Constraining a hybrid volatility basis set model fo r aging of 1 wood burning emissions using smog chamber experimen ts 2

12 Semi-volatile and intermediate volatility organic c ompounds (SVOCs, IVOCs) are not 13 included in the current non-methane volatile organi c compounds (NMVOCs) emission 14 inventories but may be important for the formation of secondary organic aerosol (SOA). In 15 this study, novel wood combustion aging experiments performed at different temperatures 16 (263 K and 288 K) in a ~7 m 3 smog chamber were modelled using a hybrid volatili ty basis set 17 (VBS) box model, representing the emission partitio n ng and their oxidation against OH. We 18 combine aerosol-chemistry box model simulations wit h unprecedented measurements of non19 traditional volatile organic compounds (NTVOCs) fro m a high-resolution proton transfer 20 reaction mass spectrometer (PTR-MS) and with organi c erosol measurements from an 21 aerosol mass spectrometer (AMS). In so-doing, we ar e able to observationally-constrain the 22 amounts of different NTVOCs aerosol precursors (in the model) relative to low-volatility and 23 semi-volatile primary organic material (OM sv) which is partitioned based on current published 24 volatility distribution data. By comparing the NTVO Cs/OMsv ratios at different temperatures, 25 we determine the enthalpies of vaporization of prim a y biomass burning organic aerosols. 26 Further, the developed model allows for evaluating he evolution of oxidation products of the 27 semi-volatile and volatile precursors with aging. M ore than 30,000 box model simulations 28 were performed to retrieve the combination of param eters that fit best the observed organic 29 aerosol mass and O:C ratios. The parameters investi gated include the NTVOC reaction rates 30 and yields as well as enthalpies of vaporization an d the O:C of secondary organic aerosol 31 surrogates. Our results suggest an average ratio of NTVOCs to the sum of non-volatile and 32 Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-163, 2016 Manuscript under review for journal Geosci. Model Dev. Published: 11 August 2016 c © Author(s) 2016. CC-BY 3.0 License.


Introduction
The fact that some semi-volatile compounds can exist in either gaseous or particulate form results in considerable uncertainties in the emission inventories for fine particulate matter (PM 2.5 ) and non-methane volatile organic compounds (NMVOCs).Emissions of PM 2.5 are generally based on emission factors (EF) of primary organic aerosol (POA) which may be over-or under-predicted depending on the measurement method used (Lipsky and Robinson, 2006;Nussbaumer et al., 2008aNussbaumer et al., , 2008b.).).
In Europe, residential wood-burning emissions constitute one of the main anthropogenic sources of POA and potentially secondary organic aerosol (SOA), especially during winter periods with contribution from 15% to 50% of the total organic mass (Crippa et al., 2013;Waked et al., 2014).Thus, great effort was devoted in the past to better constrain the uncertainties related to wood burning emissions and their evolution in the atmosphere (Denier van der Gon et al., 2015;May et al., 2013).Recent year-long source apportionment studies based on ACSM (aerosol chemical speciation monitor) measurements in central Europe suggest that winter secondary organic aerosol fingerprints resembles those measured during chamber studies of biomass burning emission aging (Canonaco et al., 2015).
One of the main complications when dealing with organic aerosol (OA) is imposed by the semi-volatile and highly reactive nature of organic material (Robinson et al., 2007).
Depending on ambient conditions freshly emitted primary organic particles can undergo evaporation.The fraction of an organic compound i in the condensed phase can be inferred based on the absorptive partitioning theory of Pankow (1994) (Eq. 1).The critical parameters driving the gas-particle partitioning of this compound are its effective saturation concentration, C i * , and the total concentration of organic aerosol, C OA : Geosci.Model Dev. Discuss., doi:10.5194/gmd-2016-163, 2016 Manuscript under review for journal Geosci.Model Dev.Published: August 2016 c Author(s) 2016.CC-BY 3.0 License.
Here, is the partitioning coefficient of i (condensed-phase mass fraction).C i * is a semiempirical property (inverse of the Pankow-type partitioning coefficient, K P ), reflecting not only the saturation vapor pressure of the pure constituents ( ) p , but also the way they interact with the organic mixture (effectively including liquid phase activities).This formulation essentially determines that at high C OA almost all semi-volatile organic aerosols are in the condensed phase with only species with the highest vapour pressures remaining in the gas phase.
The volatility basis set approach (VBS) was proposed by Donahue et al., (2006) to provide a framework to enable models to represent both the chemical ageing and the associated evolving volatility of particulate organic matter in the atmosphere.The approach is to separate organics into logarithmically spaced bins of effective saturation concentrations C i * , at 298 K and it was later extended (Donahue et al., 2011(Donahue et al., , 2012) ) by introducing surrogate compounds with different carbon and oxygen numbers following the group contribution approach based on the SIMPOL method (Pankow and Asher, 2008) (Equation 2).The model becomes 2dimensional, capable of tracking compound volatility and oxidation state (O:C ratios) (Donahue et al., 2011(Donahue et al., , 2012)): where " and represent the carbon-carbon and oxygen-oxygen interactions, respectively, describes the non-ideal solution behaviour and , equal to 25, represents the reference point for pure hydrocarbons (1 µg m -3 of alkene).and are the carbon and oxygen numbers, respectively, for the #$ℎ saturation concentration ( ).For biomass burning in particular, May et al. (2013) revealed that the majority of the emitted primary OA mass is semi-volatile, with 50 to 80 % of the POA mass evaporating when diluted from plume to ambient concentrations or when heated up to 100°C in a thermodenuder.Based on their results, they proposed a volatility distribution function and enthalpies of vaporization for wood burning smoke (May et al., 2013).
Once emitted in the atmosphere, organic compounds are highly reactive towards various oxidants such as the hydroxyl radical (OH), ozone (O 3 ) and the nitrate radical (NO 3 ).These oxidants can strongly alter the chemical structure of the reacted precursors by generating ratios, consistently with the dominant representative species in that part of the parameter space.Chemical transport models (CTMs) have been increasingly updated with a VBS scheme with varying complexities (Bergström et al., 2012;Ciarelli et al., 2016;Murphy et al., 2011;Zhang et al., 2013).A recent landmark paper within the international AeroCom initiative (Tsigaridis et al., 2014) (Bessagnet et al., 2014).All models under-predicted the total measured organic fraction mainly due the uncertainties in SOA representation (Bessagnet et al., 2014).Knote et al. (2011) used the COSMO-ART model to investigate its performance as online-coupled chemistry-climate model.In their study domestic wood burning emissions were not included and POA was assumed to be non-volatile, which resulted in a severe under-prediction of OA over the studied domain (Knote et al., 2011).Bergström et al. (2012) (2010).They found the approach improved considerably the OA simulated in the model across Europe comparing to a range of observations made during the EUCAARI field campaign (Kulmala et al., 2009(Kulmala et al., , 2011) ) and from EMEP monitoring network (Tørseth et al., 2012).Recently, an important new initiative to provide improved information on residential wood combustion (RWC) emission inventory for Europe was carried out by Denier van der taken into account, especially for rural regions (Jo et al., 2013).These novel investigations highlight the critical need for a representation of semi-volatile organic species and their evolution in chemical transport models.
In this study we perform extensive box-model simulations of wood burning combustion aging experiments performed in a ~7 m 3 smog chamber at different temperatures.Most uncertain parameters namely enthalpies of vaporization of SOA, NTVOCs reaction rates and their yields were investigated by means of brute force simulations, and a best fitting solution, within acceptable physical and errors ranges, was retrieved.

Experimental Method
Beech (Fagus sylvatica) logs were combusted in a residential wood burner (model type: Avant, Attika from 2009), following the procedure described in (Heringa et al., 2012) and (Bruns et al., 2015).The resulting emissions were sampled from the chimney through a heated line (473 K), diluted by a factor of ~8-10 using an ejector diluter (473 K, DI-1000, Dekati Ltd.) and injected into the smog chamber (~7 m 3 ) through a heated line (423 K).
Emissions were only sampled during the stable flaming phase of the burn, for 11-21 min and total dilution factors ranged from ~100 to 200.Four replicate experiments were conducted at 288 K and another four experiments at 263 K.The smog chamber had an average relative humidity of 50% over all eight experiments.Another three experiments were conducted at 90% relative humidity and 263 K.After the characterization of the primary emissions, a single dose of d9-butanol (butanol-D9, 98%, Cambridge Isotope Laboratories) was injected into the chamber, to trace the OH concentration (Barmet et al., 2012).A continuous flow of nitrous acid (2.3-2.6 l min -1 , ≥99.999%, Air Liquide) into the chamber served as an OH precursor.The chamber was then irradiated with UV light (40 lights, 90-100 W, Cleo Performance, Philips) for 4.5-6 h (Platt et al., 2013).The evolution of the gas-phase and particulate phase composition and concentration were monitored in real-time throughout aging.Non-refractory primary and secondary particulate emissions were characterized using a high resolution time-of-flight aerosol mass spectrometer (AMS).Equivalent black carbon (eBC) was quantified using a 7-wavelength aethalometer (AE33 Magee Scientific Company, flow rate 2 l min -1 ) (Drinovec et al., 2015).Particle wall loss rates in the chamber were determined using the decay of eBC assuming all particles were lost equally to the walls and that condensable material partitions only to suspended particles.The average particle half-life in the chamber was 3.4±0.7 h.Non-methane organic gases with a proton affinity greater than that of water were measured using a a high-resolution proton transfer reaction mass spectrometer (PTR-ToF-MS 8000, Ionicon Analytik G.m.b.H.).The PTR-ToF-MS was operated with hydronium ([H 2 O+H] + ) as reagent, a drift tube pressure of 2.2 mbar, a drift tube voltage of 543 V and a drift tube temperature of 90°C leading to a ratio of the electric field (E) and the density of the buffer gas (N) in the drift tube (E/N) of 137 Townsend (Td).The analysis of data PTR-ToF-MS data and the identification of the precursors' chemical nature are described in Bruns et al. (2016).The elemental composition of the detected gases was analyzed using the Tofware post-processing software (version 2.4.5),running in the Igor Pro 6.3 environment (version 6.3, Wavemetrics Inc.).More than 95% of the detected peaks could be assigned to a molecular formula.Approximately 70% of the compounds' chemical structures could be assigned to the observed ions guided by previously reported compounds emitted during residential wood combustion.Here, the lumped sum of the precursors' molar concentrations will be used to constrain the total amount of NTVOCs (Table S1) in the model.
Their weighted average O:C ratio, volatility, reaction rate and carbon number will also be presented.

Box model
The modelling approach involves two steps.
(1) We first modelled the partitioning of POA for the 11 smog chamber experiments (8 experiments at RH=50% and 3 experiments at RH=90%) before the start of the aging.
This step enables constraining the amounts of material in the different volatility bins and the enthalpy of vaporization of the different surrogates used.The simulations proceeded as follows.Using already available volatility distribution data for primary wood burning emissions (Figure 1) we inferred the total amount of organic material (gas and particle phase) in the low-volatility and semi-volatile ranges (OM sv ), (0.1<In the present study, the bulk micro-physical properties of the condensed phased were not measured.Therefore, for all calculations, we assumed instantaneous reversible absorptive equilibrium of semi-volatile organic species into a well-mixed liquid phase.I.e. the model does invoke diffusion limitations within the condensed phase.These assumptions may influence our results, especially at lower temperatures (e.g. if diffusion limitations were to be considered, higher reaction rates would be required to explain the observations).However, the same assumptions are considered in CTMs and therefore we expect that resulting biases will partially cancel out, providing that the bulk phase properties of chamber and ambient aerosols are not significantly different.A total number of 3 sets were chosen to describe the evolution of organic material.The first set was used to distribute the primary emissions (set1).Two other sets were used to model the formation and evolution of SOA.Oxidation products of SVOC material arising from primary emissions were allocated to set2, whereas oxidation products from NTVOCs were allocated to set3 (Figure 3).
The specific molecular structures for each of the sets and bins were retrieved using the group contribution approach and the Van Krevelen relation (Table 1).Primary wood burning emissions were placed to range from 14 to 11 carbons (set1) in line with previous studies (Donahue et al., 2012;Koo et al., 2014) and appropriate numbers of oxygen atoms were retrieved (Eq.2).The oxidation of semi-volatile material would tend to increase the compounds' oxygen number and decrease their volatility and carbon number, due to functionalization and fragmentation.We assume that the oxidation of the primary semivolatile compounds with C 11 -C 14 decreases their volatility by one order of magnitude and yields C 9-C 10 surrogates, placed in set2, based on the work of Donahue et al. (2011Donahue et al. ( , 2012)).
Based on these assumptions and using the group contribution approach, the oxygen numbers for set 2 is predicted to vary between 2.26 and 4.56 (Figure 2).Thus, the model implicitly accounts for the addition of 1.1 to 1.5 oxygen atoms and the loss of 2.75 to 4.25 carbon atoms, with one oxidation step.
Set3, was directly constrained based on the PTR-MS data.The measurements suggested an average NTVOC carbon and oxygen number of about 7 and 1, respectively.Based on reported molecular speciation data (Kleindienst et al., 2007), we expect that the products of C 7 compounds have a C 5 -C 6 carbon backbone.These products were placed in set3 following a kernel function based on the distribution of naphthalene oxidation products.At least two oxygens atoms were added to the NTVOC mixture upon their oxidation (Figure 2 and Figure 3).The overall, O:C ratio in the whole space roughly spans the range from 0.1 to 1.0.
Multigeneration chemistry (aging) is also accounted for by the model.Gas-phase products in the semi-volatile range in set2 and set3, once formed, can further react with a rate constant of 4 x 10 -11 cm 3 molecule -1 s -1 as proposed by previous studies (Donahue et al., 2013;Grieshop et al., 2009;Robinson et al., 2007), further lowering the volatility of the products by one order of magnitude.
As the modelled species' average carbon number systematically decreases with aging, this approach effectively takes into consideration the compounds' fragmentation.In parallel, the addition of oxygen reflects the compounds' functionalization with aging and the increase in the measured O:C ratio.Therefore, unlike previous 2D-VBS schemes where functionalization and fragmentation are disentangled, the approach adopted here, by decreasing the number of carbon atoms and increasing the number of oxygens atoms, simultaneously describes both processes.
In addition to the constrains mentioned above, three parameters were determined based on experimentally constrained time-dependent OA mass and O:C ratios, i.e., NTVOCs reaction rates and yields as well as average enthalpies of vaporization values for the set 2 and 3.
Detailed explanations are presented in the next two sections.

Inferring OM sv and NTVOCs/OM sv ratios from measurements and partitioning theory
We seek to determine, based on the PTR-MS and AMS measurements of gas and particle phase organic material at t=0, the ratio NTVOCs/OM sv and the enthalpies of vaporization of compounds of the semi-volatile compounds that represent best the observations at high and low temperatures.We modelled the OA t=0 partitioning using two different proposed ∆H vapPOA for wood burning: Eq. 3 is the best fitting solution proposed by May et al. (2013), while Eq. 4 represents the lower limit for ∆H vapPOA for a solution within the range of experimental uncertainties (Figure 4 in May et al. 2013).We will refer to these solutions as SOL1 for Eq. 3, and SOL2 for Eq. 4.  and 22.6 µg m -3 .The amount of OM sv that matches the measured OA t=0 is reported for both SOL1 and SOL2.The average NTVOCs/OM sv ratios for high and low temperature experiments are reported together with the standard deviation in Table 3.For SOL1 we calculated an average ratio of 4.2±1.1 at high temperatures and 7.2±2.6 for low temperatures.
SOL2 reduces the differences in the average NTVOCs/OM sv ratios at the two temperatures, and therefore will be used to describe the dependency of the primary organic compounds.For SOL2 the overall NTVOCs/OM sv ratio between high and low temperature experiments is around 4.75.Figure 4 shows the resolved equilibrium phase partitioning (Eq. 1) between the gas and particle phase at the beginning of each of the 11 smog chamber experiments (OA t=0 ) using SOL2.As expected, most of the material is found in the gas-phase at high temperatures, while at lower temperature only part of the compounds with saturation concentrations (at 20°C) between 100 and 1000 µg m -3 would reside in the gas-phase.

Modelling of wood burning aging at low and high temperature
In this section we will focus on the emission aging.Using the NTVOCs/OM sv ratio and the enthalpies of vaporization retrieved in section 3.1.1,we modelled the eight different smog chamber experiments: No. 1, 2, 3, 4 (low temperature) and No. 8, 9, 10, 11 (high temperature) performed at the same relative humidity (RH = 50%).For each of the eight experiments we injected an average mixture of NTVOCs equal to 4.75 times the OM sv mass before the start of the aging.NTVOCs react solely with OH, whose concentration was retrieved from PTR-MS measurements.The temperature dependence of the reaction rates was also taken into account through the Arrhenius equation.The reaction rates (k OH-NTVOCs ) and yields (Y) of the NTVOCs as well as enthalpies of vaporization of SOA (∆H vapSOA ) for set2 and set3 were varied within specific physically realistic ranges.We varied k OH-NTVOCs between 2 and 4 x 10 - 11 cm 3 molec -1 s -1 in steps of 0.1 x 10 -11 cm 3 molec -1 s -1 , and yields between 0.1 and 0.4 ppm ppm -1 in steps of 0.01 ppm ppm -1 .Values for ∆H vapSOA are still highly uncertain.In this study we explored a wide range of values from 15,000 J mol -1 to 115,000 J mol -1 in steps of 20,000 J mol -1 .The model performance for each combination of i,j and k was evaluated in terms of the root mean square error (RMSE) for the eight experiments and a best fitting solution retrieved as the one that minimized the sum of the errors on both the O:C ratio and OA mass (giving the same weight on both quantities).We performed a total number of # × 8 × 9 × :;* = -21 × 31 × 6 × 82 = 31248 simulations, where :;* are the numbers of aging experiments.Figure 5 5 indicate the absolute best fitting solution (in yellow) and the ones retrieved with a likelihood-ratio test allowing for 10% error form the best fit (red diamonds).Regions with lower error are localized for k OH-NTVOCs ≥ 2.5 x 10 -11 cm 3 molec -1 s -1 between ∆H vapSOA values of 35,000 and 55,000 J mol -1 .
Figure 6 shows the modelled and measured OA mass for all the 8 aging experiments.The primary organic aerosol fraction is reported as well as the SOA fraction from SVOCs and higher volatility NTVOCs.All the low temperature experiments (No. 1, 2, 3, 4 left side of the panel) were reproduced very well along with the concentration gradients at the end of each the experiments even though the model tends in general to slightly over-predict the final OA concentration.The primary fraction slightly increases at the very beginning of the aging phase and it decreases as the experiments proceed as a result of its partitioning to the gas phase and subsequent oxidation.Most of the SOA was predicted to be formed from NTVOCs precursors and only a minor amount from SVOCs.On the other hand, for experiments conducted at higher temperature (No. 8,9,10,11) the OA mass was under-predicted except for experiment No. 8 (see also Figure S1).In this case, SVOCs contribute more significantly to SOA formation compared to low temperature experiments, although the majority of SOA still arises from NTVOCs.
Comparisons between measured and modelled O:C ratios are reported in Fig. 7. Model and observation results match very well, especially upon aging.Significant differences between measured and modelled O:C ratios at the beginning of the experiments highlight on the one hand the variable nature of primary biomass smoke emissions.This variability cannot be accounted for in the model.On the other hand, for some experiments the model underpredicts the measured O:C ratios suggesting that the model parameters describing the O:C of primary emissions are suboptimal.These parameters include directly the carbon and oxygen

Implications for large-scale models
We performed extensive box model simulations of wood burning experiments conducted at two different temperatures (263 and 288 K) in a ~7 m 3 smog chamber facility.By combining new NTVOCs measurements and already available partitioning data for primary wood burning emission, we constrained the amounts of NTVOCs that act as SOA precursors.Our estimates indicate that NTVOCs are approximately 4.75 times the amount of total organic material in the 0.1 and 1000 µg m -3 saturation concentration range (OM sv ).This ratio can be directly used in CTM models in the absence of explicit NTVOCs emissions for wood burning in combination with the proposed aging scheme.Specific parameters such as NTVOCs reaction rates (k OH-NTVOCs ), yields (Y) and enthalpies of vaporization of secondary organic aerosol (∆H vapSOA ) were varied using brute force simulations, and their values were retrieved for best fitting solutions falling within a physically realistic range.The model predicted that the majority of the SOA formed during the aging-phase arose from NTVOCs precursors and only a smaller amount from SVOCs.
Based on our best fitting solutions, we can now predict the OA mass and composition as well as SOA yields at any given temperature, emission load and OH exposure.This is illustrated in Figure 8 for 3 different OM emission loads (OM sv + NTVOCs) of 6, 60 and 600 µg m -3 and for a wide range of atmospherically relevant temperatures (from 253.15 K to 313.15 K).
Partitioning of POA depends on the temperature and the injection amounts.The primary organic aerosol mass (POA) decreases with temperature by ~0.5% K -1 on average with higher effects predicted at higher loads (0.7% K -1 at 600 µg m -3 , 0.3% at 6 µg m -3 ).The partitioning coefficient of the primary material increases by about a factor of 1.5 for a 10-fold increase in the emissions.As aging proceeds, POA mass slightly increases as a result of additional partitioning, but after an OH exposure of (1.0-1.5)x 10 7 molec cm -3 h, the trend is inversed and POA mass decreases due to the oxidation of semi-volatile primary compounds.This effect is more visible at high loads.
From Figure 8, we can also assess the impact of temperature, OH exposure and emission concentrations on SOA yields.The temperature effect on SOA yields is a function of OH exposure, aerosol load, and temperature: i.e. >? >@ ⁄ = B-@, , C' :;* 2. SOA yields increase by 0.03, 0.06 and 0.05 % K -1 on average for 6, 60 and 600 µg m -3 respectively, with higher effects predicted in general at lower temperatures.The temperature effect on the yields is also greater at higher OH exposures (except for very high loads).An analysis typically performed to estimate the volatility distribution of SOA products is based on SOA yields from chamber data performed at different precursor concentrations.We investigated the impact of the OA concentration on the yield at different temperatures and OH exposure.In Figure S2, an average change in the yield with log is shown at the different conditions: ->? > log ⁄ 2 = B-@, C' :;* 2. Note that an increase in SOA yields with the log was observed as expected.This increase is not solely due to additional partitioning, but is partially also related to changes in the actual chemical composition and hence volatility distribution of the SOA surrogates, as they age to different extents at different concentrations and different temperatures.We determined a yield increase of 4-9% for a 10-fold increase in emissions, with a higher effect at higher OH exposures and lower temperatures.
From Figure 8, one may evaluate the minimum OH exposure values required for SOA to exceed POA.SOA is predicted to exceed POA after~1.5 x 10 7 molec cm -3 h, for typical ambient concentrations and temperatures.At low temperatures (263 K) and high loads, SOA might exceed POA at an OH exposure of 9 x 10 6 molec cm -3 h, or in 2-10 hours (at OH concentrations of (1-5) x 10 6 molec cm -3 ), in line with our previously estimated values for biomass burning emissions for the typical conditions of haze events (Huang et al., 2014).
Comparatively, at 288.15K an OH exposure of 7 x 10 6 molec cm -3 h would be required for SOA to exceed POA, which might be reached within 2 hours or less at typical summer OH concentrations, i.e. (5-10) x 10 6 molec cm -3 .These results confirm previous observations that SOA formation is very rapid and the SOA fraction might exceed primary emissions within time-scales of hours, even during haze events.

Tables and Figures 427
Table 1.Properties of the VBS space.Oxygen numbers for each volatility bin were calculated 428 using the group-contribution of Donahue et al. (2011).Hydrogen numbers were calculated 429 from the van Krevelen relation (Heald et al., 2010) Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-163,2016   Manuscript under review for journal Geosci.Model Dev.Published: August 2016 c Author(s) 2016.CC-BY 3.0 License.secondary products with lower or higher volatilities.Linking partitioning and oxidation processes of thousands of emitted organic compounds is one of the main challenges in atmospheric chemistry.The VBS scheme can delineate the transformation of the surrogates upon their functionalization or fragmentation, by changing the compounds' volatility and O:C Gon et al. (2015)  and used as an input in two CTMs (PMCAMx and EMEP MSC-W) for the EUCAARI winter periods(February-March 2009).The new RWC emissions, which are Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-163,2016 Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.higher by a factor of 2-3 compared to previous emission inventories, improved the model performance for total OA (Denier van der Gon et al., 2015).Jo et al. (2013) deployed the GEOS-Chem global model to investigate the effect of using different aging constants on modelled SOA.They concluded that model simulations are improved when chemical aging is Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-163,2016   Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.
C i* < 1000 µg m -3 ), which matched the measured OA concentrations at the beginning of the experiments (OA t=0 ).The amount of OM sv was then compared to the measured Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-163,2016 Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.NTVOCs, at high and low temperatures and the enthalpies of vaporization of primary compounds were adjusted such that a comparable NTVOCs/OM sv ratio was obtained at both temperatures within our experimental variability.We tested several sets of enthalpies of vaporization characteristic of biomass burning OA derived from May et al. (2013); the different sets were all physically possible and were determined from thermodesorber data by assuming different accommodation coefficients.(2)In step 2, the obtained volatility distributions were used to model the aging of the emissions and SOA formation within a hybrid VBS framework.This framework is adapted fromKoo et al. (2014); it describes the formation and further evolution of SOA species from different families of precursors.Unlike previous 2D-VBS schemes, the molecular space was not discretised according to the species saturation concentration and oxidation state (e.g.O:C ratios), but rather every SOA surrogate was given an average molecular composition -C x H y O z ) -as a function of its volatility and the precursor it derived from.This approach significantly decreases the degree of freedom of the model, while still providing a means to evaluate the bulk aerosol oxidation state based on the knowledge of the surrogate molecular composition.The time-dependent OA mass and O:C ratios were used as model constraints.For step 2, only experiments performed at RH=50% were used, as high RH might favour further uptake of secondary organic material into the bulk phase, effectively increasing aerosol yields(Zuend and Seinfeld, 2012).Such effects are beyond the scope of this study.

Five
volatility bins ranging from 0.1 to 1000 µg m -3 in saturation concentration were used to model the partitioning of the POA and SOA fractions.The weighted average carbon and Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-163,2016 Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.oxygennumbers of the NTVOCs mixture retrieved from PTR-MS measurements were used in combination with the group contribution approach (Eq.2) to estimate the average saturation concentration for SOA precursors yielding about ~10 6 µg m -3 , which falls within the IVOC saturation concentration range limit(Donahue et al., 2012;Koo et al., 2014;Murphy and Pandis, 2009) (Figure2).
shows the total errors for the OA mass (left side) and O:C ratio (right Geosci.Model Dev.Discuss., doi:10.5194/gmd-2016-163,2016 Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.side) for different ∆H vapSOA , Y and k OH-NTVOCs.These global errors are root mean squared deviations (i.e. for the eight experiments) adjusted to the number of points per experiment.The error on the OA mass varies from a minimum of ~25% up to more than 60 % whereas the errors on the O:C ratio (Figure 5 right side) are lower and they range from approximately 15 % up to more than 30 %.For the OA mass, distinct regions with lower errors are visible in the central part of each panel with different ∆H vapSOA , representing the models that fitted best the measured OA.While a similar observation can be made for the O:C, models with high ∆H vapSOA tend to reproduce the data less faithfully.The diamonds in Figure Geosci.ModelDev.Discuss., doi:10.5194/gmd-2016-163,2016   Manuscript under review for journal Geosci.Model Dev.Published: 11 August 2016 c Author(s) 2016.CC-BY 3.0 License.number of species in set 1, and indirectly the volatility distributions and enthalpy of vaporization, which are all adopted from previous published data.The average bias in POA O:C ratios is ~30%, well within the experimental uncertainties.

Figure 1 .
Figure 1.Properties of the wood burning POA set.a) O:C ratio, b) ∆H vap c) C number d) O number.Volatility distribution and enthalpies of vaporization were taken from May et al.(2013).Carbon and oxygen numbers were calculated using the group contribution approach ofDonahue et al. (2011).Wood burning POA carbon numbers were placed from 14 to 11 and linearly interpolated between the volatility bins.

Figure 2 .
Figure 2. Properties of the wood burning POA and SOA sets.a) C number b) O number.Wood burning SOA carbon numbers were placed from 10 to 5 and linearly interpolated between the volatility bins.Oxygen numbers were calculated using the group approach of Donahue et al. (2011).NTVOCs carbon and oxygen numbers were retrieved from PTR-MS data The red bars indicate the OM emission factors.

Figure 3 .
Figure 3. Proposed oxidation scheme: an average mixture of NTVOCs compounds are allowed to react with the hydroxyl radical following a naphthalene kernel mass distribution.Secondary products in the SOA set (set3) are allowed to further react with a reaction rate of k OH = 4.0 x 10 -11 cm 3 molec -1 s -1 .Oxidation products from semi-volatile vapours from the POA set (set1) are allowed for further aging in set2.The numbers on the red arrows indicate the NTVOCs yields for each bin for the best fitting solution (ppm ppm -1 ).

Figure 4 .
Figure 4. Partitioning of wood burning POA before the start of the aging for 11 smog chamber experiments (SOL2).Gas-phase in red and particle phase in blue.

Figure 5 .
Figure 5.Total error on the OA mass (left side) and on the O:C ratio (right side).White regions have an error larger than 60% for the OA mass and 26% for the O:C ratio.The number of simulations per experiment is 3906.The red diamonds indicate the likelihood ratio test results for solutions within 10% error from the best one (yellow diamond).

Figure 6 .
Figure 6.Modelled and observed OA mass for low temperature experiments (left side) and high temperature experiments (right side).The model results for the best fitting solution

Figure 7 .
Figure 7. Modelled (black lines) and observed (red lines) O:C ratio for low temperature experiments (left side) and high temperature experiments (right side).

Table 2
reports the measured OA t=0 for all the 11 experiments, which ranges from 6.0 µg m -3