Grazia Maria Par

Lanzafame

Mme D'

Directrice Anna Barbara

Directrice Mme Hodzic Alma

Mme Sartelet

Maitre Mme Turquety Solène

M Baldasano

Professeur José Maria

Mme Camredon

Maitre De Conférences

Lisa M Couvidat

Ingénieur De Recherche

Ineris M Albinet Alexandre
email: alexandre.albinet@gmail.com

HDR Ineris M Bessagnet Bertrand

Chargé

Examinatrice Rapporteur Examinatrice

Examinateur Invité Invitée

Stohl N I Kristiansen

G M Lanzafame

D Srivastava

O Favez

N Bonnaire

L Alleman

F Couvidat

B Bessagnet

A Albinet

V Gros

Understanding organic aerosol formation processes in atmosphere using molecular markers: a combined measurements-model approach

Keywords: Aerosol, SOA, Tracers, Source apportionment, PMF, Particulate matter

Twenty-five biogenic and anthropogenic SOA markers have been measured in both gaseous and particulate (PM 10 ) phases, over one year in the Paris region (France). The measured SOA markers concentrations agreed with the values already observed worldwide. Nitroaromatic compounds (NACs) concentrations varied seasonally, with higher concentrations in the winter period. This pattern was consistent with the profile of biomass burning emissions, that increase during the colder season. Among biogenic markers, only isoprene marker concentrations increased during summer while pinene markers had no straightforward seasonal trend. An unexpected peak in most of the marker concentrations has been observed in October, in which the meteorological conditions favoured the accumulation of pollutants at high NO x conditions. SOA markers gas to particle partitioning (GPP) was more shifted towards the particulate phase than previously reported, with a scarce temperature dependency. SOA markers were used to apportion SOA sources with the SOA tracer method: the first part of the year was dominated by anthropogenic Secondary Organic Carbon (SOC), summer period was mainly composed by biogenic SOC and the last part of the year anthropogenic contribution was significant but not predominant. SOA tracer method anthropogenic SOC agreed completely with PMF estimations, while biogenic SOC estimation still require some improvements. However, the overall SOA tracer method performance can be considered sufficiently accurate for a first SOC estimation.

First of all I would like to thank all my supervisors, Bertrand, Olivier, Alex and Florian, for giving me the opportunity to do this PhD. Special thanks are due to Alexandre Florian. Alex, your scientific support was fundamental and the challenges you've put me up against have made me grow as a better scientists. Florian, you taught me everything I know about about modeling, with a lot of patience and a smile. You believed in me till the end, also when I was driving you crazy.

I wish to acknowledge Eric Villenave and Marie Camredon for their participation during my PhD Committees, providing always a fresh and interesting point of view on my job. I am also grateful to Barbara D'anna and Alma Hodzic for accepting reviewing this manuscript and to the examiners Karine Sartelet, José Maria Baldasano and Solène Turquety.

I would like to offer my special thanks to all the teams in which I worked during my PhD: -The ASUR/EMIS team: Marc and Caroline for their kindness and availability, Valerie and Marie for their smile and their patience, Laurent for his support in my mother tongue :), Tanguy for his philosophic discussions, François for the nice talks, Nicolas K. and Robin for their support during Landex (I would never have survived without your help!) and Adrien for his work during the EVORA campaign. I also would like to thank and all the other team members for the nice time spent together: Florence, Nathalie M., Nathalie B., Celine, Sebastien, Olivier L., Jessica Q., Virginie, Anne-Sophie, Serge, Isaline, Jean, Benedicte, Cecile R., Vincent F., Fabrice, Hugo, Sylvie, Marion, Warda and Aline.

-The ANAE team: Jérôme and his precious help with the GC (you are the master), Hervé for his collaboration during the EVORA campaign, Claudine C. and Faustina for their understanding and support, Serguei (you will always be the ASE god for me), Yohann, Farid, Azziz, François, Cecile L., Claudine D., Ahmad, Jean-Pierre, Sylvain, Nicolas C., Valerie, Arnaud and Hugues.

-The MOCA/EDEN team: Simone, Elsa, Cynthia, Pierre, Simon, Valentin, Alicia, Florence, Fred, Blandine, Antonio, Gaël, Anthony, Jessica and Augustin. Your good mood (and your beliefs on italian food) made easier my last year at INERIS (and you also taught me new french words ahah) My special thanks go to my family abroad, my office mates: Deep and Yunjiang, I learn a lot from both of you and please never tell anybody what we were discussing when the office door was closed :D. Lei, I know that you are still crying since I left. Camilla, one of the best person I ever met. I wish to thank also all the other students met during this journey for the nice time spent together: Eleonora and Jasmina,the only people I trust for cooking pasta in France, Alexandre "Perlana", Marta (best road trip EVER), Adrien (BBQ king), Anitha, Ibtihel, Thi Cuc, Quentin, Pierre, Manoj, Martin, Kevin, Hugo, Vincent, Victor, Anne-Sophie, Julien, Katia, Maria, Nihal and Andrea. I would also like to thank my friends spread all over the word who listened to my infinite complains during the PhD. Don't worry, I will soon complain about my job.

Finally, I wish to thank my parents and my sister that are proud of me also if they didn't fully understand what I was working on. Thanks for your constant support and encouragement. I owe your everything. Aerosols are defined as suspended solid and liquid particles in a gas. In atmospheric sciences, the term aerosol is commonly used to indicate particulate matter (PM), although the original definition of the term is referred to the gaseous and the particulate phase together.

Chapter I Introduction and objectives of the Thesis

Because of the variety of sources and processes associated to atmospheric aerosol production, their concentrations in the atmosphere vary widely regionally and seasonally. Primary aerosols are directly emitted as particles in the atmosphere by both, natural (mainly vegetation, oceans, soil resuspension, volcanoes) and anthropogenic (biomass burning, traffic exhaust, industrial activities, etc.) sources. Secondary aerosols are produced in the atmosphere as a result of physical and chemical transformations of gaseous phase compounds.

Atmospheric aerosols are classified according to their aerodynamical diameters that range from a few nanometres to several micrometres. The most widely used aerosol classes are named PM 1 , PM 2.5 and PM 10 , which correspond respectively to particles with diameter smaller than 1, 2.5 and 10 µm. Their size influences their half-life and transportation, health impacts and interactions with light.

A scheme of particle size distribution, together with the main production and removal processes associated, is presented in Figure 1.1. Fine aerosol fraction (PM 2.5 ) includes the Aitken nuclei and accumulation modes that are produced mostly from condensation of low volatility compounds and coagulation of smaller particles (ultra-fine particles). Their half-life in the atmosphere is about few days to two weeks (Kristiansen et al., 2016). These particles can either be removed from the atmosphere by dry and wet deposition or can continue to grow in size by coagulation with other particles or by condensation of low volatility compounds onto their surface [START_REF] Pöschl | Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects[END_REF]. Most of the coarse particles (larger than 2.5 µm) are directly emitted in the atmosphere [START_REF] Finlayson-Pitts | Chemistry of the upper and lower atmosphere: theory, experiments, and applications[END_REF] by anthropogenic and natural sources, which includes resuspension of dust, primary biological particles (spores, pollens…) and sea spray. In the atmosphere, coarse particles are also formed by coagulation of smaller particles and low-vapor pressure compounds on their surface. Coarse aerosol half-lives range between some minutes to several days (Seinfeld and Pandis, 1998). Typical tropospheric particles concentrations are in the range of 10 2 -10 5 cm -3 in number and 1-100 mg m -3 in mass (Krejci et al., 2005;[START_REF] Raes | Formation and cycling of aerosols in the global troposphere[END_REF][START_REF] Van Dingenen | A European aerosol phenomenology-1: physical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[END_REF]Williams et al., 2002). Aerosols are getting growing attentions from the scientific community due to their proven effects on health, climate and visibility. According to the World Health Organization (WHO), 4.2 million people died because of air pollution in 2016 [START_REF] Who | Burden of disease from Ambient Air Pollution for 2016[END_REF]. Air pollution is considered as the largest environmental health hazard in Europe (EEA, 2018).

Several epidemiological studies proved the connection between particulate air pollution and an increment of respiratory diseases and adverse effect on cardiovascular system (Heal et al., 2012;[START_REF] Seaton | Particulate air pollution and acute health effects[END_REF] that can cause premature deaths [START_REF] Apte | Addressing Global Mortality from Ambient PM2.5[END_REF][START_REF] Burnett | Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter[END_REF]. Reduced lung function, respiratory infections and asthma have been recognized as effects of both acute and chronic exposure to air pollution. Recently, evidences of air pollution effects on diabetes, obesity, systemic inflammation and neurological diseases incidence have been also provided (RCP, 2016, and references therein;[START_REF] Who | Burden of disease from Ambient Air Pollution for 2016[END_REF].

Particle hazard depends on their size: coarse particles with a diameter between 10 and 30 μm are usually deposited in the oropharyngeal region, particles with dimensions between 2 and 16 µm can reach the terminal bronchioles and finer particles (< 2 µm) penetrate further till the alveolar sacs (Figure 1.2). Finer particles can interfere in the exchanges between air and blood, introducing toxic substances in the circulatory system. The most widely known toxic substances that can be absorbed on the aerosols are metals (Cu, Pb, Cd …), polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs) and dioxins [START_REF] Finlayson-Pitts | Chemistry of the upper and lower atmosphere[END_REF][START_REF] Jacobson | Organic atmospheric aerosols: Review and state of the science[END_REF]. Adapted from: [START_REF] Nahar | In vitro, in vivo and ex vivo models for studying particle deposition and drug absorption of inhaled pharmaceuticals[END_REF] Besides health impact, aerosols can scatter and absorb infrared and visible light, contributing to climate change. Three radiative forcing (RF) have been estimated for aerosol interactions with solar light: the aerosol-radiation interaction, the aerosol-cloud interaction and the impact of black carbon (BC) on snow and ice surface albedo (IPCC, 2018).

Aerosols mostly scatter solar radiation, leading to a cooling effect, and absorb solar radiation only when composed by absorbing components as black carbon (BC), causing a warming effect. The global RF estimate for the direct aerosol-light interaction is negative (-0.35 W m -2 ), with a prevalence of the scattering effect. Besides, aerosol-cloud interactions are usually considered as indirect effects of aerosol on climate. Reflecting properties and lifetime of clouds change according to aerosol sizes and composition [START_REF] Haywood | Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review[END_REF], with consequences on the RF associated to cloud albedo. RF by aerosol effect contribution is highly uncertain, and rather a total effective radiative forcing (ERF), due to aerosol-radiation and aerosol-cloud interactions, has been calculated (-0.9 W m -2 ). Finally, the black carbon (BC) deposition reduces the snow and ice surface albedo, absorbing visible and ultraviolet light (RF +0.04). However, the contribution of this process is low compared to the previous ones.

Radiative forcing on climate between 1750 and 2011 is shown in Figure 1.

. Air quality and air pollution policies

Oxford dictionary defines air quality as -the degree to which the air in a particular place is pollution-free‖. Industrialization and urbanization led to increasing emissions that modified the composition of the atmosphere. The consciousness about air quality monitoring importance has begun after the well-known acute pollution episodes of -Los Angeles smog‖ (1949) and -London fog‖ (1952). Pollution episodes usually occur when large amounts of pollutants are emitted in conditions of weak atmospheric dispersion. During severe pollution episodes respiratory disease incidence increase and excess mortality has been registered.

Thus, in order to protect populations, concentrations of PM in ambient air are regulated, for example, in France and Europe by the European Directive 2008/50/EC (European official journal, 2008), which set concentration limit values for PM on annual (40 and 25 µg m -3 for PM 10 and PM 2.5 respectively) and daily (50 µg m -3 for PM 10 ) timescale and with a maximum number of exceedances over the calendar year (35 days per year). However, many studies shown that there is probably no PM concentration threshold below which no health impacts would be observed (WHO, 2017).

The knowledge of PM, and especially of the organic fraction, sources is essential for policy makers to apply efficient emission regulation policies. This can be achieved from measurements as nowadays, several monitoring networks are operational and perform continuous measurements of regulated pollutants, as PM 2.5 and PM 10 , together with a detailed aerosol chemical speciation. As an example, in France, air quality monitoring network is coordinated by the Central laboratory of air quality monitoring (LCSQA) in collaboration with the Official Air quality Monitoring Associations (AASQA) and several PM source apportionment studies have been performed in different locations [START_REF] Weber | Comparison of PM10 Sources Profiles at 15 French Sites Using a Harmonized Constrained Positive Matrix Factorization Approach[END_REF]. Air quality models are also widely used to forecast the air quality and help institutions to mitigate their effects. Daily air quality in France is forecasted with the air quality system Prev'Air (www.prevair.org). In addition, chemistry-transport models can also be used to apportion PM sources and have been applied in Europe during several studies (Brandt et al., 2013;Karamchandani et al., 2017;Skyllakou et al., 2014;Yang et al., 2019). Further details on source apportionment methodologies are given in section 1.2.5.

Aerosol chemical composition

Aerosols are constituted from both an inorganic and an organic fraction. The mineral fraction is dominated by sulfate, nitrate and ammonium and account for 40 to 60% of PM 1 dry mass, according to the kind of site considered (urban, urban background or rural) [START_REF] Putaud | A European aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[END_REF]Zhang et al., 2011). OA fraction can account up to 90% (Kanakidou et al., 2005;Zhang et al., 2007Zhang et al., , 2011) ) of particulate matter (PM) in ambient air (Figure 1.4). OA concentrations and compositions show large seasonal and regional variabilities and the knowledge of their sources and processes remain still poorly understood. OA is usually estimated by multiplying concentrations of OC with factors ranging from 1.5 to 2, depending on the assumed average molecular composition [START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: Primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF][START_REF] Russell | Aerosol Organic-Mass-to-Organic-Carbon Ratio Measurements[END_REF]. The use of a fixed factor to convert OC to OA may be insufficient to achieve high accuracy results [START_REF] Brown | Assessment of the effect of degradation by atmospheric gaseous oxidants on measured annual average benzo[a]pyrene mass concentrations[END_REF].

Only the identification and quantification of all OA components could make OA mass estimation possible. OA composition is extremely varied and variable and the most comprehensive OA characterization studies succeed in identifying only 10-40% of total OA [START_REF] Pöschl | Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects[END_REF]. Yellow: ammonium, Blu: nitrate; Red: sulfate; Green: organic matter; Light green: oxygenated organic aerosol (OOA) equivalent to SOA (secondary organic aerosol) estimation; Grey: primary organic aerosol (POA). Adapted from [Zhang et al., 2011].

Primary and secondary organic aerosol

Primary organic aerosols (POA) are directly emitted into the atmosphere by the combustion of biomass and fossil fuels, sea spray and resuspension of biological (plant debris, pollen, fungal spore, etc.) and anthropogenic dusts. Secondary organic aerosols (SOA) are produced in the atmosphere via gas-to-particle conversion processes of semivolatile organic compounds (SVOCs) (Hallquist et al., 2009) and account for a significant part (20-80%) of total OA (Jimenez et al., 2009;Kroll and Seinfeld, 2008;Srivastava et al., 2018;Zhang et al., 2007Zhang et al., , 2011)). Unlike primary particles, directly emitted into the atmosphere from characterized sources, secondary aerosols, including SOA, are difficult to regulate and technological constraints restrict their monitoring. A schematic representation of the processes involved in SOA formation is reported in Figure 1.5. Gaseous phase precursors are oxidized by homogeneous and heterogeneous reactions, giving low volatility compounds. These products can nucleate into new particles or condensate onto pre-existing particles. The new-born particles can keep growing by condensation processes and coagulation with other particles. and chemical composition of atmospheric aerosol particles. Source: [START_REF] Brasseur | Atmospheric Chemistry in a Changing World: An Integration and Synthesis of a Decade of Tropospheric Chemistry Research[END_REF] 1.2.2. SOA formation: precursors and their reactivity in atmosphere

VOCs sources are from both, natural (plant emission, forest fires…) and anthropogenic (traffic, industrial activities and domestic heating emissions) origins. The processes leading to SOA formation from VOCs are complex and still poorly understood. For this reason, numerical models generally underestimate the SOA fraction [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols[END_REF]. Once emitted into the atmosphere VOCs undergo (photo-) oxidation through photolysis and reaction with OH, NO 3 or O 3 . VOCs oxidation pathways occur through several steps, leading to the formation of more functionalized, less volatile and more hydrophilic compounds. These chemical species partition onto the particle phase to form SOA until reaching thermodynamic equilibrium. In the particle phase, SOA components can undergo further chemical (e.g. oligomerization) and physical (e.g. desorption, solubilization) processes.

Plant-emitted SOA biogenic precursors include isoprene, monoterpene and sesquiterpenes.

The double C=C bonds contained by all these compounds can react with the main atmospheric oxidants (OH, NO 3 and O 3 ) [START_REF] Calogirou | Gas-phase terpene oxidation products: a review[END_REF]. Monoterpenes have been identified as the major biogenic SOA precursor (Guenther et al., 1995), with a global estimated contribution between 10 and 30% to total SOA (Pye et al., 2010;Spracklen et al., 2011;Tsigaridis and Kanakidou, 2003). Isoprene SOA yields has been estimated to be low (~1%) (Carlton et al., 2009;[START_REF] Kroll | Secondary Organic Aerosol Formation from Isoprene Photooxidation[END_REF]. However, isoprene is the largest non-methane

VOCs compound emitted (600 Tg yr -1 ) on global scale (Guenther et al., 2006) and isoprene SOA can account for a maximum 30% to total OA [START_REF] Heald | Predicted change in global secondary organic aerosol concentrations in response to future climate, emissions, and land use change[END_REF]. Sesquiterpenes have lower emissions than isoprene and monoterpenes [START_REF] Acosta Navarro | Global emissions of terpenoid VOCs from terrestrial vegetation in the last millennium[END_REF], but because of their high SOA yields they may contribute significantly to the biogenic SOA budget [START_REF] Griffin | Organic aerosol formation from the oxidation of biogenic hydrocarbons[END_REF].

Anthropogenic SOA precursors are mainly aromatic compounds (toluene, naphthalene, benzene, phenols, xylene, alkylbenzenes and PAHs) and long chain (number of carbons > 7)

aliphatic alkanes (Bruns et al., 2016;Zhao et al., 2014). Their oxidation is initiated by reaction with OH, with SOA yields ranged from a few percent (Li et al., 2016;[START_REF] Ng | Secondary organic aerosol formation from m-xylene, toluene, and benzene[END_REF] for aromatic compounds to 90% (Aumont et al., 2005;Lim andZiemann, 2005, 2009) for long chain alkanes. Anthropogenic VOCs global emissions have been reported to be around 10% of total non-methane VOCs emissions [START_REF] Goldstein | Known and Unexplored Organic Constituents in the Earth's Atmosphere[END_REF][START_REF] Heald | Satellite observations cap the atmospheric organic aerosol budget[END_REF], but can become important locally in urban environments.

A schematic representation of a generic VOC oxidation pathway is reported on Figure 1.6.

VOC oxidation is initiated by the reaction with an oxidant (OH, NO 3 or O 3 ) or by photolysis.

The produced alkyl radical (R) reacts then with a molecule of oxygen, becoming an alkylperoxyradical (RO 2 ). RO 2 fate depends on the NO x regime. At high-NO x conditions RO 2 can be oxidized by NO or NO 2 , to give in the first case, the correspondent alkoxy radical (RO) or to the correspondent nitrate (RONO 2 ) and in the second case, the peroxynitrate (ROONO2).

At low NO x concentrations, the reactions with HO 2 and RO 2 become possible, forming stable products (hydroperoxydes, carboxylic acids, peroxyacids, alcohols and carbonyls) and RO radicals only for the RO 2 +RO 2 reaction. RO undergoes isomerization or decomposition to form a stable compound. The stable compounds produced (first generation oxidation products) can be further oxidized in gas phase (producing different generation on oxidation products), following the same scheme, or can partition onto the particulate phase (organic or aqueous phases). These compounds, less volatile than VOCs, are named semi-volatile organic compounds (SVOCs). SVOCs gas/particle partitioning (GPP) is discussed in the next section. droplets and aerosol water, however the dominant reactions differ due to differences in the chemical environment (pH, inorganic, and organic concentrations). Source: [START_REF] Mcneill | Aqueous Organic Chemistry in the Atmosphere: Sources and Chemical Processing of Organic Aerosols[END_REF] The main reactions involving water-soluble organic compounds (WSOC) in the aqueous phase are (Figure 1.7):

-dark reactions, as hydration, hydrolysis, self-oligomerization and ionization, -the radical oxidation by OH, HO 2, SO 4 and HSO 4 , that generates organic acids or organosulfates. At high WSOC concentrations the organic radicals created may react together to form oligomers.

-ionic reactions operated mostly by NH 4 + , SO 4 2-and HSO 4 -. Organosulfates and lightabsorbing species are produced.

-photochemical reactions: photolysis of photolabile species and activation of photosensitizers (e.g. humic and fulvic acids).

Due to the different composition of aqueous aerosol phase and cloud water, the expected predominant reactive pathways for aqueous phase SOA formation (aqSOA) are different in both media [START_REF] Mcneill | Aqueous-phase secondary organic aerosol and organosulfate formation in atmospheric aerosols: a modeling study[END_REF][START_REF] Tan | SOA from methylglyoxal in clouds and wet aerosols: Measurement and prediction of key products[END_REF]. In the aerosol aqueous phase (0<pH<3 [START_REF] Freedman | Role of pH in Aerosol Processes and Measurement Challenges[END_REF], rich in organics and supersaturated in salts) acid-catalysed reactions and inorganic-organic reactions and oligomerization are favoured. In cloud water (pH>3, less concentrated organics and salts), OH oxidation mechanism predominates.

Gas to particle partitioning of secondary compounds

SVOCs partition between gas-phase and the aqueous and organic phases of aerosols, generating SOA. The volatility of chemical species is directly proportional to their saturation vapor pressure, defined as the pressure at which the vapor of the compound is at the equilibrium with its solid (solid saturated vapor pressure) or liquid (liquid or subcooled saturated vapor pressure) phase. Saturated vapor pressure depends on temperature following a Clausius-Clapeyron relation: at higher temperatures saturated vapor pressure decreases, determining a particulate phase fraction decrement. According to Raoult's law the concentrations in the liquid phase and in the gas phase at equilibrium are linked by the following relation:

𝛾 𝐺,𝑖 (𝑃 𝑖 )𝑃 𝑖 = 𝛾 𝐿,𝑖 (𝑥 𝑖 )𝑥 𝑖 𝑃 𝑖 0 (Eq 1.1)

where P i is the partial pressure of the compound i in the gaseous phase, x i the molar fraction of the compound i in the liquid phase, P i 0 is the compound saturated vapor pressure of i and γ G,i and γ L,i are the activity coefficients respectively in the gaseous and the liquid phase.

Activity coefficients indicate the interactions between the different species in solution. When ideality is assumed, all the interactions between the mixture components are assumed identical and therefore all the activity coefficients values are taken equal to 1. Raoult's law then become:

𝑃 𝑖 = 𝑥 𝑖 𝑃 𝑖 0 (Eq 1.2)
This version of Raoult's law is commonly used to describe the gas to particle partitioning in air quality models. However, if atmospheric gases behavior can be assumed as ideal (as gases behavior is ideal at pressures far below the atmospheric pressure), this is not the case for the aerosol phase. The Raoult's law form to calculate gas/particle this equilibrium can be written as follows:

𝑃 𝑖 = 𝛾 𝐿,𝑖 𝑥 𝑖 𝑃 𝑖 0 (Eq 1.3) in which only the liquid phase activity coefficients are taken in account.

A modified version of the Raoult's law to calculate the partitioning between the gaseous and the organic phases has been proposed by (Pankow, 1994):

𝐾 𝑝,𝑖 𝑀 0 = 𝐴 𝑝 ,𝑖 𝐴 𝑔,𝑖 (Eq 1.4)

Where 𝐾 𝑝,𝑖 is the gas-particle partitioning constant (m 3 µg -1 ), 𝐴 𝑝,𝑖 is the organic phase concentration in µg m -3 and 𝐴 𝑔,𝑖 and 𝐴 𝑝,𝑖 are respectively the concentrations of the component i in the gas and in the particulate phase. This model assumes that the particulate phase of organic aerosol is constituted by a single and homogeneous phase, which is not the case for the real aerosols. 𝐾 𝑝,𝑖 is linked to the saturated vapor pressure by the following relation:

𝐾 𝑝,𝑖 = 760×𝑅×𝑇 𝑀 𝑜𝑤 𝛾 𝑖 10 6 𝑃 𝑖 0 (Eq 1.5)

where the temperature (T) is expressed in K, R is the perfect gas constant (8.206×10 -5 m 3 atm mol -1 K -1 ), 𝑀 𝑜𝑤 is the molar mass of the organic phase in g mol -1 and the saturated vapor pressure (𝑃 𝑖 0 ) is in torr.

For diluted solutions, the partitioning between the gaseous phase and the aqueous phase can be computed using Henry's law:

𝐶 𝑖 = 𝐻 𝑖 × 𝑃 𝑖 (Eq. 1.6) where 𝐶 𝑖 , 𝐻 𝑖 and 𝑃 𝑖 are respectively the aqueous phase concentration, the Henry's law constant (mol L -1 atm -1 ) and the molar partial pressure of i. In conditions of infinite dilution (𝐶 𝑖 → 0), Henry's law and Raoult's law can be combined:

𝐻 𝑖 = lim 𝐶 𝑖 →0 𝐶 𝑖 𝑃 𝑖 = 𝜌 𝑤𝑎𝑡𝑒𝑟
𝑀 𝑤𝑎𝑡𝑒𝑟 ×𝛾 𝑖 ∞ ×𝑃 𝑖 0 (Eq. 1.7) in which 𝜌 𝑤𝑎𝑡𝑒𝑟 is the water density (g L -1 ), 𝑀 𝑤𝑎𝑡𝑒𝑟 is the water molar mass (g mol -1 ) and 𝛾 𝑖 ∞ is the infinite dilution activity coefficient of the substance i in pure water:

𝛾 𝑖 ∞ = lim 𝑥 𝑖 →0 𝛾 𝑖 𝑥 𝑖 (Eq. 1.8)

𝛾 𝑖 ∞ represents the activity coefficient of i when it is surrounded only by water. Henry's law constant takes in account both the compound volatility (𝑃 𝑖 0 ) and its affinity with water (𝛾 𝑖 ∞ ).

In real conditions, aerosol aqueous phase is not pure, and the compounds are not infinitely diluted. A relative activity coefficient can be calculated by the ratio of infinite dilution and liquid phase activity coefficient:

𝜁 𝑖 = 𝛾 𝑖 𝛾 𝑖 ∞ (Eq. 1.9)
To compute gaseous to aqueous phase partitioning in the aerosol aqueous phase (no infinite diluted phase) Raoult's law can be rewritten:

𝐶 𝑖 =
𝐻 𝑖 ×𝑃 𝑖 𝜁 𝑖 (Eq. 1.10) Following the model proposed by Pankow, (1994) for the partitioning with the organic phase, an analogous gas-water partitioning coefficient, 𝐾 𝑎𝑞 ,𝑖 (m 3 µg -1 ), can be defined:

𝐾 𝑎𝑞 ,𝑖 𝐿𝑊𝐶 = 𝐴 𝑎𝑞 ,𝑖 𝐴 𝑔,𝑖 (Eq. 1.11) in which LWC is the liquid water content (µg m -3 ) and 𝐴 𝑎𝑞 ,𝑖 and 𝐴 𝑔,𝑖 are respectively the concentrations in aqueous and gaseous phases. Equation 1.11 can be rewritten in function of Henry's law constant:

𝐾 𝑎𝑞 ,𝑖 = 𝐻 𝑖 ×𝑅×𝑇 𝜌 𝑤𝑎𝑡𝑒𝑟 𝜁 𝑖 ×1.013×10 11 (Eq 1.12)

Benefits of an aerosol molecular characterization

The characterization of the SOA composition and more generally OA has been a subject of great scientific interest in the recent years. A better knowledge of OA chemical composition is necessary to apportion OA sources and plan strategies for emission reduction. An improvement in air quality requires an understanding of the composition of ambient aerosols and their major sources. Other reasons for this interest are related to the potential impacts of organic molecules on health (e.g. PAHs) or on climate (e.g. light-absorbing molecules and CCN activity) and on the particle formation processes (e.g. volatility and partitioning). OA major components, together with their sources, are reported in Table 1.1. The proportions reported are only indicative, since aerosol chemical composition varies considerably according to the season and the region considered.

Organic aerosol chemical characterization is usually performed on PM samples collected on filters. PM samples are extracted with solvents, selected according to the targeted compounds, or by thermal or laser desorption systems coupled with an analytical instrument. Analyses are performed using chromatographic separation and detection instruments, such as gas or liquid chromatography-mass spectrometry (GC or LC-MS). Online techniques (e.g. aerosol mass spectroscopy, AMS) are also used to deduce the OA degree of oxygenation, with no information on the identity of the individual compounds. As can be inferred from Table1.1, some substance classes are typically from specific sources (e.g. sugars for biomass burning)

and others may indicate the aerosol age (e.g. highly functionalized molecules as dicarboxylic acids). Specific molecules from these classes have been identified and used as molecular markers for source apportionment. Organic markers can be later used for source apportionment using statistical source-receptor models as described in the following section.

Source apportionment

Source apportionment can be performed using source-receptor models (PMF, CMB…), based on measurements (Srivastava et al., 2018), or by chemistry transport models (CTMs) [START_REF] Burr | Source apportionment of fine particulate matter over the Eastern U.S. Part II: source apportionment simulations using CAMx/PSAT and comparisons with CMAQ source sensitivity simulations[END_REF][START_REF] Wagstrom | Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model[END_REF][START_REF] Zhang | Evaluation of three probing techniques in a three-dimensional air quality model[END_REF], for which general features are presented in section 1.3.1.

Source-receptor models are based on the solution of the mass balance equation. To apply these methods, the assumptions done are: (1) stable and reproducible source profile, (2) nonreactive and non-volatile receptor species, (3) receptor data representative of the geographical area studied and ( 4) quantification of receptor and sources has been performed with equivalent or comparable methods throughout the period considered. The chemical mass balance (CMB) only considers primary sources because the determination of profiles for secondary sources is difficult to be obtained. Thus, the OA not apportioned refers to SOA.

SOA is then defined as the difference between the measured OA concentrations and the aggregated OA concentrations from all primary sources resolved by CMB. In positive matrix factorization (PMF), SOA is commonly calculated as the sum of OA loadings associated to sulfate-and nitrate-rich factors. By comparison, molecular organic markers (tracers) are source-class specific and may provide a more definitive link between factors and source classes. Molecular markers for SOA and POA can be directly included in the PMF model providing an insight into the primary-secondary split of OA sources. The effectiveness of the method depends on the molecular markers used. Besides, the SOA-tracer method developed by Kleindienst et al., (2007) allows the estimation of the SOA contributions from several biogenic and anthropogenic hydrocarbon SOA precursors to ambient OC concentrations using a series of organic molecular compounds called tracer (or marker) compounds. The SOA tracer method is based on the estimation of the SOA mass contribution using marker measured concentrations and SOA-to-marker mass fractions determined by chamber experiment. A comparison on the different existing methods to apportion SOA from filter measurements, and notably both used in this work namely PMF and SOA tracer-method, can be found in Srivastava et al., (2018). Further details on PMF and SOA tracer method are provided in Chapter II.

Two approaches are commonly used for source-oriented modelling techniques with CTMs:

the brute force method (BFM) and the tagged species method [START_REF] Belis | European guide on air pollution source apportionment with receptor models[END_REF]. BFM consist in a sensitivity analysis on emission sources contributions on total PM. Emissions from a specific set of sources can be modified, either by sectors or by geographical regions.

The comparison with a baseline run, performed with unperturbed emissions, is required to quantify the contribution of that specific source. The relation between precursor emission and PM concentrations include non-linear effects, because of the contribution of SOA formation in atmosphere. The tagged species method quantifies the source contribution using extraspecies, known as -reactive tracers‖, that undergo chemical transformation in atmosphere generating secondary products. This method is based on a conservative mass approach, in which the sum of all the secondary product concentrations is equal to the total concentration of the sources [START_REF] Yarwood | Particulate matter source apportionment technology (PSAT) in the CAMx photochemical grid model[END_REF].

Definition and use of OA markers in literature

A good aerosol source tracer should fulfil the following requirements: [START_REF]Molecular marker measurements and database[END_REF] to be unique to the source of origin, ( 2) to be produced in reasonably high yields so at sufficiently high concentrations in the atmosphere to allow for reliable quantification, (3) to be reasonably stable in the atmosphere, so that it is conserved between emission/formation and collection at a receptor location ( 4) to have a low vapour pressure so that it primarily partitioned to the particle phase, which minimizes possible underestimation from loss to the gas phase (Al-Naiema and Stone, 2017;[START_REF] Sheppard | Atmospheric tracers and the study of the general circulation of the atmosphere[END_REF]. All these conditions are rarely satisfied, so the term -marker‖ is more appropriate to define these species. In fact, recent studies demonstrated that most of the tracer lifetimes are in the range of couple of days (e.g. levoglucosan 0.7-2.2 days [START_REF] Hennigan | Levoglucosan stability in biomass burning particles exposed to hydroxyl radicals[END_REF], pinonic acid ~2. 1-3.3 days (Lai et al., 2015) and MBTCA ~1.2 days (Kostenidou et al., 2018)), which is shorter than the average particle half-life of 1 week (Seinfeld, 2015). Therefore, their use in source-receptor models for source apportionment may cause an underestimation of the source contributions (Robinson et al., 2007).

Commonly used POA markers include: levoglucosan for biomass burning (Simoneit et al., 1999), hopanes for vehicular exhaust (Lough et al., 2007) and odd carbon C29-C33 n-alkanes from vegetal detritus [START_REF] Rogge | Sources of fine organic aerosol. 4. Particulate abrasion products from leaf surfaces of urban plants[END_REF].

SOA markers from both biogenic and anthropogenic sources have been identified and reported in the literature. Monoterpene (α-and β-pinene) SOA markers include multifunctional carboxylic acid, among which the most well-known and studied are pinonic acid, pinic acid, 3-methyl-1,2,3-butanetricarboxylic acid, 3-hydroxyglutaric acid and terpenylic acid (Claeys et al., 2007[START_REF] Claeys | Terpenylic Acid and Related Compounds from the Oxidation of α-Pinene: Implications for New Particle Formation and Growth above Forests[END_REF][START_REF] Hoffmann | Molecular composition of organic aerosols formed in the α-pinene/O3 reaction: Implications for new particle formation processes[END_REF]Szmigielski et al., 2007;Yu et al., 1999). MBTCA and 3-hydroglutaric have been identified as marker for aged SOA (or second generation oxidation markers) (Claeys et al., 2007;Müller et al., 2012;Szmigielski et al., 2007). Isoprene SOA markers are methyltetrols, α-methylglyceric acid, C 5 alkene triols and their corresponding sulfate esters and nitrate esters (Claeys et al., 2004;Surratt et al., 2006). Methyltetrols are specific to low NO x conditions, while α-methylglyceric acid is produced at high NO x concentrations (Surratt et al., 2010).The most widely used marker for β-caryophyllene is β-caryophyllinic acid (Jaoui et al., 2007) (Figure 1.8). Several anthropogenic SOA markers from benzene, toluene, naphthalene and phenolic compounds oxidation have been identified (Figure 1.9). These markers are however less source specific than the biogenic ones, because their precursors can be emitted from several sources and most of them are also directly emitted. The most widely known tracer for toluene photooxidation is the 2,3-Dihydroxy-4-oxopentanoic acid (DHOPA) (Kleindienst et al., 2004), for which the formation mechanism in atmosphere has not been highlighted yet.

Phthalic acid is a tracer for naphthalene and methylnaphthalene photooxidation (Kleindienst et al., 2012) that can be also primarly emitted (Kawamura and Kaplan, 1987). Nitrated phenols and their methyl and methoxy derivatives are commonly recognized as secondary biomass burning markers (Forstner et al., 1997;Iinuma et al., 2010;[START_REF] Lin | Molecular characterization of brown carbon (BrC) chromophores in secondary organic aerosol generated from photo-oxidation of toluene[END_REF], although nitrophenols and methyl-nitrophenols have been measured also in primary emissions (Lu et al., 2019;Mkoma and Kawamura, 2013).

SOA modelling approaches

Simplified parametrizations to model secondary organic aerosol formation in the 3D CMTs have been developed, with the purpose to minimize computational costs. This kind of approach is useful for operational applications but is not sensitive to all the variations of atmospheric conditions. A brief description of the principal models used to represent SOA formation in CTMs is provided in the following paragraphs. [START_REF] Odum | Gas/particle partitioning and secondary organic aerosol yields[END_REF] developed a method to parametrize secondary aerosol formation from a specific precursor using chamber experiment data. For each precursor, the fractional aerosol yield (Y) is calculated as follow:

Two-products model

𝑌 = Δ𝑀 0 Δ𝑅𝑂𝐺 (Eq. 1.13)
where Δ𝑀 0 is the concentration of the organic aerosol mass (µg m -3 ) produced and Δ𝑅𝑂𝐺 is the amount of reactive organic gas reacted (µg m -3 ).

The overall SOA yield is given by the sum of the single product yields, calculating their partitioning as proposed by Pankow, (1994):

𝑌 = 𝑀 0 𝛼 𝑖 𝐾 𝑜𝑚 ,𝑖
1+𝐾 𝑜𝑚 ,𝑖 𝑀 0 𝑖 (Eq. 1.14) where 𝛼 𝑖 is the stoichiometric coefficient and K om,i is the partitioning coefficient of each product i. Chamber experiments data are fitted to find the best combinations of 𝛼 𝑖 and K om,i that represent the SOA mass formed. The so-developed Secondary Organic Aerosol Module (SORGAM) has been first coupled to the EuroRADM CTM, considering aromatics, higher alkane and alkenes, pinene and limonene as SOA precursors [START_REF] Schell | Modeling the formation of secondary organic aerosol within a comprehensive air quality model system[END_REF].

VBS (Volatility Basis Set) approach

This approach consists in dividing semi-volatile organic compounds in classes (-bins‖), according to their volatility. Compound volatility is evaluated through their saturation concentration (𝐶 𝑖 * ), that is linked to the saturated vapor pressure (𝑃 𝑖 0 ) with the following relation:

𝐶 𝑖 * = 𝛾 𝑖 𝑀 𝑤 𝑅𝑇 × 𝑃 𝑖 0 (Eq 1.15)
In the classic VBS approach, each volatility bin is treated as a unique compound regarding partitioning properties and reactivity. The compounds belonging to the same bin undergo a unique aging process, generating only bins of less volatile compounds.

This approach was developed initially to describe the partitioning of semi-volatile compounds in atmosphere (Grieshop et al., 2009;Robinson et al., 2007;[START_REF] Shrivastava | Effects of gas particle partitioning and aging of primary emissions on urban and regional organic aerosol concentrations[END_REF].

Afterwards, VBS approach has also been applied to develop parametrization of VOCs aging to form SOA [START_REF] Hodzic | Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation[END_REF][START_REF] Tsimpidi | Evaluation of the volatility basis-set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area[END_REF]. The oxidation is usually simulated using the same kinetic constant for all the bins, 4×10 -11 cm 3 molecule -1 s -1 , validated by comparison with measurements [START_REF] Shrivastava | Effects of gas particle partitioning and aging of primary emissions on urban and regional organic aerosol concentrations[END_REF]. An evolution of the traditional VBS approach is the VBS-2D, in which the bins are characterized also by their oxidation degree (O:C ratio). This implementation of the traditional VBS enables to diversify the SOA composition and reactivity (Donahue et al., 2011(Donahue et al., , 2012)).

Molecular surrogate approach

The main feature of this approach is the choice of molecular surrogates to represent the total SOA mass [START_REF] Pun | Secondary organic aerosol 2. Thermodynamic model for gas/particle partitioning of molecular constituents[END_REF][START_REF] Pun | Modeling Secondary Organic Aerosol Formation via Multiphase Partitioning with Molecular Data[END_REF]Pun and Seigneur, 2007). Few molecules, identified among the major photooxidation products of a specific precursor, are chosen as molecular surrogates based on their volatility. The advantages of this approach rely on the possibility to use molecular surrogate structures to calculate their activity coefficient, with the UNIversal Functional group Activity Coefficient; (UNIFAC, Fredenslund et al., 1975) and to simulate aerosol non-ideality.

The hydrophilic/hydrophobic organic (H 2 O) (Couvidat et al., 2012) mechanism has been developed with this approach, considering isoprene, monoterpenes, sesquiterpenes, toluene and xylene as precursors and it has been implemented in the 3D models Polyphemus and CHIMERE. This mechanism uses both hydrophilic (acidic or undissociated species) and hydrophobic surrogates to represent biogenic SOA and only hydrophobic surrogates to represent anthropogenic SOA (see Table 1.2). The chemical scheme used to reproduce the surrogate formation is based on experimental studies and is sensitive to high and low NO x conditions. For some biogenic compound also the oligomerization processes are taken into account.

POA are represented in this model as SVOCs. POA are splitted in three classes of volatility, POAlP, POAmP and POAhP, from the less to the most volatile. These classes have been determined by the fitting of the diluition curve of POA from diesel exhaust in Robinson et al., (2007) and represent respectively 25%, 32% and 43% of the "non-diluted" POA emissions.

Each surrogate has a partitioning constant and default structures have been assigned to these surrogates to calculate their activity coefficients. Following Grieshop et al., (2009), POAlP, POAmP and POAhP aging is simulated by reaction with OH and lead to a decrease in volatily by a factor 100. This parametrization assumes that the POA composition is constant, while it is well known that it changes according to the emission source. Although this is the main limitation of this mechanism, no exhaustive data on POA composition are available to develop an accurate parametrization for POA.

Model performances

Air quality models often underestimates SOA formation in the atmosphere. Several studies have been performed to estimate 3D CTMs performances. Tsigaridis and Kanakidou, (2003) focused on the simulation of the global SOA distribution, identifying some critical physicochemical processes in SOA modelling: the potential irreversibility of the partitioning onto the particle phase, the simulation of the aerosol mass on which SVOCs condense and the temperature dependence of the partitioning coefficients. Multiple intercomparison exercises have been performed on regional scale (Bessagnet et al., 2016;[START_REF] Mckeen | Evaluation of several PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study[END_REF]Mircea et al., 2019;[START_REF] Pernigotti | POMI: a model inter-comparison exercise over the Po Valley[END_REF]Prank et al., 2016;[START_REF] Solazzo | Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII[END_REF][START_REF] Vautard | Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project[END_REF]. [START_REF] Solazzo | Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII[END_REF] in the Air Quality Model Evaluation International Initiative (AQMEII)

compared 10 air quality model outputs and none of them succeeded to represent PM 10 mass throughout the year. They attributed the underestimation of PM 10 to a misrepresentation of organic aerosol concentrations. Prank et al., (2016) identified the lack of precursor emissions from wildland fires and explicit representation of aerosol water content as the main reasons for PM 10 underestimation in air quality models. Concerning the EURODELTA III intercomparison exercise (Bessagnet et al., 2016;[START_REF] Mircea | EURODELTA III exercise: An evaluation of air quality models' capacity to reproduce the carbonaceous aerosol[END_REF], similar performances related to the SOA modelling approach have been found (VBS vs SORGAM), with the difference that the VBS captured better the seasonal variations. SOA underestimation was attributed to a missing precursor emissions and SOA formation processes. and secondary organic aerosol (SOA) concentrations (μg m -3 ) using 6 models (CAMX, CHIMERE, CMAQ, EMEP, MINNI and RCGC). Source: [START_REF] Mircea | EURODELTA III exercise: An evaluation of air quality models' capacity to reproduce the carbonaceous aerosol[END_REF] The validation of the model outputs is normally achieved by comparison with measurements of oxidant concentration (e.g. O 3 ), meteorological parameters (T, wind speed etc.) and PM and carbonaceous species (EC/OC). However, compare directly the PM concentrations measured with the model output could be problematic because of the complex composition of the organic fraction.

PhD thesis objectives

A better knowledge of OA sources is required to apply efficient policies to mitigate pollution effects. As previously discussed, organic marker measurements are commonly used in sourcereceptor models to apportion OA sources. Source apportionment is also performed using CTMs, in which OA is often underestimated. Such underestimation increases with the photochemical aging of air masses, highlighting the incomplete identification of all SOA precursors and the poor knowledge of the processes involved in particle aging. The aim of this work was to implement a marker modelling approach in a 3D air quality model (CHIMERE).

The ability of the model to reproduce marker concentrations observed in the ambient air is indicative of the model performances in representing the overall OA and SOA concentrations.

Molecular markers are emitted and produced by the same sources and physicochemical processes of the organic aerosol bulk. A fair representation of the marker in a 3D CTM, could further allow to apportion OA sources using a hybrid CTM/Source-receptor model approach [START_REF] Habermacher | Area of Influence (AOI) Development: Fast Generation of Receptor-Oriented Sensitivity Fields for Use in Regional Air Quality Modeling[END_REF]. The marker-implemented version of CHIMERE has been developed adding to the aerosol module detailed mechanisms for marker formation and improving emission inventories. Simulation outputs have been compared with measurements. -In Chapter IV (Article 3), 5 biogenic SOA markers (pinonic acid, pinic acid, 3-methyl-1,2,3-butanetricarboxylic acid, 2-methyltetrols and α-methylglyceric acid) formation mechanisms from α-/β-pinene and isoprene have been inserted in CHIMERE and compared with measurements performed at SIRTA during 2015. Daily variations of the production rates of pinonic acid from different reaction pathways have been also investigated. A sensitivity analysis of marker concentrations to NO x regime has been carried out. Biogenic marker gas to particle partitioning have been simulated using several thermodynamic assumptions and compared with the observed gas/particle partitioning measurements. As for Chapter III, my contribution to this chapter includes the development of the marker mechanisms and the 3D simulations execution.

The Chapter V presents the main conclusions and perspectives of this work.

Chapter II

SOA markers measurements

Article I

One-year measurements of secondary organic aerosol (SOA) markers in the Paris region: concentrations, seasonality, gas/particle partitioning and use in SOA source apportionment

To be submitted in -Science of the Total Environment‖

Introduction and objectives

Aerosols (particulate matter, PM) have significant impacts on air quality and climate (Heal et al., 2012;IPCC, 2018). The organic fraction (organic aerosol (OA)) contributes to about 20 to 90% of the PM mass in ambient air (Kanakidou et al., 2005;Zhang et al., 2011Zhang et al., , 2007)). OA originates either from anthropogenic or natural sources and includes both primary OA(POA), directly emitted into the atmosphere, and secondary OA (SOA), formed in the atmosphere. SOA results from the condensation and coagulation of (photo-)oxidized volatile and/or semivolatile organic compounds (VOCs and SVOCs) (Carlton et al., 2009;Hallquist et al., 2009;Kroll and Seinfeld, 2008;Ziemann and Atkinson, 2012) and accounts for a major fraction of OA (up to 90%) (Srivastava et al., 2018b;Zhang et al., 2011Zhang et al., , 2007) ) making its characterization and apportionment essential in terms of air quality or climate impacts. However, the comprehension of SOA sources is still difficult to achieve and SOA is quite uneasily predicted by air quality models [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols[END_REF]. Different methodologies have been developed and reported to investigate OA sources (Srivastava et al., 2018b). A detailed chemical characterization of OA at a molecular level can provide insights into the OA sources as several organic compounds have been identified and recognized as tracers (or markers) of specific sources or chemical (trans-)formation processes [START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF](Srivastava et al., , 2018a(Srivastava et al., , 2018b(Srivastava et al., , 2018c and references therein) and references therein). For instance, for primary sources, levoglucosan is commonly used to trace biomass burning emissions, polyols for biogenic emissions (fungal spores), hopanes for vehicular emissions, etc… [START_REF] Cass | Organic molecular tracers for particulate air pollution sources[END_REF]Samaké et al., 2019aSamaké et al., , 2019b;;Schauer et al., 1996;Simoneit et al., 1999) .Similarly, key organic species have been identified as characteristic of secondary sources or are typical oxidation by-products of specific precursors. They are commonly referred to as SOA tracers (markers) as they can be used to apportion biogenic and anthropogenic SOA sources (Kleindienst et al., 2007a). For instance, biogenic SOA markers include pinene (and -pinene) oxidation markers such as cis-pinonic acid, pinic acid [START_REF] Christoffersen | cis-pinic acid, a possible precursor for organic aerosol formation from ozonolysis of α-pinene[END_REF][START_REF] Jang | Newly characterized products and composition of secondary aerosols from the reaction of α-pinene with ozone[END_REF][START_REF] Mutzel | Monoterpene SOA -Contribution of first-generation oxidation products to formation and chemical composition[END_REF]Yu et al., 1999), 3-methylbutane-1,2,3tricarboxylic acid (MBTCA) [START_REF] Mutzel | Monoterpene SOA -Contribution of first-generation oxidation products to formation and chemical composition[END_REF]Szmigielski et al., 2007) and 3hydroxyglutaric acid (Claeys et al., 2007). Several other pinene SOA markers have been also reported [START_REF] Jaoui | Identification and Quantification of Aerosol Polar Oxygenated Compounds Bearing Carboxylic or Hydroxyl Groups. 2. Organic Tracer Compounds from Monoterpenes[END_REF] and are used for source apportionment (Table 1). Isoprene SOA markers include α-methylglyceric acid, 2-methylthreitrol and 2-methylerythritol (Claeys et al., 2004(Claeys et al., , 2004;;[START_REF] Edney | Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated isoprene/NO X /SO 2 /air mixtures and their detection in ambient PM2.5 samples collected in the eastern United States[END_REF] while a common sesquiterperne SOA marker is βcaryophyllinic acid formed from the oxidation of β-caryophyllene (Jaoui et al., 2007). Anthropogenic SOA markers are generally less source (precursor) specific. For instance, succinic acid has been identified as a photooxidation product of cyclic olefins but it is also directly emitted by the motor vehicles [START_REF] Hatakeyama | Mechanism for the formation of gaseous and particulate products from ozone-cycloalkene reactions in air[END_REF]Kawamura and Kaplan, 1987). Typical toluene oxidation products are 2,3-dihydroxy-4-oxopentanoic acid (DHOPA), nitrophenols and methylnitrophenols (Forstner et al., 1997;Kleindienst et al., 2004). Phthalic acid has been proposed as SOA marker for the photooxidation of naphthalene and methylnaphthalenes (Kleindienst et al., 2012). All these SOA precursors are both emitted by biomass burning and fossil fuel combustions. Nitroguaiacols and methylnitrocatechols are considered as specific products of the phenolic compounds oxidation (Iinuma et al., 2010;Yee et al., 2013) and their precursors are largely emitted by biomass burning (Bruns et al., 2016;Iinuma et al., 2010). In addition, phthalic acid, nitrophenols, methyl-nitrophenols and methylnitrocatechols could be also directly emitted (Wang et al., 2017;Lu et al., 2019;Mkoma and Kawamura, 2013).

As defined, a tracer should be should be unique to the source (precursor) of origin, formed in reasonably high yields to induce quantifiable concentrations in the ambient air, stable in the atmosphere, and so conservative between its emission/formation and its collection at a receptor location, and should have a low vapour pressure, and so mainly associated with the particulate phase, to minimize possible underestimation from loss to the gaseous phase. However, as already specified above for source specificity, all these conditions are rarely (and probably never) fulfilled and, in this case, the term marker is more appropriate. In fact, such compounds may react in the atmosphere by photochemical processes involving sunlight and atmospheric oxidants (O 3 , NO x , radicals OH, NO 3 …). For most of the SOA markers, data about their stability or atmospheric lifetimes are scarce or not available. They are usually based on empirical calculations (Nozière et al., 2015) and the exact values are only available for few ones (e.g. cis-pinonic acid ~2. 1-3.3 days(Lai et al., 2015) and MBTCA ~1.2 days (Kostenidou et al., 2018)). SOA markers are then semi-volatile compounds however, their gas/particle partitioning (GPP) is still poorly documented (Al-Naiema and Stone, 2017;[START_REF] Bao | Gas/particle partitioning of low-molecular-weight dicarboxylic acids at a suburban site in Saitama, Japan[END_REF][START_REF] Isaacman-Vanwertz | Ambient Gas-Particle Partitioning of Tracers for Biogenic Oxidation[END_REF][START_REF] Lutz | Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere[END_REF][START_REF] Thompson | Field intercomparison of the gas/particle partitioning of oxygenated organics during the Southern Oxidant and Aerosol Study (SOAS) in 2013[END_REF]Xie et al., 2014;[START_REF] Yatavelli | Semicontinuous measurements of gas-particle partitioning of organic acids in a ponderosa pine forest using a MOVI-HRToF-CIMS[END_REF] while this parameter is essential in terms of further use in source apportionment. Understanding SOA markers GPP is difficult, since SOA composition is highly variable and the GPP process cannot be considered at the equilibrium. Simple parametrizations normally based on Raoult and Henry's laws do not succeed in correctly representing the SOA GPP [START_REF] Lutz | Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere[END_REF].

Finally, SOA markers have been widely measured, and used for source apportionment, in the USA and in southeast Asia, notably in China, but are poorly documented in Europe (Srivastava et al., 2018a). The SOA chemistry is expected to be quite different in Europe compared to USA and/or Asia due to the differences in the predominant SOA precursor and emission sources. For example, biogenic emissions in the USA and in China are largely dominated by isoprene (Guenther et al., 2006;[START_REF] Hantson | Global isoprene and monoterpene emissions under changing climate, vegetation, CO2 and land use[END_REF], while other biogenic VOCs, such as monoterpenes, are significantly emitted in Europe [START_REF] Simpson | Biogenic emissions in Europe: 1. Estimates and uncertainties[END_REF][START_REF] Steinbrecher | Intra-and inter-annual variability of VOC emissions from natural and semi-natural vegetation in Europe and neighbouring countries[END_REF]. Similarly, coal combustion contribution to PM (and OA) is largely significant in China (Huang et al., 2014) but not in western Europe and biomass burning for residential heating purposes is commonly used in Europe [START_REF] Crippa | Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach[END_REF] but not necessarily in the USA (Denier van der [START_REF] Denier Van Der Gon | Particulate emissions from residential wood combustion in Europe -revised estimates and an evaluation[END_REF][START_REF] Viana | Contribution of residential-combustion to ambient air pollution and greenhouse gas emissions[END_REF]. In addition, most of the measurements of SOA markers in Europe were in rural or forest areas. This did not give any information about the influence of anthropogenic sources and it is not relevant for urban air quality purposes (Srivastava et al., 2018b).

In this work, we investigated, over a year, the concentrations of 25 SOA markers, in both, particulate and gaseous phase, in the Paris region (France) in order to (1) to compare the concentration levels observed with the ones reported in the literature in Europe and worldwide, (2) to study their temporal variations and seasonality together with their sources or the chemical processes involved, (3) to document the GPP of the SOA markers including the investigation of the influence of the temperature on this parameter and ( 4) to apportion the different SOA sources based on the use of the SOA markers into two methodologies namely, SOA tracer method and positive matrix factorization (PMF). The results obtained using both methods have then been compared.

Experimental

2.1. Sampling site and sample collection PM 10 and gaseous phases were collected, every third day, from mid-November 2014 to mid-December 2015 on quartz fiber filters (Tissu-quartz, Pallflex, Ø = 150 m) and polyurethane foams (PUF, Tisch Environmental, L = 75 mm), respectively, at the SIRTA facility (Site Instrumental de Recherche par Télédétection Atmosphérique, 2.15° E; 48.71° N; 150 m a.s.l; http://sirta.ipsl.fr). This site is located 25km SW from the Paris city centre [START_REF] Haeffelin | SIRTA, a ground-based atmospheric observatory for cloud and aerosol research[END_REF] and is part of the ACTRIS European network (Aerosol, Clouds and Trace gases Research InfraStructure, www.actris.eu). The location is surrounded by forests, agricultural fields, residential areas and commuting roads, and is representative of the suburban background air quality conditions of the Ile-de-France region (Paris), the most populated area in France (Crippa et al., 2013a;[START_REF] Petit | Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF 2 ) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength Aethalometer[END_REF]Petit et al., , 2017;;Sciare et al., 2011;[START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]. Samplings were achieved on 24h-basis (from 8am to 8am, UTC), using a high-volume sampler (DA-80, Digitel, 30 m 3 h -1 ) and the sampling head was not heated to avoid any additional sampling artifact [START_REF] Albinet | Sampling precautions for the measurement of nitrated polycyclic aromatic hydrocarbons in ambient air[END_REF]. Prior to sampling, filters were pre-baked at 500 °C for 12 h and PUFs were cleaned using pressurized solvent extraction (ASE 350, Thermo) using hexane (1 cycle) and acetone (2 cycles): 80 °C, 100 bars, 5 min heat time, 15 min static time (Zielinska2008). Once collected, particulate and gaseous phase samples (n=130 +15 field blanks) were wrapped in aluminium foils and stored in polyethylene bags at <-18°C until the analysis. Shipping of the samples to the different laboratories for analyses have been done by express post using cool boxes (<5°C). Note, as no denuder (for oxidants or to trap the SVOCs in the gaseous phase) has been used for the samplings, we are aware that results could be biased due to some sampling artifacts (positive, overestimation of the concentrations, or negative, underestimation, due to the sorption or desorption on/from the filter of semi-volatile species and/or due to the degradation/formation of chemical species by heterogeneous processes involving atmospheric oxidants) [START_REF] Albinet | A new ozone denuder for aerosol sampling based on an ionic liquid coating[END_REF][START_REF] Goriaux | Field Comparison of Particulate PAH Measurements Using a Low-Flow Denuder Device and Conventional Sampling Systems[END_REF][START_REF] Mader | Gas/Solid Partitioning of Semivolatile Organic Compounds (SOCs) to Air Filters. 3. An Analysis of Gas Adsorption Artifacts in Measurements of Atmospheric SOCs and Organic Carbon (OC) When Using Teflon Membrane Filters and Quartz Fiber Filters[END_REF]Turpin et al., 2000and references therein). However, especially on annual basis, the errors induced should be relatively small by comparison to the overall measurement uncertainties [START_REF] Brown | Assessment of the effect of degradation by atmospheric gaseous oxidants on measured annual average benzo[a]pyrene mass concentrations[END_REF].

Off-line chemical analyses

Particulate phase was extensively characterized for many chemical species (n=175). Elemental and organic carbon (EC/OC) were measured using a Sunset lab analyser and following the EUSAAR-2 thermo-optical protocol (Cavalli et al. 2010). Major ions (Cl -, NO ), were quantified using ion chromatography after filter extraction in ultrapure water (18 MΩ) [START_REF] Guinot | Beijing aerosol: Atmospheric interactions and new trends[END_REF]. Both EC/OC and ion analyses fulfilled the recommendations of the European standard procedures EN 16909 andEN 16913, respectively (CEN, 2017a, 2017b). Thirty seven elements and metals, notably including Ca, Ti, Mn, Fe, Ni, Sb, Cu,Zn, V, and Pb, were measured using ICP-AES or ICP-MS after filter digestion using nitric acid [START_REF] Alleman | PM10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone[END_REF]CEN, 2005;[START_REF] Mbengue | Molecular composition of dicarboxylic acids, ketocarboxylic acids, α-dicarbonyls and fatty acids in atmospheric aerosols from Tanzania, East Africa during wet and dry seasons[END_REF]. Anhydrosugars, including known biomass burning markers (levoglucosan, mannosan and galactosan) and 3 polyols (arabitol, sorbitol and mannitol) were quantified using LC-PAD [START_REF] Verlhac | Interlaboratory comparison organized for the European laboratories involved in the analysis of levoglucosan and its isomers[END_REF][START_REF] Yttri | An intercomparison study of analytical methods used for quantification of levoglucosan in ambient aerosol filter samples[END_REF]. Particulate polycyclic aromatic hydrocarbons (PAHs) and their oxygenated and nitrated derivatives (oxy-and nitro-PAHs) were extracted using a QuEChERS-like (Quick Easy Cheap Effective Rugged and Safe) procedure with acetonitrile (ACN) as solvent. As described below, a single extraction for PUF samples was performed for the analysis of both, SOA markers, PAHs and PAH derivatives. For both phases, 22 PAHs, 27 oxy-and 31 nitro-PAHs were quantified by UPLC/UV-Fluorescence and GC/NICI-MS, respectively (Albinet et al., 2006[START_REF] Albinet | A really quick easy cheap effective rugged and safe (QuEChERS) extraction procedure for the analysis of particle-bound PAHs in ambient air and emission samples[END_REF][START_REF] Albinet | A simple QuEChERSlike extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICI MS[END_REF]Srivastava et al., 2018c;Tomaz et al., 2016). PAH analyses have been performed following the recommendation of European standard procedures EN 15549 andTS 16645 (CEN, 2014, 2008) including the control of extraction efficiency using the NIST standard reference material SRM1649b (urban dust). Oxy-and nitro-PAH extraction efficiencies were also checked using the same SRM. All the concentration values obtained were in good agreement with the certified, reference or indicative values form the NIST certificate or with the ones reported in the literature [START_REF] Albinet | Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material 1649a (urban dust) and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionisation[END_REF][START_REF] Albinet | A really quick easy cheap effective rugged and safe (QuEChERS) extraction procedure for the analysis of particle-bound PAHs in ambient air and emission samples[END_REF][START_REF] Albinet | A simple QuEChERSlike extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICI MS[END_REF]. Twenty-five SOA markers were quantified using the extraction and analytical procedures described briefly here and in the Supplementary Material (SM) and as reported previously (Albinet et al., 2019). The list of the chemicals used together with their purity, CAS number and suppliers is also specified (Table S1). Particulate SOA markers extraction was achieved using a QuEChERS-like extraction procedure as described previously (Albinet et al., 2019). 47mm filter punches were placed in centrifuge glass tubes (∅ = 16 mm, L=100mm, screw cap with PTFE septum face; Duran), spiked with a known amount (800 ng) of 4 deuterated SOA surrogate standards (Table S2) and 7 mL of methanol (MeOH) were added for the extraction. The tubes were shaken using a multi-tube vortexer for 1.5 min (DVX-2500, VWR), then centrifuged for 10min at 4500 rpm (Sigma 3-16 PK). Supernatant extracts (5 ml) were collected and reduced to dryness under a gentle nitrogen stream to remove any trace of MeOH or water and avoid additional consumption of derivatizing reagent. Extracts were then reconstructed into 100 µl of ACN. PUF samples were extracted with acetone using pressurized liquid extraction (ASE 350, Thermo; two cycles: 80 °C, 100 bars, 5 min heat time, 15 min static time) (Tomaz et al., 2016). Extracts were reduced under a gentle nitrogen stream to a volume of about 200 μL (Zymark, Turbovap II) and adjusted to 2 mL with ACN. Next, a known amount of labelled SOA surrogate standards (100 ng) was added to 500µL of the extract and then reduced to dryness under a gentle nitrogen stream and finally dissolved into 50 µl of ACN. This step was necessary to eliminate any residual acetone left. Note that, the surrogates have not been added before PUF extractions at that time, but extraction tests have been performed later, using spiked PUFs with a standard solution of SOA markers, and showed extraction efficiencies in the range of 20 to 70 %. All the extracts have been subjected to derivatization (silylation) using an equal proportion (ratio sample extract to derivatizing reagent of 1:1) of N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) for 30 minutes at 60°C. SOA marker analyses were achieved by GC/MS (Agilent 7890A GC coupled to 5975C MS, EI, 70eV). Authentic SOA marker standards (liquids or solids) were used for the quantification of most of the compounds (Table S1). For molecules with no authentic available standards, quantification was performed using the response factor of the most similar compound among the others measured: 3-(2-hydroxy-ethyl)-2,2-dimethylcyclobutanecarboxylic acid (HCCA) was quantified as pinic acid (PniA); 3-isopropylpentanedioic acid (IPPA), 3-acetylpentanedioic acid (APDA), 3-acetylhexanedioic acid (AHDA), 2-hydroxy-4,4-dimethylglutaric acid (HDGA) were quantified as 3-hydroxyglutaric acid (HGA); 3methyl-6-nitrocatechol (3M6NC) was quantified as 3-methyl-5-nitrocatechol (3M5NC). All compounds were quantified by internal calibration using appropriate deuterated surrogates except methylnitrocatechols (SM, (Albinet et al., 2019)).

SOA markers analysis quality control/quality assurance

Fifteen field blanks for both, particulate and gaseous phases, were collected, stored and analysed simultaneously with the ambient air samples. SOA marker concentrations were corrected from the field blanks. Overall, field blank concentrations were for most of the compounds below the limit of quantification (LOQ) or represented less than30% of the average ambient concentrations observed. LOQs, defined as the lowest concentration of the compound that can be determined for a signal to noise ratio S/N = 10, were estimated using the lowest calibration standard solution (Table S2). Concentrations in the samples < LOQ were replaced by LOQ/2. SOA markers extraction efficiencies were also checked using the NIST SRM1649b (urban dust). Results obtained were compared with the only concentration values available in literature (Albinet et al., 2019, Table S3). Note that to date, certified concentration values in any SRM do not exist for any SOA marker. Succinic acid (SuA), phthalic acid (PhA), αmethylglyceric acid (MGA), 2,3-dihydroxy-4-oxopentanoic acid (DHOPA), cis-pinonic acid (PnoA), 3-hydroxyglutaric acid (HGA), pinic acid (PniA), 3-methylbutane-1,2,3-tricarboxylic acid (MBTCA) and β-caryophyllinic acid (CarA) concentrations obtained were relatively in good agreement with the ones reported previously. The differences for 2-Methylerythritol (MET), 3-Methyl-5-nitrocatechol (3M5NC) and 4-Methyl-5-nitrocatechol (4M5NC) were more significant. Note that, additional and new individual concentration values are given for nitrophenols and nitroguaiacols in this SRM.

2.4. On-line measurements PM 10 were monitored by TEOM-FDMS (1405F model, Thermo) following the technical specifications of the standard method EN 16450 (CEN, 2017c). Black carbon (BC) concentrations were measured using a multi-wavelength aethalometer (AE33, Magee Scientific) and corrected from the filter-loading effect with the real-time compensation algorithm using both simultaneous light attenuation measurements [START_REF] Drinovec | The filter-loading effect by ambient aerosols in filter absorption photometers depends on the coating of the sampled particles[END_REF]. BC was discriminated between its two main sources, i.e. wood burning (BC wb ) and fossil fuel (BC ff ) using the -aethalometer model‖ [START_REF] Sandradewi | Comparison of several wood smoke markers[END_REF]. For these calculations, absorption Angström exponents of 1.7 and 0.9 were used for BC wb and BC ff , respectively [START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF][START_REF] Zotter | Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol[END_REF]. NO x and O 3 were monitored using T200UP and T400 analysers (Teledyne API), respectively, following the standard operating procedures (SOPs) from the ACTRIS network (CEN, 2012a(CEN, , 2012b)). Meteorological parameters such as, temperature, relative humidity (RH), wind direction, wind speed and planetary boundary level height (PBL) were measured at the main SIRTA facility located at about 5 km east from the sampling site.

SOA source apportionment

3.1. SOA tracer method Estimation of the different SOA fractions associated with individual gaseous precursors was achieved applying the SOA tracer method developed by Kleindienst et al., (2007). In such method, secondary organic carbon (SOC) mass fractions are determined using conversion factors, obtained from smog chamber experiments, allowing to calculate SOC loadings from SOA marker concentrations From the smog chamber experiments, the SOA mass fraction for each precursor have been calculated using Eq. ( 1):

𝑓 𝑆𝑂𝐴,𝑝𝑟𝑒𝑐 = 𝑡𝑟 𝑖 𝑖 𝑆𝑂𝐴 (1)
where f SOA,I is the ratio of the sum of the concentrations of all the measured SOA markers 𝑡𝑟 𝑖 𝑖 , to the total SOA formed from the individual class of precursor -prec‖. The SOA mass fractions were obtained using gravimetric measurements to convert them into SOC mass fractions (𝑓 𝑆𝑂𝐶 ) using SOA/SOC mass ratios (Eq. ( 2)).

𝑓 𝑆𝑂𝐶,𝑝𝑟𝑒𝑐 = 𝑓 𝑆𝑂𝐴,𝑝𝑟𝑒𝑐 𝑆𝑂𝐴 𝑆𝑂𝐶 (2)
Here, SOC mass fractions, from anthropogenic and biogenic origins, were estimated according to a subset of markers analysed following the procedure proposed by [START_REF] Rutter | Preliminary assessment of the anthropogenic and biogenic contributions to secondary organic aerosols at two industrial cities in the upper Midwest[END_REF] and described in the SM (Tables S6).

The main limitation of this methodology is the limited number of SOA markers identified for specific known gaseous organic precursors (so far, only isoprene, α-pinene, βcaryophyllene, naphthalene and toluene) and the few SOA/SOC data available in the literature (Srivastava et al., 2018b). Biomass burning SOA is not estimated using the SOA-tracer method. Neglecting this SOA source might lead to significant underestimation of the total wintertime SOC concentrations in Europe due to relatively high contributions of residential wood burning during the cold season [START_REF] Ciarelli | Modelling winter organic aerosol at the European scale with CAMx: evaluation and source apportionment with a VBS parameterization based on novel wood burning smog chamber experiments[END_REF][START_REF] Denier Van Der Gon | Particulate emissions from residential wood combustion in Europe -revised estimates and an evaluation[END_REF][START_REF] Petit | Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF 2 ) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength Aethalometer[END_REF]Puxbaum et al., 2007;Srivastava et al., 2018b;[START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]. Here, the biomass burning SOA fraction was evaluated using the measured concentrations of methylnitrocatechols, previously demonstrated as secondary photooxidation products of phenolic compounds (e.g., cresols, methoxy phenols) (Iinuma et al., 2010) and known to account for a major fraction of SOA biomass burning (Bruns et al., 2016). Values were taken from the literature (Iinuma et al., 2010) to calculate f SOA using Eq. ( 1) with ∑[tr] i = 821 ng m -3 ; [SOA]= 8293 ng m -3 . The SOA mass fractions were converted into SOC mass fractions using SOA to SOC mass ratios. A ratio of 2, typical for SOA [START_REF] Aiken | O/C and OM/OC Ratios of Primary, Secondary, and Ambient Organic Aerosols with High-Resolution Time-of-Flight Aerosol Mass Spectrometry[END_REF], was used to estimate the SOC mass fraction linked to the oxidation of phenolic compounds emitted from biomass burning, following Eq. ( 2) (Table S6). To the best of our knowledge, the only other study including biomass burning SOA mass fractions in the SOA-tracer method has been performed recently by [START_REF] Al-Naiema | Secondary organic aerosols from aromatic hydrocarbons and their contribution to fine particulate matter in Atlanta, Georgia[END_REF].

Positive matrix factorization (PMF)

We previously showed, that the use of key secondary organic markers into source-receptor models permits the resolution of several SOA sources (Srivastava et al., 2018b(Srivastava et al., , 2018c[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF](Srivastava et al., , 2019 and references therein) and references therein). SOA marker concentrations, together with other key species from the extended PM chemical characterization, were included into the data matrix of a PMF analysis performed to apportion the different PM and SOA sources. Details about the PMF analysis and results obtained are reported in the SM.

Results and discussion

4.1. . SOA marker concentration levels and comparison with literature data

Annual mean and median total SOA marker concentrations (gaseous + particulate phases), together with minimum, maximum, mean concentrations during the cold (October, November, December, January, February and March) and warm periods (April, May, June, July, August and September), and average particulate fractions, are presented in Table 1.

Overall, individual SOA concentration levels observed at SIRTA were in the same range than those reported worldwide (Tables S4 andS5, particulate phase only), from few pg m -3 , for both, biogenic and anthropogenic compounds, up to 0.1-1 µg m -3 for nitroaromatic compounds (NACs) (Table 1).

As expected, and mentioned before, the particulate concentrations of isoprene SOA markers at SIRTA (MGA, MTR and MET) were significantly lower than the ones reported in North and South America and in China due to lower isoprene emissions (Guenther et al., 2006;[START_REF] Hantson | Global isoprene and monoterpene emissions under changing climate, vegetation, CO2 and land use[END_REF]. This is also true for some pinene SOA markers such as MBTCA and HGA. Interestingly, the differences were significant only for these 2 nd generation photooxidation products (Claeys et al., 2007;Müller et al., 2012;Szmigielski et al., 2007) while the concentrations of the 1 st generation products such as pinic and cis-pinonic acids, were comparable anywhere. Finally, except for Honk Kong [START_REF] Hu | Contributions of isoprene, monoterpenes, β -caryophyllene, and toluene to secondary organic aerosols in Hong Kong during the summer of 2006[END_REF], CarA concentrations at SIRTA were comparable to worldwide reported values.

For the anthropogenic SOA markers, the particulate concentrations observed at SIRTA were all comparable with the ones reported worldwide except for the methylnitrocatechols that were largely higher than in China, probably less impacted by biomass burning emissions.

These results confirmed that precursor emissions, together with the chemical processes involved, are significantly different in Europe than in America and Asia. A comparison of the SOA marker concentrations measured at SIRTA (in particulate phase only) with the ones previously reported in Europe is probably more relevant and is presented here after. b Quantified using pinic acid as response factor.

c Quantified by internal calibration using 3-hydroxyglutaric acid as response factor.

Fig. 1 shows a direct comparison of selected particulate SOA marker concentrations (commonly measured, from different precursors and usually used for SOA source apportionment) measured at SIRTA to the ones previously reported for several locations in Europe. European data include 4 urban sites and, as an example, one forest and 2 rural sites.

Other compounds are presented in Table S5.

SOA marker particulate phase concentrations observed displayed the same order of magnitude at SIRTA than the ones measured at other sites. However, biogenic markers are generally in the lower concentration ranges (Table S5.1 and S5.2), due to the low biogenic influence on this site. Only β-caryophillinic acid concentrations (0.8 ng m -3 ) are higher than the only other value measured in Europe (0.3 ng m -3 in Marseille).

Anthropogenic marker concentrations at SIRTA are lower than in the rest of Europe, except for SuA and methylnitrocatechols, that are respectively in the lower and in the higher range of European concentrations (Fig. 1, Table S5.1 and S5.2). These comparisons are consistent with results from previous studies showing that SIRTA was highly influenced by anthropogenic emissions, and especially by biomass burning in wintertime (Bressi et al., 2014;Crippa et al., 2013b;[START_REF] Petit | Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF 2 ) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength Aethalometer[END_REF]Srivastava et al., 2018a;[START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]. (Alier et al., 2013), Grenoble (France) (Srivastava et al, 2018c) and Ljubljana (Slovenia) (Kitanovski et al., 2012). Rural: Mainz (rural urban, Germany) (Zhang et al., 2010), Hamme (Belgium) (Kahnt et al., 2013). Forest: Jülich (Germany) (Kourtchev et al., 2008). Note, the sum of MNC isomers, and not 3M5NC concentrations, is reported for Hamme.

The comparison of the average total SOA marker concentrations observed during the cold and warm periods gives some first clues in the seasonality of these compounds (Table 1) that is further discussed into details into the following section. For biogenic species, the average sum of monoterpene SOA marker concentrations in warm and cold periods were 34 and 26 ng m -3 , respectively. Although the total monoterpene marker budget varies according to the temperature, with higher concentrations in the warmer periods, the individual compounds did not show the same behaviour. Most of the biogenic marker concentrations were 1.5 times lower in the cold period, except for HDGA, that was quite stable, and HCCA, MBTCA, and HGA, for which cold period concentrations were 1.5 to 2 times higher than in warm period.

For isoprene SOA markers, mean total concentrations were 4.5 times higher (from 1.6 to 7.6 ng m -3 ) during the warm period. Into details, MGA, MTR and MET concentrations were about 2.7, 1.6 and 10 times higher in this season than in the cold period. Finally, βcaryophyllinic acid mean concentrations were higher in the colder period (1.5 ng m -3 ) than in the warmer period (0.4 ng m -3 ) by a factor 3.7. As for HCAA, MBTCA and HGA, this was mainly due to the high concentrations observed in October/November and this will be further discussed in section 4.3. This may also suggest that the seasonal trend of biogenic markers did not depend only from the precursor emissions.

Anthropogenic SOA marker concentrations were overall higher in cold season than in warm season. SuA, PhA and DHOPA concentrations were respectively 1.9, 2 and 5.8 times higher during the cold period. Nitroaromatic compounds (NACs) dominated over the other markers measured during all the year. They all showed higher concentrations in the cold period (1.7 to 245 times higher) depending on the compound. Major differences between the cold and the warm periods were observed for 2NPh (× 245) and for 4-NG, 4M5NC and 3M5NC (× 20), due to large emissions of their precursors by biomass burning from residential heating (Bruns et al., 2016;Iinuma et al., 2010).

SOA marker temporal evolutions and seasonality

The temporal evolutions of selected biogenic (PnoA, PniA, MBTCA, HGA, MGA and MET+MTR) and anthropogenic (PhA, DHOPA and 3M5NC) SOA markers (gaseous + particulate phase concentrations) are shown on Figs. 2 and3, respectively, together with the temperature. All the other compounds are presented individually in the SM (Figs S2 to S8). Anthropogenic SOA markers are also presented with two primary anthropogenic markers: levoglucosan, a well-known biomass burning molecular tracer (Simoneit et al., 1999) and 1nitropyrene (1-NP) emitted by diesel engines (Keyte et al., 2016;Zielinska et al., 2004a). This latter was used to trace vehicular emissions (Srivastava et al., 2018a(Srivastava et al., , 2018c[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF] as in France in 2015 80% of the total vehicles fleet (cars+heavy trucks) was composed by diesel engines (CCFA, 2016) and as supported by the correlations with BC ff and NO x (Fig S11 and Fig S17,r=0.73 for both,n=130,p<0.05). Fig. 2. Temporal variations of selected total biogenic SOA markers concentrations (gaseous + particulate phases) at SIRTA (2015). Upper panel shows the temperature, central panels show the first (PnoA and PniA) and second generation (HGA and MBTCA) pinene SOA markers and the lower panel show, isoprene SOA markers (tetrols and MGA). Tetrols is the sum of 2methylerythritol (MET) and 2-methylthreitrol (MTR). Fig. 3. Temporal variations of selected total anthropogenic SOA markers concentrations (gaseous + particulate phases) at SIRTA (2015). Upper panel shows the temperature, central panel shows PhA together with 1-nitropyrene (1-NP) (diesel emission marker) and lower panel shows DHOPA and 3M5NC together with levoglucosan (marker for primary biomass burning emissions). Biogenic SOA marker concentrations were expected to increase during summer due to the increase of biogenic emissions and photochemical activity enhancement [START_REF] Guenther | Seasonal and Spatial Variations in Natural Volatile Organic Compound Emissions[END_REF][START_REF] Tarvainen | Temperature and light dependence of the VOC emissions of Scots pine[END_REF]. Here, only isoprene SOA markers (e.g., MGA and MET+MTR) showed clear seasonal variations with higher concentrations observed in summer while no significant seasonal trend could be observed for all the pinene SOA makers. Several previous studies, in China and US, investigated the annual variations of biogenic SOA markers concentrations in particulate phase. They showed a systematic summer increase for isoprene SOA marker and a fluctuating seasonal trend for pinene markers [START_REF] Ding | Spatial and seasonal trends in biogenic secondary organic aerosol tracers and water-soluble organic carbon in the southeastern United States[END_REF][START_REF] Ding | Spatial and seasonal variations of secondary organic aerosol from terpenoids over China[END_REF][START_REF] Feng | Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers[END_REF].The only study reporting PnoA, PniA and MBTCA seasonal variations in Europe (Mainz, Germany, particulate phase (Zhang et al.2010) found a general increase of these compounds in summer. The different seasonal trends of these marker families are related to their precursor emission annual variations. Monoterpene emissions are regulated almost entirely by the temperature, while isoprene emissions depend also on the light irradiance and the leaf area index (Guenther et al., 2006). Thus, in cold period, monoterpene emissions are still significant, while isoprene emissions are close to zero [START_REF] Oderbolz | A comprehensive emission inventory of biogenic volatile organic compounds in Europe: improved seasonality and land-cover[END_REF]. No significative correlations (r>0.7) between biogenic SOA markers and trace gases or meteorological data have been highlighted at SIRTA. Only MGA and MET correlated with temperature (r=0.58 and 0.63, n=130, p<0.05). Considering the difference in the factors driving isoprene and monoterpene emissions, the lack of a definite seasonal trend for monoterpene SOA was not surprising. All anthropogenic SOA markers concentrations were enhanced in colder periods, but no definite seasonal trend could be highlighted for PhA and SuA. Nitrophenols and nitroguaiacols concentrations were higher only in the first part of the year. Significative correlations with levoglucosan have been observed for 2NPh (0.72, n=130, p<0.05, Fig. S15) and for methylnitrocatechols (r² for 4M5NC, 3M6NC and 3MNC of 0.79, 0.74 and 0.81, respectively, n=130, p<0.05, Fig. S15). The concentration increase of the biomass burning related markers at low temperature was expected, since in Europe biomass burning is a large source of PM due to residential heating (Denier van der [START_REF] Denier Van Der Gon | Particulate emissions from residential wood combustion in Europe -revised estimates and an evaluation[END_REF]Puxbaum et al., 2007;[START_REF] Viana | Contribution of residential-combustion to ambient air pollution and greenhouse gas emissions[END_REF]. No significant correlations between any anthropogenic secondary markers, 1NP, trace gases and meteorological data have been observed (Fig. S15 andS17).

Focus on the October/November 2015 period

For most of the biogenic and anthropogenic molecular markers (primary and secondary, except MTR, MET and NACs), a strong and not necessarily expected increase in the concentrations was observed in fall (01/10 to 15/11) (Figs 2,3 and Figs S2 to S8). Higher concentrations of oxygenated organic aerosol (OOA, SOA proxy) have been also reported in this period at SIRTA from Aerosol Chemical Speciation Monitor (ACSM) measurements [START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]. Based on monthly data (Fig. 4), October and November 2015 showed average temperatures about 11°C, for which biogenic emissions are expected to be relatively reduced. RH in October rose of 10% compared to the month before, reaching a value of 80% and kept similar values in November (84%). Wind speed reached the annual minimum with a mean speed of 2.5 m s -1 in October and about 3 m s -1 until mid-November (Fig; S13). The PBL height was lower compared to the previous months (around 450 and 530m for October and November). October and November 2015 were very dry compared to previous years, with significant low precipitations (Météo-France, 2015). All these conditions inhibited the vertical mixing of the pollutants in the atmosphere causing an accumulation of the pollutants during October and in the first part of November. Then, the high wind speed in the second part of November increased the dispersion of the pollutants (Fig. S13).

Several studies reported a strong effect of O 3 and NO x concentrations on SOA composition and formation yields (Capouet et al., 2008;[START_REF] Dommen | Laboratory observation of oligomers in the aerosol from isoprene/NO x photooxidation[END_REF][START_REF] Eddingsaas | α-pinene photooxidation under controlled chemical conditions -Part 2: SOA yield and composition in low-and high-NO x environments[END_REF][START_REF] Kroll | Secondary Organic Aerosol Formation from Isoprene Photooxidation[END_REF]Lin et al., 2013;[START_REF] Presto | Secondary Organic Aerosol Production from Terpene Ozonolysis. 2. Effect of NOx Concentration[END_REF][START_REF] Sarrafzadeh | Impact of NO x and OH on secondary organic aerosol formation from β-pinene photooxidation[END_REF][START_REF] Xie | Light Absorption of Secondary Organic Aerosol: Composition and Contribution of Nitroaromatic Compounds[END_REF][START_REF] Xu | Simulated impact of NO<sub>x</sub> on SOA formation from oxidation of toluene and m-xylene[END_REF]. October mean ozone concentration was very low (16 ppb, annual mean 28 ppb) and NO x monthly mean was the highest (14.2 ppb) observed in 2015 (annual mean 9 ppb). High NO x concentrations were probably due increase in road traffic usually observed in this period, as supported by the increase in BC ff and 1-NP concentrations (Fig. S11). In November, O 3 (24 pbb) and NO x (6.3 ppb) concentrations were similar to the annual averages. Under high NO x conditions, the formation of methyltetrols is inhibited and MGA is the main molecular marker produced (Surratt et al., 2010). High values of the ratio MGA/(MET+MTR) were observed during this period (Fig. S12) highlighting the impact of the higher NO x concentrations on SOA formation. High NO x conditions, when combined with significant biomass burning emissions, should also induce an increase in NACs concentrations (Harrison et al., 2005a(Harrison et al., , 2005b;;Wang et al., 2019;[START_REF] Yuan | Secondary formation of nitrated phenols: insights from observations during the Uintah Basin Winter Ozone Study (UBWOS) 2014[END_REF] but it was not observed in our case. The low levoglucosan concentrations observed during this period suggested a moderate contribution of biomass burning (Fig. 3, S18 and S20). All the pinene SOA markers, except IPPA, were mainly, on average, associated to the particulate phase with low variations at higher and lower temperatures. Compared to the literature, in which F p values ranged from 0.1 to 0.6, PnoA was largely in particulate phase but showed a shift to the gaseous phase with increasing T (F p from 1 to 0.65 with T <7°C and T>15°C) (Al-Naiema and Stone, 2017;[START_REF] Lutz | Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere[END_REF][START_REF] Yatavelli | Semicontinuous measurements of gas-particle partitioning of organic acids in a ponderosa pine forest using a MOVI-HRToF-CIMS[END_REF]. Similarly, PniA (median F p ~0.9), TerA (median F p ~0.8) and HGA (median F p >0.9) partitioned more to the particulate phase than previously reported [START_REF] Yatavelli | Semicontinuous measurements of gas-particle partitioning of organic acids in a ponderosa pine forest using a MOVI-HRToF-CIMS[END_REF] [START_REF] Lutz | Gas to Particle Partitioning of Organic Acids in the Boreal Atmosphere[END_REF][START_REF] Thompson | Field intercomparison of the gas/particle partitioning of oxygenated organics during the Southern Oxidant and Aerosol Study (SOAS) in 2013[END_REF]. Finally, MBTCA was mainly particulate (median F p 1) with no change according to the temperature as shown previously [START_REF] Kristensen | Denuder/filter sampling of organic acids and organosulfates at urban and boreal forest sites: Gas/particle distribution and possible sampling artifacts[END_REF][START_REF] Yatavelli | Semicontinuous measurements of gas-particle partitioning of organic acids in a ponderosa pine forest using a MOVI-HRToF-CIMS[END_REF]. Isoprene SOA markers GPP showed also a low temperature dependency. MGA F p ranged from 0.85 to 0.90 whatever the temperature bin considered. MTR partitioned in the gaseous phase only above 15°C, while MET median F p ranged from 0.6 to 0.8. Except for MTR, these results were in agreement with the measurements performed by Al-Naiema and Stone, (2017) (for MGA average F p 0.85, for MET and MTR average F p 63). Finally, β-caryophyllinic acid was observed only in particulate phase (F p = 1). Among anthropogenic acid SOA markers, only PhA GPP was significantly affected by temperature. SuA and DHOPA median F p were above 0.8 in all bins, while PhA median F p decreased from 0.7 to 0.5 following the temperature increase. Literature data for SuA and PhA are discordant on F p estimation and temperature dependency, with F p ranging from 0.5 to 0.9 for SuA [START_REF] Bao | Gas/particle partitioning of low-molecular-weight dicarboxylic acids at a suburban site in Saitama, Japan[END_REF]Limbeck et al., 2001) and from 0.2 to 0.7 for PhA (Al-Naiema and Stone, 2017; [START_REF] Kristensen | Denuder/filter sampling of organic acids and organosulfates at urban and boreal forest sites: Gas/particle distribution and possible sampling artifacts[END_REF]Limbeck et al., 2001). In agreement with our observations, Al-Naiema and Stone, (2017) showed that DHOPA was 100% associated to the particulate phase.

No common behaviour for NACs GPP could be higlighted. Nitrophenol (2NPh, 4NPh and 2M4NPh) gaseous phase fractions predominated largely (median F p <0.3). Our estimation for 2NPh GPP agreed with the values reported by Cecinato et al., (2005) (F p = 0.25), while their 4NPh F p estimation (F p = 0.82) was higher. Al-Naiema and Stone, (2017) reported low 4NPh F p (= 0.3) in agreement with our results. Interestingly, both NG isomers, namely 4NG and 5NG, were in total opposition with 4NG completely in the gaseous phase, as in Al-Naiema and Stone, (2017), and 5NG fully associated to the particulate phase. 3M6NC and 4M5NC were mainly associated to the particulate phase whatever the T° bin considered (1>F p >0.9), as previously reported (Al-Naiema and Stone, 2017). Only 3M5NC showed a temperature dependency with a shift to the gaseous phase at higher T° (F p from 0.1 to 0.5). These results highlighted that all the SOA markers studied here seemed mainly associated to the particulate phase, including the compounds usually considered as semi-volatile. For instance, the high functionalisation may decrease alkanoic acids volatility [START_REF] Yatavelli | Semicontinuous measurements of gas-particle partitioning of organic acids in a ponderosa pine forest using a MOVI-HRToF-CIMS[END_REF] and may explain the large association to the particulate as previously observed for low molecular weight dicarboxylic acids (Bilde et al., 2015;[START_REF] Booth | Solid state and sub-cooled liquid vapour pressures of substituted dicarboxylic acids using Knudsen Effusion Mass Spectrometry (KEMS) and Differential Scanning Calorimetry[END_REF][START_REF] Falkovich | Low molecular weight organic acids in aerosol particles from Rondônia, Brazil, during the biomass-burning, transition and wet periods[END_REF][START_REF] Huisman | Vapor pressures of substituted polycarboxylic acids are much lower than previously reported[END_REF]Limbeck et al., 2001;[START_REF] Mochida | Spatial distributions of oxygenated organic compounds (dicarboxylic acids, fatty acids, and levoglucosan) in marine aerosols over the western Pacific and off the coast of East Asia: Continental outflow of organic aerosols during the ACE-Asia campaign[END_REF][START_REF] Saxena | Water-soluble organics in atmospheric particles: A critical review of the literature and application of thermodynamics to identify candidate compounds[END_REF]. The GPP temperature dependence observed was generally low. However, GPP estimation for such compounds is still highly uncertain and the results obtained here highlighted that temperature cannot fully explain their GPP in the atmosphere and other parameters must be considered (aerosol chemical composition, water content…) notably for modelling purposes [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF][START_REF] Liu | Aerosol Phase State and Its Link to Chemical Composition and Liquid Water Content in a Subtropical Coastal Megacity[END_REF][START_REF] Pye | On the implications of aerosol liquid water and phase separation for organic aerosol mass[END_REF][START_REF] Shahpoury | Evaluation of a conceptual model for gas-particle partitioning of polycyclic aromatic hydrocarbons using poly-parameter linear free energy relationships[END_REF].

4.5. SOA source apportionment 4.5.1. SOA tracer method Fig. 6 shows the annual evolution of SOC sources estimated using the SOA tracer method. The highest SOC concentrations were observed in February, October and November (2014 and 2015) accounting for about 9, 20 and 15% of OC. Through the year, three different periods with different SOC characteristics can be highlighted: the first period, from November 2014 until end of March 2015 was mainly dominated by anthropogenic SOC from the photooxidation of phenolic compounds and toluene, including minor contribution from the SOC formed through the oxidation of naphthalene. All of these species are largely emitted by biomass burning [START_REF] Baudic | Seasonal variability and source apportionment of volatile organic compounds (VOCs) in the Paris megacity (France)[END_REF]Bruns et al., 2016;Iinuma et al., 2010;Nalin et al., 2016). Wood combustion used for residential heating is usually a predominant source for PM and S/VOCs during the cold period in the Paris region (Bressi et al., 2014;Crippa et al., 2013b;[START_REF] Petit | Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF 2 ) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength Aethalometer[END_REF]Srivastava et al., 2018a;[START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]. The stable meteorological conditions (low boundary layer height and low wind speed, Fig. S10 andS11) together with high S/VOC precursor concentrations from these emissions were favourable to enhance anthropogenic SOA formation in this period. The second period, from April until the end of September, was mainly dominated by biogenic SOC from pinene oxidation. High pinene emissions are usually noticed during the warmer months [START_REF] Guenther | Seasonal and Spatial Variations in Natural Volatile Organic Compound Emissions[END_REF]Guenther et al., 2006;[START_REF] Tarvainen | Temperature and light dependence of the VOC emissions of Scots pine[END_REF], together with high solar flux (Fig. S10) and oxidant concentrations (i.e. O 3 and OH) that could facilitate high biogenic SOC formation [START_REF] Docherty | Apportionment of Primary and Secondary Organic Aerosols in Southern California during the 2005 Study of Organic Aerosols in Riverside (SOAR-1)[END_REF][START_REF] Feng | Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers[END_REF]Kleindienst et al., 2007b;[START_REF] Lewandowski | Primary and secondary contributions to ambient PM in the midwestern United States[END_REF][START_REF] Sheesley | Trends in Secondary Organic Aerosol at a Remote Site in Michigan's Upper Peninsula[END_REF]Shrivastava et al., 2007;Zhang et al., 2009). In addition, increase in the biogenic emissions at SIRTA has already been found to be linked with the temperature rise (Zhang et al., 2019). During the last period, from October until end of December 2015, biogenic SOC (from pinene) still accounted for a significant fraction of the total SOC, however, anthropogenic SOC from toluene and naphthalene oxidation contributed also significantly. Other biogenic SOC, from isoprene and β-caryophyllene oxidation, and anthropogenic SOC (phenolic compounds oxidation), showed low contributions to the total SOC mass. As explained before (section 4.2.1.), from October to mid-November, favourable meteorological conditions including increase in the relative humidity, low wind speed, low planetary boundary level height, low precipitation and -warm‖ weather (mean temperature of 11°C) induced a poor atmospheric vertical mixing with a stagnation of the pollutants over a long period, and further favored SOA formation. Additionally, this period also observed high NO x levels compared to the annual average due probably to the road traffic increase leading to larger primary pollutant emissions (Fig. S11). Together with favorable meteorological conditions, this resulted in an enhanced SOA formation. In fact, at low temperatures, reduction in biogenic emissions is usually observed, however, prevalent anthropogenic emissions (high NO x conditions) could induce an enhancement in the formation of biogenic SOA (at least from isoprene) as already shown previously (Claeys et al., 2004;[START_REF] Edney | Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated isoprene/NO X /SO 2 /air mixtures and their detection in ambient PM2.5 samples collected in the eastern United States[END_REF][START_REF] Kroll | Secondary Organic Aerosol Formation from Isoprene Photooxidation[END_REF]Surratt et al., 2006Surratt et al., , 2010)). Finally, in December 2015, the SOC composition/concentrations was found to be different from the SOC composition/concentrations observed in November-December 2014. The discrepancies could be linked to the temperature differences noticed during these periods and to the differences in terms of anthropogenic activities (low levoglucosan concentrations for instance, Fig. 3) and meteorological conditions. Fig. 6. Temporal evolution of the identified sources to SOC mass concentrations at SIRTA (2015) using the SOA tracer method.

Comparison of SOA tracer method and PMF outputs

The SOA tracer method witnesses several limitations due to the use of laboratory developed mass fractions to estimate SOC mass from the individual class of precursors, that are probably not representative of all the existing atmospheric conditions, and only determined for a limited number of SOA markers (Srivastava et al., 2018a). As suggested by Srivastava et al., (2018a), the use of a combination of different SOC estimation methodologies to apportion the SOC concentrations is necessary to get a higher level of confidence in the results obtained. Here, we compared the SOA tracer method with the outputs from a PMF analysis. Details on PMF analysis and results are provided in the SM (Fig. S18-S20). The specific PMF SOC apportionment results are shown on Fig. S21. Fig. 7. shows the comparison of the SOC concentrations estimated using both methods. Fig. 7. Comparison of the SOC concentrations estimated using PMF analysis and SOA tracer method. For SOA tracer method, anthropogenic SOC includes SOA from naphthalene, toluene and phenolic compounds; biogenic SOC includes SOA from pinene, isoprene and βcaryophyllene. For PMF analysis, anthropogenic SOC includes ASOA-1 (biomass burning + traffic) and ASOA-2 (biomass burning) factors; biogenic SOC includes BSOA-A (marine) and BSOA-2 (monoterpenes + isoprene) factors. Other PMF SOC was apportioned based the on nitrate-and sulfate-rich factors. Overall, total SOC concentrations evaluated by both methods were in good agreement through the year. Significant differences were only observed in March and in summer (from May to August) with a systematic underestimation by the SOA tracer method. March was characterized by an intense PM pollution event (PM 10 > 50 µg m -3 for at least 3 consecutive days) over a three weeks period (Fig. S20). This type of event is typical of the late winterearly spring period in North western Europe and is characterized by large contributions of secondary organic species such as ammonium nitrate (and in a lesser extent, ammonium sulfate too), long range transportation and significant aging. Detailed discussions about the PM and OA sources of the March 2015 PM pollution event can be found elsewhere (Petit et al., 2017;Srivastava et al., 2018a[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF]. The additional SOC contribution in the PMF method (other SOC, Fig; 7) was apportioned based on the nitrate and sulfate-rich factors (Srivastava et al., 2018a) (Fig. S21). Even with advanced source apportionment method, we failed to get any better description due to the lack of specific molecular markers for such aged SOA [START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF]. Similarly, as no specific markers and/or 𝑓 𝑆𝑂𝐶 have been reported in the literature for any other SOA classes, especially for organonitrates and organosulfates, the SOA tracer method underestimated the total SOC concentrations in such conditions. This is clearly one of the main limitations of this method during such highly processed PM pollution episode and it is further discussed in another paper with the comparison of several SOC apportionment methods (Srivastava et al., 2018b). Into details, the anthropogenic SOC contribution for the SOA tracer method accounted for the SOC formed from the oxidation of naphthalene, toluene and phenolic compounds while 2 anthropogenic secondary fractions (ASOA-1 (biomass burning + traffic) and ASOA-2 (biomass burning) factors) were apportioned by the PMF model. A very good agreement was noticed (r 2 =0.9, slope=0.94, n=123, p<0.05) thanks to the similar species used in both approaches and to the inclusion of methlynitrocatechols into the SOA tracer method according to the SOA and SOC fractions calculated from Iinuma et al., (2010) data. We estimated that the use of the fractions proposed by [START_REF] Al-Naiema | Secondary organic aerosols from aromatic hydrocarbons and their contribution to fine particulate matter in Atlanta, Georgia[END_REF] would have lead to a lowering of phenolic SOC by a factor 10. The biogenic SOC fraction resolved using the SOA tracer method was the sum of the SOC formed from the oxidation of isoprene, pinene and caryophyllene while biogenic SOC from the PMF model accounted two biogenic secondary fractions from marine origin (dimethylsulfide oxidation, BSOA-1) and from the oxidation of isoprene/monoterpenes (BSOA-2) (Figs 6, 7 and S21). Temporal profiles of biogenic SOC estimated using both approaches showed significant disagreement, especially in summer and fall. The biogenic SOA mass in summer included a large fraction of marine origin SOA only apportioned by the PMF. Again, as no specific 𝑓 𝑆𝑂𝐶 value has been determined for the DMS oxidation, this fraction could not be accounted using the SOA tracer method, explaining the significant differences observed between both methods. In fall, the differences between both methods with a large overestimation by the SOA tracer method were probably due to the 𝑓 𝑆𝑂𝐶 from smog chamber experiments that may be different and not representative of the atmospheric conditions observed in this period (NO x concentrations, humidity, etc…).

Conclusions

Twenty fivebiogenic and anthropogenic SOA markers were measured in gaseous and particulate phase during a whole year (2015) in a European suburban site (SIRTA, Paris suburbs). The measured marker concentrations were in the same order of magnitude of the values observed worldwide, with some differences due to different precursor emissions (e.g. higher isoprene marker concentrations in America or lower biomass burning marker concentrations in China than in Europe) or aerosol aging (e.g. fresher aerosol in Marseille compared to SIRTA). For isoprene and biomass burning markers opposite seasonal trends were identified, with respectively higher concentrations in summer and winter. No definite seasonal variations have been observed for pinene and other anthropogenic markers. Stable atmospheric conditions combined with high NO x regime caused an unexpected increase in concentrations for OC and some markers during October and the first part of November. Most of the markers were less volatile than previously reported in literature, with low F p variations with temperature. Surprisingly, different GPP has been measured for some isomers (4NG and 5NG or 3M5NC, 3M6NC and 4M5NC). Finally, SOA markers were used to apportion SOA sources. Biomass burning SOA was predominant in the first part of the year (until April) and pinene SOA afterwards.SOA tracer approach was validated by comparison with PMF: estimations for anthropogenic SOC were in good agreement, while biogenic SOC was underestimated by SOA tracer method in summer and in March. The discrepancy observed is probably due to missing sources or tracers specific to the processes. To date, less studies on SOA marker concentrations have been performed in Europe. More studies are required in order to better apportion SOA sources and identify missing sources and tracers specific for European air quality conditions.

Sample analysis

The analyses of the SOA markers were achieved using GC/MS (Agilent 7890A GC coupled to 5975C MS) in electron ionization mode (EI, 70 eV).1 μL of the extracts was injected in the splitless mode at 250 °C using a programmed temperature vaporizer injector system (CIS-4 Gerstel, with a Restek Sky single baffle liner with wool, 3 × 2 × 71 mm). Compounds were separated using a capillary column with equivalent 5% phenyl-methylpolysiloxane phase 60 m × 0.25 mm × 0.25 μm (Agilent J&W DB5-MS with 10 m guard column) with the following temperature programs: start at 70 °C for 1 min, then ramped to 260 °C at 5 °C min -1 , followed by a ramp to 290 °C at 20 °C min -1 , further followed by a ramp to 300 °C at 5 °C min -1 , then ramped at 10 °C min -1 to 320°C held for 10 min. The carrier gas (He) flow was set to 1.5 mL min -1 throughout the analysis and transfer line heated at 310 °C. The ion source and quadrupole temperatures were 230 and 150 °C, respectively. Analyses were performed in the selected ion monitoring mode (SIM). Autotune parameters were adopted for the electron multiplier conditions with a gain factor of 10. Monitored ions and dwell times are shown in Table S2. The quantification of the SOA markers was based on 8 to 10-points calibration curves (gravimetrically diluted stock standard solutions prepared in acetonitrile from 5 to 5000 pg µl -1 ; 0.99 > r² > 0.90 for all compounds). Most of the SOA marker compounds were quantified using authentic standards and labelled surrogate standards by internal calibration except for both methylnitrocatechols (external calibration) (Tables S1 andS2). 3,3, Positive matrix factorization (PMF) is based on a weighted least squares fit, where the weights are derived from the analytical uncertainty and provides an optimal solution by minimizing the residuals [START_REF] Paatero | Least squares formulation of robust non-negative factor analysis[END_REF][START_REF] Paatero | Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values[END_REF].The U.S. Environmental Protection Agency (US-EPA) PMF 5.0 software has been used to resolve the PM/SOA sources.

SOA tracer method

Criteria for the selection of species

The selection of the input data for the PMF analyses is an important step, as already shown previously (Srivastava et al., 2018a(Srivastava et al., , 2018b)). The selection of the species was based on the following criteria: signal-to-noise ratio (S/N) [START_REF] Paatero | Discarding or downweighting high-noise variables in factor analytic models[END_REF], inclusion of compounds where at least 40% of total data points were above the detection limit, compounds considered as specific markers of a given source (e.g., 2-methylerythritol acid (2-MT) (SOA marker of isoprene oxidation), 2,3-dihydroxy-4-oxopentanoic acid (DHOPA) (SOA marker of toluene oxidation), methylnitrocatechol isomers (SOA from phenolic compounds oxidation mainly emitted by biomass burning)) (Carlton et al., 2009;Iinuma et al., 2010;[START_REF] Kleindienst | Contribution of Primary and Secondary Sources to Organic Aerosol and PM 2.5 at SEARCH Network Sites[END_REF](Table S7). In addition, BC ff and BC wb have been used here instead of EC to improve the separation of primary emission sources such as biomass burning and traffic emissions. Note, only 20% of the species quantified has been finally used in order to keep a ratio of 1/3 between PM input species and the number of samples. Finally, a total number of 34 chemical species including inorganic species and metals, along with BC ff , BC wb , OC and molecular organic markers were used as input data for the PMF analysis (Table S7).PM 10 concentrations were included as the total variable in the model to determine the source contributions. The optimization of the final solution including the number of factors and the application of constraints were carried out following the same protocols as detailed by Srivastava et al., (2018a). Briefly, wide range of factors (from 6 to 13) were explored including several constraints applied on the base run to obtain clear chemical source profiles. The details related to the constraints applied on each factor profile are given in Table S8. No significant difference (p>0.05) was observed in the source chemical profiles between the base and the constrained runs (Table S9). Bootstrapping of the selected solution showed stable results with ≥ 88 out of 100 bootstrap mapped factors (Table S10). Finally, the 11-factor output provides the most reasonable solution for the selected dataset (Fig. S18 andS19). A good agreement was also noticed between the reconstructed PM 10 contributions from all sources and the measured PM 10 concentrations (Fig. S20). OA concentrations related to the PMF factors were further calculated applying OC-to-OA conversion factors specific to each source, i.e., 1.7 for biomass burning (Puxbaum et al., 2007), 1.2 for vehicular emissions [START_REF] Drooge | Particle size-resolved source apportionment of primary and secondary organic tracer compounds at urban and rural locations in Spain[END_REF] and 2.0 for secondary organics [START_REF] Mohr | Characterization of Primary Organic Aerosol Emissions from Meat Cooking, Trash Burning, and Motor Vehicles with High-Resolution Aerosol Mass Spectrometry and Comparison with Ambient and Chamber Observations[END_REF]. Identified aerosol sources are discussed individually below.

. Primary traffic emissions

Due to the constraint applied, this factor was apportioned based on the high proportion of BC ff (52%), several metals, such as Cu (53%), Sb (54%), Pb (40%) and Zn (38%) and 1-NP (59%) all considered typical species from road traffic emissions (Keyte et al., 2016;Pant and Harrison, 2012;Srivastava et al., 2016;Sternbeck et al., 2002). Note, more than half of 1-NP, good marker of diesel emissions (Keyte et al., 2016;Schulte et al., 2015;Zielinska et al., 2004aZielinska et al., , 2004b)(Fig. S18)was attributed to this factor without applying any constraint. The use of 1-NP improved the resolution of this factor thanks to its good correlation with both, BC ff and NO x (Fig. S11). Primary traffic sources accounted for 11% of the PM 10 mass on a yearly average (Fig. S21), with an annual mean concentration of 2.1 µg m -3 . This value was consistent with previous studies conducted in the Paris region (14% of PM 2.5 ; (Bressi et al., 2014), 11% of PM 1 (Petit et al., 2014)and 5-% of PM 10 (Srivastava et al., 2018a;[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF]

Biomass burning

Biomass burning factor was characterized by the presence of levoglucosan (levo) (58%), mannosan (42%) and BC wb (35%), including significant contributions of PAHs (B[a]P (24%), B[ghi]P (27%), and In [START_REF]Molecular marker measurements and database[END_REF]2,3-cd]P (32%)). The identified factor showed typical seasonal variations linked to biomass burning emissions, with maximum contributions observed in winter (38%) (Fig. S18). This source accounted for 6% of the PM 10 mass on a yearly average (1.2 µg m -3 ) (Fig. S20). Additionally, OC/levoglucosan ratio (=5.2) was in the range of the values generally reported in previously studies performed in the Paris region (Bressi et al., 2014;Sciare et al., 2011).

Fungal spores

This source factor was characterized by the large proportions of polyols (arbitol (96%) and sorbitol (65%)) (Fig. S18), markers of primary biogenic emissions mainly originating from fungal spores and bacteria [START_REF] Bauer | Arabitol and mannitol as tracers for the quantification of airborne fungal spores[END_REF][START_REF] Caseiro | Wood burning impact on PM10 in three Austrian regions[END_REF][START_REF] Rogge | Organic marker compounds in surface soils of crop fields from the San Joaquin Valley fugitive dust characterization study[END_REF][START_REF] Yttri | Source apportionment of the carbonaceous aerosol in Norway – quantitative estimates based on 14 C, thermal-optical and organic tracer analysis[END_REF]. The factor contributed approximately to 8% of the PM 10 mass on an annual average, corresponding to a concentration of 15 µg m -3 . As expected, high contributions during summer-autumn period were observed (Samaké et al., 2019a;Samaké et al., 2019b;Srivastava et al., 2018b).

Sea salt

This factor showed major contributions of Na + (75%) and Mg 2+ (62%), highlighting the characteristic sea salt particles (Fig. S18). The Mg 2+ /Na + ratio (0.09) was close to the sea water ratio (i.e., 0.11) [START_REF] Curran | Seasonal characteristics of the major ions in the high-accumulation Dome Summit South ice core, Law Dome, Antarctica[END_REF]Seinfeld and Pandis, 1998) and similar to the values found in the Paris region (0.1-0.13) (Bressi et al., 2014;Srivastava et al., 2018a). In addition, this factor also showed high Cl -content (84%), indicating the influence of fresh sea salt aerosols, with an annual contribution of 10%to the PM 10 mass (Fig. 20) and a yearly average concentration of 2.0 µg m -3 .

Dust

This factor showed a relatively high content of mineral elements such as Ti (53%), Ca 2+ (49%), and Al (54%), commonly originating from soils and road dust [START_REF] Andersen | Ambient particle source apportionment and daily hospital admissions among children and elderly in Copenhagen[END_REF][START_REF] Mossetti | Assessing the impact of particulate matter sources in the Milan urban area[END_REF][START_REF] Querol | Speciation and origin of PM10 and PM2.5 in Spain[END_REF][START_REF] Yin | Major component composition of urban PM10 and PM2.5 in Ireland[END_REF] (Fig. S16). This factor accounted for 15% of the PM 10 mass on an annual average corresponding to an average concentration of 2.9 µg m -3 (Fig. S18).

Sulfate-rich secondary aerosols

The factor (sulfate-rich) was characterized based on high loading of SO 42-(51%) including significant contributions from oxalate (24%) with an annual average concentration of 1.3 µg m -3 and accounting for approximately 7% of the PM 10 mass (Figs. S19 andS20). The source showed very constant temporal trend throughout the year except with very high concentrations during the spring PM pollution event (PM 10 > 50 µg m -3 for at least 3 consecutive days) (Fig. S20). High contributions were related to long range transport and ageing processes highlighted previously for this same pollution event (Petit et al., 2017a;Srivastava et al., 2018a;[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF].

Nitrate-rich secondary aerosols

The factor (nitrate-rich) was characterized by high contributions of NO 3 -and NH 4 + (74% and 66% of species mass being attributed to this factor, respectively), with an annual average concentration of 4.9 µg m -3 and accounting for approximately 25% of the PM 10 mass (Figs. S19 andS20). The source showed a very distinctive seasonal variation with very high concentrations during the late winter-early spring period (Fig. S20). The high contributions of ammonium nitrate observed during this period was linked to secondary formation processes and long range transport of aged air masses and is typical of the late winter-early spring period in North-western Europe (Petit et al., 2017a;Srivastava et al., 2018a;[START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements[END_REF][START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF].

8.9. Biogenic SOA-1 (marine) This factor showed very high loading (77%) of methanesulfonic acid (MSA), a known secondary oxidation product of dimethylsulfide (DMS). DMS is emitted by phytoplankton and several types of anaerobe bacteria and released from the ocean into the atmosphere [START_REF] Charlson | Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate[END_REF][START_REF] Chasteen | Volatile Organic Sulfur Compounds of Environmental Interest: Dimethyl Sulfide and Methanethiol. An Introductory Overview[END_REF]Crippa et al., 2013;[START_REF] Zorn | Characterization of the South Atlantic marine boundary layer aerosol using an aerodyne aerosol mass spectrometer[END_REF]. This factor accounted for 10% of the PM 10 mass on annual average (2.1 µg m -3 ) (Fig. S20) and followed a clear temporal variation with higher contributions during summer with a maximum contribution of 36% to the PM 10 mass in agreement with phytoplankton blooms observed in the North Sea in summer season (Figs. S19 andS22), and was also confirmed by the results obtained from the Concentration-Weighted Trajectory (CWT) analysis (Figure S23).

Biogenic SOA-2

Biogenic SOA-2 factor was resolved by using the SOA markers for oxidation of isoprene (αmethylglyceric acid (α-MGA and 2-methylerythritol (2-MT)) and pinene (3-hydroxy-4,4dimethylglutaric acid and 3-acetylpentanedioic acid ) (Carlton et al., 2009;[START_REF] Edney | Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated isoprene/NO X /SO 2 /air mixtures and their detection in ambient PM2.5 samples collected in the eastern United States[END_REF][START_REF] Lewandowski | Primary and secondary contributions to ambient PM in the midwestern United States[END_REF]Kleindienst et al., 2007). The factor showed very high loadings of these SOA markers (64-93% of the total mass of each compound), except for 3-hydroxy-4,4dimethylglutaric acid (37%) (Fig. S19). Note, the separation of both biogenic SOA sources was not possible and resulted in a non-stable solution (poor bootstrap). Biogenic SOA factor showed a very low contribution to the PM 10 mass of 2% on an annual average, corresponding to a concentration of 0.5 µg m -3 (Fig. S20). High concentrations (up to 7% of PM) observed in the summer were in good agreement with the higher biogenic activity and larger emissions of isoprene and pinene during the warm months (Kleindienst et al., 2007;Shrivastava et al., 2007;Srivastava et al., 2018b;[START_REF] Zhang | Six-year source apportionment of submicron organic aerosols from near-continuous measurements at SIRTA (Paris area, France)[END_REF]Zhang et al., , 2009)).

Anthropogenic SOA-1

The factor was characterized by high loadings of succinic acid (69%), phthalic acid (73%), and DHOPA (68%) (Fig. S19). Phthalic acid and DHOPA are considered as markers of SOA formation from naphthalene and toluene photooxidation, respectively (Al-Naiema and Stone, 2017; Kleindienst et al., 2004Kleindienst et al., , 2007Kleindienst et al., , 2012)), while succinic acid is also a known oxidation product of anthropogenic precursors (Kawamura and Bikkina, 2016). Thus, this factor seemed to follow the characteristic of SOA from anthropogenic sources, including combustion processes such as biomass burning and traffic. This source accounted for approximately 2% of the total PM 10 mass, with a concentration of 0.4 µg m -3 (Fig. S20). 8.12. Anthropogenic SOA-2 This factor was apportioned based on high fractions of 4-methyl-5-nitrocatechol (4-Me5Nc) and 3-methyl-5-nitrocatechol (3-Me5Nc) (85% of each species mass being attributed to this factor) (Fig. S19). Methyl-nitrocatechols are typical oxidation product of phenolic compounds such as cresols, mainly originating from biomass burning emissions (Bruns et al., 2016;Iinuma et al., 2010). In addition, this factor also showed significant contributions of levoglucosan (23%), mannosan (23%), BC wb (2%), some PAHs (B[a]P (38%), B[ghi]P (31%), and In [START_REF]Molecular marker measurements and database[END_REF]2,3-cd]P (29%)) and DHOPA (13%). Therefore, this factor illustrated the characteristic of biomass burning SOA. The source showed a low contribution to the PM 10 mass of 4% on annual average corresponding to a concentration of 0.8 µg m -3 (Fig. S19and S20). However, high contributions (11%) were observed during winter when the biomass burning emissions were predominant.

Table S7. List of input species in the PMF model.

PM 10

Mn Indeno [1,2,3-cd] CWT calculations were performed using the ZeFir Igor package (Petit et al., 2017b). Details about all these calculations have been reported previously (Petit et al., 2017a).

Chapter III

Model to measurements comparison: Anthropogenic markers

Article II

Modelling organic aerosol markers in 3D air quality model. Part 1:

Anthropogenic organic markers.

To be submitted in -Atmospheric chemistry and physics‖

Modelling organic aerosol markers in 3D air quality model. Part 1:

Anthropogenic organic markers.

G.M. Lanzafame Abstract.

Air quality models often underestimates the secondary organic aerosol (SOA) fractions in total organic aerosol (OA). The great variability of OA sources and composition make it difficult to validate model performances by in situ measurements (GPP). For these reasons, we developed and inserted in a 3D

model the formation mechanisms of some anthropogenic marker: levoglucosan (primary marker), nitroaromatic compounds, DHOPA and phthalic acid (secondary markers). Simulation outputs have been compared with measurements performed in 10 urban sites in France in winter for levoglucosan and for secondary marker at SIRTA, in the Paris region, all over 2015. Gas to particle partitioning thermodynamic and spatial sensitivity have been examined.

Levoglucosan is well estimated in most of the sites, except for western cities, in which residential wood burning emissions are probably underestimated. A great incertitude in levoglucosan concentrations is due to its recently proved semivolatility, that is reproduced by our model with a particulate phase fraction that varies between 20 and 80%. Our results suggest also a strong GPP spatial sensitivity to humidity for levoglucosan. Temporal variations and concentrations of secondary markers are well reproduced for nitrophenol and nitroguaiacols, and misrepresented and underestimated for methylnitrocatechol, DHOPA and phthalic acid. Precursors emissions and formation pathways are probably missing. GPP is well reproduced for nitroguaiacols, nitrophenols and DHOPA in non-ideal conditions and a protective effect towards degradation for the transfer to the particulate phase has been identified. Except for DHOPA (almost non-volatile), marker total concentrations are significantly affected by gas phase dry and wet deposition.

Introduction and objectives

Air quality models are numerical tools to forecast and monitor air pollution events and assess air quality control policies. By using data such as emissions and meteorology, chemistry transport models (CTMs)

simulate the main processes involved in the evolution of pollutant concentrations in the atmosphere.

However, the model performances depend strongly on the model parametrizations and configurations.

Outputs from different models are often discordant (Bessagnet et al., 2016). Organic aerosol (OA) is a major fraction of the fine particulate matter (PM) (Kanakidou et al., 2005;Zhang et al., 2007) originating from both, anthropogenic and biogenic sources. Whereas primary organic aerosol (POA) are directly emitted into the atmosphere, secondary organic aerosols (SOA) are produced by atmospheric (photo-)chemical reactions. Their formation occurs via photooxidation of the volatile organic compounds (VOCs) leading to semi volatile organic compounds (SVOC) products that partition between gas and particle phases. The SOA formation depends on multiple factors (reactants concentrations, meteorological parameters, various emission sources…) and their representation in air quality model remains challenging (Carlton et al., 2009;Hallquist et al., 2009;Kroll and Seinfeld, 2008;Ziemann and Atkinson, 2012).

Due to the great number of organic species, emissions sources (biomass burning, road traffic, vegetation, etc….) and phenomena involved in OA formation (aqueous-phase reactions, gas-phase reactions, oligomerization, gas-particle partitioning), it is not straightforward to analyze by comparison to in-situ observations of OA, the reasons for a lack of performances of air quality models. One way to provide information that could be used for the evaluation of CTM is to study molecular OA tracers (markers). Molecular markers from specific sources have been experimentally identified and are currently used for source apportionment (Kleindienst et al., 2007;Lough et al., 2007;Simoneit et al., 1999) and their partitioning under ambient conditions has been evaluated in several studies (Al-Naiema and Stone, 2017; Lanzafame et al., 2020;Xie et al., 2014;Yee et al., 2013). Levoglucosan, nitrophenols, nitroguaiacols, methylnitrocatechols, 2,3-dihydroxy-4-oxopentanoic acid (DHOPA), phthalic acid are examples of molecules commonly used as molecular markers.Levoglucosan has been identified as a lignin pyrolysis products and it is commonly used as a primary marker for biomass burning (Simoneit et al., 1999). Nitrated phenolic compounds (nitrophenols, nitroguaiacols and methylnitrocatechols) are usually considered as photooxidation products (secondary markers) of biomass burning VOCs (Iinuma et al., 2010;Lauraguais et al., 2014;[START_REF] Olariu | FT-IR study of the ring-retaining products from the reaction of OH radicals with phenol, o-, m-, and p-cresol[END_REF] but nitrophenols and methylnitrocatechols have also been detected in primary biomass burning emissions (Lu et al., 2019;Wang et al., 2017). DHOPA has been identified as a marker for toluene SOA (Kleindienst et al., 2007). Phthalic acid has been proposed as a marker for naphthalene and methylnaphthalene photooxidation (Kleindienst et al., 2012), but it has also been observed as a degradation product of phthalates emitted by plastic materials [START_REF] Hankett | Molecular Structural Changes of Plasticized PVC after UV Light Exposure[END_REF] and as a primary marker emitted by vehicular exhaust (Kawamura and Kaplan, 1987). Although all the source apportionment tracer-based methods assume that these compounds are stable and low volatile (Nozière et al., 2015;Schauer et al., 1996;Srivastava et al., 2018), they can be degraded in atmosphere by sunlight and radical reactions and evidences of their semi volatile behavior have been provided (Bannan et al., 2017;Booth et al., 2012;Lanzafame et al., 2020;Xie et al., 2014). Based on these considerations, OA tracers should be rather called markers.

In this study, we propose to use modelling of specific OA markers and to compare the modelled concentrations to both measurements in the gas phase and in the particle phase to better evaluate the performance of air quality models. Model to measurements comparison can provide insights on the accuracy of emissions and chemical pathways representations in the model. Additionally, comparing concentrations in both the gas phase and the particle phase of specific SOA markers can be used to evaluate directly the capacity of models to reproduce GPP.

One of the challenges to include GPP in 3D air quality models is to evaluate their thermodynamic properties such as the subcooled liquid saturated vapor pressure (P sat ) and the vaporization enthalpy (ΔH vap ). However, only a few studies were performed on the marker thermodynamic properties and their partitioning in ambient air (Bannan et al., 2017;Bilde et al., 2015;Booth et al., 2011;[START_REF] Oja | Vapor Pressures and Enthalpies of Sublimation of D -Glucose, D -Xylose, Cellobiose, and Levoglucosan[END_REF]Xie et al., 2014). As OA marker are generally solid at ambient conditions, P sat at ambient temperature is calculated from the solid-state vapor pressure and the fusion enthalpy (Booth et al., 2012). The condensation of SVOC is also influenced by the aerosol non-ideality and aerosol viscosity. For simplification aerosol models often assume the ideality (no short-, medium-and longrange interactions between the compounds) whereas the partitioning is strongly dependent on nonideality (Couvidat et al., 2012[START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF]. Moreover, viscosity mixing affects the diffusion of the marker inside the particle, slowing down the exchange processes with the gaseous phase [START_REF] Shrivastava | Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol[END_REF]. [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF] showed that considering the viscosity of the organic phase could lead to a strong increase of particle-phase concentrations of high volatility compounds.

This paper focuses on the development of chemical mechanisms of some anthropogenic markers: levoglucosan and several secondary SOA markers (nitrophenols, nitroguaiacols, methylnitrocatechols, DHOPA and phthalic acid) and presents model to measurements comparisons of concentrations and GPP at SIRTA in the Paris region. To our knowledge, this is the first time that the anthropogenic markers mentioned are modelled in a 3D CTM. Model to measurements comparison of concentrations were also carried out for levoglucosan at several sites over France. Finally, some sensitivity analyses on GPP are presented in the final part of the paper.

Model development

A gaseous and aqueous phase mechanism for the formation of 14 organic markers in ambient air has been developed and inserted in the 3D chemistry-transport model CHIMERE 2017β (Couvidat et al., 2018). The CHIMERE 2017β aerosol module was extensively described in Couvidat et al., (2018).

CHIMERE uses a sectional approach, with particles separated into 9 bins from 10nm to 10µm. The evaporation/condensation of semi-volatile compounds is represented with the algorithm of [START_REF] Pandis | Secondary organic aerosol formation and transport -II. Predicting the ambient secondary organic aerosol size distribution[END_REF] using thermodynamic equilibria computed with the thermodynamic modules ISORROPIA for inorganics (Fountoukis and Nenes, 2007) and the secondary organic aerosol processor (SOAP, Couvidat and Sartelet, 2015) for organics. Coagulation of particles is represented as in [START_REF] Sartelet | Simulation of aerosols and gas-phase species over Europe with the POLYPHEMUS system: Part I -Model-to-data comparison for[END_REF].

H 2 SO 4 nucleation parametrization is simulated as in [START_REF] Kulmala | Parameterizations for sulfuric acid/water nucleation rates[END_REF].

The model takes into account the wet and dry deposition of particles as a function of their wet diameter as presented in Couvidat et al., (2018). The dry deposition of the gas-phase concentration of SVOC can be estimated according to their Henry's law constant as in in Bessagnet et al., (2010). Similarly, the model can also consider the in-cloud and the below-cloud scavenging of SVOC.

The marker formation mechanism is added to the gas-phase mechanism MELCHIOR2 (Derognat et al., 2003) and to the Hydrophilic/Hydrophobic Organics (H²O) SOA formation mechanism (Couvidat et al., 2012) already inserted in CHIMERE. In the H²O mechanism, SOA are produced by the photooxidation of the major volatile organic compounds (VOCs: isoprene, monoterpenes, sesquiterpenes, toluene and xylene). POA is treated as SVOCs and split in 3 surrogate species with different volatility and aging products. Following Majdi et al., (2019), the H²O mechanism has been refined by adding SOA formation for the oxidation of several precursors from biomass burning: phenol, cresol, catechol, benzene, furan, guaiacol, syringol, naphthalene and methylnaphthalene.

Overview of the marker mechanism

The marker version of CHIMERE 2017β includes 267 species and 757 reactions, against the 69 species and 215 reactions in the base version.

For secondary markers, the precursors considered are toluene, guaiacol, phenoll, isoprene and α-/βpinene. Mechanisms for markers formation were sought in literature, taken from the Master Chemical Mechanism (MCM, version 3.3.1) and missing information (not found in the literature and using MCM)

were taken from the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A, Camredon et al., 2007) for the gaseous phase chemistry and from the Cloud Explicit Physico-chemical Scheme (CLEPS 1.0, Mouchel-Vallon et al., 2017) for the aqueous phase chemistry.

These mechanisms are developed with the following procedure: the precursors are photolyzed, oxidized or hydrolyzed according to kinetics and branching ratios obtained from laboratory data, and when laboratory data are not available, the reactivity is estimated by analogy with similar better characterized systems or with structure activity relationships (SARs). The photooxidation mechanisms are usually initiated in the gaseous phase by reaction with OH, NO 3 and also O 3 for isoprene and α-/β-pinene.

Further reaction steps consider also the reactivity with HO 2 , RO 2 , NO and NO 2 , photolysis, hydrolysis and aqueous-phase reactions. To simulate aqueous phase reactivity, the substrate and OH radicals partition between the gaseous and aqueous phases according to its Henry's law constant and solubilization enthalpy. The value chosen for OH Henry's law constant is 25 M -1 atm (Lelieveld and Crutzen, 1991). Following Couvidat et al. (2013), the aqueous-phase concentration of OH has been assumed to be half of the concentration at the equilibrium, to take in account the consumption by the other species dissolved. The detailed mechanisms for anthropogenic markers are discussed below and reported in the supplementary material. The mechanisms for the biogenic markers are presented in the companion paper.

The gas to particle partitioning of markers and some reaction intermediates is computed with SOAP (Couvidat and Sartelet, 2015), briefly described in the next section. The GPP is estimated by the model by using P sat and ΔH vap . These parameters have been sought in literature, measured data has been preferred when available. For the markers with no published data, SARs estimations have been inserted

in the model or used to deduce semi-empiric values related to experimental data for similar molecules.

Henry's law constants (K H ) used for marker deposition have been calculated with SOAP.

Marker GPP computation

In SOAP, the user chooses whether the SVOC species are hydrophilic (condensed onto the aqueous phase of particles), hydrophobic (condensed onto the organic phase) or both (condensed on both phase).

By using the molecular structure assigned to each species, the model computes the activity coefficients with the UNIversal Functional group Activity Coefficient; (UNIFAC, Fredenslund et al., 1975) model for short-range interactions (interactions between solvent species) and the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC, (Zuend et al., 2008(Zuend et al., , 2010[START_REF] Zuend | New and extended parameterization of the thermodynamic model AIOMFAC: calculation of activity coefficients for organic-inorganic mixtures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl, and aromatic functional groups[END_REF]Zuend and Seinfeld, 2012) model for the medium-range and long-range interactions (interactions between solvent species and ionic species) and uses them to simulate the non-ideality of aerosols. These structure activity relationship models include parameterizations to calculate activity coefficients of aerosol mixtures containing water, several organic functional groups (e.g. carboxyl, hydroxyl aldehyde, aromatic carbon …) and inorganic ions for AIOMFAC. As some NO 2 parameters are missing, the parameters for alkanes were used instead.

P sat and ΔH vap are required as input data for the model. To compute the hydrophilic partitioning, the value of the Henry's law constant is used. It can be either provided by the user or be computed from the saturation vapor pressure and the activity coefficient at infinite dilution.

SOAP can use either a dynamic approach or an equilibrium approach (assuming instantaneous partitioning) to compute OA formation. For computational purposes, the equilibrium approach was used here. The effect of modeling with SOAP non-ideality and non-equilibrium was studied in a previous paper [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF] In the equilibrium approach, the partitioning between the organic phase and the gaseous phase is estimated according to a modified version of Raoult's law that evaluates the organic compounds absorption by the organic phase (Pankow, 1994):

𝐴 𝑝 ,𝑖 𝐴 𝑔,𝑖 = 𝐾 𝑝,𝑖 𝑀 0 (Eq.1)
where 𝐴 𝑝,𝑖 is in the organic phase concentration (μg m -3 ) of i, 𝐴 𝑔,𝑖 the gas-phase concentration (μg m -3 ) of i, 𝑀 0 the organic phase concentration (μg m -3 ) and 𝐾 𝑝,𝑖 the organic-phase partitioning coefficient (m 3 μg -1 ):

𝐾 𝑝,𝑖 = 760 × 8.202× 10 -5 × 𝑇 𝑀 𝑜𝑤 𝛾 𝑖,𝑜𝑟𝑔 𝑃 𝑖 0 ×10 6 (Eq.2)
where T is the temperature (in K), 𝑀 𝑜𝑤 the organic phase mean molar mass (g mol -1 ), 𝛾 𝑖,𝑜𝑟𝑔 is the activity coefficient of i and 𝑃 𝑖 0 the saturation vapor pressure (torr).

Assuming infinite dilution, the partitioning between gaseous phase and the aqueous phase can be evaluated with Henry's law. In SOAP, a modified version of this law, suitable for all dilution conditions, is used:

𝐴 𝑎𝑞 ,𝑖 𝐴 𝑔,𝑖 = 𝐾 𝑎𝑞 ,𝑖 𝐴𝑄 (Eq.3)
where 𝐴 𝑎𝑞 ,𝑖 is the concentration of species i in the organic phase (μg m -3 ), 𝐴 𝑔,𝑖 the gaseous phase concentration of i (μg m -3 ), AQ the aqueous phase organics + inorganics concentration (μg m -3 ) and 𝐾 𝑎𝑞 ,𝑖 is the partitioning coefficient in the aqueous phase (m 3 μg -1 ):

𝐾 𝑎𝑞 ,𝑖 = 𝐾 𝐻,𝑖 𝑅𝑇 𝜌 𝑤𝑎𝑡𝑒𝑟 𝜁 𝑖 ×1.013×10 11 × 18 𝑀 𝑎𝑞 (Eq. 4)
where 𝐾 𝐻,𝑖 is the i Henry's law constant (M -1 atm), 𝜌 𝑤𝑎𝑡𝑒𝑟 is the aqueous phase density (kg m -3 ), 𝑀 𝑎𝑞 is the molar mass of the aqueous phase (g mol -1 ) considering the presence of other compounds and 𝜁 𝑖 is the ratio of the i activity coefficient in the aqueous phase with the activity coefficient at infinite dilution in water:

𝜁 𝑖 = 𝛾 𝑖,𝑎𝑞 𝛾 𝑖,𝑎𝑞 ∞ (Eq. 5)
𝛾 𝑖,𝑎𝑞 ∞ is computed with UNIFAC (Fredenslund et al., 1975). 𝛾 𝑖,𝑎𝑞 ∞ is computed multiplying the activity coefficients for long-, medium-and short-range interactions (Zuend et al., 2008).

𝐾 𝑎𝑞 ,𝑖 depends on temperature through a Clausius-Clapeyron relation as for the organic phase partitioning coefficient. A modification of partitioning coefficient to consider the acidic dissociation (by using acid dissociation constants) in the aqueous phase is added as in [START_REF] Pun | Modeling Secondary Organic Aerosol Formation via Multiphase Partitioning with Molecular Data[END_REF]. The concentrations of water and inorganics are taken from the results given by ISORROPIA.

Anthropogenic OA marker formation in the atmosphere: mechanisms and GPP parameters

The mechanisms developed for the anthropogenic markers, together with the parameters chosen to simulate their GPP at the equilibrium, are described below. For simplification, only the reactions involved in the marker formation pathways are discussed. The competitive reactions with other radicals, which do not lead to the formation of the studied markers, are also considered in the mechanism and reported in the SM. The molecules are named as in MCM or using MCM-like names. The thermodynamics properties of the markers are summarized in Table 1.

Levoglucosan

Levoglucosan degradation in atmosphere occurs in the gaseous and in the aqueous phase. In the gaseous phase, levoglucosan reacts with the OH radical following the kinetic rate parameter proposed by [START_REF] Bai | The OH-initiated atmospheric reaction mechanism and kinetics for levoglucosan emitted in biomass burning[END_REF] of 2.21×10 -13 cm 3 molecule -1 s -1 . The degradation in the aqueous phase by OH radicals was also taken into account based on the the kinetic parameter measured by [START_REF] Yang | Photooxidation of levoglucosan in atmospheric aqueous aerosols[END_REF] (2.1×10 9 M - 1 s -1 ). Evidences for a semivolatile behavior of Levoglucosan are provided in Xie et al., (2014). The values chosen for levoglucosan P sat and ΔH vap are respectively 1.45×10 -6 torr and 52 kJ mol -1 (measured values, 298 K) (Booth et al., 2011).

Nitroguaiacols

Nitroguaiacols are the photooxidation products of guaiacol, known as a primary compound emitted by biomass burning [START_REF] Mcdonald | Fine Particle and Gaseous Emission Rates from Residential Wood Combustion[END_REF]. In the atmosphere, the photooxidation can be initiated by both, reaction with the hydroxy or with the nitrate radical (Fig. S1). All nitroguaicols are represented as a single species since the SARs used to estimate the saturated vapor pressure are not able to distinguish the isomers.

For the degradation with OH, Coeur-Tourneur et al., (2010) observed in a chamber experiment a kinetic rate of 7.5×10 -11 cm 3 molecule -1 s -1 . Lauraguais et al., (2014) measured the gas-phase photooxidation yields for the guaiacol reaction with OH. With a yield of 10%, 4-nitroguaiacol was found to be the major product, followed by a mixture of 3-and 6-nitroguaicol (yield 6%). Since no detailed mechanism was available in the literature, a yield of 16% has been used to model nitroguaiacols from OH-initiated guaiacol oxidation reaction.

The experimental value for the kinetic rate parameter of guaiacol withNO 3 is 3.2×10 -12 cm 3 molecule -1 s -1 (Yang et al., 2016). The nitro-derivatives have been found to be the major products of this reaction (Yang et al., 2016), but no yield was provided. We assumed that the nitroguaiacols mixture has a yield of 100%. since no degradation kinetic rate of nitroguaiacols has been implemented in the gas-phase mechanism (due to lack of data in literature), the nitroguaiacol concentrations may be overestimated.

Nitroguaiacol P sat and ΔH vap were estimated with a semi-empiric method, taking experimental nitrocatechol P sat and ΔH vap (7,50×10 -6 torr and 39 kJ mol -1 respectively) (Booth et al., 2012) as reference. SARs P sat and ΔH vap have been calculated with EPIsuite (US EPA, 2015) for nitrocatechol and nitroguaiacol to estimate the effect of the -OH substitution with -OCH 3 . The P sat ratio and the ΔH vap difference have been used as correction factors to calculate semi-empirical values, giving respectively 4.61×10 -5 torr and 30.9 kJmol -1 (details in Tables S8, S9 and S10).

Nitrophenols

The precursors of nitrophenols are toluene, benzene and phenol. In MCM there is no distinction for the nitrophenol isomer formation yields. The photooxidation process of toluene is started by reaction with OH, with a kinetic rate parameter of 1.8×10 -12 e (340/T) cm 3 molecule -1 s -1 (T being the temperature in K).

This reaction generates 4 oxidation products: the benzyldioxidanyl radical (MCM name C6H5CH2O2), cresol, a dioxabicyclic-alcoholic-peroxy-hydroxy-hexene radical (TLBIPERO2) and oxobutenyl oxirane carbaldehyde (TLEPOXMUC) (see Fig 1, branching ratios are 0.07, 0.18, 0.65 and 0.10 respectively).

C6H5CH2O2 is the intermediate for nitrophenol formation.

The NO 3 initiated photooxidation can also produce C6H5CH2O2, but MCM does not include this reaction. Huang et al., (2014) performed a computational study on the NO 3 -initiated photooxidation of toluene. They assessed that the major isomer generated by H abstraction is the benzyl radical, with a rate constant of 6.10×10 -17 cm 3 molecule -1 s -1 . We assumed that the yield of the benzyl radical in this reaction is 100%.

The successive steps lead to the formation of the benzaldehyde as intermediate, involving reactions with HO 2 , NO and photolysis. A multi-step benzaldehyde photooxidation generate the phenate radical, that reacts with NO 2 to give the nitrophenol. Phenol and benzene are also considered as nitrophenols precursors. Phenol is both a primary compound and the photooxidation intermediate from benzene.

Benzene reacts with OH giving phenol, and phenate is produced by the reaction of phenol with OH and NO 3 . All these reactions were coded in MCM and reported in Fig. 2. The mechanism produces a mixture of 2-and 4-nitrophenol. No evidence was provided for the formation of 3-nitrophenol (Cecinato et al., 2005). Branching ratios for nitrophenol formation are not available in the literature, however some ambient air measurements in both, gaseous and particulate phases, reported that 4-nitrophenol is the most abundant isomer (Cecinato et al., 2005). 4-nitrophenol thermodynamic parameters have therefore been chosen to simulate nitrophenols GPP. P sat value is 3.86×10 -5 torr, measured by Bannan et al., (2017). Experimental ΔH vap were not available, so the arithmetic mean of SARs values (Table S1 and S3, calculated with UManSysProp [START_REF] Topping | UManSysProp v1.0: an online and open-source facility for molecular property prediction and atmospheric aerosol calculations[END_REF])), 51.16kJ mol -1 , has been used.

DHOPA

DHOPA has been recognized as a photooxidation product of toluene (Kleindienst et al., 2004) but to the best of our knowledge, no mechanism about the formation of DHOPA has been reported in the literature.

The 4-oxo-2-pentenal (C5DICARB) is one of the major ring-opening photooxidation products of toluene, with a yield of 4% (Bierbach et al., 1994). We supposed that this compound is the main intermediate leading to DHOPA formation. It has also been demonstrated that 2-methylfuran can be a precursor of C5DICARB, with experimental yields of 0.31, 0.60 and 0.70% (Aschmann et al., 2014;Gómez Alvarez et al., 2009 andBierbach et al., 1995, respectively). In our model both precursors were taken into account. A yield of 31% (Aschmann et al., 2014) and a kinetic rate parameter of 6.19×10 -11 cm 3 molecule -1 s -1 (Bierbach et al., 1992) were assumed to simulate C5DICARB formation from 2methylfuran (Fig. 3 and Table S2).

Among the OH-initiated photooxidation products of toluene listed in the previous paragraph, three of them are candidates as potential precursors of DHOPA: TLBIPERO2, TLEPOXMUC and CRESOL.

TLBIPERO2 and TLEPOXMUC are photooxidized to give 3 intermediates: a dioxabicyclic-alcooliccarbonyl-hydroxy-hexene (TLOBIPEROH), the (3E)-2,5-dioxohex-3-enal (C6125CO) and the main intermediate C5DICARB (Fig. 4). From these intermediates two pathways have been identified to produce DHOPA (Fig. 5, Tables S3, S4 andS5). CRESOL photooxidation produce the 4-oxo-1pentenoic acid (C5CO14OH), an intermediate of the second pathway described below.

The first pathway starts with the reaction of C5DICARB with OH, that produces the (3-hydroxy-1,4dioxopentan-2-yl) dioxidanyl (C5DICARBO2) with a branching ratio of 0.52. The successive reaction with RO 2 radicals yields 20% of 2,3-dihydroxy-4-oxo-pentanal (C514CO23OH). In MCM, the only product of C514CO23OH reaction with OH is the 2-hydroxy-3,4-oxo-pentenal, generated from the reaction onto one of the three possible reactive sites (the two hydroxyls and the aldehyde). To form DHOPA, the H-abstraction on the aldehyde is required. The oxidation mechanism from C514CO23OH

was therefore taken from GECKO-A, that estimate 43% of chances for the H-abstraction on the aldehyde to take place. The radical produced reacts with HO 2 or RO 2 to form DHOPA with a yield of 15% and 30%, respectively. Moreover, since C514CO23OH is an aldehyde, it is expected to be oxidized in the aqueous phase to the correspondent carboxylic acid (Tilgner and Herrmann, 2010), DHOPA. The reactivity in the aqueous phase has been simulated in analogy with 2,4-dihidroxy-3oxobutanal (CLEPS 1.0). The partitioning of C514CO23OH between the gaseous and the aqueous phase has been evaluated using the SOAP Henry's constant (3.9×10 7 M -1 atm) and solubilization enthalpy (ΔH sol =63.8 kJ/mol) (inputs P sat =8.56×10 -4 torr and a ΔH vap of 83.8 kJ mol -1 (US EPA, 2015)).

Aqueous phase branching ratios and kinetics for C514CO23OH reactions are respectively 0.29 and 1.2×10 9 M -1 s -1 with OH as oxidant and 0.47 and 1×10 6 M -1 s -1 with NO 3 as oxidant.

The second pathway starts with the formation of the [(2E)-4-oxopent-2-enoyl]dioxidanyl radical (C5CO14O2) from C6125CO and TLOBIPEROH by photolysis or reaction OH and from the reaction of C5DICARB reaction with NO 3 . C5CO14O2 reacts with HO 2 and RO 2 (branching ratios 0.15 and 0.3) to give the 4-oxo-1-pentenoic acid (C5CO14OH). C5CO14OH can also be formed from the photooxidation of CRESOL initiated by OH and NO 3 (see Fig. 6). In MCM, the C5CO14OH reaction does not lead to the formation of DHOPA. In analogy with the MCM reactivity of the 3-esen-2,5-dione (C4DBDIKET), it has been supposed a C5CO14OH-OH reaction yields of 47% for (1-carboxy-2hydroxy-3-oxobutyl)dioxidanyl (C5CO14OH2OO, analogous DMKOHO2, kinetic=1.03×10 -10 cm 3 molecule s -1 ). C5CO14OH2OO reacts then with RO 2 with a rate of 8.8×10 -13 cm 3 molecule s -1 , producing 20% of DHOPA.

DHOPA gaseous phase degradation by reaction with OH has a kinetic of 2.42×10 -11 cm 3 molecule -1 s -1 estimated from GECKO-A. DHOPA aqueous phase degradation is simulated by analogy with the 2,4dihydroxy-3-oxobutanoic acid in CLEPS, with a kinetic rate of 8.1×10 8 M -1 s -1 . S5).

Methyl-nitrocatechols

The methyl-nitrocatechols (MNCATECH) are produced from the photooxidation of cresol. Cresol is primary emitted by biomass burning and can also be produced from toluene photooxidation (see paragraph 2.2.3 and 2.2.4). All the three possible isomers, orto-, meta-and para-cresol, have been detected in atmosphere and all the three are precursors of MNCATECH (Iinuma et al., 2010). There is no information in MCM about differences in the reactivity of o-, m-and p-cresol, so they were assumed to react in the same way. Because of this, MNCATECH is the sum of all isomers that can be produced.

The mechanism for MNCATECH production is entirely reported in MCM and consists only in three steps (Fig. 6). CRESOL is oxidized by OH and form MCATECHOL (73%). MCATECHOL reacts with OH and NO 3 producing the (2-hydroxy-6-methylphenyl) oxidanyl radical (MCATEC1O) NO 2 addition to MCATEC1O generate MNCATECH.

MNCATECH P sat and ΔH vap were estimated as for NGUAIACOL with a semi-empiric method.

Nitrocatechol experimental values (Booth et al., 2012) have been used as reference. The effect of the methyl addition on P sat and ΔH vap has been estimated through SARs values calculated by EPIsuite (US EPA, 2015). Semi-empiric MNCATEC P sat and ΔH vap are respectively 3.20×10 -6 torr and 41.7 kJ mol -1 .

Phthalic Acid

Phthalic acid secondary formation in atmosphere is traditionally attributed to naphthalene and methylnaphthalene photooxidation (Kleindienst et al., 2012). No mechanism for naphthalene photooxidation has been provided in MCM. The formation pathway of phthalic acid from naphthalene proposed by Kautzman et al., (2010) has been taken as reference for the development of our mechanism (Fig. S2, Table S6).

Naphthalene OH-oxidation yields 45% of 2-formylcinnammaldehyde (FORMCINNAL, mean of 30-60% yield range reported in Kautzman et al., (2010)) with a rate constant of 2.4×10 -11 cm 3 molecule -1 s -1 (Phousongphouang and Arey, 2002). Kautzman et al., (2010) proposed an alternative pathway that produces phthalaldehyde as OH-initiated naphthalene photooxidation first generation product. [START_REF] Sasaki | Products of the gas-phase OH and NO3 radical-initiated reactions of naphthalene[END_REF] identified 67% of the first generation photooxidation products for the naphthalene-OH reaction. FORMCINNAL was the major product while phthalaldheyde was not observed. We supposed that the alternative pathway is negligible compared to the first one. In our mechanism phthalaldehyde (PHTHALDIAL) is a second generation photooxidation product.

FORMCINNAL-OH photooxidation can occur on the formyl double bond, yielding 35% of FCINNALOOOH, and on the aldehyde. The reaction rate and the branching ratios are calculated considering the analogous MCM reactions of pentene and pentaldehyde. The calculated overall reaction rate (8.3×10 -11 cm 3 molecule -1 s -1 ) is in good agreement with the values previously reported (7.7×10 -11 and 2.1×10 -11 cm 3 molecule -1 s -1 (Aschmann et al., 2013;Nishino et al., 2009)). O 3 -FORMCINNAL reactivity is simulated using the rate of 1.8×10 -18 cm 3 molecule -1 s -1 (Aschmann et al., 2013) and a yield of 50% for FCINNALOOOH, in analogy with the alkenes in MCM. FORMCINNAL photolyzes with a rate of 0.66×J(NO 2 ) (Nishino et al., 2009) yielding 33% of PHTHALDIAL. The other products of this reactions are PHTHALDIAL precursors and their reactivity is parametrized in analogy with 2-pentenale in GECKO-A (Fig. S2 and Table S6). FCINNALOOOH reacts with NO (MCM generic RO 2 +NO, rate= 2.7×10 -12 e (-360/T) cm 3 molecule -1 s -1 ) giving PHTHALDIAL.

PHTHALDIAL reacts with OH (k=2.3×10 -11 cm 3 molecule -1 s -1 , yield 85%, Wang et al., 2006) and NO 3 (k=2.8×10 -12 cm 3 molecule -1 s -1 , 2 × MCM generic constant for RO 2 +NO reaction) giving the phthalanydride (PHTHALAN). PHTHALDIAL photolyzes producing 35% of PHTHALAN and 53% of PHTHALIDE (J (aromatic carbonyl) /J (NO2) =0.030, Wang et al., 2006). PHTHALIDE reaction with OH produces 60% of PHTHALAN (k=8×10 -13 cm 3 molecule -1 s -1 , Wang et al., 2006).

PHTHALAN produces phthalic acid by reaction with OH in the gaseous phase and hydrolysis in the aqueous phase (SIDS, 2005). PHTHALAN half-life times in both phases (SIDS, 2005) have been used

to estimate the degradation rates (7×10 -13 cm 3 molecule -1 s -1 and 9.9×10 -3 M -1 s -1 ). PHTHALAN ΔH sol (61.6 kJ mol -1 ) and K H (4.12×10 5 M -1 atm) have been calculated with SOAP, using P sat =5.84×10 -3 torr and ΔH vap =65 kJ mol -1 (Crooks and Feetham, 1946).

Phthalic acid degradation in the atmosphere has been parametrized by reaction with OH (k=1.23×10 -12 cm 3 molecule -1 s -1 , half-life of 13 d -1 , 500,000 OH/cm³ ,SIDS, 2005). Experimental P sat of 1.26×10 -5 torr and a ΔH vap of 40 kJ mol -1 have been chosen (Booth et al., 2012).

Comparison measurements model

Configuration

The simulations have been performed at 0.06°x0.125° longitude/latitude resolution all over France. The number of vertical layers were 9 till 500 hPa. Meteological data were taken from data of the Integrated Forecasting System (IFS) model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Chemical boundary conditions were obtained from a lower (0.25°x0.4°) resolution simulation performed on the European domain.

Annual anthropogenic emissions, classified by sector, were taken from the EMEP inventory (methodology of [START_REF] Vestreng | Review and Revision. Emission data reported to CLRTAP Tech. Rep[END_REF]. Denier van der Gon et al. ( 2015) has shown that POA emissions are greatly underestimated due to a strong underestimation of residential wood burning emissions by a factor of 3 over Europe (between 1 and 10 depending on the countries) because emissions of condensable were often not considered. To consider this underestimation, POA emissions were corrected by applying an emission correction factor taken from the TNO inventory.

For each sector, primary organic aerosol emissions are transformed into emissions of SVOC. The SVOC emissions are split into several compounds with different volatilities. For biomass burning, SVOC are split into BOAlP (26 % of emissions, K p = 18.3 m 3 /µg), BOAmP (31 % of emissions, K p = 0.04 m 3 /µg) and BOAhP (43 %, K p = 0.00023 m 3 /µg) to follow the dilution curve of POA in [START_REF] May | Gas-particle partitioning of primary organic aerosol emissions: 3. Biomass burning[END_REF]. For other sources, emissions are split into POAlP (25 % of emissions, K p = 1.1 m 3 /µg), POAmP (32 % of emissions, K p = 0.011 m 3 /µg) and POAhP (43 %, K p = 0.00031 m 3 /µg) to follow the dilution curve of POA in Robinson et al., (2007). The aging of these compounds is simulated by their reaction with OH, using a kinetic rate constant of 2 × 10 -11 molecules -1 cm 3 s -1 . Following Grieshop et al. (2009), aging is assumed to lead to a decrease of volatility by a factor of 100 (the products BSOAlP, BSOAmP, BSOAhP, SOAlP, SOAmP and SOAhP are, respectively, less volatile by a factor of 100 than their precursors BOAlP, BOAmP, BOAhP, POAlP, POAmP and POAhP). A default -average‖ structure representative of atmospheric POA has been assigned to these surrogates to calculate their activity coefficients. This structure is composed by 40% of C23H47COOH, 5% of C8H17CH = CHC7H14COOH, 15% of 4-(2-propio)-syringone, 12% of C29H60 and 28% of 2-carboxybenzoic acid (EPRI, 1999). This approach assumes that the particle composition does not change according to the emitting source. Although we are conscious that this is not true, exhaustive molecular data for POA are still not available, so we choose this simple POA representation.

The speciation of emissions in the CHIMERE preprocessor for emission inventories is based on the data provided by [START_REF] Passant | Speciation of UK emissions of non-methane volatile organic compounds[END_REF]. Since some important precursors for biomass burning aerosols, notably the non-traditional VOCs (NTVOCs) [START_REF] Chrit | Modeling organic aerosol concentrations and properties during winter 2014 in the northwestern Mediterranean region[END_REF], were missing, an update of the speciation was required (Table 2). Emission speciation was taken from Nalin et al., (2016) and [START_REF] Schauer | Measurement of Emissions from Air Pollution Sources. 3. C 1 -C 29 Organic Compounds from Fireplace Combustion of Wood[END_REF].

When available, data from Nalin et al., (2016) ADEME) estimated that 80% of the wood used for domestic heating in France is hard wood [START_REF] Pouet | Etude sur le chauffage domestique au bois: marches et approvisionnement[END_REF]. Pine and oak emission factors have been chosen as representative of soft and hard wood, respectively. The weighted mean of oak (80%) and pine (20%) emission factors has been inserted in the model to represent biomass burning emissions in France. Also 2-methylfuran biomass burning emissions (DHOPA precursor) were included in the model and normalized according to toluene (1:1) following the experimental emission factors averages reported in [START_REF] Mcdonald | Fine Particle and Gaseous Emission Rates from Residential Wood Combustion[END_REF].

For the phenolic compounds (phenol, cresol and guaiacol) only emissions from biomass burning have been updated. Although emissions from other sectors, notably livestock [START_REF] Borhan | Comparison of seasonal phenol and p-cresol emissions from ground-level area sources in a dairy operation in central Texas[END_REF][START_REF] Cai | Odorous chemical emissions from livestock operations in United States[END_REF][START_REF] Hobbs | Emissions of volatile organic compounds originating from UK livestock agriculture[END_REF], have been reported in literature, no data suitable for a robust estimation of the emissions have been provided.

The markers have been considered both hydrophilic and hydrophobic. In the reference run, dry and wet deposition removal of gas-phase concentrations was not considered.

Measurements

The measurements compared with the model belong to different research and monitoring programs, performed in France in 2014-2015 (Fig. 7). Among the 12 sampling sites presented, 11 are urban and suburban and represent the most populated areas of France. The other is rural and less influenced by anthropic activities. This set of measurements is representative of French air quality conditions. The details about the sampling sites, online and offline measurements and analytical procedures are reported elsewhere (Lanzafame et al., 2019, Favez, 2016;Tomaz et al., 2016) et al., (2019). The list of the SOA marker measured is reported in Table S11. Levoglucosan annual data for 2015 were available in the EBAS database (http://ebas.nilu.no/) for the OPE sampling site. This site is representative of rural environment and far from any anthropic influence (description available from http://www.andra.fr). The other sites are part of the French regional air quality monitoring network (AASQA). Particulate phase samples (PM 10 ) have been collected in the winter 2014-2015 during a campaign for the quantification of biomass burning impact on urban areas (Favez, 2016). Levoglucosan has been quantified and the Biomass Burning Organic Carbon (OC wb ) has been estimated using specific conversion factors from levoglucosan for each site (the estimation method is reported in Favez, (2016) and briefly described in the SM, section 2.1). The estimated OC wb to levoglucosan ratio, details of sampling locations, periods and frequency for each site are reported in Table S12, for simplification in the main text each site will be named by the correspondent urban area, except for SIRTA and OPE.

Results and discussion

Simulations are performed over Europe and France for year 2015. In a first part, the results of the model are compared to available measurements over France in order to evaluate the performance. In a second part, sensitivity analysis on GPP are also performed. In the western cities Nantes, Poitiers and Bordeaux, simulations show an even stronger underestimation: median modelled concentrations are respectively 0.7, 0.10 and 0.9 µg m -3 against the measured 0.28, 0.35, 0.48 µg m -3 ,respectively. 25th and 75th percentiles are strongly underestimated by the model: for

Model to measurement comparison

Levoglucosan

Nantes and Bordeaux the modelled 75th percentile has the same value of the measured 25th percentile (0.13 and 0.17 µg m -3 respectively), for Poitiers the modelled 75th percentile (0.16 µg m -3 ) is lower than the measured 25th percentile (0.18 µg m -3 ). For Nantes, Poitiers and Bordeaux, MNB values are -0.71, -0.78 and -0.78 and F p values are 0.60, 0.62, 0.54 respectively.

For stations in southeastern France (Lyon and Grenoble), results of the model are close to observations, median measured values for levoglucosan are respectively 0.29 and 0.60 µg m -3 while modelled values are 0.32 and 0.49 µg m -3 . In these 2 sites levoglucosan is slightly overestimated, with MNB values of 0.10 and 0.12. The interquartile ranges estimation is close to observed values and the correlation is higher for Lyon (0.75) and lower for Grenoble (0.48). F p values are 0.68 for Lyon and 0.67 for Grenoble.

In the Mediterranean cities (Niceand Marseille), levoglucosan concentrations are highly underestimated.

Median modelled values (0.07 and 0.02 µg m -3 respectively) are very low compared to the measured ones (0.47 and 0.24 µg m -3 respectively), such as interquartile ranges (modelled values are respectively 0.02-0.17 and 0.01-0.09 µg m -3 and measured values are 0.26-0.66 and 0.16-0.39 µg m -3 ). This result is confirmed by MNBs values of -0.75 for Nice and -0.79 for Marseille. The correlation is higher for Marseille (0.67) and lower for Nice (0.38). The F p modelled values are lower than for all the other cities (0.29 for Nice and 0.26 for Marseille).

Annual median particulate phase levoglucosan concentrations calculated for annual sites are lower than for winter sites because biomass burning emissions are lower in the warmest season. Median modelled values, 0.02 µg m -3 for both sites, underestimate the measured medians of 0.06 and 0.05 µg m -3 respectively for SIRTA and OPE. The modelled interquartile range for OPE (0.01-0.08 µg m -3 ) is well estimated (measured range 0.006-0.10 µg m -3 ), while at SIRTA the 25 th and the 75 th percentile (respectively 0.002 and 0.08 µg m -3 ) are underestimated (measured interquartile range 0.01-0.21 µg m -3 ). MNB value is negative for SIRTA, but positive for OPE (modelled concentrations are probably affected by some local maximum). Correlations are high (0.72 for SIRTA and 0.78 for OPE) and F p values are around 0.45 for both sites. The same parameters have been also calculated for the winter period (concentrations excluded from 15/04 to 15/11), considering 58 samples for SIRTA and 23 for OPE. Winter measured median are higher than the ones calculated for the whole year, assuming the values of 0.28 and 0.10 µg m -3 respectively for SIRTA and OPE. Modelled median is underestimated for SIRTA (0.09 µg m -3 ) and well estimated for OPE (0.11 µg m -3 ). The interquartile range is underestimated for SIRTA (measured 0.15-0.48 µg m -3 , modelled 0.04-0.16 µg m -3 ) and overestimated for OPE, with lower 25 th percentile (measured and modelled respectively 0.07 and 0.05 µg m -3 ) and higher 75 th percentile (measured and modelled respectively 0.13 and 0.17 µg m -3 ). Correlations are high (0.66 and 0.78 for SIRTA and OPE respectively) and the MNB values are lower than the annual ones (-0.39 and 0.19 respectively). Mean modelled F p in winter is higher than in the whole year for both sites (0.66 for SIRTA and 0.68 for OPE). S12). February is the month in which higher concentrations of levoglucosan have been observed at SIRTA during 2015.

While it seems that the spatial distribution of concentrations may be well represented in the North of Moreover, concentrations in the particle phase may be strongly affected by GPP. Whereas one can expect levoglucosan to be almost entirely in the particles [START_REF] Locker | The use of levoglucosan to assess the environmental impact of residential wood-burning on air quality[END_REF], the model simulates a rather high fraction of the levoglucosan in the gas phase.

In Fig. 9 the levoglucosan simulated particulate phase fraction on the total (gas+particulate) concentration all over France is reported for February 2015. From the northern to the southern part of France particulate phase fraction gradually decrease. In the north the fraction is between 0.8 and 0.85, it decreases to 0.6-0.75 in the central and north-western part and reaches the lower values between 0.2 and 0.5 in the southern part. The spatial distribution observed in the model can be partially explained by the combined effect of temperature and humidity variations (Fig. S8 andS9). In the northern part of France, the higher humidity (RH>80%) may favor the transfer of levoglucosan in the particulate phase, while in the warmest regions (T>278 K) levoglucosan will be more volatilized. The very low particlephase concentrations observed in the southeastern cities (where the particle phase fraction of levoglucosan ranges from 20 to 60%) can be partly explained by GPP.

The influence of particle composition and thermodynamics on levoglucosan partitioning as well as the uncertainties on the GPP calculation are discussed in paragraph 4.2.1. S12). In Reims (north), the model reproduces well the lower concentrations observed and the temporal trends.All the peaks are predicted, but the model cannot reach the higher measured concentrations and the extent of the peaks. Modelled gaseous phase concentrations are around 30% of the total levoglucosan and their impact on the model to measurement comparison is not significant. GPP is therefore not expected to affect simulated concentrations for these stations. Similar results are obtained for Rouen and Strasbourg.

In Lyon, southeast of France, the simulated levoglucosan concentrations reproduce both the order of magnitude and the temporal variations measured. In some days, the gaseous phase contribution to the total modelled concentrations is not negligible (up to 90%) and the total levoglucosan concentrations are higher than the measured particulate phase. A few peaks simulated by the model do not appear in the observations. Slightly better results are obtained when modelled particle-phase concentrations is compared to the measured particle-phase concentrations (MNB 0.10, correlation 0,75) than when the comparison is done with the total concentrations (MNB 0.94, correlation 0.69), especially for March and April. In Grenoble, not far from Lyon, the model estimation is similar with the one observed in Lyon from January till April. In November and December levoglucosan is underestimated and the gaseous phase contribution is required to better represent the observed concentrations.

In Marseille, situated in the southof France (Mediterranean coast), temporal evolutions are well represented (correlation of 0.67). While concentrations are underestimated when modelled particlephase concentrations are used for the comparison, the order of magnitude is well represented by the model when the gaseous phase concentration is included. In Nice (same geographical region as Marseille), concentrations can also be reproduced when the gas-phase concentration is included. The model cannot reproduce the high concentrations observed between the 08/01 and the 18/01. These results may indicate that the underestimation of concentrations can potentially be due to an overestimation of the gas-phase fraction for these two cities.

At SIRTA, Figure, the modelled levoglucosan concentrations match well with the lower concentrations in the first part of the year, but do not reach the concentration of the major peak observed the 13/02.

From May to October concentrations are low both for the measurements and for the model. At the end of the year, the peaks observed in measurements are not reproduced by the model. Although gaseous phase concentration is not negligible in some periods, its contribution is not essential to reproduce levoglucosan concentrations. Better results are obtained when modelled particle-phase concentrations is compared to the measured particle-phase concentrations (MNB -0.44, correlation 0.72) than when the comparison is done with the total concentrations (MNB 1.1, correlation 0.57)

Modelled levoglucosan at OPE has the same magnitude order and similar temporal variations of the measured levoglucosan. In the first part of the year the modelled gaseous phase has high concentrations and the sum of both phases is higher than the measured particulate phase. In the last part of the year gaseous phase contribution to the total levoglucosan is lower. Better results are obtained when modelled particle-phase concentrations is compared to the measured particle-phase concentrations (MNB 0.61, correlation 0.78) than when the comparison is done with the total concentrations (MNB 4.6, correlation 0.73).

The comparisons of measured and modelled OM concentrations temporal variation (Fig. 11 for Bordeaux, Reims, Lyon and Marseille, Fig. S5 for Rouen, Strasbourg, Nantes, Poitiers, Grenoble and Nice, Fig. 12 for SIRTA, data not provided for OPE) can help to understand the reasons of underestimation of levoglucosan concentrations. OM is generally underestimated in the cities in which levoglucosan is underestimated by a similar factor. In western (Bordeaux, Nantes and Poitiers, MNBs -0.60, -0.63 and -0.67) and northern (Reims and Rouen, MNBs -0.41 and -0.49) as the underestimation are very similar between OM and levoglucosan, the underestimation of the biomass burning emissions over these areas is a likely explanation.

For South-eastern France, as for levoglucosan, the OM is well estimated by the model, with a slight overestimation for Lyon and Grenoble (MNBs 0.18 and 0.34) and a little underestimation for Strasbourg (MNB -0.20).

For Marseille and Nice, OM underestimation is lower compared to levoglucosan underestimation (MNBs of -0.69 and -0.53). Since the total modelled levoglucosan in these cities matches well with measurements, more investigations on the GPP computation and on the biomass burning emissions are required. The mismatching between simulation and measurements could be due to an underestimation of biomass burning emissions, to a similar bias between the GPP of levoglucosan and the GPP of SVOC from biomass burning or to a cumulation of various factors (emissions, GPP, aging, meteorology, etc…).

At SIRTA the simulated OM reproduce well the measurements from January to the beginning of May, while (similarly to levoglucosan) for the rest of the year, measured OM is underestimated.

Nitroguaiacols and nitrophenols

As already specified in section 2.2.2, the modelled nitroguaiacols are the sum of the 3-,4-and 6nitroguaiacol. Among these isomers, only 4-nitroguaiacol has been measured. According to (Lauraguais et al., 2014), 4-nitroguaiacol is the major nitroguaiacol isomer produced in atmosphere from the OHinitiated photooxidation (63% of the total nitroguaiacols produced by the OH-initiated photooxidation of guaiacol). However, since no isomer specific yield from the NO 3 -initiated photooxidation have been provided, it is not possible to estimate properly 4-nitroguaiacol percentage on the total nitroguaiacols.

For these reasons, simulated nitroguaiacols total concentrations are compared to 4-nitroguaiacol measured concentrations, so a certain underestimation in measurements is expected.

Nitroguaiacols concentrations are simulated with the right order of magnitude. The modelled higher concentrations (mean 25.8 ng m -3 ) at the beginning of the year (January to March) matches well with the observations (mean 20.8 nµg m -3 ), while in April-May the model (mean 14 ng m -3 ) overestimates the measurements (mean 1.5 ng m -3 ). In summer nitroguaiacol concentrations are very low in both model and measurements (under 6 and 1 ng m -3 respectively). The weak increase of concentrations observed at the end of the year has been well reproduced by the model.

Nitrophenols (2-and 4-nitrophenol sum) concentrations are well simulated for most of the year: the model succeeds to capture the high concentrations observed from January to April and the strong decrease of concentrations during summer. However, at the end of the year, the model underestimates concentrations by a factor 11 and is not able to reproduce the peaks observed. Nitrophenols precursors are toluene, benzene and phenol. The percentage of nitrophenols produced from toluene in February has been determined to be less than 1%, while primary phenol account for 40% of nitrophenols production.

Therefore, benzene is the most important precursor for nitrophenols.

Methylnitrocatechols and DHOPA

Methylnitrocatechols (sum of 3-methyl-5-nitrocatechol, 4-methyl-5-nitrocatechol and 3-methyl-6nitrocatechol) are underestimated by the model by a factor 60 during the cold season (January-April and September-December) and by a factor 4 in the warmest season (May-August). In winter, methylnitrocatechols measured concentrations are higher and their time evolution is well reproduced.

During spring-summer, measured Methylnitrocatechols is very low, while the model simulates a strong increase of concentrations in April-May and multiple small peaks in the rest of the period. The performances of the model in reproducing methylnitrocatechols temporal variations depend on the seasonal contribution of the different emission sectors. In winter, biomass burning is expected to be the main contributor, while in summer the sum of other emission sector (such us vehicular) contributions is higher. On these bases, we can assert that biomass burning methylnitrocatechols are more underestimated than the methylnitrocatechols from other sources.

DHOPA modelled concentrations are underestimated by a factor higher than 1000. Moreover, the model failed to capture the temporal variations of DHOPA concentrations: at the beginning of the year modelled DHOPA is higher than at the end of the year, while measured concentrations variations are opposite.

To evaluate the uncertainty due to the mechanism developed to model DHOPA concentrations from toluene photooxidation, a simpler parametrization, based on estimations from Gao et al. (2019), has been tested:

𝑇𝑂𝐿𝑈𝐸𝑁𝐸 + 𝑂𝐻 → 1.1 × 10 -5 𝐷𝐻𝑂𝑃𝐴 (R1)
The DHOPA concentrations from toluene simulated with this reduced mechanism is higher by 40% than the DHOPA concentrations from toluene (excluding primary cresol) simulated by our model with an extended formation mechanisms (section 2.2.4). While it could be possible that the concentrations simulated by the extended mechanism are underestimated, it seems unlikely than errors on the chemical mechanism could explain a factor 1000 as the two mechanisms lead to concentrations in the same order of magnitude.

As methylnitrocatechols and DHOPA can both be formed from toluene and cresol, an evaluation of cresol and toluene contribution to simulated methylnitrocatechols and DHOPA concentrations have been performed for the month of February. Only 17% of the total modelled methylnitrocatechols turn out to be toluene generated, so we can think that primary cresol is the major precursor for methylnitrocathechols in the model. We estimated that 4% of the modelled DHOPA is produced by the dioxabicyclic-alcoholic-peroxy-hydroxy-hexene radical (TLBIPERO2) and oxobutenyl oxirane carbaldehyde (TLEPOXMUC) initiated pathways. The toluene derived DHOPA, including the secondary cresol pathway, has been estimated to account for 15% of the total simulated DHOPA.

According to these results, cresol is the main precursor for both DHOPA and methylnitrocatechols.

One possible reason for the underestimation of DHOPA and methylnitrocatechols could be missing sources (especially for DHOPA that could be formed from numerous other precursors). [START_REF] Hobbs | Emissions of volatile organic compounds originating from UK livestock agriculture[END_REF] estimated that 19% of NMVOC emission from Livestock in UK could be cresol. Based on the amount of cresol in residential wood burning emissions in Table 2 and by using emission estimates from the EMEP inventory, emissions from Livestock could therefore account for 20 times more cresol than residential wood burning. However, these emissions would not explain the high concentrations in winter and the low concentrations in summer that seem to indicate a high contribution from biomass burning.

Moreover, in our model methylcatechol (MCATECHOL in the mechanism) has been considered exclusively as intermediate, while it has been measured in primary biomass burning emissions [START_REF] Gonçalves | Inventory of fine particulate organic compound emissions from residential wood combustion in Portugal[END_REF]Hatch et al., 2018). The eventual primary methylcatechol contribution to methylnitrocatechol concentrations has been estimated by adding to the model methylcatechol emissions ratios (normalised to levoglucosan emissions). from [START_REF] Gonçalves | Inventory of fine particulate organic compound emissions from residential wood combustion in Portugal[END_REF].

Methylnitrocatechols concentrations may increase by a factor 10, demonstrating that methylcatechol contribution to methylnitrocatechols total concentrations is not negligible. Wang et al. (2017) measured methylnitrocatechols primary emissions from biomass burning sources.

Based on this study, it only accounts for a small fraction of the PM 2.5 emissions (between 0.0001% and 0.07%). For a concentration of biomass burning OA of 10 µg/m 3 , primary methylnitrocatechol concentrations would be at most around 7 ng m-3 . In that state of knowledge, it is difficult to definitively conclude on the reasons for these underestimations. Missing precursors seem to be a likely explanation for DHOPA and methylnitrocatechols underestimation.

Phthalic acid

Phthalic acid model to measurements comparison is presented in Fig. 12. The measured phthalic acid concentrations are below 25 ng m -3 , with higher concentrations observed between mid-October and the beginning of November. The modelled naphthalene-generated phthalic acid is two order of magnitude lower than the observations and the temporal variations are also misrepresented.

The potential formation of phthalic acid from naphthalene in our model has been evaluated using the experimental phthalic acid fraction in naphthalene SOA (0.02) measured by Kleindienst et al., (2012).

The amount of phthalic acid estimated by the experimental fraction is 20 times lower than the phthalic acid simulated with the extended mechanism. Since also this method fails in estimating measured phthalic acid concentrations, it seems unlikely that the concentrations of phthalic acid in ambient air can be explained by the oxidation of naphthalene alone. Other phthalic acid sources should be considered to explain the discrepancies observed. Recent studies assess that phthalates concentrations are very high in urban environment [START_REF] Barreca | Photodegradation of selected phthalates on mural painting surfaces under UV light irradiation[END_REF][START_REF] He | Abundance and Sources of Phthalic Acids, Benzene-Tricarboxylic Acids, and Phenolic Acids in PM2.5 at Urban and Suburban Sites in Southern China[END_REF][START_REF] Simoneit | Combustion Products of Plastics as Indicators for Refuse Burning in the Atmosphere[END_REF][START_REF] Teil | Atmospheric fate of phthalate esters in an urban area (Paris-France)[END_REF] and their degradation in atmosphere can produce phthalic acid [START_REF] Hankett | Molecular Structural Changes of Plasticized PVC after UV Light Exposure[END_REF].In Paris urban area phthalate estersatmospheric levels are around 55 ngm -3 (Teil et al., 2006). Phthalate esters could be significant precursors of phthalic acid in atmosphere, but no enough information have been provided in the literature to quantify their contribution to total phthalic acid concentrations. Moreover, phthalic acid have been also quantified in primary vehicular emissions (Kawamura and Kaplan, 1987). Biomass burning is one of the major sources for most of the markers simulated in this study. Lanzafame et al. (in prep) reported significative correlations between some of these markers and levoglucosan for measurements. To evaluate if the relative contribution of biomass burning to marker concentrations is well represented in the model, the correlations between levoglucosan and secondary anthropogenic markers in measurements and model are compared. The values are reported in Table 4.

Correlation between secondary markers and levoglucosan

DHOPA-levoglucosan measured and modeled correlations are similar and indicate a weak correlation (respectively 0.55 and 0.52). Both in measurements and model DHOPA is not attributed totally to biomass burning, because its precursors,toluene and cresol, are emitted also by other sources (Chan et al., 2009;[START_REF] Hobbs | Emissions of volatile organic compounds originating from UK livestock agriculture[END_REF][START_REF] Na | Concentrations of volatile organic compounds in an industrial area of Korea[END_REF].

Measured methylnitrocatechols and levoglucosan correlates well (0.70), demonstrating that methylnitrocatechol is a good marker for biomass burning in atmosphere. However, the correlation between these two compounds in the model (0.2) is scarce, since, as DHOPA, methylnitrocatechols precursors in the model are toluene and cresol. These results indicate that a significant contribution from biomass burning is probably missing for methylnitrocatechols.

For nitroguaiacols and nitrophenols, the correlations with levoglucosan are around 0.6 for both the model and the measurements. These non-negligible correlations are not strong enough to attribute completely these markers to biomass burning, but the similarity of the modelled and measured values is indicative of the high accuracy in representing their precursors emissions (guaiacol, toluene and phenol) in the model.

Phthalic acid correlates scarcely (0.20) in measurements with levoglucosan and even less in the model (0.03). In fact, in our model, naphthalene is emitted both from domestic and industrial biomass burning with the same percentages. The different contribution of the 2 sources throughout the year may cause the low correlation calculated in the model. Other sources for phthalic acid should be considered.

GPP estimations

Levoglucosan: spatial variability and model sensitivity to thermodynamic assumptions

Figure 13 shows the spatial variability of simulated daily averaged concentrations of levoglucosan fraction in the particulate phase over Europe for February 2015 (chosen to be representative of the colder season with high biomass burning emissions). Several sensitivity simulations have been performed to understand the influence of the molecular interactions on the aerosol formation and partitioning. In the -reference‖ run, levoglucosan is both hydrophobic and hydrophilic and all the interactions with the aerosol organic and inorganic components are considered. In the -ideal‖ case, levoglucosan is still considered hydrophobic and hydrophilic, but the interactions with the other aerosol organic components are not considered. Non-ideal and ideal tests have also been performed by considering that levoglucosan is only hydrophilic (-Hyphi‖ and -Hyphi ideal‖ tests respectively) or only hydrophobic (-Hypho‖ and -Hypho ideal‖). A map of levoglucosan total (gas+particle) concentrations during February 2015 in Europe is provided in the Supplementary material (Fig. S11). The differences simulated on the total concentrations are under 5µg m -3 . In the -reference‖ run levoglucosan particulate phase fraction on the land varies from 0.2 to 1. In central Europe, the fraction is higher, with values starting from 0.75 to the maximum observed in Belgium, Luxemburg and Holland. In the northern Europe particulate phase faction keeps high values (>0.6), with some local minimum (0.2-0.4) in the Northern part of Norway and Sweden. In Southern Europe, levoglucosan particulate phase fraction drops to lower values (0.2-0.4) over wide regions of Spain, Portugal and Greece.

In the -ideal‖ test, much higher aerosol fractions are simulated. The lower fraction observed on the land is between 0.75 and 0.85 (wide regions of Spain and Portugal, some spots in Norway and Sweden). In a big region covering almost all the central Europe, levoglucosan is completely in the particulate phase (particulate phase fraction 0.95-1) and more than 85% of levoglucosan is in the particulate phase in the rest of the domain.

The -hydrophilic‖ test gives similar results to the -reference‖, with only some small local differences.

Similarly, the -hydrophilic ideal‖ test gives similar results to the ideal test. In both, the reference and -ideal‖ tests, most of partitioning is due to the hydrophilic partitioning.

On the contrary, hydrophobic levoglucosan partitioning is shifted towards the gaseous phase. In the -hydrophobic non-ideal‖ parametrization, the levoglucosan particle-phase fraction ranges between 0 and 0. Levoglucosan partitioning in -reference‖ and -hydrophilic run‖ is clearly influenced by humidity (Fig S11). In the drier regions (relative humidity <0.70%) less than 40% of levoglucosan is in the particulate phase.

All these results indicate that the hydrophilic partitioning is more shifted towards the particle phase than the hydrophobic partitioning and that non-ideality leads to a strong decrease of the particle phase fraction. In all the tests, the fraction of levoglucosan in the gas phase cannot be neglected. The gas phase fraction represents even most of the levoglucosan in some areas. These results can be surprising as levoglucosan is often considered as non-volatile [START_REF] Fraser | Using Levoglucosan as a Molecular Marker for the Long-Range Transport of Biomass Combustion Aerosols[END_REF]Simoneit et al., 1999) but some studies showed that the gas-phase fraction can be significant (Parshintsev et al., 2011).

The highest incertitude in this GPP parametrization is associated to the saturated vapor pressure and vaporization enthalpy choice. In this study, the values measured by Booth et al., (2011) were used. To our knowledge, no other measurements of levoglucosan P sat have been performed. Xie et al., (2014) performed computation of the levoglucosan partitioning but with a much lower saturation vapor pressure (1.8×10 -7 torr against 1.45×10 -6 torr for the value used in this study) and higher vaporization enthalpy. This lower saturation vapor pressure was taken from Parshintsev et al., (2011) who did not explain how this value was estimated. It may therefore be possible that the value used in the present study is overestimated. Using the value from Parshintsev et al. (2011) would lead to a significant underestimation of the particle-phase levoglucosan that would almost appear as non-volatile.

In the non-ideal scenarios, the particle average composition considered (detailed in the section configuration) influences the GPP and may be a cause of uncertainty. The model cannot simulate all the factors governing aerosol composition, such as the emitting sources and the type of environment considered (Ruehl et al., 2011). High percentages of WSOC (Water Soluble Organic Compounds)

providing an efficient substrate for levoglucosan condensation have been revealed in biomass burning aerosols [START_REF] Gao | Water-soluble organic components in aerosols associated with savanna fires in southern Africa: Identification, evolution, and distribution[END_REF]. However, due to lack of information on the composition of primary aerosols, the H²O mechanism assumes that primary aerosols are apolar.

GPP of secondary markers at SIRTA.

Tests on GPP computation reference (ideal aerosol, completely hydrophobic or hydrophilic marker in non-ideal and ideal aerosol) for nitroguaiacols, nitrophenols, DHOPA, methylnitrocatechols and phthalic acid have been performed during February, mid-June to mid-July and October 2015. These months have been chosen as the more representative of all the annual weather and emission conditions.

The test outputs are shown in Fig. 14, together with the results of measurements (-obs‖) and of the reference simulation (ref, markers both hydrophilic and hydrophobic, non-ideal aerosol) GPP. All the markers in the only -hydrophilic‖ test partition similarly to the -reference‖ simulation. Slight differences between the -reference‖ and the -hydrophilic‖ tests (~5% both in median and interquartile range F p ) and between the -ideal‖ and -hydrophilic ideal‖ tests (~18% for the median and interquartile range F p ) have been reported for all the markers, except DHOPA. Hence, the -hydrophilic‖ and -hydrophilic ideal‖ run outputs are very similar to the -reference‖ and -ideal‖ simulation outputs and will not be commented in the text.Hydrophobic marker F p in non-ideal aerosol is close to zero for all the markers, with no variability indicating that all the compounds studied can be considered strongly hydrophilic.

In measurements, nitroguaiacol is mostly in the gaseous phase with a measured median F p lower than 5%. In all the tests performed, the particulate phase fraction remains very low and the median value never exceeds 5%. The -reference‖ run result gives a median F p (0.002) lower than the one observed (0.02), with narrower F p distributions (interquartile ranges are respectively 0.16 for measurements and 0.025 for the -reference‖). The partitioning on the ideal aerosol is similar to the measuredone: F p median values are similar (-ideal‖ median F p 0.15), and their distribution is wider than all the other tests (interquartile range 0.047), but narrower than in the measurements. In the -ideal‖ simulations, hydrophobic nitroguaiacol is less volatile than in the non-ideal -hydrophobic‖ simulation (median F p are respectively 0.002 and 0.0005).

Similar to nitroguaiacol, nitrophenol GPP is shifted toward the gaseous phase (all the F p are below 0.14). Median F p values are lower than in the -obs‖ (0.02) for the -reference‖ (0.014) and higher for the -ideal‖ (0.03) simulations. The F p distribution in the observations is narrower than in the -reference‖ and in the -ideal‖ run (interquartile ranges are respectively 0.02, 0.053 and 0.11). -Hydrophobic ideal‖ test F p distribution is wider than in the -hydrophobic‖ non-ideal test (interquartile ranges 0.026 and 0.008), median F p value are lower (respectively 0.005 and 0.003) than in the measurements and in the -reference‖ run. DHOPA F p is shifted towards the particulate phase for the measurements, with median values close to 1. In the -reference‖ run, DHOPA partitioning is also shifted to the particulate phase, but the median value is lower (0.9) than in the -obs‖. In the -ideal‖ aerosol parametrization, F p values are close to the observed ones, with a median value slightly lower than 1. Hydrophobic DHOPA in the ideal aerosol has a median F p around 0.2 and 25 th and 75 th percentiles around 0.1 and 0.6 respectively, while in non-ideal aerosol hydrophobic DHOPA is almost completely in the gaseous phase, with a median F p of 0.005 and interquartile range of 0.035.

Methylnitrocatechols GPP is shifted towards the particle phase in the measurements (median F p of 0.9) whereas, in all the model runs, methylnitrocatechols are mainly present in the gas phase (median F p ranges between 0.02 and 0.2) and F p is strongly underestimated. -Reference‖ run F p are lower than 0.2, while for the -ideal‖ simulation, methylnitrocatechols are less volatile with median, 25th and 75th percentile values around 0.2, 0.1 and 0.6 respectively. The median F p for the -ideal hydrophobic‖ simulation (0.035) is higher than in the non-ideal -hydrophobic‖ simulation (0.009).

Phthalic acid median particulate phase fraction in measurements is 65%. The observed F p are very variable, with values between 0.13 and 1. In all the simulation phthalic acid is mainly in the gas phase, with 90th F p percentile values below 0.5. In the -reference‖ run, GPP has a narrow distribution around a median value lower than 5%. In the -ideal‖ simulation, F p median value is around 15%, F p distribution are wide and reach the maximum value (0.45) for particulate phase fraction. In the -hydrophobic ideal‖ simulation, phthalic acid is almost totally in the gaseous phase (90th F p percentile below 0.2).

In general, we can assert that the partitioning of the markers is driven mainly by their hydrophilicity and that the model tends to underestimate the particle phase fraction. Marker partitioning in the -ideal‖ simulations was found to be closer to the measurements because in the -ideal‖ simulations the particle phase fraction is higher. These considerations should be used carefully because of the approximations used in the model: some functional group, or the combination of functional groups, could be not well represented in the SAR used to estimate the activity coefficients (e.g. the NO 2 group), the saturation vapor pressure used in this study are highly uncertain, modelled OM composition could be not representative of real OM (an apolar default structure is used for primary compounds, and the model could strongly underestimate the concentrations of hydrophilic aerosols), and OM is strongly underestimated in summer.

The sensitivity analysis (ideal aerosol, completely hydrophobic or hydrophilic marker in non-ideal and ideal aerosol) on daily mean particulate phase distribution of secondary markers all over Europe during February 2015are reported in Fig. S14, S15, S16, S17 and S18. As already commented for levoglucosan, the hydrophilic partitioning dominates over hydrophobic partitioning. Higher F p values (from 0.02 to 0.2 for nitroguaiacol, from 0.05 to 0.3 for nitrophenols, from 0.1 to 0.5 for methylnitrocatechols, from 0.95 to 1 for DHOPA and from 0.2 to 1 for phthalic acid) are usually observed in central Europe (North of France, Germany, Italy, Austria, Hungary, Poland, Holland, Belgium, on the Baltic coast and southern Scandinavian peninsula) for the tests including hydrophilic partitioning and non-ideal aerosol (-reference‖ and -hydrophilic‖ simulations). Lower hydrophilic (-reference‖ and -hydrophilic‖ simulations) F p have been simulated in the drier regions (Spain and Portugal) for all the markers and for nitroguaiacol, nitrophenol and phthalic acid also in the colder regions (north of Scandinavian peninsula) (F p from 0.01 to 0.05 for nitroguaiacol, methylnitrocatechols and phthalic acid, from 0.005 to 0.02 for nitrophenols and from 0.6 to 0.8 for DHOPA). Hydrophobic markers F p distribution is similar to the hydrophilic, with higher values in central Europe and lower values in southern and northern Europe. Hydrophobic F p are lower than the hydrophilic by a factor 20 for nitroguaiacol, 5 for nitrophenol, 10 for methylnitrocatechol and DHOPA and 25 for phthalic acid.

Aerosol ideality favors the partition to the particulate phase for all the markers in both hydrophilic and hydrophobic conditions, increasing F p by a factor ranged between 0.5 to 2.

Figure 14.

From the top to the bottom: nitroguaiacol, methylnitrocatechols and DHOPA gas to particle partitioning thermodynamic test. On the right side, the particulate phase fractions calculated for measurements (obs) and simulated with reference (ref), ideal aerosol (ideal), hydrophobic marker (hypho), hydrophilic marker (hyphi), hydrophobic marker and ideal aerosol (hypho ideal) and hydrophilic marker and ideal aerosol (hyphi ideal) parametrizations are shown.

Influence of partitioning and gas-phase dry deposition of secondary markers on total concentrations

As effect of the gas-phase oxidation and the different gas and particle deposition rates, GPP may influence total marker concentrations. Some tests on total marker concentration variations have been performed assuming a completely non-volatile and volatile behavior.

Moreover, in the -reference‖ simulations, dry and wet deposition of gas-phase markers were not considered (only particle deposition has been considered in the -reference‖ run). The influence of this phenomena is also tested (as described in part 2). The results of the different tests are compared with the -reference‖ run (no dry and wet deposition of gas-phase markers) in Fig. 15.

As in the -reference‖ simulation, nitroguaiacols are almost entirely in the gas-phase, total concentrations are very similar to those of the -volatile‖ case. -Non-volatile‖ nitroguaiacols total concentrations are lower by 30% than those of the -reference‖ simulation. As no gas-phase degradation of nitroguaiacols has been considered in the mechanism, the main loss process is the particle-phase deposition in the -reference‖ simulation. Therefore; the increase of particle-phase concentrations leads to a decrease of concentrations. As in observations, F p 75 th percentile value is around 17%, the uncertainty on F p calculation can be expected to be of a few percent only.

Similar considerations can be applied to nitrophenol, for which F p values are close to 0 in the reference run. Total concentrations in the -volatile‖ and -reference‖ simulations are similar, while concentrations for the -non-volatile‖ simulation decrease by 20%. indicating that the increase in Fp values lead to more removal due to particulate phase deposition. Indeed, nitrophenols half-life has been estimated to be around 12 days for the period tested (by using simulated radical concentrations for the period), which is longer that the average particle half-life of 1 week (Seinfeld, 2015). The degradation in the gaseous phase represent a slower loss mechanism compared to the particulate phase deposition. The uncertainty on F p calculation is even lower than for nitroguaiacol, considered the lower F p values.

DHOPA partitioning is shifted preferentially toward the particulate phase both in measurements and in the -reference‖ simulation. Compared to the -reference‖ simulation, DHOPA concentrations are lower by 70% in the -volatile‖ simulation but increase by a factor 2 in the -non-volatile‖ simulation. Even though DHOPA is almost non-volatile in the -reference‖ simulation, the differences in total concentrations between the -non-volatile‖ and the -reference‖ runs show that DHOPA concentrations depend strongly on GPP. This result can be explained by the fast degradation kinetic of DHOPA in the gaseous phase (estimated half-life of0.84 d -1 for the period), that can be considered the major loss process for this marker (DHOPA can be degraded also in the aqueous phase). Therefore, the partitioning toward the particulate phase protects DHOPA from degradation. Despite DHOPA F p values in the -reference‖ simulation are close to those in the measurements (0.9), the uncertainty on total concentrations associated to the GPP estimation is high (up to a factor 2) because of the high degradation rate in the gaseous phase. As methylnitrocatechols are almost entirely volatile in the -reference‖ simulation, their total concentrations are close to those of -volatile‖ run. In the -non-volatile‖ simulation methylnitrocatechols total concentrations increase by a factor 2. In this case, the transfer to the particulate phase protects methylnitrocatechols from their degradation mechanisms, that occur only in the gas phase (estimated half-life of 0.5 d -1 ). As in measurements methylnitrocatechols are almost entirely inside the particle, the uncertainty on the simulated methylnitrocatechols total concentrations should be considered high (up to a factor 2).

In the -reference‖ simulation, phthalic acid F p is low and total concentrations are similar to those of the -volatile‖ simulation. Concentrations in the -non-volatile‖ simulation descreased by 20%.

Therefore, the partitioning toward the particulate phase does not protect phthalic acid from degradation (half-life of 16.6 d -1 ). In conclusion, naphthalene-generated phthalic acid concentrations may be slightly overestimated (by less than 20%) due to the discrepancy in F p values between the model and measurements.,

These results highlight that total concentrations may be strongly influenced by GPP for some of the compounds (DHOPA, methylnitrocatechol, phthalic acid). However, the uncertainty due to GPP for these compounds is low compared to the differences between the model and measurements. The gap cannot be explained by GPP alone.

The influence of gaseous phase deposition on total marker concentrations is clearly linked to the volatility of the marker itself with a high effect for high volatile markers. For DHOPA, no significant effect of gas-phase deposition has been simulated (because the compound is almost non-volatile). A decrease of concentrations by a factor around 6 are simulated for nitroguaiacols and nitrophenol, which are volatile in both the model and measurements. While total concentrations were close to measurements in the -reference‖ simulation, considering the gas-phase deposition lead to a strong underestimation of concentrations. This drop of model performance when gas-phase deposition is considered can be either due to an underestimation of marker formation which compensate gas-phase deposition or due to an overestimation of the deposition rate of SVOC by CHIMERE. For methylnitrocatechol and phthalic acid, the reductions simulated (30% and 75% respectively) due to gasphase deposition are probably strongly overestimated. In the measurements, the compounds are mostly present in the particle phase whereas simulated F p are close to 0.

Evaluation of the OA-tracer approach to evaluate the wood-burning OM: Analysis of OMwb pLEVO-1 variations

Constant ratios between the wood burning organic matter (OM wb ) and particulate phase levoglucosan (pLEVO -1 )are often used to evaluate the contribution of wood-burning aerosol to organic aerosol (Herich et al., 2014;Puxbaum et al., 2007;Schmidl et al., 2008). Favez, (2016) calculated for the sites monitored in this study site-specific OM wb pLEVO -1 ratios to estimate the amount of OM wb in total OM from levoglucosan measurements (details in SM). However, as shown in section 4.2.1, the partitioning of levoglucosan may depend strongly on the hydrophilic properties, on the environmental conditions and on non-ideality. Moreover, the partitioning of levoglucosan probably differs from the partitioning of wood burning SVOC. The OM wb pLEVO -1 should therefore depends on environmental conditions. The theoretical OM wb pLEVO -1 can be computed with the following algorithms:

The particulate phase levoglucosan concentration (pLEVO) can be estimated with the following equation:

𝑝𝐿𝐸𝑉𝑂 = (𝑔 + 𝑝)𝐿𝐸𝑉𝑂 × 𝐾 𝑝 ,𝑙𝑒𝑣𝑜 𝑂𝑀 1+𝐾 𝑝 ,𝑙𝑒𝑣𝑜 𝑂𝑀 (8)
where (g+p)LEVO is the total levoglucosan concentration and K p,levo is the levoglucosan partioning constant.

The organic matter produced from wood burning (OM wb ), constituted by i components, can be calculated as follows:

𝑂𝑀 𝑤𝑏 = (𝐴 𝑝,𝑤𝑏 ,𝑖 + 𝐴 𝑔,𝑤𝑏 ,𝑖 ) × 𝐾 𝑤𝑏 ,𝑝 ,𝑖 𝑂𝑀 1+𝐾 𝑤𝑏 ,𝑝 ,𝑖 𝑂𝑀 𝑖 (9)
where A p,wb,i and A g,wb,i are respectively the particulate and gaseous phase concentrations of the i component and 𝐾 𝑤𝑏 ,𝑝,𝑖 is his partitioning constant. According to Eq. ( 8) and ( 9), pLEVO and OM wb depend on the total OM and on temperature (Eq. ( 2)). The calculated OM wb pLEVO -1 ratio is: 10), this ratio would remain constant only if K p,wb,i =K p,levo (all SVOCs from wood burning have the same volatility of levoglucosan). In order to evaluate from a theoretical point of view, the validity of these assumptions, the variations of the simulated OM wb pLEVO -1 are studied. The simulated OM wb pLEVO -1 as a function of total OM at 275, 280 and 285 (±0.5) K are reported in Fig. 16for the reference simulations as well as the ideal and the hydrophobic ideal test (by taking all the point of the domain close to the selected temperature by ± 0.5 K). The ratio has been calculated for levoglucosan concentrations greater than 0.1µg m -3 . The selected temperatures have been chosen to be representative of wintertime data, in which OM wb and levoglucosan emissions are expected to be higher.

All the tests indicate strong variations of the OM wb pLEVO -1 and varies strongly under 10 µg m 3 of OM whereas above 10 µg m -3 , the results indicate weak of this ratio. In the hydrophobic ideal test, the ratio decreases fast at OM values below 10 µg m -3 (from 26 to 18) and at higher OM masses keeps a value around 18, following an inverse Odum-like curve. SVOC wb concentrations grow faster than levoglucosan concentrations when OM is increasing.

In the ideal test, the OM wb pLEVO -1 ratio varies with OM according to an Odum-like curve between 8 and 16. This trend is determined by the relative variations of levoglucosan and OM wb volatilities: levoglucosan is almost non-volatile (its partitioning does not depend on OM) while the condensation of SVOC wb increase with OM (and follow the dilution curve ofMay et al., ( 2012)).

When non-ideality is considered, strong variability of this ratio is simulated, especially under 10 µg m - 3 where the ratio varied in the simulations between 6 and 40. Nonetheless, this ratio tends to increase with OM and decrease with temperature. Above 10 µgm -3 , most of the ratios are around 20.

Whatever the temperature regime, at OM values below 10 µg m -3 , the constant ratio approach commonly used to calculate OM wb cannot be validated because of the great variability. It can therefore be difficult to evaluate precisely the contribution of OM wb from levoglucosan alone. However, this method could be considered as accurate for regions characterized by high OM values (e.

Conclusions

A mechanism for the formation of molecular markers has been implemented in the 3D chemistry transport model CHIMERE.To our knowledge, this is the first time that anthropogenic OA markers have been modeled with a 3D CTM. Using marker modeling can give insights on the model performances and on several processes (such as GPP, chemistry, emissions) involving OM formation.

Levoglucosan, a primary compound, is reproduced by the model with the same performances as OM.

An underestimation of the residential wood burning emissions for some cities in France is probably the cause of the mismatching between measurements and simulations outputs.

Concerning secondary markers, nitroguaiacol and nitrophenol concentrations are well simulated by the model, while methylnitrocatechol, DHOPA and phthalic acid are strongly underestimated, and their temporal variations are not consistent with the measurements. This effect may depend on an underestimation of precursor emissions (e.g. lack of emissions from some sectors), missing precursors (e.g. methylcatechol for methylnitrocatechol), non-accounted primary marker emissions (methylnitrocatechols and phthalic acid have been detected in emissions) or missing chemical pathways.Precursor emissions is probably the main source of uncertainty for these compounds. Due to the variety and the lack of data on the detailed composition of emissions, it is difficult to evaluate precisely the emissions from human activities: precursors emissions can be easily underestimated if some sector is missing.

The GPP of markers has been simulated with SOAP by considering non-ideality. For nitroguaiacol, nitrophenols and DHOPA the model reproduces well the GPP, but in general the model underestimates the measured marker F p . A strong GPP influence on total concentrations has been found for the markers with fast degradation in the gas-phase, for which the transfer to the particle-phase protect their degradations (e.g. DHOPA). Low volatile marker total concentrations are scarcely influenced by wet and dry gas deposition, while volatile markers total concentrations are significantly affected. Although the uncertainty connected to GPP estimation is not negligible, the discrepancies between model and measurements cannot be explained by GPP alone.

For levoglucosan, the model found that the compound is semivolatile which particle-phase fraction between 20 and 100%. Recent studies proved levoglucosan semi volatility (Bertrand et al., 2018;[START_REF] May | Volatility of Organic Molecular Markers Used for Source Apportionment Analysis: Measurements and Implications for Atmospheric Lifetime[END_REF], that is reproduced in our model using the P sat and ΔH vap values measured by Booth et al., (2011). However, the greater incertitude on the GPP estimations relies on the P sat and ΔH vap values chosen. In the case of levoglucosan, Booth et al., (2011) has determined a value of P sat which is higher by one order of magnitude than the one used in other studies (Parshintsev et al., 2011). Using the value of Parshintsev et al. (2011) (as done by Xie et al., (2014)) would have given a much higher F p and levoglucosan would have appear as almost non-volatile.

A theoretical study of OM pLEVO -1 demonstrates that levoglucosan contribution to OM wb is not constant at OM values typical of European aerosol (<10 µg m -3 ) contrary to what is assumed in numerous studies. For higher OM concentrations, the ratio become constant and this approach can be validated. The model estimate that all the markers considered in this paper are very hydrophilic and that assuming that those compounds only condense on the organic phase of particles (as often assumed for SVOC by air quality models) lead to a significant underestimation of the particle phase concentrations. Moreover, their partitioning was found to strongly depend on the environmental conditions. In our simulations, humidity may play a key role in levoglucosan GPP estimation, but no measurements of both phases are available to quantify this effect.

The impact of the other approximations often done in air quality models model should also be considered: ideality, equilibrium and non-viscous aerosol. [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF] implemented the SOAP model in the CTM Polair3D [START_REF] Sartelet | Simulation of aerosols and gas-phase species over Europe with the POLYPHEMUS system: Part I -Model-to-data comparison for[END_REF] of air quality platform Polyphemus (Mallet et al., 2007). They tested the model using both an equilibrium and a dynamic approach, ideal and non-ideal aerosol configurations. They found that non-ideality strongly affects the condensation of hydrophilic organic compounds on the aerosol and lead to a decrease of 33% the hydrophilic SOA concentrations.

The opposite findings with our study can be justified by the fact that levoglucosan and the other organic markers in this study are more polar and hydrophilic than the ones in [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF]. To simulate correctly activity coefficients, the composition of organic aerosols must be estimated correctly, which may be very challenging in the current state of knowledge. [START_REF] Kim | Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model[END_REF] also simulated the influence of particle viscosity on hydrophobic compounds GPP using a dynamic approach. In a dynamic inviscid approach, particulate phase concentrations are close to equilibrium, while in a dynamic viscous approach hydrophobic SOA concentration increase significantly for volatile compounds.

Modeling SVOCs in atmosphere is not a simple task but may provide meaningful information to understand better OA formation and behavior in atmosphere. Further studies on aerosol dynamics, thermodynamic parameters measurements and emissions are required to improve the understanding, and consequently the modeling, of atmospheric processes.

Table S3. First pathway for DHOPA formation: gaseous phase. Mechanism from GECKO-A (Aumont et al., 2005;Camredon et al., 2007).All the kinetic constants are in cm 3 molecule -1 s -1 .

Table S4. First pathway for DHOPA formation: aqueous phase. Mechanism from (Mouchel-Vallon et al., 2017) aqueous phase reactions -Analogy with 2,4-dihidroxy-3-oxobutanal for C514CO23OH and 2,4-dihydroxy-3-oxobutanoic acid for DHOPA.All the kinetic constants are in M -1 s -1 .

K H and ΔH vap for C514CO23OH are 3.9x10 7 M -1 atm and 63.8 kJ mol -1 (SOAP,Couvidat and Sartelet, 2015) K H and ΔH vap for DHOPA are 7.24x10 10 M -1 atm and 116 kJ mol -1 (SOAP, Couvidat and Sartelet, 2015) Table S5. Second pathway for DHOPA formation. Analogy with C4DBDIKET MCM v3.2 (Jenkin et al., 1997;Saunders et al., 2003) mol/cm 3 SRC-AOPWIN (SIDS, 2005) *The chemical mechanistic information was taken from the Master Chemical Mechanism, MCM v3.2 (Bloss et al., 2005;Jenkin et al., 1997Jenkin et al., , 2003;;Saunders et al., 2003), via website: http://mcm.leeds.ac.uk/MCM. (Booth et al., 2011) 1.80×10 -7 (Parshintsev et al., 2011) 1.77×10 *not specified if the value is measured or calculate, not specified if it is a subcooled or a solid saturated vapor pressure (Booth et al., 2012) 95.7 123.5 40 a. Small polyacids: N-J and M-S are not considered b. N-J and M-N are not considered c. N-J and Comp. are not considered d. M-J and Comp. are not considered e. M-S and Comp. are not considered f. N-J and M-S are not considered g. Geometric average of the SAR methods estimations h. Average of the SAR methods estimations i. Ratio method: EPIsuite (P sat marker /P sat reference ) * measured(P sat reference ) l. Difference method: EPIsuite (ΔH marker -ΔH reference ) + measured(ΔH reference )

Experimental

Table S11. Secondary markers quantified in SIRTA (2015) samples. (Eq.S1) where f= (OC wb levoglucosan -1 ) is a source-specific factor. Literature experimental values for f are ranged between 3 and 15 (Herich et al., 2014;Puxbaum et al., 2007;Schmidl et al., 2008). These differences could explained by the different burning conditions (leading to different particle composition). A mean factor of 7 has been proposed by Puxbaum et al., (2007) for different European sites and used in this study.However, to avoid any overestimation, a site-specific upper limit for f have been fixed considering the 3 lowest OC tot levoglucosan -1 ratio mean. OM wb has been estimated using a factor 1.8 [START_REF] Favez | Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France)[END_REF].

Model vs Measurements

Meteorological data

Chapter IV

Model to measurements comparison: Biogenic markers

Article III

Modelling organic aerosol markers in 3D air quality model.

Part 2: Biogenic organic markers.

To be submitted in -Atmospheric chemistry and physics NO x regime affect pinic acid and methyltetrols formation, that depend strongly on HO 2 anf RO 2 radicals concentrations. Strong daily varitions for pinonic acid formation processes have been observed. MBTCA and α-methylglyceric acid formation mechanisms are sensitive to aqueous phase reactivity representation.

GPP has been well simulated for MBTCA and methyltetrols, for which the hydrophilic nonideal partitioning is predominant. For the other markers particulate phase fraction is underestimated. The gap between model and measurements can be explained by the synergy of chemical pathway representation, GPP estimation and OM simulation.

Introduction and objectives

Secondary organic aerosols (SOA) constitute an important fraction (20-80%) of total organic aerosol (OA) (Jimenez et al., 2009;Kroll and Seinfeld, 2008;Srivastava et al., 2018a;Zhang et al., 2007Zhang et al., , 2011a)).Their formation occurs in the atmosphere, through nucleation and condensation of volatile organic compounds (VOCs) (photo-) oxidation products. VOCs are emitted in the atmosphere by biogenic and anthropogenic sources and their reactivity has been widely characterized. In Europe, SOA has been reported as the major contributor to OA in summer [START_REF] Bozzetti | Size-Resolved Identification, Characterization, and Quantification of Primary Biological Organic Aerosol at a European Rural Site[END_REF](Bozzetti et al., , 2017b(Bozzetti et al., , 2017a;;Canonaco et al., 2015;[START_REF] Bozzetti | Size-Resolved Identification, Characterization, and Quantification of Primary Biological Organic Aerosol at a European Rural Site[END_REF]Lanz et al., 2010;Reyes-Villegas et al., 2016;Schlag et al., 2016) and biogenic sources contribution has been assumed to be large (Bonvalot et al., 2016;Vlachou et al., 2018;[START_REF] Huang | High secondary aerosol contribution to particulate pollution during haze events in China[END_REF].

Several chamber experiments have been conducted to study SOA yields from biogenic volatile organic compounds (BVOCs) under different conditions (e.g. temperature, humidity, NO x regime). Isoprene and monoterpenes have been recognized as the most important biogenic aerosol precursors (Guenther et al., 1995;[START_REF] Heald | Predicted change in global secondary organic aerosol concentrations in response to future climate, emissions, and land use change[END_REF]. SOA mass yield and composition dependence on the BVOC oxidation initiation process has been established (Draper et al., 2015;Surratt et al., 2010;[START_REF] Zhao | Intermediate Volatility Organic Compound Emissions from On-Road Diesel Vehicles: Chemical Composition, Emission Factors, and Estimated Secondary Organic Aerosol Production[END_REF] and the so-determined mass yields are often used to design biogenic SOA (BSOA) formation mechanism in the atmosphere [START_REF] Friedman | SOA and gas phase organic acid yields from the sequential photooxidation of seven monoterpenes[END_REF]. However, this approach do not consider the great variability associated to BSOA production at different atmospheric conditions and increases uncertainties in predicting the total SOA budget in 3D CTMs.

Isoprene SOA have been modelled in the 3D CTM CMAQ considering high and low NO x regimes (Pye et al., 2013). The formation of some known isoprene SOA oxidation products (markers) has been simulated in the aerosol aqueous phase, using detailed chemical mechanisms. The modelled aerosol mass is composed by α-methylglyceric acid (formed under high NO x conditions), methyltetrols (formed under low NO x conditions) (Surratt et al., 2010) but also organosulfates and organonitrates. The performances of the model have been evaluated by comparison with marker measurements performed on several sampling sites across US and with the results of a classic 2-product Odum parametrization for isoprene SOA.

They found that the detailed mechanism outputs correlated better than the 2-products model with measurements. Similarly, Couvidat et al., (2013) The parameters individued as critical for SOA yields were the NO x regime (for α-pinene ozonolysis) and the temperature.

In all these studies the gas to particle partitioning was either not considered, e.g. non-volatile markers as in Pye et al., (2013), or treated using a -classical‖ approach, with ideal aerosol and no partitioning in the hydrophilic phase (Pankow, 1994).

The hydrophobic and hydrophilic gas-to-particle partitioning has been considered by [START_REF] Chrit | Modelling organic aerosol concentrations and properties during ChArMEx summer campaigns of 2012 and 2013 in the western Mediterranean region Atmos[END_REF], which model isoprene, monoterpene and sesquiterpene SOA formation in the 3D CTM Polyphemus. They used known markers, such as methyl glyceric acids or 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA), as surrogates for the biogenic SOA. However, this kind approach exclude the validation of the gas-to-particle partitioning by direct comparison with the marker measurements.

In this study, biogenic SOA marker formation in ambient air is investigated by using a multistep reaction scheme inserted in a 3D chemistry transport model. Detailed formation mechanism for pinonic acid, pinic acid, MBTCA, 2-methyltetrols and α-methylglyceric acid have been developed and the simulation output has been compared to measurements perfomed at SIRTA sampling station, in Paris region. Gas to particle partitioning (GPP) is simulated with the Secondary Organic Aerosol Processor (SOAP) thermodynamic model able to compute the non-ideal partitioning between the gas, organic and aqueous phases of semivolatile organic compounds. The simulated GPP has been evaluated, with a focus on thermodinamical sensitivity. Sensitivity analysis to radical concentrations has also been carried out. In the companion paper, the anthropogenic SOA marker formation is presented.

Model overview

A Gaseous and aqueous phase chemical mechanism for the formation of 11 organic marker have been developed and inserted in the 3D chemistry-transport model CHIMERE 2017β (Couvidat et al., 2018). In this paper 5 biogenic markers formation mechamisms and simulation outputs are presented, as a part of the marker implemented version of CHIMERE.

The biogenic precursors considered are α-/β-pinene and isoprene. Anthropogenic markers mechanisms and simulations are already described in the companion paper. A description of CHIMERE 2017β aerosol module is available in the companion paper and in Couvidat et al., (2018). Thermodynamic equilibria have been simulated by ISORROPIA (Fountoukis and Nenes, 2007) for inorganics and by the Secondary Organic Aerosol ProcessorSOAP (Couvidat and Sartelet, 2015) for organics. SOAP estimates the partitioning of organic markers by using their saturation vapor pressure and takes into account the condensation on both the organic and aqueous phases by estimating activity coefficients with the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) parameterizations.

Development of the marker mechanism

The gaseous and aqueous phase chemical mechanism of CHIMERE have been modified to insert the marker formation mechanisms. The atmospheric chemistry in CHIMERE is represented with the MELCHIOR2 (Derognat et al., 2003) mechanism and SVOC formation by the Hydrophilic/Hydrophobic Organics (H²O) (Couvidat et al., 2012) The marker formation is inititiated by the oxidation of toluene, guaiacol, phenol, isoprene and α-/β-pinene. Mechanistic data were taken from literature, the Master Chemical Mechanism (MCM, version 3.3.1) (Bloss et al., 2005;Jenkin et al., 1997Jenkin et al., , 2003;;Saunders et al., 2003), from the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) (Camredon et al., 2007) and from the Cloud Explicit Physico-chemical Scheme (CLEPS 1.0) (Mouchel-Vallon et al., 2017) for the aqueous phase chemistry. In all these mechanisms, experimental kinetic data are preferred when available, otherwise mechanistics data are estimated through structure activity relationships (SARs) or determined by analogy to similar better characterized reactions.

The OH oxidation in the aqueous phase is modelled assuming for both OH and the substrate an instantaneous equilibrium with the gaseous phase, computed using the Henry's law constant (K H )and the solubilization enthalpy (ΔH sol ) (calculated with SOAP for the substrate).

OH Henry's constant has been assumed to be 25.0 M -1 atm (Lelieveld and Crutzen, 1991) and its consumption by reactive aqueous species has been taken in account dividing by a factor 2 the concentrations at the equilibrium (Couvidat et al., 2013).

SOAP has been used to evaluate the marker gas to particle partitioning (GPP), providing as input the subcooled saturated vapor pressure (P sat ) and the evaporation enthalpy (ΔH vap )

(Table 1). SOAP is a powerful tool in which the user can choose for each molecule to compute the hydrophilic (condensation onto the aqueous phase) and/or the hydrophobic (condensation onto the organic phase) partitioning considering a non-ideal aerosol, at the equilibrium or in a dynamic configuration. The hydrophilic partitioning is estimated according to a modified version of Henry's law, dependent on the infinite dilution activity coefficient(𝛾 𝑖,𝑎𝑞 ∞ ) calculated with the UNIversal Functional group Activity Coefficient (UNIFAC) (Fredenslund et al., 1975) and on activity coefficient in the aqueous phase 𝛾 𝑖,𝑎𝑞 computed with the AIOMFAC parameterizations (Zuend et al., 2008(Zuend et al., , 2010[START_REF] Zuend | New and extended parameterization of the thermodynamic model AIOMFAC: calculation of activity coefficients for organic-inorganic mixtures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl, and aromatic functional groups[END_REF]Zuend and Seinfeld, 2012). The hydrophobic partititoning is estimated with a non-ideal Raoult's law (Pankow, 1994). Experimental P sat and ΔH vap have been preferred for markers GPP evaluation when available, semi-empiric values and SARs estimation have been used as alternative.

When experimental data are not available, P sat and ΔH vap were estimated from the measured P sat and ΔH vap for a similar molecule by applying correction factors, based on SARs ratios (for P sat ) or differences (for ΔH vap ) between the two molecules.

Further details are described in the companion paper.In the mechanism descriptions, only the reactions directly involved in the formation pathways of biogenic markers are shown, however the competitive reactions (reactions that lead to a loss of intermediate species) are also taken into account in the model. The full mechanism for pinonic and pinic acid is available online (MCM website: http://mcm.leeds.ac.uk/MCM/) and in supplementary materials for MBTCA (Table S1) and isoprene markers (Table S2).

α-and β-pinene markers

In this section pinonic acid, pinic acid and 3-methylbutane-1,2,3-tricarboxylic acid (MBTCA) formation pathways are presented. Pinonic and pinic acid are commonly recognized as first generation photooxidation products [START_REF] Jaoui | Mass balance of gaseous and particulate products analysis from α-pinene/NO x/air in the presence of natural sunlight[END_REF], while MBTCA is a second generation product (Müller et al., 2012). The gaseous phase mechanism for the first generation product formation have been provided by MCM. For MBTCA, a formation mechanism from pinonic acid based on Müller et al., (2012) have been used.The names assigned to the intermediates in MCM have been kept, in order to assure the reproducibility of the model and the possible interaction between different pathways. The kinetic constants provided by MCM have not been reported in the text.

Pinonic Acid

The only precursor of pinonic acid in MCM is α-pinene (APINENE). α-pinene photooxidation is initiated by reaction with OH, NO 3 and O 3 and leads to pinonic acid formation through several reactions. Pinonaldheyde (PINAL) has been identified as the major intermediate for this process in the 3 pathways.

The OH initiated process (Fig. 1) generate two pinonic acid precursors: the 3-hydroxy-and the 2-hydroxy-isomers of (2,6,6-trimethylbicyclo[3.1.1]heptan-2-yl)dioxidanyl (APINAO2 and APINBO2) with the branching ratios respectively of 0.57 and 0.35. APINAO2 and APINBO2 oxidation pathways procede similarly, involving the reactions with:

-NO 3 , producing 100% of PINAL from both isomers.

-NO, producing 77% of PINAL and 23% of the 3-and the 2-hydroxyperoxy-2,6,6trimethylbucyclo respectively. APINANO3 and APINBNO3 react with OH giving respectively PINAL and the 2-hydroxy-2,6,6-trimethylbicyclo[3.1.1]heptan-3-one (APINBCO).

-HO 2 , leading to the formation of the hydroperoxydes conjugates (stoechiometric coefficient of 0.9, the 3-and the 2-hydroxyperoxy-2,6,6trimethylbucyclo APINAOOH and APINBOOH respectively).

APINAOOH and APINBOOH undergo photolysis generating 100% of PINAL.

-RO 2 , producing respectively for APINAO2 and APINBO2 70% and 60% of PINAL, 30% and 20% of 3,6,6-trimethylbicyclo[3.1.1]heptane-2,3-diol (APINBOH), and 20%

of APINBCO only from APINBO2. APINBOH react with OH producing APINBCO. The competitive reactions are not reported in figure, but considered in the model.

The NO 3 initiated α-pinene photooxidation (Fig. 2) gives 35% of [2,6,6-trimethyl-3-(nitrooxy)bicyclo[3.1.1]heptan-2-yl]dioxidanyl (NAPINAO2) and 65% of [2,6,6-trimethyl-2-(nitrooxy)bicyclo[3.1.1]heptan-3-yl]dioxidanyl (NAPINBO2). The oxidation pathway proceed similarly to the one already described for OH-initiated mechanism. The two intermediates react with:

-NO 3 and NO, producing 100% of PINAL from both isomers.

-HO 2 , leading to the formation of the hydroperoxydes conjugates (branching ratio=0.9, the 2-and the 3-hydroperoxy-2,6,6-trimethylbicyclo[3.1.1] ,6,6-trimethyl-3-oxobicyclo[3.1.1]heptan-2-yl nitrate (NC101CO) at 298K (the competitive reaction with OH is temperature dependent, while the current reaction is not). NC101CO undergoes photolysis producing C96CO3.

-RO 2 , generating respectively for NAPINAO2 and NAPINBO2 90% and 80% of According to Tilgner and Herrmann, (2010) aldehydes can be oxidized to carboxylic acids in aqueous phase, therefore PINAL oxidation to PINONIC in the aqueous phase has been considered. The partitioning with the aqueous phase has been evaluated using Henry's law constant (K H =809 M atm -1 ) and solubilization enthalpy (ΔH sol =61.2 kJ mol -1 ) calculated with SOAP, based on the Psat from [START_REF] Hallquist | Atmospheric fate of carbonyl oxidation products originating from α-pinene and Δ3-carene: determination of rate of reaction with OH and NO3 radicals, UV absorption cross sections, and vapor pressures[END_REF] (P sat =3.83x10 -2 torr). PINAL oxidation by OH and NO 3 has been modelled in analogy with 2,3 dioxobutenale (CLEPS 1.0): PINAL aq +OH→0.65*PINONIC k=2×10 9 M -1 s -1 (R 1)

PINAL aq +NO 3 →0.65*PINONIC k=2×10 6 M -1 s -1 (R 2)

PINONIC degradation in gas and aqueous phase produce MBTCA (Müller et al., 2012) and is discussed in section 2.2.3. Experimental pinonic acid P sat (5.86×10 -6 torr) and ΔH vap (86kJ mol -1 ) from Booth et al., (2011) have been inserted in SOAP as input for GPP calculation.

Pinic Acid

According to MCM, pinic acid is produced from α-pinene ozonolysis and from the OH-, NO 3and O 3 -initiated oxidation of β-pinene. The major intermediate for pinic acid formation, the nopinone, is produced only by β-pinene oxidation.

C109O2 is the precursor of pinic acid produced by α-pinene reaction with O 3 (yield 27%) (Fig. 3 and4). C109O2 reacts with NO and NO 3 producing for both reactions 80% of 2,2- Since no experimental value for PINIC subcooled vapor pressure and vaporization enthalpy has been found in literature, a semi-empiric estimation from experimental Booth et al., (2011) pinonic acid values has been performed to simulate the GPP. The P sat and ΔH vap estimated with this method are respectively 1.01×10 -7 torr and 80 kJ mol -1 .

MBTCA

MBTCA is a so called -second generation‖ photooxidation product of α-and β-pinene. Müller et al., (2012) proposed a formation mechanism of MBTCA from pinonic acid (first generation photooxidation product) composed by 3 pathways. These pathways are started with H-abstraction by OH from pinonic acid C-4 (3.5%), C-5 (18%) and C-7 (0.8%) carbon atoms, named respectively pathways A, B and C. The overall MBTCA yields of the pathway A, B and C have been estimated by Müller et al., (2012) to be respectively 1.6, 0.0004 and 0.011. In our mechanism, only the pathways with higher yields, A and C, have been considered which are favoured by high NO x conditions (Table S1).

In MAE partitioning between the gaseous and aqueous phases is assumed instantaneous and is computed with an Henry's law constant of 1.2×10 The gaseous phase degradation of MAE by reaction with OH (k=1×10 -12 molecule cm -3 s - 1 (Lin et al., 2013)) has been considered as competitive for the reaction R11. The degradation kinetic of MGA in the gaseous phase has been evaluated with GECKO-A:

MGA+OH→OH k=9.9×10 -12 molecule cm -3 s -1 (GECKO-A) (R 12)
Due to lack of data on thermodynamic properties of MAE, SIMPOL (Pankow and Asher, 2008) SARs estimations are used to estimate the values of P sat and ΔH vap (respectively of 1.4×10 -5 torr and 91.7 kJ mol -1 ).

Comparison measurements model

Configuration: model resolution and domain

The reference run for model to measurements comparison has been performed all over France (resolution 0.06°×0.125° longitude x latitude). Several runs on European domain (resolution 0.25°x0.4°) have been performed to obtain boundary conditions and to perform sensitivity tests (for which European domain base run will be taken as reference). Nine vertical layers have been simulated till 500 hPa. Meteorological data from the operational analysis of the Integrated Forecasting System (IFS) were taken from the European Centre for Medium-Range Weather Forecasts (ECMWF). The sector-specific EMEP inventory (methodology described in [START_REF] Vestreng | Review and Revision. Emission data reported to CLRTAP Tech. Rep[END_REF] has been used for annual anthropogenic emissions. Biogenic emissions have been computed with the Model of Emissions and Gases and Aerosol from Nature (MEGAN) 2.1 algorithm [START_REF] Guenther | The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions[END_REF]. MEGAN simulates BVOCs emissions considering several parameters concerning the sources (such as the plant functional type (PFT) and the leaf area index (LAI)) and the meteorological data (for example T and the photosynthetically active radiation (PAR)).An example of MEGAN output for isoprene, αand β-pinene is reported in Fig. 8.

Biogenic aerosols formation from isoprene, monoterpenes and sesquiterpenes is parametrized with the hydrophilic/hydrophobic organic (H 2 O) aerosol model (Couvidat et al., 2012). In this mechanism the precursors are oxidized by OH, NO 3 and O 3 (under low NO x and high NO x conditions) to give more oxidized and less volatile compounds, that are surrogates of biogenic secondary aerosols. Surrogate and marker GPP has been calculated with SOAP. A non-ideal configuration (taking into account interactions between organics and with inorganics) assuming thermodynamic equilibrium, in which both the hydrophilic and the hydrophobic partitioning has been evaluated, was chosen. Wet and dry deposition of particles in the reference run has been computed as in Couvidat et al., (2018) , using the method described by to Bessagnet et al., (2010) for dry deposition of organic vapors. The influence of dry and wet (in-cloud and the below-cloud scavenging) deposition of the gas-phase concentration has been tested separately. (Ile-de-France) which is the most populated area in France (Crippa et al., 2013;[START_REF] Petit | Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF 2 ) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength Aethalometer[END_REF]Petit et al., , 2017;;Sciare et al., 2011;Srivastava et al., 2018b). Both particle-(PM 10 ) and gas-phase samples have been collected every third day from the 19/11/2014 to the 15/12/2015, for 24h.

25 SOA markers have been quantified by GC/EI-MS after derivatization, using authentic standards. These samples were extensively characterized and all the measurements details are reported in Lanzafame et al., (2020). In this paper only the biogenic markers measurements will be shown with the aim of comparing to the model output.

Results and discussion

Model to measurement comparison

Table 2 shows measured and modelled annual median concentrations, 25 th and 75 th quartiles, and mean particulate phase fractions (F p ), together with correlation coefficients (r) and mean fractional bias (MFB) for the 5 biogenic SOA markers. Measured median total (gas + particle) concentration values are higher for all the markers by factors that seem independent of the precursors. The correlations between observations and simulations are low and exceed 0.3 only for α-methylglyceric acid.

Pinonic acid measured median concentration (2.12 ng m -3 ) is 3 times higher than the modelled one (0.71 ng m -3 ). Over the whole period, measured concentrations are higher and more dispersed than in the simulations, with measured and modelled interquartile ranges between 0.94-3.89 and 0.43-2.25 ng m -3 , respectively. The correlation coefficient between simulated pinonic acid and measurements is low (0.28). MFB value is negative (-0.51), indicating that the model underestimates measurements. The model underestimate also the particulate phase fraction, with a simulated mean F p of 0.12 against the observed value of 0.68.

Pinic acid modelled median concentration (0.011 ng m -3 ) is 50 times lower than median measured concentration (0.51 ng m -3 ). 25 th and 75 th percentiles are underestimated respectively by a factor 450 and 25 (interquatile ranges are 0.0005-0.043 ng m -3 in simulations and 0.22-1.05 ng m -3 in measurements). In agreement with the other underestimation indications, MFB has a high negative value (-1.68). Correlation coefficient is positive but low (0.22). GPP is misrepresented: in simulations pinic acid is quite volatile, being only at 37% in the particulate phase, while in measurements is almost completely in the particulate phase (94%).

MBTCA concentrations and variability are underestimated by the model. The median concentration in the simulation is 0.14 ng m -3 and the interquartile range is equal to 0.192 ng m -3 , median measured concentration and interquatile range are 4.5 times higher (median 0.64 ng m -3 , interquartile range 0.88 ng m -3 ). The correlation coefficient is low (0.2) and MFB value is close to -1. Partitioning is well represented in the model, in which MBTCA is completely in particulate phase, as in measurements, which is coherent with the low saturation vapor pressure (2.58×10 -10 torr) used in this study.

For methyltetrols measured (1.47 ng m -3 ) and modelled (0.76 ng m -3 ) median concentrations differ by a factor 2. The simulated 25 th interquartile (0.14 ng m -3 ) is underestimated compared to measurements (0.79 ng m -3 ) by a factor higher than 5, while the gap between 75 th quartiles is only of 22% (modelled and measured values are respectively 3.05 and 3.72 ng m -3 ).

Correlation is positive but low (0.28) and not significative, MFB has been calculated to be -0.59. The simulated particulate phase fraction is 0.56, 15% lower than the measured one of 0.64.

The marker with the strongest underestimation in this study is α-methylglyceric acid. The modelled median and quartiles (median 0.20 ng m -3 , 25 th and 75 th quartiles are respectively 0.03 and 0.71 ng m -3 ) differ from measured values (median 0.0018 ng m -3 , 25 th and 75 th quartiles are respectively 0.0003 and 0.0052 ng m -3 ) by a factor higher than 100 and MFB is equal to -1.77. However, α-methylglyceric acid simulation has the highest, though not significative, correlation with measurements observed in this paper (0.32). In measurements α-methylglyceric acid is less volatile, with a mean particulate phase fraction of 0.89 while the simulated value is lower (0.54).

The significative underestimation can be due to a precursor emission underestimation, a wrong representation of the marker formation pathways and a wrong estimation of radical

concentrations. An analysis of markers temporal variability at different atmospheric conditions is required in order to understand to which extent every process can influence marker formation. Moreover, the gas toparticle partitioning is strongly underestimated for pinonic acid, pinic acid, α-methylglyceric acid that may be due to an overestimation of the saturation vapor pressure. Model to measurements annual comparison (gas+particle phase) for pinic acid, pinonic acid and MBTCA are shown in Fig. 9. Each marker is underestimated by a different factor and the temporal variations are not well represented. However, the model to measurements comparison for pinene marker is not straightforward, since no clear annual evolution has been observed in measurements (Lanzafame et al., 2020).

The concentrations simulated for pinic acid are below 0.1 ng m -3 for most of the year, except for some peak observed between May and August. On the opposite, measured concentrations are normally above 0.1 ng m -3 , except for most of winter and the beginning of spring, and several peaks have been observed thoughout the year. The maximal concentration have been simulated in summer (02/07), with a value around 5 ng m -3 , while the maximum measured value (7.75 ng m -3 ) has been observed in April (26/04). The overall underestimation is lower in summer (from May to August, MFB -1.2) than in winter (MFB -1.9). The model is not able to reproduce the moderate concentrations (between 0.002 and 3.5 ng m -3 ) measured from September to the end of the year.

Although the highest simulated pinonic acid concentration (22.5 ng m -3 , 29/08) is higher than the measured one (11 ng m -3 , 13/10), the model output is lower than measurements for most of 2015. The low pinonic acid measured concentrations at the beginning of the year (January-April) are slightly underestimated (MFB -0.2) by the model. In the rest of the year, the peak concentrations are higher than measurements, while the lower concentrations are underestimated. The average MFB calculated from May to December is around -0.7. Only for this marker, model and measurements variations partially match from May to August.

MBTCA measured average summer (May-August) concentration (1.2 ng m -3 ) is lower than the winter average (0.7 ng m -3 ), while simulated concentrations have an average of 0.2 ng m -3 both during the warmer and the colder period. MFB value is close to -1 thoughout the year.

The maximal measured concentration is 10 times higher than the simulated maximum (respectively 10.3 ng m -3 on 03/11 and 1 ng m -3 on 10/10). Peak concentrations are scarcely reproduced and the increase of concentrations at the end of the year is not reproduced by the model. Methyltetrols are represented in the model with the right magnitude order. The model simulates well the overall annual trend, with an increase of concentrations in summer.

However, mean concentrations for the colder period is underestimated by a factor 2 (measured and modelled mean are respectively 1.4 and 0.7 ng m -3 ) and the summertime simulated mean (14.5 ng m -3 ) is 1.5 times higher than in measurements (9.9 ng m -3 ). The mean simulated concentrations calculated for the warmer period are biased from the maximum (175 ng m -3 ) simulated on 02/07. This anomalous concentration is due to the high isoprene emissions generated in the south of France and in Germany (Fig. S1), in which temperature in those days (from 29/06 to 03/07) is higher than in the rest of the year.

According to Guenther et al. (2006), biogenic emissions depend on hydrological stress and no biogenic emissions are possible when the soil moisture is below the wilting point). However, in the MEGAN version inserted in CHIMERE, the soil moisture effect on biogenic emissions is not considered. The volumetric soil water content (θ) over this period is around 0.157 m3 m - conditions (θ m <θ <θ m +0.06) biogenic emissions are reduced by a factor ranged between 0 and 1 (Guenther et al., 2006) . For this reason, CHIMERE likely overestimates strongly the emissions over this period, explaining why the 02/07 peak has not been observed in measurements and the maximum has been rather measured on 15/07 (47 ng m -3 ). MFB mean value is quite low in summer (-0.2, from May to August) and higher in winter (-0.76) where the concentrations are very low (under 6 ng m -3 ).

α-methylglyceric acid is underestimated in the model by a factor 10 2 . Simulated concentrations are higher in summer than in winter, with a similar seasonal trend with measurements. The maximal simulated concentration is 40 pg m -3 (on 12/06), while the maximal measured concentration is 3.3 ng m -3 (on 23/08). The reason for this high underestimation may be linked to the parameters used to simulate α-methylglyceric acid formation in the aqueous phase from MAE. To quantify the influence of the parameters used for aqueous phase MAE oxidation on total α-methylglyceric acid concentrations, MAE Henry's law constant has been taken higher by a factor 10 (K H =1.2×10 6 kJ mol -1 ) and its kinetic constant has been increased to the same value used for IEPOX oxidation, as in Pye et al., (2013). This test was performed from the 15/06 and the 15/07.We noticed an increase by a factor 190 of α-methylglyceric acid simulated concentrations at SIRTA, and an increment of the European maximum by a factor 10 2 (Fig. S3). This new parametrization brings αmethylglyceric acid concentrations to the same order of magnitude of the measured ones, leading to an overestimation by a factor 2 at SIRTA. These results fit better the measurements than the ones in the reference run, nevertheless theyare still not sufficiently accurate.The choice of both Henry's law and aqueous phase kinetic constants must be considered critical for a good representation of α-methylglyceric acid formation in the aerosol phase and more experimental values should be provided to reach a more accurate result.

The model succeeds in representing the seasonal variations observed for biogenic markers, with no definite annual trend for pinene markers and higher isoprene marker concentrations in summer. The underestimation of simulated markers can depend from different factors, such as the radical concentration estimation and the OM mass and composition in the model. The next section is dedicated to a sensitivity analysis of marker formation towards radicals concentrations change.

Model to measurements comparison for OM at SIRTA during 2015 is reported in Fig. S4.

OM is well represented in the first part of the year (January to April) and underestimated by a factor ~5 for the rest of the year (May to December). This gap may play a decisive role on GPP estimation, especially for biogenic markers, which concentrations are expected to be higher in summer (e.g. α-methylglyceric acid). According to Pankow, (1994), the ratio between particulate and gaseous phase concentrations is directly proportional to OM, which means that OM underestimation lead to an underestimation of the particulate phase fraction and potentially of the total marker concentrations. The influence of GPP on total marker concentrations is discussed in section 4.3.

Influence of chemical regime on total marker concentrations

As already reported in the model development section, very detailed mechanisms have been inserted in CHIMERE to simulate marker formation with many reaction steps dependening on radical concentrations. Some compounds may be very sensitive to the chemical regime (low-NO x or high-NO x conditions). Moreover, different gas-phase mechanism may simulate very different radical concentrations [START_REF] Kim | Comparison of two gas-phase chemical kinetic mechanisms of ozone formation over Europe[END_REF][START_REF] Kim | Comparison of Different Gas-Phase Mechanisms and Aerosol Modules for Simulating Particulate Matter Formation[END_REF]Sarwar et al., 2013). It is therefore important to test the robustness of this novel approach. For this purporse, the sensitivity to MBTCA concentrations at SIRTA are scarcely sensitive to NO, HO 2 and RO 2 variations (Fig. S6). Whereas the formation of MBTCA should be favoured by high NO x conditions, the competition between NO and HO 2 in the oxidation process scarcely affect MBTCA concentrations in the model. This is due to the fact that MBTCA is formed almost entirely (around 80%) by the aqueous-phase oxidation of the pinonic acid in the model (for which no dependence on NO x concentrations was taken into account).

Isoprene markers

Isoprene markers sensitivity tests to radical concentrations have been reported in Fig. S7 for methyltetrols and in Fig S8 for α-methylglyceric acid. As already underlined in the method development section, the relative concentrations of these markers are expected to be strictly dependent on the NO x regime.

In the -half NO‖ simulation methyltetrols concentrations slightly increased (4% of variation) while in the -double NO‖ run methyltetrols concentrations decrease of 36%. On the other hand, methyltetrols concentrations respectively increase (by 38%) and decrease (by 34%) in the -double HO 2 ‖ and in the -half HO 2 ‖ runs. These results are consistent with the fact that methyltetrols are low NO x regime markers (Surratt et al., 2010). α-methylglyceric acid variations with the NO x regime are opposite compared to methyltetrols variations but remain low (below 10%). For both marker no sensitivity to NO 3 and RO 2 concentrations has been observed.

Pinonic acid formation process: spatial and daily variability

Pinonic acid is produced by α-pinene atmospheric degradation, initiated by 3 competitive reaction with OH, NO 3 and O 3 as oxidant (see section 2.2.1). According to the local radical concentrations, the degradation may be dominated by the reaction with one of oxidant.

However, it should not be assumed that the final amount of pinonic acid produced by each pathway depend only from the α-pinene oxidation initiation reaction. The fractional oxidation initiation rates (fraction of α-pinene reacting with the considered oxidant) of α-pinene with OH, NO 3 and O 3 over Europe are shown in Fig. 12, together with pinonic acid fractional amount (amount of pinonic acid produced by the reaction with one of the oxidants divided by total pinonic acid concentrations) from the 3 pathways. From the comparison between the fractional oxidation initiation rates and pinonic acid fractional amount we can deduce that:

-Although NO 3 fractional reaction rate predominate over OH and O 3 reaction rates in central Europe, the pinonic acid fractions produced by this pathway is the lowest.

-A large part of pinonic acid (20% to 50%) is formed by the OH initiated pathway.

-O 3 initiated pathways dominates pinonic acid formation in the Scandinavian peninsula (where To understand why pinonic acid formation by the NO 3 initiated pathway is a minor contributor of pinonic acid formation, the processes influencing pinonic acid formation have been further investigated. In Fig. 13, the daily variations of α-pinene, pinonaldehyde and pinonic acid concentrations, together with production and degradation rates of the principal species involved in pinonic acid formation, are presented. The results shown are averaged all over Europe and integrated on all the altitude level of the model, for the period included between the 15/06 and the 15/07/2015.

Pinonic acid vertically integrated concentrations are slightly above 3 µg m -2 in the nighttime and decrease below 1 µg m -3 during the daytime. α-pinene and pinonaldheyde vary daily as pinonic acid: higher concentrations (~70 and ~90µg m -2 respectively) have been simulated before 4:00 UTC and after 21:00 UTC and lower concentrations (minimum ~25 and ~10 µg m -2 respectively) in the rest of the day. The nighttime α-pinene degradation, that occurs mainly by reaction with NO 3 (rate ~7 ng m -2 s -1 ), is the predominant process of pinonaldehyde formation, as previously reported [START_REF] Calogirou | Gas-phase terpene oxidation products: a review[END_REF]. In the daytime, α-pinene+NO 3 reaction rate is very low (0.5 ng m -2 s -1 ) and α-pinene reaction with OH is the main degradation process (maximal rate ~5 ng m -2 s -1 ). The α-pinene+O 3 reaction ratehas a lower diurnal vary less between night and day, with a minimum around 7:00 UTC (2 ng m -2 s -1 ) and maximum around 21:00 UTC (4 ng m -2 s -1 ). In the gas-phase, pinonaldehyde is quickly degradated by OH in the first hours of the day (peak rate of ~4 ng m -2 s -1 at 5 UTC), while the oxidation rate with NO 3 as oxidant is always neglectable.

However, pinonic acid formation from pinonaldehyde in the gaseous phase requires several oxidation steps and these processes are not indicative of the total pinonic acid formation yield.

The processes directly involved in pinonic acid formation are shown in Fig. 13c. APINBOO, C96CO3 and pinonaldehyde (via aqueous phase reactions) are pinonic acid direct precursors (see section 2.2.1). APINBOO reaction rate with H 2 O is almost constant during all the day, keeping a value around 0.1 ng m -2 s -1 . C96CO3 reactions with HO 2 and RO 2 are faster during daytime (from 7:00 to 18:00 UTC), with a peak rate of 0.2 ng m -2 s -1 at 12 UTC. This is the major pinonic acid production process. The pinonaldehyde reaction with OH in the aqueous phase is the second process for pinonic acid yield, with a peak rate of 0.15 ng m -2 s -1 at 3:00 UTC. This rate is low during most of the day and correspond to the maximal pinonic acid degradation rate (0.2 ng m -2 s -1 ), occurring by aqueous phase reaction with OH at the same time of the day. The other processes (pinonaldehyde+NO 3 in the aqueous phase and pinonic acid degradation by OH in the gas-phase) rates are negligible compared to the rates already commented.

The courbe gathering all these processes has a minimum at 4:00 UTC (rate next to 0) and a maximum around 12:00 UTC. Pinonic acid is produced mostly during the daytime, between 9:00 and 18:00 UTC, in which the major process is the C96CO3 oxidation. However, pinonic acid formation from APINBOO (radical formed from α-pinene+O 3 ) dominates pinonic acid after 18:00 UTC for a large part of the night. In the first hours of the morning, aqueous phase reactions become dominant and the total balance between production and degradation in the aqueous phase is negative, part of the pinonic acid produced in the gaseous phase is degradated together with the pinonic acid produced in the aqueous phase. Due to the aqueous phase degradation, pinonic acid concentrations decrease sharply between 3:00 to 9:00 UTC, and start to increase after, when the gaseous phase processes are predominant.

Therefore, the high O 3 -pinonic acid fractional yield shown in MBTCA is almost completely in the particulate phase in the measurements and the simulations (minimum 10 th percentile F p 0.96). In measurements, 100% of MBTCA is in the particulate phase, with no variability. the median F p are above 0.99 for all the simulations.

The model was able to reproduce the partitioning of methyltetrols. Methyl tetrols are semivolatile both in measurements and in the -reference‖ run, with median F p values respectively of 0.65 and 0.63. F p distribution is also similar: for measurements and reference 25 th percentiles are respectively 0.29 and 0.20, 75 th percentiles are 0.99 and 0.95. Interquartile range is slightly higher for -reference‖ (0.75) compared to the -obs‖ (0.70). Taking only into account the hydrophobic partitioning, methyltetrols are totally in the gaseous phase, with a F p 75 th percentile of 0.02 (median of 0.003). Assuming an ideal partitioning would lead to an overestimation of F p values (median Fp of 0.98 for the ideal simulation).

In measurements α-methylglyceric acid has low volatility, having 75% of their F p higher than 0.71 (25 th percentile). Median measured F p is 0.91 and the interquartile range is quite narrow (0.26). In the -reference‖ run, α-methylglyceric acid has a median F p of 0.60 and 50% of F p values are between 0.24 and 0.92 (respectively 25 th and 75 th quartiles). A great variability is associated with this simulation (interquartile range 0.68). As for methyltetrols, only taking into account the hydrophobic partitioning, would lead to almost all the α-methylglyceric acid in the gaseous phase, with a F p 75 th percentile of 0.02 (median of 0.003). Assuming an ideal partitioning lead to F p similar with the measurements (median F p 0.91) except for the lower 25 th quartile value (0.50).

GPP is underestimated for most of the markers, excluded MBTCA and methyltetrols. Great incentitude in GPP computation is related to the choice of the marker thermodynamic parameters (e.g. P sat , K H ). The value chosen for α-methylglyceric acid was very uncertain as it was calculated with the SIMPOL (Pankow and Asher, 2008) SARs estimations whereas P sat (of the marker or a similar molecule) from measurements was used for the other compounds.

P sat value avaible in the literature could also be very uncertain. Moreover for pinic and pinonic acids, which are mainly hydrophobic, the underestimation of OM may lead to a significant underestimation of the simulated particulate phase fraction.

Influence of partitioning and gas-phase dry deposition on total marker concentrations

Several multiphase processes are involved in the formation and degradation mechanisms of the simulated markers. As a consequence, the GPP may affect the simulated concentrations, condensation in the particle phase can for example protect the molecule from its gas-phase degradation. To quantify the GPP influence on marker concentrations some tests have been performed assuming: (1) completely volatile markers, (2) completely non-volatile markers. A third test was also performed to evaluate the potential effect of the dry and wet deposition of gas-phase markers. The outputs at SIRTA of -volatile‖, -non-volatile‖ and -deposition‖ simulations have been compared with the -reference‖ run, in which markers are semivolatile (according to their P sat ) and only particulate phase deposition has been considered (Fig. 15). The sensitivity tests have been performed during February, mid-June to mid-July and October. The tests are not performed for MBTCA for which the non-volatility was reproduced and because in the -non-volatile‖ simulation MBTCA formation would be prevented. The effect of the tests on concentrations at SIRTA are shown.

Pinonic and pinic acid are less volatile in measurements (median F p respectively 0.82 and 0.98) than in the -reference‖ run (median F p respectively 0.05 and 0.66). The gap observed in comparison with the -reference‖ for pinonic acid and pinic acid for the -volatile‖ (respectively -12% and -45%) and the -non-volatile‖ (increase by a factor 4.5 and 2.15) simulations suggests a protective effect from degradation of the condensation onto the particle phase. For both markers the principal loss mechanism is the gas-phase oxidation by OH, for which average half lifes in the tested periods (considering the simulated OH concentrations) have been evaluated to be respectively ~3 d -1 and 2.8 d -1 for pinonic and pinic acid. These marker half-lifes are considerably shorter than the average particle half-life of 1 week for particles (Seinfeld, 2015). This result suggests that simulated total concentrations could be significantly higher and could explain the gap between measurements and observations. As the CHIMERE species BiMT (representing methyltetrols and other IEPOX derived SOA) was already present in the model, methyltetrols are the only markers for which gas-phase deposition has been already considered in the -reference‖. -Volatile‖ methyltetrols concentrations decrease by 63% while -non-volatile‖ methyltetrols concentrations increase by 61%. Since no degradation mechanism has been inserted for methyltetrols in the model, the only degradation process for methyltetrols is deposition. Therefore, the variations observed between the -volatile‖ and -non-volatile‖ simulations depends on the relative deposition rate of gaseous and particulate phase. GPP effect on total concentrations of methyltetrols at SIRTA can be quite high. However, as shown in section 4.3.1, GPP was reproduced with the right order of magnitude in the model. α-methylglyceric acid variations with volatility are coherent with the ones observed for pinonic acid and pinic acid. A concentration decrease in the -volatile‖ (by 25%) parametrization is due to an enhancement of gas-phase removal process. In the -non-volatile‖ simulation, α-methyl glyceric acid concentrations increase by a factor 2.41, because of the partitioning protecting effect. The incertitude due to GPP estimation for this marker (gap between median measured and simulated F p 0.31) should be considered lower than the one associated to pinonic acid, which measured and -reference‖ F p have a gap of 0.77, and similar to the one associated to pinic acid (median F p gap between measurement and -reference‖ of 0.32).

A strong effect of gas-phase deposition was simulation with a decrease of concentrations close to a factor 2 for pinonic acid, pinic acid and α-methylglyceric acid indicating that gasphase deposition may be a very efficient removal process. However, the extent of this removal process is probably overestimated due to the fact that F p values are strongly underestimated for these 3 compounds. acid was found not to be sensitive to NO concentrations. However, [START_REF] Kim | Comparison of Different Gas-Phase Mechanisms and Aerosol Modules for Simulating Particulate Matter Formation[END_REF] showed that methyl-glyceric acid formation is strongly dependent on the NO 2 /NO ratio.

Moreover, it should be also strongly dependent on the particle pH. The high underestimation observed for MBTCA concentrations may be due also to the lack of a low NO x conditions formation mechanism.

Our results seem to indicate that the good estimation of GPP is critical for SOA modeling. For some makers (pinonic acid, pinic acid, α-methylglyceric acid) F p is significantly underestimated in the model in all conditions. Pinonic acid and pinic acid partitioning is mostly hydrophobic, and thus strictly dependent on OM mass and OM composition. The underestimation of Fp can (at least partly) be attributed to OM mass underestimation. αmethylglyceric acid partitioning is mostly hydrophilic and a great incertitude in its partitioning should be attributed to the P sat estimation by SARs. MBTCA and methyltetrols are mostly hydrophilic and their partitioning is well estimated. Our results showed that estimating the non-ideal hydrophilic partitioning is essential to reproduce the partitioning of methyltetrols and that assuming an ideal hydrophobic partitioning would lead to a strong underestimation of F p .

Part of the gap observed between model and measurements for pinonic acid, pinic acid and αmethylglyceric acid may depend on an overestimation of removal processes entity in the gas phase. Various effect could modify marker volatility (such as aerosol viscosity), which makes markers less volatile than simulated and prevent them from the loss processes occurring in the gaseous phase. Similarly, considering these markers more volatile than observed may lead to an overestimation of their gas-phase deposition, which seems to decrease the total concentrations by a factor 2. GPP can be considered a critical parameter for the simulated pinonic acid concentrations, that underestimate measurements by a factor 3. (Piletic et al., 2013a) MGA+OH->OH k=9.9×10 -12 (GECKO-A)

Temporal variations

Sensitivity analysis

Molecular markers model/measurements comparison: knowledge of physicochemical processes and emissions

A mechanism for the formation of five anthropogenic (single species or several isomers including nitrophenols, nitroguaiacols, methylnitrocathecols, DHOPA and phthalic acid) and five biogenic (pinonic acid, pinic acid, MBTCA, methyltetrols and α-methylglyceric acid)

SOA marker has been developed and inserted into the 3D CTM CHIMERE, together with primary levoglucosan emissions. Marker concentrations have been simulated and compared with measurements at SIRTA (1-year data, in 2015) and at 10 urban sites in France for levoglucosan for the winter 2014-2015 period.

Levoglucosan concentrations were simulated with the right order of magnitude. In the western part of France, levoglucosan underestimation was linked to the general underestimation of OA from wood burning in the model. Based on levoglucosan saturation vapor pressure reported in the literature, levoglucosan was found to be semi-volatile with a major dependence on humidity. The use of fixed ratios (conversion factors) to estimate OA from wood burning from levoglucosan concentrations has been investigated from a theoretical point of view.

Results obtained highlighted the accuracy of this approach under high biomass burning influences (high OM concentrations due to this source) and during OM concentration peaks (PM pollution events in winter related to biomass burning from residential heating). In future studies, levoglucosan non-volatility should be re-considered and the quantification in gas phase during field campaigns should be addressed together with its partitioning under different humidity conditions.

Besides, SOA molecular marker such as nitrophenols and nitroguaiacols from both, measurements and model, agreed well in terms of concentrations and temporal variations. For methyltetrols, concentrations were simulated with the right magnitude order, but other markers were largely underestimated (till 1000 times lower) probably due to different factors no well described in the model, such as their emission/ precursor sources, formation pathways and/or thermodynamic properties, depending on the marker considered. Finally, GPP was well reproduced for nitroguaiacols, nitrophenols, DHOPA, MBTCA and methyltetrols. Additionally, the use of the MOVI (Multi-Orifice Volatilization Impactor) or FIGAERO inlet (Filter Inlet for Gases and AEROsols) would allow to estimate the saturated vapor pressure of the aerosol components identified and this would be of great value to improve and decrease the model uncertainties.

For biogenic SOA markers, simulation results showed that MBTCA and MGA formation seemed highly dependent on the aqueous phase formation processes. More specifically, 80%

of MBTCA was produced by the aqueous phase oxidation of pinonic acid and MGA formation seemed better represented when methacrylic acid epoxide (MAE) Henry's law constant was increased by a factor 10 and oxidised with the IEPOX kinetic constant. Pinic acid and methyltetrols formation was sensitive to the NO x regime. When NO concentrations were doubled, a decrease around 40% in marker formation was observed. In addition, strong daily variations have been observed in the formation rates of the main processes involved in pinonic acid formation from α-pinene. Despite of the higher rate of the NO 3 +α-pinene reaction compared to the ones with OH and O 3 , the oxidation pathways initiated by the latter ones gave higher pinonic acid yields.

Thus, the development of extended aqueous phase mechanisms (liquid phase such as cloud droplets but also in the aqueous aerosol phases) for VOC oxidation (number of carbon > 4)would be required, in order to take in account these processescurrently neglected. For this purpose, experimental studies on the degradationon soluble SOA intermediates (e.g. pinonaldehyde) in aqueous phases (aerosols or clouds) should be performed. In addition, the investigation of all the phases together, gaseous, particulate and aqueous phases during measurement field campaigns should be investigated simultaneously and ideally, at high -time resolution (< 1 hour).

Finally, the effective competition between NO x and HO 2 or RO 2 oxidants is difficult to evaluate due tohigh uncertainties in HO 2 and RO 2 estimations in the model. A direct comparison of HO 2 and RO 2 model outputs with with measurements performed simultaneously with OA marker measurements would be helpful to lower the model uncertainties of the biogenic OA (but also probably anthropogenic) marker simulations.

Gas/particle partitioning evaluation

Finally, all the results obtained showed that the major implicationof the GPP on OA markersis linked to the protective effect to gaseous phase degradation and deposition due the mass transfer to the particulate phase. If considered completely volatile, concentrations of low volatility compounds as DHOPA decrease till a factor 10 because of the degradation process occurring in gaseous phase. Concentrations of volatile compounds, as nitrophenols and nitroguaiacols, could decrease by a factor 6 when gas phase deposition is considered. In order to fill the gap between model and measurements, additional measurements of OA marker thermodynamic parameters,such as saturated vapor pressure, should be performed, since literature data are often discordant and for some molecular markers (e.g. α-methylglyceric acid and DHOPA) no experimental value is available. Secondly, a dynamic approach may be used to simulate particles composition, that changes according to the region and seasons, including their viscosity. This kind of partitioning could be represented using a Lagrangian approach. Additional studies on dry and wet deposition of gaseous phase compounds should be also performed, since markers concentrations in the gas phase are very sensitive to these processes, that may be overestimated.

Molecular markers for source apportionment

Organic molecular markers are useful species to apportion OA sources. The use of SOA markers in both, SOA tracer method and PMF allowed to apportion SOA at SIRTA over the year 2015. Overall, total SOC concentrations evaluated by both methods were in good agreement through the year. Significant differences were only observed in March and in summer (from May to August) with a systematic underestimation by the SOA tracer method.

These results highlighted that one of the main limitations of the SOA tracer method occurred under highly processed period, such as in March due to missing markers for SOA classes such as organonitrates and organosulfates while it was apportioned in the PMF based on the nitrate and sulfate-rich factors. Similarly, in the PMF, part the biogenic SOA was apportioned using markers specific of marine biogenic SOA (dimethylsulfide oxidation) while no specific 𝑓 𝑆𝑂𝐶 value has been determined for the DMS oxidation and this fraction could not be accounted using the SOA tracer method.

A successful simulation of OA (SOA or POA) markers may represent a useful diagnostic tool in the apportionment of OA sources using CTM and in the understanding of the variability of atmospheric processes by comparison with long term measurements. Further, we could expect to use such model outputs in a dual approach combining first OA marker concentrations simulated and then, their use in a source-receptor model or based on an improved SOA tracer method to apportion SOA over larger scales than only based on measurements.

Figure 1 . 1 .

 11 Figure 1.1. Schematic representation of principal modes, sources and aerosol formation and removal mechanisms. Adapted from:[START_REF] Whitby | California aerosols -their physical and chemical characteristics[END_REF]

  Metals and PAHs are regulated in Europe by the European Directive 2004/107/CE (European official journal, 2004).

Figure 1 . 2 .

 12 Figure 1.2. Airways of the adult lung and particle deposition in function of their sizes.

  3. Higher positive contributions to the total anthropogenic ERF are given by well mixed greenhouse gases (WMGHG), including CO 2 . Tropospheric ozone positive RF is in part compensated by the stratospheric ozone negative RF. The only forcing agents giving significative negative ERF are aerosols-radiation and aerosol-cloud interactions. These effects mitigate but not fully contrast WMGHG ERF, and total balance of anthropogenic ERFs results positive.

Figure 1 . 3 .

 13 Figure 1.3. Bar chart for radiative forcing (hatched) and effective radiative forcing (solid) by concentration change between 1750 and 2011. Uncertainties (5 to 95% confidence range) aregiven for RF (dotted lines) and ERF (solid lines). Source:[IPCC, 2018]

Figure 1 . 4

 14 Figure 1.4 Average chemical composition of PM 1 for different types of site. Pink: chloride;

Figure 1 .

 1 Figure 1.5 . Major microphysical and chemical processes that influence the size distribution

Figure 1 . 6 Figure 1 . 7 .

 1617 Figure 1.6 VOC atmospheric degradation reactions proceeding through formation of an alkylor substituted alkyl radical. Source:[Ziemann and Atkinson, 2012]

Figure 1 . 8

 18 Figure 1. 8 Structures of some biogenic SOA markers divided by their precursor.

Figure 1 . 1 .

 11 Figure 1. 9Structures of some anthropogenic SOA markers divided by their precursor.

Figure 1 .

 1 Figure 1.10 Schematic representation of a 3D chemistry transport model (CHIMERE). [c] mod and [c] obs are respectively the modelled and the observed chemical concentrations field.Adapted from:[START_REF] Menut | CHIMERE 2013: a model for regional atmospheric composition modelling[END_REF]

Figure 1 .

 1 Figure 1. 11 From EURODELTA III intercomparison exercise: Comparison between observed and simulated total organic matter (TOM), total primary organic matter (TPOM)

  The discrepancies observed have been investigated considering the possible model uncertainty, including primary emissions, chemical reactivity and GPP parameterization. This PhD manuscript is divided in 4 chapters. The main results, together with the methods used, are presented in 3 scientific articles:-In Chapter II (Article 1) the annual measurements of biogenic and anthropogenic SOA markers performed at the SIRTA facility (25 km SW of Paris city centre) during 2015 (every third day) are shown. Literature data are provided for comparison with SOA markers concentrations measured at SIRTA. The seasonal variations of 25 SOA marker concentrations and gas/particle partitioning are examined and discussed. SOA sources have been also apportioned using the SOA tracer method and the results obtained are compared with PMF outputs. This work constituted also the validation basis of the model developments made to simulate OA marker concentrations using CHIMERE. My contribution to the work presented in this chapter includes the development of the analysis method for the SOA markers in gaseous and particulate phase and the analysis of SOA markers in the gaseous phase.-In Chapter III (Article 2), the primary emissions of levoglucosan, together with the formation mechanisms of 5 anthropogenic SOA markers (nitrophenols, nitroguaiacols, methyl nitrocatechols, phthalic acid and 2,3-dihydroxy-4-oxopentanoic acid) have been implemented in the 3D CTM (CHIMERE). The simulation outputs of the SOA markers are compared with the annual (2015) measurements made at SIRTA and for levoglucosan with field observation performed during winter 2014-2015 at 10 urban locations over France. The sensitivity of gas/particle partitioning to spatial distribution and thermodynamic assumptions have been examined to highlight the key parameters in OA simulation. I personally developed the marker mechanisms and performed the 3D simulations presented in this chapter.

Fig. 1 .

 1 Fig. 1. Comparison of annual average particulate SOA marker and OC concentrations observed at SIRTA (mid-November 2014 -mid-December 2015) with literature values reported for several European locations. Urban: Marseille (France) (El Haddad et al., 2011), Barcelona (Spain) (road street, RS) (van Drooge et al., 2012) (urban, URB)(Alier et al., 2013), Grenoble (France)(Srivastava et al, 2018c) and Ljubljana (Slovenia)(Kitanovski et al., 2012). Rural: Mainz (rural urban, Germany)(Zhang et al., 2010), Hamme (Belgium)(Kahnt et

Fig. 4 .

 4 Fig. 4. Monthly average values observed for temperature (°C), relative humidity (%), wind speed (m s -1 ), planetary boundary layer height(PBL) (km), NO x and O 3 concentrations (ppb) at SIRTA from November 2014 to December 2015. October and November 2015 are highlighted in red.

-

  Fig. S1. SIRTA sampling station location.

3 .

 3 Fig. S2. Temporal variations of total (gaseous+particulate phases) concentrations of 3-

Fig. S4 .

 S4 Fig. S4. Temporal variations of total (gaseous + particulate phases) concentrations of β-

Fig. S5 .

 S5 Fig. S5. Temporal variations of total (gaseous + particulate phases) concentrations of 2-

  Fig. S8.Temporal variations of total (gaseous + particulate phases) concentrations of

4 .

 4 Fig.S9. Temporal variations of organic carbon (OC) and PM 10 concentrations at SIRTA

Fig. S12 .

 S12 Fig. S12. Temporal variations of MGA/(MET+MTR) ratio observed at SIRTA (2015).

Fig. S17 .

 S17 Fig. S17. Correlation matrix between molecular markers (primary and secondary),PM

  Fig. S18. Source profiles and temporal evolution of biomass burning, primary traffic

Fig. S19 .

 S19 Fig. S19. Source profiles and temporal evolution of sulfate rich, nitrate rich, biogenic SOA-1,

Fig. S20 .

 S20 Fig. S20. Annual average contributions (left) and temporal evolution (right) of the identified

Fig. S21 .

 S21 Fig. S21. Annual average contributions (left) and temporal evolution (right) of the identified

Fig. S22 .

 S22 Fig. S22. Worldwide distribution of chlorophyll in mg m -3 for summer 2015 from satellite

Fig. S23 .

 S23 Fig. S23. Concentration-Weighted Trajectory (CWT) results for biogenic SOA-1 (marine).

Figure 1 .Figure 2 .

 12 Figure 1.First step of toluene photooxidation (MCM)

Figure 3 .

 3 Figure 3. DHOPA formation from 2-methyl furan. Further details on the photooxidation of the 4-oxo-2pentenal (C5DICARB) are reported on Fig.5. The 2-methylfuran reactions with O 3 and NO 3 , have been considered as competitive for the reaction with OH (TableS5).

Figure 4 .

 4 Figure 4. Photooxidation of 4-Methyl-5-hydroxy-4,6-epidioxy-2-cyclohexene-1-peroxyde (TLBIPERO2) and of 4-Methyl-5-hydroxy-4,6-epidioxy-2-cyclohexene-1-peroxyde (TLEPOXMUC) leading to the formation of 4-Methyl-5-hydroxy-4,6-epidioxy-2-cyclohexene-1-peroxyde (TLOBIPEROH), 4-oxo-2-pentenal (C5DICARB) and (3E)-2,5-dioxohex-3-enal (C6125CO). Only the reactions involved in the DHOPA formation pathway are shown. The competitive reactions were considered in the model but omitted on this figure. A P sat of 1.95×10 -6 torr and a ΔH vap of 111 kJ mol -1 have been inserted in SOAP to calculate the GPP. This values are respectively the geometric mean and the arithmetic mean of the Multiphase system online property prediction (UManSysProp, Topping et al., 2016) SARs estimations. (Tables S8, S9 and S10).

Figure 5 .

 5 Figure 5. DHOPA formation pathways from the identified intermediates namely 4-Methyl-5-hydroxy-4,6-epidioxy-2-cyclohexene-1-peroxyde (TLOBIPEROH), 4-oxo-2-pentenal (C5DICARB) and (3E)-2,5-dioxohex-3-enal (C6125CO). The first formations pathway has been developed with GECKO and includes both, gaseous and aqueous phases. The second formation pathway was developed in analogy with the 3-esen-2,5-dione (C4DBDIKET) in gaseous phase only. Competitive reactions to the major reaction pathways were all considered in the model but are not shown here.

Figure 6 .

 6 Figure 6. Cresol photooxidation to methyl-nitrocatechols and 4-oxo-1-pentenoic acid, an intermediate for DHOPA formation. The reactions not involved in the formation pathway are not displayed but considered in the model.

Figure 7 .

 7 Figure 7. Map of France reporting the sampling stations mentioned in this study. Black points representannual campaign sampling sites, red points winter time campaigns. Urban and suburban sites are named by the closer agglomeration, except for SIRTA (25 km SO of Paris). OPE is a rural site.

France

  and the southeastern cities, concentrations are strongly underestimated in Western France and in Mediterranean cities.Different factors can influence the distribution of levoglucosan particulate phase concentrations in the model: for example, the emissions could be strongly underestimated over the western cities, especially for the city of Bordeaux, which has low modeled concentrations but high measured concentrations. For Nice and Marseille, part of the underestimation could be due to the low resolution of the model and bilinear interpolation of CHIMERE between cells onto the earth and the sea (with very low concentrations) as the stations are close to the sea (respectively ~1.75 and ~3.15 km).

Figure 8 .

 8 Figure 8. Mean daily particulate phase levoglucosan distribution (µg m -3 ) simulated in France in February 2015. Sampling sites are highlighted by squares filled according to the mean concentration observed every 3 to 6 days (sampling details in TableS12).

Figure 9 .

 9 Figure 9. Modeled particulate phase fraction of levoglucosan in France in February 2015. The sampling sites are highlighted by crosses.

Figure 10 .

 10 Figure 10. Model to measurements comparison of levoglucosan concentrations in 4 urban sites (Bordeaux, Reims, Lyon and Marseille) in France during the winter 2014/2015. Continuous lines are particulate phase measurements (red) and model (black). The dotted blue line represents the sum of modelled gaseous and particulate phase levoglucosan.

Figure 11 .

 11 Figure 11. Model to measurements comparison of organic matter (OM) concentrations in 4 urban sites (Bordeaux, Reims, Lyon and Marseille) in France during the winter 2014/2015.

Figure 12 .

 12 Figure 12. Annual evolution of OM, levoglucosan, nitroguaiacol, nitrophenols, DHOPA, methylnitrocatechols and phthalic acid concentrations at SIRTA, 2015.

  3 on the land. The higher fraction is simulated over Belgium and the North of Italy, where the simulated levoglucosan total concentrations are the highest (FigS11). Much higher particle-phase fractions (10 to 90%) are simulated in the -hydrophilic ideal‖ test (but the fraction is still much lower than for the hydrophilic ideal and hydrophilic tests). The lower values (fraction 0.10-0.20) have been observed in most of the Scandinavian peninsula and in the north UK. In the rest of Europe particulate phase levoglucosan fractions are between 0.30 and 0.75, except for Spain and Portugal (0.2-0.3) and 2 local maximums in Belgium and in the north of Italy.

Figure 13 .

 13 Figure 13. Maps of Levoglucosan particulate phase fraction over Europe during February 2015. The following thermodynamic conditions are shown from the top left panel to the bottom right panel: reference (non-ideal aerosol, levoglucosan is both hydrophobic and hydrophilic), ideal, hydrophilic (Hyphi), hydrophilic ideal (Hiphi ideal), hydrophobic (Hypho) and hydrophobic ideal (Hypho ideal).

Figure 15 .

 15 Figure 15.From the top left to the bottom right: nitroguaiacol, nitrophenol, DHOPA, methylnitrocatechols and phthalic acid thermodinamic tests. Total modelled semivolatile (reference)

  g. Bordeaux and Lyon, Fig 10) or during OM peaks.

Figure 16 .

 16 Figure 16. Odum-like curves representing OM wb pLEVO -1 as a function of total OM at 275, 280 and 285 K. From the top to the bottom: reference, ideal and hydrophobic ideal aerosol runs are shown. Hydrophobic ideal run follows an inverse Odum parametrization, which theoretical curves are reported.

  Experimental studies estimate biomass burning aerosols measuring levoglucosan. OC wb is determined using the following equation: 𝑂𝐶 𝑤𝑏 = 𝑓 × 𝑙𝑒𝑣𝑜𝑔𝑙𝑢𝑐𝑜𝑠𝑎𝑛

Figure S3 .

 S3 Figure S3. Model to measurements comparison in 6 urban sites all over France in winter 2014-2015.

Figure S4 .

 S4 Figure S4. Model to measurements comparison at OPE sampling site in 2015.

Figure S5 .

 S5 Figure S5. Comparison of measured and modelled organic matter in 7 sites in France during winter 2014-2015.

Figure S7 .

 S7 Figure S7. Correlation of measured and modelled levoglucosan and secondary markers. From top left to right bottom panel: nitroguaiacol, methylnitrocatechols, phthalic acid, DHOPA, nitrophenol.

Figure S8 .

 S8 Figure S8. Mean daily temperature in France in February 2015.

Figure S9 .

 S9 Figure S9. Mean daily relative humidity in France during February 2015.

Figure S10 .

 S10 Figure S10. Mean daily temperature in Europe in February 2015.

Figure S11 .

 S11 Figure S11. Mean daily relative humidity in Europe during February 2015

Figure. S13 .

 S13 Figure. S13. Spatial distribution of levoglucosan daily particulate phase fraction in an -ideal inorganic‖ configuration during February 2015 in Europe. In the ideal inorganic configuration both the interactions between organics and inorganics are not taken in account.

Figure S14 .

 S14 Figure S14.Nitroguaiacol particulate phase fraction variability in Europe during February 2015. The following thermodynamic conditions are shown from the top left panel: -reference‖ (non-ideal aerosol, nitroguaiacol is both hydrophobic and hydrophilic), -ideal‖, -hydrophilic‖, -hydrophilic ideal‖, -hydrophobic‖ and -hydrophobic ideal‖.

Figure S15 .

 S15 Figure S15.Methylnitrocatechols particulate phase fraction variability in Europe during February 2015. The following thermodynamic conditions are shown from the top left panel: ‖reference‖ (non-ideal aerosol, methylnitrocatechols is both hydrophobic and hydrophilic),-ideal‖, -hydrophilic‖, -hydrophilic ideal‖, -hydrophobic‖ and -hydrophobic ideal‖.

Figure S16 .

 S16 Figure S16.DHOPA particulate phase fraction variability in Europe during February 2015. The following thermodynamic conditions are shown from the top left panel: -reference‖ (nonideal aerosol, DHOPA is both hydrophobic and hydrophilic), -ideal‖, -hydrophilic‖, -hydrophilic ideal‖, -hydrophobic‖ and -hydrophobic ideal‖.

Figure S17 .

 S17 Figure S17.Phthalic acid particulate phase fraction variability in Europe during February 2015. The following thermodynamic conditions are shown from the top left panel: -reference‖ (non-ideal aerosol, phthalic acid is both hydrophobic and hydrophilic), -ideal‖, -hydrophilic‖, -hydrophilic ideal‖, -hydrophobic‖ and -hydrophobic ideal‖.

Figure S18 .

 S18 Figure S18.Nitrophenol acid particulate phase fraction variability in Europe during February 2015. The following thermodynamic conditions are shown from the top left panel: -reference‖ (non-ideal aerosol, nitrophenol is both hydrophobic and hydrophilic), -ideal‖, -hydrophilic‖, -hydrophilic ideal‖, -hydrophobic‖ and -hydrophobic ideal‖.

  studied the formation of IEPOX SOA and the importance of the Henry's law constant of IEPOX. The authors determined that a Henry's law constant for IEPOX around 2×10 7 M atm -1 would be necessary to explain concentrations of IEPOX SOA observed over Europe during different campaigns. This value was close to the one measured by[START_REF] Nguyen | Organic aerosol formation from the reactive uptake of isoprene epoxydiols (IEPOX) onto non-acidified inorganic seeds[END_REF] onto sea salts (3×10 7 M atm -1 ).Jo et al., (2019) recently developed a 3-surrogate parametrization for isoprene SOA formation that can reproduce well spatial and temporal variation of IEPOX SOA with lower computational costs. However, this parametrization cannot reproduce the diurnal variations due to the chemical and meteorological variations.Several studies focuses on modeling of monoterpene SOA in chamber experiments.Jenkin, (2004) succeded in reproducing the certains features (e.g. the high contribution of multifunctional acids) of α-pinene ozonolysis SOA, using a detailed SOA formation mechanism from the Master Chemical Mechanism (MCM) v3, optimized with chamber experiments yields.Capouet et al., (2008) developed the Biogenic hydrocarbon Oxidation and related Aerosol formation Model (BOREAM) that include a quasi-explicit gas phase mechamism for the first oxidation generation products of α-and β-pinene and a simplified representation of the further photoooxidation processes.[START_REF] Roldin | The role of highly oxygenated organic molecules in the Boreal aerosol-cloud-climate system[END_REF] inserted the MCM+PRAM mechanism for α-pinene OH-and O 3 -initiated oxidation in the 3D CTM ADCHEM, using a Langrangian approach. They succeeded to simulate new particle formation (NPF) episodes and high oxidized molecules (HOM) SOA in a forested site in Finland during spring with high accuracy. However, no SOA formation from isoprene or α-pinene+NO 3 oxidation has been modelled and no specific molecule formation was targeted in their simulations.[START_REF] Roldin | The role of highly oxygenated organic molecules in the Boreal aerosol-cloud-climate system[END_REF] simulated a near explicit mechanism combining MCM and the peroxy radical autoxidation mechanism (PRM,[START_REF] Roldin | The role of highly oxygenated organic molecules in the Boreal aerosol-cloud-climate system[END_REF] to model SOA mass yields from isoprene, α-and β-pinene, limonene and β-caryophyllene in an oxidative flow reactor (OFR).

Figure 1 .

 1 Figure 1.OH-initiated α-pinene oxidation process in atmosphere according to MCM v3.3.1.

  heptan-3-yl nitrate (APINBNO3 and APINANO3), and 10% of NC101CO only from NAPINBO2. APINBNO3 and APINANO3 reactions have been already commented in the OH initiated pathway.

Figure 2 .

 2 Figure 2. NO 3 -initiated α-pinene oxidation process in atmosphere according to MCM v3.3.1.The competitive reactions are not reported in figure, but considered in the model.

Figure 3 .

 3 Figure 3. O3-initiated α-pinene oxidation mechanism. Just the reactions directly involved in the pathway are displayed, the other possible reactions are considered in the model. The double arrow stands for multiple chemical steps (see Fig.4).

  4).

dimethyl- 3 -

 3 photolysis produce C89CO3. C89CO3 reacts with NO 3 , RO 2 and HO 2 giving respectively 100, 70 and 44% of 3-(carboxymethyl)-2,2-dimethylcyclobutane-1-carboxylic acid (C811CO3). The C89CO3 reaction with HO 2 produce also respectively 15% and 41% of 2,2-dimethyl-3-(2oxoethyl)cyclobutane-1-carboperoxoic acid (C89CO3H) and 2,2-dimethyl-3-(2oxoethyl)cyclobutane-1-carboxylic acid (C89CO2H). C89CO3 is also at equilibrium with his peroxiacetylnitrate (C89PAN). C811CO3 is produced by C89CO3H and C89CO2H photolysis, and also by OH-oxidation of C89CO2H. C811CO3 is at the equilibrim with his correspondent PAN (C811PAN), and is the direct precursor of pinic acid (PINIC), by reaction with RO 2 and HO 2 (stoechiometric coefficients are respectively 0.3 and 0.15). HO 2 oxidation has also the 3-(2-hydroperoxy-2-oxoethyl)-2,2-dimethylcyclobutane-1-carboxylic acid (C811CO3H) as product, that restore C811CO3 by photolysis and OH oxidation.

Figure 4 .

 4 Figure 4. Pinic acid formation pathway from {2-[2,2-dimethyl-3-(2-oxoethyl)cyclobutyl]-2-oxoethyl}dioxidanyl (C109O2), an α-pinene ozonolysis product.

Figure5.

  Figure5. OH-initiated β-pinene photooxidation to nopinone. The reactions of the mechanism not directy involved in the formation pathway are not shown.

Figure6. NO 3 -

 3 Figure6. NO 3 -initiated β-pinene photooxidation to nopinone.The reactions of the mechanism not directy involved in the formation pathway are not shown.

Figure 7 .

 7 Figure 7. O 3 -initiated β-pinene photooxidation to nopinone and the successive oxidation steps to 2,2-dimethyl-3-(oxoacetyl)cyclobutane-1-carbaldehyde (C89CO3).The reactions of the mechanism not directy involved in the formation pathway are not shown.

  pathway A, the C-4 alkyl radical (R31) oxididation by NO leads to the opening of the aliphatic cycle with formation of a carboxyl (47%, R33). The successive step consists in a 1,5-shift (99%) and another NO-oxidation, with the production of the 2-(carboxymethyl)-4hydroxy-3,3-dimethyl-5-oxohexanoyl (R35). R35 is oxidized by NO and after a 1,5 shift the 5,6-dicarboxy-3-hydroxy-4,4-dimethyl-2-oxohexan-3-yl radical is produced (R37). The last step to form MBTCA (yield 98%) involves NO and the loss of an acetyl.After the first reaction step with NO (which has {2-[3-(carboxymethyl)-2,2dimethylcyclobutyl]-2-oxoethyl}oxidanyl radical (R11) as product), the pathway C is splitted in 2 sub-path, leading both to the formation of MBTCA. 98% of R11 is degraded, losing formaldehyde, into 3-(carboxymethyl)-2,2-dimethylcyclobutane-1-carbonyl radical (R12-path referred hereafter as C1) while the residual 2% undergoes 1,5 shift giving the 2-(carboxymethyl)-4-(hydroxyacetyl)-3,3-dimethylcyclobutyl radical (R1'2-path referred hereafter as C2). The path C1 progresses through 5 NO reaction steps, together with one decarboxylation and two 1,5 shifts, to produce MBTCA. In the C2 pathway, 4 reactions with NO take place, with two 1,5 shifts and a final loss of the hydroxyacetyl radical to form MBTCA.

Figure 8 .

 8 Figure 8. Biogenic emissions of isoprene, α-and β-pinene (mg m -2 ) all over Europe computed with MEGAN 2.1 between 15/06/2015 and 15/07/2015.

Figure 9 . 4 . 1 . 2

 9412 Figure 9. α-and β-pinene markers model to measurements comparison during 2015 at SIRTA, France. Measurements have been compared to model output on French domain.

Figure 10 .

 10 Figure 10. Isoprene markers model to measurements comparison during 2015 at SIRTA, France. The model output shown is from simulations performed on French domain.

Figure 11 .

 11 Figure 11. Pinonic acid formation sensitivity to radical concentrations. Reference and test runs have been performed on European domain. The results shown are the mean daily concentrations at SIRTA, during February, mid-June to mid-July and October.

O 3

 3 is the main oxidant of α-pinene), Ireland (but with very low simulated concentrations of pinonic acid) and in several areas with strong biogenic emissions (like Southern France, Portugal or the Balkans)and UK. A high fraction was expected for the Scandinavian peninsula in which the O 3 initiation rate is high.

Figure 12 .

 12 Figure 12. Spatial distribution of α-pinene fractional reaction rate (on the top) for the reactions with OH, NO 3 and O 3 (respectively left, middle and right panels) over Europe. On

  Fig. 12 are explained by the low sensitivity to the daily variations of pinonic acid production rate from APINBOO compared to the other processes. The NO 3 initiated pathway only produces low concentrations of pinonic acid because during nighttime pinonaldehyde oxidation is very low while the accumulated pinonaldehyde during the night is fastly oxidized by aqueous-phase reactions. However, the pinonic acid formed by the aqueous phase reacts rapidly and is destroyed. During the day, most of the pinonic acid is formed by the OH initiated pathway.

Figure 13 .

 13 Figure 13. Mean daily variations of (a) α-pinene, pinonaldehyde and pinonic acid concentrations (µg m -2 ); (b) α-pinene+O 3 , α-pinene+NO 3 , α-pinene+OH, pinonaldehyde+NO 3 and pinonaldehyde+OH rates (ng m -2 s -1 ); (c) APINBOO, C96CO3, pinonic acid+OH or NO 3 (both in gaseous and aqueous phase) degradation rates (ng m -2 s -1 ). All the results shown are the mean of European domain, integrated over all the atmospheric levels (till 500 hPa), simulated from 15/06 to 15/07/2019.

Figure 14 .

 14 Figure 14. Particulate phase fraction (F p ) thermodynamic sensitivity for (from the top to the bottom): pinonic acid, pinic acid, MBTCA, methyltetrols and α-methylglyceric acid at SIRTA. The boxes represent from left to right: measurements (obs), reference (ref), ideal aerosol (ideal), hydrophobic (hypho) and hydrophilic (hyphi) partitioning in non -ideal aerosol, hydrophobic (hypho ideal) and hydrophilic (hyphi ideal) partitioning in ideal aerosol. The tests have been performed during 3 months: February, mid June-mid July and October. The measurements data shown for the comparison have been taken from the same period.

Figure 15 .

 15 Figure 15. Simulated total (gas+particle) marker concentrations at SIRTA as a function of the concentrations for the reference simulation where markers are not considered semivolatile (-completely volatile or non-volatile) and when gaseous phase deposition is considered. From the top left to the bottom right: pinonic acid, pinic acid, α-methylglyceric acid and methyltetrols tests are presented. The tests have been performed during February, mid-June to mid-July and October.

Figure S4 .

 S4 Figure S4. Annual trend of measured and simulated organic matter (OM) at SIRTA during 2015.

Figure S5 .

 S5 Figure S5. Sensitivity analysis to radical concentrations on pinic acid formation. Test and reference runs have been performed on European domain.The results shown are the mean daily concentrations at SIRTA, during February, mid-June to mid-July and October.

Figure S6 .

 S6 Figure S6. MBTCA formation sensitivity to radical concentrations. Simulations have been performed on European domain. The results shown are the mean daily concentrations at SIRTA, during February, mid-June to mid-July and October.

Figure S7 .

 S7 Figure S7. Methyltetrols sensitivity to radical concentrations. All the runs have been performed on European domain. The results shown are the mean daily concentrations at SIRTA, during February, mid-June to mid-July and October.

Figure S8 .

 S8 Figure S8. Sensitivity of α-methylglyceric acid formation to radical concentrations. All the outputs are from simulations on European domain. The results shown are the mean daily concentrations at SIRTA, during February, mid-June to mid-July and October.

  mechanisms are highly uncertain and experimental mechanistic studies are needed to support the parametrization developed here. In this context, chamber experiments couldbe performed in different conditions of temperature, humidity and NO x concentrations to elucidate the formation pathways of these species. Both, gaseous and particulate phases, must be characterized. Advanced instrumentation such as (TD)-ToF-CiMS (thermal-desorption-time-of-flight-chemical ionization mass spectrometry), CHARON-PTR-ToF-MS (chemical analysis of aerosol online-proton transfer reaction-time-of-flight-massspectrometry) and EESI-ToF-MS(extractive electrospray ionization-time-of-flight-massspectrometry) would be the most appropriate to monitor online and study the gas/particulate intermediate species formed.

Table 1 .

 1

1. Prominent organic particulate matter (OPM) components. Source:

[START_REF] Pöschl | Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects[END_REF]

Table 1 . 2

 12

. Properties of the SOA surrogate species used in H 2 O. Adapted from:

[Couvidat et al., 2012]

Table 1 .

 1 Total atmospheric concentrations (gaseous + particulate phases, ng m -3 ) of biogenic and anthropogenic SOA markers measured at SIRTA (France) from mid-November 2014 to mid-December 2015. Annual average and mean values together with the average concentrations for the cold and warm period, the minimum and the maximum observed values, are reported. Average particulate fraction (F p ) is also specified (%).

	Compound	Abbreviation	Annual mean (

min -max) Annual median Cold period a mean Warm period a mean F p (%)

	Pinene
	cis-Pinonic acid	PnoA	2.8(0.1-11)	2.1	2.2	3.4	68
	Pinic acid	PniA	0.8(<0.1-7.8)	0.5	0.7	1.0	94
	3-(2-Hydroxy-ethyl)-2,2-
	dimethylcyclobutane-	HCCA	1.1(<0.1-16.9)	0.6	1.4	0.7	75
	carboxylic acid b
	3-Methylbutane-1,2,3-tricarboxylic acid	MBTCA	1.0(<0.1-10.3)	0.6	1.4	0.6	99
	3-Hydroxyglutaric acid	HGA	2.2(<0.1-16.4)	0.8	2.6	1.7	78
	Terpenylic acid	TerA	0.3(<0.1-4.7)	0.1	0.2	0.3	51
	3-Hydroxy-4,4-dimethylglutaric acid c	HDGA	0.3(<0.1-2.3)	0.2	0.3	0.3	44
	3-Acetyl-pentanedioic acid c	APDA	4.2(0.1-43.4)	2.3	3.4	5.1	64
	3-Acetyl-hexanedioic acid c	AHDA	6(<0.1-37.1)	4.0	5.0	7.2	60
	3-Isopropylpentanedioic acid c	IPPA	10.9(<0.1-17.7)	11.1	8.9	13.3	2
	Isoprene
	α-Methylglyceric acid	MGA	0.5(<0.1-3.3)	0.2	0.3	0.8	89
	2-Methylthreitol	MTR	0.8(<0.1-6.2)	0.5	0.6	1.0	38
	2-Methylerythritol	MET	3.1(<0.1-40.8)	1.0	0.7	6.0	58
	β-Caryophyllene
	β-Caryophyllinic acid	CarA	1(<0.1-21.9)	0.4	1.5	0.4	25
	Anthropogenic SOA acids
	Succinic acid	SuA	8.3(<0.1-53.3)	5.0	10.5	5.6	88
	Phthalic acid	PhA	2.7(<0.1-25.1)	1.5	3.5	1.7	51
	2,3-Dihydroxy-4-oxopentanoic acid	DHOPA	1(<0.1-9)	0.5	1.6	0.3	72

Nitroaromatic compounds (NACs)

	2-Nitrophenol	2NPh	52.7(<0.1-1086.3)	0.6	96.8	0.4	69
	4-Nitrophenol	4NPh	6.9(<0.1-27.9)	5.3	8.6	5.0	78
	2-Methyl-4-nitrophenol	2M4NPh	1.6(<0.1-12.1)	1.1	2.4	0.7	9
	4-Nitroguaiacol	4NG	7.9(<0.1-98.1)	2.3	13.9	0.7	6
	5-Nitroguaiacol	5NG	0.4(<0.1-6.2)	0.1	0.6	0.1	7
	4-Methyl-5-nitrocatechol	4M5NC	18.9(0.1-321)	1.5	33.6	1.6	95
	3-Methyl-6-nitrocatechol	3M6NC	4.4(1.7-39.6)	3.0	5.6	3.0	88
	3-Methyl-5-nitrocatechol	3M5NC	15.9(0.1-263.8)	1.4	28.0	1.4	67
	a Cold and warm periods were defined as follows: cold period included October, November (2014 and 2015),
	December (2014 and 2015), January, February and March months, and warm periods included April, May, June,
	July, August and September months. Average temperatures during both period of about 7.4 and 17°C,
	respectively.

  found F p ~0.5 for both PniA and TerA, F p ~0.7 for HGA,[START_REF] Kristensen | Denuder/filter sampling of organic acids and organosulfates at urban and boreal forest sites: Gas/particle distribution and possible sampling artifacts[END_REF] measured respectively mean F p ~0.38 and 0.29 for PniA and TerA). Overall, our observations were in agreement with the literature, showing that PniA is less volatile than PnoA

Table S1 .

 S1 List and characteristics of the chemicals, gases and solvents used for SOA marker analyses.

	1389
	Compound	Supplier	CAS number	Purity
	cis-Pinonic acid	Sigma-Aldrich	61826-55-9	98 %
	Pinic acid	Santai labs	473-73-4	95 %
	Terpenylicacid	Interbioscreen	116-51-8	95 %
	3-Methylbutane-1,2,3-tricarboxylic acid (MBTCA)	TRC	77370-41-3	98 %
	3-Hydroxyglutaric acid	Santa Cruz Biotechnology	638-18-6	95 %
	α-Methylglyceric acid	Tractus/Wonderchem	21620-60-0	95 %
	2-Methylerythritol	Sigma-Aldrich	58698-37-6	90 %
	2-C-Methyl-D-Threitol	Sirius Chemicals	310887-92-4	99 %
	β-Caryophyllinic acid	TRC	957055-11-7	97 %
	Succinic acid	Sigma-Aldrich	110-15-6	99 %
	Phthalic acid	Sigma-Aldrich	88-99-3	> 99.5 %
	2,3-Dihydroxy-4-oxopentanoic acid (DHOPA)	TRC	37520-06-02	98 %
	2-Nitrophenol	Fisher	88-75-5	99 %
	4-Nitrophenol	Fisher	100-02-7	99 %
	2-Methyl-4-nitrophenol	Sigma-Aldrich	99-53-6	97 %
	4-Nitroguaiacol	Fisher	3251-56-7	97 %
	5-Nitroguaiacol	Fisher	636-93-1	97 %
	4-Methyl-5-nitrocatechol	TRC/Carbosinth	68906-21-8	96-98 %
	3-Methyl-5-nitrocatechol	Santai labs	5378-76-7	97 %
	Deuterated compounds
	Succinic acid-2,3,3,3-d 4	C/D/N isotopes	14493-42-6	99 %
	Meso-erythritol-1,1,2,4,4-d 6	C/D/N isotopes	149-32-6(unlabelled)	99.1 %
	2-Nitrophenol-d 4	Cambridge isotopes	93951-78-1	98 %
	1,9-Nonanedioic-d 14	C/D/N isotopes	119176-67-9	99 %
		Solvents, reagents
	Methanol	Sigma-Aldrich	67-56-1	> 99.9 %
	Acetonitrile	VWR	75-05-8	> 99.9 %
	N-Methyl-N-(trimethylsilyl)
	trifluoroacetamide (MSTFA) + 1%	Thermo	24589-78-4	99 %
	trimethylchlorosilane (TMCS)
		Gases
	Helium	Air liquide	7440-59-7	99.9999 %
	Nitrogen	Air liquide	7727-37-9	99.9999 %

Table S2 .

 S2 GC/MS conditions used for SOA marker analysis and typical instrumental limits of 1390

	1391	quantification (LOQ).
		Compound	Monitored ions(m/z)	Retention time(min)	Dwell time (ms)	LOQ (pg injected)

Table S3 .

 S3 SOA marker concentrations (mg kg -1 ) (dry-mass basis) in SRM 1649b (urban dust). obtained here and comparison with literature data.

	Compounds	Mean (this study) a	Standard deviation (this study) a	Mean (Albinet et al., 2019)	Standard deviation (Albinet et al., 2019)
	Succinic acid α-Methylglyceric acid Succinic acid	d4 115.1 2.8	147, 251 8.6 129, 247 0.2	111.0 3.3	19.54 19.61	35.0 1.3	-1.1
	DHOPA	α-Methylglyceric acid	1.8	219, 306 0.1	1.2		19.74	0.8	0.7
	2,3-Dihydroxy-4-oxopentanoic acid cis-Pinonic acid 5.9	218, 350 1.0	5.9		24.71	4.4	1.6
	cis-Pinonic acid 3-Hydroxyglutaric acid 3-Hydroxyglutaric acid Terpenylic acid Pinic acid	8.4 8.7	171, 125 185, 349 0.7 158, 229 1.1	8.5 12.8		25.27 26.26 26.60	2.5 4.9	1.1 1.0 1.4
	3-(2-Hydroxy-ethyl)-2,2-Phthalic acid 129.9 dimethylcyclobutane-carboxylic acid a 3-Hydroxy-4,4-dimethylglutaric acid b 2-Nitrophenol 0.7 3-Acetylpentanedioic acid b 4-Nitrophenol 85.4	158 229 8.1 259,377 0.1 111, 276 23.9	89.7 ND ND		26.69 27.02 27.15	30.7 ND ND	1.4 1.0 1.0
	Pinic acid 2-Methyl-4-nitrophenol	22.6	129, 171 11.7	ND		28.50	ND	1.4
	Phthalic acid 3-Acetyl hexanedioicacid b 4-Nitroguaiacol 4.8 3-Isopropylpentanedioic acid b 5-Nitroguaiacol 15.1 2-Nitrophenol-d4 2-Methylerythritol c 198.3	221, 295 204, 317 3.1 213, 229 8.0 155, 200 14.9	ND ND 28.9	29.04 29.70 29.80 21.955	ND ND 33.5	1.3 1.0 1.0 -
	MBTCA	2-Nitrophenol		4.0	151, 196 1.5	2.1	21.994	2.1	0.2
	4-Nitrophenol 2-Methyl-4-nitrophenol β-Caryophyllinic acid 4-Nitroguaiacol 4-Methyl-5-nitrocatechol 11.1 0.1	196, 211 210, 225 <0.1 211, 226 0.4	0.4 3.8	24.878 27.158 28.66	0.3 3.3	0.7 0.7 2.6
	5-Nitroguaiacol 3-Methyl-5-nitrocatechol	11.2	211, 226 <0.1	3.3	28.997	2.7	4.1
	Meso-erythritol-1,1,2,4,4-d6	208, 220			24.50	100	-
		2-Methylthreitol			203, 219			23.23	1.3
		2-Methylerythritol			117, 219			25.74	4.5
	1,9-Nonanedioic acid-d14		213, 331			31.10	-
	3-Methylbutane-1,2,3-tricarboxylic acid	204, 245			30.34	5.5
		β-Caryophyllinic acid		117, 200			36.08	2.9
	4-Methyl-5-nitrocatechol c		296, 313			31.06	1.6
	3-Methyl-6-nitrocatechol c		298, 313			32.10	4.1
	3-Methyl-5-nitrocatechol c		298, 313			32.28	100	4.1

a n=6 full replicates (extraction and analysis, triplicate injections). Mean values and standard deviations calculated considering all the results obtained. ND: not determined.

2. Literature data 1409

Table S4 .

 S4 List of studies reporting SOA marker concentrations in ambient air for different locations worldwide. 1410

		Jϋlich, Germany	Jul	Forest	July 2003	PniA, MBTCA, HGA, MGA, MTR, AHDA, MGA, MTR, MET, DHOPA	(Kourtchev et al., 2008b)
	Triangle Park, USA	TP	Semi-rural	Annual mean (2003)	MET, SuA PnoA, PniA, HCCA, HGA, HDGA,	(Kleindienst et al, 2007b)
		K-puszta, Hungary	KP2	Forest	Summer 2003	PniA, MBTCA, HGA, SuA APDA, AHDA, IPPA, MGA, MTR,	(Kourtchev et al., 2009)
	Silkeborg, Denmark	Sil	Forest	April-May 2008	PnoA, PniA, MBTCA, TerA MET, CarA, DHOPA	(Kristensen and Glasius,
	Sampling location, Country Centreville, USA Brasschaat, Belgium	Abbreviation Cen Bra	Rural Site typology * Forest impacted by	June 2004 Period June-July 2007	MTR, MET SOA markers measured PnoA, PniA, MBTCA, TerA	2011) (Clements and Seinfeld, Reference (Gómez-González et al., 2007)
	Europe Centreville, USA	Cen2	urban pollution Rural	May-August 2005	PnoA, PniA, HCCA, MBTCA, HGA,	2012) (Kleindienst et al., 2010)
	Rome, Italy North and South America	Rom	Urban	February-April 2003	2NPh, 4NPh, 2M4NPh HDGA, APDA, AHDA, MGA, MTR,	(Cecinato et al., 2005)
		Mainz, Germany Atlanta, USA	Maz Atl	Rural urban Urban	June 2006-May 2007 PnoA, PniA, MBTCA, PhA, 2NPh, 4NPh June 2006 MTR, MET MET, CarA, PhA, DHOPA	(Zhang et al., 2010) (Clements and Seinfeld,
		Marseille, France Medina, USA	Mar Med	Urban Rural	July 2008 July-August 2009	PnoA, PniA, MBTCA, HGA, MGA PnoA, PniA, HCCA, MBTCA, HGA,	(El Haddad et al., 2011) 2007) (Piletic et al., 2013)
		Barcelona, Spain Atlanta, USA	BCN Atl2	Urban Urban	February-March May-August 2005	SuA, PhA PnoA, PniA, HCCA, MBTCA, HGA, HDGA, APDA, AHDA, MGA, MTR,	(van Drooge et al., 2012) (Kleindienst et al., 2010)
					2009	HDGA, APDA, AHDA, MGA, MTR, MET, CarA, PhA, DHOPA
	Ljubljana, Slovenia Amazonian Forest, Brasil	Lju Ama	Urban background Forest	Winter 2010 July 2000	4NPh, 2M4NPh, 4NG, 4M5NC, 3M6NC, MET, CarA, PhA, DHOPA HGA	(Kitanovski et al., 2012) (Claeys et al., 2004)
	Asia	Birmingham, USA	Bir	Urban	June 2006	3MNC MTR, MET	(Clements and Seinfeld,
		Barcelona, Spain Hong Kong, China	BUB HK	Urban background Urban	September-October July 2006	PnoA, MBTCA, HGA, MGA, MTR, MBTCA, HGA, HDGA, APDA, AHDA,	(Alier et al., 2013) 2007) (Hu et al., 2008)
		Birmingham, USA	Bir2	Urban	2010 May-August 2005	MET, SuA, PhA PnoA, PniA, HCCA, MBTCA, HGA, IPPA, MGA, MTR, MET, CarA, SuA,	(Kleindienst et al., 2010)
		Barcelona, Spain	BRS	Urban, road street	September-October	PnoA, MBTCA, HGA, MGA, MTR, HDGA, APDA, AHDA, MGA, MTR, PhA, DHOPA	(Alier et al., 2013)
		Beijing, China	Bei	Urban	2010 Summer 2016	MET, SuA, PhA MET, CarA, PhA, DHOPA 4NPh, 2M4NPh, 4M5NC, 3M6NC,	(Wang et al., 2019)
		Grenoble, France Pensacola, USA	Gr Pen	Urban background Suburban	Annual mean (2013) June 2006	PnoA, MBTCA, HGA, MGA, MET, MTR, MET 3M5NC	(Srivastava et al., 2018b) (Clements and Seinfeld,
		Tsuen Wan, China	TW	Urban background	Summer 2012	SuA, PhA, DHOPA, 3M5NC, 4M4NC 4NPh, 2M4NPh, 4M5NC, 3M6NC,	2007) (Chow et al., 2015)
		Paris, France Pensacola, USA	Par Pen2	Suburban Suburban	March 2015 May-August 2005	PnoA, PniA, MBTCA, HGA, MGA, PnoA, PniA, HCCA, MBTCA, HGA, 3MNC	(Srivastava et al., 2018a) (Kleindienst et al., 2010)
		Mumbai, India	Mum	Urban background	February 2007 June 2006 and	MTR, MET, SuA, PhA, DHOPA, MET, CarA, PhA, DHOPA MTR, MET, CarA, DHOPA 3M5NC, 4M4NC HDGA, APDA, AHDA, MGA, MTR, PnoA, PniA, HCCA, MBTCA, HGA,	(Fu et al., 2016a)
	Seiffen, Germany Montseny, Spain Hamme, Belgium Detling, UK Hyytiälä, Finland K-puszta, Hungary Hyytiälä, Finland Toronto, Canada Riverside, USA Pearl river delta, China Tianhu, China Cleveland, USA Tazhong, China Bakersfield, USA Hetian, China Pasadena, USA Cape Hebdo, Japan	Sei MSY Ham Det HY KP HY2 Tor Riv PRD Tia Cle Taz Bak Het Pas CHe	Rural Rural Background Rural background Rural Forest Forest Forest Urban Urban Rural Rural Industrial Desert Urban/ industrial Desert Urban/ industrial Maritime/forested	October 2007-March2008 February-March 2009 2010-2011 Winter 2012 Summer/Fall 2004 Summer 2003 July-August 2005 2000-2001 August 2005 Summer 2008 March 2012-July-August 2009 February 2013 April 2008 May-June 2010 April 2008 May-June 2010 October 2009-February 2012	4M5NC, 3M6NC, 3M5NC SuA, PhA NPh, MNC 4NPh, 4M5NC MGA, MTR, MET MGA, MTR, MET PniA, HGA, MTR, MET PnoA PnoA, PniA, HCCA, MBTCA, HGA, PnoA, PniA, HGA, MBTCA, MGA, MTR, MET, CarA, DHOPA HDGA, APDA, AHDA, MGA, MTR, PnoA, PniA, MBTCA, HGA, HDGA, MET, CarA, PhA, DHOPA PnoA, PniA, HCCA, MBTCA, HGA, MGA, MTR, MET, PhA, DHOPA PnoA, PniA, MBTCA, HGA, MGA, HDGA, APDA, AHDA, MGA, MTR, MTR, MET, CarA MET, CarA, PhA, DHOPA MBTCA, HGA, HDGA, APDA, AHDA, PnoA, PniA, MBTCA, HGA, MGA, MTR, MET, CarA MGA, MTR, MET, DHOPA PniA, MBTCA, HGA, HDGA, APDA, PnoA, PniA, MBTCA, HGA, MGA, MTR+MET, CarA	(Iinuma et al., 2010) (van Drooge et al., 2012) (Kahnt et al., 2013) (Mohr et al., 2013) (Kourtchev et al., 2005) (Ion et al., 2005) (Kourtchev et al., 2008a) (Cheng et al., 2011) (Ding et al., 2012) (Kleindienst et al., 2010) (Yuan et al., 2018) (Piletic et al., 2013) (Fu et al., 2016b) (Fu et al., 2016b) (Lewandowski et al., 2013) (Lewandowski et al., 2013)

[START_REF] Zhu | Seasonal variations of biogenic secondary organic aerosol tracers in Cape Hedo, Okinawa[END_REF]

Table S5 .

 S5 Concentrations of SOA markers (ng m -3 ) (particulate phase only) and of OC (µg m -3 ) reported in previous studies and from this study. 1413Sampling locations and periods are specified in TableS4. PM size fractions studied is also specified. 1414

	1415	PM size fraction PM size fraction PM2.5	PM5 PM2.5	TSP PM2.5	PM2.5 PM2.5	PM1 PM2.5	PM10 PM2.5	PM1 PM2.5	PM1 PM2.5	PM10 PM2.5	PM10 PM2.5	PM10 PM2.5	PM10 PM2.5	PM1 PM2.5	PM1 PM2.5	PM10 PM2.5	PM2.5 PM2.5	PM2.5 PM10	PM2.5 PM2.5	PM2.5 PM2.5	PM1 PM2.5	PM2.5 PM2.5	PM2.5 TSP	-	PM2.5	PM2.5 PM10
		# Sum of both isomers; *Median concentration																		This work
			Bir2	Pen	Pen2	Tor	Riv	Cle	Bak	Pas	TP	Cen Cen2 Med Ama	HK	Bei	TW Mum PRD	Tia		Taz	Het	CHe NNR	(annual
																											average)
		PnoA	1.4	Rom	Maz 1.3	Mar 3.6	BCN 4.8	Lju 0.2	BUB BRS	Gr 0.7	Par	Sei 2.1	MSY Ham 0.	Det	HY	KP	HY2 3.7	Jul 1.7	KP2 5.8	1	Sil	Bra 0.2	Atl 0.12	Atl2	Bir 1.9
		PnoA PniA	2.8		1.2 1.8	15.0	1.20	0.2	15.4	8.0 0.1	13.1 9.4	4.0	14.4	0.30					1.7	0.7	1.3		3.0 0.5	1.8* 0.2	0.62	3.2	0.8
		PniA HCCA	10.0		2.3 8.1	4.5	2.	2.7			2.1	0.8	16.2	2.1					7.7* 0.6	4.2	12.2		1.5	0.5*	2.5	0.9
		HCCA MBTCA	43.7		39.4		6.9	19.6	3.2	4.2			47.2	23.9		73.5			0.3		9.3		<0.01		0.81	4.3	1.0
		MBTCA HGA	46.5		6.9 44.1	5.0	19.2	21.3	5.5 5.5	4.1 10.8	4.8 28.2	0.4	46.9	22.1	12	83.8			8	6.8 2.6	15.8 11.8	3.0 0.2	2.7* 0.4	0.48	23.9	2.1
		HGA TerA				3.5				4.5	3.1	4.7	2.2						7.6	16.1*	5.8	19.7				24.2	0.2
		TerA HDGA	5.0		5.6		1.5	1.8	0.4	0.6	6.0		5.9	2.3		9.7					6.9			0.8	2.4*	0.2
		HDGA APDA	11.0		10.4		4.9	7.8	2.0	2.1	9.2		19.1	8.2		18.5										2.6	2.0
		APDA AHDA	5.6		4.9		3.0	3.8	1.6	1.1	34.9		7.6	3.2		4.7										5.4	5.1
		AHDA IPPA										1.3					8.4										2.4	0.2
		IPPA MGA	22.7		9.8		14.7	18.4	6.7	2.7	11.7		24.1	22.2		1.0			0.8	7.7	2.1		3.6	0.2	0.42	0.5
		MGA MTR MTR MET	49.0 149	1.6 4.7	28.7 53.4	1.8	2.9 5.1	30.0 58.0	2.1 2.8 2.7 5.4	1.9 1.4 1.0 2.8	1.4 7.2 19.1 30.6	0.5 0.6 4.2 12.2	66.4 115	45.3 63.2	40.9 #	8.3 20.3	1.1 3.3	7.6* 7.5*	4.0* 0.4 1.0	2.3 4.4 25.6 65.9	8.1 17.7	0.06 0.03	6.4* # 0.1 0.2	3.5 1.02 #	18.1 25.2	0.7 4.5 1.8
		MET CarA	1.2		0.9		0.8	0.9	6.5	3.5	7.4		0.4	1.8		12.5	12.6 21.0* 8.4* 3.2	10.8 2.9			0.05		9.7	55.0	14.8 0.8
		CarA SuA				0.3											20.7				17.8						0.3 13.1	7.4
		SuA PhA	5.2		6.0		17.0 16.9	3.8	7.3 1.7	6.2 4.6	10.6	14.4	3.5	11.4 3.9		2.1				8.1	15.2 4.0				1.5	1.7
		PhA DHOPA	1.9		5.7 2.0		8.6 1.5	1.9	3.9 0.4	3.6 1.0	3.6 1.8	3.8	1.9	6.6 2.3		1.7			0.3		1.5					4.0	0.9
		DHOPA 2NP										4.9	1.5														1.4	< 0.1
		2NP 4NP		3.5	1.8											0.7* #		2.2	0.5								0.1
		4NP 2M4NP		17.8	3.8				1.8								<0.1		0.3								0.1
		2M4NP 4NG		7.8					0.8									0.8									0.1
		4NG 5NG							0.4																		0.1
		5NG 4M5NC																0. 6	0.3								24.0
		4M5NC 3M6NC 3M6NC 3M5NC							29.0 6.2			15.7	41.2	2.0 2.9		2.2* #	8.20	0.1 0.4	0.1 0.6								4.6 20.1
		3M5NC OC (µg m -3 ) OC (µg m -3 ) 13.5		5.9	22.0 3.8	5.5	4	34.0 19.4 2.3	2.1 5.2	3.5 3.7	15.5 5.6 4.1	30.3 6.8 6.5	0.4 6.5	2.5 1.5	4.0* 2.0	5.7	2.4	4.2 6.9	2.2* 9.9	4.6	6.3	4			1.7	7.5	3.3

Table S6 .

 S6 EstimatedSOA

	Chemical species	f SOA adjusted	f SOC adjusted
	PnoA	0.0219	0.0301
	PniA	0.0090	0.0124
	HCCA	0.0101	0.0138
	MBTCA	0.0118	0.0162
	HGA	0.0243	0.0334
	HDGA	0.0024	0.0033
	APDA	0.0235	0.0324
	AHDA	0.0600	0.0824
	IPPA	0.0029	0.0039
	MGA	0.0630	0.1550
	MTR	0.0630	0.1550
	MET	0.0630	0.1550
	CarA	0.0109	0.0230
	PhA	0.0206	0.0402
	DHOPA	0.0040	0.0079
	4M5NC	0,0489	0,0979
	3M6NC	0.0093	0.0186
	3M5NC	0.0409	0.0818
	8.

mass fractions (f SOA ) and SOC mass fractions (f SOC ).

Positive matrix factorization (PMF) results.

Table S9 .

 S9 Comparison of source profiles before and after constraints. Results of observed P-value for each factor obtained using t-test. 1649

			Biogenic	Primary	Biogenic	Sulfate-	Dust	Nitrate-	Biomass	Fungal	Sea salt	Anthrop	Anthropogeni
			SOA-1	traffic	SOA-2	rich		rich	burning	spores	ogenic	c SOA-2
							emissions						SOA-1
	P-value	0.36	0.75	0.20	0.22	0.17	0.26	0.18	0.09	0.47	0.17	0.10
	(p<0.05)
	1650
	1651	Table S10. Results from bootstrap runs obtained for the final solution.
							Biogenic	Primary	Biogenic	Sulfate-	Dust Nitrate Biomass
							SOA-1	traffic	SOA-2	rich		burning
								emissions
										pyrene
			OC			Pb	1-Nitropyrene
			BC ff	Sb		Oxalate
			BC wb		Ti		MSA
			Cl -			Zn	α-Methylglycericacid
			NO 3	-		V	2-Methylerythritol
			SO 4	2-	Al	3-Hydroxy-4,4-dimethylglutaric acid
			Na +		Levoglucosan	3-Acetylpentanedioic acid
			NH 4	+	Mannosan	Succinicacid
			K +			Arabitol		DHOPA
			Mg 2+	Mannitol	Phthalic acid
			Ca 2+	Benzo[a]pyrene	4-Methyl-5-nitrocatechol
			Cu			Benzo[g,h,i]perylene	3-Methyl-5-nitrocatechol

  1, 2 , F. Couvidat 1, * , O. Favez1, * , A. Albinet 1 and B. Bessagnet 1, 2 1 INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France 2 Sorbonne Universités, UPMC, 75252 PARIS cedex 05, France Correspondence to Florian Couvidat(florian.couvidat@ineris.fr)

Table 1

 1 Thermodynamic properties of the modelled anthropogenic organic markers. P sat and ΔH vap are used as input to calculate ΔH vap and K H with SOAP(Couvidat and Sartelet, 2015).

	Modelled species	Measured markers	P sat at 298K (torr)	ΔH vap (KJmol -1 )	ΔH solv (KJmo l -1 )	K H at 298 K (M -1 atm)
	DHOPA	DHOPA	1.95×10 -6	111.0	116.1	7.25×10 10
	LEVO	Levoglucosan	1.45×10 -6	52.0	61.6	2.26×10 10
	MNCATECH Methylnitrocatechols*	3.20×10 -6	41.7	68.9	1.30×10 9
	NGUAIACOL	4-Nitroguaiacol	4.61×10 -5	30.9	51.7	5.60×10 7
	NPHEN	Nitrophenols**	3.86×10 -5	51.2	63.8	3.90×10 7
	PHTHALIC	Phthalic Acid	1.26×10 -5	40.0	46.7	1.20×10 8

*Sum of

3-methyl-5-nitrocatechol, 4-methyl-5-nitrocatechol and 3-methyl-6-nitrocatechol

. Thermodynamic properties of a generic methylnitrocatechol. **Sum of 2-nitrophenol and 4-nitrophenol. Thermodynamic properties of 4-nitrophenol.

Table 2 .

 2 Marker precursor percentages in non-methane hydrocarbon VOC (NMVOC) emissions from the different sectors. Non-industrial combustion plants emissions (mainly biomass burning from domestic heating) have been especially updated in this study.

		BENZENE	CRESOL GUAIACOL MEFURAN NAPHTHALENE PHENOL TOLUENE
	SNAP
	Combustion in energy and	1.60 a						1.10 a
	transformation industries
	Non-industrial combustion	3.16 b	2.27 b	1.60 b	1.31 c	0.20 d	2.85 b	1.31 b
	plants (domestic heating)
	Combustion in manufacturing	12.70 a				0.20 a		2.00 a
	industry
	Extraction and distribution of fossil fuels and geothermal	0.80 a					0.20 a	1.10 a
	energy
	Solvents and other product	0.40	a						0.20	a
	use
	Road transport								5.20	a
	Other mobile sources and	2.54	a						5.21	a
	machinery
	Waste treatment and	3.00	a						4.70	a
	disposal
	Agriculture	0.30	a						1.10	a

Table 3

 3

	shows the comparison between modelled and measured particulate phase. Medians,
	interquartile (25th-75th) ranges, correlation coefficients, Mean Normalised Bias (MNB), modelled
	particulate phase fraction (F p ) and number of observations are shown for each site. The SIRTA and the
	OPE stations have the highest number of samples (127 and 59 respectively) with measurements
	throughout all the year. For the other stations, samples have been collected only during winter with a
	sample number from 28 (for Strasbourg) to 58 (for Marseille and Nice).
	Measured and modelled levoglucosan particulate phase median concentrations are below 1 µg m -3 for
	all the sites.

For stations in the north of France (Rouen, Reims and Strasbourg) median modelled concentrations are half of the measured ones (54, 41 and 43% less respectively). The modelled interquartile ranges underestimate the measured ones by a factor around 2. TheMNBs values calculated (-0.50, -0.36 and -

Table 3 .

 3 Median and interquartile range (25 th 75 th) of measurements and model outputs for particulate phase levoglucosan daily concentrations in 13 sites in France. Number of samples, correlation coefficients, Mean Normalized Bias (NMB) and particulate phase fraction (F p ) are also reported.

	Stations	Number of samples	Measured Median (interquartile range) µg m -3	Modelled Median (interquartile range) µg m -3	r	MNB	Modelled Fp
	Winter sites
	Rouen	48	0.28 (0.15-0.49)	0.13 (0.07-0.21) 0.35	-0.50	0.65
	Reims	46	0.22 (0.12-0.37)	0.13 (0.07-0.19) 0.63	-0.36	0.72
	Strasbourg	28	0.52 (0.33-0.91)	0.30 (0.13-0.57) 0.53	-0.43	0.67
	Nantes	48	0.28 (0.13-0.57)	0.07 (0.03-0.13) 0.82	-0.71	0.60
	Poitiers	47	0.35 (0.18-0.68)	0.10 (0.04-0.16) 0.65	-0.78	0.62
	Bordeaux	49	0.43 (0.17-1.31)	0.09 (0.03-0.17) 0.70	-0.78	0.54
	Lyon	49	0.29 (0.20-0.54)	0.32 (0.11-0.54) 0.75	0.10	0.68
	Grenoble	50	0.60 (0.39-1.04)	0.49 (0.22-0.80) 0.48	0.12	0.67
	Nice	58	0.47 (0.26-0.66)	0.07 (0.02-0.17) 0.38	-0.75	0.29
	Marseille	58	0.24 (0.16-0.39)	0.02 (0.01-0.09) 0.67	-0.79	0.26
	Annual sites
	SIRTA	127	0.06 (0.01-0.21)	0.02 (0.002-0.08) 0.72	-0.44	0.45
	OPE*	59	0.05 (0.006-0.10)	0.02 (0.01-0.08) 0.78	0.61	0.44
	Annual sites -winter period
	SIRTA	58	0.28 (0.15-0.48)	0.09 (0.04-0.16) 0.66	-0.39	0.66
	OPE*	23	0.10 (0.07-0.13)	0.11 (0.05-0.17) 0.78	0.19	0.68
	*rural site
	In Fig. 8 the daily modelled mean concentrations of particulate phase levoglucosan during February
	2015 is shown. The sampling sites are indicated by squares filled with the mean of levoglucosan

measurements performed every 3 to 6 days (sampling frequency is reported in Table

Table 4 .

 4 Correlation coefficients (p<0.05) of measured and modelled secondary markers with levoglucosan.

		Levoglucosan
		measured Modelled
	DHOPA	0.55	0.52
	Methylnitrocatechols	0.70	0.20
	4-nitroguaiacol	0.61	0.59
	Nitrophenols	0.58	0.59
	Phthalic acid	0.28	0.03

Table S6 .

 S6 via website: http://mcm.leeds.ac.uk/MCM.All the kinetic constants are in cm 3 molecule -1 s -1 . Phthalic Acid formation from naphthalene. When not specified, kinetic constants are in cm 3 molecule -1 s -1 .

	1.3	Phthalic acid
		FORMETOO+NO->NO+X	k=2.66×10 -12
	C514CO23OH+OH->OH+0.28*C5134CO2OH+0.28*CH3COCHOHCOCHO+0.43*C514CO23OHOO C514CO23OH+NO3->NO3+CH3COCHOHCOCHO NAPHTHALENE+OH->OH+0.45*FORMCINNAL k=2.4×10 -11 FORMETOO+HO2->HO2+X -13 k=1.79×10 FORMETOO+NO3->NO3+X -12 k=2.30×10 FORMETOO+RO2->RO2+0.25*PHTHALDIAL -12 k=5×10 C514CO23OH->CO2H3CHO C514CO23OH->CO2H3CHO NAPHTHALENE+NO3->NO3+NONAPHTH -28 PHTHALDIAL+OH->OH+0.85*PHTHALAN k=2.3×10 -11 k=3.4×10 PHTHALDIAL+NO3->NO3+PHTHALAN k(T)=2.8×10 -12 exp(-1860/T)	k=6.26×10 -11 , k from MCM,branching ratio from GECKO-A (Phousongphouang and Arey, 2002) 0.45 is the average yield (30-60%)(Kautzman et al., k=4.54×10 -12 J(Z)=photorate(RCHO) from MCM J(Z)=photorate(CH3COE) from 2010) Phousongphouang and Arey, (Wang et al., 2006) 2x generic constant RO2+NO3 2003 MCM (KNO3AL)*
	C514CO23OHOO+HO2->HO2+0.45*C514CO23OHOOH+0.40*CH3COCHOHCHO+0.15*DHOPA FORMCINNAL+OH->OH+0.35*FCINNALOOOH K 298K total =8.83×10 -11 PHTHALDIAL+O3->O3+X k=2.0×10 -18 C514CO23OHOO+NO->NO+CH3COCHOHCHO C514CO23OHOO+NO3->NO3+CH3COCHOHCHO Literature: PHTHALDIAL->0.35*PHTHALAN+0.53*PHTHALIDE J(Z)=photorate(PHTHA (Nishino et al., 2009)= C514CO23OHOO+oRO2->oRO2+0.70*CH3COCHOHCHO+0.30*DHOPA C514CO23OHOO+obio->obio+0.70*CH3COCHOHCHO+0.30*DHOPA C514CO23OHOO+CH3O2->CH3O2+0.70*CH3COCHOHCHO+0.30*DHOPA C514CO23OHOO+CH3COO->CH3COO+0.70*CH3COCHOHCHO+0.30*DHOPA Figure S2. Phthalic acid formation pathway -11 L) 7.7×10 (Aschmann et al., -11 PHTHALIDE+OH->OH+0.6*PHTHALAN k=8×10 -13 2013)= 2.1×10 FORMCINNAL+O3->O3+0.5*FCINNALOOOH k=1.8×10 -18 PHTHALAN+OH->OH+PHTHALICACID K=7.50×10 -13	35% reacts on the double Analogy with malonic k(T)=3.2×10 -13 exp(925/T) k(T)=8.1×10 -12 exp(270/T) bond: K 298K (pentene)=3.13x10-aldehyde (MALDIAL)* 11 * (Wang et al., 2006) k=5×10 -12 k=1×10 -11 k=1×10 -11 k=1×10 -11 K 298K (pentaldehyde)=2.85x10-11 * K 298K (pentaldehyde)x2=5.70x1 (Wang et al., 2006) * 0-11 k=1×10 -11 (Aschmann et al., 2013) Half time life= 21.4d -1 ,
	DHOPA+OH->OH+X		k=2.42×10 -11 Reactivity in analogy with BUTENAL and all the alkenes [OH]=5x10 -5 3 mol/cm * SRC-AOPWIN (SIDS, 2005)
		FORMCINNAL+NO3->NO3+FCINNALNO3 PHTHALAN->PHTHALICACID	k=4.3×10 -14 k hydrolysis =9.9×10 -3 s -1	(Aschmann et al., 2013) K react =ln2/70s
		FORMCINNAL->	J(Z)=photorate(FORMC K H =41×10 4	(Nishino et al., 2009),products Half-life time from (SIDS,
		0.33*PHTHALDIAL+0.12*FORMETOO+0.33FCINNAL	I) ΔH=61.6	yield in analogy with 2-2005) at 0<pH<6
		OO		pentenale from GECKO-A ΔH sol calculated with SOAP
	C514CO23OH+OH->OH+0.29*DHOPA C514CO23OH+NO3->NO3+0.47*DHOPA FCINNALOOOH+NO->NO+PHTHALDIAL DHOPA+OH->OH+X FCINNALOOOH+HO2->HO2+X PHTHALICACID+OH->OH+X FCINNALOOOH+NO3->NO3+X	k=1.2×10 9 k(T)=2.7×10 -k=1.0×10 6 12 exp(360/T) k=8.1×10 8 k(T)=2.91×10 -13 exp(1300/T) k=1.23×10 -12 k=2.3×10 -12	using generic constant RO2+NO P sat and ΔH vap from(Crooks and Feetham, (KRO2NO) * 1946) generic constant RO2+HO2 * Half time life= 13d -1 , (KRO2HO2) generic constant RO2+NO3 [OH]=5x10 -5
				(KRO2NO3)	*
		FCINNALOOOH+oRO2->oRO2+X	k=2.5×10 -13	Analogy with 2-hydroxy-2-
		FCINNALOOOH+obio->obio+X	k=2.5×10 -13	phenylethyl
		FCINNALOOOH+CH3O2->CH3O2+X	k=2.5×10 -13	peroxide(STYRENO2)*
		FCINNALOOOH+CH3COO->CH3COO+X	k=2.5×10 -13
	C5CO14OH+OH-FCINNALOO+HO2->HO2+0.4*PHTHALDIAL >OH+0.47*C5CO14OH2OO+0.53*C5CO14CO2 C5CO14OH+O3->O3+X FCINNALOO+NO->NO+PHTHALDIAL	k(T)=6.4×10 -k=1.03×10 -10 13 exp(925/T) k=5×10 -18 k(T)=8.1×10 -	GECKO-A, in analogy with 2-pentenale (Camredon et al., 2007)
	C5CO14OH->X	J(Z)=photorate(CH3COE) exp(270/T) 12
	C5CO14OH2OO+HO2->HO2+X FCINNALOO+NO2-> NO2+X C5CO14OH2OO+NO->NO+X FCINNALOO+NO3->NO3+PHTHALDIAL	k=(1/T)=3.3×10 -8 /T k(T)=2.91×10 -13 exp(1300/T) k(T)=2.7×10 -12 exp(360/T) k=5×10 -12
	C5CO14OH2OO+NO3->NO3+X FCINNALOO+ oRO2->oRO2+0.7*PHTHALDIAL	k=2.3×10 -12 k=1×10 -11
	C5CO14OH2OO+oRO2->oRO2+0.2*DHOPA C5CO14OH2OO+obio->obio+0.2*DHOPA FCINNALOO+obio->obio+0.7*PHTHALDIAL C5CO14OH2OO+CH3O2->CH3O2+0.2*DHOPA FCINNALOO+CH3O2->CH3O2+0.7*PHTHALDIAL	k=8.8×10 -13 -11 k=1×10 k=8.8×10 -13 k=8.8×10 -13 k=1×10 -11
	C5CO14OH2OO+CH3COO->CH3COO+0.2*DHOPA FCINNALOO+CH3COO->CH3COO+0.7*PHTHALDIAL	k=8.8×10 -13 k=1×10

Table S8 .

 S8 SARs used to estimate the Psat at 298K. All the estimations are performed through the online platform UManSysProp[START_REF] Topping | UManSysProp v1.0: an online and open-source facility for molecular property prediction and atmospheric aerosol calculations[END_REF]

	Vapor pressure at 298K (torr)
	Marker	Literature	Min	Max	Chosen Value
	DHOPA	a, g		1.67×10 -7	6.09×10 -5	1.95×10 -6
	LEVO	b	1.45×10	-6

Table S10 .

 S10 Marker vaporization enthalpies. ΔHvap is estimated though the Clausius Clapeyron equation when possible.

	ΔH vap (KJ mol	-1 )
	Marker						Literature	Min	Max	Chosen Value
	DHOPA	a, h					92.1	122.5	111
	LEVO	b						52 (Booth et al., 2011)	86.5	116.8	52
								84 (Xie et al., 2014)
	3-Methyl-5-					Semi-empiric, reference: nitrocatechol	l	66.1	78.0	41.7
	Nitrocatechol	c
	Nitrocatechol	c		39(Booth et al., 2012)	62.5	73.3	39
	4-Nitroguaiacol	d	Semi-empiric, reference: nitrocatechol	l	55.4	64.2	30.9
	4-Nitrophenol	e, h	46.2	56.8	51.16
	Phthalic acid

f 40

Table S12 :

 S12 Sampling site locations, periods, frequency and estimated OC wb levoglucosan-1

	Talence		Bordeaux	-0.59° E 44.80° N	16/11/2014 -15/04/2015	3	4.6
	Lyon Centre		Lyon	4.85° E 45.76° N	16/11/2014 -15/04/2015	3	6.1
	Les Frenes		Grenoble	5.73° E 45.16° N	14/11/2014 -16/04/2015	3	5.9
	Arson		Nice	7.27° E 43.71° N	16/11/2014 -15/04/2015	3	7.1
	5 avenues		Marseille	5.40° E 43.30° N	16/11/2014 -15/04/2015	3	7.1
	OPE		rural	5.50° E 48.56° N	01/01/2015 -26/12/2015	6	ND
	3.1	Levoglucosan
						Sampling
						frequency	OC wb
				Geographical		(1 day every	levogluco
	Site name		Urban area	coordinates	Sampling Period	n days)	san -1
	Jean d'Aulan		Reims	4.02° E 49.22° N	28/11/2014 -15/04/2015	3	6.8
	SIRTA		Paris suburbs	2.15° E 48.72° N	19/11/2014 -15/12/2015	3	ND
	STG Nord		Strasbourg	7.78° E 48.61° N	25/12/2014 -16/03/2015	3	4
	Cim Bouteillerie	Nantes	-1.54° E 47.22° N	16/11/2014 -15/04/2015	3	5.2
	Augouard		Poitiers	0.35° E 46.58° N	16/11/2014 -15/04/2015	3	5.1

Table 1 .

 1 mechanisms. H²O was implemented as inMajdi et al., (2019) by adding several aerosol precursors from biomass burning. Therefore, the marker version of CHIMERE has isoprene, monoterpenes, sesquiterpenes, toluene, xylene, phenol, cresol, catechol, benezene, furan, guaiacol, syringol, naphthalene and methylnaphthalenes as SOA precursors. Subcooled saturated vapor pressure (P sat ), evaporation and solubilization enthalpies (ΔH vap and ΔH sol ) and Henry's law constant (K H ) for the modelled biogenic markers. ΔH sol and K H have been calculated with SOAP(Couvidat and Sartelet, 2015).

	Marker	P sat 298K (torr)	ΔH vap (KJmol -1 )	ΔH sol (KJmol -1 )	K H (M -1 atm)
	Pinonic Acid	5.86×10 -6	86	81.7	2.3×10 7
	Pinic Acid	1.01×10 -7	80	84.2	1.1×10 10
	MBTCA	2.85×10 -10	135	145.2	1.37×10 14
	Methylthreitols*	1.94×10 -6	106.4	107.6	3.3×10 10
	α-methylglyceric acid	1.4×10 -5	91.7	98.5	6.31×10 9
	*sum of 2-methylerythritol and 2-methylthreitol

  (hydroperoxymethyl)-6,6-dimethylbicyclo[3.1.1]heptan-2-ol (BPINBOOH). BPINAOOH and BPINBOOH photolysis produce nopinone. BPINBOOH can also react with OH, giving C918CHO. C918CHO is photolysed into nopinone and generates 2 other nopinone precursors by reaction with OH or NO 3 .

	(BPINAOOH)	and	2-
			1]heptan-2-yl]dioxidanyl (BPINBO2). BPINAO2 and BPINBO2
	generate nopinone reacting with NO (yield 76% for both), NO 3 (yield 100% for both) and RO 2
	(yields 70 and 60% respectively). BPINAO2 and BPINBO2 reaction with RO 2 give
	respectively 30 and 20% of	2-(hydroxymethyl)-6,6-dimethylbicyclo[3.1.1]heptan-2-ol
	(BPINAOH).	BPINBO2+RO 2	produce	also	20%	of	2-hydroxy-6,6-
	dimethylbicyclo[3.1.1]heptane-2-carbaldehyde (C918CHO). BPINAOH oxidation by OH
	give 100% of C918CHO. BPINANO2 and BPINBO2 react with NO, producing the
	correspondent nitrate, respectively [2-(hydroxymethyl)-6,6-dimethylbicyclo[3.1.1]heptan-2-
	yl]azonic acid (BPINANO3) and [(2-hydroxy-6,6-dimethylbicyclo[3.1.1]heptan-2-
	yl)methyl]azonic acid (BPINBNO3). BPINANO3 and BPINBNO3 OH-oxidation products are
	respectively nopinone and C918CHO. The HO 2 -oxidation of BPINANO2 and BPINBO2
	produce	90%	of	(2-hydroperoxy-6,6-dimethylbicyclo[3.1.1]heptan-2-yl)methanol

  5 M atm -1 . This value was estimated by Pye et al., (2013) by using Henry Win. As for IEPOX, MAE is hydrolysed in cloud droplets and

	aerosol water depending on pH:
	MAE aq +H + →MGA	k=1.7×10 -3 M s -1 (Piletic et al., 2013) (R 11)

Table 2 .

 2 Model to measurement comparison for 5 SOA markers at SIRTA, from 19/11/2014 to 15/12/2015. Measured and modelled median concentration, 25 th and 75 th percentiles and mean particulate phase fraction (F p ) are shown, together with the correlation (r, p<0.01) and the mean fractional bias (NFB) between the 2 time series.

		Measured		Modelled
		Median (25 th -75 th	Mean	Median (25 th -	Mean	R	MFB
		quartiles)	F p	75 th quartiles)	F p
		concentration (ng m -3 )		concentration
				(ng m -3 )
	Pinonic acid	2.12 (0.94-3.89)	0.68	0.71 (0.43-2.25)	0.12	0.28	-0.51
	Pinic acid	0.51 (0.22-1.05)	0.94	0.011 (0.0005-	0.37	0.22	-1.68
				0.043)
	MBTCA	0.65 (0.31-1.19)	0.99	0.14 (0.088-	0.99	0.20	-0.99
				0.28)

at SIRTA whereas the wilting point (θ m ) is equal to 0.166 m 3 m -3 (Fig.S2). In these

Acknowledgements

Vapor pressure Boiling point Abbreviations (Nannoolal et al., 2008) (Nannoolal et al., 2004) N-N (Nannoolal et al., 2008) (Joback and Reid, 2007) N-J (Nannoolal et al., 2008) (Stein and Brown, 1994) N-S (Myrdal and Yalkowsky, 1997) (Nannoolal et al., 2004) M-N (Myrdal and Yalkowsky, 1997) (Joback and Reid, 2007) M-J (Myrdal and Yalkowsky, 1997) (Stein and Brown, 1994) M-S (Compernolle et al., 2011) Independent from T b Comp. EPIsuite (US EPA, 2015) EPIsuite (US EPA, 2015) EPIsuite Table S9. Marker subcooled saturated vapor pressure at 298 K. Literature and chosen values.

Supplementary Material

One-year measurements of secondary organic aerosol (SOA) markers in the Paris region: concentrations, seasonality, gas/particle partitioning and use in SOA source apportionment

Supplementary Material

Modelling organic aerosol markers in 3D air quality model. Part 1:

Anthropogenic organic markers.

G.M. Lanzafame Correspondence to Florian Couvidat(florian.couvidat@ineris.fr)

Mechanisms 1.1 Nitroguaiacol

Figure S1. Guaiacol photooxidation

Table S1. Mechanisms of nitroguaiacols formation. All the kinetic constants are in cm 3 molecule -1 s -1 .

DHOPA

Table S2.DHOPA formation from 2-methyl furan.All the kinetic constants are in cm 3 molecule -1 s -1 .

GUAIACOL+NO3->NO3+NGUAIACOL k=3.2×10 -12 (Yang et al., 2016) GUAIACOL+OH->OH+0.16*(MM GUAI /MM NGUAI )*NGUAIACOL k=7.5×10 - 11 K from (Coeur-Tourneur et al., 2010) Yields from (Lauraguais et al., 2014) MEFURAN+OH->OH+0.31*C5DICARB k=6.19×10 -11 K is from (Bierbach et al., 1992);Yield is from (Aschmann et al., 2014) MEFURAN+NO3->NO3 k=2.57×10 -11 [START_REF] Kind | Gas-phase rate constants for the reaction of NO3 radicals with furan and methyl-substituted furans[END_REF] MEFURAN+O3->O3 k=2.05×10 -17 [START_REF] Alvarado | Kinetics of the gas-phase reactions of NO3 radicals and O3 with 3methylfuran and the OH radical yield from the O3 reaction[END_REF] Modelling organic aerosol markers in 3D air quality model.

Part 2: Biogenic secondary organic markers.

G.M. Lanzafame 1, 2 , F.

Abstract

Biogenic secondary organic aerosols (SOA) have been identified as a large fraction of organic aerosol (OA) in Europe in summer. The major biogenic SOA precursors are monoterpenesand isoprene. Several chamber experiments have been performed to quantify the aerosols yields from each precursor at different conditions and have been used to develop SOA formation mechanism. These results are used in 1D and 3D air quality models to estimate SOA formation. However, SOA formation in air quality models is difficult to inverstigate due to the myriad of compounds and phenomena involved.

In this paper and its companion paper, we propose marker modelling as a way to investigate SOA formation. For the first time, a comprehensive and detailed mechanism for isoprene and α-and β-pinene SOA marker formation has been inserted in a 3D air quality model. Model outputs have been compared with measurements performed in a site located the Paris region, France.

First generation oxidation α-and β-pinene markers (pinonic acid and pinic acid) formation in the gas-phase has been modelled according to the Master Chemical Mechanism (MCM) v3.

For the second generation oxidation product MBTCA, the gas-phase formation has been modelled with the mechanism proposed by Müller et al., (2012). Isoprene SOA markers have been modelled using an updated version of Couvidat et al., (2013) parametrizations.

The concentrations of almost all the markers are underestimated and simulated with wrong temporal variations. Pinonic acid, pinic acid and MBTCA median measured values are underestimated respectively by a factor 3, 50 and 4.5. Methyltetrols are simulated with the right magnitude order, while α-methylglyceric acid is underestimated by a factor 100.

According to experimenrtal results from Aljawhary et al., (2016), MBTCA is also produced in the aqueous phase by pinonic acid reaction with OH, with a kinetic constant of 3.3x10 9 M -1 s -1 and a maximum yield of 1%. The K H (2.28×10 7 M -1 atm) and ΔH sol (81.68 kJ/mol) of pinonic acid have been calculated with SOAP, using the P sat and ΔH vap values reported in section 2.2.1 and in Table 1.

According to Aljawhary et al., (2016) MBTCA loss process in the aqueous phase occur by reaction with OH (k=3.1×10 8 M -1 s -1 ). The parameters to compute the transfer from the gaseous to the aqueous phase have been computed with SOAP (K H =1.37×10 14 M -1 s -1 , ΔH sol =145.21 kJ mol -1 ) from the P sat (2.58×10 -10 torr) and ΔH vap (135 kJ mol -1 ) of MBTCA measured by Kostenidou et al., (2018).

Isoprene markers

Isoprene (C5H8) photooxidation is strongly dependent on the NO x regime. The mechanisms have been developed following Couvidat et al., (2013) and using MELCHIOR2 (Derognat et al., 2003) as the base gaseous phase mechanism. The reaction with OH starts the oxidation process:

C5H8+OH→ISOP k=2.55×10 -11 exp(410/T) molecule cm -3 s -1 [START_REF] Atkinson | Gas-Phase Tropospheric Chemistry of Volatile Organic Compounds: 1. Alkanes and Alkenes[END_REF]

ISOP is the mixture of peroxy radicals produced from isoprene by H-abstraction and O 2 addition. The successive reactions of this species depend on the NO x regime.

Methyltetrols

Methyltetrols have been identified as low NO x isoprene markers. The major SOA formation pathway at low NO x conditions occurs in the aqueous phase via methyl-epoxy-butanediols (IEPOX) hydrolysis (Surratt et al., 2010), and to a minor extent in the gaseous phase by reaction with RO 2 (Couvidat et al., 2013). In our model, the secondary compounds produced by IEPOX hydrolysis (methyltetrols and C 5 -alkene triols) were already simulated as a single species, named BiMT. The parametrization used in this study for BiMT formation has been modified as explained below. Methyltetrol concentrations are then determined as 54% of BiMT [START_REF] Rattanavaraha | Assessing the impact of anthropogenic pollution on isoprene-derived secondary organic aerosol formation in PM<sub>2.5</sub> collected from the Birmingham, Alabama, ground site during the 2013 Southern Oxidant and Aerosol Study[END_REF].

According to Couvidat et al. (2013) In which methacrolein (MACR), an α-methylgliceric acid precursor, is also produced.

A two step mechanism lead to IEPOX formation in the gas phase:

ISOP+HO 2 →ISHP+HO k=2.05×10 -13 exp(1300/T) molecule cm -3 s -1 [START_REF] Goliff | The regional atmospheric chemistry mechanism, version 2[END_REF]) (R 5) ISHP+OH→IEPOX k=1.0×10 -10 molecule cm -3 s -1 (Couvidat et al., 2013;[START_REF] Goliff | The regional atmospheric chemistry mechanism, version 2[END_REF]) (R 6)

Differently from Couvidat et al., (2013) we supposed an IEPOX yield of 100% for this last reaction, because of the high yields reported previously (Surratt et al., 2010). The oxidation mechanism of IEPOX in the aqueous phase is treated as in Couvidat et al., (2013). IEPOX K H has been taken equal to 2x10 7 M atm -1 , as the best estimated value in Couvidat et al., (2013), which is close to the Henry's law constant onto sea salts measured by [START_REF] Nguyen | Organic aerosol formation from the reactive uptake of isoprene epoxydiols (IEPOX) onto non-acidified inorganic seeds[END_REF].

BiMT formation in cloud droplets and aerosol aqueous phase is catalyzed by acids:

IEPOX aq reacts also with nitrates and sulfates in aqueous solution, producing respectively organonitrates and sulfate esters [START_REF] Darer | Formation and Stability of Atmospherically Relevant Isoprene-Derived Organosulfates and Organonitrates[END_REF][START_REF] Eddingsaas | Kinetics and Products of the Acid-Catalyzed Ring-Opening of Atmospherically Relevant Butyl Epoxy Alcohols[END_REF]. Although sulfate esters have been reported to be stable during the SOA average lifetime, organonitrates hydrolyse fast to methyltetrols [START_REF] Darer | Formation and Stability of Atmospherically Relevant Isoprene-Derived Organosulfates and Organonitrates[END_REF]. BiMT is therefore considered the product of the IEPOX reaction with nitrate:

The competitive reaction with sulfates is reported in the supplementary material (Table S2).

BiMT degradation process has not been considered due to lack of data. A semi-empiric P sat and ΔH vap have been calculated for methyl tetrols based on threitol values measured by [START_REF] Barone | Enthalpies and entropies of sublimation, vaporization and fusion of nine polyhydric alcohols[END_REF] (threitol P sat =1.83×10 -7 torr and ΔH vap =114 kJ mol -1 ). The calculated methyl tetrols P sat and ΔH vap are respectively 1.94×10 -6 torr and 106.4 kJ mol -1 .

α-methylgliceric acid

α-methylgliceric acid (MGA) is a marker for isoprene photooxidation under high-NO x .conditions (Surratt et al., 2010). MGA formation takes places via hydrolysis of the methacrylic acid epoxide (MAE) in the aqueous phase (Lin et al., 2013). MAE is a photooxidation product of peroxymethacryloylnitrate (MPAN), generated in our model according to the MELCHIOR2 mechamism (Derognat et al., 2003). MAE has been added as pinic acid concentrations decrease by 40%, while they do not vary in -half NO‖ simulation. In parallel, pinic acid concentrations respectively increase by ~30% and decrease by ~20% in the -double HO 2 ‖ and -half HO 2 ‖ runs. These results indicates that pinic acid in the model is favoured by low-NO x conditions, as already observed by [START_REF] Eddingsaas | Kinetics and Products of the Acid-Catalyzed Ring-Opening of Atmospherically Relevant Butyl Epoxy Alcohols[END_REF]. This result is coherent with the mechanism of pinic acid formation shown in Fig. 4, where low-NO x conditions are needed for the formation of carboxylic acid functional groups.

In the -half RO 2 ‖ test pinic acid formation decrease of 33% and increase of 57% in the -double RO 2 ‖ test. We can conclude that, according to the reaction scheme used, pinic acid formation is enhanced at low NO x regimes, being RO 2 and HO 2 the principal oxidants involved. However, high incertitudes have to be attributed to HO 2 concentrations, that in may be underestimated by the model by 50% [START_REF] Ma | Winter photochemistry in Beijing: Observation and model simulation of OH and HO2 radicals at an urban site[END_REF].

In Fig. 11 the outputs of pinonic acid sensitivity analysis to radical concentrations is reported.

Pinonic acid concentrations are scarcely affected by the NO x regime and the variations observed in the -double NO‖, -half NO‖, -double HO 2 ‖ and -half HO 2 ‖ simulations are always below 10%. Low variations have been observed also in the -half RO 2 ‖ and -double RO 2 ‖ simulations, in which pinonic acid concentrations decrease and increase respectively of 14% and of 19%. However, the concentration variation effect seems not completely omogeneous for this radical and test concentrations are not perfectly linear compared to reference. Measure pinonic acid F p median value is around 0.82 and half of the values are ranged between 0.47 (25 th ) and 0.97 (75 th percentiles). On the opposite, the simulated pinonic acid partitioning is shifted towards the gaseous phase in all the simulations. In the -reference‖ simulation, 90% of the calculated F p are below 0.44, with a median value of 0.05. Pinonic acid partitioning is mainly hydrophobic and assuming ideality lead to a slight increase of F p values that cannot explain the high F p values observed in measurements. Incertitudes in the calculation of activity coefficients and the significant underestimation (by a factor 5 in summer) of the organic aerosol concentrations probably contribute to the F p underestimation, but cannot fully explain the disprepancies between observed and simulated F p .

GPP estimations

In the observations, pinic acid partition scarcely between the 2 phases, being almost completely in the particulate phase (median, 25 th and 75 th percentiles are respectively 0.98, 0.95 and 0.99). On the contrary, in simulations, pinic acid is always semivolatile. In the -reference‖ run the median F p value is 0.66. The change in volatility is quite wide, being the 25 th and the 75 th percentiles respectively 0.33 and 0.88 (interquartile range 0.55). Pinic acid is mainly hydrophobic except when ideality is assumed (similar median Fp are obtained in the -hydrophilic‖ and -hydrophibic‖ simulations). Higher F p are obtained when assuming ideality (median F p of 0.9).

Conclusions

A comprehensive and detailed chemical mechanism for molecular marker formation from isoprene and α-/β-pinene has been developed and inserted in the 3D chemistry transport model CHIMERE.Useful information about the sensitivity of aerosol formation to the radical concentrations and GPP have been obtained.

Except for some peak in summer, pinene markers (pinonic acid, pinic acid and MBTCA) are underestimated, especially in the last part of the year (from September to December).

Concerning isoprene markers, methyltetrols are simulated with the right magnitude order, but with a wrong temporality (probably due to the fact that the effect of soil mositure on isoprene emissions is not taken into account into CHIMERE), while α-methylglyceric acid are strongly underestimated (factor 100).

The sensitivity analysis performed allowed to understand the critical parameters for each marker simulation. Pinonic acid formation sensitivity to α-pinene oxidation initiation reaction and concentrations has been widely investigated. Although the α-pinene + NO 3 rate predominates in large regions of the domain, most of the pinonic acid in the model is formed by α-pinene + OH and α-pinene + O 3 reactions. The use of detailed mechanisms is required to model daily variations of aerosol formation processes, instead of constant aerosol yields from a defined precursor+radical reaction.

Pinic acid and methyltetrols concentrations are affected by NO x regime and HO 2 concentrations. Only pinic acid formation is also affected by RO 2 concentrations. The sensitivity of these markers to NO x regime was expected [START_REF] Eddingsaas | α-pinene photooxidation under controlled chemical conditions -Part 2: SOA yield and composition in low-and high-NO x environments[END_REF]Surratt et al., 2010). However, it is difficult to quantify accurately the influence of radical concentrations on marker formation, because of the high incertitude connected with the radical concentration estimation and because no measurements of HO 2 and RO 2 are available for comparison. Additionally, methyltetrol formation are overestimated during heat episodes, in which isoprene emissions are computed not taking in account the effect of soil moisture variation.

MBTCA and α-methylglyceric acid formation are sensitive to aqueous phase processes, for which parametrization are still highly uncertain (K H and k choice). When simulated with the parameters used by Pye et al., (2013) (using the same kinetic parameters as IEPOX instead of the parameters measured by Piletic et al., (2013) for MAE and using a higher henry's law constant for MAE), α-methylglyceric acid concentrations increase by a factor 200, becoming of the same magnitude order of the measurements. It may seem surprising that methylglyceric

Supplementary Material

Modelling organic aerosol markers in 3D air quality model.

Part 2: Biogenic organic markers.

G.M. Lanzafame

Figure S3

. α-methylglyceric acid daily mean concentrations over Europe from 15/06 to 15/07/2019, in the reference run (K H =1.2×10 5 kJ mol -1 and k=1.7×10 -3 M s -1 ) (a) and using a Henry's law constant increased by a factor ten (K H =1.2×10 6 kJ mol -1 ) and the same IEPOX oxidation kinetic costant in the aqueous phase (5.0×10 -2 M -1 s -1 ) (b).

Chapter V

Conclusions and perspectives

The main goal of this PhD was to improve OA modelling by implementing specific organic molecular marker emissions and formation processes into a 3D air quality model namely, CHIMERE. This work was based on a combination and comparison of results from field measurements and model outputs. A good description of the organic marker formation mechanisms in the model may improve the knowledge of OA sources, giving valuable information on: (1) the estimation of the source/precursor emissions; (2) the knowledge of the chemical processes leading to SOA formation in the atmosphere, (3) the gas/particle partitioning of OA components.

Molecular marker measurements and database

SOA marker concentrations seasonality have been examined at the SIRTA sampling site (25 km SW of Paris city centre). Observed concentrations were comparable to the ones reported in the literature for sites in Europe and for studies worldwide with similar characteristics (sampling seasons and site typologies). Overall, nitroaromatic compounds (NACs) concentrations showed higher concentrations in the winter period in agreement with the main precursor emission source of these compounds namely wood combustion used for residential heating purposes. Among biogenic SOA markers, only isoprene marker concentrations increased in summer period while pinene markers did not show any typical seasonal trend. Long term data for levoglucosan are already available and the modelling outputs obtained here may promote long term measurements of some key SOA molecular markers such as MBTCA, as already suggested in ACTRIS. Ideally, gas-phase measurements should be performed, including for markers that are traditionally considered non-volatile, as levoglucosan. In such case, prior specific sampling development and validation must be performed.

Knowledge of the emissions

For anthropogenic SOA marker simulations, emission representation appeared as the most critical parameter.Missing or underestimated precursor emissions may contribute substantially to the discrepancies between measured and simulated SOA marker concentrations. For instance, methylnitrocatechol and DHOPA concentrations (underestimated respectively by a factor 60 and 1000) could increase significantly if cresol livestock emission in France were considered, as this sector could emit 20 times more cresol than wood burning.

Methylcatechols primary emission from biomass burning, not currently considered, could also contribute significantly (factor 10) to methylnitrocatechols budget. Moreover, methylnitrocatechol and phthalic acid could be also directly emitted. Thus, anthropogenic VOC emissions inventories should be updated considering the emissions from sectors(e.g. livestock in France) and precursors (as methylcatechol) traditionally neglected. Primary emissions of the SOA markers should be also evaluated. Additionally, intermediate volatility organic compounds (IVOCs), now recognized as a large source of SOA from vehicular emissions, biomass burning and industrial sector (Hatch et al., 2018;Wu et al., 2019;Zhao et al., 2014[START_REF] Zhao | Intermediate Volatility Organic Compound Emissions from On-Road Diesel Vehicles: Chemical Composition, Emission Factors, and Estimated Secondary Organic Aerosol Production[END_REF]Zhao et al., , 2016)), should be also added to the emission inventories. In this way, the research project EVORA (Determination of Semi-Volatile Organic compounds emissions by volatility classes. Impact on air quality modelling; ADEME CORTEA 2017-2020), in which I have been involved during my PhD, has been initiated by Ineris. The main objective of this project is to evaluate the IVOC emissions from light-duty vehicles (gasoline and diesel cars from Euro 3 to Euro 5 emission standards). Thanks to a quantification by volatility classes that I developed on purpose [START_REF] Lu | Comprehensive organic emission profiles for gasoline, diesel, and gas-turbine engines including intermediate and semi-volatile organic compound emissions[END_REF][START_REF] Zhao | Intermediate Volatility Organic Compound Emissions from On-Road Gasoline Vehicles and Small Off-Road Gasoline Engines[END_REF], the emissions will be ready to use in a chemical-transport model such as CHIMERE. Such analytical strategy will be also applied to other emission sources such as biomass burning from public small to medium boiler size ranges (< 1-10 MW) within the research project ACIBIOQA (Improving knowledge of the impact of biomass boilers on Air Quality, ADEME CORTEA 2019-2022)

and must be extended in the future to the industrial sector and residential sector (wood combustion for residential heating) to get a representative emission inventory of IVOCs in France.

Knowledge of the chemical processes

Concerning anthropogenic SOA markers, the results obtained for DHOPA and phthalic acid, underestimated by 3 and 2 order of magnitude respectively, showed that their formation