Organic aerosols (OAs) are one of the main compound of submicron particulate matter PM1;. Their primary fraction originates mostly from combustion sources, such as traffic and residential heating. However, large uncertainties remain regarding their emissions . POAs have been considered non-volatile in emissions inventories and chemistry-transport models (CTMs); however, recent studies have provided clear evidence that a large portion of POA emissions partition between the gas and the particle phases . Organic species that compose POAs are often classified depending on their volatility: intermediate volatility organic compounds (IVOCs; with saturation concentration C* in the range 103–106 µg m-3), semi-volatile organic compounds (SVOCs; with saturation concentration C* in the range 0.1–103 µg m-3), or low-volatility organic compounds LVOCs; with saturation concentration C* lower than 0.1 µg m-3;.

OAs originate not only from the partitioning of POAs between the gas and the particle phases but also from secondary organic aerosol (SOA) formation through the gas-to-particle partitioning of the oxidation products of biogenic and anthropogenic volatile organic compounds (VOCs) and intermediate and semi volatile organic compounds (I/S-VOCs). The main biogenic VOC precursors are terpenes (α-pinene, β-pinene, limonene, humulene) and isoprene , while the main anthropogenic VOC precursors are aromatics e.g., toluene and xylene;.

Available measurements and modeling studies are useful in elucidating the composition and origin of OAs in different seasons . Indeed, over the Mediterranean region, the oxidation of biogenic VOCs may dominate the formation of OAs during the summer . found that I/S-VOC emissions do not influence the concentrations of OAs in summer over the Mediterranean region much, but biogenic SOAs prevail. Because biogenic emissions are low in winter, demonstrated a clear shift in the SOA origin between summer and winter during a measurement campaign from February 2012 to February 2013 conducted in Zurich using the Aerosol Chemical Speciation Monitor (ACSM; ) measurements. This last study notably highlights the importance of biogenic VOC emissions and biogenic SOA production in summer as well as the importance of residential heating in winter. performed a source apportionment study at the European scale and revealed that residential combustion (mainly related to wood burning) contributed about 60 %–70 % to SOA formation during the winter, whereas non-residential combustion and road-transportation sector contributed about 30 %–40 % to SOA formation. Moreover, residential heating can also be a source of POAs, which may make up a large fraction (20 % to 90 %) of the submicron particulate matter in winter .

Modeling OA concentrations in winter is challenging, because it mostly involves the characterization of I/S-VOC emissions and aging. Standard gridded emission inventories, such as those of the European Monitoring and Evaluation Programme (EMEP, http://www.emep.int, last access: 10 December 2018) over Europe, do not yet include I/S-VOC emissions, and their emissions are still highly uncertain. For example, estimated that emissions from residential wood combustion were underestimated by a factor of 2–3 in the 2005 EUCAARI inventory. As an indirect method of accounting for the missing organic emissions in the absence of precise emission inventories, numerous modeling studies estimate the I/S-VOC emissions from POA emissions or more recently from VOC emissions . A ratio of I/S-VOC / POA of 1.5 has been used in several air-quality studies . For example, simulated the particle composition over greater Paris during the winter MEGAPOLI campaign, and they found that simulated OAs agreed well with observed OAs when gas-phase I/S-VOCs emissions are estimated using a ratio I/S-VOC / POA of 1.5, as derived following the measurements at the tailpipe of vehicles representative of the average French vehicle . However, various ratios are used to better fit the measurements. For example, over Europe, used a I/S-VOC / POA ratio of 4, but they also used a ratio of 6 in a sensitivity simulation to better fit the observed OA concentrations in winter. used an IVOC / POA ratio of 1.5, but they also used a ratio of 3 in their high-IVOC emission scenario.

The atmospheric evolution (also known as aging) of I/S-VOCs as well as their impacts on atmospheric OA concentrations remain poorly characterized and deserve a better understanding. A widely used approach to model the aging of I/S-VOCs in CTMs is the volatility basis set (VBS) approach . I/S-VOCs are divided into several classes of volatility where each class is represented by a surrogate. When oxidized by the hydroxyl radical, it leads to the formation of surrogates of lower volatility classes. This approach tends to lead to an overestimation of simulated organic concentrations if fragmentation is not considered (formation of high-volatility surrogates during the oxidation). Although the one-dimensional basis set (1-D VBS) accounts for the volatility of the surrogates, it does not allow the representation of varying oxidation levels of OAs. The prognostic tool to date that is more powerful, the two-dimensional VBS approach (2-D VBS), although it is computationally burdensome, describes not only the aging of I/S-VOCs using not only the volatility property (C*) but also the oxidation level (the oxygen-to-carbon ratio O : C), taking into account three competing processes: functionalization, oligomerization and fragmentation . developed a 1.5-D aging VBS-type scheme that accounts for multigenerational aging, including functionalization, oligomerization and fragmentation, and that represents both the volatility and the oxidation properties of the surrogates. When oxidized by a hydroxyl radical, each surrogate leads to the formation of more-oxidized and less-volatile surrogates with a reduced carbon number. Functionalization and fragmentation are implicitly taken into account in this approach because of the increase of the oxygen number and the decrease of the carbon number of the surrogates formed. The 1.5-D VBS module is implemented within two widely used CTMs, namely the Comprehensive Air Quality Model with extensions CAMx; and the Community Multiscale Air Quality Modeling System CMAQ;. and used a simplified aging scheme with three volatility bins. When oxidized by the hydroxyl radical, each surrogate forms a less-volatile and more-oxidized surrogate that does not undergo multigenerational aging. This simplified aging scheme is implemented in the two widely used CTMs, Polyphemus and Chimere .

In winter, when anthropogenic emissions impact air quality the most, anthropogenic emissions such as toluene and xylene may also form SOAs, although they may be much less efficient than I/S-VOCs . To take into account the emissions and aging of anthropogenic VOCs that are usually not considered in CTMs (phenol; naphthalene; m-,o-,p-cresol; etc., modified the approach of by considering non-traditional VOCs (NTVOCs). They are VOCs or IVOCs, not usually taken into account in CTMs, with a saturation concentration in the low range of IVOCs.

Following , estimated these NTVOCs using chamber experiments as the mixture of phenol; m-, o-, p-cresol; m-, o-, p-benzenediol/2-methylfuraldehyde; dimethylphenols; guaiacol/methylbenzenediols; naphthalene; 2-methylnaphthalene/1-methylnaphthalene; acenaphthylene; syringol; biphenyl/acenaphthene; and dimethylnaphthalene. Furthermore, they estimated the ratio NTVOC / SVOC, where SVOC is the primary semi-volatile organic matter, to be about 4.75.

The oxidation level of OAs is important, because it is indicative of the degree of hygroscopicity, surface tension , and radiative property of the OAs in addition to its ability to act as cloud condensation nuclei (CCN) over the Mediterranean . showed that, in summer in the western Mediterranean region, OAs are highly oxidized and oxygenated. The CTM Polyphemus and Polair3d used in their study does represent this high oxidation level of OAs after adding the formation processes of highly oxidized species (autoxidation) and organic nitrate formation to the model.

The particle oxidation state is represented by the organic-mass-to-organic-carbon ratio (OM : OC). According to and , OM : OC is an index of the contribution of heteroatoms (O, H, S, N, etc.) to the organic mass; chemically processed and aged particles are expected to have higher OM : OC ratios compared to freshly emitted and unprocessed aerosols. The oxygenation state is represented by the oxygen-to-carbon ratio (O : C). It indicates the contribution of oxygen to organic molecules and the ability of carbon atoms to form bonds with oxygen.

Although the organic matter to organic carbon ratio (OM : OC) was first believed to lie between 1.2 and 1.4 , numerous studies show that OM : OC is approximately 1.6 for urban aerosols and 2.1 for non urban aerosols. developed an algorithm to deconvolve the mass spectra of OAs obtained with an Aerodyne^™ aerosol mass spectrometer (AMS) in order to estimate the mass concentrations of hydrocarbon-like and oxygenated organic aerosols (HOAs and OOAs). The mass of HOAs represents primary sources, with an OM : OC ratio close to 1.2 and O : C ratio close to 0.1, while the mass of OOAs represents secondary sources (aged and oxygenated) with an OM : OC ratio close to 2.2 and an O : C ratio close to 1 . Using this technique, found an average OM : OC ratio of 1.8 in Pittsburgh in September. Over Europe, found that secondary OAs are dominant in the OA fraction, with primary sources contributing to less than 30 % of the total mass fraction. measured a ratio OM : OC ratio over 14 cities throughout China and found that in summer, OM : OC is nearly 1.75±0.13, while the ratio is lower in winter (1.59±0.18). The OM : OC ratio is lower during winter due to the slow oxidation process owing to the low temperatures in addition the low biogenic contribution to OA mass during winter. At Ersa, over the Mediterranean and during the summer, found high OM : OC and O : C ratios (2.5 and 1 respectively). They are due to aged biogenic OAs, which were able to represent by adding the formation of extremely low-volatility species and organic nitrate to the model and by considering the formation of organosulfate.

Quantifying the effect of I/S-VOC emissions and their impact on the atmospheric organic budget as well as the OA oxidation and oxygenation levels during different seasons is challenging in spite of the recent advances concerning the description of I/S-VOCs . This work aims to evaluate how commonly used parameterizations and assumptions of I/S-VOCs emissions and aging perform to model the OA concentrations and properties in the western Mediterranean region in winter. To that end, the CTM from the air-quality platform Polyphemus is used with different parameterizations of I/S-VOCs emissions and aging.

This paper is structured as follows. Section 2 presents the setup of the air-quality model used and reference measurements. Section 3 presents the different emissions and aging mechanisms used to describe the evolution of I/S-VOCs as well as the comparison method. Section 4 compares the simulated concentrations, which are compositions of OAs for the simulations using the different parameterizations. Finally, Sect. 5 compares the measured and simulated OM : OC and O : C ratios.

The period of interest of this study is January–March 2014, hereafter referred to as the winter 2014 campaign.

In order to understand the behavior of the different parameterizations commonly used in CTMs to represent emissions and aging of I/S-VOCs in the western Mediterranean region, several simulations using different parameterizations, described in the following sections, are compared. These parameterizations are those described in , and . The differences concern the emission ratios used to estimate I/S-VOCs from POAs (R and RRH), the aging scheme (one step or multi-generational), the modeling of NTVOCs and the ratio OM : OC and volatility distribution at emissions.

The spatial distribution of OM1 concentrations averaged over the first 3 months of 2014 (Fig.  of Appendix ) shows that high OM1 concentrations are mostly located over big cities, like Marseille (2.0 µg m-3), Genoa (1.6 µg m-3), Turin (4.3 µg m-3), Milan (4.4 µg m-3), Rome (2.4 µg m-3) and Naples (2.1 µg m-3), and along maritime traffic routes, stressing that organics during wintertime are likely to be mostly of anthropogenic origins.

The simulated composition of OM1 at Ersa is shown in Fig.  for the simulations S4 and S5. In all simulations, primary and secondary organic aerosols (POAs and SOAs) from anthropogenic I/S-VOCs are the main components of the organic mass (between 60 % and 84 %). POAs tend to account for almost the same fraction of the organic mass than SOAs (between 46 % and 62 %). Similarly, in the U.S., found that the SOAs account for less than half of the modeled OA mass in winter 2005 due to the slow chemical aging during the cold season. Over Europe, in March 2009, simulated that POAs account for between 12 % and 68 % of the OAs, with an average value of 38 %. The emission sector 6 (residential heating) has a large contribution to OAs (between 31 % and 33 %). This is also in line with who found that, over Europe in March 2009, the contribution of the residential sector to OAs varies between 20 % and 45 %, with an average value of 38 %. Furthermore, this sector contributes more to SOAs (between 42 % and 52 % of SOAs from I/S-VOCs) than to POAs (between 17 % and 31 % of POAs from I/S-VOCs), because their I/S-VOC emissions are more volatile.

The contribution from aromatic VOCs is low (lower than 3 %), and when NTVOCs are considered, they represent between 18 % and 21 % of the organic mass. The model simulations performed revealed that, for winter 2014, the biogenic OA fraction is low (15 %–18 %). also estimated the biogenic contribution to the organic budget to be between 5 % and 20 % over Europe.

The statistical evaluation of the simulations is shown in Table . The model-to-measurement correlation is high for all simulations (between 76 % and 83 %). The performance criterion is satisfied for all simulations, and the goal criterion is satisfied for S2, S3, S4 and S5. The goal criterion is not satisfied for the simulation S1, which uses single-step oxidation with a biomass-burning-type volatility distribution for all anthropogenic sectors as well as for the simulation S6, which uses multi-step oxidation with NTVOCs and a high RRH ratio. The simulation S1 strongly underestimates the OM1 concentration at Ersa, whereas the simulation S6 strongly overestimates it. All the simulations tend to underestimate the OM1 concentrations at Ersa, except for the two simulations where NTVOCs are taken into account (S4 and S6), which overestimate the OM1 concentrations at Ersa. Because I/S-VOC emissions as modeled by the factor RRH are kept in those simulations, the IVOCs forming SOAs may have been counted twice by adding NTVOCs, explaining the overestimation.

Other CTMs showed the same underestimation of OM1 concentrations during winter over Europe, even when I/S-VOC emissions are taken into account . The CTM CAMx also underestimated the organic concentrations over Europe during February and March 2009 , but considerable improvement was found for the modeled OA mass, with the MFB decreasing from -61 % to -29 % when the parameterization of with NTVOCs was added.

The model-to-measurement comparison during the first 3 months of 2014 is shown in Fig.  in terms of the daily concentrations of OM1 at Ersa. Globally, the temporal variations of the simulated concentrations are well reproduced by the model. The simulation S1, which uses single-step oxidation with a biomass-burning-type volatility distribution for all anthropogenic sectors, underestimates the peaks. However, the peaks are well reproduced by the simulations S2, S3 and S5. The simulations S4 and S6, which take into account NTVOCs, overestimate the peaks. All simulations underestimate the beginning of the peak between 9 and 15 March, probably due to uncertainties in meteorology, especially rain episodes and changes in the origin of air mass.

As detailed in Sect. , the differences between the simulations S2 and S1 originate in differences in the volatility distribution of emissions from anthropogenic sectors other than residential heating. In the simulation S2, a less-volatile distribution is used than in the simulation S1, leading to larger OA concentrations in the particle phase. This difference in the volatility distribution makes a large difference in the OA concentrations, removing the strong underestimation simulated in simulation S1 (the MFB is -55 % in S1 and only -23 % in S2).

Considering multi-step aging for all anthropogenic sectors also leads to an increase of OA concentrations (the MBF of the simulation S3 is -11 %, which is lower in absolute value than the simulation S2). However, the influence of the multi-step aging (difference between S2 and S3 shown in Fig.  of Appendix ) is smaller than the influence of the volatility distribution (difference between S1 and S2 shown in Fig.  of Appendix ). This larger influence of the volatility distribution than the multi-step aging is true not only at Ersa but also over the whole Mediterranean domain, where the average RMSE between the simulations S1 and S2 is 0.01 µg m-3 (impact of volatility), compared to 0.005 µg m-3 for the RMSE between the simulations S2 and S3 (impact of multi-step aging).

At Ersa, increasing the ratio RRH from 1.5 to 4 (difference between simulation S3 and S2 shown in Fig.  of Appendix ) has almost the same impact as considering the multi-step aging (difference between simulations S5 and S2 shown in Fig.  of Appendix ), although the statistics are slightly better when the ratio RRH is increased from 1.5 to 4 than when multi-step aging is considered. However, this is not true over the whole Mediterranean domain, where the impact of increasing the ratio RRH from 1.5 to 4 is large over cities, whereas the impact of multi-step aging stays small (see Fig.  of Appendix ). Over the whole Mediterranean domain, the average RMSE between the simulations S2 and S5 is 0.014 µg m-3 (impact of increasing the ratio RRH from 1.5 to 4), compared to 0.005 µg m-3 for the RMSE between the simulations S2 and S3 (impact of multi-step aging).

Finally, the best statistics in terms of MFE and MFB are obtained for the simulation S5, with a one-step aging scheme, a volatility distribution typical of biomass burning for the residential sector with a ratio RRH of 4 and a volatility distribution typical of car emissions for other sectors with a ratio R of 1.5.

The oxidation state is quantified using two metrics, OM : OC and O : C, calculated as detailed in . Figure  shows the daily variations of the measured and simulated ratios for the different simulations.

The measurements at Ersa show highly oxidized and oxygenated organics; the measured OM : OC and O : C ratios at Ersa are 2.21±0.09 and 0.82±0.07, respectively. These values are lower than the index measured during summer 2013 by (2.43±0.07 and 0.99±0.06 for the measured OM : OC and O : C ratios at Ersa, respectively), due to the slower oxidation process owing to the lower temperatures during winter. The average simulated OM : OC and O : C ratios are shown in Table . Both indices are strongly underestimated by all simulations, due to the high contribution of POAs to the OM1 concentrations (POAs are less volatile and oxygenated than SOAs). The simulations using multi-step aging schemes for I/S-VOC emissions have higher OM : OC and O : C ratios, although the differences are very low; the OM : OC ratio is 1.69±0.53 in S2 (single-step) and 1.72±0.50 in S3 (multi-step). Organics in the simulations where the strength of I/S-VOC emissions from residential heating was increased (simulations S5 and S6) and have higher OM : OC and O : C ratios, because POAs and SOAs from I/S-VOCs from residential heating are more oxidized and oxygenated than POAs and SOAs from other anthropogenic sources. Similarly, organics in the simulations where NTVOCs are taken into account have higher OM : OC and O : C ratios, because in the model, NTVOCs lead to very oxidized and oxygenated OAs. However, the simulated ratios OM : OC and O : C stay underestimated (1.85±0.38 and 0.60±0.24 at most, compared to 2.21±0.09 and 0.82±0.07 in the measurements).

The underestimation of the O : C ratio may be due to an underestimation of oxidants' concentrations and secondary aerosol formation. Figure  shows that the model tends to underestimate ozone concentrations (the modeled and measured average concentrations between 21 January and 24 February 2014 are 46.2 and 68.0 µg m-3). However, the O : C ratio stays underestimated even during the days where ozone is well modeled. It is difficult to come to a conclusion on the underestimation of oxidants, because measurements were not performed for other oxidants than ozone, such as OH, which probably has other sources than ozone photolysis in winter.

This study shows a ground-based comparison of both modeled organic concentrations and properties to measurements performed at Ersa (the cape of Corsica, France) during winter 2014. This work aims to evaluate how commonly used parameterizations and assumptions of intermediate and semi-volatile organic compound (I/S-VOC) emissions and aging perform in modeling OA concentrations and properties in the western Mediterranean region in winter. To that end, the chemistry-transport model from the air-quality platform Polyphemus is used with different parameterizations of I/S-VOC emissions and aging (different volatility distribution emissions, single-step oxidation vs multi-step oxidation within a Volatility Basis Set framework, including non-traditional volatile organic compounds NTVOCs). Winter 2014 simulations are performed and compared to measurements obtained with an ACSM at the background station of Ersa in the north of the island of Corsica. In all simulations, OAs at Ersa are mainly from anthropogenic sources (only 15 % to 18 % of OAs are from biogenic sources). The emission sector 6 (residential heating) has a large contribution to OAs (between 31 % and 33 %). The contribution from aromatic VOCs is low (lower than 3 %). NTVOCs, as modeled with the parameterization of , represent between 18 % and 21 % of the organic mass. For most simulations, the concentrations of OAs compare well to the measurements.

Over the whole western Mediterranean domain, the volatility distribution at the emission influences the concentrations more strongly than the choice of the parameterization that may be used for aging (single-step oxidation vs multi-step oxidation). Modifying the volatility distribution of sectors other than residential heating leads to a decrease of 29 % in OA concentrations at Ersa, while using the multi-step oxidation parameterization rather than the single-step one leads to an increase of 13 %. The best statistics are obtained using two configurations; the first one is a one-step aging scheme, a volatility distribution typical of biomass burning for the residential sector with an I/S-VOC / POA ratio of 4 at emissions, and the second one is a multi-generational aging scheme, a volatility distribution typical of car emissions for other sectors with a R I/S-VOC / POA ratio of 1.5 at emissions.

Both the OM : OC and O : C ratios are underestimated at Ersa in all simulations. The largest simulated OM : OC ratio is equal to 1.85±0.83, compared to 2.21±0.09 in the measurements. For the summer campaign, improved the simulated OM : OC ratio by adding the formation mechanisms of organic compounds with extremely low volatility from the autoxidation of monoterpenes and organic nitrate from monoterpene oxidation. Similarly, the formation of organic nitrate and highly oxygenated organic molecules from the autoxidation of aromatic precursors should be added in order to better reproduce the observed OA oxidation and oxygenation levels.

However, adding these new OA formation pathways may lead to an increase in OA concentrations, suggesting that the actual parameterizations may need to be revisited, for example by better characterizing their deposition. Because the volatility distribution at the emission is the parameter influencing the concentrations the most, further experimental research should therefore focus on characterizing it for the different sectors. The emissions and formation of compounds with very low volatility should also be further investigated to represent the aerosol characteristics observed.