Bertrand Bessagnet
email: bertrand.bessagnet@ineris.fr

Laurent Menut

Gabriele Curci

Alma Hodzic

Bruno Guillaume

Catherine Liousse

Sophie Moukhtar

Betty Pun

Christian Seigneur

Michael Schulz

Michaël Schulz

M Schulz

Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols

Keywords: Secondary organic aerosols, Modeling, Isoprene, Monoterpenes, Elemental carbon, Organic carbon

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Introduction

Particulate matter (PM) pollution control is one of the main challenges highlighted by the Thematic Strategy on Air Pollution (CAFE as Clean Air For Europe), adopted by the European Commission in October 2005, under its 6 th Environmental Action Program (Decision N o 1600/2002/EC of the European Parliament and of the Council of July 22, 2002 laying down the Sixth Community Environment Action Programme). The CAFE strategy states that particulate matter (and especially fine particles with diameter smaller than and equal to 2.5 µm -PM 2.5 ) is responsible today for an average reduction of life expectancy of about 8 months in Europe: recent epidemiological studies highlight the role of the smallest part of these particles on health [START_REF] Schlesinger | The health relevance of ambient particulate matter characteristics: coherence of toxicological and epidemiological inferences[END_REF][START_REF] Lee | The adverse effects of fine particle air pollution on respiratory function in the elderly[END_REF][START_REF] Heinrich | Fine particles, a major threat to children[END_REF]. Fine particles are composed of a large fraction of organic and elemental carbon (OC and EC) that can be directly injected in the atmosphere [START_REF] Putaud | A European aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[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]). The so-called secondary organic aerosols (SOA) are formed by chemical reactions of volatile organic compounds (VOCs) in the atmosphere including monoterpenes and isoprene emitted by vegetation [START_REF] Kroll | Review: chemistry of secondary organic aerosol: formation and evolution of low-volatility organics in the atmosphere[END_REF][START_REF] Surratt | Chemical composition of secondary organic aerosol formed from the photooxidation of isoprene[END_REF]. While in winter domestic wood burning emissions are the main sources of OC with sizable additional contribution from fossil fuel combustion, SOA mainly originating from non fossil sources largely contribute to OC in summer. Based on the CARBOSOL measurements [START_REF] Pio | Climatology of aerosol composition (organic versus inorganic) at nonurban sites on a west-east transect across Europe[END_REF][START_REF] Legrand | Summary of the CARBOSOL project: present and retrospective state of organic versus inorganic aerosol over Europe[END_REF][START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF], non fossil sources were expected to represent 63-76% of the total carbon (TC = OC + EC). Moreover, 50-80% of OC is water soluble and suggests that OC has to be considered in discussing the role of clouds on climate over Europe [START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF][START_REF] Pio | Climatology of aerosol composition (organic versus inorganic) at nonurban sites on a west-east transect across Europe[END_REF]). Humic-like substances (HULIS) are other contributors of water soluble compounds (3-7%) and are directly emitted by biomass burning and possibly formed by chemical reactions in the atmosphere [START_REF] Mayol-Bracero | Water-soluble organic compounds in biomass burning aerosols over Amazonia 2. Apportionment of the chemical composition and importance of the polyacidic fraction[END_REF][START_REF] Graber | Atmospheric HULIS: how humic-like are they? A comprehensive and critical review[END_REF][START_REF] Lukács | Seasonal trends and possible sources of brown carbon based on 2-year aerosol measurements at six sites in Europe[END_REF][START_REF] Schmidl | Chemical characterisation of fine particle emissions from wood stove combustion of common woods growing in mid-European Alpine regions[END_REF][START_REF] Altieri | Oligomers formed through in-cloud methylglyoxal reactions: chemical composition, properties, and mechanisms investigated by ultra-high resolution FT-ICR mass spectrometry[END_REF]). Most OC is contained in oligomeric or polymeric matter [START_REF] Legrand | Summary of the CARBOSOL project: present and retrospective state of organic versus inorganic aerosol over Europe[END_REF]. OC directly emitted by the decomposition of vegetative debris is another source of the organic coarse size fraction [START_REF] Puxbaum | Size distribution and seasonal variation of atmospheric cellulose[END_REF], a contribution of 6% was attributed in remote background sites [START_REF] Bauer | The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols[END_REF]. In the frame of the CARBOSOL campaign, the modeled EC and OC concentration fields of the EMEP model were compared to measurements. OC was generally underpredicted in most of the sites certainly due to missing wood burning contributions. The model underpredicted TC in the southern Europe mainly due to a lack of SOA [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF]. For EC, the largest uncertainties probably lie in EC emissions from residential wood/fossil combustion possibly associated with both emission factors and spatial and temporal variation [START_REF] Tsyro | Modeling of elemental carbon over Europe[END_REF]. During a specific modeling intercomparison exercise, a potential large underestimation of carbonaceous species concentrations has been recently reported in Eastern Germany due to underestimated sources in Eastern Europe [START_REF] Stern | A model intercomparison study focussing on episodes with elevated PM10 concentrations[END_REF]. Over the United States, the role of isoprene in secondary organic aerosol formation has been extensively studied [START_REF] Dommen | Laboratory observation of oligomers in the aerosol from isoprene/NO x photooxidation[END_REF][START_REF] Kroll | Secondary organic aerosol formation from isoprene photooxidation[END_REF][START_REF] Van Donkelaar | Model evidence for a significant source of secondary organic aerosol from isoprene[END_REF][START_REF] Zhang | Role of isoprene in secondary organic aerosol formation on a regional scale[END_REF]. Previous studies suggest a high sensitivity to the values of the enthalpy of vaporization used in models [START_REF] Zhang | Role of isoprene in secondary organic aerosol formation on a regional scale[END_REF][START_REF] Henze | Global secondary organic aerosol from isoprene oxidation[END_REF]. Another survey showed for secondary organic carbon a 30% maximum contribution to the total secondary carbon at a southeastern US location [START_REF] Kleindienst | Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern US location[END_REF].

Objectives of the study The CHIMERE model was previously validated on PM 10 , sulfate, nitrate and ammonium components [START_REF] Bessagnet | Aerosol modeling with CHIMERE: preliminary evaluation at the continental scale[END_REF][START_REF] Bessagnet | Origin of particulate matter pollution episodes in wintertime over the Paris Basin[END_REF]Vautard et al. 2005;[START_REF] Hodzic | Long-term urban aerosol simulation versus routine particulate matter observations[END_REF]. In this work, a new SOA scheme has been implemented in CHIMERE, a new emission inventory for biogenic VOC emissions MEGAN has also been used. Our work is the first one focussing over Europe by accounting for terpene chemistry including isoprene for SOA formation at high resolution. One of the first work on SOA modeling in Europe was done by [START_REF] Schell | Modeling the formation of secondary organic aerosol within a comprehensive air quality model system[END_REF]w i t h the SORGAM model. Most of the recent modeling studies did not focus on Europe and/or used low horizontal resolutions with global models [START_REF] Liao | Biogenic secondary organic aerosol over the unites states: comparison of climatological simulations with observations[END_REF][START_REF] Tsikaridis | Secondary organic aerosol importance in the future atmosphere[END_REF]. For instance, in our research field, the most recent relevant modeling study over Europe with the EMEP model did not account for isoprene chemistry for SOA formation [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF]. Our work complements the knowledge on SOA modeling in Europe and proposes new insights. A first evaluation of the CHIMERE model is proposed for carbonaceous species by using OC and EC observations from the CARBOSOL and EMEP available data for year 2003. This year was characterized by huge fire emissions in the southern Europe. SOA issued from fire emissions are accounted for in this work. Our work investigates the contribution of fire emissions on OC concentrations.

Usual identified gaps in SOA modeling

SOA modeling remains highly uncertain and too simple, they need to be improved by adding aqueous and heterogeneous pathways, as well as taking into account the multi-step oxidation processes and their dependence on the NOx regime [START_REF] Pun | Investigative modeling of new pathways for secondary organic aerosol formation[END_REF]. One important weakness was identified to be the estimate of biogenic precursor emissions. A first step towards improving our SOA knowledge is improvements of the biogenic emission inventories [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF][START_REF] Arneth | Why are estimates of global terrestrial isoprene emissions so similar (and why is this not so for monoterpenes)?[END_REF]. Another improvement is the quality of meteorological data feeding the model. For instance, an underestimation of temperatures implies three kind of issues: (i) an underestimation of total SOA formation because of kinetic rates favored by high temperature, (ii) an underestimation of SOA concentrations because of lower biogenic emissions, and (iii) an overestimate of gas to particle transfert of semivolatile SOA. Another work [START_REF] Shrivastava | Modeling semivolatile organic aerosol mass emissions from combustion systems[END_REF] suggests that particle emission factors could be underestimated because they are calculated at given temperature, relative humidity and dilution ratio in order to avoid artefacts during the sampling process. In so doing, a non negligible fraction, the so-called SVOCs (semi-volatile VOCs), may have not been taken into account in current gas and particle emission inventories when these data are applied for modeling "real" atmospheres. Moreover, recent studies pointed out the possible role of primary SVOCs in SOA formation [START_REF] Robinson | Rethinking organic aerosols: semivolatile emissions and photochemical aging[END_REF][START_REF] Donahue | Coupled partitioning, dilution, and chemical aging of semivolatile organics[END_REF][START_REF] Schauer | Measurement of emissions from air pollution sources. 5. C 1 -C 32 organic compounds from gasoline-powered motor vehicles[END_REF]. These precursors evaporate during the emission dilution process and could be converted into the particulate phase after oxidation. The exact physical and chemical pathways to secondary organic aerosol for most parent hydrocarbons are still uncertain and during the last ten years, the condensation/sorption process has been adopted in models [START_REF] Pankow | An absorption model for gas/aerosol partition involved in the formation of secondary organic aerosol[END_REF][START_REF] Bessagnet | Origin of particulate matter pollution episodes in wintertime over the Paris Basin[END_REF] w m a ne ta l .1997). Nucleation bursts were observed in rather clean environments such as boreal forest [START_REF] Kavouras | Formation of atmospheric particles from organic acids produced by forests[END_REF], and in urban areas, assuming co-nucleation effects with sulfuric acid [START_REF] Fan | Contribution of secondary condensable organics to new particle formation: a case study in Houston, Texas[END_REF]. If in urban areas absorption certainly dominates, the issue is still open over forested and remote areas [START_REF] Svendby | Temperature dependent secondary organic aerosol formation from terpenes and aromatics[END_REF][START_REF] Wexler | What have we learned from highly time-resolved measurements during EPA's supersites program and related studies?[END_REF][START_REF] Bonn | New particle formation during α and β-pinene oxidation by O3, OH and NO3, and the influence of water vapour: particle size distribution studies[END_REF]H o l m e s2007). In clean environment (Arctic), a high amount of dicarboxylic acids such as glutaric, malonic, succinic and glutaric acid was discovered in coarse particles [START_REF] Kerminen | Size-segregated chemistry of particulate dicarboxylic acids in the Arctic atmosphere[END_REF]). These acids were observed in coarse urban and suburban aerosols corroborating the possible condensation/sorption of semi-volatile species onto pre-existing coarse particles in summer [START_REF] Jaffrezo | Size distribution of EC and OC in the aerosol of Alpine valleys during summer and winter[END_REF]o rt h e possible role of in-cloud processes [START_REF] Hsieh | Origin of low-molecular-weight dicarboxylic acids and their concentration and size distribution variation in suburban aerosol[END_REF][START_REF] Oliveira | Seasonal distribution of polar organic compounds in the urban atmosphere of two large cities from the north and south of Europe[END_REF]. Then, the way of modeling gas to particle conversion will have a strong impact on particle concentrations and compositions. Implementing nucleation processes for SOA in chemistry transport models (not yet available) could shift mass distributions towards smaller particles.

The chemistry transport model CHIMERE

General description

Given a set of NO x ,SO x ,NH 3 , PM, VOCs and CO emissions, the CHIMERE model calculates the concentrations of 44 gas-phase and aerosol species [START_REF] Schmidt | A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in western Europe[END_REF][START_REF] Bessagnet | Aerosol modeling with CHIMERE: preliminary evaluation at the continental scale[END_REF]. In this study, a version of CHIMERE for a domain covering the western Europe is used: from 14 o Wto28 o E in longitude and from 35 o N to 58.2 o N in latitude, with a constant horizontal resolution of 0.4 o × 0.4 o . The vertical grid contains 15 layers from surface to 500 hPa. Improvements have successively been brought in previous studies [START_REF] Vautard | Paris emission inventory diagnostics from ESQUIF airborne measurements and a chemistry transport model[END_REF][START_REF] Vautard | On the contribution of natural aeolian sources to particulate matter concentrations in Europe: testing hypotheses with a modelling approach[END_REF]. The model documentation can be found on the web server http://euler.lmd.polytechnique.fr/chimere. For both ozone and PM 10 , the model has undergone extensive intercomparisons on aerosol concentrations at European and city scales [START_REF] Vautard | Evaluation and intercomparison of ozone and PM10 simulations by several chemistry-transport models over 4 European cities within the city-delta project[END_REF][START_REF] Van Loon | Evaluation of long-term ozone simulations from seven regional air quality models and their ensemble average[END_REF][START_REF] Schaap | Evaluation of long-term aerosol simulations from seven air quality models and their ensemble in the eurodelta study[END_REF].

Anthropogenic gas emissions are taken from the EMEP inventory [START_REF] Vestreng | Review and revision. Emission data reported to CLRTAP[END_REF]. Three particulate model species are considered, PPM (primary particle material) that contains only mineral dust anthropogenic in origin, OC and EC. Coarse particles from the EMEP inventory is the only PPM source. Primary OC and EC assumed to be in the fine mode are issued from a specific inventory discussed in the next section. Calculation of model species emissions that are used by the CHIMERE Chemistry Transport Model, is made in several steps. The spatial emission distribution from the original grid to the CHIMERE grid is performed using an intermediate fine grid with a 0.083 o resolution issued from the GLCF dataset [START_REF] Hansen | Global land cover classification at 1km resolution using a decision tree classifier[END_REF]. Soil types are known on the fine grid allowing for a better apportionment of the emissions according to urban, rural, maritime and continental areas. Standard time variation profiles are applied to get hourly emissions from annual data, as required by the model. The other modeled species are sulfate, nitrate, ammonium, secondary organic aerosol, sea-salt (considered as inert here) and dust. The particle size distribution ranges from about 40 nm to 10 µm and is distributed into 8 bins. The 8 bins used are defined between the following intervals: 0.039, 0.078, 0.156, 0.312, 0.625, 1.25, 2.5, 5, 10 µm. The gas/particle partitioning of the ensemble Sulfate/Nitrate/Ammonium is treated by the code ISORROPIA [START_REF] Nenes | ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent marine aerosols[END_REF] implemented in CHIMERE. For the main gas and aerosols, boundary conditions are issued from a 5-year climatology (2001)(2002)(2003)(2004)(2005) of the global model LMDzT-INCA. For aerosol boundary conditions, only elemental and organic carbon, desert dust and sulfate are taken into account. The LMDzT-INCA model contains a multi-modal aerosol distribution [START_REF] Textor | Analysis and quantification of the diversities of aerosol life cycles within AeroCom[END_REF][START_REF] Schulz | Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations[END_REF], and uses the AeroCom emissions [START_REF] Dentener | Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom[END_REF]. Organic and elemental carbon are described as belonging to a soluble and insoluble accumulation mode, where ageing processes transfer constantly insoluble matter into the soluble aerosol mode.

Meteorology

The CHIMERE model is driven on a hourly base by the meteorological model MM5 for the dynamical parameters (wind, temperature, humidity, pressure). In order to be consistent with the PREVAIR operational forecast system using MM5 and CHIMERE, the current MM5 configuration [START_REF] Dudhia | A nonhydrostatic version of the Penn State/NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front[END_REF]) was chosen to be the same as the one used and validated during the last 3 years for the daily forecast [START_REF] Honoré | Predictability of European air quality: assessment of 3 years of operational forecasts and analyses by the PREV'AIR system[END_REF]. The MM5 vertical grid contains 32 levels ranging from surface to 10 hPa. The horizontal resolution is 54 km over a domain encompassing the european CHIMERE domain. The meteorological boundary conditions as well as the nudging four-dimensional data assimilation (FDDA) are performed with the sixhourly ECMWF analysis meteorological fields.

Temperature is one of the most important variable for SOA formation. Figure 1 displays a comparison of MM5 simulated 2-m temperatures against observations in summertime. Whereas the time evolution is fairly reproduced, a negative bias is observed in all the cities. The lowest temperatures are mainly affected by this negative bias. The maximum temperatures are the most underestimated in Milan, for Madrid the predicted 2-m temperatures agree well with observations. Such biases are often reported in MM5 simulations [START_REF] Jiménez | Evaluation of MM5-EMICAT2000-CMAQ performance and sensitivity in complex terrain: high-resolution application to the northeastern Iberian Peninsula[END_REF][START_REF] Hogrefe | Evaluating the performance of regional-scale photochemical modeling systems: part I -meteorological predictions[END_REF]. [START_REF] Black | Factors contributing to the summer 2003 European heatwave[END_REF]). The coarse resolution of the model can explain a part of the bias, when comparing to surface measurements in sites such as Frankfurt and Milan. These urbanized areas are surrounded by mountains or near great lakes, the model will give an averaged view of the meteorology, certainly not representative of the city located inside the corresponding model cell.

Anthropogenic OC and EC emissions over Europe

A particular effort has been put on primary OC (OCp) and EC emission inventories emitted by fossil fuel and biofuel combustion (traffic, industry, residential sources), which are key elements in EC and OC modeling. These emissions that are traditionally obtained by bottom-up approaches, are still hampered by severe uncertainties resulting from large differences in the choice of emission factors. Two major different approaches for deriving global fossil fuel and biofuel EC and OCp emission inventories are currently available [START_REF] Junker | A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860-1997[END_REF][START_REF] Bond | A technology-based global inventory of black and organic carbon emissions from combustion[END_REF], with the main difference between [START_REF] Junker | A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860-1997[END_REF] and [START_REF] Bond | A technology-based global inventory of black and organic carbon emissions from combustion[END_REF] being in technology differentiations. In the following, JUNKER2008 is the inventory described in [START_REF] Junker | A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860-1997[END_REF] and BOND2004 the one described in [START_REF] Bond | A technology-based global inventory of black and organic carbon emissions from combustion[END_REF]. At the European scale, detailed inventories are given for EC and OCp emissions in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF]). Thus, in BOND2004 each fossil fuel was considered as a detailed list of combustion technologies and emission controls with associated emission factors (EF). In JUNKER2008, for each fuel, two parameters characterize the emissions: (i) the activity sector (domestic, industrial, traffic), and (ii) the national level of development (with three levels: developed, semi-developed and developing countries). In each of these methods, the part of uncontrolled emissions is differently handled which is the origin of their largest differences. Higher estimated emissions may be found for the major fuels (coal, diesel, peat, lignite, coke) in JUNKER2008 than in BOND2004, with more controlled emissions in BOND2004 than in JUNKER2008. Harmonization between these two inventory types is in progress at a global scale. A technology-splitting approach has been adopted with the same considerations of emission controls as in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF] and in BOND2004, while keeping EF values estimated from JUNKER2008. With such a configuration, both newer combustor types and "large emitters" that still exist in Europe, are taken into account. The IIASA (International Institute for Applied Systems Analysis, http://www.iiasa.ac.at) fuel dataset [START_REF] Klimont | Estimating costs for controlling emissions of volatile organic compounds (VOC) from stationary sources in Europe[END_REF] are used, covering 35 European countries excluding Russia and Ukraine and including informations on controlled and uncontrolled fractions of fuel use, which depend on emission control set up. Waste burning is not included. Emission factors (EF) for EC and OCp are obtained from [START_REF] Junker | A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860-1997[END_REF], using a proxy when EF values are not well known. Variation of ratio EC versus total particulate matter is the usual proxy used to derive unknown EF values. This proxy is based on the relationships between EC/OCp and CO/CO 2 ratios. It is noteworthy that an improved spatial distribution of EC and OCp emissions than in previous inventories is used with traffic and domestic emissions scaled on rural and urban population densities, but industrial emissions spatialized according to their real geographical positions and magnitudes. A comparison between this inventory with [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF] is proposed in [START_REF] Guillaume | Global modelling of internally mixed size-resolved inorganic/organic aerosols with a focus on carbonaceous components[END_REF]. In both inventories, EC from brown coal and hard coal fuels dominates while slowly replaced by diesel, the relative importance of wood contribution is comparable. Fuel consumption is generally in agreement. Main differences occur in the relative importance of controlled and uncontrolled fractions of fuel use and in brown and hard coal emission factors selected for industrial and domestic sectors. Emission factors in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF], based on BOND2004, values are smaller than those in [START_REF] Guillaume | Global modelling of internally mixed size-resolved inorganic/organic aerosols with a focus on carbonaceous components[END_REF]. Industrial emissions (especially power plant emissions) are more controlled in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF] than in the inventory used in this work. These differences are particularly important for Poland emissions, much higher in this inventory than in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF]. Same differences exist for domestic emissions while traffic emissions are comparable. Finally, this European EC inventory is about 1.5 times higher than in [START_REF] Schaap | Anthropogenic black carbon and fine aerosol distribution over europe[END_REF], while in agreement with the European zoom of JUNKER2008 inventory. Both inventories have been tested in ORISAM-TM4 global transport model [START_REF] Guillaume | Global modelling of internally mixed size-resolved inorganic/organic aerosols with a focus on carbonaceous components[END_REF][START_REF] Guillaume | Modeling exceptional high concentrations of carbonaceous aerosols observed at Pic du Midi in spring-summer 2003: comparison with Sonnblick and Puy de Dôme[END_REF]. Following these results, EC and OCp European emission inventories for year 2000 and built with a 25 km × 25 km resolution, have been selected in our study. These emissions are extrapolated to the CHIMERE resolution.

Fire emissions

In the past decade, wildfires have devastated vast areas of forest and agricultural lands across Europe. In 2003 alone, more than 650,000 ha of forest area and about 45,000 ha of agricultural land have been destroyed in Europe, which released into the atmosphere large amount of smoke particles and trace gases (such as CO, CO 2 , VOCs, NO, NO 2 , etc...). The 2003 wildfire emissions contributed significantly to the enhancement of carbonaceous aerosol concentrations and perturbations of the surface radiative balance. In order to accurately assess the effects of wildfires on the atmospheric chemistry and radiative budget, the amount of emitted species needs to be quantified. In this study, and in the same way than in [START_REF] Hodzic | Wildfire particulate matter in Europe during summer 2003: meso-scale modeling of smoke emissions, transport and radiative effects[END_REF], daily wildfire emissions of particulate matter and trace gases together with their geographic locations were estimated based on satellite information including (i) the location and date of the fire event, (ii) the area burned, (iii) the fuel loading factors (mass of biomass per area), (iv) the fraction of biomass fuel burned, and (v) the emission factors. These parameters have been determined by combining data available from several satellite products according to the methodology described in [START_REF] Wiedinmyer | Estimating emissions from fires in North America for air quality modeling[END_REF]. The VOC speciation prescribed in EPA (1993) was used. A detailed description of the emission dataset is given in [START_REF] Hodzic | Wildfire particulate matter in Europe during summer 2003: meso-scale modeling of smoke emissions, transport and radiative effects[END_REF].

SOA modeling in CHIMERE

A specific SOA module has been used for this study, the complete chemical scheme implemented in CHIMERE includes biogenic and anthropogenic precursors (Table 1). Biogenic precursors include API (α-pinene and sabinene), BPI (βpinene and δ 3 -carene), LIM (limonene), TPO (myrcene and ocimene) and ISO

Reactions

Kinetic rates (molecules cm -3 s -1 ) TOL+OH → 0.004×AnA0D + 0.001×AnA1D 1.81×10 -12 exp(355/T) + 0.084×AnBmP + 0.013×AnBlP TMB+OH → 0.002×AnA0D + 0.002× AnA1D 9.80×10 -9 /T + 0.001×AnA2D

+ 0.088×AnBmP + 0.006×AnBlP NC4H10+OH → 0.07×AnBmP 1.36×10 -12 exp(190/T) -2 API+OH → 0.30×BiA0D + 0.17×BiA1D + 0.10×BiA2D 1.21×10 -11 exp(444/T) API+O3 → 0.18×BiA0D + 0.16×BiA1D + 0.05×BiA2D 1.01×10 -15 exp (-732/T) API+NO3 → 0.80×BiBmP 1.19×10 -12 exp(490/T) BPI+OH → 0.07×BiA0D + 0.08×BiA1D + 0.06×BiA2D 2.38×10 -11 exp(357/T) BPI+O3 → 0.09×BiA0D + 0.13×BiA1D + 0.04×BiA2D 1.50×10 -17 BPI+NO3 → 0.80×BiBmP 2.51×10 -12 LIM+OH → 0.20×BiA0D + 0.25×BiA1D + 0.005×BiA2D 1.71×10 -10 LIM+O3 → 0.09×BiA0D + 0.10×BiA1D 2×10 -16 TPO+OH → 0.70×BiA0D + 0.075×BiA1D 5.10× -8 /T TPO+O3 → 0.50×BiA0D + 0.055×BiA1D 7.50×10 -14 /T TPO+NO3 → 0.70×BiA0D + 0.075×BiA1D 4.30×10 -9 /T ISO+OH → 0.232×ISOPA1 + 0.0288×ISOPA2 2.55×10 -11 exp(410/T)
The surrogate SOA compounds consist of six hydrophilic species that include an anthropogenic non-dissociative species (AnA0D), an anthropogenic once-dissociative species (AnA1D), an anthropogenic twice-dissociative species (AnA2D), a biogenic non-dissociative species (BiA0D), a biogenic once-dissociative species (BiA1D) and a biogenic twice-dissociative species (BiA2D), three hydrophobic species that include an anthropogenic species with moderate saturation vapor pressure (AnBmP), an anthropogenic species with low saturation vapor pressure (AnBlP) and a biogenic species with moderate saturation vapor pressure (BiBmP), and two surrogate compounds for the isoprene oxidation products (ISOPA1, ISOPA2). T is the temperature in K.

(isoprene). Anthropogenic precursors include TOL (benzene, toluene and other mono-substituted aromatics), TMB (Trimethylbenzene and other poly-substituted aromatics), and NC4H10 (higher alkanes). SOA formation is represented according to a single-step oxidation of the relevant precursors and gas-particle partitioning of the condensable oxidation products. The gas-particle partitioning formulation has been described in detail in [START_REF] Pun | Modeling secondary organic aerosol via multiphase partitioning with molecular data[END_REF]. The overall approach consists in differentiating between hydrophilic SOA that are most likely to dissolve into aqueous inorganic particles and hydrophobic SOA that are most likely to absorb into organic particles. The dissolution of hydrophilic SOA is governed by Henry's law whereas the absorption of hydrophobic particles is governed by Raoult's law.

The large number of condensable organic compounds is represented by a set of surrogate compounds that cover the range of physico-chemical properties relevant for aerosol formation, i.e., water solubility and acid dissociation for hydrophilic compounds and saturation vapor pressure for hydrophobic compounds. These surrogate compounds were selected by grouping identified particulate-phase molecular products with similar properties. The molecular weight of each surrogate compound is determined based on its structure and functional groups. The Henry's law constant or the saturation vapor pressure of the surrogate species is derived from the average properties of the group. Other properties are estimated using the structure of each surrogate compound. Enthalpy of vaporization are given in brackets (kJ mol -1 ) for each SOA compounds: AnA0D (88), AnA1D(88), AnA2D(88), BiA0D(88), BiA1D(88), BiA2D(109), AnBmP(88), AnBlP(88), BiBmP(175). The full name of compounds are given in Table 1 caption. The absorption process in CHIMERE is implemented as in [START_REF] Bowman | Mathematical model for gas-particle parttioning of secondary organic aerosols[END_REF]. A dynamical approach is adopted to describe the gas/particle conversion since the model time-step is about 5 min and using the approach described in [START_REF] Bowman | Mathematical model for gas-particle parttioning of secondary organic aerosols[END_REF], the characteristic time for mass transfer can exceed 20 min for coarse particles.

J i = 1 τ i G i -G eq i
(1)

J i (µgm -2 s -1
) is the absorption or desorption flux of species i, τ i (s) is a characteristic time of the mass transfer that depends on particle size and the chemical properties of species i, G i is the bulk gas-phase concentration of species i and G eq i is the gasphase concentration of species i at thermodynamic equilibrium (i.e., at the surface of the particle). The equilibrium gas-phase concentrations are functions of the particle chemical composition, temperature and, for hydrophilic species, relative humidity, as described in [START_REF] Pun | Modeling secondary organic aerosol via multiphase partitioning with molecular data[END_REF].

The base SOA module was tested against smog chamber data (Odum et al. 1997) for anthropogenic compounds and for biogenic compounds [START_REF] Griffin | Organic aerosol formation from the oxidation of biogenic hydrocarbons[END_REF] and was shown to satisfactorily reproduce SOA formation for those compounds [START_REF] Pun | Modeling secondary organic aerosol via multiphase partitioning with molecular data[END_REF]). In this study, higher alkanes and isoprene were added to the original chemical mechanism. The formation of SOA from higher alkanes follows the formulation used in [START_REF] Zhang | Role of isoprene in secondary organic aerosol formation on a regional scale[END_REF] for the stoichiometric SOA yield and it is assumed that the SOA species can be represented by a hydrophobic surrogate compound with a moderate saturation vapor pressure. The formation of SOA from the oxidation of isoprene by hydroxyl radicals is represented with two surrogate products and follows the formulation prescribed in [START_REF] Kroll | Secondary organic aerosol formation from isoprene photooxidation[END_REF]; [START_REF] Zhang | Role of isoprene in secondary organic aerosol formation on a regional scale[END_REF].

Implementation of the MEGAN biogenic inventory

The previous biogenic inventory used in CHIMERE was based on Simpson's algorithms [START_REF] Simpson | Biogenic emissions in Europe. 1. estimates and uncertainties[END_REF][START_REF] Moukhtar | Monoterpene emissions from beech (fagus sylvatica) in a french forest and impact on secondary pollutants formation at regional scale[END_REF]. Forests were assumed to have a uniform tree distribution within each country. Moreover, the gas emission potentials proposed in [START_REF] Simpson | Biogenic emissions in Europe. 1. estimates and uncertainties[END_REF] are based on branch scale measurements only.

Here, the new MEGAN model (Guenther et al. 2006, v. 2.04) is implemented, it exploits most recent measurements by combining gridded and canopy scale approaches, that are more appropriate for use in CTMs since they estimate the effective burden of gases that mix and react in the boundary layer. Estimates of biogenic VOCs from vegetation and NO emissions are calculated as:

ER i = EF i × γ i (T, PPFD, LAI) × ρ i (2)
where ER i (µgm -2 h -1 ) is the emission rate of species i, EF i (µgm -2 h -1 )i sa n emission factor at canopy standard conditions, γ i (unitless) is an emission activity factor that accounts for deviations from canopy standard conditions, and ρ i is a factor that accounts for production/loss within canopy. The canopy standard conditions relevant for this study are defined as: air temperature (T) of 303 K, photosynthetic photon flux density (PPFD) of 1500 µmol m -2 s -1 at the top of the canopy, leaf area index (LAI) of 5 m 2 m -2 and a canopy with 80% mature, 10% growing and 10% old foliage. The MEGAN model parameterizes the bulk effect of changing environmental conditions using three time-dependent input variables specified at top of the canopy: temperature (T), radiation (PPFD), and foliage density (LAI). The production/loss term within canopy is assumed to be unity (ρ = 1). The equation can then be expanded as:

ER i = EF i × γ T,i × γ PPFD × γ LAI (3)
The MEGAN model provides input EF and LAI data over a global grid, herefater projected on the CHIMERE model grid. The current available choice for EFs is restricted to following species: isoprene, α-pinene, β-pinene, myrcene, sabinene, limonene, δ 3 -carene, ocimene, and nitrogen oxide. Sesquiterpene emissions are not calculated. EF's are static and refer to years 2000-2001. They are obtained summing up over several plant functional types (e.g. broadleaf and needle trees, shrubs, etc...). LAI database is given as a monthly mean product derived from MODIS observations, referred to base year 2000. Hourly emissions are calculated using 2-m temperature and short-wave radiation from MM5 model. The optimal choice for this work is the 150" resolution product proposed in the MEGAN inventory.

Figure 2 shows the differences for August 2003 between the new inventory MEGAN implemented in CHIMERE and the former approach based on the Simpson's algorithms [START_REF] Simpson | Biogenic emissions in Europe. 1. estimates and uncertainties[END_REF]. Large differences everywhere in Europe are observed for monoterpene emissions with lower emissions using the MEGAN algorithms. The differences are consistent to what recently reported in [START_REF] Arneth | Why are estimates of global terrestrial isoprene emissions so similar (and why is this not so for monoterpenes)?[END_REF] and represent the uncertainty related to BVOC emission calculations in current models. Isoprene emissions are higher with MEGAN in Poland, Spain, Italy and Portugal and lower in Greece, United Kingdom and North Africa. (1) The CARBOSOL database as described in [START_REF] Pio | Climatology of aerosol composition (organic versus inorganic) at nonurban sites on a west-east transect across Europe[END_REF]; [START_REF] Legrand | Summary of the CARBOSOL project: present and retrospective state of organic versus inorganic aerosol over Europe[END_REF] provides EC, OC observations available on a weekly basis, about 50 samples per site were analyzed. Chemical analyses are made for particles smaller than and equal to 2.5 µm.

(2) The dataset issued from the EMEP 2002-2003 campaign provides a daily sample per week with only EC and OC analyses [START_REF] Yttri | Elemental and organic carbon in PM10: a one year measurement campaign within the European Monitoring and Evaluation Programme EMEP[END_REF]) (about 25 samples per site). Analyses on particles smaller than and equal to 10 µm were performed using the thermal-optical transmission (TOT) instrument from Sunset Lab Inc., operating according to a NIOSH derived temperature program, more details can be found in [START_REF] Yttri | Elemental and organic carbon in PM10: a one year measurement campaign within the European Monitoring and Evaluation Programme EMEP[END_REF].

(3) The routine measurements at the EMEP sites (http://www.emep.int)p r o v i d e daily average PM 10 concentrations (particle diameter smaller than and equal to 10 µm). To get a site in a french forestry region the station list is completed with the specific site of Périgueux managed by the french air quality network.

The coordinates and locations in Europe of all stations used in this study are described in Table 2 and Fig. 3. For the model-to-data comparisons, concentrations are taken in the first model layer. This may induce representativity problems when the topography greatly varies from cells to cells as in mountainous regions. In order to convert the organic material (OM) concentrations to the modeled organic carbon (OC), the relation proposed in [START_REF] Turpin | Species contributions to PM 2.5 mass concentrations: revisiting common assumptions for estimating organic mass[END_REF] was used with OM = 1.6 × OC. SOA is a fraction of OM, the term SOA-C frequently used in the paper PM 10 measurements available only. For IT04, SK04, AT02 sites both PM 10 and OC, EC measurements are available (see Table 2 for the name of stations and coordinates)

refers to the carbon in the secondary organic aerosols (SOA-C), the same coefficient is used for the conversion: SOA = 1.6 × SOA -C.

SOA results and OC comparisons

In Fig. 4 and Table 3, the comparison of CHIMERE with OC measurements shows a systematic underestimation at each site. Time correlations for PDD, SIL, BE02, GB46 and IE31 are in the range 0.75-0.91. For IT04, IT08, AVE, PT01, SK04 and KPZ sites a clear underestimation by the CHIMERE model is noticed in wintertime.

The same underestimation by the EMEP model [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF]) was attributed to missing wood burning sources in these countries. A smaller underestimation is observed at the other low altitude sites (DE02, AT02, CZ03, and NL09) for the same reasons. In summertime, SOA-C largely dominate the carbon fraction in model results, in the range 90-95% for the two elevated sites PDD (France) and SIL (Germany). CHIMERE gives surprisingly high SOA-C contributions to total OC concentrations in winter for PDD, 71% and SIL, 52% for January-March and October-December periods. These findings are consistent with other CARBOSOL's findings [START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF] where high fractions of biogenic secondary organic carbon in winter, 72% at SIL and 69% at PDD, were reported. Moreover, within 2 and Fig. 3) the CARBOSOL framework at least 86% of OC was attributed to SOA-C during summer [START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF]).

The yearly average SOA concentrations (Fig. 5) range in most of the regions from 0.5 µgm -3 in the Northern Europe to 4 µgm -3 over forested regions (Black Forest, Portugal Forests, Massif-Central) and in the Pô valley, and peaking up to 6 µgm -3 in Croatia. These values are twice larger compared to those obtained by global model simulations (3.75 o × 5 o ) [START_REF] Tsikaridis | Secondary organic aerosol importance in the future atmosphere[END_REF], the dilution of emissions in larger grid cells can explain these differences. Nevertheless, the spatial pattern is very similar with a maximum from the Pô valley to the Eastern Europe. The yearly averaged contribution (ratio in %) of secondary organic aerosols to the total organic material calculated by CHIMERE is mostly in the range 30-80% in Europe (Fig. 6). The highest ratios are found over the Pyrénées and Massif Central and in Spain. Poland displays lower SOA contributions in Europe because of higher primary organic emissions from industries in this country as previously discussed. As shown in Fig. 7 the biogenic fraction of SOA dominates over continental areas exceeding 90% in a large area from Portugal to the North-East of France and over the Balkans. Over urbanized areas such as Paris and Milan only 20% of SOA is anthropogenic in origin, this is a direct consequence of the anthropogenic primary SVOCs chemistry not taken into account in this study. The current chemistry scheme with only primary VOC emissions are not able to produce large amounts of SOA. The fraction of secondary organic aerosols produced by isoprene chemistry ranges from 30% in Northern and Eastern Europe to 60% in Spain (Fig. 8). These results are consistent with those reported in [START_REF] Tsikaridis | Secondary organic aerosol importance in the future atmosphere[END_REF] where a slightly lower range from the 20% to 50% over Europe with similar spatial patterns was related. High isoprene relative contributions are found over North Africa, but as shown in Fig. 5 total SOA concentrations are low in this region.

Regarding to studies over the United States, a study using a global model [START_REF] Liao | Biogenic secondary organic aerosol over the unites states: comparison of climatological simulations with observations[END_REF]) reported a maximum concentration of 2 µgm -3 in summer with a contribution of 49.5% over the United States. By accounting for isoprene chemistry a large increase of the SOA global budget by a factor of two was reported in [START_REF] Henze | Global secondary organic aerosol from isoprene oxidation[END_REF].

The evaluation of a SOA scheme is difficult because total SOA measurements do not exist yet, only estimates will be soon available during specific campaigns. For the moment, only indirect evaluations can be carried out. Table 3 SOA chemistry acts on model OC performances. The normalized error is obviously reduced by adding SOA chemistry, particularly when the isoprene chemistry is implemented. These correlations are largely impaired at most of the sites, nevertheless, at the PDD and SIL sites during the CARBOSOL campaign the correlations are improved. The latter result makes sense, according to our biogenic emission inventory these sites are very close to biogenic emission sources. Moreover, these statistics are more representative because the CARBOSOL sites provide at least 50 samples for 2003 (versus about 25 for the EMEP campaign with only 3 to 4 data in summertime). In order to estimate the influence of SOA formation on PM concentrations, the impact of adding a SOA scheme has been evaluated on PM 10 simulations. Several EMEP background sites in Europe have been selected with more than 300 samples per station.

also displays how

As shown in Table 4, the implementation of the SOA scheme improves the time correlation at several background sites. Large improvements are observed in Spain and Slovenia where biogenic VOC emissions are very large. In Ispra (IT04), time correlations are impaired, but largely improved in Montelibretti (IT01). However, the correlation in IT01 was initially poor. This latter result is not significant compared to the slight decrease of correlation at IT04 particularly when adding SOA formed by isoprene. This shows model discrepancies for predicting biogenic SOA formation and particularly from isoprene in Italy and Slovakia. At DE08, a German station located in a forested region, a large improvement is also observed from 0.58 to 0.70. In Spain, a clear improvement is obtained by adding isoprene chemistry for SOA formation. In Slovakia, the time correlations are globally impaired. The urban site of Périgueux (PERI), a small city on the western part of the Massif Central, is located in a strong biogenic precursor emission area in France. In this region, the CHIMERE simulations exhibit a high contribution of secondary organic species (up to 80%) to the total organic carbon, as shown in Fig. 6. The temporal correlation is largely improved from 0.39 to 0.51 at this site when implementing the SOA scheme. Considering only the summer period from May 1 st to September 30, the correlation is improved from 0.24 to 0.63 at Périgueux.

EC comparisons

Figure 9 and Table 5 display EC model results against observations for each site. As for OC concentrations, the model underpredicts EC concentrations at AVE, PT01, IT08, IT04 and KPZ particularly in winter. The assumption made in [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF] about missing wood burning sources in the model could again explain this behavior. The model reproduces well the time evolution of EC concentrations at BE02, NL09, IE31 and GB46, these sites are very close to well documented sources and are located over flat areas. For Mace Head station (IE31), located along the Atlantic Ocean in Ireland, averaged EC concentrations given by the model are globally underestimated by a factor of two, 0.24 µgm -3 vs. 0.13 µgm -3 respectively for the observations and the model. The associated time correlation is very high 0.89, the model catches well the polluted air masses issued from the continent. The low temporal correlation at the two elevated sites (SIL and PDD) suggests that these sites could be influenced by long range transport of anthropogenic pollution. As monthly climatologies are used at the domain boundaries, the model is not able to capture daily intercontinental fluxes that can influence EC concentrations at elevated sites.

That could be the reason why correlations for OC concentrations are higher than those for EC concentrations at elevated sites because OC has a more local secondary origin than EC for SIL and PDD sites. As shown in Fig. 2 biogenic VOC emissions responsible for a major fraction of organic material are spread out over Europe compare to the spotty patterns of EC emissions. EC concentrations at remote places are affected by long range transport from anthropogenic emission areas [START_REF] Kasper | Seasonal variation of SO 2 , HNO 3 , NH 3 and selected aerosol components at Sonnblick (3106 m a.s.l.)[END_REF][START_REF] Hitzenberger | Contribution of carbonaceous material to cloud condensation nuclei concentrations in European background (Mt. Sonnblick) and urban (Vienna) aerosols[END_REF][START_REF] Guillaume | Modeling exceptional high concentrations of carbonaceous aerosols observed at Pic du Midi in spring-summer 2003: comparison with Sonnblick and Puy de Dôme[END_REF];K a i s e re ta l .2007; [START_REF] Jaffe | Six new episodes of trans-pacific transport of air pollutants[END_REF] and discrepancies in meteorological calculations can largely impair model concentrations. Indeed, even if the MM5 model has been already evaluated in others studies devoted to meteorological simulations, the transport process can explain such a behavior. Emissions are often located at low levels, because of cumulative errors in transport and mixing at such resolutions, it is not surprising to get such statistics on primary species at elevated sites. For gas phase chemistry, a similar behavior at remote background sites [START_REF] Honoré | Predictability of European air quality: assessment of 3 years of operational forecasts and analyses by the PREV'AIR system[END_REF]) was already shown, with better correlations for ozone concentrations (secondary species) than for 2 and Fig. 3) nitrogen oxides (primary species). Similarly, the previous analysis is consistent with the better correlations observed at some low altitude sites for EC concentrations model-to-data comparisons versus OC concentrations : AT02 (0.72 vs. 0.11), AVE (0.30 vs. 0.01), CZ03 (0.52 vs. 0.33), IT04 (0.58 vs. -0.11), IT08 (0.47 vs. 0.00) and NL09 (0.73 vs. 0.62). Indeed, the stations cited here before are mostly influenced by nearby emission areas : AT02 influenced by Vienna, AVE close to the industrial city of Aveiro, CZ03 influenced by industrial zone in the south of Poland, IT04 close to Milan, IT08 is an urban site and NL09 in the Netherlands is located within the most urbanized area in Europe.

Mass distribution of SOA

Mass distribution of aerosol species simulated by CHIMERE are shown in Fig. 10 for two background site PDD and NL09 the 1 st August 2003. For the most remote place (PDD), a peak of SOA is observed at 1 µm with a non negligible amount in particles with diameter larger than 1 µm. For NL09 close to anthropogenic particle emission areas, the peak is located at 0.4-0.5 µm. In our model, SOA are assumed to be absorbed into prexisting particles. The available surface for mass transfert and the absorbing mass drives the absorption process, so they absorb preferentially into fine particles. However, in very remote places, due to a lack of fine particles, those species can also absorb into coarse particles justifying our dynamical approach previously described.

Potential impact of forest fires

Fire emissions can be an important contributor to OC concentrations [START_REF] Langmann | Aerosol distribution over Europe: a model evaluation study with detailed aerosol microphysics[END_REF]). However, wild fire emissions do not contribute much to the atmospheric EC concentrations on a yearly average [START_REF] Tsyro | Modeling of elemental carbon over Europe[END_REF] .T h ei m p a c to ffi r e PDD NL09 (bottom) the ratio between SOA concentrations from fires and total SOA emissions depends on fire buoyancy, and in models, sensitivity to fires depends on the altitude of their release on the vertical grid [START_REF] Hodzic | Evolution of aerosol optical thickness over Europe during the August 2003 heat wave as seen from CHIMERE model simulations and POLDER data[END_REF]. Figure 11 shows the impact of adding forest fire emissions respectively on primary (a) and secondary organic material (b) estimated by CHIMERE on August 1 st -15, 2003. During this period, intense fires were recorded around the Mediterranean basin and in Portugal. CHIMERE estimates give a large contribution of these fires to the primary organic concentrations in the South West of Europe with more than 90% in Portugal and often more than 50% over the Mediterranean Sea (Fig. 11a). Moreover, wildfires emit volatile organic precursors that can be oxidized to form SOA. In Fig. 11b, model results show a limited impact on the total SOA load of secondary organic material originating from biomass burning related to VOCs, less than 10% during the more intense fire period in August 2003. This modeling results provide a response to a key question concerning the relative contribution of secondary organic carbon from VOC emitted by vegetation and biomass burning [START_REF] Gelencsér | Source apportionment of PM2.5 organic aerosol over Europe: primary/secondary, natural/anthropogenic, and fossil/biogenic origin[END_REF]), because analysis methods cannot apportion the two contributions. This low contribution is mainly explained by the difference between VOC emissions from vegetation-1700 kTon-and wildfires-75 kTon-over our modeling domain in August 2003.

Conclusion

In order to better simulate the atmosphere composition, an improved secondary organic aerosols scheme was implemented in the CHIMERE chemistry-transport model. In parallel, the implementation of the MEGAN biogenic emissions inventory was performed. Accounting for these new contributions allowed an improvement of model results over the whole year of 2003 comparing with OC, EC and PM 10 surface concentrations. Our work suggests that isoprene chemistry has a strong contribution to SOA concentrations in Europe and could explain large underestimation of OC concentrations in the southern Europe when this specific chemistry is not accounted for. Our findings are consistent with recent global model results and refine the spatial patterns of SOA concentrations in Europe. However, in this study, a clear underestimation of OC concentrations was diagnosed during winter. A possible explanation is that wood burning emissions in Portugal, Italy, Slovakia and Hungary are missing in the model emission inventory as suggested by a recent modeling work [START_REF] Simpson | Modeling carbonaceous aerosol over Europe: analysis of the CARBOSOL and EMEP EC/OC campaigns[END_REF]) carried out with a different model and other input data (meteorology and emissions). Satisfactory correlations are noticed between model and observed OC at elevated sites such as Puy de Dôme (France) and Schauinsland (Germany) investigated during the CARBOSOL campaign. In these specific cases, the contribution of SOA to the total OC even in winter is quite high that is consistent with CARBOSOL's findings. For these two elevated sites, the implementation of a complete SOA scheme involving monoterpenes and isoprene chemistry clearly improves the predicted OC concentrations. However, adding SOA schemes impair correlation factors in Portugal, Italy and Slovakia. Those discrepancies with observational data could have several origins such as deficient biogenic emission inventories, meteorological data or SOA chemistry and physics. In addition, our work suggests that during the higher fire emission periods, OC concentrations from fires can be the major part of primary organic carbon. The contribution of SOA from fire emissions remains low.

Fig. 2

 2 Fig. 2 Biogenic emissions (monoterpenes and isoprene) for August 2003 (in kg km -2 ) calculated with the former algorithm in CHIMERE and the new MEGAN inventory (a-d)

Fig. 3

 3 Fig.3Location of stations-Red triangles: OC and EC available measurements-Green circles: PM 10 measurements available only. For IT04, SK04, AT02 sites both PM 10 and OC, EC measurements are available (see Table2for the name of stations and coordinates)

Fig. 4

 4 Fig. 4 Comparisons between observed (red squares) and simulated OC concentrations (filled area) in 2003 for the CARBOSOL and EC/OC EMEP campaign datasets. Carbon in the secondary organic aerosols (SOA-C) is represented by crosshatched area. The date corresponds to the first day of sampling (see the full names and locations of each site in Table2and Fig.3)

Fig. 5

 5 Fig. 5 Average SOA concentrations (µgm -3 ) in 2003 simulated by CHIMERE

Fig. 7

 7 Fig. 7 Average contribution (%) in 2003 of biogenic SOA to the total SOA calculated by CHIMERE

Fig. 8

 8 Fig. 8 Average contribution (%) in 2003 of SOA from isoprene to the total SOA calculated by CHIMERE

Fig. 9

 9 Fig. 9 Comparisons between observed (plus symbols)andsimulated(circle symbols) EC concentrations in 2003 for the CARBOSOL and EC/OC EMEP campaign dataset. The date corresponds to the first day of sampling (see the full names and locations of each site in Table2and Fig.3)

Fig. 10

 10 Fig. 10 Daily mean mass distribution of particle components the 1 st August 2003 at background stations NL09 and PDD simulated by CHIMERE

Table 1

 1 Gas phase chemical scheme for SOA formation in CHIMERE

Table 2

 2 Names and coordinates of stations

	Station	Country	Lat. ( o N)	Long. ( o E)	Altitude (m)
	PuydeDôme(PDD a )	France	45.45	3.00
	Périgueux (PERI d )	France	45.19	0.73	≈100
	K-Puszta (KPZ a )	Hungary	46.97	19.58
	Mace Head (IE31 b,c )	Irland	53.33	-9.90
	Kollumerwaard (NL09 b,c )	The Netherlands	53.33	6.28
	Payerne (CH02 c )	Switzerland	46.82	6.95
	Tänikon (CH03 c )	Switzerland	47.48	8.90
	Langenbrügge (DE02 b,c )	Germany	52.80	10.76
	Neuglobsow (DE07 c )	Germany	53.17	13.03
	Schmücke (DE08 c )	Germany	50.65	10.77
	Zingst (DE09 c )	Germany	54.43	12.73
	Schauinsland (SIL a )	Germany	47.92	7.90
	Košetice (CZ03 b,c )	The Czech Republic	49.58	15.08
	Stara Lesna (SK04 b,c )	Slovakia	49.15	20.28
	Liesek (SK05 c )	Slovakia	49.37	19.68
	Starina (SK06 c )	Slovakia	49.05	22.27
	Illmitz (AT02 b,c )	Austria	47.77	16.77
	St. Koloman (AT04 c )	Austria	47.65	13.20
	Montelibretti (IT01 c )	Italy	42.10	12.63
	Ispra (IT04 b,c )	Italy	45.80	8.63
	San Pietro Capofiume (IT08 b )	Italy	44.48	11.33	≈10
	Braganca (PT01 b,c )	Portugal	41.82	-6.77
	Aveiro(AVE a )	Portugal	40.57	-8.63
	Penicuick (GB46 b )	Great Brittain	55.95	-3.22	≈180
	Ghent (BE02 b )	Belgium	51.05	3.72	≈10
	Keldsnor (DK05 c )	Denmark	54.73	10.73
	Niembro (ES08 c )	Spain	43.44	-4.85
	Cabo de Creus (ES10 c )	Spain	42.32	3.32
	Barcarrota (ES11 c )	Spain	38.47	-6.92
	Zarra (ES12 c )	Spain	39.09	-1.10
	Penausende (ES13 c )	Spain	41.28	-5.87
	Els Torms (ES14 c )	Spain	41.40	0.72
	O Saviñao (ES16 c )	Spain	43.23	-7.70
	Iskrba (SI08 c )	Slovenia	45.57	14.87
	Most of the stations are rural background sites, except IT08, BE02 and PERI that are urban
	background sites

a CARBOSOL sites (EC,OC) b EMEP sites of the EC/OC EMEP campaign c Routine EMEP sites (PM 10 data) d Station from the french monitoring network

Table 3

 3 Model-to-data statistics for OC concentrations

	Station Obs. 1	Total OC			OC without		OC without
						SOA-C c			isoprene SOA-C d
			Mod. 2	Corr. 3	Err. 4	Mod. 2	Corr. 3	Err. 4	Mod. 2	Corr. 3	Err. 4
	AT02 a	5.82	3.86	0.11	46.30 1.61	0.55	73.83 2.78	0.40	48.62
	BE02 a	3.97	2.16	0.90	48.69 1.46	0.87	64.93 1.94	0.92	53.68
	CZ03 a	4.98	2.84	0.33	48.79 1.36	0.51	73.55 2.23	0.47	52.10
	DE02 a	4.05	1.99	0.59	52.73 0.95	0.75	78.89 1.61	0.71	57.21
	GB46 a	1.67	0.70	0.82	63.76 0.35	0.90	81.15 0.61	0.85	68.02
	IE31 a	1.48	0.42	0.83	79.35 0.18	0.92	91.31 0.38	0.85	81.61
	IT04 a	8.30	2.49	-0.11	69.36 0.72	0.52	90.09 1.60	0.10	72.69
	IT08 a	5.76	2.48	0.00	65.33 0.66	0.45	85.62 1.60	0.10	68.46
	NL09 a	2.25	1.16	0.62	57.07 0.55	0.74	77.34 0.95	0.72	59.34
	PT01 a	5.30	0.80	-0.08	77.43 0.14	0.05	96.23 0.51	-0.02	85.52
	SK04 a	4.16	2.89	0.29	50.32 1.54	0.33	72.69 2.24	0.35	57.82
	AVE b	5.43	1.16	0.01	74.10 0.23	0.41	94.59 0.73	0.11	82.19
	KPZ b	6.46	1.33	0.25	75.59 0.51	0.52	92.64 1.03	0.44	82.22
	PDD b	1.84	0.82	0.91	56.69 0.10	0.08	90.65 0.47	0.81	69.02
	SIL b	2.47	1.74	0.75	39.82 0.32	-0.06	80.94 1.16	0.73	49.22
	1 Observation (µgm -3 )

2 Model value (µgm -3 ) 3 Correlation factor 4 Normalized error (%) a Daily average values for the EMEP campaign b Weekly average values for the CARBOSOL campaign c Complete scheme with isoprene chemistry d Only monoterpene and anthropogenic VOCs chemistry with no isoprene chemistry

Table 4

 4 Impact of the SOA scheme implementation on the temporal correlation coefficients for PM 10 concentrations observed and simulated

	Station	Corr. without	Corr. with SOA	Corr. with SOA
		SOA	(without isoprene SOA a )	(with isoprene SOA b )
	AT02	0.626	0.623	0.600
	AT04	0.504	0.562	0.633
	CH02	0.437	0.434	0.405
	CH03	0.602	0.620	0.612
	DE07	0.803	0.806	0.808
	DE08	0.580	0.651	0.703
	DE09	0.840	0.838	0.837
	DK05	0.846	0.847	0.848
	ES08	0.583	0.664	0.704
	ES10	0.177	0.221	0.275
	ES11	0.778	0.807	0.835
	ES12	0.645	0.660	0.703
	ES13	0.730	0.754	0.781
	ES14	0.526	0.543	0.587
	ES16	0.660	0.716	0.747
	IT01	0.413	0.450	0.486
	IT04	0.607	0.572	0.506
	PERI	0.391	0.488	0.512
	SI08	0.427	0.469	0.503
	SK04	0.440	0.454	0.458
	SK05	0.441	0.416	0.365
	SK06	0.435	0.412	0.353
	In bold, correlation coefficients improved by implementing the complete SOA scheme
	a Only monoterpene and anthropogenic VOCs chemistry

b Complete scheme with isoprene chemistry

Table 5

 5 Model-to-data statistics for EC concentrations

		Station	Obs. 1	Mod. 2	Corr. 3	Err. 4
		AT02 a	1.04	1.12	0.72	55.2
		BE02 a	1.65	1.70	0.77	41.2
		CZ03 a	1.05	0.90	0.52	42.3
		DE02 a	0.55	0.73	0.60	61.2
		GB46 a	0.52	0.31	0.75	44.3
		IE31 a	0.24	0.13	0.89	41.1
	1 Observation (µgm -3 )	IT04 a IT08 a	1.83 1.29	0.72 0.58	0.58 0.47	58.6 100.9
	2 Model value (µgm -3 )	NL09 a	0.47	0.54	0.73	53.3
	3 Correlation factor 4 Normalized error (%) a Daily average values for the EMEP campaign b Weekly average values for the CARBOSOL campaign	PT01 a SK04 a AVE b KPZ b PDD b SIL b	1.03 0.85 1.09 1.14 0.26 0.30	0.11 0.92 0.23 0.33 0.09 0.34	0.30 0.44 0.30 0.40 0.23 0.17	86.0 66.8 73.2 72.2 63.5 69.1

Acknowledgements This work was funded by the French Ministry in charge of Ecology (MEED-DAT). We thank David Simpson (EMEP, Norway) for providing us the CARBOSOL data, CARBOSOL was financed by European Commission (EVK2-2001-113). Yann Martinet (CITEPA, France) is acknowledged for his contribution in emission speciation. We also thank Alex Guenther (NCAR, USA) for providing us the MEGAN biogenic inventory. G. Curci has been supported by EU/FP6 CIRCE project. We thank METEOCIEL (www.meteociel.com) for providing us the meteorological data.


Khalid Daoudi
email: khalid.daoudi@inria.fr

Biswajit Das
email: biswajit.das@inria.fr

Solange Milhé De Saint Victor
email: solange.milhe-de-saint-victor@chu-bordeaux.fr

Alexandra Foubert-Samier
email: alexandra.samier-foubert@u-bordeaux.fr

Anne Pavy-Le Traon
email: pavy-letraon.a@chu-toulouse.fr

Olivier Rascol
email: olivier.rascol@univ-tlse3.fr

Wassilios G Meissner
email: wassilios.meissner@chu-bordeaux.fr

Virginie Woisard
email: woisard.v@chu-toulouse.fr

Distortion of voiced obstruents for differential diagnosis between parkinson's disease and multiple system atrophy

Keywords: Parkinson's disease, Multiple system atrophy, Dysarthria, Pathological speech processing, Consonant distortion

Parkinson's disease (PD) and the parkinsonian variant of Multiple System Atrophy (MSA-P) are two neurodegenerative diseases which share similar clinical features, particularly in early disease stages. The differential diagnosis can be thus very challenging. Dysarthria is known to be a frequent and early clinical feature of PD and MSA. It can be thus used as a vehicle to provide a vocal biomarker which could help in the differential diagnosis. In particular, distortion of consonants is known to be a frequent impairment in these diseases. The aim of this study is to investigate distinctive patterns in the distortion of voiced obstruents (plosives and fricatives). It is the first study which attempts to examine such distortions in the French language for the purpose of the differential diagnosis between PD and MSA-P (and among the very few studies if we consider all languages). We carry out a perceptual and objective analysis of voiced obstruents extracted from isolated pseudo-words initials. We first show that devoicing is a significant impairment which predominates in MSA-P. We then show that voice onset time (VOT) of voiced plosives (prevoicing duration) can be a complementary feature to improve the accuracy in discrimination between PD and MSA-P.

Introduction

Parkinson's disease (PD) is a neurological disorder principally caused by the degeneration of midbrain dopaminergic neurons, leading to slowness of movement, muscle rigidity and resting tremor. Multiple system atrophy (MSA) belongs to the group of atypical parkinsonian disorders (APD) with a poor prognosis. MSA differs from PD by a more widespread neurodegenerative process, resulting in more rapid disease progression and poor response to dopamine replacement therapy [START_REF] Woisard | Second consensus statement on the diagnosis of multiple system atrophy[END_REF][START_REF] Fanciulli | Multiple-system atrophy[END_REF]. MSA has two variants, MSA-P and MSA-C, where parkinsonism and cerebel-lar features predominate, respectively. The majority of PD and MSA-P patients manifest similar clinical features which renders very challenging a correct differential diagnosis [START_REF] Fanciulli | Multiple-system atrophy[END_REF]. There exists criteria for the diagnosis of "probable" and "possible" MSA, based on clinical or/and imaging features, but the definite MSA diagnosis requires postmortem confirmation by a neuropathological examination [START_REF] Woisard | Second consensus statement on the diagnosis of multiple system atrophy[END_REF]. Despite recent efforts, no validated biomarker is currently available for the differential diagnosis. There exists thus a strong need for such markers to improve diagnostic accuracy, particularly in early disease stages. An accurate early diagnosis is indeed essential not only in assessing prognosis and for treatment decisions, but also for understanding the underlying pathophysiology and for the development of new therapies [START_REF] Meissner | Multiple system atrophy: Recent developments and future perspectives[END_REF].

It is now well established that dysarthria, a class of motor speech impairments resulting from neurological disorders, is an early clinical feature of PD and APD. Dysarthria is mostly caused by control or execution impairment of one or more sensorimotors. PD patients develop essentially hypokinetic dysarthria [START_REF] Darley | Differential diagnostic patterns of dysarthria[END_REF][START_REF] Rusz | Speech disorders reflect differing pathophysiology in parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF] while MSA patients typically exhibit mixed dysarthria with various combination of hypokinetic and ataxic components [START_REF] Kluin | Characteristics of the dysarthria of multiple system atrophy[END_REF][START_REF] Rusz | Speech disorders reflect differing pathophysiology in parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF]. Dysarthria can manifest in all the levels of speech production [START_REF] Duffy | Motor Speech Disorders Substrates, Differential Diagnosis, and Management[END_REF]. In particular, the articulatory mechanism can be affected which causes deficits in range, strength, timing, stability and precision of articulators [START_REF] Logemann | Vocal tract control in parkinson's disease: Phonetic feature analysis of misarticulations[END_REF]. One of the most common manifestation of such deficits is imprecise consonant articulation. In the pioneer work [START_REF] Darley | Differential diagnostic patterns of dysarthria[END_REF], consonant realization was perceptually found to be one of the most deviant speech dimensions in PD.

Consonants distortion across various diseases have been typically assessed using perceptual evaluation [START_REF] Logemann | Vocal tract control in parkinson's disease: Phonetic feature analysis of misarticulations[END_REF][START_REF] Hartelius | Perceptual analysis of speech in multiple system atrophy and progressive supranuclear palsy[END_REF][START_REF] Duez | Acoustic analysis of occlusive weakening in parkinsonian french speech[END_REF][START_REF] Sachin | Clinical speech impairment in parkinson's disease, progressive supranuclear palsy, and multiple system atrophy[END_REF][START_REF] Kim | Perceptual and acoustic features of dysarthria in multiple system atrophy[END_REF][START_REF] Antolík | Consonant distortions in dysarthria due to parkinson's disease, amyotrophic lateral sclerosis and cerebellar ataxia[END_REF][START_REF] Miller | Utility and accuracy of perceptual voice and speech distinctions in the diagnosis of parkinson's disease, psp and msa-p[END_REF]. During the last 2 decades, a considerable effort has been produced to develop objective measures that assess consonant distortions in PD [START_REF] Ackermann | Voice onset time in ataxic dysarthria[END_REF][START_REF] Ackermann | Phonemic vowel length contrasts in cerebellar disorders[END_REF][START_REF] Novotny | Automatic evaluation of articulatory disorders in parkinson's disease[END_REF][START_REF] Orozco-Arroyave | Voiced/unvoiced transitions in speech as a potential bio-marker to detect parkinson's disease[END_REF][START_REF] Kim | Acoustic characteristics of fricatives /s/ and /S/ produced by speakers with parkinson's disease[END_REF][START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF][START_REF] Rusz | Distinctive speech signature in cerebellar and parkinsonian subtypes of multiple system atrophy[END_REF][START_REF] Goswami | Analysis of articulation errors in dysarthric speech[END_REF]. In these studies, voice onset time (VOT) has been the most analyzed feature but with rather contradictory outcomes [START_REF] Flint | Acoustic analysis in the differentiation of parkinson's disease and major depression[END_REF][START_REF] Fischer | Voice onset time in parkinson disease[END_REF][START_REF] Novotny | Automatic evaluation of articulatory disorders in parkinson's disease[END_REF][START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF]. On the other hand, only few studies have addressed consonants distortion in differential diagnosis between PD and APD [START_REF] Antolík | Consonant distortions in dysarthria due to parkinson's disease, amyotrophic lateral sclerosis and cerebellar ataxia[END_REF][START_REF] Saxena | Assessing speech dysfunction using bold and acoustic analysis in parkinsonism[END_REF][START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF][START_REF] Hlavnička | Dysprosody differentiate between parkinson's disease, progressive supranuclear palsy, and multiple system atrophy[END_REF][START_REF] Rusz | Distinctive speech signature in cerebellar and parkinsonian subtypes of multiple system atrophy[END_REF]. The same statement actually holds for dysarthria-based differential diagnosis in general. Indeed, while there exists a large amount of work on comparing PD and HC speech, there is only few studies on comparison/discrimination between PD and APD or between APD subgroups [START_REF] Chakraborty | Dysarthric bengali speech: A neurolinguistic study[END_REF][START_REF] Skodda | Acoustical analysis of speech in progressive supranuclear palsy[END_REF][START_REF] Huh | Differences in early speech patterns between parkinson variant of multiple system atrophy and parkinson's disease[END_REF][START_REF] Hlavnička | Automated analysis of connected speech reveals early biomarkers of parkinson's disease in patients with rapid eye movement sleep behaviour disorder[END_REF][START_REF] Brückl | Measurement of tremor in the voices of speakers with parkinson's disease[END_REF][START_REF] Li | Linear classification in speech-based objective differential diagnosis of parkinsonism[END_REF][START_REF] Das | Towards diseasespecific speech markers for differential diagnosis in parkinsonism[END_REF][START_REF] Kowalska-Taczanowska | Parkinson's disease or atypical parkinsonism? the importance of acoustic voice analysis in differential diagnosis of speech disorders[END_REF].

As for the French language, to the best of our knowledge, there exists no study comparing consonant production between PD and MSA. In [START_REF] Antolík | Consonant distortions in dysarthria due to parkinson's disease, amyotrophic lateral sclerosis and cerebellar ataxia[END_REF], a comparison has been subjectively performed (spectrogram visual inspection) but between PD, Amyotrophic Lateral Sclerosis and Cerebellar Ataxia. In this paper, we present the first study on French consonant distortion for the purpose of differential diagnosis between PD and MSA-P. We carry out a subjective and objective analysis of word-initial consonants using pseudo-words, called Logatomes [START_REF] Lalain | Design and development of a speech intelligibility test based on pseudowords in french: Why and how?[END_REF]. Among all the consonants, obstruents (plosives and fricatives) yield the most interesting results. In particular, we show that voiced obstruents manifest appealing distinctive impairments, in term of devoicing and VOT duration. We then provide a 2-dimensional analysis over these two deviant speech dimensions. This leads us to build a decision model which discriminates between PD and MSA-P with a good accuracy.

The paper is organized as follows. Section 2 describes the speech database. Section 3 presents a brief description of the analysis tools. Results are presented in Section 4. Discussion and conclusion are given in the last section.

Database

From 2018 to the time of writing this paper, a total of 43 French speakers were recruited in the framework of a research project involving the neurology and ENT departments of 2 French university hospitals (recruitment is continuing). 20 patients (5 females and 15 males) were diagnosed with idiopathic PD, with a mean age of 60 and a mean symptom duration of 4 years. 12 subjects (8 females and 4 males) were diagnosed with MSA-P, with a mean age of 67 and a mean symptom duration of 3.5 years. 11 healthy controls (HC) with a mean age of 56 (6 female and 5 male) with no history of neurological or communication disorders were recruited. ENTs carried out all the recording sessions for all participants. Each participant performed several speech tasks including sustained phonations, syllables repetition, a reading task, a monologue and a set of 25 isolated pseudo-words called Logatomes (other non-speech biosignals are also recorded). We used only the Logatomes dataset in this study. The speech signals were recorded with 48kHz sampling frequency and 16 bit resolution by a headmount condenser microphone (t.bone HC 444 TWS) placed at a distance of approximately 5cm from the speaker's mouth. Ethics approval was obtained prior to recruitment and all participants gave written informed consent.

Analysis tools

This paper is dedicated to the analysis of the production of consonants extracted from the Logatomes. The latter have the advantage of being an easy speech task for patients (independently on their native language) and easy to process (even manually). We performed an auditory and visual examination of the 25 Logatomes produced by each participant. Auditory examination was performed by listening carefully several times to all the audio files. Visual examination was carried out by inspection of waveform and wide-band spectrogram using Praat [START_REF] Boersma | Praat: doing phonetics by computer [computer program[END_REF]. As a consequence of this processing, we manually annotated all the speech signals. We followed the criteria of [START_REF] Fischer | Voice onset time in parkinson disease[END_REF] to set the boundaries of the different phonetic units, we refer to [START_REF] Fischer | Voice onset time in parkinson disease[END_REF] for details.

During the examination, we assessed the distortion of consonants (and vowels) when auditorily or/and visually perceived. This analysis showed that, among the 25 Logatomes, voiced obstruents were significantly distorded, in term of the manifestation of devoicing. We mention that this assessment was based on word-initials only (in CV format) in order to avoid coarticulation and speaking rate effects. We thus focused on the 3 voiced plosives /b/, /d/ and /g/ extracted from the Logatomes "berdo", "dirou" and "guizant", and the 3 voiced fricatives /v/, /z/, and /Z/ extracted from the Logatomes 'vonia", "zacu", and "jinin". After devoicing analysis, we performed the traditional voice onset time (VOT) analysis of the plosives using the manually segmented units. Finally we combined the two analysis, devoicing and VOT, in order to build a decision model for differential diagnosis.

For the statistical analysis, data normality of each acoustic feature was evaluated by the one-sample Kolmogorov-Smirnov test. No data was found Gaussian, the pairwise Kruskal-Wallis test to measure difference between groups was used. Statistical significance was set at a p-value p < 0.05.

Results

Devoicing analysis

Visual inspection of spectrograms

By visual inspection of the spectrograms, we assessed devoicing by the total or partial absence of voicing bars in the realization of voiced obstruents. Figure 1 shows an example of spectrograms of the consonant /g/ pronounced normally by a HC, with a partial devoicing by a PD and with a total devoicing by an MSA-P patient. We observed an other phenomenon which could be considered as partial devoicing, the occurrence of voicing bars with weak energy. However, for sake of clarity, reproducibility and to reduce subjectivity effects, we did not use this criterion in our assessment of devoicing.

We found that 67% of MSA-P and 15% of PD presented devoicing in at least one obstruent. In particular, we did not observe any devoicing of /b/ nor /d/ in PD while 42% of MSA-P showed devoicing in these consonants. This suggests that devoicing of /b/ or/and /d/ could be a signature of MSA-P. We mention here however that [START_REF] Antolík | Consonant distortions in dysarthria due to parkinson's disease, amyotrophic lateral sclerosis and cerebellar ataxia[END_REF] reported that 37% of PD presented devoicing in /d/ or /g/. In our data, only 10% of PD showed devoicing in /g/ (and thus in /d/ or /g/), while 33% of MSA-P showed devoicing in /d/ or /g/. This difference in PD might be due to the relatively small size of our dataset as compared to the one of [START_REF] Antolík | Consonant distortions in dysarthria due to parkinson's disease, amyotrophic lateral sclerosis and cerebellar ataxia[END_REF]. As for voiced fricatives, 33% of MSA-P and 15% of PD presented devoicing in at least one of /v/, /z/ and /Z/.

Objective assessment

We now provide an objective measure to detect devoicing. Given a labeled consonant, a simple way to assess total or partial absence of voicing is to consider the degree of voicing measure:

DV = number of voiced f rames total number of f rames (%)
We emphasize here that other objective criteria can be used to define and detect devoicing, our measure is however easy to interpret and to reproduce. Given a speaker, we then defined his/her degree of devoicing DV T as the minimal DV value over all the voiced obstruents. We then heuristically assessed the presence of devoicing if DV T < 50%. Using this criterion, the assessment of devoicing matched perfectly with our visual observations, that is, the same 67% of MSA-P and 15% of PD presented a DV T < 50%. The value of DV T is shown in Figure 2 for each speaker (see the projection over the DVT dimension). Along this dimension, one can note the large margin between subjects manifesting devoicing and the others. This is confirmed by the statistical test of group difference, as shown in Table 1. This suggests that devoicing is generally strong when it occurs, and thus easy to detect objectively by the standard tool Praat.

Overall, these results show that devoicing can be a valuable cue for differential diagnosis between PD and MSA-P. However, this cue alone is not sufficient to achieve this diagnosis with a high accuracy.

VOT analysis of voiced plosives

As mentioned earlier, VOT is among the most studied features in consonant distortion. VOT is generally associated with plosives and is defined as the duration between the vocal fold vibration starts relative to the release of the plosive (there exist however VOT definitions for other consonant types [START_REF] Abramson | Voice onset time (vot) at 50: Theoretical and practical issues in measuring voicing distinctions[END_REF]). In the case of voiced plosive, vibration begins before the release, VOT is thus considered as negative. When negative VOT tends to 0, it actually corresponds to a total devoicing. In order to avoid a potential dependency on speaking rate, VOT ratio (VOTR) is sometimes considered. VOTR is defined as VOT divided by the duration of whole syllable [START_REF] Fischer | Voice onset time in parkinson disease[END_REF].

The purpose of this section is to determine whether VOT analysis of voiced plosives (/b/, /d/ and /g/) can yield another distinctive cue (hopefully complementary to devoicing). Using our manual segmentation, we computed the VOT and VOTR statistical group difference between HC and PD, HC and MSA-P, PD and MSA-P. Table 1 shows the obtained p-value of each group difference. We first observed that, for VOT, statistical significance between MSA and the other groups was achieved only for /g/. More interestingly, this impairment was more severe in MSA-P than in PD. The waveforms of Figure 1 shows an example of such a distortion. This trend was confirmed by VOTR with an additional group difference between PD and HC. Globally, this is in accordance with the findings of [START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF] which reported shorter VOT and lower VOTR for MSA averaged on all voiced plosives (with Czech patients). We cannot however confirm the same statement for /b/ and /d/. On the other hand, we can confidently consider that VOT/VOTR of /g/ is a valuable cue for the differential diagnosis. However, as devoicing, this cue alone is not sufficient to achieve this diagnosis with a high accuracy.

Classification

Given the findings of the previous sections, it is natural to proceed with an analysis over the 2 deviant speech dimensions, devoicing and VOT of /g/ (V OT /g/ ). Using a decision tree classifier with a leave-one-speaker-out (LOSO) cross-validation, we obtained an accuracy of 69%, with a high sensitivity (correctly classified MSA-P) of 83% but a low specificity of 60%. The tree obtained with all data in training is shown in Figure 3 (Train accuracy 72%). This means that a mis-diagnosis of MSA-P presenting devoicing or short VOT of /g/ is unlikely. This statement does not hold for PD. Figure 2a shows the biplot of DV T w.r.t to V OT /g/ with decision thresholds obtained using all data in training. We noted that the estimated thresholds, h1 = 45% for DV T and h2 = -60ms for V OT /g/ , might be considered as not overfitted to our (small size) data. A possible justification is the following. First, it would be reasonable to consider that devoicing occurs when a patient presents a DV less that 45% in a voiced obstruent. Second, using our HC data, the mean/standard deviation of the VOT of /g/ is -103/22(ms). This is in accordance with the -109/32(ms) reported in [START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF] and [START_REF] Fischer | Voice onset time in parkinson disease[END_REF] (the latter reported the mean only). Thus h2 could be reasonably considered as a threshold above which one can confidently consider that a VOT impairment of /g/ is occurring. These two arguments need however to be confirmed by additional data and analysis.

We then carried out the same analysis as above but using VOTR. Using again a LOSO decision tree classifier, the specificity significantly increased to 83% and the sensitivity to 85%. The resulting accuracy score was 84%. The tree obtained with Figure 2b shows the biplot of DV T w.r.t to V OT R /g/ with decision thresholds obtained using all data in training. We see now that, along the V OT R /g/ dimension, a separation appears between the 3 MSA-P and 5 PD which were confused using V OT /g/ (right top rectangle of 2b).

This time however we can not argue that the VOTR threshold (-0.27) could be consider as reasonable decision threshold as we do not have reference values of VOTR. This threshold might thus be overfitted to our data. Therefore, given the small amount of instances of /g/ and its following vowels, we cannot confidently claim that VOTR is a better feature for discrimination than VOT. These results show however that the prevoicing duration of /g/ (and probably all voiced plosives) could be a complementary cue to devoicing of obstruents in order to achieve a high accuracy differential diagnosis between PD and MSA-P.

Overall, the results (along with literature reporting) show that devoicing of voiced obstruents and VOT of /g/ are 2 distinctive and deviant speech dimensions which are worth considering in the differential diagnosis between PD and MSA-P.

Discussion and conclusion

This work constitutes the first study that attempts to highlight distinctive cues in the distortion of French voiced obstruents realization in PD and MSA-P. Our results partially confirmed previous findings on VOT with other languages [START_REF] Saxena | Assessing speech dysfunction using bold and acoustic analysis in parkinsonism[END_REF][START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF]. Indeed, we found that VOT of the voiced plosive /g/ was significantly reduced in MSA-P while it was natural for most PD. On the other hand, VOT in not the only factor of the distortion of voiced plosives (and obviously fricatives). We showed indeed that the absence of voicing leads was the main factor of voiced obstruents distortions and is the most distinctive cue between PD and MSA-P (in the production of voiced obstruents). Moreover, there was a perfect match in devoicing assessment between perceptual and objective evaluations. This supports a potential use of devoicing in clinical practice as an additional tool for the examination of patients with a suspicion of MSA-P. We also showed that the combination of VOT and devoicing can significantly improve the differential diagnosis accuracy.

VOT impairment can be explained by a difficulty in initiating articulation resulting from a deficit in maintaining the speech motor program [START_REF] Tykalova | Distinct patterns of imprecise consonant articulation among parkinson's disease, progressive supranuclear palsy and multiple system atrophy[END_REF]. The latter is a characteristic of hypokinetic dysarthria which is a known feature in both PD and MSA. An accurate production of word-initial voiced plosives requires a precise coordination between glottal opening and articulatory closure. Devoicing is a manifestation of an impairment of such coordination. This is a characteristic of ataxic dysarthria which is known to manifest in MSA. Our results are thus in accordance with the consensus that PD develop essentially hypokinetic dysarthria while MSA develop a mixed type dysarthria. More importantly, since ataxia seems to be responsible for devoicing, the latter might manifest in early disease stages. If proven, devoicing would thus constitute a valuable deviant speech dimension to consider in early differential diagnosis.

There are some limitations to our study. The most significant one is obviously the relatively small size of the dataset due to the difficulty of recruiting patients, particularly with a rare disease such as MSA-P. We are however still continuing the effort of recruitment. Moreover, the dataset is unbalanced in gender, we cannot thus exclude that our findings are biased by gender-specific effects. Another limitation is that we used only one consonant instance per speaker, we do not thus know how the results stand to intra-speaker pronunciation variability. We can expect however that the effect of such variability is reduced by the restriction to word-initials. From this perspective, our study should be considered as a promising first step in the analysis of French voiced obstruents in PD and MSA-P. Our findings need to be confirmed by additional data. This is the purpose of our on going research.

Figure 1 :

 1 Figure 1: Example of no/partial/total devoicing of /g/ in a HC/PD/MSA-P (top). Example of normal/shorter/vanishing VOT of /g/ for the same HC/PD/MSA-P (bottom)

Figure 2 :

 2 Figure 2: Biplot of DV T (%) w.r.t to V OT /g/ and V OT R /g/ (dotted line represent decision thresholds using all data in training); (3) means that 3 MSA-P patients have same coordinates (total devoicing)

Figure 3 :

 3 Figure 3: Decision tree using DV T and V OT /g/ or V OT R /g/ (in green) dimensions for discrimination between PD and MSA-P

Author contribution: 1 )

 1 Research project: A. Conception, B. Organisation, C. Execution 2) Acoustic Analysis: A. Design, B. Execution, C. Re-view&Critique 3) Manuscript: A. Writing of the first draft, B. Review & Critique KD: 1A-C, 2A+B, 3A. BD: 2B, 3B. APL, AFS, OR: 1A, 3B. SM, VW: 1A+C, 3B. WM: 1A-C, 3B.

Table 1 :

 1 Univariate

	Feature Consonant	HC vs PD HC vs MSA-P PD vs MSA-P p-value
	DVT	All	0.12	0.0008	0.009
		/b/	0.36	0.15	0.41
	VOT	/d/	0.8	0.9	0.66
		/g/	0.07	0.004	0.03
		/b/	0.08	0.001	0.029
	VOTR	/d/	0.26	0.03	0.161
		/g/	0.002	0.0009	0.014

statistical group difference of DVT and VOT/VOTR (for the 3 voiced plosives /b/,/d/ and /g/). Bold values indicate significant group difference (p < 0.05)

This work is funded by the French research agency (ANR), project Voice4PD-MSA n o ANR-16-CE19-0010-01.