Bruno Guillaume

Florian Couvidat

Guillaume Siour

Jean-Christophe Péré

Lynda Hamaoui-Laguel

Marc Stéfanon

Sophie Moukhtar

Robert Egalement

Laurence Rosset

Laurent Rouïl

Robert Menut

Philippe Vautard

Raymond Hubert

Cointe

Anthony Ung

Audrey Fortems-Cheiney

Augustin Colette

Charline Pennequin

Edouard Debry

Elsa Real

Etienne Terrenoire

Frédéric Tognet

Frédérik Meleux

Guido Pirovano

Hilel Dergaoui

Jérome Drevet

Laure Malherbe

Laurent Létinois

Marion Devilliers

Marjorie Delias

Marta Garcia Vivanco

Maxime Beauchamp

Vincent Lemaire

Je

Aerosol RObotic NETwork AirBase The European Air quality database AMS Aerosol Mass Spectrometer ANR

Keywords:

SUMMARY

Based on a synthesis of research works carried out by the World Health Organization, air pollution is considered as a major risk for human health, especially particulate matter. Various international protocols, guidelines drive national and European regulations on air quality.

Modelling is a suitable tool for studying air quality but these numerical tools require constant improvements, so they can be used in an operationally way for the public authorities in charge of national air quality management.

In -Processes and model development. My research activities started on aerosol modelling, including microphysics and chemistry of secondary aerosols. Afterwards, I worked on natural sources of particles and how they influence air quality.

-Improvement of the resolution at the European scale. Now the current capacity of computers allows high-resolution simulations. I recently worked on the development of high-resolution emission inventories and the development of pragmatic approaches to improve the simulations at the urban scale.

-Multi models analysis. After several intercomparison modelling exercises, I coordinate now a European project to improve chemical transport models used in the regulatory framework.

-Scenario Analysis. I have participated in numerous emission reduction scenario studies for public authorities and more recently for the European Environment Agency for which we have studied the influence of ammonia emission reductions on air quality.

-Interactions Air Quality / Climate. I participated to the supervision of a thesis on the impact of aerosols on climate. This work led to several publications with a focus on the impact of wildfires on local meteorology. In addition, an air quality modelling chain under climate change pressures has been built as part of a PRIMEQUAL project, I was the coordinator.

It is an applied research activity aiming at developing operational tools and modelling suites to support the public authorities. This research is based on sustainable funding from the ministry in charge of Ecology and other sources as the European/National research projects (7 th Framework Programme, LIFE, ANR), and national programs such as PRIMEQUAL.

RESUME

Sur la base d'une synthèse de travaux de recherches réalisée par l'Organisation Mondiale de la Santé, la pollution de l'air est considérée comme un risque majeur pour la santé et particulièrement la pollution particulaire. Différents protocoles internationaux, recommandations, valeurs guides ont abouti à une réglementation Européenne et nationale sur la qualité de l'air. La modélisation est un outil approprié pour surveiller et analyser la qualité de l'air, mais ces outils numériques nécessitent sans cesse d'être améliorés, évalués afin qu'ils puissent être utilisés de façon opérationnelle pour les pouvoirs publics en charge de la politique nationale de la qualité de l'air.

Dans le dispositif national, ma première contribution a été d'équiper l'outil national CHIMERE (modèle de chimie transport co-développé par l'INERIS et l'IPSL-LMD) d'un module d'aérosol que j'avais développé lors de mon doctorat. Par la suite, j'ai développé les axes de recherches INERIS autour du modèle CHIMERE. Mes travaux de recherche se divisent en cinq axes principaux :

-Processus et développement du modèle. Mes activités de recherches ont démarré sur la modélisation des aérosols, notamment la chimie des aérosols secondaires. J'ai travaillé ensuite sur la prise en compte d'autres sources de particules notamment naturelles et leur impact sur la qualité de l'air.

-Amélioration de la résolution du modèle. La puissance actuelle de calcul autorise maintenant des simulations à haute résolution. J'ai travaillé récemment sur l'élaboration de cadastres à haute résolution et sur la mise au point de méthodes pragmatiques pour améliorer les simulations à jusqu'à l'échelle urbaine.

-Analyse multi modèles. Après de nombreuses participations à des exercices d'intercomparaisons de modèles, je coordonne un projet Européen afin d'améliorer les modèles de chimie transport utilisé dans le cadre réglementaire.

-Analyse de scénarios. J'ai participé à de nombreuses études de scénarios de réductions d'émissions polluantes pour les pouvoir publics et plus récemment pour l'Agence Européenne de l'Environnement pour laquelle nous avons étudié l'influence de réduction d'ammoniac sur la qualité de l'air.

-Interactions Qualité de l'air /Climat. J'ai participé à l'encadrement d'une thèse sur l'impact des aérosols sur le climat, travail qui a abouti à de nombreuses publications notamment sur l'étude de l'impact des feux sur la météorologie. En outre, une chaine de modélisation de la qualité de l'air sous contrainte de l'évolution du climat a été bâtie dans le cadre d'un projet PRIMEQUAL que j'ai coordonné.

Il s'agit d'une activité de recherche appliquée ayant pour but de répondre in fine à des besoins opérationnels pour les pouvoirs publics. Ces recherches s'appuient sur des financements publics pérennes du MEDDE, des projets de recherche Européens (7 ième PCRD, LIFE) et nationaux (ANR), des programmes nationaux comme PRIMEQUAL.

Introduction and context

For the World Health Organisation (WHO), air pollution is a major environmental risk to health and particularly particulate matter (PM). The most health-damaging particles are those with a diameter of 10 microns or less, (PM 10 ), which can penetrate and lodge deep inside the lungs. PM is responsible for a lost of life expectancy particularly when we consider long-term exposure to PM 2.5 [START_REF] Martinelli | Air particulate matter and cardiovascular disease: A narrative review[END_REF]. Based on expert evaluations, WHO (2005) provides some guidelines for PM, respectively 10 µg m -3 for PM 2.5 and 20 µg m -3 for PM 10 to be not exceeded on an annual basis. Particles also play a role on the evolution of climate via direct and indirect effects (IPCC, 2013). In Europe, PM is still a major problem for regional air quality (EEA, 2013a), and the member states have to take measures to reduce the exposure to comply with EU standards driven by international guidelines and regulations.

Air quality Management in France, the role of INERIS

Air quality management is one of the missions of the French authorities supported by the National Institute on Industrial Environment and Risks (INERIS) at the national level. In France, three entities are in charge of air quality:

-Ministry in charge of Ecology supported by INERIS for the implementation of European directive and the assessment of environmental policies, -A network of Air quality local NGOs in charge of the monitoring at local scale and the communication to the public, -A national lab (LCSQA) that optimizes and supervises the national strategy for air quality management. This laboratory is composed of three institutions: INERIS, "Ecole des Mines de Douai" and the "Laboratoire National d'Essai".

INERIS provides expertise and support to the French authorities and private bodies in the field of industrial risks. Air quality management and the assessment of the environmental policies related to air pollution are one of the main important missions of the institute. INERIS hosts the PREV'AIR system a pioneer system for air quality forecast developed in collaboration with the National Centre for Scientific Research (CNRS). This system was created under the auspices of the French Ministry in charge of Ecology. It was implemented in 2003 with the aim of generating and publishing daily air quality forecasts and maps resulting from numerical simulations on different spatial scales. The numerical models developed by INERIS are regularly used to evaluate emission reduction strategies in order to comply with the European and National legislations. Recently, INERIS has developed an integrated tool to simulate the effects of climate change on air quality and to provide concrete elements to policy makers on health impacts, economic and societal issues related to climate and environmental policies. INERIS has also a strong experience to manage crisis situations in the field of air quality like large wild fires, industrial accidents, volcanic eruptions; the institute can deploy a large panel of tools to provide a quick impact analysis to the public authorities.

Legislation influencing Air Quality management

At the international level, the Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone in Gothenburg (Sweden) on 30 November 1999 has been adopted. The Protocol sets emission ceilings for 2010 for four pollutants: sulphur, NOx, VOCs and ammonia. These ceilings were negotiated on the basis of scientific assessments of pollution effects and abatement options. Parties whose emissions have a more severe environmental or health impact and whose emissions are relatively cheap to reduce will have to make the biggest cuts. Once the Protocol is fully implemented, Europe's sulphur emissions should be cut by at least 63%, its NOx emissions by 41%, its VOC emissions by 40% and its ammonia emissions by 17% compared to 1990. The Protocol was amended in 2012 to include national emission reduction commitments to be achieved in 2020 and beyond. Several of the Protocol's technical annexes were revised with updated sets of emission limit values for both key stationary sources and mobile sources, as well as with emission ceilings for fine particulate matter. This protocol was established under the frame of The Convention on Long-range Transboundary Air Pollution. This convention is one of the central means for protecting our environment. It has, over the years, served as a bridge between different political systems and as a factor of stability in years of political change. It has substantially contributed to the development of international environmental law and has created the essential framework for controlling and reducing the damage to human health and the environment caused by transboundary air pollution.

The Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) is an international treaty that sets binding obligations on industrialized countries to reduce emissions of greenhouse gases. The UNFCCC is an environmental treaty with the goal of preventing dangerous anthropogenic (i.e., human-induced) interference of the climate system. According to the UNFCCC website, the Protocol "recognises that developed countries are principally responsible for the current high levels of GHG emissions in the atmosphere as a result of more than 150 years of industrial activity, and places a heavier burden on developed nations under the principle of 'common but differentiated responsibilities'. -The merging of most of existing legislation into a single directive (except for the fourth daughter directive) with no change to existing air quality objectives -New air quality objectives for PM 2.5 (fine particles) including the limit value and exposure related objectives -exposure concentration obligation and exposure reduction target -The possibility to discount natural sources of pollution when assessing compliance against limit values -The possibility for time extensions of three years (PM 10 ) or up to five years (NO 2 , benzene) for complying with limit values, based on conditions and the assessment by the European Commission.

We can cite directives in other media like the directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. The water quality can be influenced by atmospheric deposition and some pollutants like heavy metals (HM) or persistent organic pollutants (POPs) are both relevant for directives regulating the pollutant concentrations in water and air compartments (Bessagnet, 2011;Gonzalez et al., 2012).

Other EU legislations regulate the emissions for targeted sectors, for instance the

Modelling tools in the strategy of Air Quality management

The implementation and the respect of international, European and national legislations have conducted the parties, the member states to maintain air quality measurement networks to assess the evolution of air pollution, their impact on ecosystems and health.

The EMEP programme (Co-operative Programme for Monitoring and Evaluation of the Longrange Transmission of Air Pollutants in Europe) relies on three main elements: (1) collection of emission data, (2) measurements of air and precipitation quality and ( 3) modelling of atmospheric transport and deposition of air pollutions. Through the combination of these three elements, EMEP fulfils its required assessment and regularly reports on emissions, concentrations and depositions of air pollutants, the quantity and significance of transboundary fluxes and related exceedances to critical loads and threshold levels.

In the air quality directive 2008/50/EC, modelling is often cited as a technique to be used to assess air quality. The added-value of using models for AQ management can be summarized as follows (Rouïl and Bessagnet, 2014):

-Mapping air pollutant concentrations fields and air pollution patterns: to get an overview of air pollutant concentrations over the whole targeted territory, even where no measurement data exists. Mapping is necessary for improving communication and raising awareness of the general public and competent authorities. It gives the most comprehensive representation of air pollution patterns.

-Forecasting air pollution levels in a near or remote future. Modelling is the only way to assess the potential evolution of concentrations in the future according to the variation of factors such as meteorology and emissions. Short term forecasting (commonly up to two days ahead) has the objective to inform the general public (e.g. sensitive population affected by asthma and other respiratory diseases) and authorities (e.g. who may also wish to implement short term action plans in case of pollution episodes). Long term scenario analysis aims at assessing the impact of emission control strategies. Impact assessment studies are necessary for decision making, and more generally urban planning. For instance at INERIS we host the French national air quality system PREV'AIR (Honoré et al., 2008;Rouïl et al., 2009).

-Understanding the air pollution phenomenology: analysing the model parameterisations, their sensitivity to changes and the model results in a large number of situations increases the capacity of the expert to interpret air pollution and to identify the most important drivers with regard to various meteorological situations. This is particularly relevant when episodes with persistent exeedances of regulatory threshold values occur: understanding the determining factors (local sources, long range transport, natural events, etc...) helps in targeting the most efficient emission control strategies.

Why PM is still an Air Quality issue in Europe?

An extract of EU air quality standards for the Particulate Matter (PM) is presented in Table 1. PM 10 and PM 2.5 are respectively the concentrations of particles with diameter smaller than 10 and 2.5 µm. Request was subject to assessment by the Commission. In such cases within the time extension period the limit value applies at the level of the limit value + maximum margin of tolerance (35 days at 75µg/m3 for daily PM 10 limit value, 48 µg/m 3 for annual PM 10 limit value). (b) Indicative limit values to be reviewed by the European Commission in 2013 * Target value, ** Limit value Under EU law a limit value is legally binding from the date it enters into force subject to any exceedances permitted by the legislation. A target value is to be attained as far as possible by the attainment date and so is less strict than a limit value.

Considering the exceedance of the PM 10 daily limits several countries show a high level of number of exceedances. In Europe (EU27), the annual evolution in 2009 of the number of exceedance of the PM 10 daily limit is presented in Figure 1. This evolution shows a maximum of exceedances in winter particularly for urban stations mainly due to stagnant meteorological situation with higher residential heating emissions. In March and April a second peak is observed due to ammonium nitrate episodes very frequent in early spring (Bessagnet et al., 2005). 2 Processes and model developments

The chemistry transport model CHIMERE

The rapid growth of urban areas and increased industrialisation have created the need for air quality assessment and motivated the first regional-scale studies on anthropogenic pollution in the early 1990s (Fenger, 2009, among others). The first systematic measurements have been implemented by the air quality agencies in the source regions, which often coincided with the most densely populated areas. One of the first targeted pollutants was sulphur dioxide due to its effects on acid rain and forest ecosystems.

As a result of emission reductions enforced in the industrial activity sector, concentrations of sulphur dioxide were greatly reduced in the 1990s. Then the focus was shifted to other gaseous pollutants such as ozone and nitrogen oxides that were shown to have adverse health effects on populations. More recently, particles have become a priority. In parallel, research on biogenic pollution has long been more modest in contrast to anthropogenic sources. Having no possible influence on biogenic pollutants, the research community has perceived these sources, perhaps erroneously, as less intense or less important to study. Even if air pollution was considered as a local and mostly urban problem, it has been shown that ozone and its precursors may be transported over long distances. Therefore, to study local pollution and represent effects of anthropogenic and biogenic emissions, chemistry and transport on the local pollution budget, models need to integrate processes over large spatial scales. While early models were based on statistical assumptions and could not account for sporadic changes in the atmospheric forcing, the past two decades have seen the development of deterministic and Eulerian models. Some of these models are very complex and dedicated to field or idealised studies of a few days, over specific regions. Others are dedicated to long-range transport only. In addition, some models allow fast simulations and are thus suitable for daily forecast, [START_REF] Monks | Atmospheric composition change -global and regional air quality[END_REF].

The CHIMERE model has been in development for more than fifteen years and is intended to be a modular framework available for community use.

Aerosol microphysic in CHIMERE

Historically, the 0D model was created during my Ph. D (1997Ph. D ( -2000) ) at the Laboratoire d'aérologie (Toulouse University). The main objective was to develop an aerosol module able to account for Secondary Organic Aerosol (SOA) formation. The first developments are described in Bessagnet (2000). The 0D model has been coupled latter with the global model TM4 to simulate the evolution of carbonaceous aerosols at global scale (Guillaume et al., 2007).

In Bessagnet et al. (2004) the implementation in CHIMERE is detailed with a first evaluation at the regional scale. The model was also evaluated at a more local scale over the Paris area in Hodzic et al. (2005). Atmospheric aerosols are represented by their size distributions and compositions. The sectional representation described by [START_REF] Gelbard | Simulation of multicomponent aerosol dynamics[END_REF] has been used for the density distribution function. The sectional approach is quite useful to solve the governing equation for multicomponent aerosols. It discretizes the density distribution in a finite number of bins (Warren, 1986). Thus, all particles in section l have the same composition and are characterized by their mean diameter d l . The aerosol module uses 6 to 12 bins in general usually in the range 10nm to 40 µm, following a constant geometrical progression. For a given x as x=ln(m); with m the particle mass, is the density distribution, Q being the mass concentration function.

(µg m -3 ) is the mass concentration of component k in section l.

Usually in models one can distinguish three major representations of the particle size distribution (PSD) in air quality models: continuous, sectional and modal. In the modal representation, the PSD is modelled by several log-normal distributions, also called modes. Usually, the modes are: the nuclei mode, the accumulation mode and the coarse mode. The accuracy of this approach is limited by the number of modes. In the sectional representation the particle size spectrum is divided into a finite number of sections (or bins), and the PSD is approximated by the integrated number, surface, mass or volume concentrations over each section, depending on the particle characteristics of interest.

In Bessagnet et al. (2004) we see that several processes have been introduced in CHIMERE to handle the aerosols. The trickiest phenomenon to simulate is the Condensation/Evaporation process, some hybrid methods [START_REF] Debry | Technical Note: A new SIze REsolved Aerosol Model (SIREAM)[END_REF] allow to reduce the computation time. In CHIMERE an Euler scheme has been adopted able to conserve the mass but as shown by [START_REF] Devilliers | A New Algorithm to Solve Condensation/Evaporation for Ultra Fine, Fine, and Coarse Particles[END_REF], this algorithm is not suitable for the conservation of the number of particles. Certainly in future new algorithms like the one developed by [START_REF] Devilliers | A New Algorithm to Solve Condensation/Evaporation for Ultra Fine, Fine, and Coarse Particles[END_REF] should be implemented in 0D and 3D applications to both conserve the number and the mass. Actually, the regulations at the emission currently accounts for the Particle number (EURO VI regulation for vehicle exhaust of emissions). Moreover, chemistry transport models will be more and more strongly coupled with meteorological models and the number of particles is an important variable for the cloud formation influencing the indirect effect of aerosol on the radiative budget. The coagulation is an important process for the removal of the smallest particles; it influences very little the total mass of PM 2.5 and PM 10 but has a strong influence on the number of particles.

The nucleation process is certainly the most uncertain phenomenon and so far most of the parameterizations exist for inorganic vapours involving inorganic species like the sulphuric acid (see [START_REF] Holmes | A review of particle formation events and growth in the atmosphere in the various environments and discussion of mechanistic implications[END_REF][START_REF] Zhang | Créteil) Simulation de la matière particulaire dans la région parisienne[END_REF] for a detailed bibliography). Usually the nucleation rate J is a function of the sulphuric acid concentration as:

With C the concentration of sulphuric acid, k and P are two coefficients fitted with theoretical concepts and experimental measurements. The spread of variation of these coefficients is rather large in the literature. Regarding the nucleation of organics, some studies have shown that organics may play an important role in the observed nucleation events in the forested region due to the availability of high biogenic volatile organic compound (VOC) emissions and resultant concentrations. However, apart the homogeneous nucleation theory, more elaborated parameterisations do not exist in the literature yet. The way to describe the gas-to-particle conversion as a nucleation process or through the condensation process or both is still an open question for SOA [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols[END_REF]. This will have a strong impact on particle concentrations and compositions. Implementing nucleation processes for SOA in chemistry transport models could shift mass distributions towards smaller particles.

In Bessagnet and Rosset (2001), we applied the 0D aerosol module developed in Bessagnet (2000) to a car exhaust plume. For this purpose we have introduced a fractal dimension to the system in order to account for larger surface for the condensation processes and coalescence processes in the microphysic evolution. The fractal model developed in Bessagnet and Rosset (2001) allows us to simulate particle evolutions in car exhaust plumes during their dilution process. Most of the simulation results agree with published experimental works. In order to study the pollutants only emitted by vehicles, no entrainment of ambient particles in the plume has been retained in the simulations, only temperature and humidity effects on physico-chemical processes being considered. Immediately after the tailpipe, rapid evolution towards typical urban concentrations is observed. A short critical distance here of about 5m in the reference case has been defined, assuming a typical background particle load of 10 5 particles cm -3 .

Wet deposition

Particle scavenging by precipitation is difficult to predict. Particles can be scavenged either by coagulation with cloud droplets or by precipitating droplets. Particles also act as cloud condensation nuclei to form new droplets. This latter process of nucleation is the most efficient one in clouds/fogs. In-cloud scavenging in CHIMERE is rather different compare to similar models. The in-cloud scavenging transfer rate F for particles is computed in two steps, based on the classical approach in one step [START_REF] Berge | Coupling of wet scavenging of sulphur to clouds in a numerical weather prediction model[END_REF]: F 1 indicates the transfer of particles into droplets and F 2 indicates the scavenging of droplets in case of rain precipitation (Pernigotti et al., 2012):

Where w the cloud water content (g cm -3 ), C is the concentration of the aerosol (g cm -3 ), C aq is the corresponding concentration in the droplet phase (g cm -3 ), is a transfer coefficient (cm 3 g -1 s -1 ), Pr is the precipitation rate (g cm -2 s -1 ) and h the height of the given grid box (cm). The advantage of this two steps scavenging process with transfer rates F 1 and F 2 is to lead to a droplets aerosol concentration which is considered as a loss from the aerosol side but which allows for aerosol particles to reappear whenever the cloud disappears without precipitation (Figure 3).

Chemistry of secondary aerosols

The chemistry of sulphate in clouds was introduced with a set of reactions in the aqueous phase assuming the equilibrium for the gas to droplet diffusion of reacting gases: Actually, the cloud sulphate chemistry strongly depends on the pH of droplets; an increase of pH tends to limit the production of sulphates. The characteristics of CHIMERE was to authorise the evolution of the pH in the range [4][5][6] with a calculation accounting for the ammonium, sulphates and nitrates concentrations in water. Of course, the calculation is not accurate because several ions are missing like calcium and sodium for instance.

A SOA scheme was implemented in CHIMERE in [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols[END_REF] as shown in Table 2. 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 vapour 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 vapour pressure of the surrogate species is derived from the average properties of the group.

For the SOA chemistry, biogenic emissions from the vegetation are very important but uncertain. For ozone, we show for a specific episode in 2003 (Moukhtar et al., 2005) that the effect of changing the emission parameterisation is not very sensitive for O 3 but with differences of about 30% between two emission modules the effect is expected to be linear for the SOA production. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1) is a modelling framework for estimating fluxes of 147 biogenic compounds between terrestrial ecosystems and the atmosphere using simple mechanistic algorithms to account for the major known processes controlling biogenic emissions, this model is implemented in CHIMERE after [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF] but a new version [START_REF] Guenther | The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions[END_REF] has to be implemented in the model. This SOA scheme has to be improved with new modules accounting with a more complete aerosol chemistry as in [START_REF] Couvidat | Modeling secondary organic aerosol in an urban area: application to Paris, France[END_REF]. The model developed by [START_REF] Couvidat | Modeling secondary organic aerosol in an urban area: application to Paris, France[END_REF] treats the formation of SOA with two kinds of surrogate species: hydrophilic species (which condense preferentially on an aqueous phase) and hydrophobic species (which condense only on an organic phase). These surrogate species are formed from the oxidation in the atmosphere of volatile organic compounds (VOC) by radicals (HO and NO 3 ) and ozone. These VOC are either biogenic (isoprene, monoterpenes and sesquiterpenes) or anthropogenic (mainly aromatic compounds). This model includes the formation of aerosols from different precursors (biogenic precursors, aromatics), and semi-volatile organic compounds (SVOC) from traffic. Another way that will be investigated is the generation of new schemes based on the simplification of master mechanism that are now suited for the formation of SOA modelling [START_REF] Aumont | Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach[END_REF].

The implementation of module able to simulate the oxidation of POA (Primary Organic Aerosols) is also to be developed but in that case the first step is to develop reliable POA emission inventories. So far there is no common methodology in the world to handle these species in chemistry transport models.

In Bessagnet et al. (2010) we investigated the potential role of dry deposition velocity of semi-volatile species (Figure 4). Dry deposition of semi-volatile organic compounds (SVOC) is not currently treated in most chemical transport models of air quality and this omission has been identified as a possible major source of uncertainty. The effect of dry deposition of SVOC on the concentration of secondary organic aerosols (SOA) is investigated in summertime with the chemical transport model CHIMERE that simulates SOA concentrations by means of molecular SOA surrogate species. Omitting dry deposition could overestimate SOA concentrations by as much as 50%. This overestimation is larger during night time due to higher relative humidity.

Coarse nitrate formation

Most of the nitrate mass is found in the aerosol fine mode (smaller than 2.5 µm, PM 2.5 ) in the form of ammonium nitrate [START_REF] Putaud | A European aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[END_REF]. Although the sub-micron fraction of nitrate is prevalent in winter and spring, a coarse-mode fraction has been clearly identified in several studies particularly in summer. Coarse nitrate is often associated with sea salts and crustal elements. Several formation mechanisms have been proposed. In sodium-rich marine conditions, nitric acid produces sodium nitrate, while over continental areas, nitric acid reacts with calcium and magnesium carbonates found on crustal materials from local soil erosion or desert dust and forms calcium and magnesium nitrates.

The coarse nitrate formation is still rarely included in chemistry-transport models. So far in the EMEP chemistry transport model [START_REF] Simpson | The EMEP MSC-W chemical transport model -technical description[END_REF], a simple reaction accounting for the relative humidity is used with the kinetic rate k aero =1.0 × 10 -5 when RH>90%, and k aero = 5.0 × 10 -6 for RH<90%. The relative humidity is a proxy for the presence of sea salts and then sodium.

In Hodzic et al. (2006a) we evaluated the significance of the heterogeneous formation of particulate nitrate using a mechanistic scheme involving the reaction of nitric acid with calcium as shown in Figure 5. We introduced this heterogeneous reaction in the CHIMERE model and analysed its impact on total nitrate, nitric acid and aerosol particles, by statistical comparison of one year long simulations with EMEP measurements over Europe. Several values of the nitric acid uptake coefficient γ (in [START_REF] Hanisch | The heterogeneous reactivity of gaseous nitric acid on authentic mineral dust samples, and on individual mineral and clay mineral components[END_REF] parameterization were tested in order to investigate the sensitivity to this parameter, and we proposed an optimal value for our study. The model performance in simulating nitrates and PM 10 significantly improves when the heterogeneous nitrate formation was included. This process also greatly improves the simulation of coarse nitrate by comparison with field campaign measurements near Paris city (Figure 6).

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The main difficulty for the activation of such a parameterisation in CHIMERE in an operational way is to develop a reliable wind blown dust emission module over Europe in order to have the right order of magnitude of Calcium concentrations. Thus, this parameterisation is not active in the current CHIMERE version and we can observe a clear lack of nitrate in summer time. In the EURODELTA III exercise (Bessagnet et al., 2014a) we can clearly observe in Figure 7 the two different behaviours of CHIMERE in wintertime (2009 period) with a quite well reproduction of observed values and in summertime (2006 period) with a systematic underestimate of observations particularly in the south of the domain.

Temporal variation of emissions

Emission inventories usually provide annual totals which generally need to be disaggregated on a relevant temporal scale for use in models (both deterministic and statistical ones). An hourly description of emissions might be required for running local and regional air quality models. This can be achieved in three steps by using (i) monthly profiles (or seasonal profiles), (ii) weekly and (iii) hourly profiles. The profiles are usually available by country, activity sectors and pollutants.

However, when local and national data and expertise are lacking, modellers can use for the diurnal profile a unique profile (Menut et al., 2012) for all compounds and countries (Figure 8). This might be inappropriate and further investigations on the temporal profiles may be necessary to improve model inputs. 9. The distinction between days is crucial since the traffic intensity is different on working days compared to weekends. For example, the morning traffic is very low for Saturdays and Sundays, compared to the other days of the week. Figure 10 presents some examples of these new hourly diurnal profiles of NO 2 concentrations for several cities in Europe. It is assumed that this variable is a good indicator of the road traffic activity in the cities. The difference between cities is large, highlighting the fact that traffic emissions do not have the same variability in time from one country to another. However, the profiles for two cities in the same country show roughly the same profile, confirming that the emission profile of mobile sources depends on the lifestyle and customs in a given country. The "degree day" is an indicator used as a proxy variable to express the daily energy demand for heating. The degree day for a day "j" is defined as:

where T D is the daily mean 2m temperature. Therefore, a first guess daily modulation factor could be defined as:

where N = 365 days.

Considering that SNAP 2 emissions are not only related to the air temperature (e.g. emissions due to production of hot tap water), a second term is introduced in the formula by means of a constant offset C. To better assess the influence of this offset, C can be expressed as a fraction of (degree day annual average).

Considering:

where A is defined by user (A = 0.1 for this application), we can express

And

The daily modulation factor ( ) is therefore defined as:

Note that is mass conservative over the year and replaces the original monthly and daily modulation factors. The choice of A is left to the user to express the relative weight of hot water production with respect to heating. For this application, we set A = 0.1 in order to replicate as much as possible the original CHIMERE profiles during the warmer season. As an illustration, Figure 11 shows the 2009 daily modulation factor applied to the SNAP 2 emissions at three locations both geographically and climatically different: Katowice (Poland), Paris (France) and Madrid (Spain). The highest factors for the three locations are correctly seen during the winter period and the lowest ones during the summer. This means, for example, that during the cold periods the emission from SNAP 2 can be up to three times more intense (e.g. beginning of January for Madrid or end of February for Paris) than during the spring or the autumn periods. It is interesting to notice that in Katowice during the beginning of the year the factors are relatively lower than at the two other locations, meaning that over this period the difference between the daily mean temperature and the annual mean is lower in Katowice than at the two other locations. This does not mean that temperature in Katowice during January was higher than Madrid and Paris, but simply that the latter ones experience a higher variability than Katowice between January and the summer season. Indeed, it is worth noting that in Katowice the modulation factor during the summer is often greater than 0.1, indicating that in several cases, the daily temperature in Katowice is lower than 20°C. Inversely, at the end of the year, all locations experienced a cold outbreak of the same intensity relatively to their local annual mean temperature.

Challenges on certain types of emissions

Fire emissions I performed a first implementation of fire in CHIMERE using the GFED emission inventory (detailed in [START_REF] Mu | Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide[END_REF]. As mentioned in Turquety et al. (2013), fire emissions in the south of Europe can contribute a lot to the total of PM emissions. For these 14 countries reported in Table 3, total fire emissions represent 28% of the total anthropogenic emissions for PM 2.5 according to the methodology described by Turquety et al. (2013). This contribution is certainly more important in summertime. In Europe in winter and early spring when most of the exceedances of the PM 10 daily limits occur the influence of fires will be limited on average.

One important point to address in the future will be the way to inject the fire emissions in the atmosphere. Depending on the fire intensity the fires generate their own dynamics that can lead to the injection of pollutants up to the stratosphere. In most of air quality models an injection of fire emissions is commonly done within the PBL.

Dust emissions

We addressed the problem of the way to account for dust in CHIMERE in Vautard et al. (2005). In this study, I contributed to the implementation of a first parameterisation of wind blown dust emissions. We examine the impact of the introduction of local erosion on PM 10 model skill. Since our goal was not to develop a sophisticated erosion scheme for temperate regions, we followed the classical approach of dust emission modelling in a simplified manner based on the [START_REF] Marticorena | Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme[END_REF]. We also introduced a resuspension scheme. Resuspension of freshly deposited small particles lying at the surface by turbulent wind is a distinct process, their extraction resulting from the imbalance between adhesive and lifting forces. Such particles can originate from the atmosphere or the biosphere, and are particularly easy to extract shortly after deposition [START_REF] Loosmore | Evaluation and development of models for resuspension of aerosols at short times after deposition[END_REF]. The emission flux can be written as:

where f(w) is a function of the soil water content, u * the friction velocity and P is a constant tuned in order to approximately close the PM 10 mass. The soil water modulation factor is vanishing for wet soil (gravimetric soil water content of 0.2 or more, as before, and is equal to 1 for dry soil, w less than 0.1).

The parameterisation improved the model performances in terms of correlations that allow us to be confident in the scientific relevance of the parameterisation. However, if these parameterisation was able to capture an emission signal depending on the wind speed and the soil moisture three main weaknesses were identified : (i) the parameters for erosion like the saltation roughness length was set constant to 5x10 -4 m, (ii) the erosion scheme was applied to all type of landuse and (ii) both resuspension and erosion schemes were too sensitive to the soil moisture that is a parameter sometimes very different between meteorological drivers (use of different schemes, climatologies, etc...). 12.

A first improvement to the scheme of Vautard et al. (2005) is the dependence of the saltation parameters to the model surface characteristics over land, assumed constant in that study. In the present work, this aspect is refined and dust saltation only for cropland is affected representing the whole contribution of arable surface. For instance, this change eliminates unrealistic dust fluxes diagnosed over mountainous areas. A second improvement concerns the soil description. In order to specifically work on this episode, additional information about these soils was added in the model land use. Finally, a third improvement concerns the size distribution of emissions. In our case, we had two types of input information to constrain the size distribution all along the plume (from the emissions to the most remote areas):

-the size distribution of emitted dust after measurement campaigns over Europe as described by [START_REF] Alfaro | Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distribution in source areas[END_REF], which leads to mass median diameters for three emissions mode that is Dp = 1.5, 6.7 and 14.2 µm, and -the PM 2.5 /PM 10 concentrations ratio after surface measurements recorded in Houtem (Belgium) and Harwell (UK).

A first step to improve the dust emission modules was to explore the sensitivity of the drag partition scheme and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets (Menut et al., 2013a). We also compared the use of the drag partition scheme to a widely used preferential source approach in global models. I contributed to the reflection on how to extend the scheme developed for desert areas to European soils. This work has lead to a new universal parameterisation in CHIMERE [START_REF] Briant | Homogeneized modeling of mineral dust emissions over Europe and Africa using the CHIMERE model[END_REF] and currently in test for the CHARMEX experiment [START_REF] Menut | Analysis of the atmospheric composition during the summer 2013 over the Mediterranean area using the CHARMEX measurements and the CHIMERE model[END_REF].

Road dust emissions

Road dusts can be defined as particulate matter generated from the road surface due to road surface wear. Tyre and brake wear may also be important "non-exhaust" emission sources. A complete review of resuspension is available in [START_REF] Amato | Urban air quality: The challenge of traffic non-exhaust emissions[END_REF]. Particles generated by these frictional processes may be emitted directly to the air or deposit on the road surface and road shoulder and be re-suspended later due to vehicle induced turbulence and/or interaction between the tyres and the surface. Moreover, dust emitted in the vicinity of for example cities can be deposited on urban roads. Finally, the available mass for re-suspension may also be increased by the addition of sand or salt to the road surface, particularly important in countries where traction control methods such as traction sand and/or de-icing material are applied. This phenomenon results in enhanced production of road dust and salt that deposits on snow piles on the roadside during winter.

When the snow melts in spring and the surface dries up, traffic induced turbulence causes the particles to be suspended into the air, and hence the PM 10 (particulate matter with a diameter of 10 µm or less) concentrations increase significantly.

Resuspension is not included in emission inventories and official emission reporting. This will affect compliance/uncompliance to the forthcoming revised PM National Emission Ceilings Directive and it is one of the reasons why models underestimate PM 10 concentrations.

It has been estimated that at least 40% of total PM 10 traffic emissions are due to road traffic re-suspension [START_REF] Luhana | Measurement of Non-exhaust Particulate Matter. Deliverable 8 from EU Project PARTICULATES e Characterization of Exhaust Particulate Emissions from Road Vehicles[END_REF]. This ratio can reach up to 90% in springtime as reported by Folsberg et al. (2005) in Stockholm. So far, only few chemistry transport models or local Gaussian or street canyon models host re-suspension modules in an operational way. However the Swedish model SIMAIR has such capacities and is used in operational policy applications [START_REF] Omstedt | A model for vehicle-induced non tailpipe emissions of particles along Swedish roads[END_REF]. As written in the Air Quality

Directive "Contributions to exceedances of particulate matter PM 10 limit values attributable to winter-sanding or -salting of roads may also be subtracted when assessing compliance with air quality limit values provided that reasonable measures have been taken to lower concentrations".

Recently, [START_REF] Berger | A generalised model for traffic induced road dust emissions. Model description and evaluation[END_REF] proposed a new and generalised road dust emission model. They based the emissions on road, tyre and brake wear rates and used the mass balance concept to describe the build-up of road dust on the road surface and road shoulder, depending on soil moisture. The model separates the emissions into a direct part and a re-suspension part, and treats the road surface and road shoulder as two different sources. The model was tested under idealized conditions as well as on two datasets in and just outside of Oslo in Norway during the studded tyre season. The model has been further developed by [START_REF] Denby | A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particles emissions (NORTRIP). Part 1: road dist loading and suspension modelling[END_REF] and applied over long past periods in Stockholm and Copenhagen.

In Spain, [START_REF] Pay | Implementation of resuspension from paved roads for the improvement of CALIOPE air quality system in Spain[END_REF] describe the inclusion of the re-suspension of particulate matter within the HERMES emission model (operationally implemented in the CALIOPE forecasting system) and the improvements obtained in the simulations of the PM10 mass over a domain covering Spain for a whole year of simulation ( 2004). The results indicate a remarkable improvement of the PM 10 predictions, reducing the biases and errors by around 15-18% (2.6 µg m -3 for the average bias in Spain). The emissions have strong local effects on the modelled particle concentration in or near the largest urban zones (up to 7 µg m -3 as the annual average), albeit those positive effects are more limited in background areas, since the deposition mechanism was found to be a significant sink for these re-suspended particles in the chemistry transport model.

Even if these models offer the possibility to evaluate at local and urban scale the emission of road dust fraction of PM 2.5 concentrations, it is not yet possible to obtain the fraction of PM attributable to sanding and salting as required by the air quality directive.

In the frame of the EU LIFE project EC4MACS (European Consortium for Modelling of Air Pollution and Climate Strategies) we have created a simple parameterisation to account for road dust emissions assuming that the exhaust emissions was a good proxy to redistribute the emissions. Moreover, the total exhaust emissions was assumed to be equivalent to the "potential emissions of road dust" modulated by weather conditions (relative humidity as a proxy of surface moisture). The algorithm is as followed: if the precipitation rate is above 0.5 mm/hr or the relative humidity greater than 80% the emission rate for road traffic resuspension is set to 0. In doing so we obtain the map in Figure 13 performed at 8km resolution over Europe. It is not surprising to observe high concentrations over urbanized areas with a contribution reaching 1 to 5 µg m -3 . The impact is weaker over background areas with a contribution of less than 0.5 µg m -3 .

So far, some attempts of road dust emission modelling have been identified in the literature but the relationship with meteorological variable need to be improved. For instance, after precipitation, the recovery of road dust emission potential follows an exponential curve, reaching 99% after 24 h in Spain and 72 h in The Netherlands (Amato et al., 2012). The timing of emissions needs to be improved both in the short-term and longterm and this has some consequences on the design of chemistry transport models. From my point of view, the main difficulty is to evaluate the available dust layer available for resuspension. Certainly the quantification of the dust load is a mix of several parameters:

-the quality of the car fleet on the road and the quality of the road -the frequency of precipitation -the global environment: arid zones in the studied domain -the local environment (proximity of croplands for instance)

and perhaps a full coupling with dust deposition need to be performed in the chemistry transport model.

Ammonia emissions

In According to official reportings, the agricultural sector represents 95% of anthropogenic ammonia emissions (e.g. EEA, 2013b). Ammonia is emitted from livestock housing and by volatilization in the atmosphere after manure and fertilizer applications. The natural fraction of ammonia emissions is difficult to estimate. It comes from the decomposition or breakdown of organic waste matter, animal waste, and the discharge of ammonia by biota. This fraction is most probably below 10% (Sapek, 2013). Ammonia is an important PM precursor of ammonium nitrate in the air. In a "real world", ammonia emissions depend on the type of cattle, manures and fertilizers, spreading practices, meteorological and soil parameters [START_REF] Sutton | The European Nitrogen Assessment -Sources, Effects and Policy Perspectives[END_REF].

The usual time profiles for ammonia currently available are not accurate enough to catch the real instantaneous emission when a parameter (temperature, soil humidity and spreading practices) largely deviates from its average value. Moreover, if annual quantities are not available for the studied year, the closest documented year is chosen assuming that inter-annual variations of emissions are small. In order to better estimate large ammonia emissions during specific meteorological conditions, lately developed emission models allow to better account for ammonia emissions and the dynamical approach used in [START_REF] Skjoth | Implementing a dynamical ammonia parametrization in the large scale air pollution model ACPDE[END_REF] shows improved results in retrieving ammonia concentrations. [START_REF] Beuning | Improved temporal resolution in process-based modelling of agricultural soil ammonia emissions[END_REF] have developed ammonia emission models able to predict ammonia emissions peaks in Canada. In Europe, an inter-comparison exercise shows that ammonia emission models provided similar emission factors [START_REF] Reidy | Comparison of models used for national agricultural ammonia emission inventories in Europe: Liquid manure systems[END_REF]. Therefore, a dynamical approach for the treatment of ammonia emissions needs to be implemented in chemistry transport models to obtain better model predictions of high particulate matter episodes.

However, ammonia in urban areas could be mainly emitted by other kinds of sources. As observed in Barcelona in summertime by [START_REF] Reche | Urban NH3 levels and sources in a Mediterranean environment[END_REF], values were significantly higher at urban background than at traffic sites, probably indicating the impact of emissions from biological sources, such as humans, sewage systems and garbage containers. Thus, the volatilization of NH 3 from the aerosol phase seems to be significant enough during summer to dominate over NH 3 emissions from traffic. Conversely, in winter levels were higher at traffic sites, suggesting a contribution from vehicle emissions. Indeed, NH 3 levels decreased by 55% with increasing distance (50 m) to the direct emissions from traffic. A significant correlation between NH 3 concentrations averaged for the different districts of the city and the number of waste containers per hour and m 2 was also obtained, highlighting the necessity for controlling and reducing the emissions from garbage collection systems.

In our work at INERIS, Hamaoui-Lagel et al. (2012) developed a dynamical emission module. This module accounts for soil moisture, temperature, wind speed and agricultural practices. The spatial distribution of ammonia emissions is rather different in spring. The ammonia emissions tend to be overestimated in Brittany compared to those computed in a "dynamical way" that attribute more emissions by crops (Figure 14). This adopted methodology is not fully operational in CHIMERE, the next improvement will be to work on a simple way to allocate the maximum of emission occurring in early spring and partly responsible for important PM episodes. This work has to be done at the European level by crossing and harmonizing data on agricultural practices and meteorology.

3 Improvement of the resolution at the European scale

The EC4MACS project

The CHIMERE chemistry transport model is used in the frame of EC4MACS1 (EU LIFE project) to add the urban dimension in the integrated assessment model GAINS 2 . Currently, GAINS is fed by source matrix receptors computed with coarse model outputs from the EMEP chemistry transport model. The former City-Delta project brought together the 17 major European urban and regional scale atmospheric dispersion models (Thunis et al., 2007) and developed a generalized methodology to describe the increments in PM 2.5 concentrations in urban background air that originate -on top of the long range transport component -from local emission sources. These relationships associate the difference in the annual mean PM 2.5 concentrations between an urban area and the average concentrations calculated over the 50 km x 50 km grid cell surrounding the city with spatial variations in emission densities of low-level sources and city-specific meteorological and topographic factors. This relation is currently in use in GAINS [START_REF] Amann | Cost-effective control of air quality and greenhouse gases in Europe , Modeling and policy applications[END_REF].

The objective of the EC4MACS methodology is to add an innovative downscaling methodology in GAINS to improve the assessment of emission reduction strategies for air quality standards compliance (Figure 15). An in-depth analysis of the AirBase3 data recorded in 2009, has been realised to establish simple relationships between yearly averages and the number of exceedances of the threshold values. The following conclusions can be drawn:

 For NO 2 : if the yearly concentrations remain under the yearly threshold value (40 µg/m 3 ), the hourly exceedance standards will also be respected.  For PM 10 : a linear relationship comes out between the number of daily exceedances of the regulatory threshold value (50 µg/m 3 ) and the annual mean concentrations (Figure 16). 2014b). The goal of these two studies are to implement a urban increment methodology in an operational way in GAINS by the use of regional CTMs with observational data to fill the gap of the knowledge related to local and processes. In doing so, we are able to evaluate the impact of emission projections from the regional to the local scales (traffic stations).

Improvement of emissions

I have directly worked on the emission pre-processor of CHIMERE by introducing new proxys to downscale the emissions. The emission inventories have various spatial resolutions that may be inconsistent with the model resolution grid. Generally, available emissions are distributed over coarse grids, which have to be refined. Therefore the first step to build up emission inventories well-suited for modelling consists in re-gridding the anthropogenic emissions available at coarse resolution onto the high resolved target grid using adequate proxies. Databases of proxies are usually freely available on web sites. Some commonly used examples are:

-USGS4 : Land use data at 1 km 2 resolution (global) -GLCF 5: Land use data at 1 km 2 resolution (global) -GLOBCOVER6 : Land use data at 300x300 m 2 resolution (global) -CORINE 7: Land use data at 300x300 m 2 resolution (Europe) -GRUMP 8: Population data 1x1 km 2 resolution (global) -EEA database9 : Population data 100x100m 2 resolution (Europe) -EPER 10: Large Source Point database (Europe)

For the land use data, usually ten to thirty (or more) categories can be identified and can be used to re-grid the emissions. A minimum of 1x1 km 2 resolution assures that the spatial scale of emissions is addressed with accuracy. As a first approach, an activity sector is associated to a land use category or a proxy (Table 4).

i) Development of the emission inventory for the reference year 2007

The first step in the emission preparation was to calculate the spatial pattern of emissions for the reference year 2007, that was selected because it was a key year for the TNO-MACC inventory [START_REF] Kuenen | MACC European emission inventory 2003-2007[END_REF]. The anthropogenic emission input was harmonized. The total emissions were then scaled to year 2009. Models used their own speciation factors for NOx, SOx and NMVOC emissions.

ii) Gridding

The gridded distribution of anthropogenic emissions was provided by INERIS and it was based on a merging of databases from:

 TNO 0.125°×0.0625° emissions for 2007 from MACC [START_REF] Kuenen | MACC European emission inventory 2003-2007[END_REF]  EMEP 0.5°×0.5° emission inventory for 2009 [START_REF] Vestreng | Twenty-five years of continuous sulphur dioxide emission reduction in Europe[END_REF]  Emission data from the GAINS database  INERIS expertise on re-gridding with various proxies (population 11 , landuse 12 , Large Point Source (LPS) data)

The TNO-MACC dataset provides two distinct datasets (i) large point sources (LPS) with the coordinates of stacks and (ii) surface emissions on a fine grid (0.125°×0.0625°). In the gridding process, the first step consisted in summing up LPS emissions from the TNO-MACC emissions inventory for 2007 with surface emissions to obtain only one type of emissions as in the EMEP inventory. For the various activity sectors (SNAP 13 ) the processing steps were the following:

 The emissions were scaled with 2009 annual totals as reported on the EMEP web site  SNAP 2: The country emissions were re-gridded with coefficients based on population density and French bottom-up data, the methodology [START_REF] Terrenoire | High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE[END_REF] was extrapolated to the whole Europe. For PM 2.5 emissions, the annual EMEP totals were kept except for the countries CZ, BA,BE, BY, ES, FR, HR, IE, LT, LU, MD, MK, NL, CS, TR. For these countries, PM 2.5 emissions from GAINS were used. Additional factors were applied on two Polish regions (×4 or ×8) for PM 2.5 and PM 10 emissions (Personal communication from IIASA 14 ). The former activity in coal mine regions still leads to high emissions of PM due to domestic uses of coal.

 SNAP 3,7,8,9,10: TNO-MACC emissions spatial distribution was used as proxy to regrid EMEP 0.5°x0.5° annual totals into the finer modelling grid. 11 The population data come from EEA database (http://www.eea.europa.eu/data-and-maps/data/populationdensity-disaggregated-with-corine-land-cover-2000-2) merged with global data (from SEDAC http://sedac.ciesin.columbia.edu ) to fill gaps in Europe. 12 US Geophysical Survey landuse data are used at 1 km resolution (http://www.usgs.gov/) 13 SNAP : Selected Nomenclature for Air Pollutants: (1) Public Power stations, (2) Comm./inst. Combustion, (3) Industrial combustion, (4) Production processes, (5) Extraction fossil fuel, (6) Solvents, (7) Road traffic, (8) Other mobile sources (trains, shipping, aircrafts, ...), ( 9) Waste treatment, (10) Agriculture, (11) Natural. 14 International Institute for Applied Systems Analysis 39/108  SNAP 1,4,5,6: EMEP 0.5°x0.5° emissions were regridded by adequate proxies ("artificial landuse", EPER 15 data for industries).

For countries where TNO-MACC emissions were not available, the EMEP 0.5°×0.5° emissions were used (Iceland, Liechtenstein, Malta and Asian countries) and regridded with adequate proxies ("artificial landuse", EPER data for industries).

The spatial pattern of PM 2.5 emission from residential emissions is presented in Figure 17.

Residential emissions are dominant in wintertime in most of countries; they come from wood burning or coal uses. Germany, Sweden, Spain clearly have the lowest levels of emissions. Romania, Poland and France have the highest levels of emissions.

Urban meteorology

In dispersion models at the urban scale, the lower part of the boundary layer is often represented by parameterizations derived from the theory of similarity of the surface layer 16 . The urban effects are then considered by changes in surface roughness and heat 15 The EPER Decision is based on Article 15(3) of Council Directive 96/61/EC concerning integrated pollution prevention and control. EPER is a web-based register, which enables the public to view data on emissions to water and air of 50 key pollutants from large and medium-sized industrial point sources in the European Union.

The register is hosted by the European Environment Agency. http://www.eea.europa.eu/data-andmaps/data/eper-the-european-pollutant-emission-register-4

16

Similarity theory starts with the identification of the relevant physical parameters that characterize atmospheric processes; then dimensionless groups are formed from these parameters, and finally experimental flux. Nevertheless, these formulations should only be used in the inertial sub-layer (ISL) which is well above the tops of buildings (Figure 18), i.e. in an average height of several tens meters. Indeed, in the sub-rough layer (RSL), i.e. the layer in the immediate vicinity of the urban canopy elements, the flow has a rather complex structure [START_REF] Raupach | A Wind-Tunnel Study of Turbulent Flow Close to Regularly Arrayed Roughness elements[END_REF] and the similarity theory cannot be applied. This first layer of CTM used at urban scales may be under two times the average of the buildings height in big cities. It can be assumed that the corresponding Reynolds stress at this level has not yet reached its maximum value and the corresponding value of vertical diffusion coefficient, named Kz is overestimated even if we can also assume that Kz might be underestimated at the second level. A limit of the Kz methods that are using the theory of similarity is identified here.

To improve models at low levels over urban area, CTM modellers have to learn from what already exists in urban meteorology modelling: TEB (Town Balance Energy), SM2-U (Soil Model for Sub-Meso scale Urbanised version), MOSES (UM Surface Exchange scheme), BEP (Building Effect parameterization, AHF+R+A (Anthropogenic heat flux), MRF-Urb (Medium-Range Forecast Urban scheme), PALM-Urb (Parallelized Atmospheric Large-eddy Model). A summary of references to respective modules, modelling results, and discussions can be found in Mahura and Baklanov (2010). Several studies deal with the so-called "urbanization" of meso-scale meteorological models (e.g., [START_REF] Martilli | An urban surface exchange parameterisation for mesoscale models[END_REF][START_REF] Dupont | Simulation of meteorological fields within and above urban and rural canopies with a mesoscale model (MM5)[END_REF][START_REF] Lee | A vegetated urban canopy model for meteorological and environmental modelling[END_REF][START_REF] Solazzo | Coupling Mesoscale Modelling with a Simple Urban Model: The Lisbon Case Study[END_REF].Those models are based on a comprehensive set of equations linking the synoptic to the street scale circulation. Further, they include parameterisations for the turbulent exchange processes of momentum and mass. [START_REF] Solazzo | Coupling Mesoscale Modelling with a Simple Urban Model: The Lisbon Case Study[END_REF] proposed a simple scheme for estimating spatially-averaged mean wind speed and the urban heat island over data is used to find functional relations between dimensionless groups. Those functional relationships are used as simplified model parametrisations a selected neighbourhood area in Lisbon, Portugal. The results show the capability of the urban model to provide more accurate mean wind and temperature profiles. Moreover, the urban model has the advantage of being cost effective, as it requires small computational resources.

For the EC4MACS project I have decided to add an attenuation coefficient for the wind speed and the Kz coefficient within the urban canopy layer. According to the previous references a coefficient of 0.5 was applied to the first CHIMERE layer to avoid a too fast diffusion of emitted urban pollutants.

Impact of the resolution on model performances

The CHIMERE model was used to simulate the transport and transformation of trace species. The model is being used by several international teams for research and environmental policy underpinning. It has been involved in many model intercomparison initiatives and is now part of the European pre-operational air composition forecasting system MACC. As an offline CTM, CHIMERE requires prescribed meteorological fields which were provided here by ECMWF (IFS model at 16km resolution). The resolution of the meteorological model does not match that of the CTM, but performing a continental scale high-resolution meteorological simulation is a challenge in itself that was ruled out of the present initiative. Pan-European high-resolution air pollutant emission fluxes are computed using a top-down approach (a downscaling technique to disaggregate coarse inventories using proxies such as point source location and population density map) to cope with the lack of input data to propose a bottom-up approach at the continental scale. Two simulations at 50 km and about 8 km resolutions were performed over Europe for the full year 2009. A resolution of 0.03125 x 0.015625 degrees (i.e. about 2km at the centre of the domain) of longitude and latitude, respectively, was chosen for the highest resolution; this high resolution simulation is performed over a 10 day period in winter 2009 (Colette et al., 2014). On average there is a clear improvement of the modelled NO 2 and PM 10 between the 50km and 8km resolution respectively in term of bias for the whole year 2009 (Figure 19). The shaded area indicates a minimum level of quality a model should fulfil for a given application; this "model quality objective" is based on the measurement uncertainty [START_REF] Thunis | Performance criteria to evaluate air quality modeling applica-tions[END_REF]. The maps in Figure 20 provide snapshots of the simulations of nitrogen dioxide at about 50 and 2 km of resolution. While the coarse run captures well the main emission hotspots, local maxima are smeared out because of the numerical diffusion of the Eulerian CTM. The improvement brought about by the increased resolution is assessed by comparison with observed NO 2 and PM 10 concentrations across Europe as reported in the AIRBASE repository (Table 5). We find that increasing the resolution improves significantly the root mean square error (RMSE), especially at urban sites. The difference between the RMSE at urban, suburban and rural stations decreases with the resolution showing that we resolve better spatial gradients. This feature is more sensitive for NO 2 than PM 10 because of the lower spatial variability of the later. Table 5 somehow questions the relevance of very high resolution simulations since the bulk of the improvement is achieved at 8km resolution for which the RMSE of daily mean NO 2 averaged over the whole monitoring network is 26% (10% for PM 10 ) lower than the 50km simulation. The 2km resolution adds only another 5% (3% for PM 10 ) to achieve an average RMSE 32% (13% for PM 10 ) lower than the coarse configuration. In addition, the improvement in the spatial correlation is only seen at rural sites (and suburban sites for PM 10 ). These findings illustrate the need to develop more sophisticated emission downscaling algorithms for such high resolution scales.

Impact of the vertical resolution

Three model configurations are used: (i) the CHIMERE mesh in the default set-up (C8: 8 levels from 995 to 500 hPa), (ii) a mesh refined along the whole vertical axis (C20: 20 levels from 995 to 500 hPa) and (iii) a mesh with a refinement near the surface (C9: 9 levels from 999 to 500 hPa). The heights of the first level are respectively 40m, 40m and 8m respectively for C8, C20 and C9 simulations. The set-up is presented in Menut et al.

(2013c), the domain covers the Paris basin with a resolution of 5 km. For ozone, concentrations usually increase with altitude. This leads to lower values near the surface with C9. The C20 concentrations are lower between 40 m and 1500 m, but higher up to 1500 m (Figure 21). The same impact is observed on the January profile, but with an inversion of differences around 700 m. This feature reflects the impact of a refined vertical transport, between the boundary layer and the free troposphere. Over Paris there is a decrease of ozone concentrations due to the titration effect between 40m and 8m for the C9 simulation. An increase is observed for NO 2 concentrations because of emissions, helping to get better urban increments over urban areas (not shown here).

This study was conducted over a highly urbanised area, known to be poorly represented in terms of sub-grid scale dynamical processes. While the first layer of C8 and C20 (40 m) is more related to the buildings top heights, C9 (8 m) is representative of the urban canopy layer and this should be expressed in the vertical mixing representation.

Discussion

Achieving a very high horizontal resolution simulation demonstrates the robustness of the selected air quality model. We discussed the added value in terms of air pollution modelling and decision support. The comparison with in-situ observations shows that model biases are significantly improved despite some spurious added spatial variability attributed to shortcomings in the emission downscaling process and coarse resolution of the meteorological fields. In Siour et al. (2013), we also have investigated the possibility to use non regular longitude/latitude grid by refining the mesh over a specific area. CHIMERE authorises this kind of grid meshes but the time step is adaptated to the smallest grid resolution and therefore the computation time is very costly.

Regarding the vertical resolution, the relevance of adding a point close to the surface has been shown and this corresponds to modify the 'urban increment' roughly defined as the difference between the city and background concentrations. This expected change is due to the combined effects of (i) higher deposition rates in remote areas (over vegetative covers) and (ii) higher concentrations of primary pollutants emitted within the urban canopy (or lower for ozone) over cities.

The proposed methodology needs to be largely improved by testing the current urban schemes in WRF and certainly by implementing new subgrid parameterisation for the urban canopy [START_REF] Mauree | Development of a multi-scale meteorological system to improve urban climate modelling[END_REF]. Two options can be taken :

we can lower the first CHIMERE layer down to 10 m consistently with the WRF simulation we can diagnosed a urban impact in the pre-treatment of the meteorology in CHIMERE by embedding subgrid schemes within CHIMERE.

Multi model analysis for model improvement

Intercomparison exercises -focus on EURODELTA

In the frame of my activities I take part to the on-going project EURODELTA since 2001. I also contributed and I was very active in the POMI (Pernigotti et ). The Task Force on Measurements and Modelling was established in 2000 to offer a forum to the Parties, the EMEP centres and other international organizations for scientific discussions to evaluate measurements and modelling and to further develop working methods and tools. In that context, the Gothenburg Protocol signed in 1999 is a multi-pollutant protocol of the Convention designed to reduce acidification, eutrophication and ground-level ozone by setting emissions ceilings for sulphur dioxide, nitrogen oxides, volatile organic compounds, fine particulate matter and ammonia.

In 2004, EDI examined the common performance of the models in predicting recent (2000) and future (2020) air quality in Europe using the concept of a model ensemble to measure robustness of predictions. The spread of predictions about the ensemble gave a measure of uncertainty for each predicted value. In a 2020 world the effect of making emission reductions for key pollutants of NOx, SO 2 , VOC and NH3 independently in France, Germany and Italy, and of NOx and SOx in sea areas, was investigated. Source-receptor relationships used in integrated assessment (IA) modelling were derived for all the models and compared to assess how model choice might affect this key input.

ED II built on this project by taking a closer look at how the different models represent the effect on pollutant impacts on a European scale of applying emission reductions to individual emission sectors (Thunis et al., 2008).

In the recent literature, several intercomparison and evaluation exercises are reported for PM models : Differently to the previous intercomparison exercises, almost all models have now been run with the same input data (emissions, meteorology, boundary conditions) and over the same domain (domain extension and resolution). Moreover, vertical profiles, depositions and meteorology were included in the evaluation to better understand the behaviour of models.

To conduct a retrospective analysis of air quality over the policy horizon of the Convention on Long Range Transport 1990 will be takes as the starting point. This was the reference year for the first accounting of a multi-pollutant, multi-effect approach to European Air Quality assessment. Over this time our ability to model (both transport and effects) has increased enormously. A retrospective view will illuminate the progress made in pollution levels using a multi model approach with state-of-the-art models. 1999 will be taken as an intermediary year (signature of the Gothenburg protocol), and 2008 will be a recent year. This retrospective analysis over three years will set the bases of a full trend analysis of the pollution over the last two decades (1) to assess the ability of models to reproduce such a large decrease of pollutant emissions and ( 2) to evaluate the impact on air quality of emission reduction strategies in force for twenty years. The chemistry transport models involved in this exercise are CHIMERE, CAMx, CMAQ, EMEP, LOTOS-EUROS and MINNI run by different European teams.

Model variability in EURODELTA III

One of the first outcomes of the EURODELTA project (Bessagnet et al., 2014a) is the high diversity of model responses with the use of common input data (meteorology, boundary conditions and emissions) and settings (resolution and domains). For instance, Figure 22 shows the mean gridded coefficients of variation for O 3 and PM 2.5 concentrations for five models in the 2006 summertime campaign. The coefficient of variation is computed as the standard deviation of model concentrations divided by the mean values of model outputs. This coefficient is usually under 10% for ozone concentrations with some higher values over the mountains, along the ship tracks. For PM 2.5 the coefficient is much more important often in the range 20 -40%. The main differences are observed over the seas mainly due to sea salts and in the south of the domain because of dust emissions. Also in the north east of Europe some differences appear due to biogenic VOC emissions that are very different from models to models. Even if the dispersion is lower for Ozone we observe for the ozone concentration during the 2009 campaign that a part of the mean bias is due to the boundary conditions. As pointed out from the statistics in Valentia Observatory (IE01) on average the MACC analysis (used as boundary conditions) has a very negative bias (about 10 to 20 µg m -3 ). Dust concentrations at this station are also very sensitive to boundary conditions and clearly an underestimate is observed for all models (Figure 23). As we will see further in this report Ozone and Dust are the two main species at the boundaries influencing regional simulations. Figure 24 represents the average concentrations of free ammonia (F-NH X ). The free ammonia is defined as the difference in molar concentrations of the total ammonium and sulphate concentrations as follows: -

. The free ammonia is the amount of ammonia available for ammonium nitrate formation, after all sulphate is neutralized. To complete the Secondary Inorganic Aerosol (SIA) analysis the G ratio is used [START_REF] Pay | Assessing sensitivity regimes of secondary inorganic aerosol formation in Europe with the CALIOPE-EU modelling system[END_REF]. This ratio indicates whether fine-particle nitrate formation is limited by the availability of HNO 3 or NH 3 . All the terms in the following equation are expressed on a molar basis:

-:

-G ratio > 1 indicates that nitric acid is limiting, -G ratio < 0 indicates the absence of free ammonia, -G ratio between 0 and 1 indicates that ammonia is available for reaction with nitric acid, but ammonia is the limiting species.

The charts of G ratio in Figure 24 show that the models simulate an overall limitation of ammonium nitrate formation by the nitric acid, with severe limitations occurring in ammonia hot spot emission areas, like the Netherlands. The G ratio is very different from country to country for a given model. This will have a direct implication on ammonia reduction scenarios analysis with different responses expected from model to model, this point will be address in on the chapter dedicated to the scenario analysis.

CHIMERE performances in EURODELTA III

The EURODELTA exercise was a very good opportunity to compare CHIMERE with other models often used in the policy making process on air quality. In the following, I give some tendencies regarding the CHIMERE performances for the 2009 campaign (a winter time period).

Ozone and nitrogen dioxide

The models (with some exceptions) are found to underestimate observed ozone concentrations. CHIMERE displays high ozone concentration particularly over water surfaces but still below the observations. This negative bias is partly explained by a systematic underestimation of the ozone provided by the boundary conditions, provided by MACC reanalysis fields. The spatial correlations are rather different among the models (0.59 for CHIMERE) between 0.39 and 0.67 for all models. Regarding the ozone vertical profiles, the models skills in reproducing the O 3 mixing ratio within the PBL are in the range of values obtained in other studies, showing a relative bias of 3%, RMSE of 17% and correlation of 0.75. The evaluation of MACC analysis data shows a better agreement with measurement in the middle than in the lower troposphere. In the troposphere the reanalysis shows bias of -5% to 10% with respect to O 3 sondes. In short, the regional models perform better than the global MACC analysis within the planetary boundary layer.

The performances for NO 2 concentrations are similar for all models (with five models underestimating and two overestimating the observations). For all models the correlations in time and space are close to 0.6.

PM 10 and PM 2.5

For PM 10 concentration the differences between models are significant and are partly associated to the models treatment of natural PM (dust and sea salts). The main PM hotspots in Poland and Po valley are simulated by all models. CHIMERE results are found to be closer to the PM 10 and PM 2.5 observations in term of bias than the other models (Figure 25). CHIMERE exhibits the lowest RMSE and the highest correlations for the PM 2.5 concentrations. Usually, all models underestimate the highest concentration values, CHIMERE can overestimate some low values. For most of stations the error on PM 10 is dominated by a wrong reconstruction of the temporal correlation.

Though the main features of observed PM spatial distribution are reproduced fairly well, the modelled regional gradients are smaller than the measured ones. For this campaign, most models tend to underestimate the PM concentrations except CHIMERE and another model which show a slight overestimation somehow counterbalanced by an overestimation of some chemical compounds.

Deposition

Regarding wet deposition most of the models show a better agreement with observations at sites with relatively low and medium measured accumulated wet deposition values. For NHx wet deposition most of the models underestimate the highest observed values. In the case of NOx wet deposition there is a general tendency to underpredict the highest accumulated wet depositions, with some differences between the models: CHIMERE calculate higher values than the other models for this period, being closer to observations. In the case of SOx wet deposition, only one model is providing the best performance for the highest observed values, most of them registered in the Atlantic area. This fact seems to be related to the natural sulphate emissions associated to sea salt processes that this model considers (note that measurements of sulphate in precipitations used here include for sea salt sulphate). Unfortunately, the uneven distribution of monitoring sites makes it difficult to decide on model performance in some areas of Europe characterized by strong differences among the models.

Strong differences are found for NOx dry deposition between this model and the rest, with CHIMERE in the group of models presenting the lowest values. As for NHx dry deposition, differences between models are not as strong as in case of SOx and NOx dry deposition, with all the models presenting a quite similar spatial pattern. In the light of the obtained results, the importance of having any kind of methodology to evaluate dry deposition is clear, especially for SOx and NOx.

Sulphur compounds

On average CHIMERE present the lowest bias for SO 2 concentrations with two other models.

The correlation coefficients between calculated and measured SO 2 are quite similar among the models. CHIMERE exhibits the highest overestimation of sulphate concentrations. The results from all models have to some extent been affected by overestimated MACC Analysis boundary conditions at the eastern boarders. Even if the sulphur chemistry is well known, the co-analysis of sulphur depositions and concentrations suggest that some improvements can be done yet on this topic, which has a direct influence on SIA chemistry.

Nitrate and ammonium

Nitrate and ammonium are the species showing the best performance in this exercise; the main regional footprint is coherent between models with some discrepancies over the Benelux and the Po valley. For nitrates, CHIMERE simulates low values mainly in the south of Europe. For the total nitrate, there is a clear difference between one group of models, which strongly overestimate the concentrations and the other group, including CHIMERE which slightly underestimates the total nitrate concentrations. In terms of root mean square error (RMSE) CHIMERE provide the best performances (Figure 26) except for the summertime campaign in 2006 (missing coarse nitrate formation).

CHIMERE with two other models underestimate the highest values of nitrate concentrations. The models behaviour for nitrates shows a clear difference between two groups of models which underestimate the high nitrate concentrations (-30 to -40%) whereas the other models slightly overestimate the observed values.

Carbonaceous species

Elemental carbon concentrations in the PM10 fraction concentrations are on average overestimated by the models, with biases up to 50-60% for CHIMERE. A first comparison against Oxydized Organic Aerosol (OOA) measurements tends to support the fact that models underestimate SOA concentrations. CHIMERE exhibits the lowest underestimations particularly in ES78 sites (Spain) but with a low correlation. In Melpitz (Germany) and Vavihill (Sweden) sites the amplitude of OOA peaks are not reproduced by the models with SOA. At this stage, it is difficult to say more on this comparison because a part of OOA can have a primary origin. Indeed, the observed OOA and Biomass Burning Organic Aerosol (BBOA) concentrations peaks seem correlated. The implementation of more sophisticated modules including more knowledge of processes and a work on the harmonization of POA emissions are pressing issues. Finally, all models underestimate the observed peak of organic matter by a factor from 2 to 6.

Natural aerosols

All the models simulate high concentrations of sea salt sodium over the oceans and seas with some differences because of the parameterisation. On average the spatial correlation is very good in the range 0.90 -0.95 for all models. CHIMERE simulate the highest concentrations over lands that are clearly overestimated with respect to the observations.

The correlations in time and space for dust concentrations in the PM 10 fraction are close to zero for all models, the spatial correlations are better for three models including an advanced dust parameterisation. This large difference in the computed correlation between these three models with respect to the other models shows that the dust parameterisations used in former group were rather suitable in reproducing dust events during the considered period.

Although most of inputs were harmonized, we can clearly observe a rather large variability of model outputs and related performance statistics for all modelled species. A different implementation of chemical and physical processes into the models as well as preprocessing of input data can explain this variability. For instance, the comparison of meteorological data actually used by the models displays differences on diagnosed variable like the PBL. The same can happen even with directly used data like wind speed or temperature because of some differences in interpolation processes. For the wind speed some discrepancies between model and observations can be observed even on native IFS meteorological data. The comparison of modelled concentrations against wind speed and PBL heights confirmed that meteorology strongly influences CTMs performances.

Particularly the temporal evolution of wind speed is most responsible of model skilfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of episodes). The reconstruction of the PBL diurnal cycle seems more influential in driving the corresponding diurnal profile of pollutants and hence the presence of systematic positive and negative bias detectable on daily basis.

Scenario analysis

Evaluation of Source Receptor matrix

A part of the work on CHIMERE at INERIS aims at evaluating the sensitivity of the EMEP model to emission cuts. The work performed in the EURODELTA project (phase I) and CITYDELTA (Thunis et al., 2007) was to compare the sensitivity of CTMs to emission reductions at background and urban scales.

A key product of the MSC-W modelling work within EMEP is the source-receptor (SR) matrices or the so-called "blame-matrices" which estimate the contribution of the emissions in any country to the depositions or air concentrations of main pollutants, ground level ozone and PM in any other country. The Integrated Assessment (IA) process uses a source-receptor relationship approach to relate emissions to their environmental and health impacts. The SRs provide a country to grid mapping whereby the change in a national emission results in a calculated change in concentration and deposition at every grid square (50 x 50 km) in the model domain. The impact end-points for each square can then be calculated using indicators of risk to ecosystems and health developed by the environmental effects and health communities. The relationships (SR) presently used in Integrated Assessment are developed from a set of scenario calculations made with the EMEP air quality model. The model uses an emission inventory that is geographically accurate at model scale and so the distribution of base-case emissions is well represented as are the base impacts. The SR are developed by changing the national emissions from the base case and calculating the response in air concentrations and depositions. The change is distributed over all the emission sources in proportion to their contribution and so the geographic distribution of sources is assumed invariant.

In EURODELTA phase II (Thunis et al., 2008(Thunis et al., -2010)), I have contributed with CHIMERE simulations to the evaluation of emission reductions based on sectoral approaches. As described above, it is highly unlikely that the cost optimisation approach used in the IA process would distribute emission reductions in such an even manner across sectors. It is much more likely that the sectoral burdens will be different. This would mean that, in a future lower emission world, the geographic pattern of emissions would change. Furthermore, because proximity of sources to the receptors is important, the positioning of sources relative to the most sensitive receptors may be important in the optimisation process. For example sectors with emissions that closely follow the population distribution may contribute more to impacts involving human health than sectors which do not.

EUDORELTA II therefore set out to take a first look at whether there are differences in the size of effect of emission reductions if they are applied to single sectors compared with all sectors (scenario 'ALL').

Regarding particulate matter concentrations we can draw some conclusions:

-All the models agree that there are differences in effectiveness of emission reductions between sectors. This is broadly consistent with a physical interpretation that the more effective reductions are for sectors where proximity of the source of emission to people is greatest. Thus, higher effectiveness is seen from sectors emitting at low level and distributed according to population and lower effectiveness is seen for sectors emitting from large point sources. Point sources are fewer in number, emissions are released from great height (taking plume rise into account) and generally the association with populated areas is less. Quantitatively, effectiveness depends on the distance from the source as concentrations decrease with distance. Assimilation of results into SR's that reflect the influence of changes in A on B also reduces effectiveness as the geographic area of B increases and includes more distant lands.

-The differences between sectors is greater for population weighted compared with non-weighted concentrations.

-The above is true whether the impact is assessed EU wide or in the country in which the emissions control takes place.

-All models show that the 'ALL' scenario gives a significantly different effectiveness to the sectoral effectiveness and this applies to all the pollutants contributing to PM 2.5 concentrations (NOx, SOx, PPM 2.5 ).

-The sectoral response is not the same in all countries and is different for each pollutant and in particular the potency of ammonia emissions as they affect PM 2.5 is much larger (by a factor of two) in the UK than for other countries.

As an example, the effect of NOx reductions on PM 2.5 concentrations is shown in Figure 27 (population weighted case). The reduction is mainly achieved through the nitrate contribution. The third bar in each chart is now the effect of a change in the transport sector (SNAP 7). There is again a difference in overall response between countries with emission changes in Germany having the greatest transboundary effect on PM 2.5 concentrations. The models are consistent with sectoral trends in all countries. Sector 1 and sector 3 have lower response than the "all" scenario and sector 7 (road traffic emissions) has a greater response. The population weighted responses are overall greater than the non-weighted responses and the differential between sector 7 and sector 1 increases when population is accounted for. For the Dutch contribution, it is quite different: the influence seems most important in the South part of France near Lyon. Ammonia from The Netherlands is transported in these regions and can react with nitric acid locally produced, to form ammonium nitrate. Germany seems to have the most significant impact over the northeast and southwest of France, while Italy emissions have solely an influence in the very south eastern part of France. In Figure 28, the temporal variation of the delta for each country at Gennevilliers is presented. For the first episode (February 21 st ), only Germany contributes significantly to PM 10 concentrations near Paris (up to µg m -3 ) until the 20 February. Belgium and The Netherlands have a weak influence. The influence of Italy is important on 21 February (up to 10 µg m -3 ). For the second pollution episode (March 21 st ), Germany, The Netherlands and Belgium impact significantly (between 6 and 20 µg m -3 ), and as for the first episode, Italy brings a contribution the days after. Generally, Italy has a specific effect compared to other countries. Its geographic location involves a timeshifted effect when the northeast flux turns southeast over Paris. As discussed previously, Figure 28 confirms the local origin of the last event on March 26 th , with only few µg m -3 of PM concentrations due to surrounding countries on 24-29 March. Removing all French anthropogenic emissions allows to assess the influence of French emissions on PM concentrations observed in the rest of Europe. During the studied episodes, France has only an influence on Spain. But at the end of the episodes, when the northeast flux veers southeast and west, France has successively a significant influence on PM concentrations in Great Britain, Belgium, Netherlands, Germany and up to Eastern Europe to a lesser extent. In this paper we clearly highlight the role of ammonia emissions and the transboundary pollution from a country to another country. The particular role of ammonia will be assessed in the following sections.

Emission scenarios at the French scale

I have studied several scenarios for the French authorities to investigate the role of ammonia and nitrogen dioxide emissions. Some simulations with CHIMERE were performed for the period of January to April 2007 to study the impact of reduction of ammonia and nitrogen dioxide on concentrations of PM 10 particles. We investigated the impact of these two species because they are responsible for the formation of ammonium nitrate that is responsible for 40 to 50% of PM peaks in winter and early spring. Figure 30 shows the spatial patterns of ammonia emission reduction in France only versus Europe emission reductions. We can see that in the north of France the impact of reducing over the whole Europe enhances the reduction by a factor 1.5 -2 compared to a reduction only over France. This clearly shows the added value of a European view on emission reductions and confirms the transboundary effect of PM pollution. Two additional scenarios on ammonia emission reduction were compared over France:

-Scenario A: ammonia emission reductions (-30%) on the agricultural sector are applied homogenously over France -Scenario B: ammonia emission reductions (-30%) on the agricultural sector are applied with a regional weight proportional to the regional ammonia emissions,

This comparison aims at understanding if a more important effort in France for the most important emitting regions (Brittany and agricultural regions) has a positive impact on the abatement on PM concentrations. The result in Figure 31 show that PM concentrations decrease more on the west part of France on average for scenario B but less on the Eastern and Northern France. Unfortunately most of the exeedances of PM limit values in France are located in the North East part of France. One of the reasons of such behaviour is that during PM episodes the Brittany ammonia emissions are rejected over the sea because these PM outbreaks occur under specific meteorological conditions with easterly winds.

The role of ammonia emissions at the European level

In Bessagnet et al. (2014b) we have studied the impact of ammonia emissions on PM limit values in Europe. Three air quality models were used to assess the ammonia emission reduction scenarios on PM 10 and PM 2.5 air quality limit values in Europe. In term of SIA chemical regime, even if nitric acid is known to be the limiting component on average in Europe, most of scenario modelling studies have shown a large impact of ammonia emission reductions on PM concentrations and their exceedances.

In 2009, AirBase data show that 612 monitoring stations exceeded the PM 10 daily limit value of 50 µg m -3 more than 35 days. According to the models, the number of remaining stations exceeding the PM 10 daily standards after the implementation of the Gothenburg protocol in 2020 (2020GOT) is 481, 486 and 536 respectively for CHIMERE, EMEP and LOTOS-EUROS. This indicates that the Gothenburg protocol alone may contribute to a 12 to 21% reduction in the number of stations showing exceedances of the PM 10 daily limit values and further measures must be considered to reduce PM concentrations all across Europe. More ambitious measures are needed at the EU level in addition with special plans that are required to treat the remaining hot spots in Europe. A 30% reduction of ammonia emissions in addition to the Gothenburg protocol scenario (2020G30) enhances the reduction of the number of exceeding station. From the Gothenburg Protocol scenario, this reduction reaches 40, 40 and 23 stations respectively for CHIMERE, EMEP and LOTOS-EUROS models.

Most of those stations no longer exceeding the daily limit values are urban, suburban or traffic stations: 30, 31 and 21 stations respectively for CHIMERE, EMEP and LOTOS-EUROS models. An analysis carried out on scenario impacts shows that the model's response on daily exceedances might be underestimated because models tend to underestimate the high concentrations of PM components and particularly the nitrate and ammonium concentrations.

LOTOS-EUROS gives a lower decrease of PM concentrations whatever the type of scenario applied. The reasons are difficult to explain because for the simulations performed here, all models ran with different boundary conditions and the parameterizations of processes are different. Model evaluation points out a large underestimation of LOTOS-EUROS on the sulphate concentrations. In addition, LOTOS-EUROS uses a compensation point for the calculation of ammonia emission fluxes, resulting in a different estimate of average NH 3 and ammonium concentrations. This implies a different chemical regime that could explain a lower sensitivity to ammonia emission reductions for LOTOS-EUROS. The most important reduction of the number of stations in exceedance is expected in Italy and Poland because the initial number of exceeding stations is important, which is high at these countries. The Benelux, West of Germany and North of Italy exhibit the highest concentration decreases expected due to ammonia emission reductions.

The simulated reductions of annual mean PM 2.5 concentration, due to a further reduction of 30% NH 3 emissions from agriculture compared to the Gothenburg scenario, indicate that the reduction may reach 8% (CHIMERE) to 10% (EMEP) in some areas. The most important reductions in % are observed in the main source areas: over the south of England, north of France, Benelux, Germany, Czech Republic and Poland for CHIMERE (Figure 32) and EMEP; for LOTOS-EUROS the spatial pattern of reductions is more spread over Europe with highest reductions over Poland and Sweden for the PM 2.5 concentrations. The percentage reduction of the PM 10 annual mean concentrations over Europe are lower than for PM 2.5 , as expected, ranging from 3 to 8% over most of Europe. This study shows that the implementation of the Gothenburg protocol will be an important step towards compliance with PM limit values over Europe, but is far from assuring compliance in 2020. Further measures to reduce PM concentrations across Europe must therefore be considered. The linearity study presented in this report shows that an increase of ammonia emission reductions higher than 10% enhances the efficiency of the emission reductions on annual concentrations and exceedances of PM limit values. Why ammonia emission reduction so efficient?

As previously described, the free ammonia F-NH x is defined as the difference in molar concentrations of the total ammonium and of sulphate concentrations as follows: -. The free ammonia is the amount of ammonia available, after neutralizing sulphate, for ammonium nitrate formation mainly. To complete the analysis, the G ratio is used. This ratio indicates whether fine-particle nitrate formation is limited by the availability of HNO 3 or NH 3 . All the terms in the following equation are expressed on a molar basis:

-,  G ratio > 1 indicates that nitric acid is limiting,  G ratio < 0 indicates the ammonia is severely limiting,  G ratio between 0 and 1 indicates ammonia is available for reaction with nitric acid, but ammonia is the limiting species.

On average, G ratio > 1 for the three models over Europe, thus predicting on average a HNO 3 limited chemical regime in 2020GOT. This is consistent with [START_REF] Pay | Assessing sensitivity regimes of secondary inorganic aerosol formation in Europe with the CALIOPE-EU modelling system[END_REF] findings but under "present days" emissions. However, the ammonia emission reductions seem effective to reduce the PM 2.5 concentrations. Figure 33 shows that the probability frequency distribution of G ratio for the 2020GOT scenario for CHIMERE is rather uniform for SIA hourly concentrations larger than 1 µg m -3 , for all grid points. The median G ratio is 1.2 (hence indeed greater than unity and HNO 3 limiting) when considering all SIA concentrations larger than 1 µg m -3 but distribution shifts towards smaller value when using a cut-off at 40 µg m -3 (with a median of 0.94). This clearly indicates that larger SIA concentrations will be more affected by ammonia emission reductions. The embedded plot in Figure 33 shows the distribution of SIA decrease (in %) from 2020GOT to 2020G30 as a function of G ratio in 2020GOT for SIA concentrations larger than 40 µg m -3 . SIA variations are mainly negative up to -30% close to G ratio =1, the SIA decrease ratio drops to zero when G ratio increases. This chart explains why even if the chemical regime is HNO 3 limited on average, there is still an impact of ammonia emission reduction on SIA concentrations and exceedances of PM standards. Therefore, the ammonia emission reduction will be more efficient on average close to urbanized area where the chemical regime for SIA formation will be more often limited by ammonia. 6 Interactions Air Quality and Climate

Impact of aerosols on Meteorology

Creation of the CHIMERE aerosol optical module

The OPTSIM (OPTical properties SIMulation) software has been developed to calculate optical properties and Lidar attenuated backscattered profiles from aerosol concentrations calculated by chemistry transport models (Stromatas et al., 2012) and are characterized by elevated single-scattering albedo (SSA) (0.95-0.96 between 440 and 1020 nm). Comparisons of simulations with AERONET measurements show that aerosol physical-optical properties (size distribution, AOT, SSA) have been well simulated over Moscow in terms of intensity and/or spectral dependence. Secondly, modelled aerosol optical properties have been used as input in the radiative transfer code of WRF to evaluate their direct radiative impact. Simulations indicate a significant reduction of solar radiation at the ground (up to 80-150Wm -2 in diurnal averages over a large part of eastern Europe due to the presence of the aerosol plume. This ADRF causes an important reduction of the near-surface air temperature between 0.2 and 2.6 on a regional scale. Moscow has been affected by the aerosol plume, especially between 6 and 10 August. During this period, aerosol causes a significant reduction of surface shortwave radiation (up to 70-84Wm-2 in diurnal averages) with a moderate part (20-30 %) due to solar absorption within the aerosol layer. The resulting feedbacks lead to a cooling of the air up to 1.6°C at the surface and 0.1°C at an altitude of 1500-2000m (in diurnal averages), that contribute to stabilize the atmospheric boundary layer (ABL) (Figure 35). Indeed, a reduction of the ABL height of 13 to 65% has been simulated during daytime in presence of aerosols. This decrease is the result of a lower air entrainment as the vertical wind speed in the ABL is shown to be reduced by 5 to 80% (at midday) when the feedback of the ADRF is taken into account. However, the ADRF is shown to have a lower impact on the horizontal wind speed, suggesting that the dilution of particles would be mainly affected by the weakening of the ABL development and associated vertical entrainment. Indeed, CHIMERE simulations driven by the WRF meteorological fields including this ADRF feedback result in a large increase in the modelled near-surface PM 10 concentrations (up to 99 %). This is due to their lower vertical dilution in the ABL, which tend to reduce model biases with the ground PM 10 values observed over Moscow during this specific period.

Impacts of future air pollution mitigation strategies on the aerosol direct radiative forcing over Europe

Projections of aerosol emissions for 2030 have been recently generated and implemented in a comprehensive chemistry-transport model to analyse the future evolution of the aerosol radiative forcing over Europe. In Pere et al. (2012), numerical developments based on an off-line coupling between the regional chemistry-transport model CHIMERE (extended by an aerosol optical module) and the radiative transfer code GAME have been implemented in order to simulate the interaction of physico-chemically resolved aerosols with radiation at regional scale. This novel approach is used to examine the shortwave aerosol direct radiative forcing response to two air pollution reduction scenarios for 2030 over Europe. Our study suggests that measures introduced to improve future air quality could have large implication on the aerosol climate forcing over Europe. Results of simulations indicate that abatement of aerosols in the near future could lead to a decrease of the aerosol cooling effect at the surface and at the top of the atmosphere over the main anthropogenic emission regions. Especially over the Moscow region, different strategies of reduction for scattering sulphate and absorbing black carbon aerosols between the two scenarios could result, however, in either a reduction or an enhancement in atmospheric radiative forcing.

Impact of Climate change on PM concentrations

Impact of Meteorological drivers on PM modelling

Predicted air pollutant concentrations are very sensitive to physical variables such as wind, temperature and specific humidity and diagnosed turbulent parameters (friction velocity, boundary layer height). A direct error concerning the meteorological fields used has a more or less direct and linear impact on concentrations, whatever the model used. In recent years, the forecast systems have evolved further and they now use meso-scale models, driven by global meteorological fields. These models are more adapted to fine resolutions and use more relevant land cover data and turbulence parameterizations. In Europe, the American meso-scale meteorological models MM5 [START_REF] Grell | A description of the fifthgeneration Penn State/NCAR mesoscale model (MM5)[END_REF] and WRF [START_REF] Skamarock | A description of the Advanced Research WRF version 2[END_REF] are the most widely used because they are easy to implement and to change. For forecasts, new systems are under development, which are based on the Integrated Forecast System (IFS) operated in ECMWF 17 .

Various types of uncertainty studies on aerosol predictions have been performed, I have contributed to some of them:

 The impact of orography on meteorological parameters [START_REF] Carvalho | Influence of topography and land use on pollutants dispersion in the Atlantic coast of Iberian Peninsula[END_REF]  The impact of horizontal resolution of meteorology on model calculations [START_REF] Baertsch-Ritter | Effects of various meteorological conditions and spatial emission resolutions on the ozone concentration and ROG/NOx limitationin the Milan area (I)[END_REF][START_REF] Menut | Impact of meteorological data resolution on the forecasted ozone concentrations during the ESCOMPTE IOP 2a and 2b[END_REF])  The impact of meteorological parameters on aerosols formation such as temperature, humidity and vertical mixing [START_REF] Haywood | Global sensitivity studies of the direct radiative forcing due to anthropogenic sulfate and black carbon aerosols[END_REF][START_REF] Penner | Climate forcing by carbonaceous and sulfate aerosols[END_REF][START_REF] Easter | Binary Homogeneous Nucleation: Temperature and Relative Humidity Fluctuations, Nonlinearity, and Aspects of New Particle Production in the Atmosphere[END_REF])  The impact of meteorology on gas/particle partitioning and the dry deposition of SOA (Bessagnet et The formation of ammonium nitrate (NH 4 NO 3 ) is very sensitive to temperature while primary pollutants will be strongly dependent on stability conditions. As previously 67/108 discussed, dust particles emissions will be very sensitive to wind speed and soil moisture. Soil moisture is a critical parameter very different from model to model and leads to large differences in emissions fluxes. The way to diagnose the vertical diffusion coefficient K z strongly impacts the concentrations of primary pollutants. Vertical turbulent mixing takes place only in the boundary layer. Often, in the formulation of K z , a minimal K z is assumed, with a value about 0.01 m 2 /s. The change of this value directly affects PM concentrations. The temperature cold bias problem was addressed in Jimenez and Dudhia (2012). They proposed a correction, i.e. a parameterization that is based on the concept of a momentum sink term and makes use of the standard deviation of the subgrid-scale orography as well as the characteristics of the topographic field. Both the drag generated by the unresolved terrain and the possibility of an increase in the speed of the flow over mountains and hills. It could be shown that WRF presents a low wind speed bias, which is considered in the corrected scheme. The surface wind simulation over a complex-terrain region that is located in the northeast of the Iberian Peninsula has been improved with the inclusion of the new parameterization. In particular, the underestimation of the wind speed spatial variability resulting from the mentioned biases can be corrected. The importance of selecting appropriate grid points to compare with observations was also examined. The wind speed from the nearest grid point is not always the most appropriate one for this comparison, nearby ones can be more representative. The new scheme not only improves the climatological winds but also the intra-diurnal variations in the mountains, where the default WRF shows limitations in reproducing the observed wind behaviour. This effect on wind speed will have an impact on the dilution of primary particles.

Meteorological downscaling

Regarding the precipitations, WRF-IPSLcm precipitation amount is larger than in WRF-ERAi and in observations, even though seasonal modulation is weak in the two cases and interannual variability have comparable ranges. The differences are marked in the winter season and they could raise some large differences in the water cycle treatment (cloud formation, fog occurrences) that could lead to significant differences in the sulphur chemistry and then the production of sulphate and at last the secondary inorganic aerosol equilibrium (sulphate, ammonium and nitrate).

Impact of global boundary conditions on Regional air quality

Regional chemistry transport models are usually initialized and driven at the boundaries (top and lateral) by global model outputs or measurements-derived profiles. Usually for PM, global model outputs are used in most regional models like CHIMERE (Vautard et al., 2005) and EMEP [START_REF] Simpson | The EMEP MSC-W chemical transport model -technical description[END_REF] Regarding the time frequency of PM boundary conditions to be used in models, some studies provide some guidance. In the frame of the AQMEII project, Schere et al. (2012) showed that for both O 3 and PM 10 , using 3-hourly fields at the boundaries contributes to obtaining a slightly larger variability that is more in agreement with the observations for O 3 and NO 2 . The time variability is impaired for PM 10 showing that the predictability of dust events (intensity and occurrence) remains difficult, as demonstrated by Menut et al. (2009).

In [START_REF] Borge | Influence of boundary conditions on CMAQ simulations over the Iberian Peninsula[END_REF] simulations performed with the CMAQ model suggested that model performances were affected by spatial and seasonal factors, the results indicated that model-derived dynamic boundary conditions improved CMAQ predictions when compared to those based on static concentrations prescribed in the boundaries.

Concerning the initial boundary conditions, simulation results from [START_REF] Samaali | On the influence of chemical initial and boundary conditions on annual regional air quality model simulations for North America[END_REF] suggested the use of a spin-up period of longer than one week for a large (continental) domain and long-term simulation of PM 2.5 and O 3 rather than the 2-4 days commonly assumed in the literature.

From this literature review, dust and sulphates at the boundaries appear to be the most important species that influence regional air quality modelling.

Air quality and Climate Change scenarios

Most of the future emission scenarios studied between 2000 and 2010 for the public authorities did not include climate change. A constant meteorological year was used for present and future emissions. In the SALUT'AIR 19 project a PRIMEQUAL project that I have coordinated, the first goal was to create a full chain accounting for the effects of climate change and emission changes for future projections and the final objective was to appreciate the benefits of a better air quality compared to cost of emission reductions through a cost/benefit analysis. This modelling chain is described in Colette et al. (2013).

To quantify changes in air pollution over Europe at the 2050 horizon, we designed a comprehensive modelling system that captures the external factors considered to be most relevant, and that relies on up-to-date and consistent sets of air pollution and climate policy scenarios. Global and regional climate as well as global chemistry simulations are based on the recent representative concentration pathways (RCP) produced for the Fifth Assessment Report (AR5) of the IPCC (Intergovernmental Panel on Climate Change) whereas regional air quality modelling is based on the updated emissions scenarios produced in the framework of the Global Energy Assessment. We have explored two diverse scenarios: a reference scenario where climate policies are absent (2050-REF) and a mitigation scenario (2050-MIT) which limits global temperature rise to within 2°C by the end of this century. This first assessment of projected air quality and climate at the regional scale based on CMIP5 (5 th Coupled Model Intercomparison Project) climate simulations is in line with the existing literature using CMIP3. The discrepancy between air quality simulations obtained with a climate model or with meteorological reanalyses is pointed out. Sensitivity simulations show that the main factor driving future air quality projections is air pollutant emissions, rather than climate change or intercontinental transport of pollution. Whereas the well documented "climate penalty" that weights upon ozone (increase of ozone pollution with global warming) over Europe is confirmed, other features appear less robust compared to the literature, such as the impact of climate on PM 2.5 . The quantitative disentangling of external factors shows that, while several published studies focused on the climate penalty bearing upon ozone, the contribution of the global ozone burden is somewhat overlooked in the literature.

A closer look in the average individual aerosol components over Western Europe is provided in Figure 37. Note that individual PM components sum up to PM 10 , instead of the PM 2.5 that are used elsewhere in the paper because of their higher relevance for air quality purposes. Sulphate concentrations are higher in the GCM-historical simulations compared to the ERA-hindcast because of enhanced sulphur aqueous chemistry. On the contrary, nitrate concentrations are similar because of the high availability of free ammonia (defined as the total ammonia minus sulphate, in moles) in the atmosphere.

All the secondary aerosols decrease in the future as a result of decreasing anthropogenic emission of precursors. The most prominent feature in the projection of aerosol composition is the increase of the relative importance of natural aerosols such as dust and sea salts in the future (right panel in Figure 37), so that in the most stringent scenario the fraction of crustal material becomes dominant in PM 10 . Secondary organic aerosols are the only species that maintain their relative importance due to the contribution of biogenic precursors in their formation process. As far as secondary inorganic aerosols are concerned it is worth mentioning that the small increase of NH 3 emissions in the GEA projections (Colette et al., 2012) is not reflected in the projected formation of particulate ammonium whereas NH 3 emissions increase by 22 and 21% for the reference and mitigation scenarios, respectively. Between 2005 and 2050, we find that ammonium decreases from 4μg m -3 in the GCM-historical 2005 simulation to 1.4 and 0.5 μg m -3 in the reference and mitigation projections, respectively. This feature emphasises the probable limiting role of NOx emissions through the availability of HNO 3 in rural areas (Hamaoui-Laguel et al., 2014) that do exhibit a strong decrease in the future. The reason why such behaviour is not reported in coarse global chemistry transport model projections deserves further investigation [START_REF] Fiore | Global air quality and climate[END_REF][START_REF] Shindell | Radiative forcing in the ACCMIP historical and future climate simulations[END_REF]. While in the historical simulations nitrate represents about 33% of non natural particles, this fraction reaches 39-42% in 2050.

In terms of exposure, we find that population-weighted PM 2.5 decreases by 61.8 (±3.1) and 78.0% (±1.8) in the reference and mitigation scenarios, respectively. It appears that air quality legislation (which is identical in both scenarios) somewhat dominates the relative change in exposure to PM 2.5 , the impact of the climate policy (which differs in both scenarios) is not as large as observed for the exposure to ozone. Regarding the contribution of climate penalty versus emission changes effects for the PM 2.5 concentrations, the small benefit brought about by climate change is largely dominated by the response attributed to changes in air pollutant emissions, while the contribution of boundary conditions is moderate. We note however that there is no consensus whether climate change constitutes a penalty or a benefit for particulate matter [START_REF] Jacob | Effect of climate change on air quality[END_REF]. At the same time a considerable attention is devoted to the investigation of direct and indirect impact of aerosols on climate. Increasing the robustness of the anticipated impact of climate change on particulate matter should become a key research priority in the coming years.

Conclusions

My contribution on aerosol modelling researches are motivated by operational needs for the French authorities (i) to comply with the European regulations driven by international guidelines and protocols, (ii) understands and analyses the effects of emissions scenarios on air quality. The CHIMERE model was used to analyse PM 10 episodes in France that often occur in early spring. We showed an important contribution of foreign countries but as we could see in a more recent study for the French authorities the national emissions are the main contributors of this kind of episodes. In such episodes we showed the importance of ammonia emissions even if ammonia concentrations are in excess in the atmosphere. The work on the influence of ammonia emissions on exceeding of PM air quality standards was an important input for the revision of the directive on national emission ceilings so that the EU complies with the international protocol on emissions (Gothenburg protocol).

In collaboration with the LMD-IPSL we worked on a specific dust event that lead to very large PM concentrations in the Western Europe in March 2007. Some member states erroneously attributed this event to a Saharan dust outbreak whereas we proved that this event was due to a dust storm from Ukraine. This event was an opportunity to improve the dust module in CHIMERE and to initiate a new development in order to separate dust from between long range transport (from arid zones) and European dust. Indeed, from a legal point of view, if dust comes from arid zones outside Europe any exceedances due to dust outbreaks of limit values can be subtracted.

I have contributed to the creation of the CHIMERE optical module (OPTSIM) through the cosupervision of a Ph. D thesis. The optical module has been created with the possibility to select different type of aerosol mixing. We could use this module to calculate the radiative responses to mitigation strategies, to evaluate the retroaction of aerosols on meteorology during the Russian fires, or to evaluate CHIMERE with available optical properties.

Climate change is a strong driver of the evolution of air pollutant concentrations. For ozone, we showed that pessimistic scenarios make inefficient the emission reduction strategies on air pollution. For particles, the effect of climate is unclear, the concentrations of primary compounds could benefit from higher temperature with a slight decrease expected in the future, but the formation of secondary species could be enhanced by higher temperatures particularly in springtime. new dimension of "mixing" will have an implication on health impact studies and will play a role on the way to treat the links between clouds and aerosols.

The links between aerosol and aqueous chemistry is very poor in current CTMs, in most models the hypothesis of physical/chemical equilibrium is done. Even if the aerosol PM concentrations under rainy conditions seem less interesting for air quality; on an annual basis this could have a strong impact. We saw that the mechanistic way to describe the in cloud deposition in CHIMERE can lead to a complete removal of aerosols whereas classic (but less realistic) parameterisations avoid this artefact. Moreover, the pH of droplets controls the formation of sulphate but this requires the concentrations of the major ions and so far, only nitrate, ammonium and sulphate are used, but we miss much more species that could intervene in the pH calculation. For all these reasons, a proper aqueous chemistry module has to be implemented in CHIMERE. In MESO-NH, a cloud chemistry module has been implemented [START_REF] Leriche | A cloud chemistry module for the 3-D cloud-resolving mesoscale model Meso-NH with application to idealized cases[END_REF] that could be a first basis for CHIMERE.

Transport and Urban meteorology

So far chemistry transport models can be applied up to the urban scale to get urban background concentrations of pollutants. However, several studies have shown that the improvement of resolution must be accompanied by (i) an improved resolution of emission inventories by the use of bottom-up emissions, and (ii) by an improvement of the urban meteorology. Currently, WRF proposes urban canopy layer that can be used to simulate air quality within the urban layer [START_REF] Zhang | Formation of organic aerosol in the Paris region during the MEGAPOLI summer campaign: evaluation of the volatility-basis-set approach within the CHIMERE model[END_REF] but this is certainly insufficient to simulate meteorological variable within the urban canopy (below 15 m) where most of traffic emissions occur. [START_REF] Mauree | Development of a multi-scale meteorological system to improve urban climate modelling[END_REF] proposes a new sugrid model able to describe the meteorology within the urban canopy and this kind of models could be included in WRF or directly in the CHIMERE pre-processing.

The long range transport of thin layers is also a challenging issue in chemistry transport models that tend to be very diffusive along the vertical axis. In Colette et al. (2011) we showed the ability of CHIMERE to transport volcanic ashes from Iceland to France in the free troposphere, however it was very difficult to get the thin layer of volcanic ashes in the model because of the vertical layer resolution and the local stabilisation of the air masses.

Moreover, currently, meteorological modelling is facing challenges related to time calculations and storage. Meteorological modellers are working on the next generation of meteorological models MPAS, as Model for Prediction Across Scales. MPAS is comprised of fluid flow solvers using unstructured centroidal Voronoi meshes and a C-grid staggering of the prognostic variables. The primary development partners are the climate modelling group at Los Alamos National Laboratory (COSIM) and the National Center for Atmospheric Research. The goal is to gain computing time by refining the mesh on region of interest and stretching it elsewhere. Certainly this new kind of meshes should be more appropriated for air quality modelling because we know a priori the regions of interest, i.e. the most urban areas where exceedances of air quality standards are the most often recorded.

RAYONNEMENT NATIONAL ET INTERNATIONAL

Coordination de projets

Invitation à des colloques

1) 2014 Chine Trois conférences à Ghuangzhou, Beijing et Wuhan: Gestion de la qualité de l'air en France, organisée par l'Ambassade de France à Pékin.

2) 2011 Paris, Palais de la Découverte, Conférence Science en débat : Besoin d'air pur? Conférence publique.

3) 2010 Norvège, Agence de l'Environnement: Use of the Prev'Air system for air quality forecasting and monitoring at regional scale and its applications for cities

  the national system of air quality monitoring, my first contribution was to equip the national modelling tool CHIMERE (chemistry transport model co-developed by INERIS and IPSL-LMD) with an aerosol module I developed during my Ph.D. Later, I developed at INERIS a panel of research activities around the development of CHIMERE. My research activities are broken down into five main areas:

Figure 1 :

 1 Figure 1: Monthly evolution of the number of exceedances of the PM 10 daily limit in 2009 for different typologies of stations (By courtesy of Maxime Beauchamp)

Figure 2 :--

 2 Figure 2: Description of the INERIS strategy for the development of air quality modelling suite and its applications This report is a synthesis of my work on the modelling of aerosols for air quality management. Throughout the document, the references in which I am (co-)author are in bold characters. A complete Curriculum Vitae is provided in Annex 1. My contribution to the development of the air quality modelling chain and its application for PM modelling can be broken down in five topics corresponding to the five chapters of this document. For these fives topics, I indicate the name of students and Post-docs I have co-supervised: -Processes and model developments (Marc Stefanon -Master, Florian Couvidat -Master, Alma Hodzic -Ph.D, Etienne Terrenoire -Post-Doc) -Improvement of the resolution at the European scale (Etienne Terrenoire -Post-Doc) -Multi model analysis for model improvement -Scenarios analysis -Air quality/Climate interactions (Jean-Christophe Péré -Ph.D and Post-doc)

Figure 3 :

 3 Figure 3: Two step wet deposition in CHIMERE This way of simulating the wet deposition allow to model the concentration of particulate species in droplets but the difficulty is to fit the coefficient that should depend on the aerosol composition. In Pernigotti et al. (2012) several tests were performed to analyse the importance of this coefficient and it appears that the current coefficient in those time in CHIMERE was to strong and could lead to a total removal of particles in fogs. Currently in 2014, in the frame of the ongoing project EURODELTA we are still performing tests to fit the best coefficient based on observations both on wet deposition and air concentrations for PM components.

Figure 4 :

 4 Figure 4: Differences in % calculated as: , with C SIM-C the SOA concentrations in the base case with SVOC dry deposition velocities set to zero, C SIM-D the SOA concentrations with non-zero SVOC dry deposition values, by considering constant mean diurnal atmospheric stability.

Figure 5 :

 5 Figure 5: Scheme representing various fundamental steps of the heterogeneous uptake of gases on mineral dust particles: (1) gas phase diffusion of HNO 3 to the particle surface, (2) the uptake at the surface, (3) reaction in the bulk phase and (4) desorption of gaseous products.

Figure 6 :

 6 Figure 6: Daily(D) and nightly(N) mean coarse-mode (diameter > 2 µm) nitrate concentrations (µg m -3 ) observed during ESQUIF campaign over Paris region from 19 to 26 July (white) and simulated by CHIMERE reference run (gray) and the run that includes the heterogeneous formation of nitrate according to Hanisch and Crowley (2001) parameterization (γ=0.1).

Figure 7 :

 7 Figure 7: Mean nitrate concentrations in the PM 10 fraction for a one month period in winter (25 Feb -26 Mar 2009) and summer (1 st Jun -30 Jun 2006). The colour circles are the available EMEP observations for the corresponding periods.

Figure 8 :

 8 Figure 8: Emissions hourly factors of the 10 SNAP anthropogenic activities sectors (Monday) traditionally used in chemistry transport models (from Menut et al., 2012) For example, Menut et al. (2012) proposed a set of new profiles for the traffic emissions calculated from NO 2 traffic measurements. NO 2 is a good proxy to reallocate in time the road traffic gas and PM emissions. The emission factors are separately estimated for each weekday as presented in Figure9. The distinction between days is crucial since the traffic intensity is different on working days compared to weekends. For example, the morning traffic is very low for Saturdays and Sundays, compared to the other days of the week. Figure10presents some examples of these new hourly diurnal profiles of NO 2 concentrations for several cities in Europe. It is assumed that this variable is a good indicator of the road traffic activity in the cities. The difference between cities is large, highlighting the fact that traffic emissions do not have the same variability in time from one country to another. However, the profiles for two cities in the same country show roughly the same profile, confirming that the emission profile of mobile sources depends on the lifestyle and customs in a given country.

Figure 9 :Figure 10 :

 910 Figure 9: Hourly factors estimated in Barcelona for all weekdays and week-end -Source : Menut et al. (2012)

Figure 11 :

 11 Figure 11: Daily modulation factors (Fj) apply for the SNAP 2 emissions over the city of Katowice, Paris and Madrid for the year 2009

Table 3 :

 3 Average (2003-2011) total annual emissions in Gg by country for the main pollutants, for the fire emissions (F) and the EMEP anthropogenic emissions (A). Only countries for which fire CO emissions are larger than 30 Gg yr -1 on average, and for which anthropogenic emissions are available in the EMEP inventory, are reported (Source: Turquety et al., 2013)

Figure 12 :

 12 Figure 12: Surface PM 10 concentrations (µg m -3 ) simulated by CHIMERE the 25 March 2007 06:00 UTC

Figure 13 :

 13 Figure 13: Impact of road traffic resuspension on PM 10 (in µg m -3 ) for January 2009, first attempt of simulation in the frame of the EC4MACS project (not published)

  our group, I have largely contributed to launch and follow the first year a Ph. D work on ammonia emissions published by Hamaoui-Lagel et al. (2014).

Figure 14 :

 14 Figure 14: Differences in ammonia emissions between the EMEP emissions (right panel) and the VOLT'AIR model embedded in CHIMERE in spring 2007 over France.

Figure 15 :

 15 Figure 15: EMEP background outputs as input data for GAINS. The objective of EC4MACS is to add the urban dimension in GAINS with the CHIMERE model.

Figure 16 :

 16 Figure 16: Relation between the number of daily exceedances and the annual mean concentrations according 2009 AirBase data

Figure 17 :

 17 Figure 17: Emission patterns of PM 2.5 emissions from the residential sector (SNAP2), units are in logarithm for clarity.

Figure 18 :

 18 Figure 18: Urban meteorology and air quality modelling

Figure 19 :

 19 Figure 19: Model versus Observations scatter plots at 50 km (triangles) and 8 km (dots) for all urban stations in Europe for NO 2 (left) and PM 10 (right) (full year 2009).

Figure 20 :

 20 Figure 20: Maps of NO 2 (µg/m 3 ) on January 10, 2009 at 16:00 UTC in the 50km (left) and 2km (right) resolution CHIMERE simulations.

Figure 21 :

 21 Figure 21: Mean modelled ozone vertical profiles at 15:00 UTC in January and August 2009 over Paris. The profiles are averaged for each month of January (left side) and August (right side).

  [START_REF] Mckeen | Evaluation of several real-time PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study[END_REF], vanLoon et al., (2007),Vautard et al. (2007),[START_REF] Hayami | MICS-Asia II: model intercomparison and evaluation of particulate sulfate, nitrate and ammonium[END_REF],[START_REF] Stern | A model inter-comparison study focussing on episodes with elevated PM10 concentrations[END_REF],[START_REF] Smyth | A comparative performance evaluation of the AURAMS and CMAQ air quality modelling systems[END_REF]), Vautard et al. (2009), Solazzo et al. (2012a,b),Pernigotti, et al. (2013). Most of these model intercomparison exercises were performed at the regional scale with chemistry transport models. In one of the most recent exercise, AQMEII(Solazzo et al., 2012a,b), models clearly tend to underestimate PM10 background concentrations in US and EU regions. Model results for PM 2.5 concentrations showed better performances but large uncertainty remains in the devoted to the simulation of secondary organic aerosols.The new ED III exercise is to use and interpret the EMEP intensive measurements by making a retrospective analysis of the campaigns and informing on the contribution of some emission sources to the pollutant concentrations. Currently I am the coordinator of this new phase of EURODELTA(Bessagnet et al., 2014a).Four EMEP campaign are used to evaluate the model:

Figure 22 :

 22 Figure 22: Mean gridded coefficients of variation for O 3 and PM 2.5 concentrations for five models in the 2006 summertime campaign

Figure 23 :

 23 Figure 23: Time series of O 3 and Dust in Valentia Observatory (Ireland) for the 2009 campaign (CHIMERE is in blue)

Figure 24 :

 24 Figure 24: G ratio for the 2009 campaign for five countries (Italy, Great Britain, Germany, the Netherlands, Spain) for the models.

Figure 25 :

 25 Figure 25: Error statistic (RMSE, Biases and correlations) of PM 2.5 concentrations (in µg m -3 ) for the four EURODELTA campaigns for the seven models (CHIMERE in blue).

Figure 26 :

 26 Figure 26: Error statistic (RMSE, Biases and correlations) of Nitrate concentrations (in µg m -3 ) for the four EURODELTA campaigns for the seven models (CHIMERE in blue).

Figure 27 :

 27 Figure 27: Effect of NOx emission reductions in France, Germany, Spain and UK on secondary particulate concentrations (aggregation for all grid cells belonging to a given country). The Impacts shown are for the EU25 and concentrations are population weighted.

Figure 28 :

 28 Figure 28: Temporal variation of the differences (µg m -3 ) between the simulations WB (without Belgium), WN (without The Netherlands), WG (without Germany), WI (without Italy) simulations and the base case with all emissions.

Figure 29

 29 Figure29clearly shows that for quantities of absolute reduction very close in mass, ammonia reduction proves much more effective than NOx emission reductions with a factor of 2 to 4. This result is easily explained by the chemistry of ammonium nitrate formation. NH 3 is lighter than the NO 2 molecule by a factor 2.7, less mass of NH 3 is required to form one molecule of ammonium nitrate. Other factors intervene as the location of the emitting areas of NOx and NH 3 , the model provides an integrated view of all effects. We also note that the largest expected reduction in reducing NH 3 emissions over the whole of France is at the Paris basin while decreasing NOx traffic, the most significant reduction is located over the Loire and Rhone Valley.

Figure 29 :

 29 Figure 29: PM 10 concentration decrease (µg m -3 ) expected for the January-April 2007 period for a 50% ammonia emission reduction on agriculture (left), and a 50% reduction emission of NOx traffic emissions (right)

Figure 30 :

 30 Figure 30: PM 10 concentration decrease (µg m -3 ) expected for the January-April 2007 period for a 50% emission reduction of agricultural ammonia in France only (left) and on the whole Europe (right)

Figure 31 :

 31 Figure 31: PM 10 concentration decrease (µg m -3 ) expected for the January-April 2007 period for scenario A (left) and B (right) compare to the reference simulation

Figure 32 :

 32 Figure 32: Percentage of PM 2.5 concentration reduction (µg m -3 ) considering an additional effort of -30% reduction on ammonia to the Gothenburg Protocol (GP) scenario compared to the sole GP emission.

Figure 33 :

 33 Figure 33: Distribution of G ratio in 2020GOT for three different ranges of SIA hourly concentrations for CHIMERE (18% of G ratio that are not represented on the graph are negative for SIA concentrations above 1 µg/m 3 ). The embedded chart gives the SIA decrease in % from 2020GOT to 2020G30 as a function of G ratio in 2020GOT for SIA concentrations larger than 40 µg m -3 .

Figure 34 :

 34 Figure 34: (a) Daily mean simulated AOTtot at 440 nm (core-shell mixing) on 27 March 2003 with corresponding observed values at AERONET sites (if available) (circles). (b) AOT at 550 nm retrieved by the MODIS sensor.

Figure 35 :

 35 Figure 35: Diurnal-averaged near surface air temperature (in °C) simulated with and without aerosols and observed at the meteorological station of Moscow Domodedov (Left panel). Temporal evolution of the atmospheric boundary layer (ABL) (Top right) and near-surface horizontal wind speed (in ms-1) (Bottom left) for 8 August, simulated with and without aerosols and measured at the Moscow meteorological station.

Figure 36

 36 Figure36illustrates the impact of using a meso-scale model to refine the resolution of a meteorological re-analysis(Menut et al., 2013b). The daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe is called E-OBS18 , ERAi is the ERA-interim global ECMWF reanalysis, IPSLcm is the IPSL global coupled climate model (From Institut Pierre-Simon Laplace). The downscaling with a meso-scale model strongly affects the meteorological simulations. The Weather Research and Forecasting (WRF) model tends to increase the temperature cold bias (by -2K) and the precipitation positive bias by +1 mm/day. Over land, WRF tends to increases the wind speed by ±1 to 2 m/s, independently of the global model used.

Figure 36 :

 36 Figure 36: Diurnal cycles of 2m temperature (K) and 10m wind speed (m/s) for the periods DJA and JJA. Calculations are done over the western/central Europe modelled domain for the 1989-2005 period. E-OBS is the observational dataset, ERAi is the ERA-interim global ECMWF reanalysis, IPSLcm is the IPSL global coupled climate model, WRF is the mesoscale meterological model.

Table 6 :

 6 Contributions to annual average area-weighted fine aerosol (PM 2.5 ) surface aerosol concentrations (SAC) (units: µg/m 3 ) over each receptor region. 'Total' indicates total fine aerosol (PM 2.5 ) concentrations including ammonium sulfate, black carbon (BC), organic mass (OM), and fine dust; 'Domestic' indicates aerosol concentrations resulting from local emissions; Background is the difference between 'Total' and 'Domestic' concentrations. The percent contribution from each aerosol species to each category (i.e., 'Total', 'Domestic', 'Background') is also quantified. Note: ''DMS'' represents sulfate aerosols derived from DMS (dimethyl sulphide), while ''Sulfate'' in the 'Background' category represents sulfate contributed from ROW (ROW= ships, airplanes, volcanoes, etc.). NA: North America, SA : South America, EU : Europe, FSU : Former Soviet Union, AF: Africa, IN: India, EA: East Asia, SE: Southeast Asia, AU: Australia, ME: Middle East. (Source: Liu et al., 2009)

Figure 37 :

 37 Figure 37: Average aerosol composition over land surfaces of Western Europe for the two simulations corresponding to the present-day conditions (GCM-historical and ERA-hindcast) as well as the two scenarios for 2050 (reference and mitigation). Absolute concentrations are given on the left panel and relative contributions to total PM 10 (expressed in percentages) are given on the right panel.

4 )

 4 Tous les ans depuis 2006, invitation à présenter les travaux sur la modélisation de la qualité de l'air dans le cadre de la TFMM (Task Force on Measuement and Modelling, group de travail ONU) Enseignement Pas d'enseignement régulier, seulement une intervention à l'ENSIACET pour l'option Environnement en 2006.FICHE DE POSTE INERIS Assure les missions et les responsabilités liées à la fonction de Responsable de l'Unité MOCA  Contribue à l'orientation et au développement des activités,  Garant de leur réalisation, il anime une équipe d'Ingénieurs et des techniciens dans le domaine de dispersion et de la transformation chimique des polluants dans l'atmosphère : o modélisation de la qualité de l'air à l'échelle régionale et continentale, o mécanique des fluides et simulation de la dynamique atmosphérique, o modélisation des particules, o développements numériques dans les grands codes de calculs scientifiques… Dans ce domaine :  Participe en son nom propre ou pour le compte de l'INERIS à des Comités d'Experts Nationaux ou Internationaux dans son domaine d'activité,  A la demande du Responsable de Pôle, peut coordonner et assurer le suivi de programmes techniques pouvant impliquer l'ensemble des Unités du Pôle, voire d'autres Unités de la DRC ou de l'INERIS,  Assure en collaboration avec le CNRS la coordination du développement des modules du code CHIMERE et l'édition de ses versions opérationnelles,  Assure l'encadrement de doctorants affectés aux projets de recherche de l'Unité,  Participe à la réalisation du programme d'appui scientifique et technique de l'INERIS au Ministère de l'Écologie et du Développement Durable,  En particulier, pour une utilisation dans le cadre des négociations internationales sur le contrôle des pollutions atmosphériques transfrontières, assurer la conception scientifique et la programmation informatique d'un module « particules » intégré dans le code CHIMERE Continental du CNRS-LMD,  Fournit une assistance méthodologique aux associations de surveillance de la qualité de l'air,  Réalise des prestations d'études, d'expertise et de conseil à des clients publics ou privés, Mène des activités de recherche,  Contribue aux activités de l'Institut en matière de veille scientifique et technologique, de diffusion des connaissances (communications, publications…), de formation.

  There are 192 parties to the convention: 191 states (including all the UN members except Andorra, Canada, South Sudan and the United States) and the European Union. The United States signed but did not ratify the Protocol and Canada withdrew from it in 2011. The Protocol was adopted by Parties to the UNFCCC in 1997, and entered into force in 2005. , but leaves it largely to the Member States to decide which measures -on top of Community legislation for specific source categories -to take in order to comply. This directive is currently under revision.In Europe, concentrations of certain air pollutants (NO 2 , SO 2 , PM 10 , PM 2.5 , benzene, etc...) are regulated by the Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe that entered into force on 11 June 2008. This Directive includes the following key elements:

International protocols are strong drivers for the elaboration of the EU legislation and their transposition in national laws. To comply with the Gothenburg protocol, Directive 2001/81/EC of the European Parliament and the Council on National Emission Ceilings for certain pollutants (NEC Directive) sets upper limits for each Member State for the total emissions in 2010 of the four pollutants responsible for acidification, eutrophication and ground-level ozone pollution (sulphur dioxide, nitrogen oxides, volatile organic compounds and ammonia)

  For vehicle exhaust emissions the so called EURO regulations have been created in Europe to limit the emissions of NOx, THC, NMHC, CO and PM from vehicles. Recently a standard on Particulate Number has been introduced in 2008 and consolidated in the EU regulation No 459/2012 of 29 May 2012 amending Regulation (EC) No 715/2007 of the European Parliament and of the Council and Commission Regulation (EC) No 692/2008 as regards emissions from light passenger and commercial vehicles (Euro 6).

Directive 2008/1/EC of the European Parliament and of the Council of 15 January 2008 concerning integrated pollution prevention and control. The purpose of this Directive is to achieve integrated prevention and control of pollution arising from the activities listed in the Annex of the directive. It lays down measures designed to prevent or, where that is not practicable, to reduce emissions in the air, water and land from the above mentioned activities.

Table 1 : EU air quality standards for PM

 1

	Pollutant	Concentration	Averaging	Legal nature	Permitted
			period		exceedances
					each year
				LV entered into force
		50 µg m -3	24 hours	1.1.2005 (a)	35
	Particles (PM 10 )			LV entered into force
		40 µg m -3	year	1.1.2005 (a)	n/a
	Fine particles	25 µg m -3
	(PM 2.5 )

Menut et al. (2013d).

	quantification over
	continental scales. Designed for both the research community and operational agencies,
	the CHIMERE model needs to be computationally stable and provide robust results. This
	means that the model needs to be able to estimate pollution peaks at the right time and
	location, but also to be able to diagnose low pollution conditions and avoid false alerts. As
	a research tool, the model needs to be modular enough to allow adding new processes or
	testing specific physico-chemical interactions. The last model developments are described
	in

It includes the necessary state of the-art parameterisations to simulate reasonable pollutant concentrations, but remains also computationally efficient for forecast applications. CHIMERE is also frequently used for field experiment analysis studies, long-range transport and trend

Table 2 : Gas phase chemical scheme for SOA formation in CHIMERE

 2

In Bessagnet et al. (2008) we improved the Vautard et al. (2005) methodology

  by studying a dust event observed in Europe on23-25 March 2007. Surface observations in Central Europe showed huge concentrations of particulate matter (PM). At the same time, dust models diagnosed a Saharan dust outbreak flowing from Sahara to Europe. However, Lidar measurements and surface stations in Eastern Europe diagnosed a dust event originating from Ukraine related to chernozemic-erodible lands. This outbreak of European dust travelled through Europe up to the West coast (France and UK) as shown in Figure

Table 4 : Landuse and associated proxy Emission sector or type Landuse category or Proxy

 4

	Agriculture	"crops" and "grass land"
	Road Traffic	Population and roads
	Industry	"Artificial areas"
	Residential heating	population
	Forest fires	"forests" and "shrubs"
	Industry	Large Source Points
	For the EC4MACS project and the EURODELTA initiative, I have developed a new pre-
	processing performed in several steps.

Table 5 : Model performances (RMSE: root mean square error, Cor: average spatial correlation) of each configuration over the whole modelling domain between January 9 and 16, 2009 Pollutant Station type 50km 8 km 2 km RMSE (µg/m 3 ) Cor RMSE (µg/m 3 ) Cor RMSE (µg/m 3 ) Cor NO 2

 5

		Urban	29.95	0.70	21.52	0.65	18.93	0.62
		Surburban	17.03	0.60	12.30	0.58	11,77	0.56
		Rural	7.80	0.54	6.67	0.64	6.68	0.68
		Urban	34.14	0.57	31.28	0.46	30.42	0.43
	PM 10	Surburban	17.55	0.49	15.42	0.51	14.54	0.53
		Rural	6.96	0.51	6.26	0.63	6.01	0.63

.

  It was developed to model both Level 1 observations and level 2 aerosol Lidar retrievals in order to compare model results to measurements: the level 2 enables to estimate the main properties of aerosols plume structures, but may be limited due to specific assumptions. This module has been designed to read the outputs of the CHIMERE model.Among the work that lead to the development of the module, I have contributed to the creation of this module in Péré

et al. (2009) and Péré et al. (2010).

  The goal was to generalise the work performed by Hodzic et al.(2004, 2006b), by implementing the possibility to activate different type of mixing assumption for the aerosols. The computation of optical properties is useful to (i) evaluate air quality models with optical properties (from satellite like MODIS or AERONET/PHOTONS network) and (i) to offer the possibility to calculate radiative forcings that can be used in turn in meteorological models. Comparisons between simulations and observations are made over Western Europe for the year 2003 but also for one specific case focused on ammonium nitrate aerosols. Observed AOT displays a seasonal cycle (with highest values during summer) rather well reproduced by the model but biases with observational data have been found depending on seasons. In fall, winter and early spring, modelled AOT values agree well with AERONET retrievals with small negative biases. Focus on a pollution episode of ammonium nitrate origin during March 2003 reveals that CHIMERE is able to well reproduce the fine mode volume size distribution retrieved by AERONET (Figure34), leading to good agreements between modelled and observed AOT. In late spring and summer, AERONET AOT values are underpredicted by the model, which could be due to uncertainties in modelling secondary species. Three different mixing states are modelled, external, internally homogeneous and core-shell mixing.

	Three optical properties: Aerosol Optical Thickness (AOT), column-averaged Single
	Scattering Albedo (SSA) and asymmetry parameter (g) are calculated at 440 nm, 675 nm,
	870 nm and 1020 nm (AERONET wavelengths) under three hypotheses on the particle
	mixing state (external, internally homogeneous and core-shell). Furthermore and in
	addition to optical calculations, an original development has been made to estimate
	column volume size distributions in CHIMERE, directly comparable with AERONET
	retrievals.

Meij et al., 2009 ; Pernigotti et al., 2012)

	al., 2010)
	 The relationship between meteorological variables and PM concentrations (Mues et
	al., 2012)
	 The impact of model MM5 configuration on aerosol predictions (de

  . My technical contribution in Vautard et al. (2005) was to introduce PM boundary conditions in CHIMERE using global CTMs.Very few studies investigated the impact of the long range transport of PM to Europe. The hemispheric transport of PM was assessed by[START_REF] Brandt | An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport of air pollution[END_REF] with a chemistry transport model, for particles, the contributions from North America to Europe is around 0.9% (∼0.05 μg/m 3 ).[START_REF] Liu | Evaluating inter-continental transport of fine aerosols: (1) Methodology, global aerosol distribution and optical depth[END_REF] proposed a receptor modelling study with a global chemistry transport model in order to evaluate domestic versus background origins of PM 2.5 concentrations in several regions of the world (Table6). Their results show that PM 2.5 from outside Europe contributed to 30 % of the background PM 2.5 concentrations in Europe. The main species contributing to the PM 2.5 is dust followed by sulphates. Organic matter and black carbon concentrations have according to[START_REF] Liu | Evaluating inter-continental transport of fine aerosols: (1) Methodology, global aerosol distribution and optical depth[END_REF] mainly a domestic origin in Europe.

  To achieve these operational goals, I have been involved since 2001 in the development of the CHIMERE model particularly on the aerosol module. This module was implemented in 2002 with a first evaluation over Europe performed in 2004. I implemented in 2008 a new SOA module in CHIMERE with a new set of chemical reactions. I participated to several model intercomparison exercises between 2003 and 2014 with the main European chemistry transport models. Clearly, the CHIMERE aerosol module is a state of the art model with an underestimate of PM mainly driven by a systematic underestimation of organic compounds particularly during episodes. The works performed on emissions and the resolutions allowed us to curb the usual underestimate of CTMs. All the models in Europe benefited from this work through the EURODELTA 3 project I am currently coordinating.

  1) 2010-2013 Coordinateur du programme SALUT'AIR: Évaluation des StrAtégies de LUTte contre la pollution de l'AIR à longue distance dans le contexte du changement climatique. 2) 2012-Présent Coordinateur du programme EURODELTA 3 (programme de travail pour la convention sur le transport des polluants à longue distance) 3) 2007-Présent Co-coordinateur du développement du modèle CHIMERE 4) 2010-2013 Coordination INERIS du travail réalisé dans le projet UE LIFE EC4MACS (European Consortium for Modelling of Air Pollution and Climate Strategies) Present Expert national pour le suivi de la convention CEE-ONU sur le transport des polluants à longue distance (Protocole de Goteborg) 2) 2007-2011 Expert national COST 728: Enhancing meso-scale meteorological modelling capabilities for air pollution and dispersion applications.

	Appartenance à des réseaux d'experts
	1) 2006-

3) 2007-2014 Expert EIONET (European Environment Information and Observation

Network)

European Consortium for Modelling of Air Pollution and Climate Strategies

The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-Model provides a consistent framework for the analysis of co-benefits reduction strategies from air pollution and greenhouse gas sources (http://gains.iiasa.ac.at/gains)

AirBase is a public air quality database containing air quality monitoring information for more than 30 participating countries throughout Europe (http://www.eea.europa.eu/themes/air/airbase)

http://landcover.usgs.gov/

http://glcf.umiacs.umd.edu/

http://postel.mediasfrance.org/en/PROJECTS/Preoperational-GMES/GLOBCOVER/

http://www.eea.europa.eu/themes/landuse/interactive/clc-download

http://sedac.ciesin.columbia.edu/gpw/

http://dataservice.eea.europa.eu/

http://www.eea.europa.eu/data-and-maps/data/member-states-reporting-art-7-under-the-europeanpollutant-release-and-transfer-register-e-prtr-regulation-8

http://www.ecmwf.int/

http://eca.knmi.nl/download/ensembles/download.php

Évaluation des StrAtégies de LUTte contre la pollution de l'AIR à longue distance dans le contexte du changement climatique

Remerciements

Perspectives

In this section, I propose some research perspectives to improve the capabilities of CHIMERE for its use in air quality management.

Emissions

The use of High Performance Computing Centres allows simulating air quality on wider domains with higher resolutions. The improvement of the horizontal resolution highlights the weaknesses of European emission inventories. A first improvement performed at INERIS was to better regrid the residential emissions but we have to further now. Indeed, some European countries have very fine resolution inventories (France, UK, Spain at least) we could inject in our emission CHIMERE preprocessor. My goal is to develop in 2015 an emission inventory at 4km resolution in Europe with the best proxies and available national inventories.

Aerosol modules evolve quite quickly and embed advanced SOA modules which require SVOC's emissions. The problem is that we do not know exactly those emissions and the estimates are very different in the studies. Our goal will be to define an harmonized methodology to calculate those emissions for two key sectors: residential and traffic emissions.

Emission of natural dust in Europe is still a challenging issue. The new parameterization developed by [START_REF] Briant | Homogeneized modeling of mineral dust emissions over Europe and Africa using the CHIMERE model[END_REF] applied for the CHARMEX experiment clearly show some overestimate of the dust emissions over Europe. My objective is to use more Calcium measurements is needed to constraint this parameterisation, these observations are available for several EMEP campaigns in Europe.

Road traffic resuspension is not included in emission inventories and can be responsible for a large fraction of PM nearby roads. Our work will be to develop a specific module to calculate these emissions. As for natural dust emissions the role of past precipitations will be crucial to evaluate the the thickness of the dust layer over the road.

Aerosol modelling

The SOA scheme in CHIMERE needs to be revisited to account for the last developments in that domain. Our objective is also to develop a new SOA module with the 0D model GECKO-A developed at LISA [START_REF] Aumont | Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach[END_REF], we can expect an operational module for CHIMERE in 2017. The general idea is to develop a simplified 0D module based on the "real world" given by the GECKO-A module.

We also have to implement new aerosol modules able to simulate accurate number distributions and the possibility to simulate different type of mixing for a given size bin. Such a module is in preparation at INERIS [START_REF] Debry | Development of a Size and Composition Resolved 0D Aerosol Model with a Sectional Approach in Order to Simulate Efficiently External Mixing Within the Chemical Transport Model CHIMERE[END_REF] thanks to the previous works performed by [START_REF] Devilliers | A New Algorithm to Solve Condensation/Evaporation for Ultra Fine, Fine, and Coarse Particles[END_REF] and [START_REF] Dergaoui | Modeling coagulation of externally mixed particles: Sectional approach for both size and chemical composition[END_REF]. The possibility to use the

Air quality and Climate

In Europe the next challenges for air quality will be the management of specific pollution episodes like the last March 2014 PM outbreak that occurred in France. This episode was due to a combination of agricultural and traffic/industrial emissions. In the frame of climatic projections, we need to verify the impact of more stagnant and mild conditions in springtime on PM episodes. The impact of climate on PM requires a deeper analysis because the decrease of SOx emissions should enhance the formation of ammonium nitrate, however a rise of temperature should evaporate this latter species, then a dedicated study on winter and springtime periods will be performed.

The chemistry transport models provide deposition fluxes that are not sufficiently exploited. There is also a need to analyse the deposition flux of species (SOx, NOx, NHx and O 3 dry deposition) to evaluate the impact on ecosystems in the future. Liousse and L. Rouil (2004). Aerosol modeling with CHIMERE -preliminary evaluation at the continental scale. Atmospheric Environment 38 (18): 2803-2817.

 Hodzic, A., B. Bessagnet and R. Vautard (2006). A model evaluation of coarse-mode nitrate heterogeneous formation on dust particles. Atmospheric Environment 40 [START_REF] Pernigotti | Impact of meteorology on air quality modeling over the Po valley in northern Italy[END_REF]: 4158-4171.

 Bessagnet, B., L. Menut, G. Aymoz, H. Chepfer and R. Vautard (2008)  Bessagnet, B., C. Seigneur and L. Menut (2010). Impact of dry deposition of semivolatile organic compounds on secondary organic aerosols. Atmospheric Environment 44 (14): 1781-1787.

 Bessagnet, B., Beauchamp, M., Guerreiro, C., de Leeuw, F., Tsyro, S., Colette, A., Meleux, F., Rouïl, L., Ruyssenaars, P., Sauter, F., Velders, G. J. M., Foltescue, V. L., van Aardennee, J. (2014) Can further mitigation of ammonia emissions reduce exceedances of particulate matter air quality standards?, Environmental Science & Policy, Volume 44,