussed where relevant.
Some simplifications were applied in this work during the mechanism generation to reduce the size of the gas-phase 110 chemical mechanisms: (1) the maximum generations of oxidation for each mechanism was set at six based on prior GECKO-A modeling results for n-alkanes (Aumont et al., 2012) and sensitivity studies performed in this work for α-pinene oxidation, where increasing the number of generations beyond six did not result in significant changes in the evolution of the gas and particle phases; (2) species with vapor pressure below 10 -13 atm (equivalent to C* of 1.02 × 10 -3 µg m -3 for species with a mean molecular weight of 250 g mol -1 ) were considered non-volatile and therefore treated as end products during gas-phase mechanism generation 115 (Valorso et al., 2011); (3) position isomers were lumped if the production yield of a species was lower than 10 −3 (Valorso et al., 2011). The chemical mechanisms generated for this study included: 1.4 × 10 6 reactions and 2 × 10 5 oxidation products for αpinene; 6.5 × 10 5 reactions and 9.3 × 10 4 oxidation products for limonene; and 1.3 × 10 6 reactions and 1.8 × 10 5 oxidation products for camphene. These mechanisms were then implemented in a box model to simulate the evolution of gaseous organic compounds and SOA formation (Aumont et al., 2005(Aumont et al., , 2012Camredon et al., 2007). Gas/particle partitioning of stable organic compounds 120 was calculated according to the saturation vapor pressure of each organic compound and assuming thermodynamic equilibrium between the gas and an ideal (activity coefficients = 1), homogeneous, and inert condensed phase. The saturation vapor pressures were estimated using the Nannoolal method (Nannoolal et al., 2008), which performs relatively well compared to other estimation methods when used to simulate SOA formation during α-pinene oxidation experiments (Valorso et al., 2011). Condensed-phase reactions are not currently represented in GECKO-A; the limitations and implications of which are discussed where relevant..

GECKO-A generated oxidation mechanisms
The monoterpene reaction schemes are generated by GECKO-A using established protocols, as described in Aumont et al. (2005). First, the mechanism generator analyzes the structure of the compound to determine the reactive sites and the plausible reaction pathways. Reaction products and initial branching ratios are based on experimental data when available. Otherwise, the reaction products and rate constants are estimated based on structure-activity relationships (SARs). The initial reaction rate 130 constants of the monoterpenes with OH, O3, and NO3 were based on data from Atkinson and Arey (2003a). For α-pinene + OH, the initial branching ratios are based on data from Peeters et al. (2001). For subsequent reaction steps with α-pinene + OH, and for the limonene and camphene mechanisms, reaction products and branching ratios are based on SARs.

OH reaction scheme
The reaction pathways of OH-initiated oxidation of α-pinene, limonene, and camphene up to the formation of 1st-135 generation stable products are shown in Figs. 1, 2, and 3, respectively. For figure clarity, inorganic species formed (including OH) are not shown. The initial reaction steps proceed mainly by the addition of OH to the C=C double bond or by hydrogen abstraction.
This leads to the formation of hydroxyalkyl radicals (HO2) which react rapidly with O2 to form peroxy radicals (RO2). The peroxy radicals can combine with NO, RO2 or HO2 to form stable products. The peroxy radicals can also lose an oxygen atom through reaction with NO to form alkoxy radicals, which is consistent with observations reported by Atkinson and Arey (1998) and 140 Calogirou et al. (1999). For α-pinene oxidation, the hydroxyalkyl radicals primarily react with O2 to form peroxy radicals, which then react with NO, RO2 or HO2 to form stable products, many with a four-membered ring, or lose an oxygen atom to form alkoxy radicals. As observed by Lee et al. (2006b), the alkoxy radicals undergo subsequent reactions leading to formation of formaldehyde, acetone, and multifunctional products including pinonaldehyde. For limonene oxidation, reaction of the peroxy radicals with NO/NO3/RO2 followed by O2 addition and NO to NO2 conversion leads to the formation of limononaldehyde or limonaketone and 145 formaldehyde, which are consistent with observations reported by Lee et al. (2006b). Alternatively, the peroxy radicals react with NO/NO3/RO2 to form ring-opened peroxy radicals, which further react to form multifunctional products. For camphene, the hydroxyalkyl radicals react rapidly with O2 to form hydroxyalkylperoxy radicals. The hydroxyalkylperoxy radicals subsequently react with NO, RO2, and HO2 to form stable products, or react with NO/NO3/RO2 to form hydroxyalkoxy radicals. The hydroxyalkoxy radicals then either decompose to form camphenilone (a bicyclic product) and formaldehyde, or react with O2 to 5 form five-membered ring hydroxyperoxy radicals, which further react to form multifunctional products. The reaction pathway of OH addition to the exocyclic double bond of camphene as represented in GECKO-A is in agreement with the observations made by Gaona-Colmán et al. (2017) and Reissell et al. (1999), as well as by Baruah et al. (2018) in their DFT study of OH-initiated oxidation of camphene. While camphene and α-pinene are structurally bicyclic, their 1 st generation products resulting from the decomposition of the bicyclic hydroxyalkoxy radicals differ; camphene primarily forms five-membered ring 1 st generation products 155 while α-pinene primarily forms four-membered ring 1 st generation products. Limonene, which is monocyclic, primarily forms ringopened 1 st generation products when its monocyclic hydroxyalkoxy radicals decompose.

O3 reaction scheme
The initial oxidation pathways of O3-initiated oxidation of α-pinene, limonene, and camphene are shown in Figs. S1, S2, and S3, respectively. The reaction starts with the addition of O3 to the C=C double bond of the parent compound to form an ozonide, 160 which rapidly undergoes bond cleavage to form a biradical Criegee intermediate bearing a carbonyl substituent for terpenes with an endocyclic double bond, or a biradical Criegee intermediate and a carbonyl for terpenes with an exocyclic double bond. The Criegee intermediate can stabilize by collisions and/or decompose (after possible rearrangement) to form peroxy radicals. The stabilized Criegee intermediates (SCI) undergo bimolecular reactions with H2O, CO, NO and/or NO2. The peroxy radicals then react with HO2/NO/RO2 to form stable products or react with NO/NO3/RO2 to form alkoxy radicals. For α-pinene, the alkoxy 165 radicals either react with O2 or decompose to form formaldehyde and peroxy radicals. The peroxy radicals further react to form peroxy acid, carboxylic acid, and CO2. For limonene, the alkoxy radical reactions primarily lead to the formation of organic nitrates, organic hydroperoxides, carboxylic acids, and peroxy acids. For camphene, the ozonide decomposes to form (1) camphenilone, a stable bicyclic product that has been observed experimentally by Calogirou et al. (1999) and Hakola et al. (1994); and (2) a bicyclic peroxy radical and formaldehyde, consistent with the camphene + O3 mechanism reported by Gaona-Colmán et al. (2017). The 170 bicyclic peroxy radical reacts with HO2/NO/RO2 to form stable products or reacts with NO/NO3/RO2 to form alkoxy radical which then further reacts to form five-membered ring products.

NO3 reaction scheme
The initial oxidation pathways of NO3-initiated oxidation of α-pinene, limonene, and camphene are shown in Figs. S4, S5, and S6, respectively. The NO3 radical attacks the C=C double bond to form a nitratoalkyl radical which undergoes rapid 175 reaction with O2 to form a nitratoalkylperoxy radical. The nitratoalkylperoxy radicals of all three compounds react similarly in three ways: (1) with NO to form dinitrates; (2) with HO2 or RO2 to form nitratocarbonyls, nitratoalcohols, and nitratoperoxides (Calogirou et al., 1999); and (3) with NO/NO3/RO2 to form nitratoalkoxy radicals, which react further to form multifunctional products.

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The objective of the chamber reactivity simulations was to compare GECKO-A model output with published SOA chamber data (Table 1). No attempt was made to strictly reproduce the conditions of a given chamber experiment. Since the first objective of this study focuses on the ability of the model to capture the major trends observed in chamber data (e.g., SOA yields and major oxidation products), the simulation conditions were therefore set to mimic (or be representative of) typical chamber conditions. Comparative analyses were performed for the precursors α-pinene and limonene, since they are among the well-studied 185 monoterpenes in environmental chambers and sufficient data exist for measurement-model comparison. These chamber reactivity simulations included photooxidation (P) and dark ozonolysis (DO) conditions, which were differentiated by the initial concentrations of NO, HONO, and O3 as shown in Table 1. For both the P and DO conditions, the initial hydrocarbon mixing ratios were set at a relatively low (50 ppb) and a relatively high (150 ppb) level as compared with published chamber studies. This resulted in a total of four chamber reactivity simulations for each monoterpene precursor. It is noted that the simulations are unable 190 to capture HOM formation via RO2 autoxidation and subsequent dimerization (Ehn et al., 2014), that may have occurred in the chamber studies, particularly under DO conditions. In each simulation, 1 µg m -3 of organic seed with molecular weight of 250 g mol -1 was added to initiate gas/particle partitioning.
The objective of the controlled reactivity simulations was to examine SOA formation by camphene in the context of wellstudied monoterpenes, specifically α-pinene and limonene, under controlled conditions (Table 2). In these simulations, the gas-195 phase chemistry was not controlled by the individual precursors, but by other organic compounds as occurs in the ambient atmosphere;, allowing a straightforward comparison of terpene oxidation mechanisms under the controlled reactivity conditions..
A mixture of ethane (10 ppb) and formaldehyde (50 ppb) was used to buffer (i.e. control) the gas-phase reactivity.. The NOx and O3 mixing ratios were held constant at values of 1 ppb and 30 ppb, respectively, throughout the simulation. Hence, RO2 reacted equally with HO2 and NO, and the levels of the oxidants did not change when relatively small amounts of precursor were added.

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Hence, the levels of the oxidants did not change when relatively small amounts of precursor were added in the simulation and therefore allows a straightforward comparison of oxidation mechanisms of the various terpenes. The NOx and O3 mixing ratios were held constant at values of 1 ppb and 30 ppb respectively throughout the simulation. The controlled reactivity simulations included 0.1 ppb of initial precursor and 10 µg m -3 of organic seed. All box-model simulations were performed under fixed conditions: temperature was heldfixed at 298 K and relative 205 humidity was held at 5 % to represent mean chamber conditions; and the solar zenith angle, required to calculate photolysis frequencies, was set at 50 o to represent mean daytime solar spectra, except for dark ozonolysis conditions where no photolysis was considered. SOA data (see Table S1) were compiled from 12 published chamber studies (e.g., Chen et al., 2017;Griffin et al., 1999;Kim and Paulson, 2013;Kourtchev et al., 2014;Ng et al., 2007;Yu et al., 1999) in which α-pinene or limonene was used as a precursor and final SOA mass, SOA yield, and reacted hydrocarbon concentration (HC) were reported (at least two of the three quantities). For 215 the α-pinene photooxidation data, there is an apparent cluster around an SOA yield of 0.2 for SOA mass < 150 µg m -3 with which the model agrees (Fig. 4a). The scatter in the data is due to differences in experimental conditions (e.g., temperature and NOx mixing ratios). As previously observed, SOA yields of α-pinene tend to be higher at lower temperatures and lower NOx conditions (higher initial VOC/NOx ratios) (Kim and Paulson, 2013;Pathak et al., 2007b). For example, the two relatively high SOA yields (0.38 at 29.3 µg m -3 and 0.46 at 121.3 µg m -3 ) had relatively low initial NOx concentrations (Ng et al., 2007), while the two 220 relatively low SOA yields (0.059 at 44 µg m -3 and 0.06 at 4.5 µg m -3 ) had relatively high initial NOx concentrations (Kim and Paulson, 2013;Ng et al., 2007). For mass loadings > 150 µg m -3 , α-pinene photooxidation SOA yield data plateaus at approximately 0.3, which also is captured by the model. In contrast, for limonene photooxidation, experimental data show a linear trend in the SOA yield as a function of SOA mass (for SOA mass > 25 µg m -3 ), and the SOA yield does not plateau at higher SOA mass loadings. The observed linear trend in SOA yield as a function of SOA mass is reflected in the model simulations (Fig. 4c). For α-7 pinene ozonolysis (Fig. 4b), there is an apparent cluster around an SOA yield of 0.2 for SOA mass < 200 µg m -3 . with which At SOA mass < 100 µg m -3 , the the modeled SOA yield agrees is within range of the observations, towards the lowest values; between SOA mass > 100 µg m -3 and < 200 µg m -3 , the modeled SOA yield is lower than the observations (two data points Overall, the model simulations agree well with the observed trends in SOA yield as a function of SOA mass. The largest discrepancies are for α-pinene ozonolysis, in which SOA mass is underpredicted relative to the observations. The contribution of HOM formation from RO2 autoxidation is expected to be more important under such conditions, when the lifetime of RO2 is sufficiently long for autoxidation to compete with biomolecular reactions and monoterpene oxidation by O3 is greater than by OH 235 leading to higher HOM yields (Ehn et al., 2014;Jokinen et al., 2015). The inclusion of HOM formation and subsequent dimerization would lead to an increase in predicted SOA mass in both the α-pinene and limonene ozonolysis simulations. An increase in SOA mass due to HOM formation and subsequent dimerization would improve the measurement-model agreement for α-pinene, but would also lead to an overprediction of SOA mass for limonene. In addition, a non-negligible contribution of HOM monomers and dimers to the particle phase would increase the calculated O/C ratio, and increase the measurement-model discrepancy further  Kourtchev et al. (2015) in which the reported O/C for OH-initiated α-pinene SOA was higher than for α-pinene SOA initiated by ozonolysis. The same trend was predicted for limonene. Generally, the simulated O/C values were high relative to values reported from chamber studies. Reported average O/C values from chamber studies range from 0.3 to 0.65 for α-pinene 250 photooxidation (e.g., Lambe et al., 2015;Pfaffenberger et al., 2013), 0.22 to 0.55 for α-pinene ozonolysis (e.g., Chen et al., 2011;Chhabra et al., 2010;Kourtchev et al., 2015), and 0.23 to 0.5 for limonene ozonolysis (e.g., Draper et al., 2015;Heaton et al., 2007;Walser et al., 2008). Factors known to affect the O/C ratios include mass loading, OH ex posureexposure (defined as the integral of OH concentration and residence time (Lambe et al., 2015)), and oligomerizationaccretion product formation (Chhabra et al., 2010;Reinhardt et al., 2007). Shilling et al. (2009) showed the dependency of O/C ratios on mass loadings for α-pinene, in which 255 O/C ratio decreased from 0.45 to 0.38 as mass loading increased from 0.5 to 15 µg m -3 . Mass loading is not likely driving the differences between simulations and observations here, since the simulated mass loadings were similar to the mass loadings of the chamber experiments (e.g., Chhabra et al., 2011;Shilling et al., 2009) with which the O/C ratios were compared. Regarding OH exposure, calculated OH exposures for the photooxidation simulations (Table 3) were within the typically reported OH exposure ranges (5.4×10 10 -4.0×10 11 molec cm -3 s) from the chamber photooxidation experiments (e.g., Lambe et al., 2015;Pfaffenberger et 260 al., 2013). Therefore, tThe lower observed O/C values may be partially explained by the loss of H2O in condensed-phase reactions (Claflin et al., 2018;Ziemann and Atkinson, 2012),during oligomerization (Chhabra et al., 2010;Reinhardt et al., 2007), a process that waswhich were likely occurring in the experiments (e.g., Bakker-Arkema and Ziemann, 2020; Kenseth et al., 2018) e.g., but was were not represented in the GECKO-A simulations.

Chamber reactivity simulations
The results from the simulations using the lower hydrocarbon mixing ratio (LHC) and higher hydrocarbon mixing ratio (HHC) were qualitatively similar. Thus, here and in subsequent sections, only the results for the LHC simulations are shown and discussed; the corresponding figures for the HHC simulations are provided in the supplement. Figures 5a and 5b show the chemical structures and molecular formulae of the top 10 products by mass in the gas and particle phases at the end of the α-pinene photooxidation simulation. The top 10 gas-phase products (dominated by carbonyl, carboxyl, and nitrate functional groups) account 270 for 46 % of the reacted α-pinene carbon mass, with acetone being the top contributor. Two of the top 10 gas-phase products, pinonic acid (i.e.
(3-acetyl-2,2-dimethylcyclobutyl)acetic acid) and pinonaldehyde (i.e. (3-acetyl-2,2dimethylcyclobutyl)acetaldehyde) are among the most commonly reported products in experimental studies (e.g., Lee et al., 2006b). The top 10 particle-phase products (dominated by carbonyl, carboxyl, hydroxyl, hydroperoxide, and nitrate functional groups) account for 42 % of the SOA mass and 7 % of the reacted α-pinene carbon mass. For limonene photooxidation (Figs. S10 275 and S11), the top 10 gas-phase products account for 34 % of reacted limonene, while the top 10 particle-phase products account for 50 % of the SOA mass and 20 % of the reacted limonene carbon mass. The top 10 particle-phase products are dominated by dinitrate and carbonyl functional groups, indicating the possible influence of multigeneration products from peroxy radicals + NO reactions.

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Given the skill of the model in representing published chamber data at both macroscopic and molecular levels, the model was used to explore the carbon budget during photooxidation and ozonolysis simulations. The time evolution of SOA yields for αpinene and limonene during photooxidation and ozonolysis, as simulated by GECKO-A, is shown in Figs. 7a and 7b respectively.
Also shown are the corresponding final SOA mass concentrations. As has been previously reported (Lee et al., 2006b), limonene had a higher SOA yield than α-pinene under both photooxidation and ozonolysis conditions.

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The time evolution of the carbon budget during the photooxidation and ozonolysis simulations is shown in Figs. 7c to 7f.
During photooxidation (Fig. 7c), the precursors were oxidized largely by OH and O3 (see Fig. S7 for the relative fractions of precursor reacting with each oxidant), forming organic oxidation products in the gas phase. These gaseous oxidation products partitioned into the particle phase if their volatility was low enough. Oxidation products that remained in the gas phase reacted with OH, NO3, and/or O3, or were photolyzed if a chromophore was present; these subsequent gas-phase reactions formed 300 additional oxidation products that partitioned to the particle phase or continued to react in the gas phase. At the end of 12 hours of photooxidation, the α-pinene system was dominated by organic oxidation products in the gas phase (70 %), with the remaining fractions being organic oxidation products in the particle phase (8 %) and CO+CO2 (22 %). The high yield of gas-phase organics is largely influenced by the high concentrations of acetone and volatile C8 to C10 species (see Fig. 5a for top gas-phase products and Fig. S9a for the gas-and particle-phase product distribution). As shown in the α-pinene + OH reaction scheme (Fig. 1) acetone 305 is formed when the monocyclic alkoxy radical decomposes via O2 addition. For limonene photooxidation (Fig. 7e), the concentration of acetone is lower than for α-pinene and more of the C8 to C10 species are further oxidized and partitioned into the particle phase (Fig. S9c). This resulted in a final distribution of 50 % gas-phase organic products, 20 % particle-phase organic products, and 30 % CO+CO2. The simulated acetone yields are qualitatively consistent with experimental data that have shown yields of acetone from α-pinene photooxidation (Lee et al., 2006b;Wisthaler et al., 2001) can be up to four orders of magnitude 310 higher than from limonene photooxidation (Lee et al., 2006b;Reissell et al., 1999).
For the α-pinene ozonolysis system (Fig. 7d), at the end of the simulation 88 % of the carbon is gas-phase organic products, 7 % particle-phase organic products, and 5 % CO+CO2. For limonene ozonolysis (Fig. 7f), 50 % of the carbon fraction is gasphase organics, 43 % particle-phase organics, and 7 % CO+CO2. The higher particle-phase fraction for limonene ozonolysis is a result of the C8 and C10 organic products of limonene being more highly functionalized and thus partitioned to the particle phase 315 (Figs. S9d and S13); whereas the C8 and C10 organic products of α-pinene are more volatile and partitioned to the gas phase (Figs. 6a and S9b).

Controlled reactivity simulations
The GECKO-A simulations captured trends (e.g., SOA yields and major products) observed in chamber studies (section 3.1) for α-pinene and limonene, two common terpene model surrogates. Therefore, tThe GECKO-A model was then used to 320 perform a detailed study of SOA formation from camphene under idealized ("controlled reactivity") atmospheric conditions, which were compared with analogous simulations for α-pinene and limonene.

Gas-phase chemistry
Time-dependent mixing ratios of HO2, OH, and NO3 are shown in Fig. 8 for the controlled reactivity simulations performed at 0.1 ppb of HCo (camphene, α-pinene, or limonene) and 10 µg m -3 of organic seed. The O3 and total NOx levels were 325 fixed so that the oxidant (OH, O3, and NO3) levels remained stable during the simulations. The reaction rate of camphene with O3 is extremely slow (two and three orders of magnitude lower than the rate constants for α-pinene+O3 and limonene+O3 respectively (Atkinson and Arey, 2003a)); thus camphene predominately reacts with OH in the simulations, while α-pinene and limonene react with O3 and OH (see Fig. S30 for relative fractions).. The O3 and total NOx levels were fixed so that the oxidant (OH, O3, and NO3) levels remained stable during the simulations. The time profiles of HO2, OH, and NO3 were independent of the precursor, 330 confirming that the gas-phase oxidant levels were controlled by the added ethane and formaldehyde. This allows for a comparative assessment of the monoterpenes. The calculated lifetime of RO2 with HO2/NO was < 60 s, and thus it is assumed that these biomolecular RO2 reactions would be dominant, and the absence of HOM formation via RO2 autoxidation in GECKO-A did not significantly impact the results and conclusions derived from these simulations. The reaction rate of camphene with O3 is extremely slow (two and three orders of magnitude lower than the rate constants for α-pinene+O3 and limonene+O3 respectively (Atkinson 335 and Arey, 2003a)); thus camphene predominately reacts with OH in the simulations, while α-pinene and limonene react with O3 and OH (see Fig. S30 for relative fractions). Figure 9 illustrates the simulated SOA yields as a function of atmospheric aging time (Fig. 9a) and the SOA yield as function of reacted HC concentration (Fig. 9b)  (1)

Simulated SOA formation
where [OH]atm is the atmospheric OH concentration (2 × 10 6 molecule cm -3 was assumed) and [OH]sim is the simulated OH concentration. Camphene was predicted to form more SOA (0.26 µg m -3 ) than α-pinene (0.14 µg m -3 ) but less than limonene (0.42 µg m -3 ) after 14.5 hours of aging time (Fig. 9a). The simulation results in Fig. 9b show that camphene, which reacts predominantly 345 with OH (Fig. S30), forms low volatility products (more SOA at lower ∆HC) at the start of the reaction than α-pinene and limonene.
However, after the precursor is completely consumed, the SOA yield of limonene exceeds that of camphene. The shorter lifetime and chemical structure, including the presence of two double bonds, contribute to the relatively high SOA yield of limonene. As previously reported (Lee et al., 2006b), and as simulated herein, limonene had the highest SOA yield among well studied monoterpenes. However, the final SOA yield of camphene was relatively high, approximately twice that of α-pinene. 350 Figure 10 shows the product distribution in the gas-and particle-phases after 72 hours (equivalent to 14.5 hours of atmospheric OH aging time) for the controlled reactivity simulations. While thousands of secondary species are formed during the oxidation of a given monoterpene, only species that contribute  0.01 % of the total gas-or particle-phase mass were included in Fig. 10. Also, all C1 species, as well as seven of the C2 gas-phase products (whose concentrations were largely a direct result of 355 ethane chemistry) were omitted from Fig. 10. For camphene (Fig. 10a), the particle phase is largely dominated by C10 species with 3 to 5 functional groups, followed by highly functionalized C7 species (typically with 4 to 5 functional groups). Similarly, for limonene (Fig. 10b), the particle phase is dominated by C10 species with 4 to 5 functional groups, followed by C7 to C9 species with 4 to 5 functional groups. However, for α-pinene (Fig. 10c), there is a broad distribution of C8 to C10 products (with 3 to 4 functional groups) contributing to the particle phase. Generally, the volatility of particle-phase products from camphene and 360 limonene was lower than from α-pinene. As shown in Fig. 10a, a large fraction of gas-phase products from camphene, as compared to limonene, is composed of C9 and C10 products whose volatility was not low enough to partition to the particle phase. This further explains the SOA yields shown in Fig. 9b, where limonene SOA yield exceeded camphene SOA yield at the end of the simulation. Figure 11 shows the final mass percentages of α-pinene, camphene, and limonene particle-phase oxidation products 365 grouped into three volatility categories. The volatility categories were assigned based on the calculated mass saturation concentrations (C*) of the simulated products. C* was calculated based on the equilibrium absorption coefficient equation, as defined by Odum et al. (1996) and Pankow (1994). Log C* values in the range of < -3.5, -3.5 to -0.5, and -0.5 to 2.5 were assigned respectively as extremely low-volatility, low-volatility, and semi-volatile organic compounds (ELVOCs, LVOCs, and SVOCs) (Chuang and Donahue, 2016;Zhang et al., 2015). Limonene, which had the highest simulated SOA yield among the three studied 370 monoterpenes, was largely LVOCs (59 %), followed by ELVOCs (24 %) and then SVOCs (17 %). Camphene SOA was also largely LVOCs (67 %), followed by SVOCs (28 %), and then a significantly lower fraction of ELVOCs (4 %) than limonene. In contrast, α-pinene SOA was dominated by SVOCs (50 %), followed by LVOCs (48 %), and then ELVOCs (2 %). For experimental studies of α-pinene ozonolysis, Zhang et al. (2015) reported a fractional contribution of ~68 % SVOCs to final SOA mass, which is similar to the contribution predicted using GECKO-A.

Gas-and particle-phase product distribution
For α-pinene and camphene, intermediate-volatility organic compounds (IVOCs) were less than 1 % of the SOA mass. The pProduct volatility distributions can be influenced by gas-phase RO2 autoxidation, and condensed-phase reactions, which were not considered here. While HOM formation likely played a minor role in these controlled reactivity simulations, the monomer building blocks of known accretion reactions were predicted for all monoterpenes studied. Thus, it is expected that accretion product formation could occur under these conditions, leading to changes in the simulated volatility distributions.For experimental 380 studies of α-pinene ozonolysis, Zhang et al. (2015) reported a fractional contribution of ~68 % SVOCs to final SOA mass, which is similar to the contribution predicted using GECKO-A.

Using α-pinene limonene as a surrogate for camphene
For the controlled reactivity simulations, the final SOA mass and yield of camphene (0.26 µg m -3 , 0.46) were between the final SOA mass and yield of α-pinene (0.14 µg m -3 , 0.25) and limonene (0.42 µg m -3 , 0.74). This suggests that camphene could 385 potentially be represented in models as a 50/50 mixture of α-pinene + limonene, for which SOA parameterizations currently are available Pathak et al., 2007b;Zhang et al., 2006). To test this, a controlled reactivity simulation was run with 50 ppt α-pinene + 50 ppt limonene; simulation results were then compared with the simulation results for 0.1 ppb of camphene. Figure 12a shows that while the slopes of the SOA yield curves differ over the course of the reaction, the SOA masses (0.26 µg m -3 for 50 % α-pinene + 50 % limonene and 0.26 µg m -3 for camphene) and yields (0.46 for 50 % α-pinene + 50 % limonene and 0.47 390 for camphene) were approximately equal at the end of the simulation. However, the end of simulation particle-phase volatility distributions ( Fig. 12b) are notably different. The 50 % α-pinene + 50 % limonene simulation had a significantly higher fraction (25 %) of ELVOCs, influenced by the low volatility limonene products, than the camphene simulation (4 %). These results suggest that while the final SOA mass and yield of the 50/50 α-pinene + limonene mixture were representative of camphene, the properties (e.g., volatility) of the particle-phase products were not. The volatility distributions will influence the formation of SOA at the 395 lowest mass loadings and will also influence changes in SOA mass as a function of dilution, with the surrogate mixture (50 % αpinene + 50 % limonene) producing less volatile SOA than predicted for camphene. Thus, the extent to which camphene can be represented by α-pinene + limonene will depend on the application. To improve the representation of camphene, a second simulation was run with 50 ppt α-pinene + 50 ppt limonene, where the rate constants of α-pinene and limonene were replaced with the rate constants of camphene during the chemical mechanism generation. However, the representation of camphene SOA by the 400 50/50 α-pinene + limonene mixture did not improve (resulted in higher final SOA yield of 0.51) when the rate constants of αpinene and limonene were replaced with those of camphene (Fig. 12a). Also, representing camphene by the limonene mechanism with camphene rate constants did not improve the representation of camphene SOA (see Fig. S33). This illustrates the importance of both the reaction rate constants and structure on SOA formation from monoterpenes.
To demonstrate the potential impact of including a parameterized representation of SOA formation by camphene in air 405 quality models, SOA mass and yields were predicted for three wildland fire fuels based on the measured monoterpene distributions in Hatch et al. (2015) for black spruce, and Hatch et al. (2019) for Douglas fir and lodgepole pine. The top five monoterpenes by emissions factor (mass of compound emitted/mass fuel burned) represent ~70-80 % of the total monoterpene emission factor (EF) for each of these fuels. These top five monoterpenes were used to represent SOA formation from monoterpenes for each fuel by normalizing the monoterpene EF for each fuel; assigning α-pinene as the model surrogate for all measured compounds except 410 limonene, including camphene; and then reassigning camphene as 50 % α-pinene and 50 % limonene. SOA mass concentrations and yields were predicted assuming a background PM level of 50 µg m -3 and HC = 10 ppb and using published two-product SOA parameters based on Griffin et al. (1999) (Table S3) and volatility basis set (VBS) parameters (low NOx, dry) based on Pathak et al. (2007b) (for α-pinene) and Zhang et al. (2006) (for limonene) ( Table S4). The two model parameterizations were used to represent a range of potential outcomes. The SOA yields using the two-product parameters were lower than predicted here for α-415 pinene (~0.1), but similar for camphene (~0.6); using the VBS parameters, the yields were similar for α-pinene (~0.2) but higher than predicted here for camphene (~0.9). The total OA mass loadings in the parameterized SOA calculations were a factor of 3-6 higher than in the GECKO-A controlled reactivity simulations, which is consistent with the higher SOA yield for camphene predicted using the VBS parameters. The results of the SOA calculations are summarized in Table 4. For lodgepole pine, there is no change in SOA mass, because camphene is not one of the top five monoterpenes by EF. However, for fuels in which camphene 420 contributed significantly to the measured monoterpene EF, SOA mass increased by 43-50 % for black spruce and by 56-108 % for Douglas fir.

Conclusions
While camphene is a ubiquitous monoterpene, measured in significant quantities from both biogenic and pyrogenic sources, little is known about SOA formation from camphene and there are no published parameterizations to represent camphene 425 SOA in air quality models. GECKO-A simulations suggest that the initial organic oxidation products of camphene are of low volatility and can condense at low OA mass loadings; lower than oxidation products predicted for α-pinene and limonene. Predicted final SOA yields for camphene in the controlled reactivity simulations (~45 %) were in between those predicted for α-pinene (25 %) and limonene (~75 %), suggesting that SOA formation from camphene can be represented in air quality models assuming a 50/50 (α-pinene/limonene) surrogate mixture. The predicted SOA yields do not account for condensed-phase accretion reactions, 430 which could occur under the simulation conditions. Calculations based on measured monoterpene distributions for three wildland fire fuels illustrate that accounting for camphene, in this case using the surrogate mixture and published SOA parameterizations for α-pinene and limonene, increased predicted SOA mass from monoterpenes by 43-108 %. This demonstrates the potential impact of representing SOA formation from camphene in air quality models, and the need for an appropriate parameterization. The surrogate mixture appears to represent the SOA mass and yield of camphene well, but not necessarily the volatility distribution of 435 the products. The SOA mass, yields, and product volatility distributions can also be influenced by gas-phase HOM formation and subsequent dimerization, and condensed-phase accretion reactions, which were not considered here. Further modeling and/or experimental studies are needed to develop and test a suitable SOA parameterization for representing camphene in air quality models; including a robust assessment of the role of gas-phase HOM formation via RO2 autoxidation, and condensed-phase accretion reactions, on SOA composition and yields under a range of atmospherically relevant conditions.

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The authors declare that they have no conflict of interest.

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Results are shown for camphene, α-pinene, and limonene after 72 hours of oxidation under controlled reactivity condition.
The markers are sized by the ratio of their mixing ratio (in ppbC) to the initial mixing ratio of the precursor (in ppbC).
The colors of the markers are scaled by volatility (represented by saturation concentration, C*).